Initial commit: yu-zhi-ran platform with automation integration
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#!/usr/bin/env python3
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"""
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将 content/ideas/ 目录下的 Markdown 选题文件转换为 JSON 格式
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供 content creator 脚本使用
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"""
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import os
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import sys
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import json
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import re
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from pathlib import Path
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from datetime import datetime
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PROJECT_ROOT = Path(__file__).parent.parent
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IDEAS_DIR = PROJECT_ROOT / "content" / "ideas"
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DATA_DIR = PROJECT_ROOT / "automation" / "data"
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OUTPUT_FILE = DATA_DIR / "sustainability_topics.json"
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def extract_field(content, field_name):
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"""从 Markdown 中提取字段值"""
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# 支持 **字段名**：值 或 字段名：值 格式
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patterns = [
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rf"\*\*{re.escape(field_name)}\*\*\s*[:：]\s*(.+?)(?:\n|$)",
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rf"{re.escape(field_name)}\s*[:：]\s*(.+?)(?:\n|$)",
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]
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for pattern in patterns:
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match = re.search(pattern, content, re.MULTILINE)
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if match:
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return match.group(1).strip()
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return None
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def extract_list(content, start_keyword):
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"""提取列表数据（如数据/案例）"""
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lines = content.split('\n')
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result = []
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capturing = False
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for line in lines:
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if start_keyword in line:
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capturing = True
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continue
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if capturing:
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if line.strip().startswith(('**', '#', '-', '*', '1.', '2.')):
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if re.match(r'^(#|\*\*|-|\*|\d+\.)\s', line):
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result.append(line.strip())
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elif line.strip() == '' or line.startswith('##'):
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break
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return result
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def parse_evaluation_matrix(content):
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"""解析选题评估矩阵表格"""
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scores = {}
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lines = content.split('\n')
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in_table = False
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for line in lines:
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if '|' in line and '---' not in line and '维度' not in line:
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parts = [p.strip() for p in line.split('|')]
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if len(parts) >= 3:
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dimension = parts[1]
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score_str = parts[2]
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try:
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score = int(score_str)
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scores[dimension] = score
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except:
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pass
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if '**总分**' in line:
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total_match = re.search(r'\*\*总分\*\*\s*\|\s*\*\*(\d+)\*\*', line)
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if total_match:
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scores['总分'] = int(total_match.group(1))
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return scores
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def md_to_topic(md_path):
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"""将单个 Markdown 文件转换为 topic 字典"""
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with open(md_path, 'r', encoding='utf-8') as f:
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content = f.read()
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# 提取标题 (第一行 # 开头)
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title_match = re.search(r'^#\s+(.+)$', content, re.MULTILINE)
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title = title_match.group(1).strip() if title_match else md_path.stem
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# 提取基础字段
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field = extract_field(content, '领域')
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format_type = extract_field(content, '形式')
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word_count = extract_field(content, '预估字数')
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core_concept = extract_field(content, '核心观点')
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audience_pain = extract_field(content, '受众痛点')
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unique_angle = extract_field(content, '独特角度')
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data_cases = extract_list(content, '数据/案例')
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estimated_days = extract_field(content, '预估完成时间')
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priority_str = extract_field(content, '优先级')
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publish_date = extract_field(content, '预计发布时间')
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status = extract_field(content, '状态') or '待处理'
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# 解析优先级为分数
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priority_map = {'高': 10, '中': 7, '低': 4}
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priority_score = priority_map.get(priority_str, 5)
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# 解析评估矩阵
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evaluation = parse_evaluation_matrix(content)
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total_score = evaluation.get('总分', 0)
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# 生成 topic ID
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topic_id = md_path.stem.split('-')[0] # 如 "001-上海阳台种菜一年.md" -> "001"
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# 构建 topic 对象
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topic = {
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"id": topic_id,
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"title": title,
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"field": field or "未知",
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"format": format_type or "未指定",
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"word_count": word_count,
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"core_concept": core_concept,
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"audience_pain": audience_pain,
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"unique_angle": unique_angle,
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"data_cases": data_cases,
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"estimated_days": estimated_days,
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"priority": priority_str,
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"priority_score": priority_score if priority_score > 0 else (total_score if total_score > 0 else 5),
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"publish_date": publish_date,
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"status": status,
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"evaluation": evaluation,
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"total_score": total_score,
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"cases": [], # 关联的案例ID列表，待填充
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"source_file": md_path.name,
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"created_at": datetime.now().isoformat()
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}
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return topic
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def main():
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"""主函数：导入所有 Markdown 选题文件"""
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if not IDEAS_DIR.exists():
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print(f"错误：选题目录不存在 {IDEAS_DIR}")
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return
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# 只导入主选题文件（格式：NNN-标题.md），排除 research/compliance 等辅助文件
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md_files = []
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for f in IDEAS_DIR.glob("*.md"):
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if f.name == "README.md":
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continue
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# 排除 research 和 compliance 文件
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if f.name.endswith('-research.md') or f.name.endswith('-compliance.md'):
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continue
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# 匹配 001-xxx.md 格式
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if re.match(r'^\d{3}-.+\.md$', f.name):
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md_files.append(f)
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if not md_files:
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print("未找到选题文件")
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return
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print(f"找到 {len(md_files)} 个选题文件，开始导入...")
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topics = []
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for md_file in sorted(md_files):
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print(f" 处理: {md_file.name}")
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topic = md_to_topic(md_file)
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topics.append(topic)
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print(f" 标题: {topic['title']}")
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print(f" 总分: {topic['total_score']}")
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print(f" 状态: {topic['status']}")
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# 确保输出目录存在
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DATA_DIR.mkdir(parents=True, exist_ok=True)
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# 写入 JSON
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with open(OUTPUT_FILE, 'w', encoding='utf-8') as f:
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json.dump(topics, f, ensure_ascii=False, indent=2)
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print(f"\n✅ 已导入 {len(topics)} 个选题到 {OUTPUT_FILE}")
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# 统计
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ready_topics = [t for t in topics if t['status'] != '已发布']
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print(f"📊 可用选题数: {len(ready_topics)}")
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avg_score = sum(t['total_score'] for t in ready_topics) / len(ready_topics) if ready_topics else 0
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print(f"🎯 平均评分: {avg_score:.1f}")
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if __name__ == "__main__":
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main()
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