Initial commit: yu-zhi-ran platform with automation integration
This commit is contained in:
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#!/usr/bin/env python3
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"""
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可持续性内容收集脚本
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每天凌晨5:00运行，收集全球可持续性趋势信息，提炼选题和案例
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"""
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import os
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import sys
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import yaml
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import json
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import datetime
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import logging
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from pathlib import Path
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import feedparser
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import requests
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import re
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from typing import Dict, List, Optional, Tuple
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import hashlib
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from dataclasses import dataclass, asdict
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import subprocess
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# 项目根目录
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PROJECT_ROOT = Path(__file__).parent.parent
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sys.path.insert(0, str(PROJECT_ROOT))
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# 配置路径
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CONFIG_DIR = PROJECT_ROOT / "config"
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DATA_DIR = PROJECT_ROOT / "automation" / "data"
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LOGS_DIR = PROJECT_ROOT / "automation" / "logs"
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TODAY = datetime.datetime.now().strftime("%Y-%m-%d")
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# 日志配置
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logging.basicConfig(
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level=logging.INFO,
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format='%(asctime)s - %(name)s - %(levelname)s - %(message)s',
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handlers=[
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logging.FileHandler(LOGS_DIR / f"collector_{TODAY}.log"),
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logging.StreamHandler()
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]
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)
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logger = logging.getLogger(__name__)
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@dataclass
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class SustainabilitySource:
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"""可持续性信息源"""
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name: str
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type: str # rss, web, api, report, local
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url: Optional[str] = None # 可为空（如本地源）
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update_frequency: str = "daily"
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credibility: str = "medium"
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focus: str = "可持续性"
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@dataclass
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class SustainabilityCase:
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"""可持续性案例"""
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id: str
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country: str
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category: str # 子领域：城市农业、零浪费生活等
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title: str
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core_idea: str
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data_facts: str
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global_advantage: str
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china_pain_point: str
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localization_suggestion: str
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mvp_action: str
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source_url: str
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credibility_rating: str # ⭐⭐ ⭐⭐⭐
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china_applicability: str # ⭐ ⭐⭐ ⭐⭐⭐
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collection_date: str
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status: str = "待验证"
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@dataclass
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class SustainabilityTopic:
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"""可持续性选题"""
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id: str
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title: str
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cases: List[str] # 关联的案例ID列表
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audience: str # 目标受众
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china_pain_points: str
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localization_solution: str
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mvp_actions: str
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estimated_length: int
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priority_score: float
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status: str = "待处理" # 待处理/待审查/待发布/已发布
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lock_by: Optional[str] = None # 被哪个任务锁定
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lock_at: Optional[str] = None # 锁定时间
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created_at: Optional[str] = None # 创建时间
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class SustainabilityCollector:
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"""可持续性内容收集器"""
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def __init__(self):
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self.load_config()
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self.today_dir = DATA_DIR / "sustainability_raw" / TODAY
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self.today_dir.mkdir(parents=True, exist_ok=True)
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# 结果存储
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self.new_cases: List[SustainabilityCase] = []
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self.new_topics: List[SustainabilityTopic] = []
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def load_config(self):
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"""加载配置文件"""
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with open(CONFIG_DIR / "sources.yaml", "r", encoding='utf-8') as f:
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self.config = yaml.safe_load(f)
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with open(CONFIG_DIR / "wecom_config.yaml", "r", encoding='utf-8') as f:
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self.wecom_config = yaml.safe_load(f)
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self.sources = []
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for source_group in self.config["sustainability_sources"].values():
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for source_info in source_group:
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# Handle both 'url' and 'base_url' in config
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source_info = source_info.copy()
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if 'base_url' in source_info and 'url' not in source_info:
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source_info['url'] = source_info.pop('base_url')
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# Provide defaults for missing optional fields
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source_info.setdefault('update_frequency', 'daily')
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source_info.setdefault('focus', '可持续性')
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# Filter to only fields accepted by SustainabilitySource
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allowed_keys = {'name', 'type', 'url', 'update_frequency', 'credibility', 'focus'}
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filtered_info = {k: v for k, v in source_info.items() if k in allowed_keys}
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self.sources.append(SustainabilitySource(**filtered_info))
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logger.info(f"加载了 {len(self.sources)} 个信息源")
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def load_local_cases_from_db(self) -> List[SustainabilityCase]:
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"""从本地案例库加载历史案例，用于降级生成选题"""
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local_cases = []
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db_file = DATA_DIR / "sustainability_cases.json"
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if db_file.exists():
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try:
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with open(db_file, 'r', encoding='utf-8') as f:
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cases_data = json.load(f)
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# 取最近50个案例（按日期倒序）
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recent_cases = cases_data[-50:] if len(cases_data) > 50 else cases_data
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for case_dict in recent_cases:
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# 转换为 dataclass
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case = SustainabilityCase(**case_dict)
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local_cases.append(case)
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logger.info(f"从本地数据库加载了 {len(local_cases)} 个历史案例")
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except Exception as e:
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logger.error(f"读取本地案例库失败: {e}")
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return local_cases
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def load_local_cases_from_markdown(self) -> List[SustainabilityCase]:
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"""从 Markdown 案例文件解析案例（备用）"""
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local_cases = []
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md_file = PROJECT_ROOT / "strategy" / "全球案例数据库-v1.md"
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if not md_file.exists():
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return local_cases
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try:
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content = md_file.read_text(encoding='utf-8')
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# 简单解析：按 "#### ID:" 分割案例
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import re
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blocks = re.split(r'#### ID:', content)
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for block in blocks[1:]: # 第一个是引言
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case_data = {
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'id': 'LOCAL-UNKNOWN',
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'country': 'Global',
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'category': '未分类',
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'title': '',
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'core_idea': '',
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'data_facts': '',
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'global_advantage': '',
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'china_pain_point': '',
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'localization_suggestion': '',
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'mvp_action': '',
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'source_url': '',
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'credibility_rating': '⭐⭐',
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'china_applicability': '⭐⭐',
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'collection_date': TODAY
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}
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# 提取字段
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title_match = re.search(r'标题[：:]\s*(.+)\n', block)
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if title_match:
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case_data['title'] = title_match.group(1).strip()
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case_data['id'] = f"LOCAL-{hashlib.md5(title_match.group(1).encode()).hexdigest()[:6].upper()}"
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country_match = re.search(r'国家[：:]\s*(.+)\n', block)
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if country_match:
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case_data['country'] = country_match.group(1).strip()
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field_match = re.search(r'领域[：:]\s*(.+)\n', block)
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if field_match:
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field = field_match.group(1).strip()
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# 映射到子领域
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category_map = {
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'远程工作方式': '城市农业',
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'数字游民政策': '低碳出行',
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'AI副业服务': '环保科技产品',
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'一人公司模式': '循环消费',
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'未来技能趋势': '可持续饮食'
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}
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case_data['category'] = category_map.get(field, field[:4] if len(field) > 4 else field)
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core_match = re.search(r'核心观点[：:]([\s\S]*?)(?=数据/事实|$)', block)
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if core_match:
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case_data['core_idea'] = core_match.group(1).strip()[:500]
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data_match = re.search(r'数据/事实[：:]([\s\S]*?)(?=全球优势|$)', block)
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if data_match:
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case_data['data_facts'] = data_match.group(1).strip()[:200]
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global_match = re.search(r'全球优势[：:]([\s\S]*?)(?=中国痛点|$)', block)
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if global_match:
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case_data['global_advantage'] = global_match.group(1).strip()[:200]
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pain_match = re.search(r'中国痛点[：:]([\s\S]*?)(?=本土化建议|$)', block)
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if pain_match:
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case_data['china_pain_point'] = pain_match.group(1).strip()[:200]
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local_match = re.search(r'本土化建议[：:]([\s\S]*?)(?=MVP行动|$)', block)
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if local_match:
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case_data['localization_suggestion'] = local_match.group(1).strip()[:200]
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mvp_match = re.search(r'MVP行动[：:]([\s\S]*?)(?=来源URL|$)', block)
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if mvp_match:
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case_data['mvp_action'] = mvp_match.group(1).strip()[:200]
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url_match = re.search(r'来源URL[：:]\s*(.+)\n', block)
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if url_match:
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case_data['source_url'] = url_match.group(1).strip()
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case = SustainabilityCase(**case_data)
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local_cases.append(case)
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logger.info(f"从 Markdown 案例库解析了 {len(local_cases)} 个案例")
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except Exception as e:
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logger.error(f"解析 Markdown 案例库失败: {e}")
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return local_cases
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def fetch_rss_feed(self, source: SustainabilitySource) -> List[Dict]:
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"""获取RSS订阅内容"""
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try:
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feed = feedparser.parse(source.url)
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articles = []
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for entry in feed.entries[:10]: # 限制数量
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# 检查是否包含可持续性关键词
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content = entry.get('summary', entry.get('description', ''))
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title = entry.get('title', '')
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# 可持续性关键词匹配
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sustainability_keywords = [
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'sustainable', 'green', 'eco', 'circular', 'climate',
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'carbon', 'zero waste', 'renewable', 'recycle',
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'环保', '可持续', '碳中和', '循环经济', '零浪费'
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]
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if any(keyword.lower() in (title + content).lower() for keyword in sustainability_keywords):
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articles.append({
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'title': title,
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'url': entry.get('link', ''),
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'content': content,
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'published': entry.get('published', ''),
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'source_name': source.name
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})
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logger.info(f"从 {source.name} 获取到 {len(articles)} 篇可持续性文章")
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return articles
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except Exception as e:
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logger.error(f"获取RSS失败 {source.name}: {e}")
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return []
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def fetch_web_content(self, source: SustainabilitySource) -> List[Dict]:
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"""获取网页内容（简化版，实际需要更复杂的抓取）"""
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# 简化实现：只记录，不实际抓取
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logger.info(f"网页信息源 {source.name} 需要手动处理")
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return []
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def analyze_article(self, article: Dict) -> Optional[SustainabilityCase]:
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"""分析文章内容，提炼案例"""
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try:
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content = article['content']
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title = article['title']
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# 提取关键数据（简化版，实际可用NLP）
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data_patterns = [
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r'(\d+\.?\d*)\s*(?:percent|%|百分比)',
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r'(\d+\.?\d*)\s*(?:million|billion|万|亿)',
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r'(\d+\.?\d*)\s*(?:tons|tonnes|吨)',
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r'(\d+\.?\d*)\s*(?:reduction|increase|减少|增加)'
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]
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data_points = []
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for pattern in data_patterns:
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matches = re.findall(pattern, content, re.IGNORECASE)
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if matches:
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data_points.extend(matches[:3]) # 限制数量
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if len(data_points) < 2:
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logger.info(f"文章数据不足: {title}")
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return None
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# 确定国家（简化判断）
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countries = ['China', 'Japan', 'Germany', 'US', 'UK', 'Sweden', 'Netherlands']
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country = 'Global' # 默认
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for c in countries:
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if c.lower() in content.lower():
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country = c
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break
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# 确定子领域
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categories = self.config["sustainability_categories"]
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category = categories[0] # 默认第一个
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for cat in categories:
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if any(keyword in content.lower() for keyword in [cat.lower(), cat[:4].lower()]):
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category = cat
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break
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# 生成案例ID
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case_id = f"SUS-{hashlib.md5(title.encode()).hexdigest()[:8].upper()}"
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# 提取核心观点（简化版）
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# 实际应用中可用AI提取，这里用前100字符
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core_idea = content[:200] if len(content) > 200 else content
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# 生成中国痛点（基于类别模板）
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china_pains = {
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"城市农业": "中国城市空间小、光照不足、怕邻居投诉",
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"零浪费生活": "中国垃圾分类执行难、环保产品溢价高",
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"低碳出行": "中国电动车充电难、城市规划不支持",
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"循环消费": "中国二手文化不成熟、维修成本高",
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"能源效率": "中国能源价格波动、设备更换成本高",
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"可持续饮食": "中国预制菜泛滥、有机食品价格高",
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"环保科技产品": "中国消费者关注价格多于环保"
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}
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china_pain = china_pains.get(category, "中国相关数据不足，需本土化验证")
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# 生成案例
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case = SustainabilityCase(
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id=case_id,
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country=country,
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category=category,
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title=title,
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core_idea=core_idea,
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data_facts=f"数据点: {', '.join(data_points[:3])}",
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global_advantage="需进一步分析全球优势",
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china_pain_point=china_pain,
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localization_suggestion="需基于中国现实调整实施",
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mvp_action="建议先小规模试点验证",
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source_url=article['url'],
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credibility_rating="⭐⭐" if article['source_name'] in ['GreenBiz', 'Sustainable Brands'] else "⭐",
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china_applicability="⭐⭐",
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collection_date=TODAY
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)
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return case
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except Exception as e:
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logger.error(f"分析文章失败: {e}")
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return None
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def generate_topic_from_cases(self, cases: List[SustainabilityCase]) -> Optional[SustainabilityTopic]:
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"""从案例组合生成选题"""
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if len(cases) < 2:
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return None
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# 按类别分组
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category_cases = {}
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for case in cases:
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if case.category not in category_cases:
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category_cases[case.category] = []
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category_cases[case.category].append(case)
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# 选择案例数最多的类别
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main_category = max(category_cases, key=lambda k: len(category_cases[k]))
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main_cases = category_cases[main_category]
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if len(main_cases) < 2:
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return None
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# 生成选题ID
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topic_id = f"TOPIC-.{hashlib.md5((main_category + TODAY).encode()).hexdigest()[:6].upper()}"
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# 组合标题
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case_titles = [case.title[:30] for case in main_cases[:2]]
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topic_title = f"{main_category}新趋势: {case_titles[0]}与{case_titles[1]}的中国落地路径"
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# 计算优先级分数
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priority_weights = self.config["topic_priority"]
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priority_score = (
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priority_weights["audience_match"] * 0.8 + # 受众匹配度预估
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priority_weights["data_availability"] * 0.9 + # 数据可得性
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priority_weights["uniqueness"] * 0.7 + # 独特性
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priority_weights["executability"] * 0.6 + # 可执行性
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priority_weights["brand_fit"] * 0.9 # 品牌契合度
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)
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topic = SustainabilityTopic(
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id=topic_id,
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title=topic_title,
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cases=[case.id for case in main_cases],
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audience="城市焦虑青年（26-35岁）",
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china_pain_points=f"{main_category}在中国面临的主要问题",
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localization_solution="国际案例中国化适配方案",
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mvp_actions="读者可立即尝试的3个行动",
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estimated_length=2500,
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priority_score=round(priority_score, 2)
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)
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return topic
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|
||||
def save_results(self):
|
||||
"""保存收集结果"""
|
||||
# 保存案例
|
||||
cases_file = self.today_dir / "new_cases.json"
|
||||
with open(cases_file, 'w', encoding='utf-8') as f:
|
||||
json.dump([asdict(case) for case in self.new_cases], f, ensure_ascii=False, indent=2)
|
||||
|
||||
# 保存选题
|
||||
topics_file = self.today_dir / "new_topics.json"
|
||||
with open(topics_file, 'w', encoding='utf-8') as f:
|
||||
json.dump([asdict(topic) for topic in self.new_topics], f, ensure_ascii=False, indent=2)
|
||||
|
||||
# 更新主数据库
|
||||
self.update_main_database()
|
||||
|
||||
logger.info(f"保存了 {len(self.new_cases)} 个案例和 {len(self.new_topics)} 个选题")
|
||||
|
||||
def update_main_database(self):
|
||||
"""更新主数据库（简化版）"""
|
||||
# 实际应更新Notion/数据库，这里仅保存到文件
|
||||
main_cases_file = DATA_DIR / "sustainability_cases.json"
|
||||
main_topics_file = DATA_DIR / "sustainability_topics.json"
|
||||
|
||||
# 读取现有数据
|
||||
existing_cases = []
|
||||
existing_topics = []
|
||||
|
||||
if main_cases_file.exists():
|
||||
with open(main_cases_file, 'r', encoding='utf-8') as f:
|
||||
existing_cases = json.load(f)
|
||||
|
||||
if main_topics_file.exists():
|
||||
with open(main_topics_file, 'r', encoding='utf-8') as f:
|
||||
existing_topics = json.load(f)
|
||||
|
||||
# 合并新数据
|
||||
all_cases = existing_cases + [asdict(case) for case in self.new_cases]
|
||||
all_topics = existing_topics + [asdict(topic) for topic in self.new_topics]
|
||||
|
||||
# 保存（限制总数）
|
||||
with open(main_cases_file, 'w', encoding='utf-8') as f:
|
||||
json.dump(all_cases[:100], f, ensure_ascii=False, indent=2)
|
||||
|
||||
with open(main_topics_file, 'w', encoding='utf-8') as f:
|
||||
json.dump(all_topics[:50], f, ensure_ascii=False, indent=2)
|
||||
|
||||
def send_wecom_notification(self):
|
||||
"""发送企业微信通知"""
|
||||
try:
|
||||
# 调用通知脚本
|
||||
notification_script = PROJECT_ROOT / "scripts" / "wecom_notifier.py"
|
||||
if not notification_script.exists():
|
||||
logger.warning("企业微信通知脚本不存在")
|
||||
return
|
||||
|
||||
# 准备通知数据
|
||||
notification_data = {
|
||||
"task": "sustainability_collection",
|
||||
"time": datetime.datetime.now().strftime("%Y-%m-%d %H:%M"),
|
||||
"topic_count": len(self.new_topics),
|
||||
"case_count": len(self.new_cases),
|
||||
"source_count": len(self.sources),
|
||||
"details_link": str(self.today_dir.relative_to(PROJECT_ROOT))
|
||||
}
|
||||
|
||||
data_file = self.today_dir / "notification_data.json"
|
||||
with open(data_file, 'w', encoding='utf-8') as f:
|
||||
json.dump(notification_data, f, ensure_ascii=False)
|
||||
|
||||
# 运行通知脚本
|
||||
result = subprocess.run(
|
||||
[sys.executable, str(notification_script), str(data_file)],
|
||||
capture_output=True,
|
||||
text=True,
|
||||
cwd=PROJECT_ROOT
|
||||
)
|
||||
|
||||
if result.returncode == 0:
|
||||
logger.info("企业微信通知发送成功")
|
||||
else:
|
||||
logger.error(f"通知发送失败: {result.stderr}")
|
||||
|
||||
except Exception as e:
|
||||
logger.error(f"发送通知失败: {e}")
|
||||
|
||||
def run(self):
|
||||
"""主运行流程"""
|
||||
logger.info("开始可持续性内容收集")
|
||||
|
||||
# 1. 从所有信息源收集
|
||||
all_articles = []
|
||||
for source in self.sources:
|
||||
if source.type == 'rss':
|
||||
articles = self.fetch_rss_feed(source)
|
||||
all_articles.extend(articles)
|
||||
elif source.type == 'web':
|
||||
articles = self.fetch_web_content(source)
|
||||
all_articles.extend(articles)
|
||||
elif source.type == 'api':
|
||||
# TODO: 实现API抓取
|
||||
pass
|
||||
elif source.type == 'local':
|
||||
# 本地源不产生新文章，后续降级处理
|
||||
pass
|
||||
|
||||
logger.info(f"总共收集到 {len(all_articles)} 篇可持续性文章")
|
||||
|
||||
# 2. 分析文章，提炼案例
|
||||
for article in all_articles[:20]: # 限制分析数量
|
||||
case = self.analyze_article(article)
|
||||
if case:
|
||||
self.new_cases.append(case)
|
||||
|
||||
# 3. 降级策略：如果外部源没有收集到足够案例，使用本地案例库
|
||||
if len(self.new_cases) < 2:
|
||||
logger.warning(f"外部源案例不足 ({len(self.new_cases)} < 2)，启动降级策略")
|
||||
|
||||
# 优先：从本地JSON数据库加载最近案例
|
||||
local_cases = self.load_local_cases_from_db()
|
||||
if len(local_cases) < 2:
|
||||
# 备用：从Markdown案例库解析
|
||||
local_cases = self.load_local_cases_from_markdown()
|
||||
|
||||
if local_cases:
|
||||
# 随机选取2-3个本地案例作为本次选题的案例基础
|
||||
import random
|
||||
selected = random.sample(local_cases, min(3, len(local_cases)))
|
||||
self.new_cases.extend(selected)
|
||||
logger.info(f"降级：使用了 {len(selected)} 个本地案例")
|
||||
else:
|
||||
logger.error("降级失败：本地案例库为空")
|
||||
|
||||
# 4. 生成选题
|
||||
if self.new_cases:
|
||||
topic = self.generate_topic_from_cases(self.new_cases)
|
||||
if topic:
|
||||
# 标记为今日创建，并添加锁字段（表示未被占用）
|
||||
topic.created_at = datetime.datetime.now().isoformat()
|
||||
topic.lock_by = None
|
||||
topic.lock_at = None
|
||||
# 确保状态为「待处理」
|
||||
topic.status = "待处理"
|
||||
self.new_topics.append(topic)
|
||||
|
||||
# 5. 保存结果
|
||||
self.save_results()
|
||||
|
||||
# 6. 发送通知
|
||||
self.send_wecom_notification()
|
||||
|
||||
logger.info(f"收集完成: {len(self.new_cases)} 案例, {len(self.new_topics)} 选题")
|
||||
return len(self.new_cases), len(self.new_topics)
|
||||
|
||||
def main():
|
||||
"""主函数"""
|
||||
try:
|
||||
collector = SustainabilityCollector()
|
||||
case_count, topic_count = collector.run()
|
||||
|
||||
# 返回结果码
|
||||
if case_count > 0 or topic_count > 0:
|
||||
print(f"SUCCESS: Collected {case_count} cases and {topic_count} topics")
|
||||
sys.exit(0)
|
||||
else:
|
||||
print("WARNING: No new content found")
|
||||
sys.exit(1)
|
||||
|
||||
except Exception as e:
|
||||
logger.error(f"收集任务失败: {e}")
|
||||
print(f"ERROR: {e}")
|
||||
sys.exit(1)
|
||||
|
||||
if __name__ == "__main__":
|
||||
main()
|
||||
Reference in New Issue
Block a user