916117771
8b2590c5b5
- 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
589 lines
23 KiB
Python
589 lines
23 KiB
Python
#!/usr/bin/env python3
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# -*- coding: utf-8 -*-
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"""
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上期正码与当期特码关联规律分析脚本
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分析维度：
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1. 上期正码平均值与下期特码的差值分布
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2. 上期正码范围[min,max]与下期特码的关系
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3. 上期正码与下期特码的最短距离分布
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4. 上期正码和值尾数与下期特码尾数的关系
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5. 上期正码覆盖区间与下期特码所在区间的关系
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6. 上期正码波色分布与下期特码波色的关系
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7. 上期特码与下期特码的转移关系
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"""
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import re
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from collections import defaultdict
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from pathlib import Path
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# 波色映射表
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COLOR_MAP = {
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1: '红', 2: '红', 3: '蓝', 4: '蓝', 5: '绿', 6: '绿',
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7: '红', 8: '红', 9: '蓝', 10: '蓝', 11: '绿', 12: '红',
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13: '红', 14: '蓝', 15: '蓝', 16: '绿', 17: '绿', 18: '红',
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19: '红', 20: '蓝', 21: '绿', 22: '绿', 23: '红', 24: '红',
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25: '蓝', 26: '蓝', 27: '绿', 28: '绿', 29: '红', 30: '红',
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31: '蓝', 32: '绿', 33: '绿', 34: '红', 35: '红', 36: '蓝',
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37: '蓝', 38: '绿', 39: '绿', 40: '红', 41: '蓝', 42: '蓝',
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43: '绿', 44: '绿', 45: '红', 46: '红', 47: '蓝', 48: '蓝',
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49: '绿'
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}
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def get_range(num):
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"""获取数字所在的区间"""
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if 1 <= num <= 10:
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return '小号(1-10)'
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elif 11 <= num <= 30:
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return '中号(11-30)'
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else:
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return '大号(31-49)'
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def get_tail(num):
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"""获取数字的尾数"""
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return num % 10
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def parse_sql_file(filepath):
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"""解析SQL文件，提取历史数据"""
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content = Path(filepath).read_text(encoding='utf-8')
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# 解析INSERT语句
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pattern = r"INSERT INTO `fa_history` VALUES \((\d+), (\d+), (\d+), (\d+), (\d+), (\d+), (\d+), (\d+), '([^']+)'"
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matches = re.findall(pattern, content)
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history = []
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for match in matches:
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history.append({
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'expect': int(match[0]),
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'num1': int(match[1]),
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'num2': int(match[2]),
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'num3': int(match[3]),
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'num4': int(match[4]),
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'num5': int(match[5]),
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'num6': int(match[6]),
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'num7': int(match[7]),
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'openTime': match[8]
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})
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# 按期号排序
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history.sort(key=lambda x: x['expect'])
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return history
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def analyze():
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"""主分析函数"""
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# 解析数据
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history = parse_sql_file(r'C:\Users\91611\Desktop\fa_history.sql')
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print("=== 数据概览 ===")
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print(f"总期数: {len(history)}")
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print(f"期号范围: {history[0]['expect']} - {history[-1]['expect']}")
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print()
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total_predictions = len(history) - 1
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# ============ 维度1: 上期正码平均值与下期特码的差值分布 ============
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print("=== 维度1: 上期正码平均值与下期特码的差值分布 ===")
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avg_diffs = []
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hit_count_avg = 0
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prediction_range = 10
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for i in range(len(history) - 1):
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current = history[i]
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next_record = history[i + 1]
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# 计算上期正码平均值
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avg = (current['num1'] + current['num2'] + current['num3'] +
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current['num4'] + current['num5'] + current['num6']) / 6
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# 与下期特码的差值
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diff = next_record['num7'] - avg
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avg_diffs.append(round(diff))
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# 预测范围：平均值±10
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predict_min = max(1, int(avg) - prediction_range)
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predict_max = min(49, int(avg) + prediction_range)
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if predict_min <= next_record['num7'] <= predict_max:
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hit_count_avg += 1
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# 统计差值分布
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diff_stats = defaultdict(int)
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for d in avg_diffs:
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diff_stats[d] += 1
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print("差值分布（差值=下期特码-上期正码平均值）:")
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for diff in sorted(diff_stats.keys()):
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count = diff_stats[diff]
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percent = round(count / total_predictions * 100, 2)
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print(f" 差值 {diff}: {count}次 ({percent}%)")
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# 差值范围分布
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print("\n差值范围分布:")
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ranges = [
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('[-40,-20]', lambda d: -40 <= d < -20),
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('[-20,-10]', lambda d: -20 <= d < -10),
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('[-10,0]', lambda d: -10 <= d < 0),
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('[0,10]', lambda d: 0 <= d < 10),
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('[10,20]', lambda d: 10 <= d < 20),
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('[20,40]', lambda d: 20 <= d <= 40)
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]
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for range_name, condition in ranges:
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count = sum(1 for d in avg_diffs if condition(d))
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percent = round(count / total_predictions * 100, 2)
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print(f" {range_name}: {count}次 ({percent}%)")
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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})")
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print()
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# ============ 维度2: 上期正码范围[min,max]与下期特码的关系 ============
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print("=== 维度2: 上期正码范围[min,max]与下期特码的关系 ===")
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in_range_count = 0
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below_range_count = 0
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above_range_count = 0
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range_width_stats = defaultdict(lambda: {'在范围内': 0, '低于范围': 0, '高于范围': 0})
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for i in range(len(history) - 1):
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current = history[i]
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next_record = history[i + 1]
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nums = [current['num1'], current['num2'], current['num3'],
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current['num4'], current['num5'], current['num6']]
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min_num = min(nums)
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max_num = max(nums)
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range_width = max_num - min_num
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if min_num <= next_record['num7'] <= max_num:
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in_range_count += 1
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relation = '在范围内'
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elif next_record['num7'] < min_num:
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below_range_count += 1
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relation = '低于范围'
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else:
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above_range_count += 1
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relation = '高于范围'
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range_width_stats[range_width][relation] += 1
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print("下期特码位置分布:")
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print(f" 在上期正码范围内: {in_range_count}次 ({round(in_range_count / total_predictions * 100, 2)}%)")
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print(f" 低于上期正码范围: {below_range_count}次 ({round(below_range_count / total_predictions * 100, 2)}%)")
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print(f" 高于上期正码范围: {above_range_count}次 ({round(above_range_count / total_predictions * 100, 2)}%)")
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print("\n范围宽度与特码位置关系:")
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for width in sorted(range_width_stats.keys()):
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stats = range_width_stats[width]
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width_total = sum(stats.values())
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print(f" 范围宽度{width} (共{width_total}期):")
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for relation, count in stats.items():
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percent = round(count / width_total * 100, 2)
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print(f" {relation}: {count}次 ({percent}%)")
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avg_range_width = sum(max([h['num1'], h['num2'], h['num3'], h['num4'], h['num5'], h['num6']]) -
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min([h['num1'], h['num2'], h['num3'], h['num4'], h['num5'], h['num6']])
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for h in history[:-1]) / total_predictions
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print(f"\n平均范围宽度: {round(avg_range_width, 2)}")
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print()
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# ============ 维度3: 上期正码与下期特码的最短距离分布 ============
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print("=== 维度3: 上期正码与下期特码的最短距离分布 ===")
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min_distances = []
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prediction_hit_dist = 0
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for i in range(len(history) - 1):
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current = history[i]
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next_record = history[i + 1]
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nums = [current['num1'], current['num2'], current['num3'],
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current['num4'], current['num5'], current['num6']]
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min_dist = min(abs(next_record['num7'] - num) for num in nums)
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min_distances.append(min_dist)
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# 预测范围：每个正码±3
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predicted = set()
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for num in nums:
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for p in range(max(1, num - 3), min(50, num + 4)):
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predicted.add(p)
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if next_record['num7'] in predicted:
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prediction_hit_dist += 1
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dist_stats = defaultdict(int)
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for d in min_distances:
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dist_stats[d] += 1
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print("最短距离分布:")
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for dist in sorted(dist_stats.keys()):
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count = dist_stats[dist]
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percent = round(count / total_predictions * 100, 2)
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print(f" 距离 {dist}: {count}次 ({percent}%)")
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close_hit = sum(1 for d in min_distances if d <= 5)
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very_close_hit = sum(1 for d in min_distances if d <= 3)
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print(f"\n最短距离≤5的比例: {round(close_hit / total_predictions * 100, 2)}% ({close_hit}/{total_predictions})")
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print(f"最短距离≤3的比例: {round(very_close_hit / total_predictions * 100, 2)}% ({very_close_hit}/{total_predictions})")
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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})")
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# 计算预测范围大小
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nums_sample = [history[0]['num1'], history[0]['num2'], history[0]['num3'],
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history[0]['num4'], history[0]['num5'], history[0]['num6']]
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predicted_sample = set()
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for num in nums_sample:
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for p in range(max(1, num - 3), min(50, num + 4)):
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predicted_sample.add(p)
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print(f"预测范围大小: 约{len(predicted_sample)}个数字")
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print()
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# ============ 维度4: 上期正码和值尾数与下期特码尾数的关系 ============
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print("=== 维度4: 上期正码和值尾数与下期特码尾数的关系 ===")
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sum_tail_relations = defaultdict(lambda: defaultdict(int))
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tail_same_count = 0
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tail_diff1_count = 0
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tail_diff2_count = 0
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tail_prediction_hit = 0
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for i in range(len(history) - 1):
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current = history[i]
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next_record = history[i + 1]
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sum_val = current['num1'] + current['num2'] + current['num3'] + \
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current['num4'] + current['num5'] + current['num6']
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sum_tail = get_tail(sum_val)
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next_tail = get_tail(next_record['num7'])
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sum_tail_relations[sum_tail][next_tail] += 1
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# 计算尾数差异（考虑环形）
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tail_diff = abs(sum_tail - next_tail)
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if tail_diff > 5:
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tail_diff = 10 - tail_diff
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if tail_diff == 0:
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tail_same_count += 1
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elif tail_diff == 1:
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tail_diff1_count += 1
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elif tail_diff == 2:
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tail_diff2_count += 1
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# 预测：和值尾数±2范围内的尾数
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predict_tails = {sum_tail, (sum_tail + 1) % 10, (sum_tail - 1 + 10) % 10,
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(sum_tail + 2) % 10, (sum_tail - 2 + 10) % 10}
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if next_tail in predict_tails:
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tail_prediction_hit += 1
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print("尾数关系分布:")
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print(f" 尾数相同: {tail_same_count}次 ({round(tail_same_count / total_predictions * 100, 2)}%)")
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print(f" 尾数相差1: {tail_diff1_count}次 ({round(tail_diff1_count / total_predictions * 100, 2)}%)")
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print(f" 尾数相差2: {tail_diff2_count}次 ({round(tail_diff2_count / total_predictions * 100, 2)}%)")
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print("\n上期和值尾数→下期特码尾数转移矩阵:")
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for sum_tail in range(10):
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if sum_tail in sum_tail_relations:
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stats = sum_tail_relations[sum_tail]
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max_tail = max(stats, key=stats.get)
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max_count = stats[max_tail]
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total = sum(stats.values())
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percent = round(max_count / total * 100, 2)
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others = [f"{t}({c})" for t, c in stats.items() if t != max_tail]
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print(f" 和值尾数{sum_tail} → 最可能尾数{max_tail} ({max_count}次, {percent}%), 其他: {', '.join(others)}")
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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})")
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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")
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print()
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# ============ 维度5: 上期正码覆盖区间与下期特码所在区间的关系 ============
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print("=== 维度5: 上期正码覆盖区间与下期特码所在区间的关系 ===")
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range_cover_stats = defaultdict(lambda: defaultdict(int))
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range_transfer_stats = defaultdict(lambda: defaultdict(int))
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hit_in_covered_range = 0
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for i in range(len(history) - 1):
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current = history[i]
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next_record = history[i + 1]
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nums = [current['num1'], current['num2'], current['num3'],
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current['num4'], current['num5'], current['num6']]
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covers = set(get_range(num) for num in nums)
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cover_str = '+'.join(sorted(covers))
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next_range = get_range(next_record['num7'])
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range_cover_stats[cover_str][next_range] += 1
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range_transfer_stats[len(covers)][next_range] += 1
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if next_range in covers:
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hit_in_covered_range += 1
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print("上期正码覆盖区间→下期特码区间转移:")
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for cover, next_stats in sorted(range_cover_stats.items(), key=lambda x: -sum(x[1].values())):
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total_cover = sum(next_stats.values())
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print(f" {cover} (共{total_cover}期):")
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for next_range, count in next_stats.items():
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percent = round(count / total_cover * 100, 2)
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print(f" → {next_range}: {count}次 ({percent}%)")
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print("\n上期正码覆盖区间数量与下期特码分布:")
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for cover_count, stats in sorted(range_transfer_stats.items()):
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total_cover = sum(stats.values())
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print(f" 覆盖{cover_count}个区间 (共{total_cover}期):")
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for range_name, count in stats.items():
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percent = round(count / total_cover * 100, 2)
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print(f" {range_name}: {count}次 ({percent}%)")
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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})")
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print()
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# ============ 维度6: 上期正码波色分布与下期特码波色的关系 ============
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print("=== 维度6: 上期正码波色分布与下期特码波色的关系 ===")
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color_distribution_stats = defaultdict(lambda: defaultdict(int))
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dominant_color_stats = defaultdict(lambda: defaultdict(int))
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dominant_prediction_hit = 0
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expanded_color_prediction_hit = 0
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for i in range(len(history) - 1):
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current = history[i]
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next_record = history[i + 1]
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nums = [current['num1'], current['num2'], current['num3'],
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current['num4'], current['num5'], current['num6']]
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colors = defaultdict(int)
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for num in nums:
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colors[COLOR_MAP[num]] += 1
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color_str = f"红{colors['红']}蓝{colors['蓝']}绿{colors['绿']}"
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next_color = COLOR_MAP[next_record['num7']]
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color_distribution_stats[color_str][next_color] += 1
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# 主导波色
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dominant_color = max(colors, key=colors.get)
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dominant_color_stats[dominant_color][next_color] += 1
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if dominant_color == next_color:
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dominant_prediction_hit += 1
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# 扩展到两种波色
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top2_colors = sorted(colors, key=colors.get, reverse=True)[:2]
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if next_color in top2_colors:
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expanded_color_prediction_hit += 1
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print("上期正码波色分布→下期特码波色转移 (出现5次以上的):")
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sorted_color_dist = sorted(color_distribution_stats.items(),
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key=lambda x: -sum(x[1].values()))
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for dist, next_stats in sorted_color_dist:
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total_dist = sum(next_stats.values())
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if total_dist >= 5:
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print(f" {dist} (共{total_dist}期):")
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for next_color, count in next_stats.items():
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percent = round(count / total_dist * 100, 2)
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print(f" → {next_color}: {count}次 ({percent}%)")
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|
|
|
print("\n上期主导波色→下期特码波色转移:")
|
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for dominant, next_stats in dominant_color_stats.items():
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|
total_dom = sum(next_stats.values())
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|
print(f" 主导{dominant} (共{total_dom}期):")
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for next_color, count in next_stats.items():
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percent = round(count / total_dom * 100, 2)
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print(f" → {next_color}: {count}次 ({percent}%)")
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|
|
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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})")
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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})")
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print()
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|
|
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# ============ 维度7: 上期特码与下期特码的转移关系 ============
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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))
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|
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() |