feat: 彩票智能分析技能 v1.0 - 双色球/大乐透集成学习模型
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#!/usr/bin/env python3
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# -*- coding: utf-8 -*-
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"""
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增强版彩票智能分析系统 v2.0
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===============================
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核心升级：
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1. 采集近200期数据（原100期）
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2. AI逐期回滚推演：从第200期开始->模拟分析机选->比对实开->回滚优化
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3. 方法论自优化：每轮回滚后总结方法论并迭代升级
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4. 目标：胜出率(概率占比) >= 51%
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用法:
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python enhanced_lottery.py --action full_report --lottery ssq --count 5
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python enhanced_lottery.py --action rollback --lottery ssq
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python enhanced_lottery.py --action self_evolve --lottery ssq --iterations 10
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"""
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import json, os, sys, math, random, copy, datetime
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from collections import Counter
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BASE_DIR = os.path.dirname(os.path.abspath(__file__))
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DATA_DIR = os.path.join(BASE_DIR, "data")
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os.makedirs(DATA_DIR, exist_ok=True)
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LOTTERY_RULES = {
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"ssq": {
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"name": "双色球",
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"red_count": 6, "red_max": 33, "blue_count": 1, "blue_max": 16,
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"draw_days": [2, 4, 7], "draw_time": "21:15",
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},
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"dlt": {
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"name": "大乐透",
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"red_count": 5, "red_max": 35, "blue_count": 2, "blue_max": 12,
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"draw_days": [1, 3, 6], "draw_time": "20:25",
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}
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}
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DEFAULT_WEIGHTS = {
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"frequency": 0.25,
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"hot_number": 0.20,
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"missing": 0.15,
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"tail": 0.10,
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"zone": 0.08,
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"odd_even": 0.06,
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"big_small": 0.05,
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"sum_value": 0.04,
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"probability": 0.04,
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"consecutive": 0.03,
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}
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# ============================================================
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# 增强版分析引擎（200期 + 10维度深度分析）
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# ============================================================
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def _normalize_rec(rec):
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"""标准化记录，兼容新旧格式（blue vs blues）"""
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if "blues" not in rec or not rec["blues"]:
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if "blue" in rec:
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rec["blues"] = rec["blue"] if isinstance(rec["blue"], list) else [rec["blue"]]
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return rec
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def _get_blues(rec):
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"""获取blue值，兼容新旧格式"""
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if "blues" in rec and rec["blues"]:
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raw = rec["blues"]
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elif "blue" in rec:
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raw = rec["blue"]
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else:
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raw = []
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return raw if isinstance(raw, list) else [raw]
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class EnhancedAnalyzer:
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"""使用近200期数据，10维度权重可调，输出结构化分析结果"""
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def __init__(self, data, lt, weights=None):
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self.lt = lt
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self.rule = LOTTERY_RULES[lt]
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# 标准化并取近200期
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normed = [_normalize_rec(d) for d in data]
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self.data = sorted(normed, key=lambda x: int(x["issue"]), reverse=True)[:200]
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self.weights = weights if weights else copy.deepcopy(DEFAULT_WEIGHTS)
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self.rmax = self.rule["red_max"]
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self.bmax = self.rule["blue_max"]
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self.rcnt = self.rule["red_count"]
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self.bcnt = self.rule["blue_count"]
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self.mid = self.rmax / 2
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def analyze(self):
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"""完整分析，返回结构化分析结果"""
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if not self.data:
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return None
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# 1. 频率分析
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all_r, all_b = [], []
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for rec in self.data:
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all_r.extend(int(x) for x in rec["reds"])
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bl = _get_blues(rec)
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all_b.extend(int(x) for x in bl)
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rf = Counter(all_r)
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bf = Counter(all_b)
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max_rf = max(rf.values()) or 1
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max_bf = max(bf.values()) or 1
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rf_score = {i: rf.get(i, 0) / max_rf for i in range(1, self.rmax + 1)}
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bf_score = {i: bf.get(i, 0) / max_bf for i in range(1, self.bmax + 1)}
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# 2. 热号分析（最近20期）
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hot = self.data[:20]
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hot_r = [int(x) for r in hot for x in r["reds"]]
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hot_rf = Counter(hot_r)
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max_hr = max(hot_rf.values()) or 1
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hot_score = {i: hot_rf.get(i, 0) / max_hr for i in range(1, self.rmax + 1)}
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# 3. 遗漏值分析
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r_miss = {}
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for n in range(1, self.rmax + 1):
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cnt = 0
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for r in self.data:
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if n in [int(x) for x in r["reds"]]:
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break
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cnt += 1
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r_miss[n] = cnt
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max_rm = max(r_miss.values()) or 1
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rm_score = {n: math.sin(m / max_rm * math.pi) * 0.8 + 0.2 for n, m in r_miss.items()}
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b_miss = {}
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for n in range(1, self.bmax + 1):
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cnt = 0
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for r in self.data:
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ns = [int(x) for x in _get_blues(r)]
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if n in ns:
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break
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cnt += 1
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b_miss[n] = cnt
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max_bm = max(b_miss.values()) or 1
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bm_score = {n: math.sin(m / max_bm * math.pi) * 0.8 + 0.2 for n, m in b_miss.items()}
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# 4. 奇偶模式
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oe_counter = Counter()
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for rec in self.data:
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ns = [int(x) for x in rec["reds"]]
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odd_count = sum(1 for n in ns if n % 2 == 1)
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oe_counter[f"{odd_count}:{self.rcnt - odd_count}"] += 1
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most_oe = oe_counter.most_common(1)[0][0] if oe_counter else "3:3"
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# 5. 大小模式
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bs_counter = Counter()
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for rec in self.data:
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ns = [int(x) for x in rec["reds"]]
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big_count = sum(1 for n in ns if n > self.mid)
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bs_counter[f"{big_count}:{self.rcnt - big_count}"] += 1
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most_bs = bs_counter.most_common(1)[0][0] if bs_counter else "3:3"
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# 6. 和值分析
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sums = [sum(int(x) for x in r["reds"]) for r in self.data]
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avg_sum = sum(sums) / len(sums) if sums else 0
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std_sum = math.sqrt(sum((s - avg_sum) ** 2 for s in sums) / len(sums)) if sums else 0
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# 7. 尾数分布
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tail_counter = Counter()
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for rec in self.data:
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for ns in rec["reds"]:
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tail_counter[int(ns) % 10] += 1
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max_tf = max(tail_counter.values()) or 1
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tail_score = {t: tail_counter.get(t, 0) / max_tf for t in range(10)}
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# 8. 区间分布
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zones = [(1, 11), (12, 22), (23, self.rmax)] if self.lt == "ssq" else [(1, 12), (13, 24), (25, self.rmax)]
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zone_balance = []
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for zs, ze in zones:
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count = sum(1 for r in self.data for n in [int(x) for x in r["reds"]] if zs <= n <= ze)
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expected = len(self.data) * self.rcnt / len(zones)
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zone_balance.append(count / expected if expected else 1.0)
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# 9. 连号分析
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consec_counter = Counter()
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for rec in self.data:
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ns = sorted(int(x) for x in rec["reds"])
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consec = sum(1 for i in range(len(ns) - 1) if ns[i + 1] - ns[i] == 1)
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consec_counter[consec] += 1
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most_consec = consec_counter.most_common(1)[0][0] if consec_counter else 0
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# 10. 综合评分
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r_comp = {}
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for n in range(1, self.rmax + 1):
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s = rf_score.get(n, 0) * self.weights["frequency"]
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s += hot_score.get(n, 0) * self.weights["hot_number"]
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s += rm_score.get(n, 0.5) * self.weights["missing"]
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s += tail_score.get(n % 10, 0.5) * self.weights["tail"]
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for i, (zs, ze) in enumerate(zones):
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if zs <= n <= ze and zone_balance[i] < 0.85:
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s += 0.10
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break
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r_comp[n] = round(s, 4)
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# 蓝球复合评分
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recent_blue = []
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for r in self.data[:20]:
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recent_blue.extend(int(x) for x in (r["blues"] if isinstance(r["blues"], list) else [r["blues"]]))
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rbf = Counter(recent_blue)
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max_rbf = max(rbf.values()) or 1
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b_comp = {}
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for n in range(1, self.bmax + 1):
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s = bf_score.get(n, 0) * 0.35 + bm_score.get(n, 0.5) * 0.40 + (rbf.get(n, 0) / max_rbf) * 0.25
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b_comp[n] = round(s, 4)
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result = {
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"data_range": {
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"total": len(self.data),
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"start": self.data[-1]["issue"],
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"end": self.data[0]["issue"]
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},
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"red_composite": r_comp,
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"blue_composite": b_comp,
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"patterns": {
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"most_oe": most_oe,
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"most_bs": most_bs,
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"avg_sum": round(avg_sum, 1),
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"std_sum": round(std_sum, 1),
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"sum_range": [round(avg_sum - std_sum, 1), round(avg_sum + std_sum, 1)],
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"zone_balance": {f"Z{i+1}": round(zb, 3) for i, zb in enumerate(zone_balance)},
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"most_consec": most_consec,
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},
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"weights_used": copy.deepcopy(self.weights),
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}
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return result
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def recommend(self, analysis, count=5):
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"""基于分析结果生成推荐号码"""
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if not analysis:
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return []
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r_comp = analysis["red_composite"]
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b_comp = analysis["blue_composite"]
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pat = analysis["patterns"]
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sorted_r = sorted(r_comp.keys(), key=lambda n: r_comp[n], reverse=True)
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sorted_b = sorted(b_comp.keys(), key=lambda n: b_comp[n], reverse=True)
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target_oe = [int(x) for x in pat["most_oe"].split(":")]
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target_bs = [int(x) for x in pat["most_bs"].split(":")]
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sum_min, sum_max = pat["sum_range"]
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seed = int(datetime.datetime.now().timestamp() * 1000) % 1000000
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rng = random.Random(seed)
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results = []
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for _ in range(count * 3):
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top_r = sorted_r[:20]
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weights_r = [max(r_comp[n], 0) for n in top_r]
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total_w = sum(weights_r)
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if total_w <= 0:
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weights_r = [1] * len(top_r)
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total_w = len(top_r)
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probs_r = [w / total_w for w in weights_r]
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reds = set()
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attempts = 0
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while len(reds) < self.rcnt and attempts < 100:
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n = rng.choices(top_r, weights=probs_r, k=1)[0]
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reds.add(n)
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attempts += 1
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if len(reds) < self.rcnt:
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remaining = [n for n in range(1, self.rmax + 1) if n not in reds]
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rng.shuffle(remaining)
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reds.update(remaining[:self.rcnt - len(reds)])
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reds = sorted(reds)
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top_b = sorted_b[:8]
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weights_b = [max(b_comp[n], 0) for n in top_b]
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total_bw = sum(weights_b) or 1
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probs_b = [w / total_bw for w in weights_b]
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if self.bcnt > 1:
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blues = list(rng.choices(top_b, weights=probs_b, k=self.bcnt))
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# 确保后区号码不重复
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while len(set(blues)) < len(blues):
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for j in range(len(blues)):
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if blues.count(blues[j]) > 1:
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remaining = [n for n in range(1, self.bmax + 1) if n not in blues[:j] + blues[j+1:]]
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blues[j] = rng.choice(remaining)
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else:
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blues = list(rng.choices(top_b, weights=probs_b, k=self.bcnt))
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score = sum(r_comp.get(n, 0) for n in reds) / self.rcnt
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score += sum(b_comp.get(n, 0) for n in blues) / self.bcnt
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score /= 2
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oe = sum(1 for n in reds if n % 2 == 1)
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bs = sum(1 for n in reds if n > self.mid)
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total_sum = sum(reds)
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bonus = 0
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if oe == target_oe[0]:
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bonus += 0.05
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if bs == target_bs[0]:
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bonus += 0.05
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if sum_min <= total_sum <= sum_max:
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bonus += 0.05
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results.append({
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"reds": reds, "blues": blues,
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"score": round(score + bonus, 4),
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"oe": f"{oe}:{self.rcnt - oe}",
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"bs": f"{bs}:{self.rcnt - bs}",
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"sum": total_sum,
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})
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seen = set()
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unique = []
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for r in sorted(results, key=lambda x: x["score"], reverse=True):
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key = tuple(r["reds"]) + tuple(r["blues"])
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if key not in seen:
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seen.add(key)
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unique.append(r)
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return unique[:count]
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# ============================================================
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# AI 逐期回滚推演引擎
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# ============================================================
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class AIRollbackEngine:
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"""
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AI逐期回滚推演：
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从第200期开始 -> 使用当时可用的历史数据进行分析 -> 生成推荐
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-> 与实开号码比对 -> 记录命中率 -> 自动调整权重
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-> 进入下一期... 直至最新一期
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"""
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def __init__(self, lt, data, initial_weights=None):
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self.lt = lt
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self.rule = LOTTERY_RULES[lt]
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# 标准化所有记录（兼容 blue/blues 混用）
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normed = [_normalize_rec(d) for d in data]
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self.all_data = sorted(normed, key=lambda x: int(x["issue"])) # 从旧到新
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self.weights = initial_weights if initial_weights else copy.deepcopy(DEFAULT_WEIGHTS)
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self.rollback_history = []
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self.methodology_log = []
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def run(self):
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"""执行完整回滚推演：从第200期开始逐期推演至最新一期"""
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total_periods = len(self.all_data)
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if total_periods < 200:
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return {"error": f"数据不足200期，当前仅{total_periods}期", "available": total_periods}
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start_idx = 199 # 第200期（0-based索引）
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total_simulations = 0
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total_red_hits = 0
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total_blue_hits = 0
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possible_red = 0
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possible_blue = 0
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methodology_snapshots = []
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print(f"{'='*60}")
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print(f" AI逐期回滚推演引擎 - {self.rule['name']}")
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print(f" 数据总量: {total_periods}期 | 推演范围: 第200期 ~ 第{total_periods}期")
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print(f" 推演总轮次: {total_periods - start_idx} 期")
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print(f"{'='*60}")
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for i in range(start_idx, total_periods):
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current_issue = self.all_data[i]["issue"]
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historical = self.all_data[:i]
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actual = self.all_data[i]
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actual_reds = [int(x) for x in actual["reds"]]
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actual_blues = [int(x) for x in _get_blues(actual)]
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analyzer = EnhancedAnalyzer(historical, self.lt, self.weights)
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analysis = analyzer.analyze()
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if not analysis:
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continue
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recommendations = analyzer.recommend(analysis, count=5)
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if not recommendations:
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continue
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best_pick = recommendations[0]
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pred_reds = best_pick["reds"]
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pred_blues = best_pick["blues"]
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red_hit = len(set(pred_reds) & set(actual_reds))
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blue_hit = len(set(pred_blues) & set(actual_blues))
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total_red_hits += red_hit
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total_blue_hits += blue_hit
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possible_red += self.rule["red_count"]
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possible_blue += self.rule["blue_count"]
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total_simulations += 1
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self.rollback_history.append({
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"issue": current_issue,
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"iteration": total_simulations,
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"pred_reds": pred_reds,
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"pred_blues": pred_blues,
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"actual_reds": actual_reds,
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"actual_blues": actual_blues,
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"red_hit": red_hit,
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"blue_hit": blue_hit,
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"score": best_pick["score"],
|
||||
"weights_at_time": copy.deepcopy(self.weights),
|
||||
})
|
||||
|
||||
self._optimize_weights(red_hit, blue_hit, pred_reds, actual_reds)
|
||||
|
||||
if total_simulations % 5 == 0:
|
||||
red_acc = total_red_hits / possible_red * 100 if possible_red else 0
|
||||
blue_acc = total_blue_hits / possible_blue * 100 if possible_blue else 0
|
||||
methodology_snapshots.append({
|
||||
"iteration": total_simulations,
|
||||
"issue": current_issue,
|
||||
"weights": copy.deepcopy(self.weights),
|
||||
"red_accuracy": round(red_acc, 2),
|
||||
"blue_accuracy": round(blue_acc, 2),
|
||||
})
|
||||
|
||||
if total_simulations % 10 == 0 or total_simulations == 1:
|
||||
red_acc = total_red_hits / possible_red * 100 if possible_red else 0
|
||||
blue_acc = total_blue_hits / possible_blue * 100 if possible_blue else 0
|
||||
overall_acc = (total_red_hits + total_blue_hits) / (possible_red + possible_blue) * 100
|
||||
print(f" [推演 {total_simulations:3d}] 期{current_issue} "
|
||||
f"红{red_hit}/{self.rule['red_count']} 蓝{blue_hit}/{self.rule['blue_count']} "
|
||||
f"| 累计 红{red_acc:.1f}% 蓝{blue_acc:.1f}% 综合{overall_acc:.1f}%")
|
||||
|
||||
# 最终统计
|
||||
avg_red = total_red_hits / possible_red * 100 if possible_red else 0
|
||||
avg_blue = total_blue_hits / possible_blue * 100 if possible_blue else 0
|
||||
overall_hits = total_red_hits + total_blue_hits
|
||||
overall_possible = possible_red + possible_blue
|
||||
overall_acc = overall_hits / overall_possible * 100 if overall_possible else 0
|
||||
win_probability = overall_acc
|
||||
|
||||
hit_dist = {0: 0, 1: 0, 2: 0, 3: 0, 4: 0, 5: 0, 6: 0}
|
||||
for entry in self.rollback_history:
|
||||
total_hit = entry["red_hit"] + entry["blue_hit"]
|
||||
if total_hit in hit_dist:
|
||||
hit_dist[total_hit] += 1
|
||||
|
||||
result = {
|
||||
"lottery": self.lt,
|
||||
"total_simulations": total_simulations,
|
||||
"start_issue": self.all_data[start_idx]["issue"],
|
||||
"end_issue": self.all_data[-1]["issue"],
|
||||
"final_weights": self.weights,
|
||||
"accuracy": {
|
||||
"red_accuracy": round(avg_red, 2),
|
||||
"blue_accuracy": round(avg_blue, 2),
|
||||
"overall_accuracy": round(overall_acc, 2),
|
||||
"win_probability": round(win_probability, 2),
|
||||
},
|
||||
"hits": {
|
||||
"total_red_hits": total_red_hits,
|
||||
"total_blue_hits": total_blue_hits,
|
||||
"possible_red": possible_red,
|
||||
"possible_blue": possible_blue,
|
||||
},
|
||||
"hit_distribution": hit_dist,
|
||||
"methodology_snapshots": methodology_snapshots,
|
||||
"weight_evolution": self._get_weight_evolution(),
|
||||
}
|
||||
return result
|
||||
|
||||
def _optimize_weights(self, red_hit, blue_hit, pred_reds, actual_reds):
|
||||
"""根据推演结果动态调整10维度权重"""
|
||||
iteration = len(self.rollback_history) + 1
|
||||
lr = max(0.003, 0.01 - iteration * 0.0001)
|
||||
|
||||
hit_ratio = (red_hit + blue_hit) / (self.rule["red_count"] + self.rule["blue_count"])
|
||||
|
||||
if hit_ratio >= 0.5:
|
||||
self.weights["frequency"] = min(0.35, self.weights["frequency"] * (1 + lr))
|
||||
self.weights["hot_number"] = min(0.30, self.weights["hot_number"] * (1 + lr * 0.8))
|
||||
else:
|
||||
self.weights["missing"] = min(0.30, self.weights["missing"] * (1 + lr))
|
||||
self.weights["probability"] = min(0.15, self.weights["probability"] * (1 + lr * 1.2))
|
||||
self.weights["frequency"] = max(0.15, self.weights["frequency"] * (1 - lr * 0.5))
|
||||
|
||||
pred_oe = sum(1 for n in pred_reds if n % 2 == 1)
|
||||
actual_oe = sum(1 for n in actual_reds if n % 2 == 1)
|
||||
if pred_oe == actual_oe:
|
||||
self.weights["odd_even"] = min(0.12, self.weights["odd_even"] * (1 + lr))
|
||||
|
||||
pred_sum = sum(pred_reds)
|
||||
actual_sum = sum(actual_reds)
|
||||
if abs(pred_sum - actual_sum) <= 15:
|
||||
self.weights["sum_value"] = min(0.10, self.weights["sum_value"] * (1 + lr))
|
||||
|
||||
pred_consec = sum(1 for i in range(len(pred_reds) - 1) if pred_reds[i + 1] - pred_reds[i] == 1)
|
||||
actual_consec = sum(1 for i in range(len(actual_reds) - 1) if actual_reds[i + 1] - actual_reds[i] == 1)
|
||||
if pred_consec == actual_consec:
|
||||
self.weights["consecutive"] = min(0.08, self.weights["consecutive"] * (1 + lr))
|
||||
|
||||
total = sum(self.weights.values())
|
||||
if total > 0:
|
||||
for k in self.weights:
|
||||
self.weights[k] /= total
|
||||
|
||||
def _get_weight_evolution(self):
|
||||
if not self.rollback_history:
|
||||
return []
|
||||
interval = max(1, len(self.rollback_history) // 20)
|
||||
evolution = []
|
||||
for i, entry in enumerate(self.rollback_history):
|
||||
if i % interval == 0 or i == len(self.rollback_history) - 1:
|
||||
evolution.append({
|
||||
"iteration": entry["iteration"],
|
||||
"issue": entry["issue"],
|
||||
"weights": entry["weights_at_time"],
|
||||
})
|
||||
return evolution
|
||||
|
||||
|
||||
# ============================================================
|
||||
# 自进化系统（多轮迭代 + 方法论总结升级）
|
||||
# ============================================================
|
||||
|
||||
class SelfEvolvingSystem:
|
||||
"""多轮迭代运行，每轮完成后总结方法论并升级，目标胜出率 >= 51%"""
|
||||
|
||||
def __init__(self, lt, data):
|
||||
self.lt = lt
|
||||
self.data = data
|
||||
self.rule = LOTTERY_RULES[lt]
|
||||
self.best_weights = copy.deepcopy(DEFAULT_WEIGHTS)
|
||||
self.best_accuracy = 0
|
||||
self.iteration_results = []
|
||||
self.methodology_log = []
|
||||
|
||||
def run_iterations(self, max_iterations=20):
|
||||
print(f"\n{'='*60}")
|
||||
print(f" 🔬 自进化系统启动 - {self.rule['name']}")
|
||||
print(f" 目标：胜出率(概率占比) >= 51%")
|
||||
print(f" 最大迭代轮次: {max_iterations}")
|
||||
print(f"{'='*60}")
|
||||
|
||||
for iteration in range(1, max_iterations + 1):
|
||||
print(f"\n{'─'*60}")
|
||||
print(f" 第 {iteration}/{max_iterations} 轮迭代")
|
||||
print(f"{'─'*60}")
|
||||
|
||||
if iteration == 1:
|
||||
current_weights = copy.deepcopy(DEFAULT_WEIGHTS)
|
||||
else:
|
||||
current_weights = self._mutate_weights(self.best_weights, iteration)
|
||||
|
||||
engine = AIRollbackEngine(self.lt, self.data, current_weights)
|
||||
result = engine.run()
|
||||
|
||||
if "error" in result:
|
||||
print(f" ❌ 错误: {result['error']}")
|
||||
break
|
||||
|
||||
accuracy = result["accuracy"]["win_probability"]
|
||||
self.iteration_results.append({
|
||||
"iteration": iteration,
|
||||
"accuracy": accuracy,
|
||||
"weights": result["final_weights"],
|
||||
"simulations": result["total_simulations"],
|
||||
})
|
||||
|
||||
self._summarize_methodology(iteration, result)
|
||||
|
||||
if accuracy > self.best_accuracy:
|
||||
self.best_accuracy = accuracy
|
||||
self.best_weights = result["final_weights"]
|
||||
print(f" 🏆 新最佳胜出率: {accuracy:.2f}%")
|
||||
else:
|
||||
print(f" 本轮胜出率: {accuracy:.2f}% (最佳: {self.best_accuracy:.2f}%)")
|
||||
|
||||
if self.best_accuracy >= 51.0:
|
||||
print(f"\n{'='*60}")
|
||||
print(f" 🎯 目标达成！胜出率 {self.best_accuracy:.2f}% >= 51%")
|
||||
print(f"{'='*60}")
|
||||
break
|
||||
|
||||
return self._final_report()
|
||||
|
||||
def _mutate_weights(self, base_weights, iteration):
|
||||
mutated = copy.deepcopy(base_weights)
|
||||
mutation_rate = max(0.005, 0.03 - iteration * 0.001)
|
||||
for k in mutated:
|
||||
delta = random.uniform(-mutation_rate, mutation_rate)
|
||||
mutated[k] = max(0.01, mutated[k] + delta)
|
||||
total = sum(mutated.values())
|
||||
for k in mutated:
|
||||
mutated[k] /= total
|
||||
return mutated
|
||||
|
||||
def _summarize_methodology(self, iteration, result):
|
||||
acc = result["accuracy"]
|
||||
weights = result["final_weights"]
|
||||
dist = result.get("hit_distribution", {})
|
||||
|
||||
sorted_w = sorted(weights.items(), key=lambda x: x[1], reverse=True)
|
||||
top_dims = sorted_w[:3]
|
||||
weak_dims = sorted_w[-2:]
|
||||
|
||||
summary_lines = [
|
||||
f"\n === 方法论总结 - 第{iteration}轮 ===",
|
||||
f" 胜出率: {acc['win_probability']:.2f}% (红球{acc['red_accuracy']:.1f}% 蓝球{acc['blue_accuracy']:.1f}%)",
|
||||
f" 最优三维度:",
|
||||
]
|
||||
for dim, w in top_dims:
|
||||
summary_lines.append(f" - {dim}: {w:.4f}")
|
||||
summary_lines.append(f" 最弱二维度:")
|
||||
for dim, w in weak_dims:
|
||||
summary_lines.append(f" - {dim}: {w:.4f}")
|
||||
summary_lines.append(f" 命中分布:")
|
||||
for level in sorted(dist.keys()):
|
||||
if dist.get(level, 0) > 0:
|
||||
pct = dist[level] / result["total_simulations"] * 100
|
||||
summary_lines.append(f" - {level}个命中: {dist[level]}次 ({pct:.1f}%)")
|
||||
|
||||
summary = "\n".join(summary_lines)
|
||||
print(summary)
|
||||
self.methodology_log.append(summary)
|
||||
|
||||
mpath = os.path.join(DATA_DIR, f"methodology_{self.lt}_iter{iteration}.json")
|
||||
with open(mpath, "w", encoding="utf-8") as f:
|
||||
json.dump({
|
||||
"iteration": iteration,
|
||||
"accuracy": acc,
|
||||
"weights": weights,
|
||||
"hit_distribution": dist,
|
||||
"snapshots": result.get("methodology_snapshots", []),
|
||||
}, f, ensure_ascii=False, indent=2)
|
||||
print(f" [方法论已保存] {mpath}")
|
||||
|
||||
def _final_report(self):
|
||||
report = {
|
||||
"lottery": self.lt,
|
||||
"total_iterations": len(self.iteration_results),
|
||||
"best_accuracy": self.best_accuracy,
|
||||
"best_weights": self.best_weights,
|
||||
"iteration_summary": self.iteration_results,
|
||||
}
|
||||
rpath = os.path.join(DATA_DIR, f"self_evolve_{self.lt}_final.json")
|
||||
with open(rpath, "w", encoding="utf-8") as f:
|
||||
json.dump(report, f, ensure_ascii=False, indent=2)
|
||||
return report
|
||||
|
||||
|
||||
# ============================================================
|
||||
# CLI 主入口
|
||||
# ============================================================
|
||||
|
||||
def load_history(lt):
|
||||
path = os.path.join(DATA_DIR, f"{lt}_history.json")
|
||||
if os.path.exists(path):
|
||||
try:
|
||||
with open(path, "r", encoding="utf-8") as f:
|
||||
return json.load(f)
|
||||
except:
|
||||
pass
|
||||
return []
|
||||
|
||||
|
||||
def print_header():
|
||||
print("\n" + "*" * 60)
|
||||
print(" 双色球 & 大乐透 * 增强智能分析系统 v2.0")
|
||||
print(" AI回滚推演 | 方法论自优化 | 200期深度分析")
|
||||
print(" " + datetime.datetime.now().strftime("%Y-%m-%d %H:%M"))
|
||||
print("*" * 60)
|
||||
|
||||
|
||||
def show_full_report(lt, count=5):
|
||||
rule = LOTTERY_RULES[lt]
|
||||
data = load_history(lt)
|
||||
if not data:
|
||||
print(f"\n ⚠️ {rule['name']}暂无数据，请先采集")
|
||||
print(f" python3 lottery_tool.py --action fetch --lottery {lt}")
|
||||
return
|
||||
|
||||
analyzer = EnhancedAnalyzer(data, lt)
|
||||
analysis = analyzer.analyze()
|
||||
if not analysis:
|
||||
print(f"\n ⚠️ {rule['name']}分析失败")
|
||||
return
|
||||
|
||||
print(f"\n{'─'*60}")
|
||||
print(f" {rule['name']} - 增强分析报告 (200期)")
|
||||
print(f"{'─'*60}")
|
||||
dr = analysis["data_range"]
|
||||
print(f" 数据范围: {dr['total']}期 ({dr['start']} ~ {dr['end']})")
|
||||
w = analyzer.weights
|
||||
print(f" 权重体系: freq={w['frequency']:.3f} hot={w['hot_number']:.3f} miss={w['missing']:.3f}")
|
||||
|
||||
pat = analysis["patterns"]
|
||||
print(f"\n 模式分析:")
|
||||
print(f" 奇偶比: {pat['most_oe']} | 大小比: {pat['most_bs']}")
|
||||
print(f" 和值范围: {pat['sum_range'][0]} ~ {pat['sum_range'][1]} (均{pat['avg_sum']})")
|
||||
print(f" 区间分布: {pat['zone_balance']}")
|
||||
|
||||
recs = analyzer.recommend(analysis, count=count)
|
||||
print(f"\n 🎯 AI推荐号码 ({count}组):")
|
||||
for i, rec in enumerate(recs, 1):
|
||||
rs = " ".join(f"{n:02d}" for n in rec["reds"])
|
||||
bs = " ".join(f"{n:02d}" for n in rec["blues"])
|
||||
print(f" [{i}] 红球: {rs} 蓝球: {bs} (评分:{rec['score']:.3f} OE:{rec['oe']} BS:{rec['bs']})")
|
||||
|
||||
|
||||
def main():
|
||||
import argparse
|
||||
parser = argparse.ArgumentParser(description="增强版彩票智能分析系统 v2.0")
|
||||
parser.add_argument("--action",
|
||||
choices=["report", "rollback", "self_evolve"],
|
||||
default="report",
|
||||
help="report: 200期分析推荐 | rollback: 逐期回滚推演 | self_evolve: 自进化")
|
||||
parser.add_argument("--lottery", choices=["ssq", "dlt", "all"], default="ssq")
|
||||
parser.add_argument("--count", type=int, default=5, help="推荐组数")
|
||||
parser.add_argument("--iterations", type=int, default=10, help="自进化迭代轮次")
|
||||
args = parser.parse_args()
|
||||
|
||||
print_header()
|
||||
lotteries = ["ssq", "dlt"] if args.lottery == "all" else [args.lottery]
|
||||
|
||||
if args.action == "report":
|
||||
for lt in lotteries:
|
||||
show_full_report(lt, args.count)
|
||||
return
|
||||
|
||||
if args.action == "rollback":
|
||||
for lt in lotteries:
|
||||
data = load_history(lt)
|
||||
if len(data) < 200:
|
||||
print(f"\n ⚠️ {LOTTERY_RULES[lt]['name']}数据不足200期 (当前{len(data)}期)")
|
||||
print(f" 请先采集: python3 lottery_tool.py --action fetch --lottery {lt}")
|
||||
continue
|
||||
engine = AIRollbackEngine(lt, data)
|
||||
result = engine.run()
|
||||
if "error" not in result:
|
||||
acc = result["accuracy"]
|
||||
print(f"\n === 回滚推演完成 ===")
|
||||
print(f" 综合胜出率: {acc['win_probability']:.2f}%")
|
||||
print(f" 红球准确率: {acc['red_accuracy']:.1f}% | 蓝球: {acc['blue_accuracy']:.1f}%")
|
||||
|
||||
if args.action == "self_evolve":
|
||||
for lt in lotteries:
|
||||
data = load_history(lt)
|
||||
if len(data) < 200:
|
||||
print(f"\n ⚠️ {LOTTERY_RULES[lt]['name']}数据不足200期 (当前{len(data)}期)")
|
||||
continue
|
||||
system = SelfEvolvingSystem(lt, data)
|
||||
final = system.run_iterations(max_iterations=args.iterations)
|
||||
print(f"\n === 自进化最终结果 ===")
|
||||
print(f" 最佳胜出率: {final['best_accuracy']:.2f}%")
|
||||
print(f" 迭代总轮次: {final['total_iterations']}")
|
||||
print(f" 最优权重:")
|
||||
for k, v in sorted(final['best_weights'].items(), key=lambda x: x[1], reverse=True):
|
||||
print(f" {k}: {v:.4f}")
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
main()
|
||||
Reference in new issue
Block a user