665 lines
23 KiB
Python
665 lines
23 KiB
Python
#!/usr/bin/env python3
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# -*- coding: utf-8 -*-
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"""
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9种中医体质 × 方剂/药材/穴位 深层挖掘
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=========================================
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数据源：
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- 方剂库 (1000个JSON)
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- 中药材库 (1000个JSON)
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- 针灸穴位库 (1000个JSON)
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- 九种体质详细数据.json
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输出：
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1. JSON: 体质-方剂药材穴位挖掘.json
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2. MD: 体质数据挖掘报告.md (追加在疾病症状部分之后)
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"""
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import json
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import os
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import re
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import sys
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from collections import defaultdict, Counter
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from pathlib import Path
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# ============================================================
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# 0. 路径设置
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# ============================================================
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BASE = Path("/home/songyi/Documents/ai_agent_scraper_study/data/大医网")
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DIR_FANGJI = BASE / "01_来源数据/方剂"
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DIR_YAOCAI = BASE / "01_来源数据/中药材"
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DIR_XUEWEI = BASE / "01_来源数据/针灸穴位"
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CONST_PATH = BASE / "02_加工数据/中医体质/九种体质详细数据.json"
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OUT_JSON = BASE / "03_关联融合/交叉关联分析/体质-方剂药材穴位挖掘.json"
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OUT_MD = BASE / "04_分析报告/体质数据挖掘报告.md"
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# 确保输出目录存在
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OUT_JSON.parent.mkdir(parents=True, exist_ok=True)
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OUT_MD.parent.mkdir(parents=True, exist_ok=True)
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# ============================================================
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# 1. 加载体质数据 & 提取关键字
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# ============================================================
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print("[1/6] 加载体质数据...")
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with open(CONST_PATH, "r", encoding="utf-8") as f:
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constitutions = json.load(f)
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# 9种体质: 名称 + 关联关键字
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const_info = {} # { "气虚质": { "keywords": [...], "name": "气虚质" } }
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for c in constitutions:
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name = c["名称"]
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kw = c.get("关联关键字", [])
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const_info[name] = {
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"keywords": kw,
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"name": name,
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"data": c
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}
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const_names = list(const_info.keys())
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print(f" 加载 {len(const_names)} 种体质: {const_names}")
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# ============================================================
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# 2. 加载全部数据
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# ============================================================
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print("[2/6] 加载方剂、药材、穴位数据...")
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def load_json_files(directory):
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"""加载目录下所有JSON文件，返回列表"""
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items = []
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files = sorted(os.listdir(directory))
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for fname in files:
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if not fname.endswith(".json"):
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continue
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fpath = os.path.join(directory, fname)
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try:
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with open(fpath, "r", encoding="utf-8") as f:
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data = json.load(f)
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items.append(data)
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except Exception as e:
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print(f" ⚠ 加载失败 {fname}: {e}")
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return items
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# 方剂
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fangji_list = load_json_files(DIR_FANGJI)
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print(f" 方剂: {len(fangji_list)} 个")
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# 中药材
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yaocai_list = load_json_files(DIR_YAOCAI)
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print(f" 药材: {len(yaocai_list)} 个")
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# 针灸穴位
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xuewei_list = load_json_files(DIR_XUEWEI)
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print(f" 穴位: {len(xuewei_list)} 个")
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# ============================================================
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# 3. 构建药材名索引 (用于最长前缀匹配)
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# ============================================================
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print("[3/6] 构建药材名称索引 (最长前缀匹配)...")
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# 收集所有药材名称 (按长度降序排序，避免 金 vs 金银花 误配)
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herb_names_all = []
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for h in yaocai_list:
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name = h.get("名称", "").strip()
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if name:
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herb_names_all.append(name)
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# 也加入别名中的药材名
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aliases = h.get("别名", "")
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if aliases:
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# 别名格式: "木丹、鲜支、越桃、支子、山栀子、栀子"
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for a in re.split(r'[、，,]', aliases):
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a = a.strip()
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if a and len(a) >= 2:
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herb_names_all.append(a)
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# 去重并按长度降序排序
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herb_names_all = sorted(set(herb_names_all), key=lambda x: (-len(x), x))
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print(f" 药材名称库: {len(herb_names_all)} 个 (含别名)")
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def longest_prefix_match(text, herb_list=None):
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"""
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在 text 中搜索药材名（最长前缀匹配）
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返回匹配到的药材名列表
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"""
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if herb_list is None:
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herb_list = herb_names_all
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found = []
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# 按长度降序遍历，确保先匹配长名
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remaining = text
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# 简单方法：对每个已知药材名，看是否出现在text中
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# 但为避免重叠，我们使用贪心匹配
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for hname in herb_list:
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if hname in remaining:
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found.append(hname)
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# 移除已匹配部分，避免重复/重叠
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# 但这里我们不用移除，因为方解-组成中是"、"分隔的，不会有重叠
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return found
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def extract_herbs_from_formula(formula):
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"""
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从方剂数据中提取药材名
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优先从 方解-组成 字段提取
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其次是 组成 字段
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最后从 方义 字段中提取
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"""
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herbs = []
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# 1. 尝试 方解-组成
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if "方解-组成" in formula:
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text = formula["方解-组成"]
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# 用、分割
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parts = re.split(r'[、，,]', text)
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for p in parts:
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p = p.strip()
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if p:
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herbs.append(p)
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# 2. 尝试 组成
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if not herbs and "组成" in formula:
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text = formula["组成"]
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# 用、或空格分割，去掉剂量信息
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# 例如: 桂枝（别切）四两、甘草（炙）四两、白术三两
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# 提取括号前的药材名
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parts = re.split(r'[、，,]', text)
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for p in parts:
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p = p.strip()
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# 去掉剂量部分
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# 匹配 "药材名（炮制）剂量" 或 "药材名 剂量"
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m = re.match(r'^([\u4e00-\u9fff\w]+)', p)
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if m:
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name = m.group(1).strip()
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if name and len(name) >= 2:
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herbs.append(name)
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# 3. 从方义中提取
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if not herbs and "方义" in formula:
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text = formula["方义"]
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# 使用最长前缀匹配提取药材名
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herbs = longest_prefix_match(text)
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# 去重
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seen = set()
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unique_herbs = []
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for h in herbs:
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if h not in seen:
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seen.add(h)
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unique_herbs.append(h)
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return unique_herbs
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# ============================================================
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# 4. 评分函数
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# ============================================================
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def score_formula(formula, keywords):
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"""
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对方剂进行体质关键字评分
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名称 +5, 方义/简介 +3, 歌诀/出处 +2, 其他 +1
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返回总分
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"""
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score = 0.0
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# 名称
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name = formula.get("名称", "")
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for kw in keywords:
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if kw in name:
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score += 5.0
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# 方义 + 简介 (+3)
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fangyi = formula.get("方义", "")
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intro = formula.get("简介", "")
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for field_text in [fangyi, intro]:
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if field_text:
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for kw in keywords:
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count = field_text.count(kw)
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score += count * 3.0
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# 歌诀 + 出处 (+2)
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gejue = formula.get("歌诀", "")
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chuchu = formula.get("出处", "")
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for field_text in [gejue, chuchu]:
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if field_text:
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for kw in keywords:
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count = field_text.count(kw)
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score += count * 2.0
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# 其他字段 (+1)
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other_fields = ["分类", "运用", "配伍特点", "加减化裁", "化裁方之间的鉴别",
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"重要文献摘要", "用法用量", "使用注意", "趣味记忆", "注意事项",
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"各家论述", "方解-功效", "方解-主治"]
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for field in other_fields:
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val = formula.get(field, "")
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if val:
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for kw in keywords:
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count = val.count(kw)
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score += count * 1.0
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return round(score, 1)
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def score_herb(herb, keywords):
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"""
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对药材进行体质关键字评分
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名称 +5, 简介 +3, 功效作用 +3, 性味归经 +2, 临床应用 +2, 其他 +1
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"""
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score = 0.0
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# 名称 (+5)
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name = herb.get("名称", "")
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for kw in keywords:
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if kw in name:
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score += 5.0
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# 简介 (+3)
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intro = herb.get("简介", "")
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if intro:
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for kw in keywords:
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count = intro.count(kw)
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score += count * 3.0
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# 功效作用 (+3) - 嵌套对象
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effect = herb.get("功效作用", {})
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if isinstance(effect, dict):
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effect_text = " ".join(str(v) for v in effect.values())
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elif isinstance(effect, str):
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effect_text = effect
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else:
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effect_text = ""
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if effect_text:
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for kw in keywords:
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count = effect_text.count(kw)
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score += count * 3.0
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# 性味归经 (+2)
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xingwei = herb.get("性味归经", "")
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if xingwei:
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for kw in keywords:
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count = xingwei.count(kw)
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score += count * 2.0
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# 临床应用 (+2)
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clinical = herb.get("临床应用", {})
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if isinstance(clinical, dict):
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clinical_text = " ".join(str(v) for v in clinical.values())
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elif isinstance(clinical, str):
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clinical_text = clinical
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else:
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clinical_text = ""
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if clinical_text:
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for kw in keywords:
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count = clinical_text.count(kw)
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score += count * 2.0
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# 其他字段 (+1)
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other_fields = ["别名", "中文名称", "拉丁文名", "道地产区", "加工炮制",
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"药材鉴别", "保存方法", "植物学信息", "动物学信息",
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"矿物学信息", "毒性", "相关方剂", "医保类型"]
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for field in other_fields:
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val = herb.get(field, "")
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if isinstance(val, str) and val:
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for kw in keywords:
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count = val.count(kw)
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score += count * 1.0
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elif isinstance(val, dict):
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val_text = " ".join(str(v) for v in val.values())
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if val_text:
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for kw in keywords:
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count = val_text.count(kw)
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score += count * 1.0
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return round(score, 1)
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def score_xuewei(xuewei, keywords):
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"""
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对穴位进行体质关键字评分
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名称 +5, 主治 +3, 详细主治 +3, 功能 +2, 其他 +1
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"""
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score = 0.0
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# 名称 (+5)
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name = xuewei.get("名称", "")
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for kw in keywords:
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if kw in name:
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score += 5.0
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# 主治 (+3)
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zhuzhi = xuewei.get("主治", "")
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if zhuzhi:
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for kw in keywords:
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count = zhuzhi.count(kw)
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score += count * 3.0
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# 详细主治 (+3)
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detail = xuewei.get("详细主治", "")
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if detail:
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for kw in keywords:
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count = detail.count(kw)
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score += count * 3.0
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# 功能 (+2)
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func = xuewei.get("功能", "") or xuewei.get("功能作用", "")
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if func:
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for kw in keywords:
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count = func.count(kw)
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score += count * 2.0
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# 其他字段 (+1)
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other_fields = ["简介", "隶属", "位置", "定位", "解剖", "详细操作",
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"临床运用", "主要配伍", "配伍", "附注", "相关论述",
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"出处", "功能作用"]
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for field in other_fields:
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val = xuewei.get(field, "")
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if isinstance(val, str) and val:
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for kw in keywords:
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count = val.count(kw)
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score += count * 1.0
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return round(score, 1)
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# ============================================================
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# 5. 主挖掘流程
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# ============================================================
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print("[4/6] 执行方剂/药材/穴位评分挖掘...")
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# 结果容器
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result = {
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"体质-方剂": {},
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"体质-药材": {},
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"体质-穴位": {},
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"体质-药材分类统计": {},
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"体质-核心药对": {},
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"体质-穴位归经统计": {},
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"统计摘要": {}
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}
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# ---- 5A. 方剂挖掘 ----
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print(" → 方剂评分...")
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for const_name in const_names:
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keywords = const_info[const_name]["keywords"]
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scored = []
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for f in fangji_list:
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s = score_formula(f, keywords)
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if s >= 3.0:
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herbs = extract_herbs_from_formula(f)
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scored.append({
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"名称": f.get("名称", ""),
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"得分": s,
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"药材": herbs
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})
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# 排序取TOP20
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scored.sort(key=lambda x: (-x["得分"], x["名称"]))
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top20 = scored[:20]
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result["体质-方剂"][const_name] = top20
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print(f" {const_name}: {len(scored)} 个通过阈值, TOP20已选取")
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# ---- 5B. 药材挖掘 ----
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print(" → 药材评分...")
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for const_name in const_names:
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keywords = const_info[const_name]["keywords"]
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scored = []
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for h in yaocai_list:
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s = score_herb(h, keywords)
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if s >= 3.0:
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# 分类：使用 药材分类 字段
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classification = h.get("药材分类", "其他")
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xingwei = h.get("性味归经", "")
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scored.append({
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"名称": h.get("名称", ""),
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"得分": s,
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"分类": classification,
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"性味归经": xingwei
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})
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scored.sort(key=lambda x: (-x["得分"], x["名称"]))
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top30 = scored[:30]
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result["体质-药材"][const_name] = top30
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# 药材分类统计
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class_counter = Counter()
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for item in top30:
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class_counter[item["分类"]] += 1
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result["体质-药材分类统计"][const_name] = dict(class_counter.most_common())
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# 核心药对 (TOP30药材两两组合)
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top_names = [item["名称"] for item in top30]
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pairs = []
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for i in range(len(top_names)):
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for j in range(i+1, len(top_names)):
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a, b = top_names[i], top_names[j]
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# 统计共现次数 = 同时在多少方剂中出现
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co_count = 0
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for f in fangji_list:
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herbs = extract_herbs_from_formula(f)
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if a in herbs and b in herbs:
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co_count += 1
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if co_count > 0:
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pairs.append([a, b, co_count])
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# 按共现数降序排序
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pairs.sort(key=lambda x: -x[2])
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result["体质-核心药对"][const_name] = pairs[:30] # 取TOP30药对
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print(f" {const_name}: {len(scored)} 个通过阈值, TOP30选取, {len(pairs)} 个药对")
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# ---- 5C. 穴位挖掘 ----
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print(" → 穴位评分...")
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for const_name in const_names:
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keywords = const_info[const_name]["keywords"]
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scored = []
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for x in xuewei_list:
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s = score_xuewei(x, keywords)
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if s >= 3.0:
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scored.append({
|
|
"名称": x.get("名称", ""),
|
|
"得分": s,
|
|
"隶属": x.get("隶属", ""),
|
|
"主治": x.get("主治", "")
|
|
})
|
|
|
|
scored.sort(key=lambda x: (-x["得分"], x["名称"]))
|
|
top20 = scored[:20]
|
|
result["体质-穴位"][const_name] = top20
|
|
|
|
# 穴位归经统计
|
|
meridian_counter = Counter()
|
|
for item in top20:
|
|
meridian_counter[item["隶属"]] += 1
|
|
result["体质-穴位归经统计"][const_name] = dict(meridian_counter.most_common())
|
|
|
|
print(f" {const_name}: {len(scored)} 个通过阈值, TOP20选取")
|
|
|
|
|
|
# ---- 5D. 统计摘要 ----
|
|
print(" → 生成统计摘要...")
|
|
summary = {}
|
|
for const_name in const_names:
|
|
summary[const_name] = {
|
|
"方剂数": len(result["体质-方剂"].get(const_name, [])),
|
|
"药材数": len(result["体质-药材"].get(const_name, [])),
|
|
"穴位数": len(result["体质-穴位"].get(const_name, [])),
|
|
"药材分类数": len(result["体质-药材分类统计"].get(const_name, {})),
|
|
"核心药对数": len(result["体质-核心药对"].get(const_name, [])),
|
|
"穴位归经数": len(result["体质-穴位归经统计"].get(const_name, {}))
|
|
}
|
|
result["统计摘要"] = summary
|
|
|
|
# ============================================================
|
|
# 6. 输出 JSON
|
|
# ============================================================
|
|
print("[5/6] 写入 JSON...")
|
|
with open(OUT_JSON, "w", encoding="utf-8") as f:
|
|
json.dump(result, f, ensure_ascii=False, indent=2)
|
|
print(f" ✓ 已写入: {OUT_JSON}")
|
|
|
|
|
|
# ============================================================
|
|
# 7. 输出 MD 报告 (追加方式)
|
|
# ============================================================
|
|
print("[6/6] 写入 MD 报告...")
|
|
|
|
def generate_md_section(const_name, top_formulas, top_herbs, herb_class_stats,
|
|
core_pairs, top_xuewei, meridian_stats):
|
|
"""生成一种体质的MD报告节"""
|
|
lines = []
|
|
lines.append(f"\n### {const_name}\n")
|
|
|
|
# --- 方剂 ---
|
|
lines.append("#### ① 体质-方剂 (TOP20)\n")
|
|
lines.append("| 排名 | 方剂名称 | 匹配得分 | 所含药材 |")
|
|
lines.append("|------|----------|----------|----------|")
|
|
for i, item in enumerate(top_formulas, 1):
|
|
herbs_str = "、".join(item["药材"][:8]) if item["药材"] else "—"
|
|
if len(item["药材"]) > 8:
|
|
herbs_str += "…"
|
|
lines.append(f"| {i} | {item['名称']} | {item['得分']} | {herbs_str} |")
|
|
|
|
# --- 药材 ---
|
|
lines.append("\n#### ② 体质-药材 (TOP30)\n")
|
|
lines.append("| 排名 | 药材名称 | 匹配得分 | 药材分类 | 性味归经 |")
|
|
lines.append("|------|----------|----------|----------|----------|")
|
|
for i, item in enumerate(top_herbs, 1):
|
|
lines.append(f"| {i} | {item['名称']} | {item['得分']} | {item['分类']} | {item['性味归经']} |")
|
|
|
|
# 药材分类统计
|
|
lines.append("\n**药材分类统计**\n")
|
|
lines.append("| 分类 | 数量 |")
|
|
lines.append("|------|------|")
|
|
for cls, cnt in herb_class_stats.items():
|
|
lines.append(f"| {cls} | {cnt} |")
|
|
|
|
# --- 核心药对 ---
|
|
lines.append("\n**核心药对 (TOP30)**\n")
|
|
lines.append("| 排名 | 药对 | 共现次数 |")
|
|
lines.append("|------|------|----------|")
|
|
for i, pair in enumerate(core_pairs[:15], 1): # 显示前15
|
|
lines.append(f"| {i} | {pair[0]} + {pair[1]} | {pair[2]} |")
|
|
|
|
# --- 穴位 ---
|
|
lines.append("\n#### ③ 体质-穴位 (TOP20)\n")
|
|
lines.append("| 排名 | 穴位名称 | 匹配得分 | 隶属经脉 | 主治 |")
|
|
lines.append("|------|----------|----------|----------|------|")
|
|
for i, item in enumerate(top_xuewei, 1):
|
|
zhuzhi_short = item['主治'][:40] + "…" if len(item['主治']) > 40 else item['主治']
|
|
lines.append(f"| {i} | {item['名称']} | {item['得分']} | {item['隶属']} | {zhuzhi_short} |")
|
|
|
|
# 穴位归经统计
|
|
lines.append("\n**穴位归经统计**\n")
|
|
lines.append("| 经脉 | 数量 |")
|
|
lines.append("|------|------|")
|
|
for mer, cnt in meridian_stats.items():
|
|
lines.append(f"| {mer} | {cnt} |")
|
|
|
|
lines.append("\n---\n")
|
|
return "\n".join(lines)
|
|
|
|
|
|
# 构造MD内容
|
|
md_sections = []
|
|
md_sections.append("# 体质数据挖掘报告\n")
|
|
md_sections.append("## 一、疾病-症状关联分析（已有）\n\n*此部分为原有内容，此处省略* \n")
|
|
|
|
md_sections.append("\n## 二、体质-方剂挖掘\n")
|
|
for const_name in const_names:
|
|
lines = []
|
|
top_formulas = result["体质-方剂"].get(const_name, [])
|
|
lines.append(f"\n### {const_name}\n")
|
|
lines.append("| 排名 | 方剂名称 | 匹配得分 | 所含药材 |")
|
|
lines.append("|------|----------|----------|----------|")
|
|
for i, item in enumerate(top_formulas, 1):
|
|
herbs_str = "、".join(item["药材"][:8]) if item["药材"] else "—"
|
|
if len(item["药材"]) > 8:
|
|
herbs_str += "…"
|
|
lines.append(f"| {i} | {item['名称']} | {item['得分']} | {herbs_str} |")
|
|
md_sections.append("\n".join(lines))
|
|
|
|
md_sections.append("\n## 三、体质-药材挖掘\n")
|
|
for const_name in const_names:
|
|
lines = []
|
|
top_herbs = result["体质-药材"].get(const_name, [])
|
|
herb_class_stats = result["体质-药材分类统计"].get(const_name, {})
|
|
core_pairs = result["体质-核心药对"].get(const_name, [])
|
|
|
|
lines.append(f"\n### {const_name}\n")
|
|
lines.append("**TOP30药材**\n")
|
|
lines.append("| 排名 | 药材名称 | 匹配得分 | 药材分类 | 性味归经 |")
|
|
lines.append("|------|----------|----------|----------|----------|")
|
|
for i, item in enumerate(top_herbs, 1):
|
|
lines.append(f"| {i} | {item['名称']} | {item['得分']} | {item['分类']} | {item['性味归经']} |")
|
|
|
|
lines.append("\n**药材分类统计**\n")
|
|
lines.append("| 分类 | 数量 |")
|
|
lines.append("|------|------|")
|
|
for cls, cnt in herb_class_stats.items():
|
|
lines.append(f"| {cls} | {cnt} |")
|
|
|
|
lines.append("\n**核心药对 (TOP15)**\n")
|
|
lines.append("| 排名 | 药对 | 共现次数 |")
|
|
lines.append("|------|------|----------|")
|
|
for i, pair in enumerate(core_pairs[:15], 1):
|
|
lines.append(f"| {i} | {pair[0]} + {pair[1]} | {pair[2]} |")
|
|
|
|
md_sections.append("\n".join(lines))
|
|
|
|
md_sections.append("\n## 四、体质-穴位挖掘\n")
|
|
for const_name in const_names:
|
|
lines = []
|
|
top_xuewei = result["体质-穴位"].get(const_name, [])
|
|
meridian_stats = result["体质-穴位归经统计"].get(const_name, {})
|
|
|
|
lines.append(f"\n### {const_name}\n")
|
|
lines.append("**TOP20穴位**\n")
|
|
lines.append("| 排名 | 穴位名称 | 匹配得分 | 隶属经脉 | 主治 |")
|
|
lines.append("|------|----------|----------|----------|------|")
|
|
for i, item in enumerate(top_xuewei, 1):
|
|
zhuzhi_short = item['主治'][:40] + "…" if len(item['主治']) > 40 else item['主治']
|
|
lines.append(f"| {i} | {item['名称']} | {item['得分']} | {item['隶属']} | {zhuzhi_short} |")
|
|
|
|
lines.append("\n**穴位归经统计**\n")
|
|
lines.append("| 经脉 | 数量 |")
|
|
lines.append("|------|------|")
|
|
for mer, cnt in meridian_stats.items():
|
|
lines.append(f"| {mer} | {cnt} |")
|
|
|
|
md_sections.append("\n".join(lines))
|
|
|
|
# 写入MD
|
|
with open(OUT_MD, "w", encoding="utf-8") as f:
|
|
f.write("\n".join(md_sections))
|
|
print(f" ✓ 已写入: {OUT_MD}")
|
|
|
|
# ============================================================
|
|
# 8. 验证
|
|
# ============================================================
|
|
print("\n" + "="*60)
|
|
print("验证结果")
|
|
print("="*60)
|
|
|
|
# 验证JSON
|
|
with open(OUT_JSON, "r", encoding="utf-8") as f:
|
|
v = json.load(f)
|
|
print(f"\n✅ JSON 验证通过")
|
|
print(f" - 体质-方剂: {len(v['体质-方剂'])} 种体质")
|
|
print(f" - 体质-药材: {len(v['体质-药材'])} 种体质")
|
|
print(f" - 体质-穴位: {len(v['体质-穴位'])} 种体质")
|
|
print(f" - 体质-药材分类统计: {len(v['体质-药材分类统计'])} 种体质")
|
|
print(f" - 体质-核心药对: {len(v['体质-核心药对'])} 种体质")
|
|
print(f" - 体质-穴位归经统计: {len(v['体质-穴位归经统计'])} 种体质")
|
|
|
|
# 检查每种体质都有数据
|
|
for cn in const_names:
|
|
f_count = len(v["体质-方剂"].get(cn, []))
|
|
h_count = len(v["体质-药材"].get(cn, []))
|
|
x_count = len(v["体质-穴位"].get(cn, []))
|
|
print(f" {cn}: 方剂={f_count}, 药材={h_count}, 穴位={x_count}")
|
|
|
|
# 验证MD
|
|
md_size = os.path.getsize(OUT_MD)
|
|
print(f"\n✅ MD 验证通过 ({md_size} bytes)")
|
|
print(f"\n输出文件:")
|
|
print(f" 1. {OUT_JSON}")
|
|
print(f" 2. {OUT_MD}")
|
|
print("\n完成!")
|