docs(predictV3): 添加predictV3算法优化研究文档和前端功能实现
- 完成Phase 11: predictV3算法优化研究文档，涵盖6个优化方向的技术分析 - 实现置信度评估功能，提供历史命中率、得分分布、多维度一致性置信度指标 - 扩展回测指标体系，新增NDCG@K、MRR、命中率分布等排名质量评估指标 - 优化转移概率算法，引入二阶马尔可夫链和多属性联合转移增强预测准确性 - 设计权重训练机制，支持网格搜索和遗传算法进行数据驱动的参数优化 - 集成组合特征挖掘功能，采用关联规则和序列模式发现号码间潜在关联 - 实现完整的前端交互界面，支持预测结果显示、置信度展示和回测验证功能 - 建立性能优化策略，包括预计算缓存、批量计算和降级策略保障响应速度
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---
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phase: 11-predictv3
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plan: 01
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type: execute
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wave: 1
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depends_on: []
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files_modified:
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- application/admin/model/History.php
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autonomous: true
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requirements:
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- PRED-02
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- PRED-05
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must_haves:
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truths:
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- "用户可以在回测结果中看到 NDCG@5 指标"
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- "用户可以在回测结果中看到 MRR 指标"
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- "用户可以看到各排名位置的命中分布统计"
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- "系统在数据不足时返回合理的默认值或提示"
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artifacts:
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- path: "application/admin/model/History.php"
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provides: "NDCG、MRR、命中分布计算方法"
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contains: "_calculateNDCG|_calculateMRR|_calculateHitDistribution"
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key_links:
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- from: "_runBacktestV3"
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to: "_calculateNDCG, _calculateMRR, _calculateHitDistribution"
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via: "method call in return statement"
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---
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# Phase 11 - Plan 01: 回测指标扩展
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## Objective
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扩展 `_runBacktestV3` 方法的回测指标，新增 NDCG@5、MRR、命中率分布等排名质量评估指标，提升算法评估能力。
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**Purpose:** 当前回测仅返回命中率(Top5)和平均排名，缺少排名质量评估指标。NDCG、MRR 是成熟的推荐系统评估指标，能更全面反映预测排名质量。
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**Output:** `History.php` 中新增 3 个计算方法，`_runBacktestV3` 返回结果扩展。
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## Tasks
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### Task 1: 实现 NDCG@5 计算（含空预测保护和公式文档）
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<read_first>
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- D:\code\php\amlhc\application\admin\model\History.php (line 3495-3560, _runBacktestV3 方法)
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</read_first>
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<action>
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在 `History.php` 文件末尾（类内）新增 `_calculateNDCG` 方法：
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```php
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/**
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* 计算 NDCG@K (Normalized Discounted Cumulative Gain)
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*
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* 公式说明:
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* - DCG (Discounted Cumulative Gain) = Σ(rel_i / log2(rank_i + 1))
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* 其中 rel_i = 1 (命中) 或 0 (未命中)，rank_i 为预测排名位置
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* - IDCG (Ideal DCG) = Σ(1 / log2(i + 1)) for i = 1..min(hits, K)
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* 即理想情况下所有命中的号码都排在最前面的DCG值
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* - NDCG = DCG / IDCG，范围 0-1，越接近1表示排名质量越好
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*
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* @param array $backtestDetails 回测详情数组，每项包含 {hit: bool, rank: int}
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* @param int $K Top-K 参数，默认5，评估前K个预测位置的排名质量
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* @return float NDCG值 (0-1范围)，空数据时返回0
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*/
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private function _calculateNDCG($backtestDetails, $K = 5)
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{
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// 边缘情况处理：空预测或无效参数
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if (empty($backtestDetails) || $K <= 0) {
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return 0;
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}
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$dcg = 0;
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$idcg = 0;
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// 计算 DCG: 命中号码的排名折损累积值
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foreach ($backtestDetails as $detail) {
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if (!isset($detail['hit']) || !isset($detail['rank'])) {
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continue; // 跳过无效数据
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}
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if ($detail['hit'] && $detail['rank'] > 0 && $detail['rank'] <= $K) {
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// DCG公式: rel / log2(rank + 1)，命中时 rel=1
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$dcg += 1 / log($detail['rank'] + 1, 2);
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}
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}
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// 计算 IDCG: 最理想情况下所有命中的 DCG（假设都排在第1位）
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$hitCount = 0;
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foreach ($backtestDetails as $detail) {
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if (isset($detail['hit']) && $detail['hit']) {
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$hitCount++;
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}
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}
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for ($i = 1; $i <= min($hitCount, $K); $i++) {
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$idcg += 1 / log($i + 1, 2);
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}
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// 返回标准化值，IDCG为0时返回0避免除零错误
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return $idcg > 0 ? round($dcg / $idcg, 4) : 0;
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}
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```
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实现要点：
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- 公式：DCG = Σ(1/log2(rank+1))，IDCG = Σ(1/log2(i+1)) for i=1..hits
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- 添加空预测保护：检查 $backtestDetails 是否为空
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- 添加数据完整性检查：确保 hit 和 rank 字段存在
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- 使用 log(rank + 1, 2) 作为折损函数，排名越靠前权重越高
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- 返回 0-1 范围的标准化值，越接近 1 表示排名质量越好
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</action>
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<acceptance_criteria>
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- grep 正则匹配: `_calculateNDCG\s*\(` 在 History.php 中存在
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- grep 匹配: `empty($backtestDetails)` 在方法中存在（空预测保护）
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- 方法返回 float 类型值
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- 包含函数级注释说明 NDCG 计算逻辑和公式
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</acceptance_criteria>
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### Task 2: 实现 MRR 和命中分布计算（含边缘情况处理）
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<read_first>
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- D:\code\php\amlhc\application\admin\model\History.php (新增的 _calculateNDCG 方法位置)
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</read_first>
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|
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<action>
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||||||
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在 `_calculateNDCG` 方法后继续新增 `_calculateMRR` 和 `_calculateHitDistribution` 方法：
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```php
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/**
|
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* 计算 MRR (Mean Reciprocal Rank)
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* 平均倒数排名，关注命中号码的具体排名位置
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*
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||||||
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* 公式说明:
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||||||
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* - MRR = Σ(1/rank_i) / N，其中 rank_i 为命中号码的排名，N 为测试总数
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||||||
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* - 未命中的测试项贡献 0 到倒数排名
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* - MRR 范围 0-1，越接近1表示命中号码平均排名越靠前
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*
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* @param array $backtestDetails 回测详情数组，每项包含 {hit: bool, rank: int}
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||||||
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* @return float MRR值 (0-1范围)，空数据时返回0
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||||||
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*/
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||||||
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private function _calculateMRR($backtestDetails)
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||||||
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{
|
||||||
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// 边缘情况处理：空预测
|
||||||
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if (empty($backtestDetails)) {
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||||||
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return 0;
|
||||||
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}
|
||||||
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|
||||||
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$reciprocalRanks = [];
|
||||||
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|
||||||
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foreach ($backtestDetails as $detail) {
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||||||
|
if (!isset($detail['hit']) || !isset($detail['rank'])) {
|
||||||
|
continue; // 跳过无效数据
|
||||||
|
}
|
||||||
|
if ($detail['hit'] && $detail['rank'] > 0) {
|
||||||
|
$reciprocalRanks[] = 1 / $detail['rank'];
|
||||||
|
} else {
|
||||||
|
$reciprocalRanks[] = 0; // 未命中记为0
|
||||||
|
}
|
||||||
|
}
|
||||||
|
|
||||||
|
return count($reciprocalRanks) > 0
|
||||||
|
? round(array_sum($reciprocalRanks) / count($reciprocalRanks), 4)
|
||||||
|
: 0;
|
||||||
|
}
|
||||||
|
|
||||||
|
/**
|
||||||
|
* 计算命中率分布
|
||||||
|
* 统计各排名位置(1-5)的命中次数分布
|
||||||
|
*
|
||||||
|
* 结构定义:
|
||||||
|
* - 返回格式: {rank_1: n, rank_2: n, rank_3: n, rank_4: n, rank_5: n}
|
||||||
|
* - rank_N 表示预测排名第N位的命中次数
|
||||||
|
* - 用于前端柱状图可视化展示
|
||||||
|
*
|
||||||
|
* @param array $backtestDetails 回测详情数组，每项包含 {hit: bool, rank: int}
|
||||||
|
* @return array 各排名(1-5)的命中次数统计，键名为 rank_1 到 rank_5
|
||||||
|
*/
|
||||||
|
private function _calculateHitDistribution($backtestDetails)
|
||||||
|
{
|
||||||
|
// 边缘情况处理：空预测返回全0分布
|
||||||
|
if (empty($backtestDetails)) {
|
||||||
|
return [
|
||||||
|
'rank_1' => 0,
|
||||||
|
'rank_2' => 0,
|
||||||
|
'rank_3' => 0,
|
||||||
|
'rank_4' => 0,
|
||||||
|
'rank_5' => 0
|
||||||
|
];
|
||||||
|
}
|
||||||
|
|
||||||
|
// 初始化分布数组，键名使用 rank_N 格式便于前端解析
|
||||||
|
$distribution = [
|
||||||
|
'rank_1' => 0,
|
||||||
|
'rank_2' => 0,
|
||||||
|
'rank_3' => 0,
|
||||||
|
'rank_4' => 0,
|
||||||
|
'rank_5' => 0
|
||||||
|
];
|
||||||
|
|
||||||
|
foreach ($backtestDetails as $detail) {
|
||||||
|
if (!isset($detail['hit']) || !isset($detail['rank'])) {
|
||||||
|
continue; // 跳过无效数据
|
||||||
|
}
|
||||||
|
if ($detail['hit'] && $detail['rank'] >= 1 && $detail['rank'] <= 5) {
|
||||||
|
$key = 'rank_' . $detail['rank'];
|
||||||
|
$distribution[$key]++;
|
||||||
|
}
|
||||||
|
}
|
||||||
|
|
||||||
|
return $distribution;
|
||||||
|
}
|
||||||
|
```
|
||||||
|
|
||||||
|
实现要点：
|
||||||
|
- MRR: 命中号码排名倒数平均值，公式 Σ(1/rank)/N
|
||||||
|
- 命中分布: 明确结构为 `{rank_1: n, rank_2: n, ..., rank_5: n}`
|
||||||
|
- 两个方法均添加空预测保护和无效数据跳过逻辑
|
||||||
|
- hit_distribution 使用 rank_N 键名格式，便于前端柱状图渲染
|
||||||
|
</action>
|
||||||
|
|
||||||
|
<acceptance_criteria>
|
||||||
|
- grep 正则匹配: `_calculateMRR\s*\(` 在 History.php 中存在
|
||||||
|
- grep 正则匹配: `_calculateHitDistribution\s*\(` 在 History.php 中存在
|
||||||
|
- grep 匹配: `empty($backtestDetails)` 在两个方法中均存在（空预测保护）
|
||||||
|
- grep 匹配: `rank_1|rank_2|rank_3|rank_4|rank_5` 在 _calculateHitDistribution 中存在
|
||||||
|
- 两个方法均包含函数级注释
|
||||||
|
</acceptance_criteria>
|
||||||
|
|
||||||
|
### Task 3: 扩展 _runBacktestV3 返回结果（含数据量检查）
|
||||||
|
|
||||||
|
<read_first>
|
||||||
|
- D:\code\php\amlhc\application\admin\model\History.php (line 3549-3556, _runBacktestV3 返回语句)
|
||||||
|
</read_first>
|
||||||
|
|
||||||
|
<action>
|
||||||
|
修改 `_runBacktestV3` 方法的返回语句，在原有返回结构中添加新指标和数据量验证：
|
||||||
|
|
||||||
|
找到以下代码段（约 line 3549-3556）：
|
||||||
|
```php
|
||||||
|
return [
|
||||||
|
'hit_rate' => $hitRate,
|
||||||
|
'avg_rank' => $avgRank,
|
||||||
|
'total_tests' => $testCount,
|
||||||
|
'total_hits' => $hits,
|
||||||
|
'details' => $details
|
||||||
|
];
|
||||||
|
```
|
||||||
|
|
||||||
|
替换为：
|
||||||
|
```php
|
||||||
|
// 计算新增指标（添加数据量检查）
|
||||||
|
$minDataThreshold = 50; // 置信度计算最小数据量阈值
|
||||||
|
|
||||||
|
// 如果测试数据不足，返回默认值并添加警告
|
||||||
|
if ($testCount < $minDataThreshold) {
|
||||||
|
$ndcg5 = 0;
|
||||||
|
$mrr = 0;
|
||||||
|
$hitDistribution = [
|
||||||
|
'rank_1' => 0,
|
||||||
|
'rank_2' => 0,
|
||||||
|
'rank_3' => 0,
|
||||||
|
'rank_4' => 0,
|
||||||
|
'rank_5' => 0
|
||||||
|
];
|
||||||
|
$dataWarning = '回测数据不足(' . $testCount . '期)，建议至少50期以获得可靠指标';
|
||||||
|
} else {
|
||||||
|
$ndcg5 = $this->_calculateNDCG($details, 5);
|
||||||
|
$mrr = $this->_calculateMRR($details);
|
||||||
|
$hitDistribution = $this->_calculateHitDistribution($details);
|
||||||
|
$dataWarning = null;
|
||||||
|
}
|
||||||
|
|
||||||
|
$precision5 = $testCount > 0 ? round($hits / ($testCount * 5) * 100, 2) : 0;
|
||||||
|
|
||||||
|
return [
|
||||||
|
'hit_rate' => $hitRate,
|
||||||
|
'avg_rank' => $avgRank,
|
||||||
|
'total_tests' => $testCount,
|
||||||
|
'total_hits' => $hits,
|
||||||
|
'details' => $details,
|
||||||
|
// 新增排名质量指标
|
||||||
|
'ndcg_5' => $ndcg5,
|
||||||
|
'mrr' => $mrr,
|
||||||
|
'hit_distribution' => $hitDistribution,
|
||||||
|
'precision_5' => $precision5,
|
||||||
|
// 数据量警告（不足时提示）
|
||||||
|
'data_warning' => $dataWarning,
|
||||||
|
'data_sufficient' => $testCount >= $minDataThreshold
|
||||||
|
];
|
||||||
|
```
|
||||||
|
|
||||||
|
注意：
|
||||||
|
- 新增指标计算放在 return 语句之前，确保 $details 数组已完整构建
|
||||||
|
- 添加最小数据量检查（50期），不足时返回默认值和警告提示
|
||||||
|
- 新增 data_warning 和 data_sufficient 字段供前端展示
|
||||||
|
</action>
|
||||||
|
|
||||||
|
<acceptance_criteria>
|
||||||
|
- grep 匹配: `ndcg_5` 在 _runBacktestV3 返回结构中存在
|
||||||
|
- grep 匹配: `mrr` 在 _runBacktestV3 返回结构中存在
|
||||||
|
- grep 匹配: `hit_distribution` 在 _runBacktestV3 返回结构中存在
|
||||||
|
- grep 匹配: `precision_5` 在 _runBacktestV3 返回结构中存在
|
||||||
|
- grep 匹配: `data_warning` 在 _runBacktestV3 返回结构中存在
|
||||||
|
- grep 匹配: `minDataThreshold` 变量在方法中存在
|
||||||
|
</acceptance_criteria>
|
||||||
|
|
||||||
|
## Verification
|
||||||
|
|
||||||
|
执行预测接口验证新指标返回：
|
||||||
|
|
||||||
|
```bash
|
||||||
|
curl -s "http://127.0.0.1:8000/admin/history/predictV3?periods=200&backtest=10" | grep -E "ndcg_5|mrr|hit_distribution|precision_5|data_warning"
|
||||||
|
```
|
||||||
|
|
||||||
|
预期结果：返回 JSON 中包含 ndcg_5、mrr、hit_distribution、precision_5、data_warning 字段。
|
||||||
|
|
||||||
|
## Success Criteria
|
||||||
|
|
||||||
|
1. `_calculateNDCG`、`_calculateMRR`、`_calculateHitDistribution` 三个方法已实现
|
||||||
|
2. 所有计算方法包含空预测保护和数据完整性检查
|
||||||
|
3. NDCG 公式在注释中完整说明：DCG = Σ(1/log2(rank+1))
|
||||||
|
4. hit_distribution 结构明确为 `{rank_1..rank_5: counts}` 格式
|
||||||
|
5. `_runBacktestV3` 返回结构包含 ndcg_5、mrr、hit_distribution、precision_5、data_warning 字段
|
||||||
|
6. 添加数据量检查，不足50期时返回警告
|
||||||
|
7. 所有新增方法包含函数级注释
|
||||||
|
|
||||||
|
## Output
|
||||||
|
|
||||||
|
完成后创建 `.planning/phases/11-predictv3/11-01-SUMMARY.md`
|
||||||
@@ -0,0 +1,325 @@
|
|||||||
|
---
|
||||||
|
phase: 11-predictv3
|
||||||
|
plan: 02
|
||||||
|
type: execute
|
||||||
|
wave: 1
|
||||||
|
depends_on: []
|
||||||
|
files_modified:
|
||||||
|
- application/admin/model/History.php
|
||||||
|
autonomous: true
|
||||||
|
requirements:
|
||||||
|
- PRED-01
|
||||||
|
must_haves:
|
||||||
|
truths:
|
||||||
|
- "用户可以看到每个预测号码的置信度百分比"
|
||||||
|
- "用户可以看到 Top5 预测的整体置信度"
|
||||||
|
- "置信度基于历史命中率、得分集中度、得分分布三个维度计算"
|
||||||
|
- "系统在数据不足时提供合理的置信度估算"
|
||||||
|
artifacts:
|
||||||
|
- path: "application/admin/model/History.php"
|
||||||
|
provides: "置信度计算方法"
|
||||||
|
contains: "_calculateConfidence|_getHistoricalHitRateByRank|_getScoreDistributionConfidence|_getScoreConcentration"
|
||||||
|
key_links:
|
||||||
|
- from: "getPredictionV3"
|
||||||
|
to: "_calculateConfidence"
|
||||||
|
via: "method call before return"
|
||||||
|
---
|
||||||
|
|
||||||
|
# Phase 11 - Plan 02: 置信度评估实现
|
||||||
|
|
||||||
|
## Objective
|
||||||
|
|
||||||
|
为预测结果添加置信度评估，帮助用户判断预测可靠性。置信度基于历史排名命中率、得分分布、得分集中度三个维度计算。
|
||||||
|
|
||||||
|
**Purpose:** 当前预测结果只有排名和得分，缺少置信度指标。用户无法判断预测结果的可信程度，置信度评估能有效辅助用户决策。
|
||||||
|
|
||||||
|
**Output:** `History.php` 中新增置信度计算方法，`getPredictionV3` 返回结果扩展 confidence 字段。
|
||||||
|
|
||||||
|
## Tasks
|
||||||
|
|
||||||
|
### Task 1: 实现置信度核心计算方法（含明确维度定义和数据量检查）
|
||||||
|
|
||||||
|
<read_first>
|
||||||
|
- D:\code\php\amlhc\application\admin\model\History.php (line 2436-2444, getPredictionV3 返回语句)
|
||||||
|
- D:\code\php\amlhc\application\admin\model\History.php (line 3495-3556, _runBacktestV3 方法)
|
||||||
|
</read_first>
|
||||||
|
|
||||||
|
<action>
|
||||||
|
在 `History.php` 类末尾新增置信度计算相关方法：
|
||||||
|
|
||||||
|
```php
|
||||||
|
/**
|
||||||
|
* 计算预测置信度
|
||||||
|
*
|
||||||
|
* 置信度组成（三个维度加权平均）:
|
||||||
|
* - 维度1: 历史排名命中率 (权重0.4) - 基于回测数据统计各排名位置的命中率
|
||||||
|
* - 维度2: 得分分布置信度 (权重0.3) - 当前号码得分与Top5得分范围的比例关系
|
||||||
|
* - 维度3: 得分集中度 (权重0.3) - Top5得分与平均得分的差距，差距越大置信度越高
|
||||||
|
*
|
||||||
|
* 加权公式:
|
||||||
|
* confidence = 0.4 * historical_hit_rate + 0.3 * score_distribution + 0.3 * score_concentration
|
||||||
|
*
|
||||||
|
* 阈值定义:
|
||||||
|
* - 高置信度: >= 70% (绿色展示)
|
||||||
|
* - 中置信度: 50-70% (橙色展示)
|
||||||
|
* - 低置信度: < 50% (红色展示)
|
||||||
|
*
|
||||||
|
* @param array $predictions 预测结果数组（Top5）
|
||||||
|
* @param array $backtest 回测结果
|
||||||
|
* @param array $scoresAll 所有号码得分详情（可选，用于集中度计算）
|
||||||
|
* @param int $minDataThreshold 最小数据量阈值，默认50期
|
||||||
|
* @return array {confidence_scores: [], overall_confidence: float, data_warning: string|null}
|
||||||
|
*/
|
||||||
|
private function _calculateConfidence($predictions, $backtest, $scoresAll = null, $minDataThreshold = 50)
|
||||||
|
{
|
||||||
|
// 数据量检查
|
||||||
|
$dataWarning = null;
|
||||||
|
$hasBacktest = $backtest && !empty($backtest['details']) && $backtest['total_tests'] > 0;
|
||||||
|
|
||||||
|
if (!$hasBacktest || $backtest['total_tests'] < $minDataThreshold) {
|
||||||
|
$dataWarning = '回测数据不足(' . ($backtest['total_tests'] ?? 0) . '期)，置信度基于估算，建议至少50期';
|
||||||
|
}
|
||||||
|
|
||||||
|
$confidenceScores = [];
|
||||||
|
|
||||||
|
// 计算Top5平均得分（用于集中度计算）
|
||||||
|
$avgScore = 0;
|
||||||
|
if (!empty($predictions)) {
|
||||||
|
$totalScore = array_sum(array_column($predictions, 'score'));
|
||||||
|
$avgScore = $totalScore / count($predictions);
|
||||||
|
}
|
||||||
|
|
||||||
|
foreach ($predictions as $idx => $pred) {
|
||||||
|
$rank = $idx + 1;
|
||||||
|
$num = $pred['num'];
|
||||||
|
$score = $pred['score'];
|
||||||
|
|
||||||
|
// 维度1: 历史排名命中率 (权重0.4)
|
||||||
|
$rankHitRate = $this->_getHistoricalHitRateByRank($rank, $backtest);
|
||||||
|
|
||||||
|
// 维度2: 得分分布置信度 (权重0.3) - 得分比例
|
||||||
|
$scoreDistribution = $this->_getScoreDistributionConfidence($score, $predictions);
|
||||||
|
|
||||||
|
// 维度3: 得分集中度 (权重0.3) - Top得分与平均得分的差距比例
|
||||||
|
$scoreConcentration = $this->_getScoreConcentration($score, $avgScore, $predictions);
|
||||||
|
|
||||||
|
// 综合置信度（加权平均）
|
||||||
|
$overallConfidence = $rankHitRate * 0.4 + $scoreDistribution * 0.3 + $scoreConcentration * 0.3;
|
||||||
|
|
||||||
|
$confidenceScores[] = [
|
||||||
|
'num' => $num,
|
||||||
|
'rank' => $rank,
|
||||||
|
'confidence' => round($overallConfidence * 100, 1),
|
||||||
|
'rank_hit_rate' => round($rankHitRate * 100, 1),
|
||||||
|
'score_distribution' => round($scoreDistribution * 100, 1),
|
||||||
|
'score_concentration' => round($scoreConcentration * 100, 1)
|
||||||
|
];
|
||||||
|
}
|
||||||
|
|
||||||
|
// 整体置信度（Top5平均）
|
||||||
|
$overallConfidence = count($confidenceScores) > 0
|
||||||
|
? round(array_sum(array_column($confidenceScores, 'confidence')) / count($confidenceScores), 1)
|
||||||
|
: 0;
|
||||||
|
|
||||||
|
return [
|
||||||
|
'confidence_scores' => $confidenceScores,
|
||||||
|
'overall_confidence' => $overallConfidence,
|
||||||
|
'data_warning' => $dataWarning
|
||||||
|
];
|
||||||
|
}
|
||||||
|
|
||||||
|
/**
|
||||||
|
* 基于历史排名获取命中率
|
||||||
|
*
|
||||||
|
* 计算方法:
|
||||||
|
* - 有回测数据时: 统计各排名的历史命中次数 / 总测试次数
|
||||||
|
* - 无回测数据时: 根据排名估算，排名越靠前置信度越高
|
||||||
|
* 估算公式: 1 - (rank - 1) * 0.15，即第1名估算85%，第5名估算25%
|
||||||
|
*
|
||||||
|
* @param int $rank 排名位置 (1-5)
|
||||||
|
* @param array $backtest 回测结果
|
||||||
|
* @return float 该排名的历史命中率 (0-1)
|
||||||
|
*/
|
||||||
|
private function _getHistoricalHitRateByRank($rank, $backtest)
|
||||||
|
{
|
||||||
|
if (!$backtest || empty($backtest['details']) || $backtest['total_tests'] == 0) {
|
||||||
|
// 无回测数据时，根据排名估算（排名越靠前置信度越高）
|
||||||
|
// 估算公式: 1 - (rank - 1) * 0.15
|
||||||
|
// 第1名: 1.0, 第2名: 0.85, 第3名: 0.70, 第4名: 0.55, 第5名: 0.40
|
||||||
|
return max(0, 1 - ($rank - 1) * 0.15);
|
||||||
|
}
|
||||||
|
|
||||||
|
// 统计各排名的历史命中次数
|
||||||
|
$rankHits = array_fill(1, 5, 0);
|
||||||
|
foreach ($backtest['details'] as $detail) {
|
||||||
|
if ($detail['hit'] && $detail['rank'] >= 1 && $detail['rank'] <= 5) {
|
||||||
|
$rankHits[$detail['rank']]++;
|
||||||
|
}
|
||||||
|
}
|
||||||
|
|
||||||
|
$totalTests = $backtest['total_tests'];
|
||||||
|
return $totalTests > 0 ? $rankHits[$rank] / $totalTests : 0;
|
||||||
|
}
|
||||||
|
|
||||||
|
/**
|
||||||
|
* 计算得分分布置信度
|
||||||
|
*
|
||||||
|
* 计算方法:
|
||||||
|
* - 得分比例 = (score - bottomScore) / (topScore - bottomScore)
|
||||||
|
* - 得分越接近第一名，置信度越高
|
||||||
|
* - 所有得分相同时返回1
|
||||||
|
*
|
||||||
|
* @param float $score 当前号码得分
|
||||||
|
* @param array $predictions 所有预测结果
|
||||||
|
* @return float 得分置信度 (0-1)
|
||||||
|
*/
|
||||||
|
private function _getScoreDistributionConfidence($score, $predictions)
|
||||||
|
{
|
||||||
|
if (empty($predictions)) return 0;
|
||||||
|
|
||||||
|
$topScore = $predictions[0]['score'];
|
||||||
|
$bottomScore = end($predictions)['score'];
|
||||||
|
|
||||||
|
if ($topScore == $bottomScore) return 1; // 所有得分相同
|
||||||
|
|
||||||
|
// 得分比例：(score - bottom) / (top - bottom)
|
||||||
|
$ratio = ($score - $bottomScore) / ($topScore - $bottomScore);
|
||||||
|
return max(0, min(1, $ratio));
|
||||||
|
}
|
||||||
|
|
||||||
|
/**
|
||||||
|
* 计算得分集中度
|
||||||
|
*
|
||||||
|
* 计算方法:
|
||||||
|
* - 集中度 = (score - avgScore) / (topScore - avgScore) 如果 score > avgScore
|
||||||
|
* - 集中度 = 0 如果 score <= avgScore
|
||||||
|
* - Top得分与平均得分差距越大，集中度越高，表示预测结果区分度明显
|
||||||
|
*
|
||||||
|
* @param float $score 当前号码得分
|
||||||
|
* @param float $avgScore Top5平均得分
|
||||||
|
* @param array $predictions 所有预测结果
|
||||||
|
* @return float 集中度置信度 (0-1)
|
||||||
|
*/
|
||||||
|
private function _getScoreConcentration($score, $avgScore, $predictions)
|
||||||
|
{
|
||||||
|
if (empty($predictions)) return 0;
|
||||||
|
|
||||||
|
$topScore = $predictions[0]['score'];
|
||||||
|
|
||||||
|
// 如果得分低于平均，集中度为0
|
||||||
|
if ($score <= $avgScore) {
|
||||||
|
return 0;
|
||||||
|
}
|
||||||
|
|
||||||
|
// 如果Top得分等于平均，所有得分相同，集中度为0.5
|
||||||
|
if ($topScore == $avgScore) {
|
||||||
|
return $score == $topScore ? 0.5 : 0;
|
||||||
|
}
|
||||||
|
|
||||||
|
// 集中度 = (score - avg) / (top - avg)
|
||||||
|
$concentration = ($score - $avgScore) / ($topScore - $avgScore);
|
||||||
|
return max(0, min(1, $concentration));
|
||||||
|
}
|
||||||
|
```
|
||||||
|
|
||||||
|
实现要点：
|
||||||
|
- **维度重命名**：将"多维度一致性"改为"得分集中度"，更明确地表示Top得分与平均得分的差距
|
||||||
|
- **加权公式明确**：`confidence = 0.4*历史命中率 + 0.3*得分分布 + 0.3*得分集中度`
|
||||||
|
- **数据量检查**：回测数据不足50期时返回警告
|
||||||
|
- **阈值明确**：>=70%高、50-70%中、<50%低
|
||||||
|
- **无数据fallback**：回测缺失时使用估算公式
|
||||||
|
</action>
|
||||||
|
|
||||||
|
<acceptance_criteria>
|
||||||
|
- grep 正则匹配: `_calculateConfidence\s*\(` 在 History.php 中存在
|
||||||
|
- grep 正则匹配: `_getHistoricalHitRateByRank\s*\(` 在 History.php 中存在
|
||||||
|
- grep 正则匹配: `_getScoreDistributionConfidence\s*\(` 在 History.php 中存在
|
||||||
|
- grep 正则匹配: `_getScoreConcentration\s*\(` 在 History.php 中存在（替代原_getDimensionConsistency）
|
||||||
|
- grep 匹配: `minDataThreshold` 在方法中存在（数据量阈值）
|
||||||
|
- grep 匹配: `score_concentration` 在返回结构中存在（替代原consistency）
|
||||||
|
- 所有方法包含函数级注释，注释中包含加权公式说明
|
||||||
|
</acceptance_criteria>
|
||||||
|
|
||||||
|
### Task 2: 在 getPredictionV3 中调用置信度计算（含数据量传递）
|
||||||
|
|
||||||
|
<read_first>
|
||||||
|
- D:\code\php\amlhc\application\admin\model\History.php (line 2413-2444, getPredictionV3 返回部分)
|
||||||
|
</read_first>
|
||||||
|
|
||||||
|
<action>
|
||||||
|
在 `getPredictionV3` 方法中，找到以下代码段（约 line 2413-2444）：
|
||||||
|
|
||||||
|
```php
|
||||||
|
// ====== 10. 历史回测验证 ======
|
||||||
|
$backtest = $skipBacktest ? null : $this->_runBacktestV3($periods, $weights, $backtestCount, $cutoffTime);
|
||||||
|
|
||||||
|
// 计算命中情况
|
||||||
|
$hitInfo = null;
|
||||||
|
...
|
||||||
|
return [
|
||||||
|
'predictions' => $predictions,
|
||||||
|
...
|
||||||
|
];
|
||||||
|
```
|
||||||
|
|
||||||
|
在 `$backtest` 计算后、`$hitInfo` 计算前，插入置信度计算代码：
|
||||||
|
|
||||||
|
```php
|
||||||
|
// ====== 10. 历史回测验证 ======
|
||||||
|
$backtest = $skipBacktest ? null : $this->_runBacktestV3($periods, $weights, $backtestCount, $cutoffTime);
|
||||||
|
|
||||||
|
// ====== 11. 置信度评估（新增）======
|
||||||
|
// 最小数据量阈值设为50期，不足时置信度基于估算
|
||||||
|
$minDataThreshold = 50;
|
||||||
|
$confidence = $this->_calculateConfidence($predictions, $backtest, null, $minDataThreshold);
|
||||||
|
|
||||||
|
// 计算命中情况
|
||||||
|
$hitInfo = null;
|
||||||
|
...
|
||||||
|
```
|
||||||
|
|
||||||
|
并修改返回结构，添加 `confidence` 字段：
|
||||||
|
|
||||||
|
```php
|
||||||
|
return [
|
||||||
|
'predictions' => $predictions,
|
||||||
|
'last_special' => $lastSpecial,
|
||||||
|
'last_expect' => $lastExpect,
|
||||||
|
'analysis' => $analysis,
|
||||||
|
'actual_result' => $actualResult,
|
||||||
|
'hit_info' => $hitInfo,
|
||||||
|
'backtest' => $backtest,
|
||||||
|
'confidence' => $confidence // 新增置信度字段
|
||||||
|
];
|
||||||
|
```
|
||||||
|
</action>
|
||||||
|
|
||||||
|
<acceptance_criteria>
|
||||||
|
- grep 匹配: `_calculateConfidence` 在 getPredictionV3 方法中被调用
|
||||||
|
- grep 匹配: `$minDataThreshold` 在 getPredictionV3 中存在
|
||||||
|
- grep 匹配: `'confidence'` 在 getPredictionV3 返回结构中存在
|
||||||
|
- 置信度计算在回测验证之后执行
|
||||||
|
</acceptance_criteria>
|
||||||
|
|
||||||
|
## Verification
|
||||||
|
|
||||||
|
执行预测接口验证置信度字段返回：
|
||||||
|
|
||||||
|
```bash
|
||||||
|
curl -s "http://127.0.0.1:8000/admin/history/predictV3?periods=200&backtest=10" | grep -E "confidence|overall_confidence|confidence_scores|score_concentration"
|
||||||
|
```
|
||||||
|
|
||||||
|
预期结果：返回 JSON 中包含 confidence、overall_confidence、confidence_scores、score_concentration 字段。
|
||||||
|
|
||||||
|
## Success Criteria
|
||||||
|
|
||||||
|
1. `_calculateConfidence` 及 4 个辅助方法已实现
|
||||||
|
2. 置信度维度重命名为：历史排名命中率、得分分布、得分集中度
|
||||||
|
3. 加权公式在注释中明确：`confidence = 0.4*历史 + 0.3*分布 + 0.3*集中度`
|
||||||
|
4. 添加数据量检查，不足50期时返回警告
|
||||||
|
5. `getPredictionV3` 返回结构包含 confidence 字段
|
||||||
|
6. 所有新增方法包含函数级注释
|
||||||
|
|
||||||
|
## Output
|
||||||
|
|
||||||
|
完成后创建 `.planning/phases/11-predictv3/11-02-SUMMARY.md`
|
||||||
@@ -0,0 +1,278 @@
|
|||||||
|
---
|
||||||
|
phase: 11-predictv3
|
||||||
|
plan: 03
|
||||||
|
type: execute
|
||||||
|
wave: 2
|
||||||
|
depends_on:
|
||||||
|
- 11-01
|
||||||
|
- 11-02
|
||||||
|
files_modified:
|
||||||
|
- public/assets/js/backend/history.js
|
||||||
|
autonomous: true
|
||||||
|
requirements:
|
||||||
|
- PRED-01
|
||||||
|
- PRED-02
|
||||||
|
must_haves:
|
||||||
|
truths:
|
||||||
|
- "用户可以在预测弹窗中看到每个号码的置信度百分比"
|
||||||
|
- "用户可以在回测结果区域看到 NDCG@5 和 MRR 指标"
|
||||||
|
- "用户可以看到各排名位置的命中分布柱状图"
|
||||||
|
- "用户可以看到数据不足时的警告提示"
|
||||||
|
artifacts:
|
||||||
|
- path: "public/assets/js/backend/history.js"
|
||||||
|
provides: "置信度和新回测指标前端展示"
|
||||||
|
contains: "renderPredict|confidence|ndcg_5|mrr|hit_distribution|data_warning"
|
||||||
|
key_links:
|
||||||
|
- from: "renderPredict"
|
||||||
|
to: "backtest.ndcg_5, backtest.mrr, confidence, backtest.data_warning"
|
||||||
|
via: "property access in rendering logic"
|
||||||
|
---
|
||||||
|
|
||||||
|
# Phase 11 - Plan 03: 前端展示优化
|
||||||
|
|
||||||
|
## Objective
|
||||||
|
|
||||||
|
更新前端 `renderPredict` 方法，展示新增的置信度指标和扩展的回测指标（NDCG、MRR、命中分布、数据警告）。
|
||||||
|
|
||||||
|
**Purpose:** 后端已计算置信度和新回测指标，前端需要将这些数据可视化呈现给用户，提升预测结果的可读性和决策辅助价值。
|
||||||
|
|
||||||
|
**Output:** `history.js` 中的 `renderPredict` 方法扩展，新增置信度展示区域和回测指标扩展展示。
|
||||||
|
|
||||||
|
## Tasks
|
||||||
|
|
||||||
|
### Task 1: 添加置信度展示区域（含数据警告提示）
|
||||||
|
|
||||||
|
<read_first>
|
||||||
|
- D:\code\php\amlhc\public\assets\js\backend\history.js (line 1700-1871, renderPredict 方法)
|
||||||
|
</read_first>
|
||||||
|
|
||||||
|
<action>
|
||||||
|
在 `renderPredict` 方法中，找到以下变量声明位置（约 line 1701）：
|
||||||
|
|
||||||
|
```javascript
|
||||||
|
var predictions = data.predictions || [];
|
||||||
|
var analysis = data.analysis || {};
|
||||||
|
var hitInfo = data.hit_info || null;
|
||||||
|
var actualResult = data.actual_result || null;
|
||||||
|
var backtest = data.backtest || null;
|
||||||
|
```
|
||||||
|
|
||||||
|
在 `backtest` 声明后添加 `confidence` 变量：
|
||||||
|
|
||||||
|
```javascript
|
||||||
|
var confidence = data.confidence || null;
|
||||||
|
```
|
||||||
|
|
||||||
|
然后在回测验证结果展示区域（约 line 1732-1751）之前，插入置信度展示区域：
|
||||||
|
|
||||||
|
在 line 1732 之前（即 `// 回测验证结果` 注释之前）插入：
|
||||||
|
|
||||||
|
```javascript
|
||||||
|
// 置信度评估展示（V2和V3版本）
|
||||||
|
if (confidence && (version === 'v2' || version === 'v3')) {
|
||||||
|
html += '<div style="background:#fff8e1;border:1px solid #ffb300;border-radius:6px;padding:12px;margin-bottom:15px;">';
|
||||||
|
html += '<div style="font-size:13px;font-weight:bold;color:#ff8f00;margin-bottom:8px;"><i class="fa fa-star-half-o"></i> 预测置信度评估</div>';
|
||||||
|
|
||||||
|
// 数据警告提示（数据不足时显示）
|
||||||
|
if (confidence.data_warning) {
|
||||||
|
html += '<div style="font-size:11px;color:#d32f2f;background:#ffebee;padding:6px;border-radius:4px;margin-bottom:8px;"><i class="fa fa-exclamation-triangle"></i> ' + confidence.data_warning + '</div>';
|
||||||
|
}
|
||||||
|
|
||||||
|
html += '<div style="display:flex;gap:20px;align-items:center;">';
|
||||||
|
html += '<div style="text-align:center;"><div style="font-size:24px;font-weight:bold;color:#ff8f00;">' + confidence.overall_confidence + '%</div><div style="font-size:12px;color:#666;">整体置信度</div></div>';
|
||||||
|
|
||||||
|
// 各排名置信度（使用得分集中度维度）
|
||||||
|
if (confidence.confidence_scores && confidence.confidence_scores.length > 0) {
|
||||||
|
html += '<div style="display:flex;gap:8px;">';
|
||||||
|
for (var i = 0; i < confidence.confidence_scores.length; i++) {
|
||||||
|
var cs = confidence.confidence_scores[i];
|
||||||
|
// 阈值定义：>=70%高(绿)、50-70%中(橙)、<50%低(红)
|
||||||
|
var confLevel = cs.confidence >= 70 ? '高' : (cs.confidence >= 50 ? '中' : '低');
|
||||||
|
var confColor = cs.confidence >= 70 ? '#4caf50' : (cs.confidence >= 50 ? '#ff9800' : '#f44336');
|
||||||
|
html += '<div style="text-align:center;padding:5px;background:#fff;border-radius:4px;">';
|
||||||
|
html += '<div style="font-size:14px;font-weight:bold;color:' + confColor + ';">' + cs.confidence + '%</div>';
|
||||||
|
html += '<div style="font-size:10px;color:#999;">#' + cs.rank + '</div>';
|
||||||
|
html += '</div>';
|
||||||
|
}
|
||||||
|
html += '</div>';
|
||||||
|
}
|
||||||
|
html += '</div></div>';
|
||||||
|
}
|
||||||
|
```
|
||||||
|
|
||||||
|
实现要点：
|
||||||
|
- 整体置信度以大数字展示，各排名置信度以小卡片形式横向排列
|
||||||
|
- 置信度分三级：高(>=70%, 绿色)、中(50-70%, 橙色)、低(<50%, 红色)
|
||||||
|
- 只在 V2 和 V3 版本中显示
|
||||||
|
- 新增 data_warning 展示：数据不足时显示红色警告提示
|
||||||
|
</action>
|
||||||
|
|
||||||
|
<acceptance_criteria>
|
||||||
|
- grep 匹配: `confidence.overall_confidence` 在 history.js renderPredict 方法中存在
|
||||||
|
- grep 匹配: `confidence.confidence_scores` 在 history.js renderPredict 方法中存在
|
||||||
|
- grep 匹配: `confidence.data_warning` 在 history.js renderPredict 方法中存在
|
||||||
|
- grep 匹配: `confLevel` 和 `confColor` 变量在 history.js 中存在
|
||||||
|
- 置信度展示区域在回测验证结果之前显示
|
||||||
|
</acceptance_criteria>
|
||||||
|
|
||||||
|
### Task 2: 扩展回测指标展示区域（含数据警告和命中分布柱状图）
|
||||||
|
|
||||||
|
<read_first>
|
||||||
|
- D:\code\php\amlhc\public\assets\js\backend\history.js (line 1732-1751, 回测验证结果展示区域)
|
||||||
|
</read_first>
|
||||||
|
|
||||||
|
<action>
|
||||||
|
在 `renderPredict` 方法中，找到回测验证结果展示区域（约 line 1732-1751），现有代码展示命中率、命中次数、平均排名三个指标。
|
||||||
|
|
||||||
|
找到以下代码段：
|
||||||
|
|
||||||
|
```javascript
|
||||||
|
html += '<div style="display:flex;gap:20px;">';
|
||||||
|
html += '<div style="text-align:center;"><div style="font-size:24px;font-weight:bold;color:#2196f3;">' + backtest.hit_rate + '%</div><div style="font-size:12px;color:#666;">命中率（Top5）</div></div>';
|
||||||
|
html += '<div style="text-align:center;"><div style="font-size:24px;font-weight:bold;color:#4caf50;">' + backtest.total_hits + '/' + backtest.total_tests + '</div><div style="font-size:12px;color:#666;">命中次数</div></div>';
|
||||||
|
html += '<div style="text-align:center;"><div style="font-size:24px;font-weight:bold;color:#ff9800;">' + (backtest.avg_rank || '—') + '</div><div style="font-size:12px;color:#666;">平均排名</div></div>';
|
||||||
|
html += '</div>';
|
||||||
|
```
|
||||||
|
|
||||||
|
替换为：
|
||||||
|
|
||||||
|
```javascript
|
||||||
|
// 回测数据警告提示
|
||||||
|
if (backtest.data_warning) {
|
||||||
|
html += '<div style="font-size:11px;color:#d32f2f;background:#ffebee;padding:6px;border-radius:4px;margin-bottom:8px;"><i class="fa fa-exclamation-triangle"></i> ' + backtest.data_warning + '</div>';
|
||||||
|
}
|
||||||
|
|
||||||
|
html += '<div style="display:flex;gap:15px;flex-wrap:wrap;">';
|
||||||
|
html += '<div style="text-align:center;padding:8px;"><div style="font-size:22px;font-weight:bold;color:#2196f3;">' + backtest.hit_rate + '%</div><div style="font-size:11px;color:#666;">命中率（Top5）</div></div>';
|
||||||
|
html += '<div style="text-align:center;padding:8px;"><div style="font-size:22px;font-weight:bold;color:#4caf50;">' + backtest.total_hits + '/' + backtest.total_tests + '</div><div style="font-size:11px;color:#666;">命中次数</div></div>';
|
||||||
|
html += '<div style="text-align:center;padding:8px;"><div style="font-size:22px;font-weight:bold;color:#ff9800;">' + (backtest.avg_rank || '—') + '</div><div style="font-size:11px;color:#666;">平均排名</div></div>';
|
||||||
|
|
||||||
|
// 新增指标：NDCG@5 和 MRR（百分比展示）
|
||||||
|
if (backtest.ndcg_5 !== undefined) {
|
||||||
|
html += '<div style="text-align:center;padding:8px;"><div style="font-size:22px;font-weight:bold;color:#9c27b0;">' + (backtest.ndcg_5 * 100).toFixed(1) + '%</div><div style="font-size:11px;color:#666;">NDCG@5</div></div>';
|
||||||
|
}
|
||||||
|
if (backtest.mrr !== undefined) {
|
||||||
|
html += '<div style="font-size:22px;font-weight:bold;color:#00bcd4;">' + (backtest.mrr * 100).toFixed(1) + '%</div><div style="font-size:11px;color:#666;">MRR</div></div>';
|
||||||
|
}
|
||||||
|
|
||||||
|
// 转移概率阶数显示（来自11-05的transition_order字段）
|
||||||
|
if (analysis && analysis.transition_order !== undefined) {
|
||||||
|
html += '<div style="text-align:center;padding:8px;"><div style="font-size:22px;font-weight:bold;color:#607d8b;">' + analysis.transition_order + '阶</div><div style="font-size:11px;color:#666;">转移概率</div></div>';
|
||||||
|
}
|
||||||
|
html += '</div>';
|
||||||
|
|
||||||
|
// 命中分布柱状图（使用rank_1..rank_5键名）
|
||||||
|
if (backtest.hit_distribution && Object.keys(backtest.hit_distribution).length > 0) {
|
||||||
|
var distribution = backtest.hit_distribution;
|
||||||
|
var maxHit = 0;
|
||||||
|
// 找最大值用于计算柱状图高度比例
|
||||||
|
for (var r = 1; r <= 5; r++) {
|
||||||
|
var key = 'rank_' + r;
|
||||||
|
if (distribution[key] > maxHit) {
|
||||||
|
maxHit = distribution[key];
|
||||||
|
}
|
||||||
|
}
|
||||||
|
|
||||||
|
html += '<div style="margin-top:10px;font-size:11px;color:#666;">命中分布（各排名命中次数）：</div>';
|
||||||
|
html += '<div style="display:flex;gap:8px;align-items:flex-end;height:60px;margin-top:5px;padding:5px;background:#f5f5f5;border-radius:4px;">';
|
||||||
|
for (var r = 1; r <= 5; r++) {
|
||||||
|
var key = 'rank_' + r;
|
||||||
|
var hitCount = distribution[key] || 0;
|
||||||
|
var barHeight = maxHit > 0 ? (hitCount / maxHit * 45) : 0;
|
||||||
|
var barColor = hitCount > 0 ? '#4caf50' : '#e0e0e0';
|
||||||
|
html += '<div style="text-align:center;min-width:50px;">';
|
||||||
|
html += '<div style="height:' + barHeight + 'px;background:' + barColor + ';border-radius:2px 2px 0 0;width:35px;margin:0 auto;"></div>';
|
||||||
|
html += '<div style="font-size:10px;color:#666;margin-top:2px;">#' + r + '</div>';
|
||||||
|
html += '<div style="font-size:11px;color:#333;font-weight:bold;">' + hitCount + '</div>';
|
||||||
|
html += '</div>';
|
||||||
|
}
|
||||||
|
html += '</div>';
|
||||||
|
}
|
||||||
|
```
|
||||||
|
|
||||||
|
实现要点：
|
||||||
|
- NDCG@5 和 MRR 以百分比形式展示（乘100后保留1位小数）
|
||||||
|
- 命中分布以简单柱状图展示，排名1-5横向排列
|
||||||
|
- 柱状图高度按最大命中次数比例计算
|
||||||
|
- 使用 rank_1..rank_5 键名格式解析分布数据
|
||||||
|
- 新增 data_warning 展示：回测数据不足时显示警告
|
||||||
|
- 转移概率阶数从 analysis.transition_order 获取（而非 backtest）
|
||||||
|
</action>
|
||||||
|
|
||||||
|
<acceptance_criteria>
|
||||||
|
- grep 匹配: `backtest.ndcg_5` 在 history.js renderPredict 方法中存在
|
||||||
|
- grep 匹配: `backtest.mrr` 在 history.js renderPredict 方法中存在
|
||||||
|
- grep 匹配: `backtest.hit_distribution` 在 history.js renderPredict 方法中存在
|
||||||
|
- grep 匹配: `backtest.data_warning` 在 history.js renderPredict 方法中存在
|
||||||
|
- grep 匹配: `rank_1|rank_2|rank_3|rank_4|rank_5` 在命中分布解析中存在
|
||||||
|
- grep 匹配: `analysis.transition_order` 在 history.js renderPredict 方法中存在
|
||||||
|
- 命中分布柱状图使用 div 元素实现
|
||||||
|
</acceptance_criteria>
|
||||||
|
|
||||||
|
### Task 3: 在预测号码卡片中显示置信度（含得分集中度展示）
|
||||||
|
|
||||||
|
<read_first>
|
||||||
|
- D:\code\php\amlhc\public\assets\js\backend\history.js (line 1828-1866, 预测号码列表渲染)
|
||||||
|
</read_first>
|
||||||
|
|
||||||
|
<action>
|
||||||
|
在预测号码列表渲染区域（约 line 1828-1866），找到号码卡片渲染代码：
|
||||||
|
|
||||||
|
在现有得分显示后添加置信度显示。找到以下代码段：
|
||||||
|
|
||||||
|
```javascript
|
||||||
|
html += '<div style="font-size:11px;color:#2e7d32;font-weight:bold;">得分:' + p.score + '</div>';
|
||||||
|
```
|
||||||
|
|
||||||
|
替换为：
|
||||||
|
|
||||||
|
```javascript
|
||||||
|
html += '<div style="font-size:11px;color:#2e7d32;font-weight:bold;">得分:' + p.score + '</div>';
|
||||||
|
|
||||||
|
// 显示置信度（V3版本）
|
||||||
|
if (version === 'v3' && confidence && confidence.confidence_scores) {
|
||||||
|
var csForNum = confidence.confidence_scores.find(function(c) { return c.num === p.num; });
|
||||||
|
if (csForNum) {
|
||||||
|
// 阈值定义：>=70%高(绿)、50-70%中(橙)、<50%低(红)
|
||||||
|
var confLevel = csForNum.confidence >= 70 ? '高' : (csForNum.confidence >= 50 ? '中' : '低');
|
||||||
|
var confColor = csForNum.confidence >= 70 ? '#4caf50' : (csForNum.confidence >= 50 ? '#ff9800' : '#f44336');
|
||||||
|
html += '<div style="font-size:10px;"><span style="color:' + confColor + ';font-weight:bold;">置信度:' + confLevel + '</span> <span style="color:#666;">(' + csForNum.confidence + '%)</span></div>';
|
||||||
|
}
|
||||||
|
}
|
||||||
|
```
|
||||||
|
|
||||||
|
实现要点：
|
||||||
|
- 在号码卡片中显示置信度等级（高/中/低）和具体百分比
|
||||||
|
- 使用与整体置信度展示相同的颜色映射阈值
|
||||||
|
- 只在 V3 版本中显示
|
||||||
|
- 置信度字段使用 score_concentration（得分集中度）维度
|
||||||
|
</action>
|
||||||
|
|
||||||
|
<acceptance_criteria>
|
||||||
|
- grep 匹配: `csForNum` 变量在 history.js 中存在
|
||||||
|
- grep 匹配: `置信度:` 在号码卡片渲染代码中存在
|
||||||
|
- 置信度显示在得分下方
|
||||||
|
- grep 匹配: `csForNum.confidence` 在号码卡片渲染中存在
|
||||||
|
</acceptance_criteria>
|
||||||
|
|
||||||
|
## Verification
|
||||||
|
|
||||||
|
1. 打开 history 页面，点击"智能预测"按钮
|
||||||
|
2. 使用 V3 版本执行预测，验证：
|
||||||
|
- 置信度评估区域是否显示
|
||||||
|
- 数据不足时是否显示警告提示
|
||||||
|
- 回测结果中是否显示 NDCG@5、MRR、命中分布柱状图
|
||||||
|
- 预测号码卡片中是否显示置信度等级和百分比
|
||||||
|
|
||||||
|
## Success Criteria
|
||||||
|
|
||||||
|
1. `renderPredict` 方法已更新，新增置信度展示区域
|
||||||
|
2. 回测结果展示区域已扩展，包含 NDCG@5、MRR、命中分布、数据警告
|
||||||
|
3. 预测号码卡片显示置信度等级和百分比
|
||||||
|
4. 命中分布使用 rank_1..rank_5 键名格式解析
|
||||||
|
5. 所有新增展示样式与原有 UI 风格一致
|
||||||
|
6. 数据警告以红色背景突出显示
|
||||||
|
|
||||||
|
## Output
|
||||||
|
|
||||||
|
完成后创建 `.planning/phases/11-predictv3/11-03-SUMMARY.md`
|
||||||
@@ -0,0 +1,346 @@
|
|||||||
|
---
|
||||||
|
phase: 11-predictv3
|
||||||
|
plan: 04
|
||||||
|
type: execute
|
||||||
|
wave: 1
|
||||||
|
depends_on: []
|
||||||
|
files_modified:
|
||||||
|
- application/admin/model/History.php
|
||||||
|
- application/admin/controller/History.php
|
||||||
|
autonomous: true
|
||||||
|
requirements:
|
||||||
|
- PRED-04
|
||||||
|
must_haves:
|
||||||
|
truths:
|
||||||
|
- "用户可以通过接口获取最优权重配置"
|
||||||
|
- "系统返回基于历史回测的权重优化结果"
|
||||||
|
- "优化结果包含各权重配置的命中率、NDCG评估"
|
||||||
|
- "网格搜索有超时保护机制"
|
||||||
|
artifacts:
|
||||||
|
- path: "application/admin/model/History.php"
|
||||||
|
provides: "权重网格搜索优化方法"
|
||||||
|
contains: "_optimizeWeightsGridSearch"
|
||||||
|
- path: "application/admin/controller/History.php"
|
||||||
|
provides: "权重优化接口入口"
|
||||||
|
contains: "optimizeWeights"
|
||||||
|
key_links:
|
||||||
|
- from: "optimizeWeights controller"
|
||||||
|
to: "_optimizeWeightsGridSearch model"
|
||||||
|
via: "method call"
|
||||||
|
---
|
||||||
|
|
||||||
|
# Phase 11 - Plan 04: 权重网格搜索优化
|
||||||
|
|
||||||
|
## Objective
|
||||||
|
|
||||||
|
实现权重网格搜索优化功能，通过预定义权重组合批量回测，找出最优权重配置，提升算法预测准确性。
|
||||||
|
|
||||||
|
**Purpose:** 当前权重为手动配置，缺乏数据驱动优化。网格搜索是一种成熟的参数优化方法，能基于历史回测数据找到更优权重组合。
|
||||||
|
|
||||||
|
**Output:** `History.php` 新增 `_optimizeWeightsGridSearch` 方法，`History.php` controller 新增 `optimizeWeights` 接口入口。
|
||||||
|
|
||||||
|
## Tasks
|
||||||
|
|
||||||
|
### Task 1: 实现权重网格搜索方法（含5种具体配置和超时保护）
|
||||||
|
|
||||||
|
<read_first>
|
||||||
|
- D:\code\php\amlhc\application\admin\model\History.php (line 2094-2113, 默认权重配置)
|
||||||
|
- D:\code\php\amlhc\application\admin\model\History.php (line 3495-3556, _runBacktestV3 方法)
|
||||||
|
</read_first>
|
||||||
|
|
||||||
|
<action>
|
||||||
|
在 `History.php` 类末尾新增权重网格搜索优化方法：
|
||||||
|
|
||||||
|
```php
|
||||||
|
/**
|
||||||
|
* 权重网格搜索优化
|
||||||
|
*
|
||||||
|
* 优化目标定义:
|
||||||
|
* - 综合评估得分 = hit_rate * 0.6 + ndcg_5 * 100 * 0.4
|
||||||
|
* - 命中率权重60%，NDCG权重40%
|
||||||
|
* - 返回综合得分最高的权重配置
|
||||||
|
*
|
||||||
|
* 5种预定义权重配置:
|
||||||
|
* - 配置1: 遗漏优先型 - omit_regression权重最高(0.25)
|
||||||
|
* - 配置2: 转移概率优先型 - transition_prob权重最高(0.25)
|
||||||
|
* - 配置3: 走势方向优先型 - trend_direction权重最高(0.25)
|
||||||
|
* - 配置4: 平衡型 - 各维度权重较均衡
|
||||||
|
* - 配置5: 组合特征优先型 - combination权重最高(0.20)
|
||||||
|
*
|
||||||
|
* @param int $periods 统计期数，范围50-500
|
||||||
|
* @param int $backtestCount 回测期数，范围10-100
|
||||||
|
* @param int $timeoutSeconds 超时限制秒数，默认60秒
|
||||||
|
* @return array {best_weights: [], best_hit_rate: float, best_ndcg: float, all_results: [], timed_out: bool}
|
||||||
|
*/
|
||||||
|
private function _optimizeWeightsGridSearch($periods = 200, $backtestCount = 50, $timeoutSeconds = 60)
|
||||||
|
{
|
||||||
|
// 超时保护：记录开始时间
|
||||||
|
$startTime = microtime(true);
|
||||||
|
$timedOut = false;
|
||||||
|
|
||||||
|
// 5种预定义权重配置（具体权重值明确）
|
||||||
|
$weightConfigs = [
|
||||||
|
// 配置1: 遗漏优先型 - 遗漏回归权重最高
|
||||||
|
[
|
||||||
|
'omit_regression' => 0.25, // 遗漏回归权重25%
|
||||||
|
'freq_regression' => 0.12, // 频率回归权重12%
|
||||||
|
'transition_prob' => 0.15, // 转移概率权重15%
|
||||||
|
'trend_direction' => 0.12, // 走势方向权重12%
|
||||||
|
'oddeven_balance' => 0.08, // 单双平衡权重8%
|
||||||
|
'bigsmall_balance' => 0.08, // 大小平衡权重8%
|
||||||
|
'zone_balance' => 0.05, // 区域平衡权重5%
|
||||||
|
'color_balance' => 0.05, // 波色平衡权重5%
|
||||||
|
'combination' => 0.10 // 组合特征权重10%
|
||||||
|
],
|
||||||
|
// 配置2: 转移概率优先型 - 转移概率权重最高
|
||||||
|
[
|
||||||
|
'omit_regression' => 0.15,
|
||||||
|
'freq_regression' => 0.10,
|
||||||
|
'transition_prob' => 0.25, // 转移概率权重25%（最高）
|
||||||
|
'trend_direction' => 0.12,
|
||||||
|
'oddeven_balance' => 0.08,
|
||||||
|
'bigsmall_balance' => 0.08,
|
||||||
|
'zone_balance' => 0.04,
|
||||||
|
'color_balance' => 0.04,
|
||||||
|
'combination' => 0.14
|
||||||
|
],
|
||||||
|
// 配置3: 走势方向优先型 - 走势方向权重最高
|
||||||
|
[
|
||||||
|
'omit_regression' => 0.12,
|
||||||
|
'freq_regression' => 0.10,
|
||||||
|
'transition_prob' => 0.15,
|
||||||
|
'trend_direction' => 0.25, // 走势方向权重25%（最高）
|
||||||
|
'oddeven_balance' => 0.08,
|
||||||
|
'bigsmall_balance' => 0.08,
|
||||||
|
'zone_balance' => 0.04,
|
||||||
|
'color_balance' => 0.04,
|
||||||
|
'combination' => 0.12
|
||||||
|
],
|
||||||
|
// 配置4: 平衡型（默认配置）- 各维度权重较均衡
|
||||||
|
[
|
||||||
|
'omit_regression' => 0.18,
|
||||||
|
'freq_regression' => 0.12,
|
||||||
|
'transition_prob' => 0.18,
|
||||||
|
'trend_direction' => 0.14,
|
||||||
|
'oddeven_balance' => 0.08,
|
||||||
|
'bigsmall_balance' => 0.08,
|
||||||
|
'zone_balance' => 0.04,
|
||||||
|
'color_balance' => 0.04,
|
||||||
|
'combination' => 0.10
|
||||||
|
],
|
||||||
|
// 配置5: 组合特征优先型 - 组合特征权重最高
|
||||||
|
[
|
||||||
|
'omit_regression' => 0.15,
|
||||||
|
'freq_regression' => 0.10,
|
||||||
|
'transition_prob' => 0.15,
|
||||||
|
'trend_direction' => 0.12,
|
||||||
|
'oddeven_balance' => 0.06,
|
||||||
|
'bigsmall_balance' => 0.06,
|
||||||
|
'zone_balance' => 0.03,
|
||||||
|
'color_balance' => 0.03,
|
||||||
|
'combination' => 0.20 // 组合特征权重20%（最高）
|
||||||
|
]
|
||||||
|
];
|
||||||
|
|
||||||
|
$bestWeights = [];
|
||||||
|
$bestHitRate = 0;
|
||||||
|
$bestNdcg = 0;
|
||||||
|
$bestCombinedScore = 0;
|
||||||
|
$allResults = [];
|
||||||
|
|
||||||
|
// 执行每种配置的回测（添加超时检查）
|
||||||
|
foreach ($weightConfigs as $configIdx => $weights) {
|
||||||
|
// 超时检查：超过限制时间则停止
|
||||||
|
$elapsedTime = microtime(true) - $startTime;
|
||||||
|
if ($elapsedTime > $timeoutSeconds) {
|
||||||
|
$timedOut = true;
|
||||||
|
break;
|
||||||
|
}
|
||||||
|
|
||||||
|
// 执行回测
|
||||||
|
$backtest = $this->_runBacktestV3($periods, $weights, $backtestCount);
|
||||||
|
|
||||||
|
$hitRate = $backtest['hit_rate'] ?? 0;
|
||||||
|
$ndcg = $backtest['ndcg_5'] ?? 0;
|
||||||
|
$avgRank = $backtest['avg_rank'] ?? 0;
|
||||||
|
$mrr = $backtest['mrr'] ?? 0;
|
||||||
|
|
||||||
|
// 综合评估得分：命中率60% + NDCG40%
|
||||||
|
$combinedScore = $hitRate * 0.6 + $ndcg * 100 * 0.4;
|
||||||
|
|
||||||
|
$result = [
|
||||||
|
'config_name' => $configIdx + 1,
|
||||||
|
'config_type' => ['遗漏优先型', '转移概率优先型', '走势方向优先型', '平衡型', '组合特征优先型'][$configIdx],
|
||||||
|
'weights' => $weights,
|
||||||
|
'hit_rate' => $hitRate,
|
||||||
|
'avg_rank' => $avgRank,
|
||||||
|
'ndcg_5' => $ndcg,
|
||||||
|
'mrr' => $mrr,
|
||||||
|
'combined_score' => round($combinedScore, 2),
|
||||||
|
'total_hits' => $backtest['total_hits'] ?? 0
|
||||||
|
];
|
||||||
|
|
||||||
|
$allResults[] = $result;
|
||||||
|
|
||||||
|
// 更新最优配置
|
||||||
|
if ($combinedScore > $bestCombinedScore) {
|
||||||
|
$bestCombinedScore = $combinedScore;
|
||||||
|
$bestHitRate = $hitRate;
|
||||||
|
$bestNdcg = $ndcg;
|
||||||
|
$bestWeights = $weights;
|
||||||
|
}
|
||||||
|
}
|
||||||
|
|
||||||
|
// 按综合得分降序排序结果
|
||||||
|
usort($allResults, function($a, $b) {
|
||||||
|
return $b['combined_score'] - $a['combined_score'];
|
||||||
|
});
|
||||||
|
|
||||||
|
return [
|
||||||
|
'best_weights' => $bestWeights,
|
||||||
|
'best_hit_rate' => $bestHitRate,
|
||||||
|
'best_ndcg' => $bestNdcg,
|
||||||
|
'best_combined_score' => round($bestCombinedScore, 2),
|
||||||
|
'all_results' => $allResults,
|
||||||
|
'periods' => $periods,
|
||||||
|
'backtest_count' => $backtestCount,
|
||||||
|
'timeout_seconds' => $timeoutSeconds,
|
||||||
|
'timed_out' => $timedOut,
|
||||||
|
'elapsed_time' => round(microtime(true) - $startTime, 2)
|
||||||
|
];
|
||||||
|
}
|
||||||
|
```
|
||||||
|
|
||||||
|
实现要点：
|
||||||
|
- **5种具体权重配置**：每种配置的权重值在代码中明确列出
|
||||||
|
- **优化目标明确**：综合得分 = hit_rate*0.6 + ndcg_5*100*0.4
|
||||||
|
- **超时保护**：添加 $timeoutSeconds 参数，默认60秒，超时后停止剩余配置测试
|
||||||
|
- **配置类型命名**：遗漏优先型、转移概率优先型、走势方向优先型、平衡型、组合特征优先型
|
||||||
|
- 返回 timed_out 标志和 elapsed_time 供前端判断
|
||||||
|
</action>
|
||||||
|
|
||||||
|
<acceptance_criteria>
|
||||||
|
- grep 正则匹配: `_optimizeWeightsGridSearch\s*\(` 在 History.php 中存在
|
||||||
|
- grep 匹配: `$weightConfigs` 数组包含5个配置项
|
||||||
|
- grep 匹配: `$timeoutSeconds` 参数在方法签名中存在
|
||||||
|
- grep 匹配: `$timedOut` 变量在方法中存在
|
||||||
|
- grep 匹配: `combined_score` 在返回结果中存在
|
||||||
|
- grep 匹配: `config_type` 在结果中存在
|
||||||
|
- 方法调用 `_runBacktestV3` 进行回测
|
||||||
|
- 方法包含函数级注释说明优化目标和配置类型
|
||||||
|
</acceptance_criteria>
|
||||||
|
|
||||||
|
### Task 2: 新增权重优化接口入口（含参数验证和超时警告）
|
||||||
|
|
||||||
|
<read_first>
|
||||||
|
- D:\code\php\amlhc\application\admin\controller\History.php (line 25, noNeedRight 数组)
|
||||||
|
- D:\code\php\amlhc\application\admin\controller\History.php (line 460-489, predictV3 方法)
|
||||||
|
</read_first>
|
||||||
|
|
||||||
|
<action>
|
||||||
|
在 `History.php` controller 中：
|
||||||
|
|
||||||
|
1. 将 `optimizeWeights` 添加到 `noNeedRight` 数组（约 line 25）：
|
||||||
|
|
||||||
|
找到：
|
||||||
|
```php
|
||||||
|
protected $noNeedRight = ['missingNum', 'trendData', ..., 'predictV3'];
|
||||||
|
```
|
||||||
|
|
||||||
|
在 `predictV3` 后添加 `'optimizeWeights'`：
|
||||||
|
|
||||||
|
```php
|
||||||
|
protected $noNeedRight = ['missingNum', 'trendData', ..., 'predictV3', 'optimizeWeights'];
|
||||||
|
```
|
||||||
|
|
||||||
|
2. 在 `predictV3` 方法后（约 line 489 之后）新增 `optimizeWeights` 方法：
|
||||||
|
|
||||||
|
```php
|
||||||
|
/**
|
||||||
|
* 权重网格搜索优化接口
|
||||||
|
* 执行多权重配置回测，返回最优权重组合
|
||||||
|
*
|
||||||
|
* 参数说明:
|
||||||
|
* - periods: 统计期数，范围50-500，默认200
|
||||||
|
* - backtest: 回测期数，范围10-100，默认30
|
||||||
|
* - timeout: 超时秒数，范围10-120，默认60
|
||||||
|
*
|
||||||
|
* 返回说明:
|
||||||
|
* - best_weights: 最优权重配置
|
||||||
|
* - best_hit_rate: 最优配置命中率
|
||||||
|
* - all_results: 所有配置测试结果
|
||||||
|
* - timed_out: 是否超时中断
|
||||||
|
*/
|
||||||
|
public function optimizeWeights()
|
||||||
|
{
|
||||||
|
if ($this->request->isAjax()) {
|
||||||
|
// 参数验证
|
||||||
|
$periods = $this->request->get('periods', 200, 'intval');
|
||||||
|
if ($periods < 50 || $periods > 500) {
|
||||||
|
$this->error('期数范围必须在 50-500 之间');
|
||||||
|
}
|
||||||
|
|
||||||
|
$backtestCount = $this->request->get('backtest', 30, 'intval');
|
||||||
|
if ($backtestCount < 10 || $backtestCount > 100) {
|
||||||
|
$backtestCount = 30; // 使用默认值而非报错
|
||||||
|
}
|
||||||
|
|
||||||
|
$timeoutSeconds = $this->request->get('timeout', 60, 'intval');
|
||||||
|
if ($timeoutSeconds < 10 || $timeoutSeconds > 120) {
|
||||||
|
$timeoutSeconds = 60; // 使用默认值
|
||||||
|
}
|
||||||
|
|
||||||
|
// 执行优化
|
||||||
|
$result = $this->model->_optimizeWeightsGridSearch($periods, $backtestCount, $timeoutSeconds);
|
||||||
|
|
||||||
|
// 超时警告提示
|
||||||
|
$message = '优化完成';
|
||||||
|
if ($result['timed_out']) {
|
||||||
|
$message = '优化超时中断，已完成' . count($result['all_results']) . '种配置测试';
|
||||||
|
}
|
||||||
|
|
||||||
|
$this->success($message, null, $result);
|
||||||
|
}
|
||||||
|
}
|
||||||
|
```
|
||||||
|
|
||||||
|
实现要点：
|
||||||
|
- 接口参数：periods（统计期数）、backtest（回测期数）、timeout（超时秒数）
|
||||||
|
- 参数范围验证，超出范围时使用默认值或报错
|
||||||
|
- 返回最优权重配置及所有测试结果
|
||||||
|
- 超时时返回警告消息和已完成的测试数量
|
||||||
|
- 需添加到 noNeedRight 允许无权限访问
|
||||||
|
</action>
|
||||||
|
|
||||||
|
<acceptance_criteria>
|
||||||
|
- grep 匹配: `optimizeWeights` 在 noNeedRight 数组中存在
|
||||||
|
- grep 正则匹配: `public function optimizeWeights\s*\(` 在 controller 中存在
|
||||||
|
- grep 匹配: `$timeoutSeconds` 在方法中存在
|
||||||
|
- grep 匹配: `$result['timed_out']` 在方法中存在
|
||||||
|
- 方法调用 `$this->model->_optimizeWeightsGridSearch`
|
||||||
|
- 方法包含函数级注释
|
||||||
|
</acceptance_criteria>
|
||||||
|
|
||||||
|
## Verification
|
||||||
|
|
||||||
|
执行权重优化接口验证返回结果：
|
||||||
|
|
||||||
|
```bash
|
||||||
|
curl -s "http://127.0.0.1:8000/admin/history/optimizeWeights?periods=200&backtest=20&timeout=60" | grep -E "best_weights|best_hit_rate|best_ndcg|all_results|timed_out|config_type"
|
||||||
|
```
|
||||||
|
|
||||||
|
预期结果：返回 JSON 中包含 best_weights、best_hit_rate、best_ndcg、all_results、timed_out、config_type 字段。
|
||||||
|
|
||||||
|
## Success Criteria
|
||||||
|
|
||||||
|
1. `_optimizeWeightsGridSearch` 方法已实现，包含 5 种预定义权重配置（权重值明确）
|
||||||
|
2. 优化目标明确：综合得分 = hit_rate*0.6 + ndcg_5*100*0.4
|
||||||
|
3. 超时保护机制已添加，默认60秒
|
||||||
|
4. `optimizeWeights` controller 接口已实现，含参数验证
|
||||||
|
5. 接口能正常返回最优权重配置及测试结果
|
||||||
|
6. 超时时返回警告消息
|
||||||
|
7. 所有新增方法包含函数级注释
|
||||||
|
|
||||||
|
## Output
|
||||||
|
|
||||||
|
完成后创建 `.planning/phases/11-predictv3/11-04-SUMMARY.md`
|
||||||
@@ -0,0 +1,455 @@
|
|||||||
|
---
|
||||||
|
phase: 11-predictv3
|
||||||
|
plan: 05
|
||||||
|
type: execute
|
||||||
|
wave: 1
|
||||||
|
depends_on: []
|
||||||
|
files_modified:
|
||||||
|
- application/admin/model/History.php
|
||||||
|
autonomous: true
|
||||||
|
requirements:
|
||||||
|
- PRED-03
|
||||||
|
must_haves:
|
||||||
|
truths:
|
||||||
|
- "转移概率计算考虑前两期状态联合决定"
|
||||||
|
- "系统在数据充足时使用二阶马尔可夫，数据不足时回退一阶"
|
||||||
|
- "预测结果中显示使用的转移概率阶数"
|
||||||
|
- "二阶马尔可夫有状态对观察次数检查，不足时回退一阶"
|
||||||
|
artifacts:
|
||||||
|
- path: "application/admin/model/History.php"
|
||||||
|
provides: "二阶马尔可夫转移矩阵构建方法"
|
||||||
|
contains: "_getTransitionMatrix2ndOrder|_calcTransitionScore2ndOrder"
|
||||||
|
key_links:
|
||||||
|
- from: "getPredictionV3"
|
||||||
|
to: "_getTransitionMatrix2ndOrder"
|
||||||
|
via: "conditional call based on data availability and state pair count"
|
||||||
|
---
|
||||||
|
|
||||||
|
# Phase 11 - Plan 05: 二阶马尔可夫转移概率增强
|
||||||
|
|
||||||
|
## Objective
|
||||||
|
|
||||||
|
改进现有一阶马尔可夫链转移概率计算，新增二阶马尔可夫链实现。考虑前两期状态联合决定当前转移概率，提升转移概率预测准确性。
|
||||||
|
|
||||||
|
**Purpose:** 现有 `_getTransitionMatrix` 仅考虑上一期状态，预测信息有限。二阶马尔可夫链利用更长历史序列，理论上预测更精准。
|
||||||
|
|
||||||
|
**Important:** 本计划是独立功能增强，不依赖其他计划。可独立执行。
|
||||||
|
|
||||||
|
**Output:** `History.php` 新增 `_getTransitionMatrix2ndOrder` 和 `_calcTransitionScore2ndOrder` 方法，`getPredictionV3` 中根据数据量和状态对观察次数选择使用一阶或二阶转移概率。
|
||||||
|
|
||||||
|
## Tasks
|
||||||
|
|
||||||
|
### Task 1: 实现二阶马尔可夫转移矩阵构建方法（含状态对观察次数检查）
|
||||||
|
|
||||||
|
<read_first>
|
||||||
|
- D:\code\php\amlhc\application\admin\model\History.php (line 2468-2493, _getTransitionMatrix 方法)
|
||||||
|
- D:\code\php\amlhc\application\admin\model\History.php (line 2452-2460, _getHeadIdx 方法)
|
||||||
|
</read_first>
|
||||||
|
|
||||||
|
<action>
|
||||||
|
在 `History.php` 类末尾新增二阶马尔可夫转移矩阵构建方法：
|
||||||
|
|
||||||
|
```php
|
||||||
|
/**
|
||||||
|
* 构建二阶马尔可夫转移矩阵
|
||||||
|
* 考虑前两期状态联合决定当前转移概率
|
||||||
|
*
|
||||||
|
* 状态空间说明:
|
||||||
|
* - 一阶马尔可夫: N个状态 (zone:5, tail:10, head:5)
|
||||||
|
* - 二阶马尔可夫: N^2个状态对 (zone:25, tail:100, head:25)
|
||||||
|
* - 状态键格式: "prev1-prev2"，如 "2-3" 表示前一期区域2、前两期区域3
|
||||||
|
*
|
||||||
|
* 数据量阈值说明:
|
||||||
|
* - 建议历史数据 >= 200期以获得稳定的二阶概率估计
|
||||||
|
* - 状态对观察次数 >= 5 才使用该状态对的二阶概率
|
||||||
|
* - 观察次数不足时返回 state_pair_insufficient 标志，供调用者回退一阶
|
||||||
|
*
|
||||||
|
* @param array $history 历史数据（降序，最新在前）
|
||||||
|
* @param string $type 类型：zone/tail/head
|
||||||
|
* @param int $minStatePairCount 状态对最小观察次数，默认5
|
||||||
|
* @return array {matrix: [], prob_matrix: [], state_totals: [], num_categories: int, sufficient_pairs: int, total_pairs: int, min_threshold: int}
|
||||||
|
*/
|
||||||
|
private function _getTransitionMatrix2ndOrder($history, $type, $minStatePairCount = 5)
|
||||||
|
{
|
||||||
|
// 升序排列（从旧到新）
|
||||||
|
$historyAsc = array_reverse($history);
|
||||||
|
|
||||||
|
// 确定类别数量和索引函数
|
||||||
|
switch ($type) {
|
||||||
|
case 'zone':
|
||||||
|
$numCategories = 5;
|
||||||
|
$getIdx = function ($num) {
|
||||||
|
if ($num <= 10) return 0;
|
||||||
|
if ($num <= 20) return 1;
|
||||||
|
if ($num <= 30) return 2;
|
||||||
|
if ($num <= 40) return 3;
|
||||||
|
return 4;
|
||||||
|
};
|
||||||
|
break;
|
||||||
|
case 'tail':
|
||||||
|
$numCategories = 10;
|
||||||
|
$getIdx = function ($num) { return $num % 10; };
|
||||||
|
break;
|
||||||
|
case 'head':
|
||||||
|
$numCategories = 5;
|
||||||
|
$getIdx = function ($num) {
|
||||||
|
if ($num <= 9) return 0;
|
||||||
|
if ($num <= 19) return 1;
|
||||||
|
if ($num <= 29) return 2;
|
||||||
|
if ($num <= 39) return 3;
|
||||||
|
return 4;
|
||||||
|
};
|
||||||
|
break;
|
||||||
|
default:
|
||||||
|
return [
|
||||||
|
'matrix' => [],
|
||||||
|
'prob_matrix' => [],
|
||||||
|
'state_totals' => [],
|
||||||
|
'num_categories' => 0,
|
||||||
|
'sufficient_pairs' => 0,
|
||||||
|
'total_pairs' => 0,
|
||||||
|
'min_threshold' => $minStatePairCount
|
||||||
|
];
|
||||||
|
}
|
||||||
|
|
||||||
|
// 状态空间: (prev1, prev2) -> current，共 numCategories^2 个前置状态
|
||||||
|
$matrix = [];
|
||||||
|
$stateTotals = [];
|
||||||
|
|
||||||
|
// 初始化矩阵结构
|
||||||
|
for ($i = 0; $i < $numCategories; $i++) {
|
||||||
|
for ($j = 0; $j < $numCategories; $j++) {
|
||||||
|
$stateKey = $i . '-' . $j;
|
||||||
|
$matrix[$stateKey] = array_fill(0, $numCategories, 0);
|
||||||
|
$stateTotals[$stateKey] = 0;
|
||||||
|
}
|
||||||
|
}
|
||||||
|
|
||||||
|
// 统计二阶转移
|
||||||
|
for ($i = 0; $i < count($historyAsc) - 2; $i++) {
|
||||||
|
$prev1 = $getIdx((int)$historyAsc[$i]['num7']);
|
||||||
|
$prev2 = $getIdx((int)$historyAsc[$i + 1]['num7']);
|
||||||
|
$current = $getIdx((int)$historyAsc[$i + 2]['num7']);
|
||||||
|
|
||||||
|
if ($prev1 < 0 || $prev2 < 0 || $current < 0) continue;
|
||||||
|
|
||||||
|
$stateKey = $prev1 . '-' . $prev2;
|
||||||
|
$matrix[$stateKey][$current]++;
|
||||||
|
$stateTotals[$stateKey]++;
|
||||||
|
}
|
||||||
|
|
||||||
|
// 统计充分观察的状态对数量（观察次数 >= minStatePairCount）
|
||||||
|
$sufficientPairs = 0;
|
||||||
|
$totalPairs = $numCategories * $numCategories;
|
||||||
|
foreach ($stateTotals as $stateKey => $count) {
|
||||||
|
if ($count >= $minStatePairCount) {
|
||||||
|
$sufficientPairs++;
|
||||||
|
}
|
||||||
|
}
|
||||||
|
|
||||||
|
// 拉普拉斯平滑处理
|
||||||
|
$probMatrix = [];
|
||||||
|
foreach ($matrix as $stateKey => $counts) {
|
||||||
|
$smoothTotal = $stateTotals[$stateKey] + $numCategories;
|
||||||
|
$probMatrix[$stateKey] = [];
|
||||||
|
for ($j = 0; $j < $numCategories; $j++) {
|
||||||
|
$probMatrix[$stateKey][$j] = ($counts[$j] + 1) / $smoothTotal;
|
||||||
|
}
|
||||||
|
}
|
||||||
|
|
||||||
|
return [
|
||||||
|
'matrix' => $matrix,
|
||||||
|
'prob_matrix' => $probMatrix,
|
||||||
|
'state_totals' => $stateTotals,
|
||||||
|
'num_categories' => $numCategories,
|
||||||
|
'sufficient_pairs' => $sufficientPairs,
|
||||||
|
'total_pairs' => $totalPairs,
|
||||||
|
'min_threshold' => $minStatePairCount
|
||||||
|
];
|
||||||
|
}
|
||||||
|
```
|
||||||
|
|
||||||
|
实现要点：
|
||||||
|
- 状态空间从 N 扩展到 N^2（zone: 25状态，tail: 100状态，head: 25状态）
|
||||||
|
- 使用拉普拉斯平滑处理避免零概率问题
|
||||||
|
- 状态键格式为 "prev1-prev2"
|
||||||
|
- **新增状态对观察次数检查**：统计 sufficient_pairs（观察>=5次的状态对数量）
|
||||||
|
- 返回 sufficient_pairs、total_pairs、min_threshold 供调用者判断是否足够稳定
|
||||||
|
</action>
|
||||||
|
|
||||||
|
<acceptance_criteria>
|
||||||
|
- grep 正则匹配: `_getTransitionMatrix2ndOrder\s*\(` 在 History.php 中存在
|
||||||
|
- grep 匹配: `$minStatePairCount` 参数在方法签名中存在
|
||||||
|
- grep 匹配: `sufficient_pairs` 在返回结构中存在
|
||||||
|
- grep 匹配: `total_pairs` 在返回结构中存在
|
||||||
|
- 方法包含 stateKey 变量（格式为 prev1-prev2）
|
||||||
|
- 方法包含函数级注释，说明状态空间和数据量阈值
|
||||||
|
</acceptance_criteria>
|
||||||
|
|
||||||
|
### Task 2: 实现二阶转移概率得分计算方法
|
||||||
|
|
||||||
|
<read_first>
|
||||||
|
- D:\code\php\amlhc\application\admin\model\History.php (新增的 _getTransitionMatrix2ndOrder 方法)
|
||||||
|
- D:\code\php\amlhc\application\admin\model\History.php (查找 _calcTransitionScore 方法位置)
|
||||||
|
</read_first>
|
||||||
|
|
||||||
|
<action>
|
||||||
|
使用 Grep 找到 `_calcTransitionScore` 方法位置后，在其附近新增二阶转移概率得分计算方法：
|
||||||
|
|
||||||
|
```bash
|
||||||
|
grep -n "_calcTransitionScore" application/admin/model/History.php
|
||||||
|
```
|
||||||
|
|
||||||
|
新增 `_calcTransitionScore2ndOrder` 方法：
|
||||||
|
|
||||||
|
```php
|
||||||
|
/**
|
||||||
|
* 计算二阶转移概率得分
|
||||||
|
*
|
||||||
|
* 计算方法:
|
||||||
|
* - 综合区域、尾号、首号三个维度的二阶转移概率
|
||||||
|
* - 各维度权重: 区域40%、尾号35%、首号25%
|
||||||
|
* - 得分范围: 0-100
|
||||||
|
*
|
||||||
|
* @param int $num 当前号码
|
||||||
|
* @param int $prev1Zone 前一期区域索引
|
||||||
|
* @param int $prev2Zone 前两期区域索引
|
||||||
|
* @param int $prev1Tail 前一期尾号索引
|
||||||
|
* @param int $prev2Tail 前两期尾号索引
|
||||||
|
* @param int $prev1Head 前一期首号索引
|
||||||
|
* @param int $prev2Head 前两期首号索引
|
||||||
|
* @param array $zoneTrans2nd 二阶区域转移矩阵
|
||||||
|
* @param array $tailTrans2nd 二阶尾号转移矩阵
|
||||||
|
* @param array $headTrans2nd 二阶首号转移矩阵
|
||||||
|
* @param array $zoneMap 号码区域映射
|
||||||
|
* @param array $tailMap 号码尾号映射
|
||||||
|
* @param array $headMap 号码首号映射
|
||||||
|
* @return float 综合转移得分 (0-100)
|
||||||
|
*/
|
||||||
|
private function _calcTransitionScore2ndOrder(
|
||||||
|
$num,
|
||||||
|
$prev1Zone, $prev2Zone,
|
||||||
|
$prev1Tail, $prev2Tail,
|
||||||
|
$prev1Head, $prev2Head,
|
||||||
|
$zoneTrans2nd, $tailTrans2nd, $headTrans2nd,
|
||||||
|
$zoneMap, $tailMap, $headMap
|
||||||
|
)
|
||||||
|
{
|
||||||
|
$zone = $zoneMap[$num];
|
||||||
|
$tail = $tailMap[$num];
|
||||||
|
$head = $headMap[$num];
|
||||||
|
|
||||||
|
$score = 0;
|
||||||
|
|
||||||
|
// 区域二阶转移得分（权重40%）
|
||||||
|
$zoneStateKey = $prev1Zone . '-' . $prev2Zone;
|
||||||
|
if (isset($zoneTrans2nd['prob_matrix'][$zoneStateKey][$zone])) {
|
||||||
|
$prob = $zoneTrans2nd['prob_matrix'][$zoneStateKey][$zone];
|
||||||
|
$score += $prob * 40;
|
||||||
|
}
|
||||||
|
|
||||||
|
// 尾号二阶转移得分（权重35%）
|
||||||
|
$tailStateKey = $prev1Tail . '-' . $prev2Tail;
|
||||||
|
if (isset($tailTrans2nd['prob_matrix'][$tailStateKey][$tail])) {
|
||||||
|
$prob = $tailTrans2nd['prob_matrix'][$tailStateKey][$tail];
|
||||||
|
$score += $prob * 35;
|
||||||
|
}
|
||||||
|
|
||||||
|
// 首号二阶转移得分（权重25%）
|
||||||
|
$headStateKey = $prev1Head . '-' . $prev2Head;
|
||||||
|
if (isset($headTrans2nd['prob_matrix'][$headStateKey][$head])) {
|
||||||
|
$prob = $headTrans2nd['prob_matrix'][$headStateKey][$head];
|
||||||
|
$score += $prob * 25;
|
||||||
|
}
|
||||||
|
|
||||||
|
return round($score, 2);
|
||||||
|
}
|
||||||
|
```
|
||||||
|
|
||||||
|
实现要点：
|
||||||
|
- 综合区域、尾号、首号三个维度
|
||||||
|
- 各维度权重：区域40%、尾号35%、首号25%
|
||||||
|
- 使用 prob_matrix 中对应状态键的概率值
|
||||||
|
</action>
|
||||||
|
|
||||||
|
<acceptance_criteria>
|
||||||
|
- grep 正则匹配: `_calcTransitionScore2ndOrder\s*\(` 在 History.php 中存在
|
||||||
|
- 方法参数包含 prev1Zone、prev2Zone 等二阶状态参数
|
||||||
|
- 方法包含 zoneStateKey、tailStateKey、headStateKey 变量
|
||||||
|
- 方法包含函数级注释说明权重分配
|
||||||
|
</acceptance_criteria>
|
||||||
|
|
||||||
|
### Task 3: 在 getPredictionV3 中集成二阶马尔可夫（含200期阈值和状态对检查）
|
||||||
|
|
||||||
|
<read_first>
|
||||||
|
- D:\code\php\amlhc\application\admin\model\History.php (line 2230-2239, 转移概率分析部分)
|
||||||
|
- D:\code\php\amlhc\application\admin\model\History.php (line 2159-2161, 历史数据量检查)
|
||||||
|
</read_first>
|
||||||
|
|
||||||
|
<action>
|
||||||
|
在 `getPredictionV3` 方法中修改转移概率分析部分（约 line 2230-2239）：
|
||||||
|
|
||||||
|
1. 找到以下代码段：
|
||||||
|
```php
|
||||||
|
// ====== 3. 转移概率分析（新增）======
|
||||||
|
// 获取转移概率矩阵数据
|
||||||
|
$zoneTransition = $this->_getTransitionMatrix($allHistory, 'zone');
|
||||||
|
$tailTransition = $this->_getTransitionMatrix($allHistory, 'tail');
|
||||||
|
$headTransition = $this->_getTransitionMatrix($allHistory, 'head');
|
||||||
|
|
||||||
|
// 上期号码的各类属性
|
||||||
|
$lastZone = $this->_getZoneIdx($lastSpecial);
|
||||||
|
$lastTail = $lastSpecial % 10;
|
||||||
|
$lastHead = $this->_getHeadIdx($lastSpecial);
|
||||||
|
```
|
||||||
|
|
||||||
|
替换为：
|
||||||
|
```php
|
||||||
|
// ====== 3. 转移概率分析 ======
|
||||||
|
// 根据历史数据量决定使用一阶或二阶马尔可夫
|
||||||
|
// 阈值条件：总期数 >= 200 且 状态对观察次数充足（>=5次的比例>=30%）
|
||||||
|
$minPeriodsThreshold = 200; // 二阶马尔可夫最小历史期数阈值（从100提升到200）
|
||||||
|
$minStatePairCount = 5; // 状态对最小观察次数
|
||||||
|
$use2ndOrder = false;
|
||||||
|
$secondOrderAvailable = false;
|
||||||
|
|
||||||
|
// 获取一阶转移概率矩阵（始终计算，作为fallback）
|
||||||
|
$zoneTransition = $this->_getTransitionMatrix($allHistory, 'zone');
|
||||||
|
$tailTransition = $this->_getTransitionMatrix($allHistory, 'tail');
|
||||||
|
$headTransition = $this->_getTransitionMatrix($allHistory, 'head');
|
||||||
|
|
||||||
|
// 获取二阶转移概率矩阵（数据充足时）
|
||||||
|
$zoneTransition2nd = null;
|
||||||
|
$tailTransition2nd = null;
|
||||||
|
$headTransition2nd = null;
|
||||||
|
$prev2Zone = 0;
|
||||||
|
$prev2Tail = 0;
|
||||||
|
$prev2Head = 0;
|
||||||
|
|
||||||
|
if (count($allHistory) >= $minPeriodsThreshold && count($allHistory) >= 2) {
|
||||||
|
// 获取前两期号码属性
|
||||||
|
$prev2Special = (int)$allHistory[1]['num7'];
|
||||||
|
$prev2Zone = $this->_getZoneIdx($prev2Special);
|
||||||
|
$prev2Tail = $prev2Special % 10;
|
||||||
|
$prev2Head = $this->_getHeadIdx($prev2Special);
|
||||||
|
|
||||||
|
// 构建二阶转移矩阵
|
||||||
|
$zoneTransition2nd = $this->_getTransitionMatrix2ndOrder($allHistory, 'zone', $minStatePairCount);
|
||||||
|
$tailTransition2nd = $this->_getTransitionMatrix2ndOrder($allHistory, 'tail', $minStatePairCount);
|
||||||
|
$headTransition2nd = $this->_getTransitionMatrix2ndOrder($allHistory, 'head', $minStatePairCount);
|
||||||
|
|
||||||
|
// 检查状态对观察次数是否充足（至少30%的状态对有足够观察）
|
||||||
|
// tail类型状态空间最大(100)，以tail为基准判断
|
||||||
|
if ($tailTransition2nd['total_pairs'] > 0) {
|
||||||
|
$sufficientRatio = $tailTransition2nd['sufficient_pairs'] / $tailTransition2nd['total_pairs'];
|
||||||
|
$secondOrderAvailable = $sufficientRatio >= 0.3; // 至少30%状态对观察>=5次
|
||||||
|
}
|
||||||
|
|
||||||
|
$use2ndOrder = $secondOrderAvailable;
|
||||||
|
}
|
||||||
|
|
||||||
|
// 上期号码的各类属性
|
||||||
|
$lastZone = $this->_getZoneIdx($lastSpecial);
|
||||||
|
$lastTail = $lastSpecial % 10;
|
||||||
|
$lastHead = $this->_getHeadIdx($lastSpecial);
|
||||||
|
```
|
||||||
|
|
||||||
|
2. 在 analysis 数组中添加转移阶数信息（约 line 2297-2317）：
|
||||||
|
|
||||||
|
找到：
|
||||||
|
```php
|
||||||
|
$analysis = [
|
||||||
|
'last_special' => $lastSpecial,
|
||||||
|
'last_expect' => $lastExpect,
|
||||||
|
'weights' => $weights,
|
||||||
|
...
|
||||||
|
];
|
||||||
|
```
|
||||||
|
|
||||||
|
在 `trend_direction` 后添加：
|
||||||
|
```php
|
||||||
|
$analysis = [
|
||||||
|
...
|
||||||
|
'trend_direction' => $trendDirection,
|
||||||
|
'transition_order' => $use2ndOrder ? 2 : 1, // 新增：转移概率阶数
|
||||||
|
'transition_available' => $secondOrderAvailable, // 二阶是否可用
|
||||||
|
'history_count' => count($allHistory), // 历史期数
|
||||||
|
'min_periods_threshold' => $minPeriodsThreshold, // 阈值
|
||||||
|
'last_zone' => $zoneLabels[$lastZone] ?? '',
|
||||||
|
...
|
||||||
|
];
|
||||||
|
```
|
||||||
|
|
||||||
|
3. 在得分计算循环中（约 line 2342-2349）修改转移概率得分计算：
|
||||||
|
|
||||||
|
找到：
|
||||||
|
```php
|
||||||
|
// === 转移概率得分 ===
|
||||||
|
$transScore = $this->_calcTransitionScore(
|
||||||
|
$num, $lastZone, $lastTail, $lastHead,
|
||||||
|
$zoneTransition, $tailTransition, $headTransition,
|
||||||
|
$zoneMap, $tailMap, $headMap
|
||||||
|
);
|
||||||
|
```
|
||||||
|
|
||||||
|
替换为：
|
||||||
|
```php
|
||||||
|
// === 转移概率得分（根据阶数选择计算方法）===
|
||||||
|
if ($use2ndOrder && $zoneTransition2nd && $tailTransition2nd && $headTransition2nd) {
|
||||||
|
$transScore = $this->_calcTransitionScore2ndOrder(
|
||||||
|
$num, $lastZone, $prev2Zone, $lastTail, $prev2Tail, $lastHead, $prev2Head,
|
||||||
|
$zoneTransition2nd, $tailTransition2nd, $headTransition2nd,
|
||||||
|
$zoneMap, $tailMap, $headMap
|
||||||
|
);
|
||||||
|
$detail['trans_order'] = 2;
|
||||||
|
} else {
|
||||||
|
$transScore = $this->_calcTransitionScore(
|
||||||
|
$num, $lastZone, $lastTail, $lastHead,
|
||||||
|
$zoneTransition, $tailTransition, $headTransition,
|
||||||
|
$zoneMap, $tailMap, $headMap
|
||||||
|
);
|
||||||
|
$detail['trans_order'] = 1;
|
||||||
|
}
|
||||||
|
```
|
||||||
|
</action>
|
||||||
|
|
||||||
|
<acceptance_criteria>
|
||||||
|
- grep 匹配: `minPeriodsThreshold` 变量在 getPredictionV3 中存在（值为200）
|
||||||
|
- grep 匹配: `minStatePairCount` 变量在 getPredictionV3 中存在（值为5）
|
||||||
|
- grep 匹配: `$secondOrderAvailable` 变量在 getPredictionV3 中存在
|
||||||
|
- grep 匹配: `sufficientRatio` 在 getPredictionV3 中存在（状态对观察比例）
|
||||||
|
- grep 匹配: `_getTransitionMatrix2ndOrder` 在 getPredictionV3 中被调用
|
||||||
|
- grep 匹配: `transition_order` 在 analysis 数组中存在
|
||||||
|
- grep 匹配: `transition_available` 在 analysis 数组中存在
|
||||||
|
- grep 匹配: `_calcTransitionScore2ndOrder` 在得分计算中被调用
|
||||||
|
- 数据量阈值设置为 200 期（而非原100期）
|
||||||
|
- 状态对观察次数检查 >= 5，比例 >= 30%
|
||||||
|
</acceptance_criteria>
|
||||||
|
|
||||||
|
## Verification
|
||||||
|
|
||||||
|
执行预测接口验证二阶马尔可夫使用情况：
|
||||||
|
|
||||||
|
```bash
|
||||||
|
curl -s "http://127.0.0.1:8000/admin/history/predictV3?periods=300&backtest=10" | grep -E "transition_order|transition_available|history_count"
|
||||||
|
```
|
||||||
|
|
||||||
|
预期结果：
|
||||||
|
- periods >= 200 且状态对观察充足时，返回 transition_order: 2
|
||||||
|
- periods < 200 或状态对观察不足时，返回 transition_order: 1
|
||||||
|
- transition_available 显示二阶是否可用
|
||||||
|
|
||||||
|
## Success Criteria
|
||||||
|
|
||||||
|
1. `_getTransitionMatrix2ndOrder` 方法已实现，包含二阶状态空间构建
|
||||||
|
2. `_calcTransitionScore2ndOrder` 方法已实现
|
||||||
|
3. `getPredictionV3` 根据数据量和状态对观察次数自动选择一阶或二阶马尔可夫
|
||||||
|
4. 数据量阈值提升到 200 期（而非原100期）
|
||||||
|
5. 状态对观察次数检查 >= 5，比例 >= 30% 才使用二阶
|
||||||
|
6. analysis 返回中包含 transition_order、transition_available 字段
|
||||||
|
7. 所有新增方法包含函数级注释
|
||||||
|
8. depends_on 已修正为空数组（独立功能）
|
||||||
|
|
||||||
|
## Output
|
||||||
|
|
||||||
|
完成后创建 `.planning/phases/11-predictv3/11-05-SUMMARY.md`
|
||||||
@@ -844,25 +844,29 @@ foreach ($weights as $key => $value) {
|
|||||||
|
|
||||||
---
|
---
|
||||||
|
|
||||||
## Open Questions
|
## Open Questions (RESOLVED)
|
||||||
|
|
||||||
1. **历史数据量是否足够支撑高级优化?**
|
1. **历史数据量是否足够支撑高级优化?**
|
||||||
- 当前默认200期统计，二阶马尔可夫和关联规则挖掘建议500期+
|
- 当前默认200期统计，二阶马尔可夫和关联规则挖掘建议500期+
|
||||||
- 需检查数据库中实际可用的历史期数
|
- 需检查数据库中实际可用的历史期数
|
||||||
- 推荐: 查询 `SELECT COUNT(*) FROM fa_history` 确认数据量
|
- 推荐: 查询 `SELECT COUNT(*) FROM fa_history` 确认数据量
|
||||||
|
- **Resolution:** 11-05 Task 3 设置100期阈值，数据不足时回退一阶马尔可夫，已在plan中处理
|
||||||
|
|
||||||
2. **权重优化结果如何持久化?**
|
2. **权重优化结果如何持久化?**
|
||||||
- 选项A: 存储到 `application/extra/predict.php` 配置文件
|
- 选项A: 存储到 `application/extra/predict.php` 配置文件
|
||||||
- 选项B: 存储到数据库配置表
|
- 选项B: 存储到数据库配置表
|
||||||
- 选项C: 每次预测时动态计算（性能成本高）
|
- 选项C: 每次预测时动态计算（性能成本高）
|
||||||
|
- **Resolution:** 11-04 采用选项C（动态计算）+ 返回结果给前端展示，不持久化。设计决策：避免过拟合特定时间段，每次获取最新优化结果
|
||||||
|
|
||||||
3. **置信度阈值如何定义?**
|
3. **置信度阈值如何定义?**
|
||||||
- 当前假设: >=70%为高，50-70%为中，<50%为低
|
- 当前假设: >=70%为高，50-70%为中，<50%为低
|
||||||
- 需根据实际回测数据调整阈值
|
- 需根据实际回测数据调整阈值
|
||||||
|
- **Resolution:** 11-02 Task 1 明确阈值定义：>=70%高（绿色）、50-70%中（橙色）、<50%低（红色），前端11-03使用相同映射
|
||||||
|
|
||||||
4. **前端如何展示新增的回测指标(NDCG、MRR)?**
|
4. **前端如何展示新增的回测指标(NDCG、MRR)?**
|
||||||
- 需设计用户友好的展示方式
|
- 需设计用户友好的展示方式
|
||||||
- 可考虑简化为"预测质量评分"单一指标
|
- 可考虑简化为"预测质量评分"单一指标
|
||||||
|
- **Resolution:** 11-03 Task 2 实现百分比显示 + 柱状图：NDCG@5/MRR以百分比展示，命中分布以柱状图可视化
|
||||||
|
|
||||||
---
|
---
|
||||||
|
|
||||||
|
|||||||
@@ -0,0 +1,158 @@
|
|||||||
|
---
|
||||||
|
phase: 11
|
||||||
|
reviewers: [codex, opencode]
|
||||||
|
reviewed_at: 2026-05-01T12:30:00+08:00
|
||||||
|
plans_reviewed: [11-01-PLAN.md, 11-02-PLAN.md, 11-03-PLAN.md, 11-04-PLAN.md, 11-05-PLAN.md]
|
||||||
|
---
|
||||||
|
|
||||||
|
# Cross-AI Plan Review — Phase 11: predictV3算法优化
|
||||||
|
|
||||||
|
## Codex Review
|
||||||
|
|
||||||
|
### Summary
|
||||||
|
|
||||||
|
计划技术上是合理的，但有几个需要注意的地方：依赖关系修复、数据验证、性能保护和边缘情况处理。整体架构遵循现有模式良好，NDCG@5 和 MRR 作为排名评估指标是适当的。
|
||||||
|
|
||||||
|
### Strengths
|
||||||
|
|
||||||
|
- 计划结构清晰，任务分解合理
|
||||||
|
- NDCG@5、MRR、命中分布都是业界标准的排名质量评估指标
|
||||||
|
- 扩展字段明确（ndcg_5、mrr、hit_distribution、precision_5）
|
||||||
|
- 整体架构遵循现有代码模式
|
||||||
|
|
||||||
|
### Concerns
|
||||||
|
|
||||||
|
| Severity | Concern |
|
||||||
|
|----------|---------|
|
||||||
|
| HIGH | **依赖关系错误**: Plan 05 (二阶马尔可夫) 不应依赖 Plans 01 和 03 — 它是独立的增强功能 |
|
||||||
|
| HIGH | **数据验证缺失**: 需要最小样本量检查 — 置信度计算建议 50+ 期，二阶马尔可夫建议 150+ 期 |
|
||||||
|
| MEDIUM | **性能保护**: 网格搜索需要超时保护和最优权重缓存机制 |
|
||||||
|
| MEDIUM | **边缘情况**: NDCG 计算需要空预测保护；命中分布需要定义明确的统计桶 |
|
||||||
|
|
||||||
|
### Suggestions
|
||||||
|
|
||||||
|
- 修复 Plan 05 的依赖关系，使其独立执行
|
||||||
|
- 添加数据量阈值验证，不足时返回提示或回退策略
|
||||||
|
- 为网格搜索添加执行超时限制（如 60 秒）
|
||||||
|
- 在 NDCG/MRR 计算前检查 `$details` 是否为空
|
||||||
|
|
||||||
|
### Risk Assessment
|
||||||
|
|
||||||
|
**MEDIUM** — 依赖关系和边缘情况需要修复，否则执行可能失败。
|
||||||
|
|
||||||
|
---
|
||||||
|
|
||||||
|
## OpenCode Review
|
||||||
|
|
||||||
|
### Summary
|
||||||
|
|
||||||
|
Phase 11 计划在功能扩展方向上合理，在现有 V3 预测算法（9维度 + 动态权重）基础上增加置信度评估、回测指标扩展、权重优化和二阶马尔可夫链增强。计划整体结构清晰，依赖关系明确，但存在若干实现细节缺失和潜在风险需要补充。
|
||||||
|
|
||||||
|
### Strengths
|
||||||
|
|
||||||
|
- Plan 01 任务分解合理，指标选择专业
|
||||||
|
- Plan 02 与回测指标扩展解耦，可独立实现
|
||||||
|
- Plan 03 依赖明确，在现有弹窗架构内实现
|
||||||
|
- Plan 04 网格搜索是系统化的超参数优化方法
|
||||||
|
- Plan 05 二阶马尔可夫是合理的算法增强方向
|
||||||
|
|
||||||
|
### Concerns
|
||||||
|
|
||||||
|
| Severity | Concern |
|
||||||
|
|----------|---------|
|
||||||
|
| HIGH | **Plan 01: NDCG 计算公式不明确** — 需要确认基于什么"理想排名"计算，relevance score 定义模糊 |
|
||||||
|
| HIGH | **Plan 01: hit_distribution 定义模糊** — 具体指什么分布？按命中排名？按命中/未命中按期数分布？ |
|
||||||
|
| HIGH | **Plan 02: "历史排名命中率"数据来源不明** — 现有 `_runBacktestV3` 不保存历史命中率数据，需要明确是实时计算还是缓存 |
|
||||||
|
| HIGH | **Plan 02: "多维度一致性"定义不明** — 具体指哪些维度之间的一致性？如何量化？ |
|
||||||
|
| HIGH | **Plan 04: 5 种权重配置未明确** — 缺少具体配置清单 |
|
||||||
|
| HIGH | **Plan 04: 优化目标不明确** — 用哪个指标作为优化目标？hit_rate？NDCG？MRR？ |
|
||||||
|
| HIGH | **Plan 05: 状态空间爆炸** — 二阶马尔可夫状态空间大（尾数 10×10=100），100 期历史数据下很多状态对从未出现，概率估计不准确 |
|
||||||
|
| HIGH | **Plan 05: 100 期阈值缺乏依据** — 二阶马尔可夫需要更长历史，建议至少 200-300 期 |
|
||||||
|
| MEDIUM | **Plan 01: precision_5 与 hit_rate 关系** — 两者都是 Top-5 命中，是否重复？建议区分或合并 |
|
||||||
|
| MEDIUM | **Plan 02: 置信度百分比计算公式** — 三个维度如何加权组合？权重比例未明确 |
|
||||||
|
| MEDIUM | **Plan 02: 数据量要求** — 历史 < 20 期时置信度不准确，缺少 fallback 策略 |
|
||||||
|
| MEDIUM | **Plan 03: UI 设计细节缺失** — 未提供具体布局建议，置信度展示样式不明确 |
|
||||||
|
| MEDIUM | **Plan 03: 命中分布图表实现** — 未说明使用 ECharts 还是 CSS，数据量大时需考虑性能 |
|
||||||
|
| MEDIUM | **Plan 04: 结果持久化** — 每次调用都重新计算？建议增加缓存 |
|
||||||
|
| MEDIUM | **Plan 04: 计算量** — 5 配置 × 50 期回测 = 250 次预测，响应时间长，建议异步 |
|
||||||
|
| MEDIUM | **Plan 05: 一阶/二阶切换逻辑** — 判断条件不明确：总期数阈值还是状态对观察次数阈值？ |
|
||||||
|
| MEDIUM | **Plan 05: 与现有一阶权重关系** — 二阶是独立维度还是替换一阶？ |
|
||||||
|
| LOW | **Plan 01: 计算性能** — 每次回测批量计算可能影响性能 |
|
||||||
|
| LOW | **Plan 02: 返回值结构** — 是否每个号码都提供置信度？ |
|
||||||
|
| LOW | **Plan 03: 前端数据量** — 命中分布是否需要分页/滚动加载？ |
|
||||||
|
| LOW | **Plan 03: 国际化** — 新增 UI 文本是否需要多语言？ |
|
||||||
|
| LOW | **Plan 05: 性能影响** — 二阶计算复杂度高于一阶 |
|
||||||
|
|
||||||
|
### Suggestions
|
||||||
|
|
||||||
|
**Plan 01:**
|
||||||
|
- 补充 NDCG 公式：DCG = Σ(1/log2(rank+1))，IDCG = Σ(1/log2(i+1)) for i=1..hits
|
||||||
|
- 明确 hit_distribution 结构：`{rank_1: n, rank_2: n, ..., rank_5: n, miss: n}`
|
||||||
|
- 添加回测结果缓存机制
|
||||||
|
|
||||||
|
**Plan 02:**
|
||||||
|
- 将"多维度一致性"改为"预测得分集中度"，基于 Top-5 得分与平均得分差距计算
|
||||||
|
- 明确加权公式：`confidence = 0.4*historical + 0.3*score + 0.3*consistency`
|
||||||
|
- 历史命中率作为 `_runBacktestV3` 副产品，调用一次获取
|
||||||
|
|
||||||
|
**Plan 03:**
|
||||||
|
- 补充 UI 设计 mockup 或具体说明
|
||||||
|
- 使用现有 ECharts 展示命中分布
|
||||||
|
- 置信度用红/黄/绿三色表示高/中/低
|
||||||
|
|
||||||
|
**Plan 04:**
|
||||||
|
- 补充 5 种预定义配置的具体权重值
|
||||||
|
- 定义优化目标：综合 hit_rate(60%) + avg_rank(40%)
|
||||||
|
- 使用后台队列异步处理，返回 task_id
|
||||||
|
|
||||||
|
**Plan 05:**
|
||||||
|
- 重新评估二阶马尔可夫必要性 — 49选1彩票数据稀疏性严重
|
||||||
|
- 替代方案：加权 N 阶马尔可夫（70% 二阶 + 30% 一阶）
|
||||||
|
- 明确判断标准：`count($history) >= 200 && $statePairCount >= 5`
|
||||||
|
|
||||||
|
### Risk Assessment
|
||||||
|
|
||||||
|
**MEDIUM** — 实现复杂度中等，数据稀疏性问题高，多处定义不明确需补充。建议优先实现 Plan 01、02、04，将 Plan 05 作为可选高阶优化或重新评估必要性。
|
||||||
|
|
||||||
|
---
|
||||||
|
|
||||||
|
## Consensus Summary
|
||||||
|
|
||||||
|
### Agreed Strengths
|
||||||
|
|
||||||
|
- 计划结构清晰，任务分解合理 ✓
|
||||||
|
- NDCG@5、MRR 是适当的排名质量评估指标 ✓
|
||||||
|
- 整体架构遵循现有代码模式 ✓
|
||||||
|
- 网格搜索是系统化的参数优化方法 ✓
|
||||||
|
|
||||||
|
### Agreed Concerns (Highest Priority)
|
||||||
|
|
||||||
|
| Priority | Concern | Source |
|
||||||
|
|----------|---------|--------|
|
||||||
|
| 1 | **Plan 05 依赖关系错误** — 不应依赖 01、03，是独立功能 | Codex + OpenCode |
|
||||||
|
| 2 | **数据量验证缺失** — 置信度需 50+，二阶马尔可夫需 150-200+ | Codex + OpenCode |
|
||||||
|
| 3 | **边缘情况处理** — NDCG/MRR 空预测保护，hit_distribution 定义模糊 | Codex + OpenCode |
|
||||||
|
| 4 | **Plan 05 状态空间爆炸** — 100 期数据下二阶马尔可夫概率估计不准 | OpenCode |
|
||||||
|
| 5 | **Plan 02 置信度维度定义不明** — "多维度一致性"如何量化 | OpenCode |
|
||||||
|
| 6 | **Plan 04 配置未明确** — 5 种权重具体值缺失，优化目标不明 | OpenCode |
|
||||||
|
| 7 | **性能影响** — 网格搜索需超时/异步，二阶马尔可夫计算量大 | Codex + OpenCode |
|
||||||
|
|
||||||
|
### Divergent Views
|
||||||
|
|
||||||
|
| Issue | Codex | OpenCode |
|
||||||
|
|-------|-------|----------|
|
||||||
|
| Plan 05 数据阈值 | 建议 150+ | 建议 200-300+，并要求状态对观察次数 >= 5 |
|
||||||
|
| precision_5 与 hit_rate | 未提及 | 认为可能重复，建议区分 |
|
||||||
|
|
||||||
|
---
|
||||||
|
|
||||||
|
## Action Items for Replanning
|
||||||
|
|
||||||
|
1. **Fix Plan 05 depends_on** → 改为 `depends_on: []`
|
||||||
|
2. **Add data validation** → 所有计算方法添加最小数据量检查和 fallback
|
||||||
|
3. **Clarify NDCG formula** → 补充完整公式到 Plan 01 Task 1
|
||||||
|
4. **Clarify hit_distribution** → 明确结构为 `{rank_1..rank_5: counts}`
|
||||||
|
5. **Clarify confidence dimensions** → 重命名"多维度一致性"为"得分集中度"
|
||||||
|
6. **Add weight configs** → Plan 04 补充 5 种具体权重配置值
|
||||||
|
7. **Raise 2nd-order threshold** → Plan 05 改为 200 期 + 状态对观察次数检查
|
||||||
|
8. **Add performance protection** → 网格搜索添加超时限制，考虑异步
|
||||||
@@ -0,0 +1,97 @@
|
|||||||
|
---
|
||||||
|
phase: 11
|
||||||
|
phase_slug: predictv3
|
||||||
|
created: 2026-05-01
|
||||||
|
---
|
||||||
|
|
||||||
|
# Phase 11: predictV3算法优化 - Validation Strategy
|
||||||
|
|
||||||
|
## Overview
|
||||||
|
|
||||||
|
本阶段为算法优化，验证重点在于：
|
||||||
|
1. 新增指标计算准确性（NDCG、MRR、置信度）
|
||||||
|
2. 二阶马尔可夫转移矩阵构建正确性
|
||||||
|
3. 权重优化结果有效性
|
||||||
|
4. 回测命中率提升验证
|
||||||
|
|
||||||
|
## Test Framework
|
||||||
|
|
||||||
|
| Property | Value |
|
||||||
|
|----------|-------|
|
||||||
|
| Framework | PHPUnit (ThinkPHP内置) |
|
||||||
|
| Config file | 无独立配置，通过 `php think unit` 运行 |
|
||||||
|
| Quick run command | `php think unit --filter HistoryTest` |
|
||||||
|
| Full suite command | `php think unit` |
|
||||||
|
|
||||||
|
## Phase Requirements → Test Map
|
||||||
|
|
||||||
|
| Req ID | Behavior | Test Type | Automated Command | File Exists? |
|
||||||
|
|--------|----------|-----------|-------------------|-------------|
|
||||||
|
| PRED-01 | 置信度计算准确性 | unit | `php think unit --filter testConfidenceCalculation` | ❌ Wave 0 |
|
||||||
|
| PRED-02 | NDCG计算准确性 | unit | `php think unit --filter testNDCGCalculation` | ❌ Wave 0 |
|
||||||
|
| PRED-03 | 二阶马尔可夫转移矩阵构建 | unit | `php think unit --filter testTransitionMatrix2ndOrder` | ❌ Wave 0 |
|
||||||
|
| PRED-04 | 权重优化收敛性 | unit | `php think unit --filter testWeightOptimization` | ❌ Wave 0 |
|
||||||
|
| PRED-05 | 回测结果完整性 | unit | `php think unit --filter testBacktestV3Extended` | ❌ Wave 0 |
|
||||||
|
|
||||||
|
## Sampling Rate
|
||||||
|
|
||||||
|
- **Per task commit:** 快速单元测试覆盖核心方法
|
||||||
|
- **Per wave merge:** 回测验证使用真实历史数据
|
||||||
|
- **Phase gate:** 全量回测（100期）验证整体命中率提升
|
||||||
|
|
||||||
|
## Wave 0 Gaps
|
||||||
|
|
||||||
|
- [ ] `tests/HistoryTest.php` — 核心预测方法单元测试
|
||||||
|
- [ ] `tests/ConfidenceTest.php` — 置信度计算测试
|
||||||
|
- [ ] `tests/BacktestMetricsTest.php` — NDCG、MRR等指标计算测试
|
||||||
|
- [ ] 共享fixtures: 历史数据模拟生成器
|
||||||
|
|
||||||
|
## Validation Dimensions
|
||||||
|
|
||||||
|
### Dimension 8: Nyquist Test Coverage
|
||||||
|
|
||||||
|
**Target:** 每个 PLAN.md 至少有一个可验证的测试命令
|
||||||
|
|
||||||
|
| Plan | Primary Test | Coverage |
|
||||||
|
|------|--------------|----------|
|
||||||
|
| 11-01 | testBacktestV3Extended | NDCG/MRR 计算准确性 |
|
||||||
|
| 11-02 | testConfidenceCalculation | 置信度计算准确性 |
|
||||||
|
| 11-03 | 手动 UI 验证 | 前端展示正确性 |
|
||||||
|
| 11-04 | testWeightOptimization | 权重优化收敛性 |
|
||||||
|
| 11-05 | testTransitionMatrix2ndOrder | 二阶马尔可夫正确性 |
|
||||||
|
|
||||||
|
### Dimension 9: Integration Verification
|
||||||
|
|
||||||
|
**End-to-End Flow:**
|
||||||
|
|
||||||
|
1. 后端 `getPredictionV3()` 返回完整数据结构（predictions + confidence + backtest）
|
||||||
|
2. 前端 `renderPredict()` 正确渲染所有新增指标
|
||||||
|
3. 回测命中率可量化对比（V2 vs V3）
|
||||||
|
|
||||||
|
**Verification Command:**
|
||||||
|
|
||||||
|
```bash
|
||||||
|
# 验证后端接口返回结构完整性
|
||||||
|
curl -s "http://localhost/history/predictV3?periods=100" | jq '.data | keys'
|
||||||
|
# 期望输出包含: predictions, confidence, backtest, analysis
|
||||||
|
|
||||||
|
# 验证 NDCG/MRR 存在
|
||||||
|
curl -s "http://localhost/history/predictV3?periods=100" | jq '.data.backtest | keys'
|
||||||
|
# 期望输出包含: hit_rate, avg_rank, ndcg_5, mrr, hit_distribution
|
||||||
|
```
|
||||||
|
|
||||||
|
## Acceptance Criteria
|
||||||
|
|
||||||
|
### Phase Gate
|
||||||
|
|
||||||
|
- [ ] NDCG@5 计算准确（单元测试通过）
|
||||||
|
- [ ] MRR 计算准确（单元测试通过）
|
||||||
|
- [ ] 置信度阈值正确（>=70%高、50-70%中、<50%低）
|
||||||
|
- [ ] 二阶马尔可夫在数据充足时启用
|
||||||
|
- [ ] 前端展示包含所有新增指标
|
||||||
|
- [ ] 回测命中率有提升（相比 V2）
|
||||||
|
|
||||||
|
---
|
||||||
|
|
||||||
|
*Phase: 11-predictv3*
|
||||||
|
*Validation strategy created: 2026-05-01*
|
||||||
@@ -0,0 +1,55 @@
|
|||||||
|
---
|
||||||
|
name: history-predict
|
||||||
|
created: 2026-04-30
|
||||||
|
type: quick
|
||||||
|
---
|
||||||
|
|
||||||
|
# 预测号码功能规划
|
||||||
|
|
||||||
|
## 目标
|
||||||
|
在 history 页面新增预测号码功能，综合历史记录多维度分析给出号码预测建议。
|
||||||
|
|
||||||
|
## 分析维度
|
||||||
|
现有系统已具备以下转移概率分析：
|
||||||
|
1. **区域转移** - zoneTransition (1-10, 11-20, 21-30, 31-40, 41-49)
|
||||||
|
2. **生肖转移** - zodiacTransition (12生肖)
|
||||||
|
3. **尾号转移** - tailNumberTransition (尾号0-9)
|
||||||
|
4. **首号转移** - headNumberTransition (首号0-4)
|
||||||
|
5. **波色转移** - colorWaveTransition (红/蓝/绿)
|
||||||
|
|
||||||
|
## 预测算法
|
||||||
|
基于最近N期特码，结合各维度转移概率矩阵：
|
||||||
|
- 根据上一期特码所在维度（区域、生肖、尾号、首号），查找转移概率最高的目标维度
|
||||||
|
- 综合各维度预测结果，计算每个号码的综合得分
|
||||||
|
- 得分 = 区域概率权重 + 生肖概率权重 + 尾号概率权重 + 首号概率权重 + 波色概率权重
|
||||||
|
|
||||||
|
## 实现步骤
|
||||||
|
|
||||||
|
### 1. 后端 Model 新增方法
|
||||||
|
- `getPrediction($periods, $weights)` - 综合预测计算方法
|
||||||
|
- 输入：历史期数、各维度权重配置
|
||||||
|
- 输出：预测号码列表（按得分排序）
|
||||||
|
|
||||||
|
### 2. 后端 Controller 新增接口
|
||||||
|
- `predict()` - AJAX 接口
|
||||||
|
- 参数：periods, weights (可选)
|
||||||
|
- 返回：预测号码列表 + 各维度分析详情
|
||||||
|
|
||||||
|
### 3. 前端 JS 新增功能
|
||||||
|
- 预测弹窗 `showPredictDialog()`
|
||||||
|
- 权重配置面板
|
||||||
|
- 预测结果渲染（号码球 + 得分 + 各维度分析说明）
|
||||||
|
|
||||||
|
### 4. 权重配置
|
||||||
|
默认权重：
|
||||||
|
- 区域转移：0.25
|
||||||
|
- 生肖转移：0.20
|
||||||
|
- 尾号转移：0.20
|
||||||
|
- 首号转移：0.15
|
||||||
|
- 波色转移：0.10
|
||||||
|
- 冷热系数：0.10
|
||||||
|
|
||||||
|
## 文件修改清单
|
||||||
|
1. `application/admin/model/History.php` - 新增 getPrediction 方法
|
||||||
|
2. `application/admin/controller/History.php` - 新增 predict 接口，更新 noNeedRight
|
||||||
|
3. `public/assets/js/backend/history.js` - 新增预测弹窗和渲染逻辑
|
||||||
@@ -0,0 +1,37 @@
|
|||||||
|
---
|
||||||
|
status: complete
|
||||||
|
created: 2026-04-30
|
||||||
|
slug: history-predict
|
||||||
|
---
|
||||||
|
|
||||||
|
# 预测号码功能完成
|
||||||
|
|
||||||
|
## 实现内容
|
||||||
|
|
||||||
|
### 1. 后端 Model (History.php)
|
||||||
|
新增 `getPrediction($periods, $weights)` 方法：
|
||||||
|
- 基于 6 个维度计算综合预测得分
|
||||||
|
- 区域转移、生肖转移、尾号转移、首号转移、波色转移、冷热系数
|
||||||
|
- 返回 Top 20 预测号码及其详细得分分析
|
||||||
|
|
||||||
|
### 2. 后端 Controller (History.php)
|
||||||
|
新增 `predict()` 接口：
|
||||||
|
- 支持 AJAX 请求
|
||||||
|
- 可配置统计期数和权重参数
|
||||||
|
- 已加入 `noNeedRight` 白名单
|
||||||
|
|
||||||
|
### 3. 前端 JS (history.js)
|
||||||
|
新增预测功能：
|
||||||
|
- `showPredictDialog()` - 预测弹窗
|
||||||
|
- `queryPredict()` - AJAX 查询
|
||||||
|
- `renderPredict()` - 结果渲染
|
||||||
|
- 支持自定义权重配置
|
||||||
|
|
||||||
|
### 4. 视图 (index.html)
|
||||||
|
新增"智能预测"按钮
|
||||||
|
|
||||||
|
## 文件变更
|
||||||
|
- `application/admin/model/History.php` (+150行)
|
||||||
|
- `application/admin/controller/History.php` (+25行)
|
||||||
|
- `public/assets/js/backend/history.js` (+180行)
|
||||||
|
- `application/admin/view/history/index.html` (+1行)
|
||||||
@@ -0,0 +1,39 @@
|
|||||||
|
## 正码与特码关联规律分析
|
||||||
|
|
||||||
|
### 数据范围
|
||||||
|
- 总期数：约500期（2025111-2026120）
|
||||||
|
- 每期数据：num1-6（正码） + num7（特码）
|
||||||
|
|
||||||
|
### 分析维度
|
||||||
|
|
||||||
|
#### 1. 正码平均值与特码差值
|
||||||
|
分析每一期的正码平均值(avg)与特码(num7)的差值分布：
|
||||||
|
- 差值 = num7 - avg(num1-6)
|
||||||
|
- 统计差值的高频范围
|
||||||
|
|
||||||
|
#### 2. 正码范围与特码关系
|
||||||
|
分析正码的[min, max]范围与特码的关系：
|
||||||
|
- 特码是否在正码范围内？
|
||||||
|
- 特码距离正码范围的距离分布
|
||||||
|
|
||||||
|
#### 3. 正码排序后与特码距离
|
||||||
|
将num1-6排序，分析特码与最近正码的距离：
|
||||||
|
- 最短距离分布
|
||||||
|
- 特码是否等于某个正码？
|
||||||
|
|
||||||
|
#### 4. 和值尾数与特码尾数
|
||||||
|
分析正码和值的尾数与特码尾数的关系：
|
||||||
|
- 同尾概率？
|
||||||
|
- 差值分布？
|
||||||
|
|
||||||
|
#### 5. 正码区间覆盖分析
|
||||||
|
将1-49分为5个区间，分析正码覆盖的区间与特码所在区间的关系：
|
||||||
|
- 特码是否出现在正码未覆盖的区间？
|
||||||
|
|
||||||
|
#### 6. 波色/生肖关联
|
||||||
|
分析正码中各波色/生肖的数量与特码波色/生肖的关系
|
||||||
|
|
||||||
|
---
|
||||||
|
|
||||||
|
### 数据提取准备
|
||||||
|
从SQL中提取所有INSERT数据进行统计分析。
|
||||||
@@ -0,0 +1,858 @@
|
|||||||
|
<?php
|
||||||
|
/**
|
||||||
|
* 上期正码与当期特码关联规律分析脚本
|
||||||
|
*
|
||||||
|
* 分析维度：
|
||||||
|
* 1. 上期正码平均值与下期特码的差值分布
|
||||||
|
* 2. 上期正码范围[min,max]与下期特码的关系
|
||||||
|
* 3. 上期正码与下期特码的最短距离分布
|
||||||
|
* 4. 上期正码和值尾数与下期特码尾数的关系
|
||||||
|
* 5. 上期正码覆盖区间与下期特码所在区间的关系
|
||||||
|
* 6. 上期正码波色分布与下期特码波色的关系
|
||||||
|
* 7. 上期特码与下期特码的转移关系
|
||||||
|
*/
|
||||||
|
|
||||||
|
// 波色映射表
|
||||||
|
$colorMap = [
|
||||||
|
1 => '红', 2 => '红', 3 => '蓝', 4 => '蓝', 5 => '绿', 6 => '绿',
|
||||||
|
7 => '红', 8 => '红', 9 => '蓝', 10 => '蓝', 11 => '绿', 12 => '红',
|
||||||
|
13 => '红', 14 => '蓝', 15 => '蓝', 16 => '绿', 17 => '绿', 18 => '红',
|
||||||
|
19 => '红', 20 => '蓝', 21 => '绿', 22 => '绿', 23 => '红', 24 => '红',
|
||||||
|
25 => '蓝', 26 => '蓝', 27 => '绿', 28 => '绿', 29 => '红', 30 => '红',
|
||||||
|
31 => '蓝', 32 => '绿', 33 => '绿', 34 => '红', 35 => '红', 36 => '蓝',
|
||||||
|
37 => '蓝', 38 => '绿', 39 => '绿', 40 => '红', 41 => '蓝', 42 => '蓝',
|
||||||
|
43 => '绿', 44 => '绿', 45 => '红', 46 => '红', 47 => '蓝', 48 => '蓝',
|
||||||
|
49 => '绿'
|
||||||
|
];
|
||||||
|
|
||||||
|
// 区间划分（1-10小号，11-30中号，31-49大号）
|
||||||
|
function getRange($num) {
|
||||||
|
if ($num >= 1 && $num <= 10) return '小号(1-10)';
|
||||||
|
if ($num >= 11 && $num <= 30) return '中号(11-30)';
|
||||||
|
return '大号(31-49)';
|
||||||
|
}
|
||||||
|
|
||||||
|
// 获取数字的尾数
|
||||||
|
function getTail($num) {
|
||||||
|
return $num % 10;
|
||||||
|
}
|
||||||
|
|
||||||
|
// 从SQL数据提取历史记录
|
||||||
|
$history = [];
|
||||||
|
$sqlFile = 'C:\Users\91611\Desktop\fa_history.sql';
|
||||||
|
$content = file_get_contents($sqlFile);
|
||||||
|
|
||||||
|
// 解析INSERT语句
|
||||||
|
preg_match_all("/INSERT INTO `fa_history` VALUES \((\d+), (\d+), (\d+), (\d+), (\d+), (\d+), (\d+), (\d+), '([^']+)'\)/", $content, $matches);
|
||||||
|
|
||||||
|
for ($i = 0; $i < count($matches[0]); $i++) {
|
||||||
|
$history[] = [
|
||||||
|
'expect' => (int)$matches[1][$i],
|
||||||
|
'num1' => (int)$matches[2][$i],
|
||||||
|
'num2' => (int)$matches[3][$i],
|
||||||
|
'num3' => (int)$matches[4][$i],
|
||||||
|
'num4' => (int)$matches[5][$i],
|
||||||
|
'num5' => (int)$matches[6][$i],
|
||||||
|
'num6' => (int)$matches[7][$i],
|
||||||
|
'num7' => (int)$matches[8][$i],
|
||||||
|
'openTime' => $matches[9][$i]
|
||||||
|
];
|
||||||
|
}
|
||||||
|
|
||||||
|
// 按期号排序
|
||||||
|
usort($history, function($a, $b) {
|
||||||
|
return $a['expect'] - $b['expect'];
|
||||||
|
});
|
||||||
|
|
||||||
|
echo "=== 数据概览 ===\n";
|
||||||
|
echo "总期数: " . count($history) . "\n";
|
||||||
|
echo "期号范围: {$history[0]['expect']} - {$history[count($history)-1]['expect']}\n\n";
|
||||||
|
|
||||||
|
// 分析结果存储
|
||||||
|
$analysisResults = [];
|
||||||
|
|
||||||
|
// ============ 维度1: 上期正码平均值与下期特码的差值分布 ============
|
||||||
|
echo "=== 维度1: 上期正码平均值与下期特码的差值分布 ===\n";
|
||||||
|
|
||||||
|
$avgDifferences = [];
|
||||||
|
$avgDiffPredictions = []; // 基于平均值的预测命中率
|
||||||
|
|
||||||
|
for ($i = 0; $i < count($history) - 1; $i++) {
|
||||||
|
$current = $history[$i];
|
||||||
|
$next = $history[$i + 1];
|
||||||
|
|
||||||
|
// 计算上期正码平均值
|
||||||
|
$avg = ($current['num1'] + $current['num2'] + $current['num3'] +
|
||||||
|
$current['num4'] + $current['num5'] + $current['num6']) / 6;
|
||||||
|
|
||||||
|
// 与下期特码的差值
|
||||||
|
$diff = $next['num7'] - $avg;
|
||||||
|
$avgDifferences[] = round($diff);
|
||||||
|
}
|
||||||
|
|
||||||
|
// 统计差值分布
|
||||||
|
$avgDiffStats = array_count_values($avgDifferences);
|
||||||
|
ksort($avgDiffStats);
|
||||||
|
|
||||||
|
echo "差值分布（差值=下期特码-上期正码平均值）:\n";
|
||||||
|
foreach ($avgDiffStats as $diff => $count) {
|
||||||
|
$percent = round($count / (count($history) - 1) * 100, 2);
|
||||||
|
echo " 差值 $diff: $count次 ($percent%)\n";
|
||||||
|
}
|
||||||
|
|
||||||
|
// 分析差值范围预测效果
|
||||||
|
echo "\n差值范围预测效果:\n";
|
||||||
|
$diffRanges = [
|
||||||
|
'[-40,-20]' => 0,
|
||||||
|
'[-20,-10]' => 0,
|
||||||
|
'[-10,0]' => 0,
|
||||||
|
'[0,10]' => 0,
|
||||||
|
'[10,20]' => 0,
|
||||||
|
'[20,40]' => 0
|
||||||
|
];
|
||||||
|
|
||||||
|
foreach ($avgDifferences as $diff) {
|
||||||
|
if ($diff >= -40 && $diff < -20) $diffRanges['[-40,-20]']++;
|
||||||
|
elseif ($diff >= -20 && $diff < -10) $diffRanges['[-20,-10]']++;
|
||||||
|
elseif ($diff >= -10 && $diff < 0) $diffRanges['[-10,0]']++;
|
||||||
|
elseif ($diff >= 0 && $diff < 10) $diffRanges['[0,10]']++;
|
||||||
|
elseif ($diff >= 10 && $diff < 20) $diffRanges['[10,20]']++;
|
||||||
|
elseif ($diff >= 20 && $diff <= 40) $diffRanges['[20,40]']++;
|
||||||
|
}
|
||||||
|
|
||||||
|
$total = count($avgDifferences);
|
||||||
|
foreach ($diffRanges as $range => $count) {
|
||||||
|
$percent = round($count / $total * 100, 2);
|
||||||
|
echo " $range: $count次 ($percent%)\n";
|
||||||
|
}
|
||||||
|
|
||||||
|
// 基于平均值±10范围的预测命中率分析
|
||||||
|
$hitCount = 0;
|
||||||
|
$predictionRange = 10; // 预测范围
|
||||||
|
for ($i = 0; $i < count($history) - 1; $i++) {
|
||||||
|
$current = $history[$i];
|
||||||
|
$next = $history[$i + 1];
|
||||||
|
|
||||||
|
$avg = ($current['num1'] + $current['num2'] + $current['num3'] +
|
||||||
|
$current['num4'] + $current['num5'] + $current['num6']) / 6;
|
||||||
|
|
||||||
|
// 预测范围：平均值±10
|
||||||
|
$predictMin = max(1, floor($avg) - $predictionRange);
|
||||||
|
$predictMax = min(49, floor($avg) + $predictionRange);
|
||||||
|
|
||||||
|
// 检查下期特码是否在预测范围内
|
||||||
|
if ($next['num7'] >= $predictMin && $next['num7'] <= $predictMax) {
|
||||||
|
$hitCount++;
|
||||||
|
}
|
||||||
|
}
|
||||||
|
echo "\n基于平均值±$predictionRange范围的预测命中率: " . round($hitCount / (count($history) - 1) * 100, 2) . "% ($hitCount/" . (count($history)-1) . ")\n";
|
||||||
|
|
||||||
|
echo "\n";
|
||||||
|
|
||||||
|
// ============ 维度2: 上期正码范围[min,max]与下期特码的关系 ============
|
||||||
|
echo "=== 维度2: 上期正码范围[min,max]与下期特码的关系 ===\n";
|
||||||
|
|
||||||
|
$inRangeCount = 0;
|
||||||
|
$belowRangeCount = 0;
|
||||||
|
$aboveRangeCount = 0;
|
||||||
|
$rangeStats = [];
|
||||||
|
|
||||||
|
for ($i = 0; $i < count($history) - 1; $i++) {
|
||||||
|
$current = $history[$i];
|
||||||
|
$next = $history[$i + 1];
|
||||||
|
|
||||||
|
// 上期正码范围
|
||||||
|
$nums = [$current['num1'], $current['num2'], $current['num3'],
|
||||||
|
$current['num4'], $current['num5'], $current['num6']];
|
||||||
|
$min = min($nums);
|
||||||
|
$max = max($nums);
|
||||||
|
$rangeWidth = $max - $min;
|
||||||
|
|
||||||
|
// 下期特码与范围的关系
|
||||||
|
if ($next['num7'] >= $min && $next['num7'] <= $max) {
|
||||||
|
$inRangeCount++;
|
||||||
|
$relation = '在范围内';
|
||||||
|
} elseif ($next['num7'] < $min) {
|
||||||
|
$belowRangeCount++;
|
||||||
|
$relation = '低于范围';
|
||||||
|
} else {
|
||||||
|
$aboveRangeCount++;
|
||||||
|
$relation = '高于范围';
|
||||||
|
}
|
||||||
|
|
||||||
|
// 统计范围宽度与关系
|
||||||
|
$rangeKey = "范围宽度$rangeWidth";
|
||||||
|
if (!isset($rangeStats[$rangeKey])) {
|
||||||
|
$rangeStats[$rangeKey] = ['在范围内' => 0, '低于范围' => 0, '高于范围' => 0];
|
||||||
|
}
|
||||||
|
$rangeStats[$rangeKey][$relation]++;
|
||||||
|
}
|
||||||
|
|
||||||
|
$total = count($history) - 1;
|
||||||
|
echo "下期特码位置分布:\n";
|
||||||
|
echo " 在上期正码范围内: $inRangeCount次 (" . round($inRangeCount / $total * 100, 2) . "%)\n";
|
||||||
|
echo " 低于上期正码范围: $belowRangeCount次 (" . round($belowRangeCount / $total * 100, 2) . "%)\n";
|
||||||
|
echo " 高于上期正码范围: $aboveRangeCount次 (" . round($aboveRangeCount / $total * 100, 2) . "%)\n";
|
||||||
|
|
||||||
|
echo "\n范围宽度与特码位置关系:\n";
|
||||||
|
ksort($rangeStats);
|
||||||
|
foreach ($rangeStats as $width => $stats) {
|
||||||
|
$widthTotal = array_sum($stats);
|
||||||
|
echo " $width (共$widthTotal期):\n";
|
||||||
|
foreach ($stats as $relation => $count) {
|
||||||
|
$percent = round($count / $widthTotal * 100, 2);
|
||||||
|
echo " $relation: $count次 ($percent%)\n";
|
||||||
|
}
|
||||||
|
}
|
||||||
|
|
||||||
|
// 计算范围宽度的平均值
|
||||||
|
$avgRangeWidth = 0;
|
||||||
|
for ($i = 0; $i < count($history) - 1; $i++) {
|
||||||
|
$current = $history[$i];
|
||||||
|
$nums = [$current['num1'], $current['num2'], $current['num3'],
|
||||||
|
$current['num4'], $current['num5'], $current['num6']];
|
||||||
|
$avgRangeWidth += max($nums) - min($nums);
|
||||||
|
}
|
||||||
|
$avgRangeWidth = round($avgRangeWidth / (count($history) - 1), 2);
|
||||||
|
echo "\n平均范围宽度: $avgRangeWidth\n";
|
||||||
|
|
||||||
|
echo "\n";
|
||||||
|
|
||||||
|
// ============ 维度3: 上期正码与下期特码的最短距离分布 ============
|
||||||
|
echo "=== 维度3: 上期正码与下期特码的最短距离分布 ===\n";
|
||||||
|
|
||||||
|
$minDistances = [];
|
||||||
|
$distanceStats = [];
|
||||||
|
|
||||||
|
for ($i = 0; $i < count($history) - 1; $i++) {
|
||||||
|
$current = $history[$i];
|
||||||
|
$next = $history[$i + 1];
|
||||||
|
|
||||||
|
// 计算上期正码与下期特码的最短距离
|
||||||
|
$nums = [$current['num1'], $current['num2'], $current['num3'],
|
||||||
|
$current['num4'], $current['num5'], $current['num6']];
|
||||||
|
|
||||||
|
$minDist = PHP_INT_MAX;
|
||||||
|
foreach ($nums as $num) {
|
||||||
|
$dist = abs($next['num7'] - $num);
|
||||||
|
if ($dist < $minDist) $minDist = $dist;
|
||||||
|
}
|
||||||
|
$minDistances[] = $minDist;
|
||||||
|
|
||||||
|
if (!isset($distanceStats[$minDist])) {
|
||||||
|
$distanceStats[$minDist] = 0;
|
||||||
|
}
|
||||||
|
$distanceStats[$minDist]++;
|
||||||
|
}
|
||||||
|
|
||||||
|
ksort($distanceStats);
|
||||||
|
echo "最短距离分布:\n";
|
||||||
|
foreach ($distanceStats as $dist => $count) {
|
||||||
|
$percent = round($count / (count($history) - 1) * 100, 2);
|
||||||
|
echo " 距离 $dist: $count次 ($percent%)\n";
|
||||||
|
}
|
||||||
|
|
||||||
|
// 分析距离≤5的命中率
|
||||||
|
$closeHitCount = count(array_filter($minDistances, function($d) { return $d <= 5; }));
|
||||||
|
echo "\n最短距离≤5的比例: " . round($closeHitCount / (count($history) - 1) * 100, 2) . "% ($closeHitCount/" . (count($history)-1) . ")\n";
|
||||||
|
|
||||||
|
$veryCloseHitCount = count(array_filter($minDistances, function($d) { return $d <= 3; }));
|
||||||
|
echo "最短距离≤3的比例: " . round($veryCloseHitCount / (count($history) - 1) * 100, 2) . "% ($veryCloseHitCount/" . (count($history)-1) . ")\n";
|
||||||
|
|
||||||
|
// 基于最短距离预测（围绕上期正码±3的范围）
|
||||||
|
$predictionHit = 0;
|
||||||
|
for ($i = 0; $i < count($history) - 1; $i++) {
|
||||||
|
$current = $history[$i];
|
||||||
|
$next = $history[$i + 1];
|
||||||
|
|
||||||
|
$nums = [$current['num1'], $current['num2'], $current['num3'],
|
||||||
|
$current['num4'], $current['num5'], $current['num6']];
|
||||||
|
|
||||||
|
// 预测范围：每个正码±3
|
||||||
|
$predicted = [];
|
||||||
|
foreach ($nums as $num) {
|
||||||
|
for ($p = max(1, $num - 3); $p <= min(49, $num + 3); $p++) {
|
||||||
|
$predicted[$p] = true;
|
||||||
|
}
|
||||||
|
}
|
||||||
|
|
||||||
|
if (isset($predicted[$next['num7']])) {
|
||||||
|
$predictionHit++;
|
||||||
|
}
|
||||||
|
}
|
||||||
|
echo "基于正码±3范围预测命中率: " . round($predictionHit / (count($history) - 1) * 100, 2) . "% ($predictionHit/" . (count($history)-1) . ")\n";
|
||||||
|
echo "预测范围大小: 约" . count($predicted) . "个数字\n";
|
||||||
|
|
||||||
|
echo "\n";
|
||||||
|
|
||||||
|
// ============ 维度4: 上期正码和值尾数与下期特码尾数的关系 ============
|
||||||
|
echo "=== 维度4: 上期正码和值尾数与下期特码尾数的关系 ===\n";
|
||||||
|
|
||||||
|
$sumTailRelations = [];
|
||||||
|
$tailSameCount = 0;
|
||||||
|
$tailDiff1Count = 0;
|
||||||
|
$tailDiff2Count = 0;
|
||||||
|
|
||||||
|
for ($i = 0; $i < count($history) - 1; $i++) {
|
||||||
|
$current = $history[$i];
|
||||||
|
$next = $history[$i + 1];
|
||||||
|
|
||||||
|
// 上期正码和值
|
||||||
|
$sum = $current['num1'] + $current['num2'] + $current['num3'] +
|
||||||
|
$current['num4'] + $current['num5'] + $current['num6'];
|
||||||
|
$sumTail = getTail($sum);
|
||||||
|
|
||||||
|
// 下期特码尾数
|
||||||
|
$nextTail = getTail($next['num7']);
|
||||||
|
|
||||||
|
// 统计尾数关系
|
||||||
|
$tailDiff = abs($sumTail - $nextTail);
|
||||||
|
if ($tailDiff > 5) $tailDiff = 10 - $tailDiff; // 考虑环形差异
|
||||||
|
|
||||||
|
if (!isset($sumTailRelations[$sumTail])) {
|
||||||
|
$sumTailRelations[$sumTail] = [];
|
||||||
|
}
|
||||||
|
if (!isset($sumTailRelations[$sumTail][$nextTail])) {
|
||||||
|
$sumTailRelations[$sumTail][$nextTail] = 0;
|
||||||
|
}
|
||||||
|
$sumTailRelations[$sumTail][$nextTail]++;
|
||||||
|
|
||||||
|
if ($tailDiff == 0) $tailSameCount++;
|
||||||
|
elseif ($tailDiff == 1) $tailDiff1Count++;
|
||||||
|
elseif ($tailDiff == 2) $tailDiff2Count++;
|
||||||
|
}
|
||||||
|
|
||||||
|
$total = count($history) - 1;
|
||||||
|
echo "尾数关系分布:\n";
|
||||||
|
echo " 尾数相同: $tailSameCount次 (" . round($tailSameCount / $total * 100, 2) . "%)\n";
|
||||||
|
echo " 尾数相差1: $tailDiff1Count次 (" . round($tailDiff1Count / $total * 100, 2) . "%)\n";
|
||||||
|
echo " 尾数相差2: $tailDiff2Count次 (" . round($tailDiff2Count / $total * 100, 2) . "%)\n";
|
||||||
|
|
||||||
|
echo "\n上期和值尾数→下期特码尾数转移矩阵:\n";
|
||||||
|
for ($sumTail = 0; $sumTail <= 9; $sumTail++) {
|
||||||
|
echo " 和值尾数$sumTail → ";
|
||||||
|
if (isset($sumTailRelations[$sumTail])) {
|
||||||
|
$maxTail = -1;
|
||||||
|
$maxCount = 0;
|
||||||
|
foreach ($sumTailRelations[$sumTail] as $nextTail => $count) {
|
||||||
|
if ($count > $maxCount) {
|
||||||
|
$maxCount = $count;
|
||||||
|
$maxTail = $nextTail;
|
||||||
|
}
|
||||||
|
}
|
||||||
|
$totalCount = array_sum($sumTailRelations[$sumTail]);
|
||||||
|
$percent = round($maxCount / $totalCount * 100, 2);
|
||||||
|
echo "最可能尾数$maxTail ($maxCount次, $percent%), 其他: ";
|
||||||
|
$others = [];
|
||||||
|
foreach ($sumTailRelations[$sumTail] as $nextTail => $count) {
|
||||||
|
if ($nextTail != $maxTail) {
|
||||||
|
$others[] = "$nextTail($count)";
|
||||||
|
}
|
||||||
|
}
|
||||||
|
echo implode(', ', $others) . "\n";
|
||||||
|
} else {
|
||||||
|
echo "无数据\n";
|
||||||
|
}
|
||||||
|
}
|
||||||
|
|
||||||
|
// 基于尾数预测命中率
|
||||||
|
$tailPredictionHit = 0;
|
||||||
|
for ($i = 0; $i < count($history) - 1; $i++) {
|
||||||
|
$current = $history[$i];
|
||||||
|
$next = $history[$i + 1];
|
||||||
|
|
||||||
|
$sum = $current['num1'] + $current['num2'] + $current['num3'] +
|
||||||
|
$current['num4'] + $current['num5'] + $current['num6'];
|
||||||
|
$sumTail = getTail($sum);
|
||||||
|
|
||||||
|
// 预测：和值尾数±2范围内的尾数
|
||||||
|
$predictTails = [
|
||||||
|
$sumTail,
|
||||||
|
($sumTail + 1) % 10,
|
||||||
|
($sumTail - 1 + 10) % 10,
|
||||||
|
($sumTail + 2) % 10,
|
||||||
|
($sumTail - 2 + 10) % 10
|
||||||
|
];
|
||||||
|
|
||||||
|
$nextTail = getTail($next['num7']);
|
||||||
|
if (in_array($nextTail, $predictTails)) {
|
||||||
|
$tailPredictionHit++;
|
||||||
|
}
|
||||||
|
}
|
||||||
|
echo "\n基于和值尾数±2范围的尾数预测命中率: " . round($tailPredictionHit / (count($history) - 1) * 100, 2) . "% ($tailPredictionHit/" . (count($history)-1) . ")\n";
|
||||||
|
echo "预测范围: 5个尾数，每个尾数对应约5个数字，共约25个数字\n";
|
||||||
|
|
||||||
|
echo "\n";
|
||||||
|
|
||||||
|
// ============ 维度5: 上期正码覆盖区间与下期特码所在区间的关系 ============
|
||||||
|
echo "=== 维度5: 上期正码覆盖区间与下期特码所在区间的关系 ===\n";
|
||||||
|
|
||||||
|
$rangeCoverStats = [];
|
||||||
|
$rangeTransferStats = [];
|
||||||
|
|
||||||
|
for ($i = 0; $i < count($history) - 1; $i++) {
|
||||||
|
$current = $history[$i];
|
||||||
|
$next = $history[$i + 1];
|
||||||
|
|
||||||
|
$nums = [$current['num1'], $current['num2'], $current['num3'],
|
||||||
|
$current['num4'], $current['num5'], $current['num6']];
|
||||||
|
|
||||||
|
// 上期正码覆盖的区间
|
||||||
|
$covers = [];
|
||||||
|
foreach ($nums as $num) {
|
||||||
|
$covers[getRange($num)] = true;
|
||||||
|
}
|
||||||
|
$coverStr = implode('+', array_keys($covers));
|
||||||
|
|
||||||
|
// 下期特码所在区间
|
||||||
|
$nextRange = getRange($next['num7']);
|
||||||
|
|
||||||
|
if (!isset($rangeCoverStats[$coverStr])) {
|
||||||
|
$rangeCoverStats[$coverStr] = [];
|
||||||
|
}
|
||||||
|
if (!isset($rangeCoverStats[$coverStr][$nextRange])) {
|
||||||
|
$rangeCoverStats[$coverStr][$nextRange] = 0;
|
||||||
|
}
|
||||||
|
$rangeCoverStats[$coverStr][$nextRange]++;
|
||||||
|
|
||||||
|
// 统计覆盖度与特码位置
|
||||||
|
$coverCount = count($covers);
|
||||||
|
if (!isset($rangeTransferStats[$coverCount])) {
|
||||||
|
$rangeTransferStats[$coverCount] = [
|
||||||
|
'小号(1-10)' => 0,
|
||||||
|
'中号(11-30)' => 0,
|
||||||
|
'大号(31-49)' => 0
|
||||||
|
];
|
||||||
|
}
|
||||||
|
$rangeTransferStats[$coverCount][$nextRange]++;
|
||||||
|
}
|
||||||
|
|
||||||
|
echo "上期正码覆盖区间→下期特码区间转移:\n";
|
||||||
|
foreach ($rangeCoverStats as $cover => $nextStats) {
|
||||||
|
$totalCover = array_sum($nextStats);
|
||||||
|
echo " $cover (共$totalCover期):\n";
|
||||||
|
foreach ($nextStats as $nextRange => $count) {
|
||||||
|
$percent = round($count / $totalCover * 100, 2);
|
||||||
|
echo " → $nextRange: $count次 ($percent%)\n";
|
||||||
|
}
|
||||||
|
}
|
||||||
|
|
||||||
|
echo "\n上期正码覆盖区间数量与下期特码分布:\n";
|
||||||
|
foreach ($rangeTransferStats as $coverCount => $stats) {
|
||||||
|
$totalCover = array_sum($stats);
|
||||||
|
echo " 覆盖$coverCount个区间 (共$totalCover期):\n";
|
||||||
|
foreach ($stats as $range => $count) {
|
||||||
|
$percent = round($count / $totalCover * 100, 2);
|
||||||
|
echo " $range: $count次 ($percent%)\n";
|
||||||
|
}
|
||||||
|
}
|
||||||
|
|
||||||
|
// 分析特码是否在上期正码覆盖的区间内
|
||||||
|
$hitInCoveredRange = 0;
|
||||||
|
for ($i = 0; $i < count($history) - 1; $i++) {
|
||||||
|
$current = $history[$i];
|
||||||
|
$next = $history[$i + 1];
|
||||||
|
|
||||||
|
$nums = [$current['num1'], $current['num2'], $current['num3'],
|
||||||
|
$current['num4'], $current['num5'], $current['num6']];
|
||||||
|
|
||||||
|
$covers = [];
|
||||||
|
foreach ($nums as $num) {
|
||||||
|
$covers[getRange($num)] = true;
|
||||||
|
}
|
||||||
|
|
||||||
|
$nextRange = getRange($next['num7']);
|
||||||
|
if (isset($covers[$nextRange])) {
|
||||||
|
$hitInCoveredRange++;
|
||||||
|
}
|
||||||
|
}
|
||||||
|
echo "\n下期特码在上期正码覆盖区间内的比例: " . round($hitInCoveredRange / (count($history) - 1) * 100, 2) . "% ($hitInCoveredRange/" . (count($history)-1) . ")\n";
|
||||||
|
|
||||||
|
echo "\n";
|
||||||
|
|
||||||
|
// ============ 维度6: 上期正码波色分布与下期特码波色的关系 ============
|
||||||
|
echo "=== 维度6: 上期正码波色分布与下期特码波色的关系 ===\n";
|
||||||
|
|
||||||
|
$colorDistributionStats = [];
|
||||||
|
$colorTransferStats = [];
|
||||||
|
$dominantColorStats = [];
|
||||||
|
|
||||||
|
for ($i = 0; $i < count($history) - 1; $i++) {
|
||||||
|
$current = $history[$i];
|
||||||
|
$next = $history[$i + 1];
|
||||||
|
|
||||||
|
// 上期正码波色分布
|
||||||
|
$colors = [];
|
||||||
|
$nums = [$current['num1'], $current['num2'], $current['num3'],
|
||||||
|
$current['num4'], $current['num5'], $current['num6']];
|
||||||
|
foreach ($nums as $num) {
|
||||||
|
$color = $colorMap[$num];
|
||||||
|
if (!isset($colors[$color])) {
|
||||||
|
$colors[$color] = 0;
|
||||||
|
}
|
||||||
|
$colors[$color]++;
|
||||||
|
}
|
||||||
|
|
||||||
|
// 波色分布字符串
|
||||||
|
$colorStr = "红{$colors['红']}蓝{$colors['蓝']}绿{$colors['绿']}";
|
||||||
|
|
||||||
|
// 下期特码波色
|
||||||
|
$nextColor = $colorMap[$next['num7']];
|
||||||
|
|
||||||
|
// 统计波色分布→特码波色
|
||||||
|
if (!isset($colorDistributionStats[$colorStr])) {
|
||||||
|
$colorDistributionStats[$colorStr] = [];
|
||||||
|
}
|
||||||
|
if (!isset($colorDistributionStats[$colorStr][$nextColor])) {
|
||||||
|
$colorDistributionStats[$colorStr][$nextColor] = 0;
|
||||||
|
}
|
||||||
|
$colorDistributionStats[$colorStr][$nextColor]++;
|
||||||
|
|
||||||
|
// 统计主导波色→特码波色
|
||||||
|
$dominantColor = array_keys($colors, max($colors))[0];
|
||||||
|
if (!isset($dominantColorStats[$dominantColor])) {
|
||||||
|
$dominantColorStats[$dominantColor] = [];
|
||||||
|
}
|
||||||
|
if (!isset($dominantColorStats[$dominantColor][$nextColor])) {
|
||||||
|
$dominantColorStats[$dominantColor][$nextColor] = 0;
|
||||||
|
}
|
||||||
|
$dominantColorStats[$dominantColor][$nextColor]++;
|
||||||
|
}
|
||||||
|
|
||||||
|
echo "上期正码波色分布→下期特码波色转移:\n";
|
||||||
|
// 按出现次数排序
|
||||||
|
$sortedColorDist = $colorDistributionStats;
|
||||||
|
uasort($sortedColorDist, function($a, $b) {
|
||||||
|
return array_sum($b) - array_sum($a);
|
||||||
|
});
|
||||||
|
|
||||||
|
foreach ($sortedColorDist as $dist => $nextStats) {
|
||||||
|
$totalDist = array_sum($nextStats);
|
||||||
|
if ($totalDist >= 5) { // 只显示出现5次以上的分布
|
||||||
|
echo " $dist (共$totalDist期):\n";
|
||||||
|
foreach ($nextStats as $nextColor => $count) {
|
||||||
|
$percent = round($count / $totalDist * 100, 2);
|
||||||
|
echo " → $nextColor: $count次 ($percent%)\n";
|
||||||
|
}
|
||||||
|
}
|
||||||
|
}
|
||||||
|
|
||||||
|
echo "\n上期主导波色→下期特码波色转移:\n";
|
||||||
|
foreach ($dominantColorStats as $dominant => $nextStats) {
|
||||||
|
$totalDom = array_sum($nextStats);
|
||||||
|
echo " 主导$dominant (共$totalDom期):\n";
|
||||||
|
foreach ($nextStats as $nextColor => $count) {
|
||||||
|
$percent = round($count / $totalDom * 100, 2);
|
||||||
|
echo " → $nextColor: $count次 ($percent%)\n";
|
||||||
|
}
|
||||||
|
}
|
||||||
|
|
||||||
|
// 基于主导波色预测命中率
|
||||||
|
$dominantPredictionHit = 0;
|
||||||
|
for ($i = 0; $i < count($history) - 1; $i++) {
|
||||||
|
$current = $history[$i];
|
||||||
|
$next = $history[$i + 1];
|
||||||
|
|
||||||
|
$nums = [$current['num1'], $current['num2'], $current['num3'],
|
||||||
|
$current['num4'], $current['num5'], $current['num6']];
|
||||||
|
$colors = [];
|
||||||
|
foreach ($nums as $num) {
|
||||||
|
$color = $colorMap[$num];
|
||||||
|
if (!isset($colors[$color])) $colors[$color] = 0;
|
||||||
|
$colors[$color]++;
|
||||||
|
}
|
||||||
|
|
||||||
|
$dominantColor = array_keys($colors, max($colors))[0];
|
||||||
|
$nextColor = $colorMap[$next['num7']];
|
||||||
|
|
||||||
|
if ($dominantColor == $nextColor) {
|
||||||
|
$dominantPredictionHit++;
|
||||||
|
}
|
||||||
|
}
|
||||||
|
echo "\n主导波色预测命中率: " . round($dominantPredictionHit / (count($history) - 1) * 100, 2) . "% ($dominantPredictionHit/" . (count($history)-1) . ")\n";
|
||||||
|
|
||||||
|
// 基于上期正码波色预测（扩展到两种最可能波色）
|
||||||
|
$expandedColorPredictionHit = 0;
|
||||||
|
for ($i = 0; $i < count($history) - 1; $i++) {
|
||||||
|
$current = $history[$i];
|
||||||
|
$next = $history[$i + 1];
|
||||||
|
|
||||||
|
$nums = [$current['num1'], $current['num2'], $current['num3'],
|
||||||
|
$current['num4'], $current['num5'], $current['num6']];
|
||||||
|
$colors = ['红' => 0, '蓝' => 0, '绿' => 0];
|
||||||
|
foreach ($nums as $num) {
|
||||||
|
$colors[$colorMap[$num]]++;
|
||||||
|
}
|
||||||
|
|
||||||
|
// 选择出现次数最多的两种波色
|
||||||
|
arsort($colors);
|
||||||
|
$topColors = array_keys(array_slice($colors, 0, 2, true));
|
||||||
|
|
||||||
|
$nextColor = $colorMap[$next['num7']];
|
||||||
|
if (in_array($nextColor, $topColors)) {
|
||||||
|
$expandedColorPredictionHit++;
|
||||||
|
}
|
||||||
|
}
|
||||||
|
echo "扩展到两种主导波色预测命中率: " . round($expandedColorPredictionHit / (count($history) - 1) * 100, 2) . "% ($expandedColorPredictionHit/" . (count($history)-1) . ")\n";
|
||||||
|
|
||||||
|
echo "\n";
|
||||||
|
|
||||||
|
// ============ 维度7: 上期特码与下期特码的转移关系（马尔可夫分析） ============
|
||||||
|
echo "=== 维度7: 上期特码与下期特码的转移关系（马尔可夫分析） ===\n";
|
||||||
|
|
||||||
|
$specialTransfer = [];
|
||||||
|
$specialRangeTransfer = [];
|
||||||
|
|
||||||
|
for ($i = 0; $i < count($history) - 1; $i++) {
|
||||||
|
$current = $history[$i];
|
||||||
|
$next = $history[$i + 1];
|
||||||
|
|
||||||
|
$currentSpecial = $current['num7'];
|
||||||
|
$nextSpecial = $next['num7'];
|
||||||
|
|
||||||
|
// 精确转移统计
|
||||||
|
if (!isset($specialTransfer[$currentSpecial])) {
|
||||||
|
$specialTransfer[$currentSpecial] = [];
|
||||||
|
}
|
||||||
|
if (!isset($specialTransfer[$currentSpecial][$nextSpecial])) {
|
||||||
|
$specialTransfer[$currentSpecial][$nextSpecial] = 0;
|
||||||
|
}
|
||||||
|
$specialTransfer[$currentSpecial][$nextSpecial]++;
|
||||||
|
|
||||||
|
// 区间转移统计
|
||||||
|
$currentRange = getRange($currentSpecial);
|
||||||
|
$nextRange = getRange($nextSpecial);
|
||||||
|
|
||||||
|
if (!isset($specialRangeTransfer[$currentRange])) {
|
||||||
|
$specialRangeTransfer[$currentRange] = [];
|
||||||
|
}
|
||||||
|
if (!isset($specialRangeTransfer[$currentRange][$nextRange])) {
|
||||||
|
$specialRangeTransfer[$currentRange][$nextRange] = 0;
|
||||||
|
}
|
||||||
|
$specialRangeTransfer[$currentRange][$nextRange]++;
|
||||||
|
}
|
||||||
|
|
||||||
|
echo "特码区间转移矩阵:\n";
|
||||||
|
foreach ($specialRangeTransfer as $fromRange => $toStats) {
|
||||||
|
$totalFrom = array_sum($toStats);
|
||||||
|
echo " $fromRange → :\n";
|
||||||
|
foreach ($toStats as $toRange => $count) {
|
||||||
|
$percent = round($count / $totalFrom * 100, 2);
|
||||||
|
echo " $toRange: $count次 ($percent%)\n";
|
||||||
|
}
|
||||||
|
}
|
||||||
|
|
||||||
|
// 找出高频转移
|
||||||
|
echo "\n高频特码转移（出现2次以上）:\n";
|
||||||
|
foreach ($specialTransfer as $from => $toStats) {
|
||||||
|
foreach ($toStats as $to => $count) {
|
||||||
|
if ($count >= 2) {
|
||||||
|
echo " 特码$from → 特码$to: $count次\n";
|
||||||
|
}
|
||||||
|
}
|
||||||
|
}
|
||||||
|
|
||||||
|
// 基于特码区间预测
|
||||||
|
$specialRangePredictionHit = 0;
|
||||||
|
for ($i = 0; $i < count($history) - 1; $i++) {
|
||||||
|
$current = $history[$i];
|
||||||
|
$next = $history[$i + 1];
|
||||||
|
|
||||||
|
$currentRange = getRange($current['num7']);
|
||||||
|
|
||||||
|
// 找出该区间最可能转移到的两个区间
|
||||||
|
$transferStats = $specialRangeTransfer[$currentRange];
|
||||||
|
arsort($transferStats);
|
||||||
|
$topRanges = array_keys(array_slice($transferStats, 0, 2, true));
|
||||||
|
|
||||||
|
$nextRange = getRange($next['num7']);
|
||||||
|
if (in_array($nextRange, $topRanges)) {
|
||||||
|
$specialRangePredictionHit++;
|
||||||
|
}
|
||||||
|
}
|
||||||
|
echo "\n基于特码区间转移预测（前2区间）命中率: " . round($specialRangePredictionHit / (count($history) - 1) * 100, 2) . "% ($specialRangePredictionHit/" . (count($history)-1) . ")\n";
|
||||||
|
|
||||||
|
echo "\n";
|
||||||
|
|
||||||
|
// ============ 综合分析：寻找40%以上命中率的规律 ============
|
||||||
|
echo "=== 综合分析：寻找40%以上命中率的规律 ===\n\n";
|
||||||
|
|
||||||
|
// 综合预测模型
|
||||||
|
echo "【综合预测模型测试】\n\n";
|
||||||
|
|
||||||
|
$combinedHits = [
|
||||||
|
'平均值±10范围' => $hitCount,
|
||||||
|
'正码±3范围' => $predictionHit,
|
||||||
|
'和值尾数±2尾数范围' => $tailPredictionHit,
|
||||||
|
'覆盖区间预测' => $hitInCoveredRange,
|
||||||
|
'主导波色预测' => $dominantPredictionHit,
|
||||||
|
'双波色预测' => $expandedColorPredictionHit,
|
||||||
|
'特码区间转移' => $specialRangePredictionHit
|
||||||
|
];
|
||||||
|
|
||||||
|
echo "各维度预测命中率汇总:\n";
|
||||||
|
$totalPredictions = count($history) - 1;
|
||||||
|
foreach ($combinedHits as $name => $hit) {
|
||||||
|
$percent = round($hit / $totalPredictions * 100, 2);
|
||||||
|
$status = $percent >= 40 ? '【达标】' : '';
|
||||||
|
echo " $name: $percent% ($hit/$totalPredictions) $status\n";
|
||||||
|
}
|
||||||
|
|
||||||
|
// 组合预测测试
|
||||||
|
echo "\n组合预测测试:\n";
|
||||||
|
|
||||||
|
$comboHits = 0;
|
||||||
|
$comboPlusHits = 0;
|
||||||
|
|
||||||
|
for ($i = 0; $i < count($history) - 1; $i++) {
|
||||||
|
$current = $history[$i];
|
||||||
|
$next = $history[$i + 1];
|
||||||
|
|
||||||
|
$nums = [$current['num1'], $current['num2'], $current['num3'],
|
||||||
|
$current['num4'], $current['num5'], $current['num6']];
|
||||||
|
|
||||||
|
// 方法1：平均值±15范围
|
||||||
|
$avg = array_sum($nums) / 6;
|
||||||
|
$avgRange = range(max(1, floor($avg) - 15), min(49, floor($avg) + 15));
|
||||||
|
|
||||||
|
// 方法2：正码±5范围
|
||||||
|
$numRange = [];
|
||||||
|
foreach ($nums as $num) {
|
||||||
|
for ($p = max(1, $num - 5); $p <= min(49, $num + 5); $p++) {
|
||||||
|
$numRange[$p] = true;
|
||||||
|
}
|
||||||
|
}
|
||||||
|
|
||||||
|
// 方法3：和值尾数±3范围的所有数字
|
||||||
|
$sum = array_sum($nums);
|
||||||
|
$sumTail = getTail($sum);
|
||||||
|
$tailRange = [];
|
||||||
|
for ($t = $sumTail - 3; $t <= $sumTail + 3; $t++) {
|
||||||
|
$actualTail = ($t + 10) % 10;
|
||||||
|
for ($n = 1; $n <= 49; $n++) {
|
||||||
|
if (getTail($n) == $actualTail) {
|
||||||
|
$tailRange[$n] = true;
|
||||||
|
}
|
||||||
|
}
|
||||||
|
}
|
||||||
|
|
||||||
|
// 组合1：平均值范围 OR 正码范围
|
||||||
|
if (in_array($next['num7'], $avgRange) || isset($numRange[$next['num7']])) {
|
||||||
|
$comboHits++;
|
||||||
|
}
|
||||||
|
|
||||||
|
// 组合2：平均值范围 OR 正码范围 OR 尾数范围
|
||||||
|
if (in_array($next['num7'], $avgRange) || isset($numRange[$next['num7']]) || isset($tailRange[$next['num7']])) {
|
||||||
|
$comboPlusHits++;
|
||||||
|
}
|
||||||
|
}
|
||||||
|
|
||||||
|
echo "组合1（平均值±15 OR 正码±5）命中率: " . round($comboHits / $totalPredictions * 100, 2) . "% ($comboHits/$totalPredictions)\n";
|
||||||
|
echo "组合2（平均值±15 OR 正码±5 OR 尾数±3）命中率: " . round($comboPlusHits / $totalPredictions * 100, 2) . "% ($comboPlusHits/$totalPredictions)\n";
|
||||||
|
|
||||||
|
// 波色+区间组合
|
||||||
|
echo "\n波色+区间组合预测:\n";
|
||||||
|
$colorRangeComboHit = 0;
|
||||||
|
for ($i = 0; $i < count($history) - 1; $i++) {
|
||||||
|
$current = $history[$i];
|
||||||
|
$next = $history[$i + 1];
|
||||||
|
|
||||||
|
$nums = [$current['num1'], $current['num2'], $current['num3'],
|
||||||
|
$current['num4'], $current['num5'], $current['num6']];
|
||||||
|
|
||||||
|
// 获取上期正码覆盖的波色和区间
|
||||||
|
$colors = ['红' => 0, '蓝' => 0, '绿' => 0];
|
||||||
|
$ranges = ['小号(1-10)' => 0, '中号(11-30)' => 0, '大号(31-49)' => 0];
|
||||||
|
foreach ($nums as $num) {
|
||||||
|
$colors[$colorMap[$num]]++;
|
||||||
|
$ranges[getRange($num)]++;
|
||||||
|
}
|
||||||
|
|
||||||
|
// 选择最可能的波色和区间
|
||||||
|
arsort($colors);
|
||||||
|
arsort($ranges);
|
||||||
|
$topColor = array_keys($colors)[0];
|
||||||
|
$topRange = array_keys($ranges)[0];
|
||||||
|
|
||||||
|
// 预测：该波色+该区间的交集
|
||||||
|
$predictNums = [];
|
||||||
|
foreach (range(1, 49) as $n) {
|
||||||
|
if ($colorMap[$n] == $topColor && getRange($n) == $topRange) {
|
||||||
|
$predictNums[$n] = true;
|
||||||
|
}
|
||||||
|
}
|
||||||
|
|
||||||
|
if (isset($predictNums[$next['num7']])) {
|
||||||
|
$colorRangeComboHit++;
|
||||||
|
}
|
||||||
|
}
|
||||||
|
echo "波色+区间交集预测命中率: " . round($colorRangeComboHit / $totalPredictions * 100, 2) . "% ($colorRangeComboHit/$totalPredictions)\n";
|
||||||
|
echo "预测范围大小: " . count($predictNums) . "个数字\n";
|
||||||
|
|
||||||
|
// 扩展波色+区间（前2波色+前2区间）
|
||||||
|
$colorRangeCombo2Hit = 0;
|
||||||
|
for ($i = 0; $i < count($history) - 1; $i++) {
|
||||||
|
$current = $history[$i];
|
||||||
|
$next = $history[$i + 1];
|
||||||
|
|
||||||
|
$nums = [$current['num1'], $current['num2'], $current['num3'],
|
||||||
|
$current['num4'], $current['num5'], $current['num6']];
|
||||||
|
|
||||||
|
$colors = ['红' => 0, '蓝' => 0, '绿' => 0];
|
||||||
|
$ranges = ['小号(1-10)' => 0, '中号(11-30)' => 0, '大号(31-49)' => 0];
|
||||||
|
foreach ($nums as $num) {
|
||||||
|
$colors[$colorMap[$num]]++;
|
||||||
|
$ranges[getRange($num)]++;
|
||||||
|
}
|
||||||
|
|
||||||
|
arsort($colors);
|
||||||
|
arsort($ranges);
|
||||||
|
$top2Colors = array_keys(array_slice($colors, 0, 2, true));
|
||||||
|
$top2Ranges = array_keys(array_slice($ranges, 0, 2, true));
|
||||||
|
|
||||||
|
$predictNums2 = [];
|
||||||
|
foreach (range(1, 49) as $n) {
|
||||||
|
if (in_array($colorMap[$n], $top2Colors) && in_array(getRange($n), $top2Ranges)) {
|
||||||
|
$predictNums2[$n] = true;
|
||||||
|
}
|
||||||
|
}
|
||||||
|
|
||||||
|
if (isset($predictNums2[$next['num7']])) {
|
||||||
|
$colorRangeCombo2Hit++;
|
||||||
|
}
|
||||||
|
}
|
||||||
|
echo "前2波色+前2区间交集预测命中率: " . round($colorRangeCombo2Hit / $totalPredictions * 100, 2) . "% ($colorRangeCombo2Hit/$totalPredictions)\n";
|
||||||
|
|
||||||
|
echo "\n";
|
||||||
|
|
||||||
|
// ============ 总结：40%以上命中率的规律 ============
|
||||||
|
echo "=== 总结：达到40%以上命中率的规律 ===\n\n";
|
||||||
|
|
||||||
|
$highHitRules = [];
|
||||||
|
foreach ($combinedHits as $name => $hit) {
|
||||||
|
$percent = round($hit / $totalPredictions * 100, 2);
|
||||||
|
if ($percent >= 40) {
|
||||||
|
$highHitRules[] = [
|
||||||
|
'name' => $name,
|
||||||
|
'percent' => $percent,
|
||||||
|
'hit' => $hit,
|
||||||
|
'total' => $totalPredictions
|
||||||
|
];
|
||||||
|
}
|
||||||
|
}
|
||||||
|
|
||||||
|
if (count($highHitRules) > 0) {
|
||||||
|
foreach ($highHitRules as $rule) {
|
||||||
|
echo "【{$rule['name']}】命中率: {$rule['percent']}% ({$rule['hit']}/{$rule['total']})\n";
|
||||||
|
}
|
||||||
|
} else {
|
||||||
|
echo "单维度分析中没有达到40%以上命中率的规律\n";
|
||||||
|
}
|
||||||
|
|
||||||
|
// 组合规律
|
||||||
|
echo "\n组合规律命中率:\n";
|
||||||
|
echo "组合1（平均值±15 OR 正码±5）: " . round($comboHits / $totalPredictions * 100, 2) . "%\n";
|
||||||
|
echo "组合2（平均值±15 OR 正码±5 OR 尾数±3）: " . round($comboPlusHits / $totalPredictions * 100, 2) . "%\n";
|
||||||
|
echo "前2波色+前2区间交集: " . round($colorRangeCombo2Hit / $totalPredictions * 100, 2) . "%\n";
|
||||||
|
|
||||||
|
echo "\n分析完成！\n";
|
||||||
@@ -0,0 +1,589 @@
|
|||||||
|
#!/usr/bin/env python3
|
||||||
|
# -*- coding: utf-8 -*-
|
||||||
|
"""
|
||||||
|
上期正码与当期特码关联规律分析脚本
|
||||||
|
|
||||||
|
分析维度：
|
||||||
|
1. 上期正码平均值与下期特码的差值分布
|
||||||
|
2. 上期正码范围[min,max]与下期特码的关系
|
||||||
|
3. 上期正码与下期特码的最短距离分布
|
||||||
|
4. 上期正码和值尾数与下期特码尾数的关系
|
||||||
|
5. 上期正码覆盖区间与下期特码所在区间的关系
|
||||||
|
6. 上期正码波色分布与下期特码波色的关系
|
||||||
|
7. 上期特码与下期特码的转移关系
|
||||||
|
"""
|
||||||
|
|
||||||
|
import re
|
||||||
|
from collections import defaultdict
|
||||||
|
from pathlib import Path
|
||||||
|
|
||||||
|
# 波色映射表
|
||||||
|
COLOR_MAP = {
|
||||||
|
1: '红', 2: '红', 3: '蓝', 4: '蓝', 5: '绿', 6: '绿',
|
||||||
|
7: '红', 8: '红', 9: '蓝', 10: '蓝', 11: '绿', 12: '红',
|
||||||
|
13: '红', 14: '蓝', 15: '蓝', 16: '绿', 17: '绿', 18: '红',
|
||||||
|
19: '红', 20: '蓝', 21: '绿', 22: '绿', 23: '红', 24: '红',
|
||||||
|
25: '蓝', 26: '蓝', 27: '绿', 28: '绿', 29: '红', 30: '红',
|
||||||
|
31: '蓝', 32: '绿', 33: '绿', 34: '红', 35: '红', 36: '蓝',
|
||||||
|
37: '蓝', 38: '绿', 39: '绿', 40: '红', 41: '蓝', 42: '蓝',
|
||||||
|
43: '绿', 44: '绿', 45: '红', 46: '红', 47: '蓝', 48: '蓝',
|
||||||
|
49: '绿'
|
||||||
|
}
|
||||||
|
|
||||||
|
def get_range(num):
|
||||||
|
"""获取数字所在的区间"""
|
||||||
|
if 1 <= num <= 10:
|
||||||
|
return '小号(1-10)'
|
||||||
|
elif 11 <= num <= 30:
|
||||||
|
return '中号(11-30)'
|
||||||
|
else:
|
||||||
|
return '大号(31-49)'
|
||||||
|
|
||||||
|
def get_tail(num):
|
||||||
|
"""获取数字的尾数"""
|
||||||
|
return num % 10
|
||||||
|
|
||||||
|
def parse_sql_file(filepath):
|
||||||
|
"""解析SQL文件，提取历史数据"""
|
||||||
|
content = Path(filepath).read_text(encoding='utf-8')
|
||||||
|
|
||||||
|
# 解析INSERT语句
|
||||||
|
pattern = r"INSERT INTO `fa_history` VALUES \((\d+), (\d+), (\d+), (\d+), (\d+), (\d+), (\d+), (\d+), '([^']+)'"
|
||||||
|
matches = re.findall(pattern, content)
|
||||||
|
|
||||||
|
history = []
|
||||||
|
for match in matches:
|
||||||
|
history.append({
|
||||||
|
'expect': int(match[0]),
|
||||||
|
'num1': int(match[1]),
|
||||||
|
'num2': int(match[2]),
|
||||||
|
'num3': int(match[3]),
|
||||||
|
'num4': int(match[4]),
|
||||||
|
'num5': int(match[5]),
|
||||||
|
'num6': int(match[6]),
|
||||||
|
'num7': int(match[7]),
|
||||||
|
'openTime': match[8]
|
||||||
|
})
|
||||||
|
|
||||||
|
# 按期号排序
|
||||||
|
history.sort(key=lambda x: x['expect'])
|
||||||
|
return history
|
||||||
|
|
||||||
|
def analyze():
|
||||||
|
"""主分析函数"""
|
||||||
|
# 解析数据
|
||||||
|
history = parse_sql_file(r'C:\Users\91611\Desktop\fa_history.sql')
|
||||||
|
|
||||||
|
print("=== 数据概览 ===")
|
||||||
|
print(f"总期数: {len(history)}")
|
||||||
|
print(f"期号范围: {history[0]['expect']} - {history[-1]['expect']}")
|
||||||
|
print()
|
||||||
|
|
||||||
|
total_predictions = len(history) - 1
|
||||||
|
|
||||||
|
# ============ 维度1: 上期正码平均值与下期特码的差值分布 ============
|
||||||
|
print("=== 维度1: 上期正码平均值与下期特码的差值分布 ===")
|
||||||
|
|
||||||
|
avg_diffs = []
|
||||||
|
hit_count_avg = 0
|
||||||
|
prediction_range = 10
|
||||||
|
|
||||||
|
for i in range(len(history) - 1):
|
||||||
|
current = history[i]
|
||||||
|
next_record = history[i + 1]
|
||||||
|
|
||||||
|
# 计算上期正码平均值
|
||||||
|
avg = (current['num1'] + current['num2'] + current['num3'] +
|
||||||
|
current['num4'] + current['num5'] + current['num6']) / 6
|
||||||
|
|
||||||
|
# 与下期特码的差值
|
||||||
|
diff = next_record['num7'] - avg
|
||||||
|
avg_diffs.append(round(diff))
|
||||||
|
|
||||||
|
# 预测范围：平均值±10
|
||||||
|
predict_min = max(1, int(avg) - prediction_range)
|
||||||
|
predict_max = min(49, int(avg) + prediction_range)
|
||||||
|
|
||||||
|
if predict_min <= next_record['num7'] <= predict_max:
|
||||||
|
hit_count_avg += 1
|
||||||
|
|
||||||
|
# 统计差值分布
|
||||||
|
diff_stats = defaultdict(int)
|
||||||
|
for d in avg_diffs:
|
||||||
|
diff_stats[d] += 1
|
||||||
|
|
||||||
|
print("差值分布（差值=下期特码-上期正码平均值）:")
|
||||||
|
for diff in sorted(diff_stats.keys()):
|
||||||
|
count = diff_stats[diff]
|
||||||
|
percent = round(count / total_predictions * 100, 2)
|
||||||
|
print(f" 差值 {diff}: {count}次 ({percent}%)")
|
||||||
|
|
||||||
|
# 差值范围分布
|
||||||
|
print("\n差值范围分布:")
|
||||||
|
ranges = [
|
||||||
|
('[-40,-20]', lambda d: -40 <= d < -20),
|
||||||
|
('[-20,-10]', lambda d: -20 <= d < -10),
|
||||||
|
('[-10,0]', lambda d: -10 <= d < 0),
|
||||||
|
('[0,10]', lambda d: 0 <= d < 10),
|
||||||
|
('[10,20]', lambda d: 10 <= d < 20),
|
||||||
|
('[20,40]', lambda d: 20 <= d <= 40)
|
||||||
|
]
|
||||||
|
for range_name, condition in ranges:
|
||||||
|
count = sum(1 for d in avg_diffs if condition(d))
|
||||||
|
percent = round(count / total_predictions * 100, 2)
|
||||||
|
print(f" {range_name}: {count}次 ({percent}%)")
|
||||||
|
|
||||||
|
print(f"\n基于平均值±{prediction_range}范围的预测命中率: {round(hit_count_avg / total_predictions * 100, 2)}% ({hit_count_avg}/{total_predictions})")
|
||||||
|
print()
|
||||||
|
|
||||||
|
# ============ 维度2: 上期正码范围[min,max]与下期特码的关系 ============
|
||||||
|
print("=== 维度2: 上期正码范围[min,max]与下期特码的关系 ===")
|
||||||
|
|
||||||
|
in_range_count = 0
|
||||||
|
below_range_count = 0
|
||||||
|
above_range_count = 0
|
||||||
|
range_width_stats = defaultdict(lambda: {'在范围内': 0, '低于范围': 0, '高于范围': 0})
|
||||||
|
|
||||||
|
for i in range(len(history) - 1):
|
||||||
|
current = history[i]
|
||||||
|
next_record = history[i + 1]
|
||||||
|
|
||||||
|
nums = [current['num1'], current['num2'], current['num3'],
|
||||||
|
current['num4'], current['num5'], current['num6']]
|
||||||
|
min_num = min(nums)
|
||||||
|
max_num = max(nums)
|
||||||
|
range_width = max_num - min_num
|
||||||
|
|
||||||
|
if min_num <= next_record['num7'] <= max_num:
|
||||||
|
in_range_count += 1
|
||||||
|
relation = '在范围内'
|
||||||
|
elif next_record['num7'] < min_num:
|
||||||
|
below_range_count += 1
|
||||||
|
relation = '低于范围'
|
||||||
|
else:
|
||||||
|
above_range_count += 1
|
||||||
|
relation = '高于范围'
|
||||||
|
|
||||||
|
range_width_stats[range_width][relation] += 1
|
||||||
|
|
||||||
|
print("下期特码位置分布:")
|
||||||
|
print(f" 在上期正码范围内: {in_range_count}次 ({round(in_range_count / total_predictions * 100, 2)}%)")
|
||||||
|
print(f" 低于上期正码范围: {below_range_count}次 ({round(below_range_count / total_predictions * 100, 2)}%)")
|
||||||
|
print(f" 高于上期正码范围: {above_range_count}次 ({round(above_range_count / total_predictions * 100, 2)}%)")
|
||||||
|
|
||||||
|
print("\n范围宽度与特码位置关系:")
|
||||||
|
for width in sorted(range_width_stats.keys()):
|
||||||
|
stats = range_width_stats[width]
|
||||||
|
width_total = sum(stats.values())
|
||||||
|
print(f" 范围宽度{width} (共{width_total}期):")
|
||||||
|
for relation, count in stats.items():
|
||||||
|
percent = round(count / width_total * 100, 2)
|
||||||
|
print(f" {relation}: {count}次 ({percent}%)")
|
||||||
|
|
||||||
|
avg_range_width = sum(max([h['num1'], h['num2'], h['num3'], h['num4'], h['num5'], h['num6']]) -
|
||||||
|
min([h['num1'], h['num2'], h['num3'], h['num4'], h['num5'], h['num6']])
|
||||||
|
for h in history[:-1]) / total_predictions
|
||||||
|
print(f"\n平均范围宽度: {round(avg_range_width, 2)}")
|
||||||
|
print()
|
||||||
|
|
||||||
|
# ============ 维度3: 上期正码与下期特码的最短距离分布 ============
|
||||||
|
print("=== 维度3: 上期正码与下期特码的最短距离分布 ===")
|
||||||
|
|
||||||
|
min_distances = []
|
||||||
|
prediction_hit_dist = 0
|
||||||
|
|
||||||
|
for i in range(len(history) - 1):
|
||||||
|
current = history[i]
|
||||||
|
next_record = history[i + 1]
|
||||||
|
|
||||||
|
nums = [current['num1'], current['num2'], current['num3'],
|
||||||
|
current['num4'], current['num5'], current['num6']]
|
||||||
|
|
||||||
|
min_dist = min(abs(next_record['num7'] - num) for num in nums)
|
||||||
|
min_distances.append(min_dist)
|
||||||
|
|
||||||
|
# 预测范围：每个正码±3
|
||||||
|
predicted = set()
|
||||||
|
for num in nums:
|
||||||
|
for p in range(max(1, num - 3), min(50, num + 4)):
|
||||||
|
predicted.add(p)
|
||||||
|
|
||||||
|
if next_record['num7'] in predicted:
|
||||||
|
prediction_hit_dist += 1
|
||||||
|
|
||||||
|
dist_stats = defaultdict(int)
|
||||||
|
for d in min_distances:
|
||||||
|
dist_stats[d] += 1
|
||||||
|
|
||||||
|
print("最短距离分布:")
|
||||||
|
for dist in sorted(dist_stats.keys()):
|
||||||
|
count = dist_stats[dist]
|
||||||
|
percent = round(count / total_predictions * 100, 2)
|
||||||
|
print(f" 距离 {dist}: {count}次 ({percent}%)")
|
||||||
|
|
||||||
|
close_hit = sum(1 for d in min_distances if d <= 5)
|
||||||
|
very_close_hit = sum(1 for d in min_distances if d <= 3)
|
||||||
|
|
||||||
|
print(f"\n最短距离≤5的比例: {round(close_hit / total_predictions * 100, 2)}% ({close_hit}/{total_predictions})")
|
||||||
|
print(f"最短距离≤3的比例: {round(very_close_hit / total_predictions * 100, 2)}% ({very_close_hit}/{total_predictions})")
|
||||||
|
print(f"基于正码±3范围预测命中率: {round(prediction_hit_dist / total_predictions * 100, 2)}% ({prediction_hit_dist}/{total_predictions})")
|
||||||
|
|
||||||
|
# 计算预测范围大小
|
||||||
|
nums_sample = [history[0]['num1'], history[0]['num2'], history[0]['num3'],
|
||||||
|
history[0]['num4'], history[0]['num5'], history[0]['num6']]
|
||||||
|
predicted_sample = set()
|
||||||
|
for num in nums_sample:
|
||||||
|
for p in range(max(1, num - 3), min(50, num + 4)):
|
||||||
|
predicted_sample.add(p)
|
||||||
|
print(f"预测范围大小: 约{len(predicted_sample)}个数字")
|
||||||
|
print()
|
||||||
|
|
||||||
|
# ============ 维度4: 上期正码和值尾数与下期特码尾数的关系 ============
|
||||||
|
print("=== 维度4: 上期正码和值尾数与下期特码尾数的关系 ===")
|
||||||
|
|
||||||
|
sum_tail_relations = defaultdict(lambda: defaultdict(int))
|
||||||
|
tail_same_count = 0
|
||||||
|
tail_diff1_count = 0
|
||||||
|
tail_diff2_count = 0
|
||||||
|
tail_prediction_hit = 0
|
||||||
|
|
||||||
|
for i in range(len(history) - 1):
|
||||||
|
current = history[i]
|
||||||
|
next_record = history[i + 1]
|
||||||
|
|
||||||
|
sum_val = current['num1'] + current['num2'] + current['num3'] + \
|
||||||
|
current['num4'] + current['num5'] + current['num6']
|
||||||
|
sum_tail = get_tail(sum_val)
|
||||||
|
next_tail = get_tail(next_record['num7'])
|
||||||
|
|
||||||
|
sum_tail_relations[sum_tail][next_tail] += 1
|
||||||
|
|
||||||
|
# 计算尾数差异（考虑环形）
|
||||||
|
tail_diff = abs(sum_tail - next_tail)
|
||||||
|
if tail_diff > 5:
|
||||||
|
tail_diff = 10 - tail_diff
|
||||||
|
|
||||||
|
if tail_diff == 0:
|
||||||
|
tail_same_count += 1
|
||||||
|
elif tail_diff == 1:
|
||||||
|
tail_diff1_count += 1
|
||||||
|
elif tail_diff == 2:
|
||||||
|
tail_diff2_count += 1
|
||||||
|
|
||||||
|
# 预测：和值尾数±2范围内的尾数
|
||||||
|
predict_tails = {sum_tail, (sum_tail + 1) % 10, (sum_tail - 1 + 10) % 10,
|
||||||
|
(sum_tail + 2) % 10, (sum_tail - 2 + 10) % 10}
|
||||||
|
if next_tail in predict_tails:
|
||||||
|
tail_prediction_hit += 1
|
||||||
|
|
||||||
|
print("尾数关系分布:")
|
||||||
|
print(f" 尾数相同: {tail_same_count}次 ({round(tail_same_count / total_predictions * 100, 2)}%)")
|
||||||
|
print(f" 尾数相差1: {tail_diff1_count}次 ({round(tail_diff1_count / total_predictions * 100, 2)}%)")
|
||||||
|
print(f" 尾数相差2: {tail_diff2_count}次 ({round(tail_diff2_count / total_predictions * 100, 2)}%)")
|
||||||
|
|
||||||
|
print("\n上期和值尾数→下期特码尾数转移矩阵:")
|
||||||
|
for sum_tail in range(10):
|
||||||
|
if sum_tail in sum_tail_relations:
|
||||||
|
stats = sum_tail_relations[sum_tail]
|
||||||
|
max_tail = max(stats, key=stats.get)
|
||||||
|
max_count = stats[max_tail]
|
||||||
|
total = sum(stats.values())
|
||||||
|
percent = round(max_count / total * 100, 2)
|
||||||
|
others = [f"{t}({c})" for t, c in stats.items() if t != max_tail]
|
||||||
|
print(f" 和值尾数{sum_tail} → 最可能尾数{max_tail} ({max_count}次, {percent}%), 其他: {', '.join(others)}")
|
||||||
|
|
||||||
|
print(f"\n基于和值尾数±2范围的尾数预测命中率: {round(tail_prediction_hit / total_predictions * 100, 2)}% ({tail_prediction_hit}/{total_predictions})")
|
||||||
|
print("预测范围: 5个尾数，每个尾数对应约5个数字，共约25个数字")
|
||||||
|
print()
|
||||||
|
|
||||||
|
# ============ 维度5: 上期正码覆盖区间与下期特码所在区间的关系 ============
|
||||||
|
print("=== 维度5: 上期正码覆盖区间与下期特码所在区间的关系 ===")
|
||||||
|
|
||||||
|
range_cover_stats = defaultdict(lambda: defaultdict(int))
|
||||||
|
range_transfer_stats = defaultdict(lambda: defaultdict(int))
|
||||||
|
hit_in_covered_range = 0
|
||||||
|
|
||||||
|
for i in range(len(history) - 1):
|
||||||
|
current = history[i]
|
||||||
|
next_record = history[i + 1]
|
||||||
|
|
||||||
|
nums = [current['num1'], current['num2'], current['num3'],
|
||||||
|
current['num4'], current['num5'], current['num6']]
|
||||||
|
|
||||||
|
covers = set(get_range(num) for num in nums)
|
||||||
|
cover_str = '+'.join(sorted(covers))
|
||||||
|
next_range = get_range(next_record['num7'])
|
||||||
|
|
||||||
|
range_cover_stats[cover_str][next_range] += 1
|
||||||
|
range_transfer_stats[len(covers)][next_range] += 1
|
||||||
|
|
||||||
|
if next_range in covers:
|
||||||
|
hit_in_covered_range += 1
|
||||||
|
|
||||||
|
print("上期正码覆盖区间→下期特码区间转移:")
|
||||||
|
for cover, next_stats in sorted(range_cover_stats.items(), key=lambda x: -sum(x[1].values())):
|
||||||
|
total_cover = sum(next_stats.values())
|
||||||
|
print(f" {cover} (共{total_cover}期):")
|
||||||
|
for next_range, count in next_stats.items():
|
||||||
|
percent = round(count / total_cover * 100, 2)
|
||||||
|
print(f" → {next_range}: {count}次 ({percent}%)")
|
||||||
|
|
||||||
|
print("\n上期正码覆盖区间数量与下期特码分布:")
|
||||||
|
for cover_count, stats in sorted(range_transfer_stats.items()):
|
||||||
|
total_cover = sum(stats.values())
|
||||||
|
print(f" 覆盖{cover_count}个区间 (共{total_cover}期):")
|
||||||
|
for range_name, count in stats.items():
|
||||||
|
percent = round(count / total_cover * 100, 2)
|
||||||
|
print(f" {range_name}: {count}次 ({percent}%)")
|
||||||
|
|
||||||
|
print(f"\n下期特码在上期正码覆盖区间内的比例: {round(hit_in_covered_range / total_predictions * 100, 2)}% ({hit_in_covered_range}/{total_predictions})")
|
||||||
|
print()
|
||||||
|
|
||||||
|
# ============ 维度6: 上期正码波色分布与下期特码波色的关系 ============
|
||||||
|
print("=== 维度6: 上期正码波色分布与下期特码波色的关系 ===")
|
||||||
|
|
||||||
|
color_distribution_stats = defaultdict(lambda: defaultdict(int))
|
||||||
|
dominant_color_stats = defaultdict(lambda: defaultdict(int))
|
||||||
|
dominant_prediction_hit = 0
|
||||||
|
expanded_color_prediction_hit = 0
|
||||||
|
|
||||||
|
for i in range(len(history) - 1):
|
||||||
|
current = history[i]
|
||||||
|
next_record = history[i + 1]
|
||||||
|
|
||||||
|
nums = [current['num1'], current['num2'], current['num3'],
|
||||||
|
current['num4'], current['num5'], current['num6']]
|
||||||
|
|
||||||
|
colors = defaultdict(int)
|
||||||
|
for num in nums:
|
||||||
|
colors[COLOR_MAP[num]] += 1
|
||||||
|
|
||||||
|
color_str = f"红{colors['红']}蓝{colors['蓝']}绿{colors['绿']}"
|
||||||
|
next_color = COLOR_MAP[next_record['num7']]
|
||||||
|
|
||||||
|
color_distribution_stats[color_str][next_color] += 1
|
||||||
|
|
||||||
|
# 主导波色
|
||||||
|
dominant_color = max(colors, key=colors.get)
|
||||||
|
dominant_color_stats[dominant_color][next_color] += 1
|
||||||
|
|
||||||
|
if dominant_color == next_color:
|
||||||
|
dominant_prediction_hit += 1
|
||||||
|
|
||||||
|
# 扩展到两种波色
|
||||||
|
top2_colors = sorted(colors, key=colors.get, reverse=True)[:2]
|
||||||
|
if next_color in top2_colors:
|
||||||
|
expanded_color_prediction_hit += 1
|
||||||
|
|
||||||
|
print("上期正码波色分布→下期特码波色转移 (出现5次以上的):")
|
||||||
|
sorted_color_dist = sorted(color_distribution_stats.items(),
|
||||||
|
key=lambda x: -sum(x[1].values()))
|
||||||
|
for dist, next_stats in sorted_color_dist:
|
||||||
|
total_dist = sum(next_stats.values())
|
||||||
|
if total_dist >= 5:
|
||||||
|
print(f" {dist} (共{total_dist}期):")
|
||||||
|
for next_color, count in next_stats.items():
|
||||||
|
percent = round(count / total_dist * 100, 2)
|
||||||
|
print(f" → {next_color}: {count}次 ({percent}%)")
|
||||||
|
|
||||||
|
print("\n上期主导波色→下期特码波色转移:")
|
||||||
|
for dominant, next_stats in dominant_color_stats.items():
|
||||||
|
total_dom = sum(next_stats.values())
|
||||||
|
print(f" 主导{dominant} (共{total_dom}期):")
|
||||||
|
for next_color, count in next_stats.items():
|
||||||
|
percent = round(count / total_dom * 100, 2)
|
||||||
|
print(f" → {next_color}: {count}次 ({percent}%)")
|
||||||
|
|
||||||
|
print(f"\n主导波色预测命中率: {round(dominant_prediction_hit / total_predictions * 100, 2)}% ({dominant_prediction_hit}/{total_predictions})")
|
||||||
|
print(f"扩展到两种主导波色预测命中率: {round(expanded_color_prediction_hit / total_predictions * 100, 2)}% ({expanded_color_prediction_hit}/{total_predictions})")
|
||||||
|
print()
|
||||||
|
|
||||||
|
# ============ 维度7: 上期特码与下期特码的转移关系 ============
|
||||||
|
print("=== 维度7: 上期特码与下期特码的转移关系（马尔可夫分析） ===")
|
||||||
|
|
||||||
|
special_transfer = defaultdict(lambda: defaultdict(int))
|
||||||
|
special_range_transfer = defaultdict(lambda: defaultdict(int))
|
||||||
|
special_range_prediction_hit = 0
|
||||||
|
|
||||||
|
for i in range(len(history) - 1):
|
||||||
|
current = history[i]
|
||||||
|
next_record = history[i + 1]
|
||||||
|
|
||||||
|
current_special = current['num7']
|
||||||
|
next_special = next_record['num7']
|
||||||
|
|
||||||
|
special_transfer[current_special][next_special] += 1
|
||||||
|
|
||||||
|
current_range = get_range(current_special)
|
||||||
|
next_range = get_range(next_special)
|
||||||
|
special_range_transfer[current_range][next_range] += 1
|
||||||
|
|
||||||
|
print("特码区间转移矩阵:")
|
||||||
|
for from_range, to_stats in special_range_transfer.items():
|
||||||
|
total_from = sum(to_stats.values())
|
||||||
|
print(f" {from_range} → :")
|
||||||
|
for to_range, count in to_stats.items():
|
||||||
|
percent = round(count / total_from * 100, 2)
|
||||||
|
print(f" {to_range}: {count}次 ({percent}%)")
|
||||||
|
|
||||||
|
# 高频特码转移
|
||||||
|
print("\n高频特码转移（出现2次以上）:")
|
||||||
|
for from_num, to_stats in special_transfer.items():
|
||||||
|
for to_num, count in to_stats.items():
|
||||||
|
if count >= 2:
|
||||||
|
print(f" 特码{from_num} → 特码{to_num}: {count}次")
|
||||||
|
|
||||||
|
# 基于特码区间预测
|
||||||
|
for i in range(len(history) - 1):
|
||||||
|
current = history[i]
|
||||||
|
next_record = history[i + 1]
|
||||||
|
|
||||||
|
current_range = get_range(current['num7'])
|
||||||
|
transfer_stats = special_range_transfer[current_range]
|
||||||
|
top_ranges = sorted(transfer_stats, key=transfer_stats.get, reverse=True)[:2]
|
||||||
|
|
||||||
|
next_range = get_range(next_record['num7'])
|
||||||
|
if next_range in top_ranges:
|
||||||
|
special_range_prediction_hit += 1
|
||||||
|
|
||||||
|
print(f"\n基于特码区间转移预测（前2区间）命中率: {round(special_range_prediction_hit / total_predictions * 100, 2)}% ({special_range_prediction_hit}/{total_predictions})")
|
||||||
|
print()
|
||||||
|
|
||||||
|
# ============ 综合分析 ============
|
||||||
|
print("=== 综合分析：寻找40%以上命中率的规律 ===")
|
||||||
|
print()
|
||||||
|
|
||||||
|
combined_hits = {
|
||||||
|
'平均值±10范围': hit_count_avg,
|
||||||
|
'正码±3范围': prediction_hit_dist,
|
||||||
|
'和值尾数±2尾数范围': tail_prediction_hit,
|
||||||
|
'覆盖区间预测': hit_in_covered_range,
|
||||||
|
'主导波色预测': dominant_prediction_hit,
|
||||||
|
'双波色预测': expanded_color_prediction_hit,
|
||||||
|
'特码区间转移': special_range_prediction_hit
|
||||||
|
}
|
||||||
|
|
||||||
|
print("各维度预测命中率汇总:")
|
||||||
|
for name, hit in combined_hits.items():
|
||||||
|
percent = round(hit / total_predictions * 100, 2)
|
||||||
|
status = '【达标】' if percent >= 40 else ''
|
||||||
|
print(f" {name}: {percent}% ({hit}/{total_predictions}) {status}")
|
||||||
|
|
||||||
|
# 组合预测测试
|
||||||
|
print("\n组合预测测试:")
|
||||||
|
|
||||||
|
combo_hits = 0
|
||||||
|
combo_plus_hits = 0
|
||||||
|
|
||||||
|
for i in range(len(history) - 1):
|
||||||
|
current = history[i]
|
||||||
|
next_record = history[i + 1]
|
||||||
|
|
||||||
|
nums = [current['num1'], current['num2'], current['num3'],
|
||||||
|
current['num4'], current['num5'], current['num6']]
|
||||||
|
|
||||||
|
# 方法1：平均值±15范围
|
||||||
|
avg = sum(nums) / 6
|
||||||
|
avg_range = set(range(max(1, int(avg) - 15), min(50, int(avg) + 16)))
|
||||||
|
|
||||||
|
# 方法2：正码±5范围
|
||||||
|
num_range = set()
|
||||||
|
for num in nums:
|
||||||
|
for p in range(max(1, num - 5), min(50, num + 6)):
|
||||||
|
num_range.add(p)
|
||||||
|
|
||||||
|
# 方法3：和值尾数±3范围
|
||||||
|
sum_val = sum(nums)
|
||||||
|
sum_tail = get_tail(sum_val)
|
||||||
|
tail_range = set()
|
||||||
|
for t in range(sum_tail - 3, sum_tail + 4):
|
||||||
|
actual_tail = (t + 10) % 10
|
||||||
|
for n in range(1, 50):
|
||||||
|
if get_tail(n) == actual_tail:
|
||||||
|
tail_range.add(n)
|
||||||
|
|
||||||
|
# 组合1
|
||||||
|
if next_record['num7'] in avg_range or next_record['num7'] in num_range:
|
||||||
|
combo_hits += 1
|
||||||
|
|
||||||
|
# 组合2
|
||||||
|
if next_record['num7'] in avg_range or next_record['num7'] in num_range or next_record['num7'] in tail_range:
|
||||||
|
combo_plus_hits += 1
|
||||||
|
|
||||||
|
print(f"组合1（平均值±15 OR 正码±5）命中率: {round(combo_hits / total_predictions * 100, 2)}% ({combo_hits}/{total_predictions})")
|
||||||
|
print(f"组合2（平均值±15 OR 正码±5 OR 尾数±3）命中率: {round(combo_plus_hits / total_predictions * 100, 2)}% ({combo_plus_hits}/{total_predictions})")
|
||||||
|
|
||||||
|
# 波色+区间组合
|
||||||
|
print("\n波色+区间组合预测:")
|
||||||
|
|
||||||
|
color_range_combo_hit = 0
|
||||||
|
color_range_combo2_hit = 0
|
||||||
|
last_predict_count = 0
|
||||||
|
last_predict_count2 = 0
|
||||||
|
|
||||||
|
for i in range(len(history) - 1):
|
||||||
|
current = history[i]
|
||||||
|
next_record = history[i + 1]
|
||||||
|
|
||||||
|
nums = [current['num1'], current['num2'], current['num3'],
|
||||||
|
current['num4'], current['num5'], current['num6']]
|
||||||
|
|
||||||
|
colors = defaultdict(int)
|
||||||
|
ranges = defaultdict(int)
|
||||||
|
for num in nums:
|
||||||
|
colors[COLOR_MAP[num]] += 1
|
||||||
|
ranges[get_range(num)] += 1
|
||||||
|
|
||||||
|
# 前1波色+前1区间
|
||||||
|
top_color = max(colors, key=colors.get)
|
||||||
|
top_range = max(ranges, key=ranges.get)
|
||||||
|
|
||||||
|
predict_nums = {n for n in range(1, 50) if COLOR_MAP[n] == top_color and get_range(n) == top_range}
|
||||||
|
|
||||||
|
if next_record['num7'] in predict_nums:
|
||||||
|
color_range_combo_hit += 1
|
||||||
|
last_predict_count = len(predict_nums)
|
||||||
|
|
||||||
|
# 前2波色+前2区间
|
||||||
|
top2_colors = sorted(colors, key=colors.get, reverse=True)[:2]
|
||||||
|
top2_ranges = sorted(ranges, key=ranges.get, reverse=True)[:2]
|
||||||
|
|
||||||
|
predict_nums2 = {n for n in range(1, 50)
|
||||||
|
if COLOR_MAP[n] in top2_colors and get_range(n) in top2_ranges}
|
||||||
|
|
||||||
|
if next_record['num7'] in predict_nums2:
|
||||||
|
color_range_combo2_hit += 1
|
||||||
|
last_predict_count2 = len(predict_nums2)
|
||||||
|
|
||||||
|
print(f"波色+区间交集预测命中率: {round(color_range_combo_hit / total_predictions * 100, 2)}% ({color_range_combo_hit}/{total_predictions})")
|
||||||
|
print(f"预测范围大小: {last_predict_count}个数字")
|
||||||
|
print(f"前2波色+前2区间交集预测命中率: {round(color_range_combo2_hit / total_predictions * 100, 2)}% ({color_range_combo2_hit}/{total_predictions})")
|
||||||
|
print(f"预测范围大小: {last_predict_count2}个数字")
|
||||||
|
|
||||||
|
print()
|
||||||
|
|
||||||
|
# ============ 总结 ============
|
||||||
|
print("=== 总结：达到40%以上命中率的规律 ===")
|
||||||
|
print()
|
||||||
|
|
||||||
|
high_hit_rules = []
|
||||||
|
for name, hit in combined_hits.items():
|
||||||
|
percent = round(hit / total_predictions * 100, 2)
|
||||||
|
if percent >= 40:
|
||||||
|
high_hit_rules.append((name, percent, hit, total_predictions))
|
||||||
|
|
||||||
|
if high_hit_rules:
|
||||||
|
for name, percent, hit, total in high_hit_rules:
|
||||||
|
print(f"【{name}】命中率: {percent}% ({hit}/{total})")
|
||||||
|
else:
|
||||||
|
print("单维度分析中没有达到40%以上命中率的规律")
|
||||||
|
|
||||||
|
print("\n组合规律命中率:")
|
||||||
|
print(f"组合1（平均值±15 OR 正码±5）: {round(combo_hits / total_predictions * 100, 2)}%")
|
||||||
|
print(f"组合2（平均值±15 OR 正码±5 OR 尾数±3）: {round(combo_plus_hits / total_predictions * 100, 2)}%")
|
||||||
|
print(f"前2波色+前2区间交集: {round(color_range_combo2_hit / total_predictions * 100, 2)}%")
|
||||||
|
|
||||||
|
print("\n分析完成！")
|
||||||
|
|
||||||
|
if __name__ == '__main__':
|
||||||
|
analyze()
|
||||||
@@ -0,0 +1,395 @@
|
|||||||
|
<?php
|
||||||
|
/**
|
||||||
|
* 正码与特码关联规律统计分析脚本
|
||||||
|
*
|
||||||
|
* 分析维度：
|
||||||
|
* 1. 正码平均值与特码差值分布
|
||||||
|
* 2. 特码是否在正码范围内
|
||||||
|
* 3. 特码与最近正码的距离分布
|
||||||
|
* 4. 和值尾数关系
|
||||||
|
* 5. 区间覆盖分析
|
||||||
|
* 6. 波色/生肖关联
|
||||||
|
*/
|
||||||
|
|
||||||
|
// 波色映射表 (从fa_num表提取)
|
||||||
|
$colorMap = [
|
||||||
|
1 => '红', 2 => '红', 3 => '蓝', 4 => '蓝', 5 => '绿', 6 => '绿',
|
||||||
|
7 => '红', 8 => '红', 9 => '蓝', 10 => '蓝', 11 => '绿', 12 => '红',
|
||||||
|
13 => '红', 14 => '蓝', 15 => '蓝', 16 => '绿', 17 => '绿', 18 => '红',
|
||||||
|
19 => '红', 20 => '蓝', 21 => '绿', 22 => '绿', 23 => '红', 24 => '红',
|
||||||
|
25 => '蓝', 26 => '蓝', 27 => '绿', 28 => '绿', 29 => '红', 30 => '红',
|
||||||
|
31 => '蓝', 32 => '绿', 33 => '绿', 34 => '红', 35 => '红', 36 => '蓝',
|
||||||
|
37 => '蓝', 38 => '绿', 39 => '绿', 40 => '红', 41 => '蓝', 42 => '蓝',
|
||||||
|
43 => '绿', 44 => '绿', 45 => '红', 46 => '红', 47 => '蓝', 48 => '蓝',
|
||||||
|
49 => '绿'
|
||||||
|
];
|
||||||
|
|
||||||
|
// 生肖映射表
|
||||||
|
$animalMap = [
|
||||||
|
1 => '马', 2 => '蛇', 3 => '龙', 4 => '兔', 5 => '虎', 6 => '牛',
|
||||||
|
7 => '鼠', 8 => '猪', 9 => '狗', 10 => '鸡', 11 => '猴', 12 => '羊',
|
||||||
|
13 => '马', 14 => '蛇', 15 => '龙', 16 => '兔', 17 => '虎', 18 => '牛',
|
||||||
|
19 => '鼠', 20 => '猪', 21 => '狗', 22 => '鸡', 23 => '猴', 24 => '羊',
|
||||||
|
25 => '马', 26 => '蛇', 27 => '龙', 28 => '兔', 29 => '虎', 30 => '牛',
|
||||||
|
31 => '鼠', 32 => '猪', 33 => '狗', 34 => '鸡', 35 => '猴', 36 => '羊',
|
||||||
|
37 => '马', 38 => '蛇', 39 => '龙', 40 => '兔', 41 => '虎', 42 => '牛',
|
||||||
|
43 => '鼠', 44 => '猪', 45 => '狗', 46 => '鸡', 47 => '猴', 48 => '羊',
|
||||||
|
49 => '马'
|
||||||
|
];
|
||||||
|
|
||||||
|
/**
|
||||||
|
* 获取数字所在的区间 (1-10, 11-20, 21-30, 31-40, 41-49)
|
||||||
|
*/
|
||||||
|
function getZone($num) {
|
||||||
|
if ($num >= 1 && $num <= 10) return 1;
|
||||||
|
if ($num >= 11 && $num <= 20) return 2;
|
||||||
|
if ($num >= 21 && $num <= 30) return 3;
|
||||||
|
if ($num >= 31 && $num <= 40) return 4;
|
||||||
|
if ($num >= 41 && $num <= 49) return 5;
|
||||||
|
return 0;
|
||||||
|
}
|
||||||
|
|
||||||
|
/**
|
||||||
|
* 计算特码到最近正码的距离
|
||||||
|
*/
|
||||||
|
function getMinDistance($sortedNums, $num7) {
|
||||||
|
$minDist = 49;
|
||||||
|
foreach ($sortedNums as $num) {
|
||||||
|
$dist = abs($num7 - $num);
|
||||||
|
if ($dist < $minDist) {
|
||||||
|
$minDist = $dist;
|
||||||
|
}
|
||||||
|
}
|
||||||
|
return $minDist;
|
||||||
|
}
|
||||||
|
|
||||||
|
// 解析SQL文件中的数据
|
||||||
|
$sqlFile = 'C:\Users\91611\Desktop\fa_history.sql';
|
||||||
|
$content = file_get_contents($sqlFile);
|
||||||
|
|
||||||
|
// 提取INSERT语句中的数据
|
||||||
|
$pattern = '/INSERT INTO `fa_history` VALUES \((\d+), (\d+), (\d+), (\d+), (\d+), (\d+), (\d+), (\d+), \'([^\']+)\'\);/';
|
||||||
|
preg_match_all($pattern, $content, $matches);
|
||||||
|
|
||||||
|
$data = [];
|
||||||
|
for ($i = 0; $i < count($matches[0]); $i++) {
|
||||||
|
$data[] = [
|
||||||
|
'expect' => (int)$matches[1][$i],
|
||||||
|
'num1' => (int)$matches[2][$i],
|
||||||
|
'num2' => (int)$matches[3][$i],
|
||||||
|
'num3' => (int)$matches[4][$i],
|
||||||
|
'num4' => (int)$matches[5][$i],
|
||||||
|
'num5' => (int)$matches[6][$i],
|
||||||
|
'num6' => (int)$matches[7][$i],
|
||||||
|
'num7' => (int)$matches[8][$i],
|
||||||
|
'openTime' => $matches[9][$i]
|
||||||
|
];
|
||||||
|
}
|
||||||
|
|
||||||
|
$total = count($data);
|
||||||
|
echo "==================== 正码与特码关联规律统计分析 ====================\n";
|
||||||
|
echo "数据总量: {$total} 期 (从 {$data[0]['expect']} 到 {$data[$total-1]['expect']})\n\n";
|
||||||
|
|
||||||
|
// ==================== 1. 正码平均值与特码差值分布 ====================
|
||||||
|
echo "==================== 1. 正码平均值与特码差值分布 ====================\n";
|
||||||
|
|
||||||
|
$diffCounts = [];
|
||||||
|
$inRange5 = 0;
|
||||||
|
|
||||||
|
foreach ($data as $row) {
|
||||||
|
$avg = ($row['num1'] + $row['num2'] + $row['num3'] + $row['num4'] + $row['num5'] + $row['num6']) / 6;
|
||||||
|
$diff = round($row['num7'] - $avg); // 四舍五入
|
||||||
|
|
||||||
|
$diffKey = $diff;
|
||||||
|
if (!isset($diffCounts[$diffKey])) {
|
||||||
|
$diffCounts[$diffKey] = 0;
|
||||||
|
}
|
||||||
|
$diffCounts[$diffKey]++;
|
||||||
|
|
||||||
|
if ($diff >= -5 && $diff <= 5) {
|
||||||
|
$inRange5++;
|
||||||
|
}
|
||||||
|
}
|
||||||
|
|
||||||
|
// 按差值排序
|
||||||
|
ksort($diffCounts);
|
||||||
|
|
||||||
|
echo "差值分布统计:\n";
|
||||||
|
foreach ($diffCounts as $diff => $count) {
|
||||||
|
$pct = round($count / $total * 100, 2);
|
||||||
|
echo " 差值 {$diff}: {$count} 次 ({$pct}%)\n";
|
||||||
|
}
|
||||||
|
|
||||||
|
echo "\n差值在 [-5, +5] 范围内的概率: " . round($inRange5 / $total * 100, 2) . "% ($inRange5/$total)\n";
|
||||||
|
|
||||||
|
// 找出高频差值区间 (>5%)
|
||||||
|
echo "\n高频差值区间 (>5%):\n";
|
||||||
|
foreach ($diffCounts as $diff => $count) {
|
||||||
|
$pct = round($count / $total * 100, 2);
|
||||||
|
if ($pct > 5) {
|
||||||
|
echo " 差值 {$diff}: {$pct}%\n";
|
||||||
|
}
|
||||||
|
}
|
||||||
|
|
||||||
|
// ==================== 2. 特码是否在正码范围内 ====================
|
||||||
|
echo "\n==================== 2. 特码是否在正码范围内 ====================\n";
|
||||||
|
|
||||||
|
$inRange = 0;
|
||||||
|
$outRange = 0;
|
||||||
|
|
||||||
|
foreach ($data as $row) {
|
||||||
|
$nums = [$row['num1'], $row['num2'], $row['num3'], $row['num4'], $row['num5'], $row['num6']];
|
||||||
|
$min = min($nums);
|
||||||
|
$max = max($nums);
|
||||||
|
|
||||||
|
if ($row['num7'] >= $min && $row['num7'] <= $max) {
|
||||||
|
$inRange++;
|
||||||
|
} else {
|
||||||
|
$outRange++;
|
||||||
|
}
|
||||||
|
}
|
||||||
|
|
||||||
|
echo "特码在正码范围内 [min(num1-6), max(num1-6)]:\n";
|
||||||
|
echo " 是: {$inRange} 次 (" . round($inRange / $total * 100, 2) . "%)\n";
|
||||||
|
echo " 否: {$outRange} 次 (" . round($outRange / $total * 100, 2) . "%)\n";
|
||||||
|
|
||||||
|
// ==================== 3. 特码与最近正码的距离分布 ====================
|
||||||
|
echo "\n==================== 3. 特码与最近正码的距离分布 ====================\n";
|
||||||
|
|
||||||
|
$distCounts = [];
|
||||||
|
$equalCount = 0; // 特码等于某正码
|
||||||
|
|
||||||
|
foreach ($data as $row) {
|
||||||
|
$nums = [$row['num1'], $row['num2'], $row['num3'], $row['num4'], $row['num5'], $row['num6']];
|
||||||
|
sort($nums);
|
||||||
|
|
||||||
|
$minDist = getMinDistance($nums, $row['num7']);
|
||||||
|
|
||||||
|
if (!isset($distCounts[$minDist])) {
|
||||||
|
$distCounts[$minDist] = 0;
|
||||||
|
}
|
||||||
|
$distCounts[$minDist]++;
|
||||||
|
|
||||||
|
if ($minDist == 0) {
|
||||||
|
$equalCount++;
|
||||||
|
}
|
||||||
|
}
|
||||||
|
|
||||||
|
ksort($distCounts);
|
||||||
|
|
||||||
|
echo "距离分布统计:\n";
|
||||||
|
foreach ($distCounts as $dist => $count) {
|
||||||
|
$pct = round($count / $total * 100, 2);
|
||||||
|
echo " 距离 {$dist}: {$count} 次 ({$pct}%)\n";
|
||||||
|
}
|
||||||
|
|
||||||
|
echo "\n特码等于某正码 (距离=0) 的概率: " . round($equalCount / $total * 100, 2) . "% ($equalCount/$total)\n";
|
||||||
|
|
||||||
|
// 距离<=5的概率
|
||||||
|
$distLE5 = 0;
|
||||||
|
for ($i = 0; $i <= 5; $i++) {
|
||||||
|
if (isset($distCounts[$i])) {
|
||||||
|
$distLE5 += $distCounts[$i];
|
||||||
|
}
|
||||||
|
}
|
||||||
|
echo "距离 <= 5 的概率: " . round($distLE5 / $total * 100, 2) . "% ($distLE5/$total)\n";
|
||||||
|
|
||||||
|
// ==================== 4. 和值尾数关系 ====================
|
||||||
|
echo "\n==================== 4. 和值尾数关系 ====================\n";
|
||||||
|
|
||||||
|
$sameTail = 0;
|
||||||
|
$tailDiffCounts = [];
|
||||||
|
|
||||||
|
foreach ($data as $row) {
|
||||||
|
$sum = $row['num1'] + $row['num2'] + $row['num3'] + $row['num4'] + $row['num5'] + $row['num6'];
|
||||||
|
$sumTail = $sum % 10;
|
||||||
|
$num7Tail = $row['num7'] % 10;
|
||||||
|
|
||||||
|
$tailDiff = abs($sumTail - $num7Tail);
|
||||||
|
|
||||||
|
if (!isset($tailDiffCounts[$tailDiff])) {
|
||||||
|
$tailDiffCounts[$tailDiff] = 0;
|
||||||
|
}
|
||||||
|
$tailDiffCounts[$tailDiff]++;
|
||||||
|
|
||||||
|
if ($sumTail == $num7Tail) {
|
||||||
|
$sameTail++;
|
||||||
|
}
|
||||||
|
}
|
||||||
|
|
||||||
|
ksort($tailDiffCounts);
|
||||||
|
|
||||||
|
echo "和值尾数与特码尾数同尾概率: " . round($sameTail / $total * 100, 2) . "% ($sameTail/$total)\n";
|
||||||
|
echo "\n尾数差值分布:\n";
|
||||||
|
foreach ($tailDiffCounts as $diff => $count) {
|
||||||
|
$pct = round($count / $total * 100, 2);
|
||||||
|
echo " 尾数差 {$diff}: {$count} 次 ({$pct}%)\n";
|
||||||
|
}
|
||||||
|
|
||||||
|
// 尾数差 <= 3 的概率
|
||||||
|
$tailDiffLE3 = 0;
|
||||||
|
for ($i = 0; $i <= 3; $i++) {
|
||||||
|
if (isset($tailDiffCounts[$i])) {
|
||||||
|
$tailDiffLE3 += $tailDiffCounts[$i];
|
||||||
|
}
|
||||||
|
}
|
||||||
|
echo "\n尾数差 <= 3 的概率: " . round($tailDiffLE3 / $total * 100, 2) . "% ($tailDiffLE3/$total)\n";
|
||||||
|
|
||||||
|
// ==================== 5. 区间覆盖分析 ====================
|
||||||
|
echo "\n==================== 5. 区间覆盖分析 ====================\n";
|
||||||
|
|
||||||
|
$zoneCoveredCounts = [0, 0, 0, 0, 0, 0]; // 覆盖0-5个区间
|
||||||
|
$zone7Covered = 0; // 特码所在区间被正码覆盖
|
||||||
|
|
||||||
|
foreach ($data as $row) {
|
||||||
|
$nums = [$row['num1'], $row['num2'], $row['num3'], $row['num4'], $row['num5'], $row['num6']];
|
||||||
|
$zones = [];
|
||||||
|
foreach ($nums as $num) {
|
||||||
|
$zone = getZone($num);
|
||||||
|
$zones[$zone] = true;
|
||||||
|
}
|
||||||
|
|
||||||
|
$coverCount = count($zones);
|
||||||
|
$zoneCoveredCounts[$coverCount]++;
|
||||||
|
|
||||||
|
$zone7 = getZone($row['num7']);
|
||||||
|
if (isset($zones[$zone7])) {
|
||||||
|
$zone7Covered++;
|
||||||
|
}
|
||||||
|
}
|
||||||
|
|
||||||
|
echo "正码覆盖区间数量分布:\n";
|
||||||
|
for ($i = 0; $i <= 5; $i++) {
|
||||||
|
$count = $zoneCoveredCounts[$i];
|
||||||
|
$pct = round($count / $total * 100, 2);
|
||||||
|
echo " 覆盖 {$i} 个区间: {$count} 次 ({$pct}%)\n";
|
||||||
|
}
|
||||||
|
|
||||||
|
echo "\n特码所在区间被正码覆盖的概率: " . round($zone7Covered / $total * 100, 2) . "% ($zone7Covered/$total)\n";
|
||||||
|
|
||||||
|
// ==================== 6. 波色/生肖关联 ====================
|
||||||
|
echo "\n==================== 6. 波色/生肖关联 ====================\n";
|
||||||
|
|
||||||
|
$color7InNums = 0; // 特码波色在正码中出现过
|
||||||
|
$animal7InNums = 0; // 特码生肖在正码中出现过
|
||||||
|
|
||||||
|
$colorMatchCounts = []; // 正码中某波色数量与特码波色匹配情况
|
||||||
|
$color7Counts = ['红' => 0, '蓝' => 0, '绿' => 0]; // 特码波色分布
|
||||||
|
|
||||||
|
foreach ($data as $row) {
|
||||||
|
$nums = [$row['num1'], $row['num2'], $row['num3'], $row['num4'], $row['num5'], $row['num6']];
|
||||||
|
|
||||||
|
$numColors = [];
|
||||||
|
$numAnimals = [];
|
||||||
|
$colorCounts = ['红' => 0, '蓝' => 0, '绿' => 0];
|
||||||
|
|
||||||
|
foreach ($nums as $num) {
|
||||||
|
$color = $colorMap[$num];
|
||||||
|
$animal = $animalMap[$num];
|
||||||
|
$numColors[$color] = true;
|
||||||
|
$numAnimals[$animal] = true;
|
||||||
|
$colorCounts[$color]++;
|
||||||
|
}
|
||||||
|
|
||||||
|
$color7 = $colorMap[$row['num7']];
|
||||||
|
$animal7 = $animalMap[$row['num7']];
|
||||||
|
|
||||||
|
$color7Counts[$color7]++;
|
||||||
|
|
||||||
|
// 特码波色是否在正码中出现过
|
||||||
|
if (isset($numColors[$color7])) {
|
||||||
|
$color7InNums++;
|
||||||
|
}
|
||||||
|
|
||||||
|
// 特码生肖是否在正码中出现过
|
||||||
|
if (isset($numAnimals[$animal7])) {
|
||||||
|
$animal7InNums++;
|
||||||
|
}
|
||||||
|
|
||||||
|
// 正码中某波色数量与特码波色
|
||||||
|
$key = $colorCounts[$color7] . '_' . $color7;
|
||||||
|
if (!isset($colorMatchCounts[$key])) {
|
||||||
|
$colorMatchCounts[$key] = 0;
|
||||||
|
}
|
||||||
|
$colorMatchCounts[$key]++;
|
||||||
|
}
|
||||||
|
|
||||||
|
echo "特码波色分布:\n";
|
||||||
|
foreach ($color7Counts as $color => $count) {
|
||||||
|
$pct = round($count / $total * 100, 2);
|
||||||
|
echo " {$color}: {$count} 次 ({$pct}%)\n";
|
||||||
|
}
|
||||||
|
|
||||||
|
echo "\n特码波色在正码中出现的概率: " . round($color7InNums / $total * 100, 2) . "% ($color7InNums/$total)\n";
|
||||||
|
echo "特码生肖在正码中出现的概率: " . round($animal7InNums / $total * 100, 2) . "% ($animal7InNums/$total)\n";
|
||||||
|
|
||||||
|
echo "\n正码中特码同波色数量分布:\n";
|
||||||
|
ksort($colorMatchCounts);
|
||||||
|
foreach ($colorMatchCounts as $key => $count) {
|
||||||
|
$pct = round($count / $total * 100, 2);
|
||||||
|
echo " {$key}: {$count} 次 ({$pct}%)\n";
|
||||||
|
}
|
||||||
|
|
||||||
|
// ==================== 总结: 高命中率规律 ====================
|
||||||
|
echo "\n==================== 总结: 可能达到40%命中率以上的规律 ====================\n";
|
||||||
|
|
||||||
|
$highHitRules = [];
|
||||||
|
|
||||||
|
// 检查各规律
|
||||||
|
if ($inRange5 / $total >= 0.4) {
|
||||||
|
$highHitRules[] = "差值在[-5,+5]: " . round($inRange5 / $total * 100, 2) . "%";
|
||||||
|
}
|
||||||
|
|
||||||
|
if ($distLE5 / $total >= 0.4) {
|
||||||
|
$highHitRules[] = "距离<=5: " . round($distLE5 / $total * 100, 2) . "%";
|
||||||
|
}
|
||||||
|
|
||||||
|
if ($zone7Covered / $total >= 0.4) {
|
||||||
|
$highHitRules[] = "特码区间被正码覆盖: " . round($zone7Covered / $total * 100, 2) . "%";
|
||||||
|
}
|
||||||
|
|
||||||
|
if ($color7InNums / $total >= 0.4) {
|
||||||
|
$highHitRules[] = "特码波色在正码中出现: " . round($color7InNums / $total * 100, 2) . "%";
|
||||||
|
}
|
||||||
|
|
||||||
|
if ($animal7InNums / $total >= 0.4) {
|
||||||
|
$highHitRules[] = "特码生肖在正码中出现: " . round($animal7InNums / $total * 100, 2) . "%";
|
||||||
|
}
|
||||||
|
|
||||||
|
if ($tailDiffLE3 / $total >= 0.4) {
|
||||||
|
$highHitRules[] = "尾数差<=3: " . round($tailDiffLE3 / $total * 100, 2) . "%";
|
||||||
|
}
|
||||||
|
|
||||||
|
if (count($highHitRules) > 0) {
|
||||||
|
foreach ($highHitRules as $rule) {
|
||||||
|
echo "- {$rule}\n";
|
||||||
|
}
|
||||||
|
} else {
|
||||||
|
echo "没有发现明显超过40%命中率的规律，以下是接近40%的规律:\n";
|
||||||
|
|
||||||
|
$allRules = [
|
||||||
|
"差值在[-5,+5]" => $inRange5 / $total,
|
||||||
|
"距离<=5" => $distLE5 / $total,
|
||||||
|
"距离<=10" => 0,
|
||||||
|
"特码区间被正码覆盖" => $zone7Covered / $total,
|
||||||
|
"特码波色在正码中出现" => $color7InNums / $total,
|
||||||
|
"特码生肖在正码中出现" => $animal7InNums / $total,
|
||||||
|
"尾数差<=3" => $tailDiffLE3 / $total,
|
||||||
|
];
|
||||||
|
|
||||||
|
// 计算距离<=10
|
||||||
|
$distLE10 = 0;
|
||||||
|
for ($i = 0; $i <= 10; $i++) {
|
||||||
|
if (isset($distCounts[$i])) {
|
||||||
|
$distLE10 += $distCounts[$i];
|
||||||
|
}
|
||||||
|
}
|
||||||
|
$allRules["距离<=10"] = $distLE10 / $total;
|
||||||
|
|
||||||
|
arsort($allRules);
|
||||||
|
foreach ($allRules as $rule => $rate) {
|
||||||
|
echo "- {$rule}: " . round($rate * 100, 2) . "%\n";
|
||||||
|
}
|
||||||
|
}
|
||||||
|
|
||||||
|
echo "\n==================== 分析完成 ====================\n";
|
||||||
@@ -0,0 +1,286 @@
|
|||||||
|
#!/usr/bin/env python3
|
||||||
|
# -*- coding: utf-8 -*-
|
||||||
|
"""
|
||||||
|
正码与特码关联规律统计分析脚本
|
||||||
|
"""
|
||||||
|
|
||||||
|
import re
|
||||||
|
from collections import defaultdict
|
||||||
|
|
||||||
|
# 波色映射表
|
||||||
|
color_map = {
|
||||||
|
1: '红', 2: '红', 3: '蓝', 4: '蓝', 5: '绿', 6: '绿',
|
||||||
|
7: '红', 8: '红', 9: '蓝', 10: '蓝', 11: '绿', 12: '红',
|
||||||
|
13: '红', 14: '蓝', 15: '蓝', 16: '绿', 17: '绿', 18: '红',
|
||||||
|
19: '红', 20: '蓝', 21: '绿', 22: '绿', 23: '红', 24: '红',
|
||||||
|
25: '蓝', 26: '蓝', 27: '绿', 28: '绿', 29: '红', 30: '红',
|
||||||
|
31: '蓝', 32: '绿', 33: '绿', 34: '红', 35: '红', 36: '蓝',
|
||||||
|
37: '蓝', 38: '绿', 39: '绿', 40: '红', 41: '蓝', 42: '蓝',
|
||||||
|
43: '绿', 44: '绿', 45: '红', 46: '红', 47: '蓝', 48: '蓝',
|
||||||
|
49: '绿'
|
||||||
|
}
|
||||||
|
|
||||||
|
# 生肖映射表
|
||||||
|
animal_map = {
|
||||||
|
1: '马', 2: '蛇', 3: '龙', 4: '兔', 5: '虎', 6: '牛',
|
||||||
|
7: '鼠', 8: '猪', 9: '狗', 10: '鸡', 11: '猴', 12: '羊',
|
||||||
|
13: '马', 14: '蛇', 15: '龙', 16: '兔', 17: '虎', 18: '牛',
|
||||||
|
19: '鼠', 20: '猪', 21: '狗', 22: '鸡', 23: '猴', 24: '羊',
|
||||||
|
25: '马', 26: '蛇', 27: '龙', 28: '兔', 29: '虎', 30: '牛',
|
||||||
|
31: '鼠', 32: '猪', 33: '狗', 34: '鸡', 35: '猴', 36: '羊',
|
||||||
|
37: '马', 38: '蛇', 39: '龙', 40: '兔', 41: '虎', 42: '牛',
|
||||||
|
43: '鼠', 44: '猪', 45: '狗', 46: '鸡', 47: '猴', 48: '羊',
|
||||||
|
49: '马'
|
||||||
|
}
|
||||||
|
|
||||||
|
def get_zone(num):
|
||||||
|
if 1 <= num <= 10: return 1
|
||||||
|
elif 11 <= num <= 20: return 2
|
||||||
|
elif 21 <= num <= 30: return 3
|
||||||
|
elif 31 <= num <= 40: return 4
|
||||||
|
elif 41 <= num <= 49: return 5
|
||||||
|
return 0
|
||||||
|
|
||||||
|
def get_min_distance(nums, num7):
|
||||||
|
return min(abs(num7 - num) for num in nums)
|
||||||
|
|
||||||
|
# 解析SQL文件
|
||||||
|
sql_file = r'C:\Users\91611\Desktop\fa_history.sql'
|
||||||
|
with open(sql_file, 'r', encoding='utf-8') as f:
|
||||||
|
content = f.read()
|
||||||
|
|
||||||
|
pattern = r"INSERT INTO `fa_history` VALUES \((\d+), (\d+), (\d+), (\d+), (\d+), (\d+), (\d+), (\d+), '([^']+)'\);"
|
||||||
|
matches = re.findall(pattern, content)
|
||||||
|
|
||||||
|
data = []
|
||||||
|
for match in matches:
|
||||||
|
data.append({
|
||||||
|
'expect': int(match[0]),
|
||||||
|
'num1': int(match[1]), 'num2': int(match[2]), 'num3': int(match[3]),
|
||||||
|
'num4': int(match[4]), 'num5': int(match[5]), 'num6': int(match[6]),
|
||||||
|
'num7': int(match[7]), 'openTime': match[8]
|
||||||
|
})
|
||||||
|
|
||||||
|
total = len(data)
|
||||||
|
|
||||||
|
# 输出到文件
|
||||||
|
output_file = r'D:\code\php\amlhc\analysis_result.txt'
|
||||||
|
with open(output_file, 'w', encoding='utf-8') as f:
|
||||||
|
f.write("==================== 正码与特码关联规律统计分析 ====================\n")
|
||||||
|
f.write(f"数据总量: {total} 期 (从 {data[0]['expect']} 到 {data[-1]['expect']})\n\n")
|
||||||
|
|
||||||
|
# ==================== 1. 正码平均值与特码差值分布 ====================
|
||||||
|
f.write("==================== 1. 正码平均值与特码差值分布 ====================\n")
|
||||||
|
|
||||||
|
diff_counts = defaultdict(int)
|
||||||
|
in_range_5 = 0
|
||||||
|
|
||||||
|
for row in data:
|
||||||
|
avg = (row['num1'] + row['num2'] + row['num3'] + row['num4'] + row['num5'] + row['num6']) / 6
|
||||||
|
diff = round(row['num7'] - avg)
|
||||||
|
diff_counts[diff] += 1
|
||||||
|
if -5 <= diff <= 5:
|
||||||
|
in_range_5 += 1
|
||||||
|
|
||||||
|
f.write("差值分布统计:\n")
|
||||||
|
for diff in sorted(diff_counts.keys()):
|
||||||
|
count = diff_counts[diff]
|
||||||
|
pct = round(count / total * 100, 2)
|
||||||
|
f.write(f" 差值 {diff}: {count} 次 ({pct}%)\n")
|
||||||
|
|
||||||
|
f.write(f"\n差值在 [-5, +5] 范围内的概率: {round(in_range_5 / total * 100, 2)}% ({in_range_5}/{total})\n")
|
||||||
|
|
||||||
|
in_range_10 = sum(diff_counts[d] for d in diff_counts if -10 <= d <= 10)
|
||||||
|
f.write(f"差值在 [-10, +10] 范围内的概率: {round(in_range_10 / total * 100, 2)}% ({in_range_10}/{total})\n")
|
||||||
|
|
||||||
|
# ==================== 2. 特码是否在正码范围内 ====================
|
||||||
|
f.write("\n==================== 2. 特码是否在正码范围内 ====================\n")
|
||||||
|
|
||||||
|
in_range = 0
|
||||||
|
for row in data:
|
||||||
|
nums = [row['num1'], row['num2'], row['num3'], row['num4'], row['num5'], row['num6']]
|
||||||
|
if min(nums) <= row['num7'] <= max(nums):
|
||||||
|
in_range += 1
|
||||||
|
|
||||||
|
f.write("特码在正码范围内 [min(num1-6), max(num1-6)]:\n")
|
||||||
|
f.write(f" 是: {in_range} 次 ({round(in_range / total * 100, 2)}%)\n")
|
||||||
|
f.write(f" 否: {total - in_range} 次 ({round((total - in_range) / total * 100, 2)}%)\n")
|
||||||
|
|
||||||
|
# ==================== 3. 特码与最近正码的距离分布 ====================
|
||||||
|
f.write("\n==================== 3. 特码与最近正码的距离分布 ====================\n")
|
||||||
|
|
||||||
|
dist_counts = defaultdict(int)
|
||||||
|
for row in data:
|
||||||
|
nums = [row['num1'], row['num2'], row['num3'], row['num4'], row['num5'], row['num6']]
|
||||||
|
min_dist = get_min_distance(nums, row['num7'])
|
||||||
|
dist_counts[min_dist] += 1
|
||||||
|
|
||||||
|
f.write("距离分布统计:\n")
|
||||||
|
for dist in sorted(dist_counts.keys()):
|
||||||
|
count = dist_counts[dist]
|
||||||
|
pct = round(count / total * 100, 2)
|
||||||
|
f.write(f" 距离 {dist}: {count} 次 ({pct}%)\n")
|
||||||
|
|
||||||
|
equal_count = dist_counts.get(0, 0)
|
||||||
|
dist_le5 = sum(dist_counts[i] for i in range(0, 6) if i in dist_counts)
|
||||||
|
dist_le10 = sum(dist_counts[i] for i in range(0, 11) if i in dist_counts)
|
||||||
|
dist_le15 = sum(dist_counts[i] for i in range(0, 16) if i in dist_counts)
|
||||||
|
|
||||||
|
f.write(f"\n特码等于某正码 (距离=0) 的概率: {round(equal_count / total * 100, 2)}% ({equal_count}/{total})\n")
|
||||||
|
f.write(f"距离 <= 5 的概率: {round(dist_le5 / total * 100, 2)}% ({dist_le5}/{total})\n")
|
||||||
|
f.write(f"距离 <= 10 的概率: {round(dist_le10 / total * 100, 2)}% ({dist_le10}/{total})\n")
|
||||||
|
f.write(f"距离 <= 15 的概率: {round(dist_le15 / total * 100, 2)}% ({dist_le15}/{total})\n")
|
||||||
|
|
||||||
|
# ==================== 4. 和值尾数关系 ====================
|
||||||
|
f.write("\n==================== 4. 和值尾数关系 ====================\n")
|
||||||
|
|
||||||
|
same_tail = 0
|
||||||
|
tail_diff_counts = defaultdict(int)
|
||||||
|
|
||||||
|
for row in data:
|
||||||
|
sum_val = row['num1'] + row['num2'] + row['num3'] + row['num4'] + row['num5'] + row['num6']
|
||||||
|
sum_tail = sum_val % 10
|
||||||
|
num7_tail = row['num7'] % 10
|
||||||
|
tail_diff = abs(sum_tail - num7_tail)
|
||||||
|
tail_diff_counts[tail_diff] += 1
|
||||||
|
if sum_tail == num7_tail:
|
||||||
|
same_tail += 1
|
||||||
|
|
||||||
|
f.write(f"和值尾数与特码尾数同尾概率: {round(same_tail / total * 100, 2)}% ({same_tail}/{total})\n")
|
||||||
|
f.write("\n尾数差值分布:\n")
|
||||||
|
for diff in sorted(tail_diff_counts.keys()):
|
||||||
|
count = tail_diff_counts[diff]
|
||||||
|
pct = round(count / total * 100, 2)
|
||||||
|
f.write(f" 尾数差 {diff}: {count} 次 ({pct}%)\n")
|
||||||
|
|
||||||
|
tail_diff_le3 = sum(tail_diff_counts[i] for i in range(0, 4) if i in tail_diff_counts)
|
||||||
|
f.write(f"\n尾数差 <= 3 的概率: {round(tail_diff_le3 / total * 100, 2)}% ({tail_diff_le3}/{total})\n")
|
||||||
|
|
||||||
|
# ==================== 5. 区间覆盖分析 ====================
|
||||||
|
f.write("\n==================== 5. 区间覆盖分析 ====================\n")
|
||||||
|
|
||||||
|
zone_covered_counts = defaultdict(int)
|
||||||
|
zone7_covered = 0
|
||||||
|
|
||||||
|
for row in data:
|
||||||
|
nums = [row['num1'], row['num2'], row['num3'], row['num4'], row['num5'], row['num6']]
|
||||||
|
zones = set(get_zone(num) for num in nums)
|
||||||
|
zone_covered_counts[len(zones)] += 1
|
||||||
|
zone7 = get_zone(row['num7'])
|
||||||
|
if zone7 in zones:
|
||||||
|
zone7_covered += 1
|
||||||
|
|
||||||
|
f.write("正码覆盖区间数量分布:\n")
|
||||||
|
for i in range(1, 6):
|
||||||
|
count = zone_covered_counts.get(i, 0)
|
||||||
|
pct = round(count / total * 100, 2)
|
||||||
|
f.write(f" 覆盖 {i} 个区间: {count} 次 ({pct}%)\n")
|
||||||
|
|
||||||
|
f.write(f"\n特码所在区间被正码覆盖的概率: {round(zone7_covered / total * 100, 2)}% ({zone7_covered}/{total})\n")
|
||||||
|
|
||||||
|
# ==================== 6. 波色/生肖关联 ====================
|
||||||
|
f.write("\n==================== 6. 波色/生肖关联 ====================\n")
|
||||||
|
|
||||||
|
color7_in_nums = 0
|
||||||
|
animal7_in_nums = 0
|
||||||
|
color_match_counts = defaultdict(int)
|
||||||
|
color7_counts = defaultdict(int)
|
||||||
|
|
||||||
|
for row in data:
|
||||||
|
nums = [row['num1'], row['num2'], row['num3'], row['num4'], row['num5'], row['num6']]
|
||||||
|
num_colors = set()
|
||||||
|
num_animals = set()
|
||||||
|
color_counts = defaultdict(int)
|
||||||
|
|
||||||
|
for num in nums:
|
||||||
|
color = color_map[num]
|
||||||
|
animal = animal_map[num]
|
||||||
|
num_colors.add(color)
|
||||||
|
num_animals.add(animal)
|
||||||
|
color_counts[color] += 1
|
||||||
|
|
||||||
|
color7 = color_map[row['num7']]
|
||||||
|
animal7 = animal_map[row['num7']]
|
||||||
|
color7_counts[color7] += 1
|
||||||
|
|
||||||
|
if color7 in num_colors:
|
||||||
|
color7_in_nums += 1
|
||||||
|
if animal7 in num_animals:
|
||||||
|
animal7_in_nums += 1
|
||||||
|
|
||||||
|
key = f"{color_counts[color7]}_{color7}"
|
||||||
|
color_match_counts[key] += 1
|
||||||
|
|
||||||
|
f.write("特码波色分布:\n")
|
||||||
|
for color in ['红', '蓝', '绿']:
|
||||||
|
count = color7_counts[color]
|
||||||
|
pct = round(count / total * 100, 2)
|
||||||
|
f.write(f" {color}: {count} 次 ({pct}%)\n")
|
||||||
|
|
||||||
|
f.write(f"\n特码波色在正码中出现的概率: {round(color7_in_nums / total * 100, 2)}% ({color7_in_nums}/{total})\n")
|
||||||
|
f.write(f"特码生肖在正码中出现的概率: {round(animal7_in_nums / total * 100, 2)}% ({animal7_in_nums}/{total})\n")
|
||||||
|
|
||||||
|
f.write("\n正码中特码同波色数量分布:\n")
|
||||||
|
for key in sorted(color_match_counts.keys()):
|
||||||
|
count = color_match_counts[key]
|
||||||
|
pct = round(count / total * 100, 2)
|
||||||
|
f.write(f" {key}: {count} 次 ({pct}%)\n")
|
||||||
|
|
||||||
|
# ==================== 总结 ====================
|
||||||
|
f.write("\n==================== 总结: 达到40%命中率以上的规律 ====================\n")
|
||||||
|
|
||||||
|
all_rules = {
|
||||||
|
"距离<=15": dist_le15 / total,
|
||||||
|
"距离<=10": dist_le10 / total,
|
||||||
|
"特码波色在正码中出现": color7_in_nums / total,
|
||||||
|
"距离<=5": dist_le5 / total,
|
||||||
|
"特码区间被正码覆盖": zone7_covered / total,
|
||||||
|
"特码在正码范围内": in_range / total,
|
||||||
|
"尾数差<=3": tail_diff_le3 / total,
|
||||||
|
"差值在[-10,+10]": in_range_10 / total,
|
||||||
|
"差值在[-5,+5]": in_range_5 / total,
|
||||||
|
"同尾": same_tail / total,
|
||||||
|
"特码生肖在正码中出现": animal7_in_nums / total,
|
||||||
|
}
|
||||||
|
|
||||||
|
sorted_rules = sorted(all_rules.items(), key=lambda x: x[1], reverse=True)
|
||||||
|
|
||||||
|
f.write("规律按命中率排序:\n")
|
||||||
|
for rule, rate in sorted_rules:
|
||||||
|
status = "[达标]" if rate >= 0.4 else ""
|
||||||
|
f.write(f" - {rule}: {round(rate * 100, 2)}% {status}\n")
|
||||||
|
|
||||||
|
f.write("\n==================== 关键发现 ====================\n")
|
||||||
|
f.write(f"""
|
||||||
|
1. 【特码波色重复规律】命中率最高 90.67%
|
||||||
|
- 特码波色在正码中出现的概率约为 90.67%
|
||||||
|
- 如果正码中有红色号码，特码有90%概率是红色波色
|
||||||
|
|
||||||
|
2. 【近距离规律】命中率很高 94.13%
|
||||||
|
- 特码距离最近正码<=10的概率约为 94.13%
|
||||||
|
- 特码往往不会离正码太远，基本在10个数字以内
|
||||||
|
|
||||||
|
3. 【区间覆盖规律】命中率较高 74.13%
|
||||||
|
- 特码所在区间被正码覆盖的概率约为 74.13%
|
||||||
|
- 将1-49分为5区间，特码有74%概率落在正码覆盖的区间
|
||||||
|
|
||||||
|
4. 【正码范围规律】命中率中等 70.67%
|
||||||
|
- 特码在正码[min, max]范围内的概率约为 70.67%
|
||||||
|
- 特码有70%概率落在正码的最小值和最大值之间
|
||||||
|
|
||||||
|
5. 【尾数差规律】命中率54.13%
|
||||||
|
- 和值尾数与特码尾数差<=3的概率约为 54.13%
|
||||||
|
- 特码尾数与正码和值尾数相差不超过3
|
||||||
|
|
||||||
|
6. 【生肖重复规律】命中率较低 36.27%
|
||||||
|
- 特码生肖在正码中出现的概率约为 36.27%
|
||||||
|
- 生肖关联性不如波色明显
|
||||||
|
|
||||||
|
7. 【特码等于正码】命中率极低 0%
|
||||||
|
- 特码等于某正码的概率为 0%
|
||||||
|
- 特码与正码完全不重复（六合彩规则）
|
||||||
|
""")
|
||||||
|
|
||||||
|
f.write("\n==================== 分析完成 ====================\n")
|
||||||
|
|
||||||
|
print(f"分析完成，结果已保存到: {output_file}")
|
||||||
@@ -0,0 +1,204 @@
|
|||||||
|
==================== 正码与特码关联规律统计分析 ====================
|
||||||
|
数据总量: 375 期 (从 2025111 到 2026120)
|
||||||
|
|
||||||
|
==================== 1. 正码平均值与特码差值分布 ====================
|
||||||
|
差值分布统计:
|
||||||
|
差值 -33: 1 次 (0.27%)
|
||||||
|
差值 -32: 2 次 (0.53%)
|
||||||
|
差值 -31: 1 次 (0.27%)
|
||||||
|
差值 -30: 3 次 (0.8%)
|
||||||
|
差值 -29: 3 次 (0.8%)
|
||||||
|
差值 -28: 1 次 (0.27%)
|
||||||
|
差值 -27: 3 次 (0.8%)
|
||||||
|
差值 -26: 2 次 (0.53%)
|
||||||
|
差值 -25: 4 次 (1.07%)
|
||||||
|
差值 -24: 5 次 (1.33%)
|
||||||
|
差值 -23: 3 次 (0.8%)
|
||||||
|
差值 -22: 4 次 (1.07%)
|
||||||
|
差值 -21: 4 次 (1.07%)
|
||||||
|
差值 -20: 8 次 (2.13%)
|
||||||
|
差值 -19: 9 次 (2.4%)
|
||||||
|
差值 -18: 5 次 (1.33%)
|
||||||
|
差值 -17: 10 次 (2.67%)
|
||||||
|
差值 -16: 9 次 (2.4%)
|
||||||
|
差值 -15: 3 次 (0.8%)
|
||||||
|
差值 -14: 13 次 (3.47%)
|
||||||
|
差值 -13: 8 次 (2.13%)
|
||||||
|
差值 -12: 9 次 (2.4%)
|
||||||
|
差值 -11: 8 次 (2.13%)
|
||||||
|
差值 -10: 9 次 (2.4%)
|
||||||
|
差值 -9: 4 次 (1.07%)
|
||||||
|
差值 -8: 13 次 (3.47%)
|
||||||
|
差值 -7: 9 次 (2.4%)
|
||||||
|
差值 -6: 10 次 (2.67%)
|
||||||
|
差值 -5: 5 次 (1.33%)
|
||||||
|
差值 -4: 5 次 (1.33%)
|
||||||
|
差值 -3: 2 次 (0.53%)
|
||||||
|
差值 -2: 8 次 (2.13%)
|
||||||
|
差值 -1: 4 次 (1.07%)
|
||||||
|
差值 0: 12 次 (3.2%)
|
||||||
|
差值 1: 6 次 (1.6%)
|
||||||
|
差值 2: 11 次 (2.93%)
|
||||||
|
差值 3: 5 次 (1.33%)
|
||||||
|
差值 4: 11 次 (2.93%)
|
||||||
|
差值 5: 3 次 (0.8%)
|
||||||
|
差值 6: 7 次 (1.87%)
|
||||||
|
差值 7: 8 次 (2.13%)
|
||||||
|
差值 8: 8 次 (2.13%)
|
||||||
|
差值 9: 4 次 (1.07%)
|
||||||
|
差值 10: 6 次 (1.6%)
|
||||||
|
差值 11: 5 次 (1.33%)
|
||||||
|
差值 12: 7 次 (1.87%)
|
||||||
|
差值 13: 4 次 (1.07%)
|
||||||
|
差值 14: 9 次 (2.4%)
|
||||||
|
差值 15: 4 次 (1.07%)
|
||||||
|
差值 16: 6 次 (1.6%)
|
||||||
|
差值 17: 6 次 (1.6%)
|
||||||
|
差值 18: 18 次 (4.8%)
|
||||||
|
差值 19: 5 次 (1.33%)
|
||||||
|
差值 20: 6 次 (1.6%)
|
||||||
|
差值 21: 11 次 (2.93%)
|
||||||
|
差值 22: 8 次 (2.13%)
|
||||||
|
差值 23: 4 次 (1.07%)
|
||||||
|
差值 24: 2 次 (0.53%)
|
||||||
|
差值 25: 1 次 (0.27%)
|
||||||
|
差值 26: 2 次 (0.53%)
|
||||||
|
差值 27: 3 次 (0.8%)
|
||||||
|
差值 28: 2 次 (0.53%)
|
||||||
|
差值 29: 1 次 (0.27%)
|
||||||
|
差值 31: 2 次 (0.53%)
|
||||||
|
差值 32: 1 次 (0.27%)
|
||||||
|
|
||||||
|
差值在 [-5, +5] 范围内的概率: 19.2% (72/375)
|
||||||
|
差值在 [-10, +10] 范围内的概率: 40.0% (150/375)
|
||||||
|
|
||||||
|
==================== 2. 特码是否在正码范围内 ====================
|
||||||
|
特码在正码范围内 [min(num1-6), max(num1-6)]:
|
||||||
|
是: 265 次 (70.67%)
|
||||||
|
否: 110 次 (29.33%)
|
||||||
|
|
||||||
|
==================== 3. 特码与最近正码的距离分布 ====================
|
||||||
|
距离分布统计:
|
||||||
|
距离 1: 103 次 (27.47%)
|
||||||
|
距离 2: 62 次 (16.53%)
|
||||||
|
距离 3: 56 次 (14.93%)
|
||||||
|
距离 4: 37 次 (9.87%)
|
||||||
|
距离 5: 38 次 (10.13%)
|
||||||
|
距离 6: 20 次 (5.33%)
|
||||||
|
距离 7: 12 次 (3.2%)
|
||||||
|
距离 8: 12 次 (3.2%)
|
||||||
|
距离 9: 7 次 (1.87%)
|
||||||
|
距离 10: 6 次 (1.6%)
|
||||||
|
距离 11: 6 次 (1.6%)
|
||||||
|
距离 12: 5 次 (1.33%)
|
||||||
|
距离 13: 4 次 (1.07%)
|
||||||
|
距离 14: 4 次 (1.07%)
|
||||||
|
距离 15: 1 次 (0.27%)
|
||||||
|
距离 20: 1 次 (0.27%)
|
||||||
|
距离 21: 1 次 (0.27%)
|
||||||
|
|
||||||
|
特码等于某正码 (距离=0) 的概率: 0.0% (0/375)
|
||||||
|
距离 <= 5 的概率: 78.93% (296/375)
|
||||||
|
距离 <= 10 的概率: 94.13% (353/375)
|
||||||
|
距离 <= 15 的概率: 99.47% (373/375)
|
||||||
|
|
||||||
|
==================== 4. 和值尾数关系 ====================
|
||||||
|
和值尾数与特码尾数同尾概率: 8.27% (31/375)
|
||||||
|
|
||||||
|
尾数差值分布:
|
||||||
|
尾数差 0: 31 次 (8.27%)
|
||||||
|
尾数差 1: 70 次 (18.67%)
|
||||||
|
尾数差 2: 53 次 (14.13%)
|
||||||
|
尾数差 3: 49 次 (13.07%)
|
||||||
|
尾数差 4: 43 次 (11.47%)
|
||||||
|
尾数差 5: 53 次 (14.13%)
|
||||||
|
尾数差 6: 42 次 (11.2%)
|
||||||
|
尾数差 7: 20 次 (5.33%)
|
||||||
|
尾数差 8: 10 次 (2.67%)
|
||||||
|
尾数差 9: 4 次 (1.07%)
|
||||||
|
|
||||||
|
尾数差 <= 3 的概率: 54.13% (203/375)
|
||||||
|
|
||||||
|
==================== 5. 区间覆盖分析 ====================
|
||||||
|
正码覆盖区间数量分布:
|
||||||
|
覆盖 1 个区间: 0 次 (0.0%)
|
||||||
|
覆盖 2 个区间: 9 次 (2.4%)
|
||||||
|
覆盖 3 个区间: 103 次 (27.47%)
|
||||||
|
覆盖 4 个区间: 194 次 (51.73%)
|
||||||
|
覆盖 5 个区间: 69 次 (18.4%)
|
||||||
|
|
||||||
|
特码所在区间被正码覆盖的概率: 74.13% (278/375)
|
||||||
|
|
||||||
|
==================== 6. 波色/生肖关联 ====================
|
||||||
|
特码波色分布:
|
||||||
|
红: 131 次 (34.93%)
|
||||||
|
蓝: 122 次 (32.53%)
|
||||||
|
绿: 122 次 (32.53%)
|
||||||
|
|
||||||
|
特码波色在正码中出现的概率: 90.67% (340/375)
|
||||||
|
特码生肖在正码中出现的概率: 36.27% (136/375)
|
||||||
|
|
||||||
|
正码中特码同波色数量分布:
|
||||||
|
0_红: 7 次 (1.87%)
|
||||||
|
0_绿: 11 次 (2.93%)
|
||||||
|
0_蓝: 17 次 (4.53%)
|
||||||
|
1_红: 35 次 (9.33%)
|
||||||
|
1_绿: 42 次 (11.2%)
|
||||||
|
1_蓝: 37 次 (9.87%)
|
||||||
|
2_红: 53 次 (14.13%)
|
||||||
|
2_绿: 46 次 (12.27%)
|
||||||
|
2_蓝: 38 次 (10.13%)
|
||||||
|
3_红: 30 次 (8.0%)
|
||||||
|
3_绿: 17 次 (4.53%)
|
||||||
|
3_蓝: 26 次 (6.93%)
|
||||||
|
4_红: 3 次 (0.8%)
|
||||||
|
4_绿: 5 次 (1.33%)
|
||||||
|
4_蓝: 4 次 (1.07%)
|
||||||
|
5_红: 3 次 (0.8%)
|
||||||
|
5_绿: 1 次 (0.27%)
|
||||||
|
|
||||||
|
==================== 总结: 达到40%命中率以上的规律 ====================
|
||||||
|
规律按命中率排序:
|
||||||
|
- 距离<=15: 99.47% [达标]
|
||||||
|
- 距离<=10: 94.13% [达标]
|
||||||
|
- 特码波色在正码中出现: 90.67% [达标]
|
||||||
|
- 距离<=5: 78.93% [达标]
|
||||||
|
- 特码区间被正码覆盖: 74.13% [达标]
|
||||||
|
- 特码在正码范围内: 70.67% [达标]
|
||||||
|
- 尾数差<=3: 54.13% [达标]
|
||||||
|
- 差值在[-10,+10]: 40.0% [达标]
|
||||||
|
- 特码生肖在正码中出现: 36.27%
|
||||||
|
- 差值在[-5,+5]: 19.2%
|
||||||
|
- 同尾: 8.27%
|
||||||
|
|
||||||
|
==================== 关键发现 ====================
|
||||||
|
|
||||||
|
1. 【特码波色重复规律】命中率最高 90.67%
|
||||||
|
- 特码波色在正码中出现的概率约为 90.67%
|
||||||
|
- 如果正码中有红色号码，特码有90%概率是红色波色
|
||||||
|
|
||||||
|
2. 【近距离规律】命中率很高 94.13%
|
||||||
|
- 特码距离最近正码<=10的概率约为 94.13%
|
||||||
|
- 特码往往不会离正码太远，基本在10个数字以内
|
||||||
|
|
||||||
|
3. 【区间覆盖规律】命中率较高 74.13%
|
||||||
|
- 特码所在区间被正码覆盖的概率约为 74.13%
|
||||||
|
- 将1-49分为5区间，特码有74%概率落在正码覆盖的区间
|
||||||
|
|
||||||
|
4. 【正码范围规律】命中率中等 70.67%
|
||||||
|
- 特码在正码[min, max]范围内的概率约为 70.67%
|
||||||
|
- 特码有70%概率落在正码的最小值和最大值之间
|
||||||
|
|
||||||
|
5. 【尾数差规律】命中率54.13%
|
||||||
|
- 和值尾数与特码尾数差<=3的概率约为 54.13%
|
||||||
|
- 特码尾数与正码和值尾数相差不超过3
|
||||||
|
|
||||||
|
6. 【生肖重复规律】命中率较低 36.27%
|
||||||
|
- 特码生肖在正码中出现的概率约为 36.27%
|
||||||
|
- 生肖关联性不如波色明显
|
||||||
|
|
||||||
|
7. 【特码等于正码】命中率极低 0%
|
||||||
|
- 特码等于某正码的概率为 0%
|
||||||
|
- 特码与正码完全不重复（六合彩规则）
|
||||||
|
|
||||||
|
==================== 分析完成 ====================
|
||||||
@@ -22,7 +22,7 @@ class History extends Backend
|
|||||||
* 无需额外权限检查的方法（但仍在 admin 模块内，需要 admin 登录）
|
* 无需额外权限检查的方法（但仍在 admin 模块内，需要 admin 登录）
|
||||||
* @var array
|
* @var array
|
||||||
*/
|
*/
|
||||||
protected $noNeedRight = ['missingNum', 'trendData', 'hotColdNumbers', 'colorWaveAnalysis', 'zodiacAnalysis', 'oddEvenAnalysis', 'bigSmallAnalysis', 'specialTrend', 'consecutiveNumbers', 'tailNumbers', 'dashboard', 'specialHeatmap', 'specialHotColdAction', 'zoneTransition', 'colorWaveTransition', 'zoneToColorTransition', 'zodiacTransition', 'tailNumberTransition', 'headNumberTransition', 'predict', 'predictV2', 'predictV3', 'optimizeWeights'];
|
protected $noNeedRight = ['missingNum', 'trendData', 'hotColdNumbers', 'colorWaveAnalysis', 'zodiacAnalysis', 'oddEvenAnalysis', 'bigSmallAnalysis', 'specialTrend', 'consecutiveNumbers', 'tailNumbers', 'dashboard', 'specialHeatmap', 'specialHotColdAction', 'zoneTransition', 'colorWaveTransition', 'zoneToColorTransition', 'zodiacTransition', 'tailNumberTransition', 'headNumberTransition', 'predict', 'predictV2', 'predictV3', 'optimizeWeights', 'predictByNormalRelation'];
|
||||||
|
|
||||||
public function _initialize()
|
public function _initialize()
|
||||||
{
|
{
|
||||||
@@ -535,5 +535,29 @@ class History extends Backend
|
|||||||
}
|
}
|
||||||
}
|
}
|
||||||
|
|
||||||
|
/**
|
||||||
|
* 基于正码关联规律的特码预测
|
||||||
|
* 核心规律：
|
||||||
|
* - 波色重复规律：90.67%特码波色与正码中某号码波色相同
|
||||||
|
* - 距离规律：94.13%特码与最近正码距离<=10
|
||||||
|
* - 区间覆盖规律：74.13%特码落在正码覆盖的区间
|
||||||
|
* - 正码范围规律：70.67%特码在正码min-max之间
|
||||||
|
*/
|
||||||
|
public function predictByNormalRelation()
|
||||||
|
{
|
||||||
|
if ($this->request->isAjax()) {
|
||||||
|
$periods = $this->request->get('periods', 100, 'intval');
|
||||||
|
if ($periods < 30 || $periods > 500) {
|
||||||
|
$periods = 100;
|
||||||
|
}
|
||||||
|
$targetExpect = $this->request->get('target_expect', '', 'trim');
|
||||||
|
$result = $this->model->getPredictionByNormalRelation($periods, $targetExpect);
|
||||||
|
if (isset($result['error'])) {
|
||||||
|
$this->error($result['error']);
|
||||||
|
}
|
||||||
|
$this->success('查询成功', null, $result);
|
||||||
|
}
|
||||||
|
}
|
||||||
|
|
||||||
|
|
||||||
}
|
}
|
||||||
|
|||||||
@@ -4290,5 +4290,543 @@ class History extends Model
|
|||||||
];
|
];
|
||||||
}
|
}
|
||||||
|
|
||||||
|
/**
|
||||||
|
* 基于正码关联规律的特码预测方法（修正版）
|
||||||
|
* 核心规律：上期正码 → 当期特码
|
||||||
|
* - 覆盖区间规律：91.44% 当期特码在上期正码覆盖的区间内
|
||||||
|
* - 正码±3距离：59.36% 当期特码与上期正码某号码距离≤3
|
||||||
|
* - 双波色预测：69.52% 当期特码波色在上期正码前2种主导波色内
|
||||||
|
* - 特码区间转移：77.54% 基于上期特码区间预测当期特码区间
|
||||||
|
* - 平均值±10：41.98% 当期特码在上期正码平均值±10范围
|
||||||
|
* - 尾数±2：50% 和值尾数与特码尾数差≤2
|
||||||
|
* @param int $periods 统计期数（用于验证历史命中率）
|
||||||
|
* @param string $targetExpect 目标期号（可选，用于回测验证）
|
||||||
|
* @return array {predictions: [], analysis: {}, hit_info: {}, backtest: {}}
|
||||||
|
*/
|
||||||
|
public function getPredictionByNormalRelation($periods = 100, $targetExpect = '')
|
||||||
|
{
|
||||||
|
$num_model = new Num();
|
||||||
|
$colorMap = $num_model->column('color', 'num');
|
||||||
|
$animalMap = $num_model->column('animal', 'num');
|
||||||
|
|
||||||
|
// 区间划分：大号(31-49)、中号(11-30)、小号(1-10)
|
||||||
|
$getBigZone = function ($num) {
|
||||||
|
if ($num <= 10) return 'small';
|
||||||
|
if ($num <= 30) return 'mid';
|
||||||
|
return 'big';
|
||||||
|
};
|
||||||
|
|
||||||
|
// 细区间划分：1-10, 11-20, 21-30, 31-40, 41-49
|
||||||
|
$getFineZone = function ($num) {
|
||||||
|
if ($num <= 10) return 0;
|
||||||
|
if ($num <= 20) return 1;
|
||||||
|
if ($num <= 30) return 2;
|
||||||
|
if ($num <= 40) return 3;
|
||||||
|
return 4;
|
||||||
|
};
|
||||||
|
|
||||||
|
// 确定预测基准
|
||||||
|
$actualResult = null;
|
||||||
|
$lastNormals = [];
|
||||||
|
$lastSpecial = 0;
|
||||||
|
$lastExpect = '';
|
||||||
|
$cutoffTime = null;
|
||||||
|
|
||||||
|
if ($targetExpect) {
|
||||||
|
$targetRow = $this->where('expect', $targetExpect)->find();
|
||||||
|
if (!$targetRow) {
|
||||||
|
return ['predictions' => [], 'error' => '期号不存在', 'target_expect' => $targetExpect];
|
||||||
|
}
|
||||||
|
$cutoffTime = $targetRow['openTime'];
|
||||||
|
$actualResult = [
|
||||||
|
'expect' => (string)$targetRow['expect'],
|
||||||
|
'num7' => (int)$targetRow['num7'],
|
||||||
|
'color' => $colorMap[$targetRow['num7']] ?? '',
|
||||||
|
'animal' => $animalMap[$targetRow['num7']] ?? '',
|
||||||
|
'bigZone' => $getBigZone($targetRow['num7']),
|
||||||
|
'openTime' => $targetRow['openTime']
|
||||||
|
];
|
||||||
|
// 获取上一期数据作为预测基准
|
||||||
|
$prevRow = $this->where('openTime', '<', $cutoffTime)->order('openTime', 'desc')->limit(1)->find();
|
||||||
|
if (!$prevRow) {
|
||||||
|
return ['predictions' => [], 'error' => '没有历史数据'];
|
||||||
|
}
|
||||||
|
for ($i = 1; $i <= 6; $i++) {
|
||||||
|
$lastNormals[] = (int)$prevRow['num' . $i];
|
||||||
|
}
|
||||||
|
$lastSpecial = (int)$prevRow['num7'];
|
||||||
|
$lastExpect = (string)$prevRow['expect'];
|
||||||
|
} else {
|
||||||
|
// 使用最新一期作为预测基准
|
||||||
|
$latest = $this->field('expect,num1,num2,num3,num4,num5,num6,num7,openTime')
|
||||||
|
->order('openTime', 'desc')->limit(1)->find();
|
||||||
|
if (!$latest) {
|
||||||
|
return ['predictions' => [], 'error' => '没有历史数据'];
|
||||||
|
}
|
||||||
|
for ($i = 1; $i <= 6; $i++) {
|
||||||
|
$lastNormals[] = (int)$latest['num' . $i];
|
||||||
|
}
|
||||||
|
$lastSpecial = (int)$latest['num7'];
|
||||||
|
$lastExpect = (string)$latest['expect'];
|
||||||
|
}
|
||||||
|
|
||||||
|
// 分析上期正码特征
|
||||||
|
$normalMin = min($lastNormals);
|
||||||
|
$normalMax = max($lastNormals);
|
||||||
|
$normalAvg = round(array_sum($lastNormals) / 6, 2);
|
||||||
|
$normalSum = array_sum($lastNormals);
|
||||||
|
|
||||||
|
// 统计上期正码波色分布（找出前2种主导波色）
|
||||||
|
$colorCounts = ['红' => 0, '蓝' => 0, '绿' => 0];
|
||||||
|
foreach ($lastNormals as $n) {
|
||||||
|
$color = $colorMap[$n] ?? '';
|
||||||
|
if (strpos($color, '红') !== false) $colorCounts['红']++;
|
||||||
|
elseif (strpos($color, '蓝') !== false) $colorCounts['蓝']++;
|
||||||
|
elseif (strpos($color, '绿') !== false) $colorCounts['绿']++;
|
||||||
|
}
|
||||||
|
// 按数量排序，取前2种
|
||||||
|
$sortedColors = [];
|
||||||
|
foreach ($colorCounts as $c => $cnt) {
|
||||||
|
$sortedColors[] = ['color' => $c, 'count' => $cnt];
|
||||||
|
}
|
||||||
|
usort($sortedColors, function ($a, $b) { return $b['count'] - $a['count']; });
|
||||||
|
$top2Colors = [$sortedColors[0]['color'], $sortedColors[1]['color']];
|
||||||
|
|
||||||
|
// 获取上期正码覆盖的细区间
|
||||||
|
$normalFineZones = [];
|
||||||
|
foreach ($lastNormals as $n) {
|
||||||
|
$zoneIdx = $getFineZone($n);
|
||||||
|
if (!in_array($zoneIdx, $normalFineZones)) {
|
||||||
|
$normalFineZones[] = $zoneIdx;
|
||||||
|
}
|
||||||
|
}
|
||||||
|
|
||||||
|
// 获取上期正码覆盖的大区间
|
||||||
|
$normalBigZones = [];
|
||||||
|
foreach ($lastNormals as $n) {
|
||||||
|
$bigZone = $getBigZone($n);
|
||||||
|
if (!in_array($bigZone, $normalBigZones)) {
|
||||||
|
$normalBigZones[] = $bigZone;
|
||||||
|
}
|
||||||
|
}
|
||||||
|
|
||||||
|
// 上期特码所在大区间
|
||||||
|
$lastSpecialBigZone = $getBigZone($lastSpecial);
|
||||||
|
|
||||||
|
// 特码区间转移概率矩阵（基于历史分析）
|
||||||
|
$zoneTransMatrix = [
|
||||||
|
'big' => ['big' => 35.77, 'mid' => 37.96, 'small' => 26.28],
|
||||||
|
'mid' => ['big' => 33.77, 'mid' => 43.51, 'small' => 22.73],
|
||||||
|
'small' => ['big' => 42.17, 'mid' => 42.17, 'small' => 15.66]
|
||||||
|
];
|
||||||
|
|
||||||
|
// 计算每个号码的预测评分
|
||||||
|
$predictions = [];
|
||||||
|
for ($num = 1; $num <= 49; $num++) {
|
||||||
|
$numColor = $colorMap[$num] ?? '';
|
||||||
|
$numBigZone = $getBigZone($num);
|
||||||
|
$numFineZone = $getFineZone($num);
|
||||||
|
|
||||||
|
// 规律1：覆盖区间规律（91.44%命中）- 细区间覆盖
|
||||||
|
$fineZoneCovered = in_array($numFineZone, $normalFineZones);
|
||||||
|
$zoneCoverScore = $fineZoneCovered ? 91 : 0;
|
||||||
|
|
||||||
|
// 规律2：正码±3距离（59.36%命中）
|
||||||
|
$minDistance = 49;
|
||||||
|
foreach ($lastNormals as $n) {
|
||||||
|
$dist = abs($num - $n);
|
||||||
|
if ($dist < $minDistance) $minDistance = $dist;
|
||||||
|
}
|
||||||
|
$distScore = $minDistance <= 3 ? 59 : ($minDistance <= 5 ? 40 : 0);
|
||||||
|
|
||||||
|
// 规律3：双波色预测（69.52%命中）
|
||||||
|
$colorInTop2 = false;
|
||||||
|
foreach ($top2Colors as $tc) {
|
||||||
|
if (strpos($numColor, $tc) !== false) {
|
||||||
|
$colorInTop2 = true;
|
||||||
|
break;
|
||||||
|
}
|
||||||
|
}
|
||||||
|
$colorScore = $colorInTop2 ? 69 : 0;
|
||||||
|
|
||||||
|
// 规律4：特码区间转移（77.54%命中）
|
||||||
|
$transProb = $zoneTransMatrix[$lastSpecialBigZone][$numBigZone] ?? 0;
|
||||||
|
// 取该区间最高转移概率的2个区间
|
||||||
|
$transProbs = $zoneTransMatrix[$lastSpecialBigZone];
|
||||||
|
$sortedTrans = [];
|
||||||
|
foreach ($transProbs as $z => $p) {
|
||||||
|
$sortedTrans[] = ['zone' => $z, 'prob' => $p];
|
||||||
|
}
|
||||||
|
usort($sortedTrans, function ($a, $b) { return $b['prob'] - $a['prob']; });
|
||||||
|
$top2TransZones = [$sortedTrans[0]['zone'], $sortedTrans[1]['zone']];
|
||||||
|
$transMatch = in_array($numBigZone, $top2TransZones);
|
||||||
|
$transScore = $transMatch ? 77 : 0;
|
||||||
|
|
||||||
|
// 规律5：平均值±10（41.98%命中）
|
||||||
|
$avgDiff = abs($num - $normalAvg);
|
||||||
|
$avgScore = $avgDiff <= 10 ? 42 : 0;
|
||||||
|
|
||||||
|
// 规律6：尾数±2（50%命中）
|
||||||
|
$numTail = $num % 10;
|
||||||
|
$sumTail = $normalSum % 10;
|
||||||
|
$tailDiff = abs($numTail - $sumTail);
|
||||||
|
$tailDiff = min($tailDiff, 10 - $tailDiff);
|
||||||
|
$tailScore = $tailDiff <= 2 ? 50 : ($tailDiff <= 3 ? 30 : 0);
|
||||||
|
|
||||||
|
// 综合评分（加权求和）
|
||||||
|
$totalScore = $zoneCoverScore * 0.30 // 覆盖区间权重最高
|
||||||
|
+ $transScore * 0.25 // 特码区间转移
|
||||||
|
+ $colorScore * 0.20 // 双波色
|
||||||
|
+ $distScore * 0.12 // 距离
|
||||||
|
+ $avgScore * 0.08 // 平均值
|
||||||
|
+ $tailScore * 0.05; // 尾数
|
||||||
|
|
||||||
|
$predictions[] = [
|
||||||
|
'num' => $num,
|
||||||
|
'score' => round($totalScore, 2),
|
||||||
|
'color' => $numColor,
|
||||||
|
'animal' => $animalMap[$num] ?? '',
|
||||||
|
'big_zone' => $numBigZone,
|
||||||
|
'fine_zone' => $numFineZone,
|
||||||
|
'zone_covered' => $fineZoneCovered,
|
||||||
|
'min_distance' => $minDistance,
|
||||||
|
'color_in_top2' => $colorInTop2,
|
||||||
|
'trans_match' => $transMatch,
|
||||||
|
'trans_prob' => $transProb,
|
||||||
|
'avg_diff' => round($avgDiff, 2),
|
||||||
|
'tail_diff' => $tailDiff
|
||||||
|
];
|
||||||
|
}
|
||||||
|
|
||||||
|
// 按评分降序排序
|
||||||
|
usort($predictions, function ($a, $b) {
|
||||||
|
return $b['score'] - $a['score'];
|
||||||
|
});
|
||||||
|
|
||||||
|
// 返回Top15推荐号码
|
||||||
|
$topPredictions = array_slice($predictions, 0, 15);
|
||||||
|
|
||||||
|
// 分析信息
|
||||||
|
$analysis = [
|
||||||
|
'last_expect' => $lastExpect,
|
||||||
|
'last_normals' => $lastNormals,
|
||||||
|
'last_special' => $lastSpecial,
|
||||||
|
'last_special_zone' => $lastSpecialBigZone,
|
||||||
|
'normal_min' => $normalMin,
|
||||||
|
'normal_max' => $normalMax,
|
||||||
|
'normal_avg' => $normalAvg,
|
||||||
|
'normal_sum' => $normalSum,
|
||||||
|
'top2_colors' => $top2Colors,
|
||||||
|
'color_counts' => $colorCounts,
|
||||||
|
'normal_fine_zones' => $normalFineZones,
|
||||||
|
'normal_big_zones' => $normalBigZones,
|
||||||
|
'zone_trans_matrix' => $zoneTransMatrix,
|
||||||
|
'rules' => [
|
||||||
|
['name' => '覆盖区间', 'rate' => '91.44%', 'desc' => '当期特码在上期正码覆盖的细区间内'],
|
||||||
|
['name' => '特码区间转移', 'rate' => '77.54%', 'desc' => '基于上期特码区间预测当期特码所在大区间'],
|
||||||
|
['name' => '双波色预测', 'rate' => '69.52%', 'desc' => '当期特码波色在上期正码前2种主导波色内'],
|
||||||
|
['name' => '正码±3距离', 'rate' => '59.36%', 'desc' => '当期特码与上期正码某号码距离≤3'],
|
||||||
|
['name' => '尾数±2', 'rate' => '50%', 'desc' => '上期正码和值尾数与当期特码尾数差≤2'],
|
||||||
|
['name' => '平均值±10', 'rate' => '41.98%', 'desc' => '当期特码在上期正码平均值±10范围']
|
||||||
|
],
|
||||||
|
'predict_next_expect' => $lastExpect ? (string)(intval($lastExpect) + 1) : ''
|
||||||
|
];
|
||||||
|
|
||||||
|
// 命中验证
|
||||||
|
$hitInfo = null;
|
||||||
|
if ($actualResult) {
|
||||||
|
$hitRank = -1;
|
||||||
|
foreach ($topPredictions as $idx => $p) {
|
||||||
|
if ($p['num'] === $actualResult['num7']) {
|
||||||
|
$hitRank = $idx + 1;
|
||||||
|
break;
|
||||||
|
}
|
||||||
|
}
|
||||||
|
$fullRank = -1;
|
||||||
|
foreach ($predictions as $idx => $p) {
|
||||||
|
if ($p['num'] === $actualResult['num7']) {
|
||||||
|
$fullRank = $idx + 1;
|
||||||
|
break;
|
||||||
|
}
|
||||||
|
}
|
||||||
|
// 分析实际结果的规律命中情况
|
||||||
|
$actualAnalysis = null;
|
||||||
|
foreach ($predictions as $p) {
|
||||||
|
if ($p['num'] === $actualResult['num7']) {
|
||||||
|
$actualAnalysis = $p;
|
||||||
|
break;
|
||||||
|
}
|
||||||
|
}
|
||||||
|
$hitInfo = [
|
||||||
|
'hit' => $hitRank > 0,
|
||||||
|
'rank_in_top' => $hitRank,
|
||||||
|
'rank_in_all' => $fullRank,
|
||||||
|
'actual_num' => $actualResult['num7'],
|
||||||
|
'actual_color' => $actualResult['color'],
|
||||||
|
'actual_animal' => $actualResult['animal'],
|
||||||
|
'actual_expect' => $actualResult['expect'],
|
||||||
|
'actual_zone_covered' => $actualAnalysis ? $actualAnalysis['zone_covered'] : false,
|
||||||
|
'actual_min_distance' => $actualAnalysis ? $actualAnalysis['min_distance'] : 99,
|
||||||
|
'actual_color_in_top2' => $actualAnalysis ? $actualAnalysis['color_in_top2'] : false,
|
||||||
|
'actual_trans_match' => $actualAnalysis ? $actualAnalysis['trans_match'] : false,
|
||||||
|
'actual_tail_diff' => $actualAnalysis ? $actualAnalysis['tail_diff'] : 99,
|
||||||
|
'actual_avg_diff' => $actualAnalysis ? $actualAnalysis['avg_diff'] : 99
|
||||||
|
];
|
||||||
|
}
|
||||||
|
|
||||||
|
// 回测验证（默认显示前50期命中详情）
|
||||||
|
$backtest = null;
|
||||||
|
if ($periods >= 30) {
|
||||||
|
$backtest = $this->_runBacktestNormalRelation($periods, 50);
|
||||||
|
}
|
||||||
|
|
||||||
|
return [
|
||||||
|
'predictions' => $topPredictions,
|
||||||
|
'all_predictions' => $predictions,
|
||||||
|
'analysis' => $analysis,
|
||||||
|
'actual_result' => $actualResult,
|
||||||
|
'hit_info' => $hitInfo,
|
||||||
|
'backtest' => $backtest
|
||||||
|
];
|
||||||
|
}
|
||||||
|
|
||||||
|
/**
|
||||||
|
* 执行正码关联规律的历史回测（修正版）
|
||||||
|
* 使用正确规律：上期正码 → 当期特码
|
||||||
|
* @param int $periods 回测期数
|
||||||
|
* @param int $detailLimit 返回详情条数
|
||||||
|
* @return array {hit_rate, avg_rank, details, rule_stats}
|
||||||
|
*/
|
||||||
|
private function _runBacktestNormalRelation($periods = 100, $detailLimit = 20)
|
||||||
|
{
|
||||||
|
$history = $this->field('expect,num1,num2,num3,num4,num5,num6,num7,openTime')
|
||||||
|
->order('openTime', 'desc')
|
||||||
|
->limit($periods + 1)
|
||||||
|
->select();
|
||||||
|
|
||||||
|
if (count($history) < 2) {
|
||||||
|
return ['error' => '数据不足'];
|
||||||
|
}
|
||||||
|
|
||||||
|
$num_model = new Num();
|
||||||
|
$colorMap = $num_model->column('color', 'num');
|
||||||
|
|
||||||
|
// 大区间划分
|
||||||
|
$getBigZone = function ($num) {
|
||||||
|
if ($num <= 10) return 'small';
|
||||||
|
if ($num <= 30) return 'mid';
|
||||||
|
return 'big';
|
||||||
|
};
|
||||||
|
|
||||||
|
// 细区间划分
|
||||||
|
$getFineZone = function ($num) {
|
||||||
|
if ($num <= 10) return 0;
|
||||||
|
if ($num <= 20) return 1;
|
||||||
|
if ($num <= 30) return 2;
|
||||||
|
if ($num <= 40) return 3;
|
||||||
|
return 4;
|
||||||
|
};
|
||||||
|
|
||||||
|
// 特码区间转移概率矩阵
|
||||||
|
$zoneTransMatrix = [
|
||||||
|
'big' => ['big' => 35.77, 'mid' => 37.96, 'small' => 26.28],
|
||||||
|
'mid' => ['big' => 33.77, 'mid' => 43.51, 'small' => 22.73],
|
||||||
|
'small' => ['big' => 42.17, 'mid' => 42.17, 'small' => 15.66]
|
||||||
|
];
|
||||||
|
|
||||||
|
$hits = 0;
|
||||||
|
$ranks = [];
|
||||||
|
$details = [];
|
||||||
|
$ruleHits = [
|
||||||
|
'zone_cover' => 0,
|
||||||
|
'trans_match' => 0,
|
||||||
|
'color_top2' => 0,
|
||||||
|
'dist_3' => 0,
|
||||||
|
'tail_2' => 0,
|
||||||
|
'avg_10' => 0
|
||||||
|
];
|
||||||
|
|
||||||
|
for ($i = 0; $i < count($history) - 1; $i++) {
|
||||||
|
$currentRow = $history[$i];
|
||||||
|
$prevRow = $history[$i + 1];
|
||||||
|
|
||||||
|
// 使用上一期的正码预测当期的特码
|
||||||
|
$lastNormals = [];
|
||||||
|
for ($j = 1; $j <= 6; $j++) {
|
||||||
|
$lastNormals[] = (int)$prevRow['num' . $j];
|
||||||
|
}
|
||||||
|
$lastSpecial = (int)$prevRow['num7'];
|
||||||
|
$actualSpecial = (int)$currentRow['num7'];
|
||||||
|
|
||||||
|
// 分析上期正码特征
|
||||||
|
$normalMin = min($lastNormals);
|
||||||
|
$normalMax = max($lastNormals);
|
||||||
|
$normalAvg = array_sum($lastNormals) / 6;
|
||||||
|
$normalSum = array_sum($lastNormals);
|
||||||
|
|
||||||
|
// 波色分布
|
||||||
|
$colorCounts = ['红' => 0, '蓝' => 0, '绿' => 0];
|
||||||
|
foreach ($lastNormals as $n) {
|
||||||
|
$color = $colorMap[$n] ?? '';
|
||||||
|
if (strpos($color, '红') !== false) $colorCounts['红']++;
|
||||||
|
elseif (strpos($color, '蓝') !== false) $colorCounts['蓝']++;
|
||||||
|
elseif (strpos($color, '绿') !== false) $colorCounts['绿']++;
|
||||||
|
}
|
||||||
|
$sortedColors = [];
|
||||||
|
foreach ($colorCounts as $c => $cnt) {
|
||||||
|
$sortedColors[] = ['color' => $c, 'count' => $cnt];
|
||||||
|
}
|
||||||
|
usort($sortedColors, function ($a, $b) { return $b['count'] - $a['count']; });
|
||||||
|
$top2Colors = [$sortedColors[0]['color'], $sortedColors[1]['color']];
|
||||||
|
|
||||||
|
// 上期正码覆盖的细区间
|
||||||
|
$normalFineZones = [];
|
||||||
|
foreach ($lastNormals as $n) {
|
||||||
|
$zoneIdx = $getFineZone($n);
|
||||||
|
if (!in_array($zoneIdx, $normalFineZones)) {
|
||||||
|
$normalFineZones[] = $zoneIdx;
|
||||||
|
}
|
||||||
|
}
|
||||||
|
|
||||||
|
// 上期特码所在大区间
|
||||||
|
$lastSpecialBigZone = $getBigZone($lastSpecial);
|
||||||
|
|
||||||
|
// 计算每个号码评分
|
||||||
|
$scores = [];
|
||||||
|
for ($num = 1; $num <= 49; $num++) {
|
||||||
|
$numColor = $colorMap[$num] ?? '';
|
||||||
|
$numBigZone = $getBigZone($num);
|
||||||
|
$numFineZone = $getFineZone($num);
|
||||||
|
|
||||||
|
// 规律1：覆盖区间（91%）
|
||||||
|
$zoneCovered = in_array($numFineZone, $normalFineZones);
|
||||||
|
$zoneCoverScore = $zoneCovered ? 91 : 0;
|
||||||
|
|
||||||
|
// 规律2：特码区间转移（77%）
|
||||||
|
$transProbs = $zoneTransMatrix[$lastSpecialBigZone];
|
||||||
|
$sortedTrans = [];
|
||||||
|
foreach ($transProbs as $z => $p) {
|
||||||
|
$sortedTrans[] = ['zone' => $z, 'prob' => $p];
|
||||||
|
}
|
||||||
|
usort($sortedTrans, function ($a, $b) { return $b['prob'] - $a['prob']; });
|
||||||
|
$top2TransZones = [$sortedTrans[0]['zone'], $sortedTrans[1]['zone']];
|
||||||
|
$transMatch = in_array($numBigZone, $top2TransZones);
|
||||||
|
$transScore = $transMatch ? 77 : 0;
|
||||||
|
|
||||||
|
// 规律3：双波色（69%）
|
||||||
|
$colorInTop2 = false;
|
||||||
|
foreach ($top2Colors as $tc) {
|
||||||
|
if (strpos($numColor, $tc) !== false) {
|
||||||
|
$colorInTop2 = true;
|
||||||
|
break;
|
||||||
|
}
|
||||||
|
}
|
||||||
|
$colorScore = $colorInTop2 ? 69 : 0;
|
||||||
|
|
||||||
|
// 规律4：距离≤3（59%）
|
||||||
|
$minDist = 49;
|
||||||
|
foreach ($lastNormals as $n) {
|
||||||
|
$dist = abs($num - $n);
|
||||||
|
if ($dist < $minDist) $minDist = $dist;
|
||||||
|
}
|
||||||
|
$distScore = $minDist <= 3 ? 59 : ($minDist <= 5 ? 40 : 0);
|
||||||
|
|
||||||
|
// 规律5：尾数≤2（50%）
|
||||||
|
$tailDiff = abs($num % 10 - $normalSum % 10);
|
||||||
|
$tailDiff = min($tailDiff, 10 - $tailDiff);
|
||||||
|
$tailScore = $tailDiff <= 2 ? 50 : ($tailDiff <= 3 ? 30 : 0);
|
||||||
|
|
||||||
|
// 规律6：平均值±10（42%）
|
||||||
|
$avgDiff = abs($num - $normalAvg);
|
||||||
|
$avgScore = $avgDiff <= 10 ? 42 : 0;
|
||||||
|
|
||||||
|
// 综合评分
|
||||||
|
$score = $zoneCoverScore * 0.30
|
||||||
|
+ $transScore * 0.25
|
||||||
|
+ $colorScore * 0.20
|
||||||
|
+ $distScore * 0.12
|
||||||
|
+ $avgScore * 0.08
|
||||||
|
+ $tailScore * 0.05;
|
||||||
|
|
||||||
|
$scores[$num] = $score;
|
||||||
|
}
|
||||||
|
|
||||||
|
// 排序找排名
|
||||||
|
$sorted = [];
|
||||||
|
for ($num = 1; $num <= 49; $num++) {
|
||||||
|
$sorted[] = ['num' => $num, 'score' => $scores[$num]];
|
||||||
|
}
|
||||||
|
usort($sorted, function ($a, $b) {
|
||||||
|
return $b['score'] - $a['score'];
|
||||||
|
});
|
||||||
|
|
||||||
|
$rank = -1;
|
||||||
|
foreach ($sorted as $idx => $item) {
|
||||||
|
if ($item['num'] === $actualSpecial) {
|
||||||
|
$rank = $idx + 1;
|
||||||
|
break;
|
||||||
|
}
|
||||||
|
}
|
||||||
|
|
||||||
|
if ($rank > 0 && $rank <= 15) {
|
||||||
|
$hits++;
|
||||||
|
}
|
||||||
|
$ranks[] = $rank;
|
||||||
|
|
||||||
|
// 统计各规律命中情况
|
||||||
|
$actualFineZone = $getFineZone($actualSpecial);
|
||||||
|
$actualBigZone = $getBigZone($actualSpecial);
|
||||||
|
$actualColor = $colorMap[$actualSpecial] ?? '';
|
||||||
|
$actualMinDist = 49;
|
||||||
|
foreach ($lastNormals as $n) {
|
||||||
|
$dist = abs($actualSpecial - $n);
|
||||||
|
if ($dist < $actualMinDist) $actualMinDist = $dist;
|
||||||
|
}
|
||||||
|
$actualTailDiff = abs($actualSpecial % 10 - $normalSum % 10);
|
||||||
|
$actualTailDiff = min($actualTailDiff, 10 - $actualTailDiff);
|
||||||
|
$actualAvgDiff = abs($actualSpecial - $normalAvg);
|
||||||
|
|
||||||
|
if (in_array($actualFineZone, $normalFineZones)) $ruleHits['zone_cover']++;
|
||||||
|
if (in_array($actualBigZone, $top2TransZones)) $ruleHits['trans_match']++;
|
||||||
|
$actualColorInTop2 = false;
|
||||||
|
foreach ($top2Colors as $tc) {
|
||||||
|
if (strpos($actualColor, $tc) !== false) $actualColorInTop2 = true;
|
||||||
|
}
|
||||||
|
if ($actualColorInTop2) $ruleHits['color_top2']++;
|
||||||
|
if ($actualMinDist <= 3) $ruleHits['dist_3']++;
|
||||||
|
if ($actualTailDiff <= 2) $ruleHits['tail_2']++;
|
||||||
|
if ($actualAvgDiff <= 10) $ruleHits['avg_10']++;
|
||||||
|
|
||||||
|
$details[] = [
|
||||||
|
'expect' => (string)$currentRow['expect'],
|
||||||
|
'actual' => $actualSpecial,
|
||||||
|
'rank' => $rank,
|
||||||
|
'hit' => $rank > 0 && $rank <= 15
|
||||||
|
];
|
||||||
|
}
|
||||||
|
|
||||||
|
$totalPeriods = count($ranks);
|
||||||
|
$hitRate = $totalPeriods > 0 ? round($hits / $totalPeriods * 100, 2) : 0;
|
||||||
|
$avgRank = $totalPeriods > 0 ? round(array_sum($ranks) / $totalPeriods, 2) : 0;
|
||||||
|
|
||||||
|
// 计算各规律实际命中率
|
||||||
|
$ruleStats = [];
|
||||||
|
foreach ($ruleHits as $rule => $count) {
|
||||||
|
$ruleStats[$rule] = [
|
||||||
|
'hits' => $count,
|
||||||
|
'rate' => $totalPeriods > 0 ? round($count / $totalPeriods * 100, 2) : 0
|
||||||
|
];
|
||||||
|
}
|
||||||
|
|
||||||
|
return [
|
||||||
|
'periods' => $totalPeriods,
|
||||||
|
'hits' => $hits,
|
||||||
|
'hit_rate' => $hitRate,
|
||||||
|
'avg_rank' => $avgRank,
|
||||||
|
'rule_stats' => $ruleStats,
|
||||||
|
'details' => array_slice($details, 0, $detailLimit)
|
||||||
|
];
|
||||||
|
}
|
||||||
|
|
||||||
|
|
||||||
}
|
}
|
||||||
|
|||||||
@@ -19,6 +19,8 @@
|
|||||||
<a href="javascript:;" class="btn btn-default btn-tailnums" title="{:__('Tail Numbers')}"><i class="fa fa-list-ol"></i> {:__('Tail Numbers')}</a>
|
<a href="javascript:;" class="btn btn-default btn-tailnums" title="{:__('Tail Numbers')}"><i class="fa fa-list-ol"></i> {:__('Tail Numbers')}</a>
|
||||||
<a href="javascript:;" class="btn btn-danger btn-specialhotcold" title="{:__('Special Hot/Cold')}"><i class="fa fa-fire"></i> 特码冷热</a>
|
<a href="javascript:;" class="btn btn-danger btn-specialhotcold" title="{:__('Special Hot/Cold')}"><i class="fa fa-fire"></i> 特码冷热</a>
|
||||||
<a href="javascript:;" class="btn btn-primary btn-numberfilter" title="筛号器"><i class="fa fa-filter"></i> 筛号器</a>
|
<a href="javascript:;" class="btn btn-primary btn-numberfilter" title="筛号器"><i class="fa fa-filter"></i> 筛号器</a>
|
||||||
|
<a href="javascript:;" class="btn btn-success btn-predict" title="智能预测"><i class="fa fa-magic"></i> 智能预测</a>
|
||||||
|
<a href="javascript:;" class="btn btn-info btn-normal-relation" title="正码关联预测"><i class="fa fa-link"></i> 正码关联预测</a>
|
||||||
<!-- <a href="javascript:;" class="btn btn-success btn-dashboard" title="{:__('Dashboard')}"><i class="fa fa-tachometer"></i> {:__('Dashboard')}</a>-->
|
<!-- <a href="javascript:;" class="btn btn-success btn-dashboard" title="{:__('Dashboard')}"><i class="fa fa-tachometer"></i> {:__('Dashboard')}</a>-->
|
||||||
<!-- <a href="javascript:;" class="btn btn-success btn-add {:$auth->check('history/add')?'':'hide'}" title="{:__('Add')}" ><i class="fa fa-plus"></i> {:__('Add')}</a>-->
|
<!-- <a href="javascript:;" class="btn btn-success btn-add {:$auth->check('history/add')?'':'hide'}" title="{:__('Add')}" ><i class="fa fa-plus"></i> {:__('Add')}</a>-->
|
||||||
</div>
|
</div>
|
||||||
|
|||||||
@@ -107,6 +107,11 @@ define(['jquery', 'bootstrap', 'backend', 'table', 'form'], function ($, undefin
|
|||||||
$(document).off('click', '.btn-predict').on('click', '.btn-predict', function () {
|
$(document).off('click', '.btn-predict').on('click', '.btn-predict', function () {
|
||||||
Controller.api.showPredictDialog();
|
Controller.api.showPredictDialog();
|
||||||
});
|
});
|
||||||
|
|
||||||
|
// 正码关联预测按钮事件
|
||||||
|
$(document).off('click', '.btn-normal-relation').on('click', '.btn-normal-relation', function () {
|
||||||
|
Controller.api.showNormalRelationDialog();
|
||||||
|
});
|
||||||
},
|
},
|
||||||
add: function () {
|
add: function () {
|
||||||
Controller.api.bindevent();
|
Controller.api.bindevent();
|
||||||
@@ -1960,6 +1965,204 @@ define(['jquery', 'bootstrap', 'backend', 'table', 'form'], function ($, undefin
|
|||||||
|
|
||||||
html += '</div>';
|
html += '</div>';
|
||||||
$('#predict-result', layero).html(html);
|
$('#predict-result', layero).html(html);
|
||||||
|
},
|
||||||
|
|
||||||
|
/**
|
||||||
|
* 显示正码关联预测弹窗（修正版）
|
||||||
|
* 核心规律：上期正码 → 当期特码
|
||||||
|
*/
|
||||||
|
showNormalRelationDialog: function () {
|
||||||
|
var html = '<div style="padding:20px;">' +
|
||||||
|
'<div style="margin-bottom:15px;background:#e8f5e9;border:1px solid #4caf50;padding:10px;border-radius:6px;">' +
|
||||||
|
' <div style="font-size:13px;font-weight:bold;color:#2e7d32;margin-bottom:8px;"><i class="fa fa-link"></i> 正码关联预测算法（修正版）</div>' +
|
||||||
|
' <div style="font-size:12px;color:#666;">' +
|
||||||
|
' <b>核心逻辑</b>：用<b>上期正码(num1-6)</b>预测<b>当期特码(num7)</b><br>' +
|
||||||
|
' <b>1. 覆盖区间规律（91.44%）</b>：当期特码在上期正码覆盖的细区间内<br>' +
|
||||||
|
' <b>2. 特码区间转移（77.54%）</b>：基于上期特码区间预测当期特码大区间<br>' +
|
||||||
|
' <b>3. 双波色预测（69.52%）</b>：当期特码波色在上期正码前2种主导波色内<br>' +
|
||||||
|
' <b>4. 正码±3距离（59.36%）</b>：当期特码与上期正码某号码距离≤3<br>' +
|
||||||
|
' <b>5. 尾数±2（50%）</b>：上期正码和值尾数与当期特码尾数差≤2<br>' +
|
||||||
|
' <b>6. 平均值±10（41.98%）</b>：当期特码在上期正码平均值±10范围' +
|
||||||
|
' </div>' +
|
||||||
|
'</div>' +
|
||||||
|
'<div class="form-group">' +
|
||||||
|
' <label>回测期数：</label>' +
|
||||||
|
' <input type="number" id="nr-periods" class="form-control" value="100" min="30" max="500" style="width:120px;display:inline-block;">' +
|
||||||
|
' <label style="margin-left:15px;">目标期号（可选）：</label>' +
|
||||||
|
' <input type="text" id="nr-target" class="form-control" placeholder="如2026120" style="width:150px;display:inline-block;">' +
|
||||||
|
' <button class="btn btn-primary" id="btn-nr-query" style="margin-left:10px;"><i class="fa fa-search"></i> 查询</button>' +
|
||||||
|
'</div>' +
|
||||||
|
'<div id="nr-result" style="margin-top:15px;"></div>' +
|
||||||
|
'</div>';
|
||||||
|
|
||||||
|
Layer.open({
|
||||||
|
type: 1,
|
||||||
|
title: '正码关联预测（上期正码→当期特码）',
|
||||||
|
area: ['900px', '750px'],
|
||||||
|
content: html,
|
||||||
|
shadeClose: true,
|
||||||
|
success: function (layero, index) {
|
||||||
|
$('#btn-nr-query', layero).on('click', function () {
|
||||||
|
Controller.api.queryNormalRelation(layero);
|
||||||
|
});
|
||||||
|
// 自动执行一次查询
|
||||||
|
Controller.api.queryNormalRelation(layero);
|
||||||
|
}
|
||||||
|
});
|
||||||
|
},
|
||||||
|
|
||||||
|
/**
|
||||||
|
* 查询正码关联预测
|
||||||
|
*/
|
||||||
|
queryNormalRelation: function (layero) {
|
||||||
|
var $btn = $('#btn-nr-query', layero);
|
||||||
|
$btn.prop('disabled', true);
|
||||||
|
$('#nr-result', layero).html('<div class="text-center"><i class="fa fa-spinner fa-spin"></i> 正在分析...</div>');
|
||||||
|
|
||||||
|
var periods = parseInt($('#nr-periods', layero).val()) || 100;
|
||||||
|
var targetExpect = $('#nr-target', layero).val().trim();
|
||||||
|
|
||||||
|
$.ajax({
|
||||||
|
url: 'history/predictByNormalRelation',
|
||||||
|
type: 'GET',
|
||||||
|
data: { periods: periods, target_expect: targetExpect },
|
||||||
|
dataType: 'json',
|
||||||
|
success: function (ret) {
|
||||||
|
if (ret.code == 1) {
|
||||||
|
Controller.api.renderNormalRelation(ret.data, layero);
|
||||||
|
} else {
|
||||||
|
$('#nr-result', layero).html('<div class="alert alert-danger">' + (ret.msg || '查询失败') + '</div>');
|
||||||
|
}
|
||||||
|
},
|
||||||
|
error: function () {
|
||||||
|
$('#nr-result', layero).html('<div class="alert alert-danger">查询失败</div>');
|
||||||
|
},
|
||||||
|
complete: function () {
|
||||||
|
$btn.prop('disabled', false);
|
||||||
|
}
|
||||||
|
});
|
||||||
|
},
|
||||||
|
|
||||||
|
/**
|
||||||
|
* 渲染正码关联预测结果
|
||||||
|
*/
|
||||||
|
renderNormalRelation: function (data, layero) {
|
||||||
|
if (!data || !data.predictions || data.predictions.length === 0) {
|
||||||
|
$('#nr-result', layero).html('<div class="alert alert-info">无预测结果</div>');
|
||||||
|
return;
|
||||||
|
}
|
||||||
|
|
||||||
|
var analysis = data.analysis || {};
|
||||||
|
var predictions = data.predictions;
|
||||||
|
var hitInfo = data.hit_info;
|
||||||
|
var backtest = data.backtest;
|
||||||
|
|
||||||
|
var html = '';
|
||||||
|
|
||||||
|
// 分析信息
|
||||||
|
html += '<div style="background:#f5f5f5;padding:10px;border-radius:6px;margin-bottom:15px;">';
|
||||||
|
html += '<div style="font-size:13px;font-weight:bold;margin-bottom:8px;"><i class="fa fa-info-circle"></i> 上期开奖信息（预测基准）</div>';
|
||||||
|
html += '<div style="font-size:12px;">';
|
||||||
|
html += '期号：<b>' + analysis.last_expect + '</b> | ';
|
||||||
|
html += '正码：<b>' + analysis.last_normals.join(', ') + '</b> | ';
|
||||||
|
html += '特码：<b>' + analysis.last_special + '</b><br>';
|
||||||
|
html += '正码范围：' + analysis.normal_min + ' ~ ' + analysis.normal_max + ' | ';
|
||||||
|
html += '平均值：' + analysis.normal_avg + ' | ';
|
||||||
|
html += '和值：' + analysis.normal_sum + '<br>';
|
||||||
|
html += '正码波色：<b>' + (analysis.top2_colors || analysis.normal_colors || []).join('/') + '</b> | ';
|
||||||
|
html += '覆盖区间：' + (analysis.normal_fine_zones || analysis.normal_zones || []).map(function(z) {
|
||||||
|
return ['1-10','11-20','21-30','31-40','41-49'][z];
|
||||||
|
}).join(',');
|
||||||
|
html += '</div></div>';
|
||||||
|
|
||||||
|
// 预测号码展示
|
||||||
|
html += '<div style="margin-bottom:15px;">';
|
||||||
|
html += '<div style="font-size:13px;font-weight:bold;margin-bottom:8px;"><i class="fa fa-star"></i> 推荐号码（Top 15）</div>';
|
||||||
|
html += '<div style="display:flex;flex-wrap:wrap;gap:8px;">';
|
||||||
|
|
||||||
|
for (var i = 0; i < predictions.length; i++) {
|
||||||
|
var p = predictions[i];
|
||||||
|
var colorHex = Controller.api.getColorByNum(p.num);
|
||||||
|
var animal = Controller.api.animalMap[p.num] || '';
|
||||||
|
|
||||||
|
var bgColor = '#fff';
|
||||||
|
var borderColor = '1px solid #ddd';
|
||||||
|
if (i < 5) {
|
||||||
|
bgColor = '#fff8e1';
|
||||||
|
borderColor = '2px solid #ffc107';
|
||||||
|
}
|
||||||
|
|
||||||
|
html += '<div style="background:' + bgColor + ';border:' + borderColor + ';padding:8px;border-radius:8px;min-width:90px;text-align:center;">';
|
||||||
|
html += '<span style="display:inline-block;width:36px;height:36px;line-height:36px;border-radius:50%;color:#fff;background:' + colorHex + ';font-weight:bold;font-size:16px;">' + p.num + '</span>';
|
||||||
|
html += '<div style="font-size:10px;color:#666;">' + animal + '</div>';
|
||||||
|
html += '<div style="font-size:11px;color:#2e7d32;font-weight:bold;">得分:' + p.score + '</div>';
|
||||||
|
html += '<div style="font-size:9px;color:#999;">';
|
||||||
|
html += (p.color_in_top2 || p.color_match) ? '✓波色 ' : '';
|
||||||
|
html += '距离' + (p.min_distance || 0);
|
||||||
|
html += '</div>';
|
||||||
|
html += '</div>';
|
||||||
|
}
|
||||||
|
html += '</div></div>';
|
||||||
|
|
||||||
|
// 规律命中情况（如果有回测）
|
||||||
|
if (backtest) {
|
||||||
|
html += '<div style="background:#e3f2fd;padding:10px;border-radius:6px;margin-bottom:15px;">';
|
||||||
|
html += '<div style="font-size:13px;font-weight:bold;margin-bottom:8px;"><i class="fa fa-chart-line"></i> 回测验证（最近' + backtest.periods + '期）</div>';
|
||||||
|
html += '<div style="font-size:12px;">';
|
||||||
|
html += '命中率（Top15内）：<b style="color:#2e7d32;">' + backtest.hit_rate + '%</b> (' + backtest.hits + '/' + backtest.periods + ')<br>';
|
||||||
|
html += '平均排名：<b>' + backtest.avg_rank + '</b> / 49<br>';
|
||||||
|
html += '<span style="color:#666;font-size:11px;">注：命中率越高越好，平均排名越低越好</span>';
|
||||||
|
html += '</div>';
|
||||||
|
|
||||||
|
// 显示前50期命中详情表格
|
||||||
|
if (backtest.details && backtest.details.length > 0) {
|
||||||
|
html += '<div style="margin-top:10px;max-height:300px;overflow-y:auto;">';
|
||||||
|
html += '<table class="table table-striped table-bordered" style="font-size:11px;">';
|
||||||
|
html += '<thead><tr><th>期号</th><th>特码</th><th>排名</th><th>命中</th></tr></thead>';
|
||||||
|
html += '<tbody>';
|
||||||
|
for (var d = 0; d < backtest.details.length; d++) {
|
||||||
|
var det = backtest.details[d];
|
||||||
|
var hitBadge = det.hit ? '<span style="color:#2e7d32;font-weight:bold;">✓</span>' : '<span style="color:#c62828;">✗</span>';
|
||||||
|
var rankColor = det.rank <= 5 ? '#2e7d32' : (det.rank <= 15 ? '#ff9800' : '#c62828');
|
||||||
|
html += '<tr>';
|
||||||
|
html += '<td>' + det.expect + '</td>';
|
||||||
|
html += '<td>' + det.actual + '</td>';
|
||||||
|
html += '<td><b style="color:' + rankColor + ';">' + det.rank + '</b></td>';
|
||||||
|
html += '<td>' + hitBadge + '</td>';
|
||||||
|
html += '</tr>';
|
||||||
|
}
|
||||||
|
html += '</tbody></table>';
|
||||||
|
html += '</div>';
|
||||||
|
}
|
||||||
|
html += '</div>';
|
||||||
|
}
|
||||||
|
|
||||||
|
// 实际命中情况（如果有目标期号）
|
||||||
|
if (hitInfo) {
|
||||||
|
var hitBg = hitInfo.hit ? '#e8f5e9' : '#ffebee';
|
||||||
|
var hitColor = hitInfo.hit ? '#2e7d32' : '#c62828';
|
||||||
|
html += '<div style="background:' + hitBg + ';padding:10px;border-radius:6px;margin-bottom:15px;">';
|
||||||
|
html += '<div style="font-size:13px;font-weight:bold;color:' + hitColor + ';">';
|
||||||
|
html += hitInfo.hit ? '✓ 命中！排名：' + hitInfo.rank_in_top + ' / 15' : '✗ 未命中，排名：' + hitInfo.rank_in_all + ' / 49';
|
||||||
|
html += '</div>';
|
||||||
|
html += '<div style="font-size:12px;">实际特码：<b>' + hitInfo.actual_num + '</b> (' + hitInfo.actual_color + '/' + hitInfo.actual_animal + ') 期号：' + hitInfo.actual_expect + '</div>';
|
||||||
|
html += '</div>';
|
||||||
|
}
|
||||||
|
|
||||||
|
// 规律说明表
|
||||||
|
if (analysis.rules) {
|
||||||
|
html += '<div style="margin-top:15px;">';
|
||||||
|
html += '<div style="font-size:13px;font-weight:bold;margin-bottom:8px;"><i class="fa fa-table"></i> 规律命中率表</div>';
|
||||||
|
html += '<table class="table table-bordered" style="font-size:12px;">';
|
||||||
|
html += '<tr><th>规律名称</th><th>命中率</th><th>说明</th></tr>';
|
||||||
|
for (var r = 0; r < analysis.rules.length; r++) {
|
||||||
|
var rule = analysis.rules[r];
|
||||||
|
html += '<tr><td>' + rule.name + '</td><td><b style="color:#2e7d32;">' + rule.rate + '</b></td><td>' + rule.desc + '</td></tr>';
|
||||||
|
}
|
||||||
|
html += '</table></div>';
|
||||||
|
}
|
||||||
|
|
||||||
|
$('#nr-result', layero).html(html);
|
||||||
}
|
}
|
||||||
}
|
}
|
||||||
};
|
};
|
||||||
|
|||||||
Reference in New Issue
Block a user