Yuzhiran Dev
195 lines
7.5 KiB
Python
195 lines
7.5 KiB
Python
#!/usr/bin/env python3
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"""
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选题引擎：多趋势加权匹配 + 趋势缺口检测 + 新选题生成
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结合采集到的热点趋势和当前选题库，推荐最优选题并检测趋势缺口
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"""
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import sys, json, logging
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from pathlib import Path
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from typing import Dict, List, Optional
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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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sys.path.insert(0, str(PROJECT_ROOT / "platform" / "backend"))
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from trends import load_trends
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from db_helper import export_topics_to_json, update_topic_status, save_topics_to_db
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from app.core.nvidia_client import call_llm
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DATA_DIR = PROJECT_ROOT / "automation" / "data"
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TRENDS_FILE = DATA_DIR / "trends.json"
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TODAY = __import__('datetime').datetime.now().strftime("%Y-%m-%d")
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logger = logging.getLogger(__name__)
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TREND_DOMAIN_MAP = {
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"远程工作": "未来工作方式",
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"AI工具": "AI与效率",
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"可持续生活": "可持续生活系统",
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"知识管理": "个人知识工厂",
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"数字生活": "科技人文交叉",
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"科技人文": "科技人文交叉",
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"个人成长": "个人成长",
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"副业": "个人成长",
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"AI创作": "AI与效率",
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"未来工作": "未来工作方式",
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"效率工具": "AI与效率",
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"家庭教育": "科技人文交叉",
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}
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def _topic_trend_score(topic: Dict, trend: Dict) -> float:
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field = (topic.get("field") or "").lower()
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title = (topic.get("title") or "").lower()
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core = (topic.get("core_concept") or "").lower()
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trend_domain = TREND_DOMAIN_MAP.get(trend.get("domain", ""), "")
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keywords = [trend.get("topic", "")] + trend.get("hot_keywords", [])
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score = 0.0
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if trend_domain and trend_domain in field:
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score += 5
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for kw in keywords:
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kw = kw.lower()
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if kw in title: score += 3
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elif kw in core: score += 2
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return score
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def match_trends_to_topics() -> List[Dict]:
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trends = load_trends()
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topics = [t for t in export_topics_to_json() if t.get('status') in ('pending', '待处理')]
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if not trends or not topics:
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return []
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topic_scores = {}
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for topic in topics:
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tid = topic["id"]
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per_trend = []
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total = 0.0
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for trend in trends:
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s = _topic_trend_score(topic, trend)
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if s > 0:
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per_trend.append({"trend": trend["topic"], "domain": trend.get("domain",""), "score": s})
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total += s
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if per_trend:
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topic_scores[tid] = {
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"topic_id": tid,
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"topic_title": topic["title"],
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"total_score": total + (topic.get("priority_score", 0) or 0) * 0.3,
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"matched_trends": sorted(per_trend, key=lambda x: -x["score"]),
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"matched_count": len(per_trend),
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}
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results = sorted(topic_scores.values(), key=lambda x: -x["total_score"])
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return results
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def detect_trend_gaps() -> List[Dict]:
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trends = load_trends()
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topics = export_topics_to_json()
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gaps = []
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for trend in trends:
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matched = False
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trend_keywords = [trend.get("topic", "").lower()] + [k.lower() for k in trend.get("hot_keywords", [])]
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for t in topics:
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title = (t.get("title") or "").lower()
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core = (t.get("core_concept") or "").lower()
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for kw in trend_keywords:
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if kw in title or kw in core:
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matched = True
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break
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if matched:
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break
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if not matched:
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gaps.append(trend)
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return gaps
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def generate_new_topics(gaps: List[Dict]) -> List[Dict]:
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if not gaps:
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return []
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prompt = f"""你是一个敏锐的内容策略师，擅长将热点转化为有价值、有传播力的选题。以下热点当前未覆盖，请为每个热点生成选题建议。
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注意：选题要兼具SEO价值（能在搜索中被找到）和社交传播力（能在平台引发讨论）。
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热点列表：
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{chr(10).join(f'- {g["topic"]}（{g.get("domain","")}）— {g.get("reason","")}。热搜词：{", ".join(g.get("hot_keywords", []))}）' for g in gaps)}
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每个选题需包含：
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- "title": 标题（20字内，包含核心关键词，有吸引力）
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- "field": 所属领域
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- "core_concept": 核心观点（一句话说清独特价值）
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- "audience_pain": 受众痛点（真实用户的困惑/焦虑/需求）
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- "unique_angle": 独特视角（差异化切入点，含SEO关键词潜力）
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- "target_platform": 最适合发布平台（知乎/小红书/微信/多平台）
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- "estimated_search_volume": 预估搜索热度（高/中/低）
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只输出 JSON 数组，不要其他文字。"""
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try:
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resp = call_llm(prompt, temperature=0.4)
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resp = resp.strip()
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if resp.startswith("```"):
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resp = resp.split("\n", 1)[1].rsplit("\n", 1)[0]
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suggestions = json.loads(resp)
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if isinstance(suggestions, list):
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return suggestions
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except Exception as e:
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logger.warning(f"新选题生成失败: {e}")
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return []
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def suggest_new_topics() -> List[Dict]:
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gaps = detect_trend_gaps()
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if not gaps:
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logger.info("所有趋势均有匹配选题，无需新增")
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return []
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logger.info(f"发现 {len(gaps)} 个趋势缺口: {[g['topic'] for g in gaps]}")
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new_topics = generate_new_topics(gaps)
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if new_topics:
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logger.info(f"生成 {len(new_topics)} 个新选题建议")
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report_path = DATA_DIR / "topic_suggestions.json"
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data = {"date": TODAY, "gaps": gaps, "suggestions": new_topics}
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DATA_DIR.mkdir(parents=True, exist_ok=True)
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report_path.write_text(json.dumps(data, ensure_ascii=False, indent=2), encoding="utf-8")
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return new_topics
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def select_best_topic() -> Optional[Dict]:
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matches = match_trends_to_topics()
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if matches:
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best = matches[0]
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logger.info(f"选题推荐: {best['topic_id']} {best['topic_title']} "
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f"(命中 {best['matched_count']} 个趋势, 综合分: {best['total_score']:.1f})")
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topic = __import__('db_helper').get_topic_by_id(best['topic_id'])
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if topic:
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return topic
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from db_helper import get_next_topic
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fallback = get_next_topic(priority='高') or get_next_topic()
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if fallback:
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logger.info(f"无趋势匹配，退回到优先级最高选题: {fallback['id']}")
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return fallback
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def print_report():
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matches = match_trends_to_topics()
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trends = load_trends()
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gaps = detect_trend_gaps()
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print(f"=== 选题匹配报告 ({TODAY}) ===\n")
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print(f"当前趋势数: {len(trends)}")
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print(f"待处理选题: {len([t for t in export_topics_to_json() if t.get('status') in ('pending', '待处理')])}")
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print(f"趋势匹配数: {len(matches)}")
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print(f"趋势缺口: {len(gaps)}\n")
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if matches:
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print(f"{'排名':>4} {'选题ID':6} {'综合分':>6} {'命中趋势':>6} {'趋势详情':30} {'选题标题'}")
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print("-" * 100)
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for i, m in enumerate(matches[:10], 1):
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trends_str = ", ".join(t["trend"][:10] for t in m["matched_trends"][:3])
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print(f"{i:>4} {m['topic_id']:6} {m['total_score']:>6.1f} {m['matched_count']:>6} {trends_str:30} {m['topic_title'][:40]}")
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if gaps:
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print(f"\n=== 趋势缺口（无匹配选题）===")
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for g in gaps:
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print(f" ⚠️ {g['topic']}（{g.get('domain','')}）— {g.get('reason','')}")
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if __name__ == "__main__":
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logging.basicConfig(level=logging.INFO)
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print_report()
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print()
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new = suggest_new_topics()
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if new:
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print(f"\n新选题建议已保存到 automation/data/topic_suggestions.json")
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