fix: 修复代码审查反馈全部问题
审查意见修复清单:
P1 列映射语义修复 (lottery.py):
- _normalize_history_format() 不再将红球2-6映射到开机号/和值特征/奇偶比等
格式A不含这些特征字段，缺失列留空，前端做降级显示
- 删除已用于构建号码列的原始分列，避免数据重复
P2 架构优化:
- 提取 Excel 兼容逻辑到公共模块 history_loader.py
lottery.py 和 app.py 共同引用，消除三处重复代码
- web_executor.py 标记为已废弃，功能已整合到 app.py
部署修复:
- 删除 deploy/lotto-web.service (旧服务)，仅保留 lotto-app.service
- 更新 deploy/DEPLOY.md: 端口5000→8085, 接口清单更新, 添加迁移说明
安全加固:
- API Token 改为环境变量读取: os.environ.get('LOTTO_API_TOKEN')
- 错误信息不再暴露内部异常，改为通用错误消息+日志记录
- 目录遍历防护改用 os.path.realpath 检查最终路径
其他:
- .gitignore 补充排除 双色球历史数据.xlsx
- app.py 引用公共模块，简化 get_statistics_data 和 load_history_dataframe
测试验证: 全部 API 测试通过，120条历史数据正确解析
Issue: BIZ-75
This commit is contained in:
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#!/usr/bin/env python3
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# -*- coding: utf-8 -*-
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"""
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history_loader.py — 双色球历史数据 Excel 公共加载模块
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统一 Excel 格式检测和列名标准化逻辑，供 lottery.py 和 app.py 共同引用。
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支持三种格式：
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格式A: Row0=新列名(期号|开奖日期|红球1~6|蓝球|特别号), Row1=旧列名(开奖时间|期数|号码|...), Row2+=数据
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格式B: Row0=标准列名(开奖时间|期数|号码|开机号|...), Row1+=数据
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格式C: 直接含"号码"列的标准 DataFrame
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"""
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import pandas as pd
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import os
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from collections import Counter
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import re
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# 标准列名（lottery.py 和 app.py 期望的列）
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LEGACY_COLUMNS = ['开奖时间', '期数', '号码', '开机号', '和值特征', '奇偶比', '大小比', '奇偶形态', '跨度', '其他']
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def load_history_dataframe(history_file):
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"""智能加载历史数据 Excel，兼容多种格式。
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返回统一的 DataFrame，使用 LEGACY_COLUMNS 列名。
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"""
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if not os.path.exists(history_file):
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return pd.DataFrame()
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raw_df = pd.read_excel(history_file, header=None)
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if raw_df.empty:
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return pd.DataFrame()
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row0_vals = raw_df.iloc[0].astype(str).tolist() if len(raw_df) > 0 else []
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row1_vals = raw_df.iloc[1].astype(str).tolist() if len(raw_df) > 1 else []
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has_legacy_in_row0 = any(col in row0_vals for col in ['开奖时间', '期数', '号码'])
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has_legacy_in_row1 = any(col in row1_vals for col in ['开奖时间', '期数', '号码'])
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has_new_cols_in_row0 = any(col in row0_vals for col in ['期号', '开奖日期', '红球 1'])
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if has_new_cols_in_row0 and has_legacy_in_row1:
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# 格式A：跳过 Row0(新列名) 和 Row1(旧列名)，用旧列名，数据从 Row2 开始
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data_df = raw_df.iloc[2:].copy()
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num_cols = len(data_df.columns)
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data_df.columns = LEGACY_COLUMNS[:min(num_cols, len(LEGACY_COLUMNS))] + \
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[f'col_{i}' for i in range(min(num_cols, len(LEGACY_COLUMNS)), num_cols)]
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elif has_legacy_in_row0:
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# 格式B：Row0 就是标准列名
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data_df = raw_df.iloc[1:].copy()
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num_cols = len(data_df.columns)
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data_df.columns = LEGACY_COLUMNS[:min(num_cols, len(LEGACY_COLUMNS))] + \
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[f'col_{i}' for i in range(min(num_cols, len(LEGACY_COLUMNS)), num_cols)]
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else:
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# 格式C：尝试默认读取
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data_df = pd.read_excel(history_file)
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if '号码' not in data_df.columns:
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# 可能是分列格式，尝试构建号码列
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red_cols = [f'红球 {i}' for i in range(1, 7)]
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if not all(c in data_df.columns for c in red_cols):
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red_cols = [f'红球{i}' for i in range(1, 7)]
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if all(c in data_df.columns for c in red_cols) and '蓝球' in data_df.columns:
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data_df['号码'] = data_df.apply(
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lambda row: _build_number_string(row, red_cols), axis=1)
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num_cols = len(data_df.columns)
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if not any(c in data_df.columns for c in LEGACY_COLUMNS[:3]):
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data_df.columns = LEGACY_COLUMNS[:min(num_cols, len(LEGACY_COLUMNS))] + \
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[f'col_{i}' for i in range(min(num_cols, len(LEGACY_COLUMNS)), num_cols)]
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data_df = data_df.reset_index(drop=True)
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return data_df
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def _build_number_string(row, red_cols):
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"""将分列红球 + 蓝球拼接为 14 位号码字符串。"""
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parts = []
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for c in red_cols:
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val = row.get(c)
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if pd.isna(val):
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return None
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s = str(int(val)) if isinstance(val, (int, float)) else str(val).strip()
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parts.append(s.zfill(2))
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blue_val = row.get('蓝球')
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if pd.isna(blue_val):
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return None
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blue_s = str(int(blue_val)) if isinstance(blue_val, (int, float)) else str(blue_val).strip()
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return ''.join(parts) + blue_s.zfill(2)
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def parse_number_string(numbers_str):
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"""解析号码字符串为 (红球列表, 蓝球)。
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支持以下格式：
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- 拼接字符串: '08121821243001' (6红球×2位 + 1蓝球×2位)
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- 加号分隔: '03,12,16,22,25,28+10'
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- 空格/逗号分隔: '08 12 18 21 24 30 01'
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"""
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if not numbers_str or pd.isna(numbers_str):
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return [], 0
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s = str(numbers_str).strip()
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# 情况1: 纯拼接字符串（14位或以上，无分隔符）
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if re.match(r'^\d{14,}$', s):
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red_balls = [int(s[i:i+2]) for i in range(0, 12, 2)]
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blue_ball = int(s[12:14])
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if all(1 <= b <= 33 for b in red_balls) and 1 <= blue_ball <= 16:
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return red_balls, blue_ball
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return [], 0
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# 情况2: 加号分隔
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if '+' in s:
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parts = s.replace(',', ' ').replace('+', ' ').split()
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if len(parts) >= 7:
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try:
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red_balls = [int(x) for x in parts[:6]]
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blue_ball = int(parts[6])
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if all(1 <= b <= 33 for b in red_balls) and 1 <= blue_ball <= 16:
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return red_balls, blue_ball
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except ValueError:
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pass
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return [], 0
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# 情况3: 正则提取数字
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number_list = re.findall(r'\d+', s)
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if len(number_list) >= 7:
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try:
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red_balls = [int(x) for x in number_list[:6]]
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blue_ball = int(number_list[6])
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if all(1 <= b <= 33 for b in red_balls) and 1 <= blue_ball <= 16:
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return red_balls, blue_ball
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except ValueError:
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pass
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return [], 0
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def compute_statistics(history_file):
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"""从历史数据 Excel 计算统计信息，返回字典。"""
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if not os.path.exists(history_file):
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return {}
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data_df = load_history_dataframe(history_file)
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red_ball_counts = Counter()
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blue_ball_counts = Counter()
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sum_values = []
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span_values = []
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for _, row in data_df.iterrows():
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s = str(row.get('号码', '')).strip()
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if len(s) >= 14:
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reds = [int(s[i:i+2]) for i in range(0, 12, 2)]
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blue = int(s[12:14])
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if all(1 <= r <= 33 for r in reds) and 1 <= blue <= 16:
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red_ball_counts.update(reds)
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blue_ball_counts[blue] += 1
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sum_values.append(sum(reds))
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span_values.append(max(reds) - min(reds))
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stats = {}
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if red_ball_counts:
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sorted_reds = sorted(red_ball_counts.items(), key=lambda x: x[1], reverse=True)
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stats['hot_reds'] = [x[0] for x in sorted_reds[:15]]
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stats['cold_reds'] = [x[0] for x in sorted_reds[-15:]]
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if blue_ball_counts:
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sorted_blues = sorted(blue_ball_counts.items(), key=lambda x: x[1], reverse=True)
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stats['hot_blues'] = [x[0] for x in sorted_blues[:8]]
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# 奇偶比/大小比统计
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odd_even_ratios = Counter()
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size_ratios = Counter()
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for _, row in data_df.iterrows():
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oe = str(row.get('奇偶比', '')).strip()
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sz = str(row.get('大小比', '')).strip()
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if oe and oe != 'nan':
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odd_even_ratios[oe] += 1
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if sz and sz != 'nan':
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size_ratios[sz] += 1
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if odd_even_ratios:
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stats['common_odd_even'] = max(odd_even_ratios, key=odd_even_ratios.get)
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if size_ratios:
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stats['common_size_ratio'] = max(size_ratios, key=size_ratios.get)
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if sum_values:
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import numpy as np
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arr = np.array(sum_values)
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stats['sum_range'] = {
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'min': int(arr.min()), 'max': int(arr.max()),
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'mean': float(arr.mean()), 'std': float(arr.std())
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}
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if span_values:
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import numpy as np
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arr = np.array(span_values)
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stats['span_range'] = {
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'min': int(arr.min()), 'max': int(arr.max()),
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'mean': float(arr.mean()), 'std': float(arr.std())
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}
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stats['history_count'] = len(data_df)
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return stats
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