配置全面迁移数据库：PromptConfig、TaskConfig动态调度、敏感词/清洗规则/趋势映射/平台标签/痛点模板全部可编辑

- 新增 PromptConfig 模型 + API，支持提示词在线编辑（16条默认）
- 调度器动态读取 TaskConfig.schedule，admin 可调执行时间
- 新增 KeywordDomainMap、SensitiveWord、ContentCleanRule、TrendFieldMapping 表
- DOMAINS、TREND_DOMAIN_MAP、PLATFORM_TAGS、china_pains、RSS关键词、priority_weights 全部迁移到 DB
- tasks.html 重构：卡片网格+配置/产出/历史/提示词四个Tab，折叠显示
- 清理冗余代码：DEFAULT_PROMPTS死代码、collector.py unreachable代码、compliance_checker bug
- strip_thinking_html 改用 DB 规则优先

scripts/collector.py

@@ -39,7 +39,48 @@ logging.basicConfig(
         logging.StreamHandler()
     ]
 )
-logger = logging.getLogger(__name__)
+DEFAULT_CHINA_PAINS = {
+    "循环消费": "以旧换新流程繁琐、二手商品信任缺失、租赁市场不规范",
+    "低碳出行": "新能源车充电设施不足、城市规划不支持骑行、通勤距离长",
+    "干净饮食": "有机食品价格高、真伪难辨、外卖为主的生活方式难以改变",
+    "零浪费生活": "环保产品溢价高、可持续选择不便、漂绿营销难以分辨",
+    "绿色家电与节能": "绿色家电初期投入高、节能效果难量化、老旧小区改造难",
+    "碳普惠": "碳账户普及率低、减排量兑换吸引力不足、公众认知有限",
+    "环保科技产品": "绿色产品溢价68%难以承受、缺乏统一认证标准、担心漂绿",
+    "AI与效率": "AI工具选择困难、数据隐私担忧、学习成本高、实际效果难验证"
+}
+
+_cached_china_pains = None
+
+def _load_china_pains():
+    global _cached_china_pains
+    if _cached_china_pains is not None:
+        return _cached_china_pains
+    try:
+        from app.database import SessionLocal
+        from app.models import CollectorCategory
+        db = SessionLocal()
+        try:
+            cats = db.query(CollectorCategory).filter(
+                CollectorCategory.is_active == True,
+                CollectorCategory.pain_template.isnot(None),
+                CollectorCategory.pain_template != ""
+            ).all()
+            if cats:
+                _cached_china_pains = {c.name: c.pain_template for c in cats}
+                logger.info(f"从DB加载 {len(_cached_china_pains)} 个类别的pain_template")
+                return _cached_china_pains
+        finally:
+            db.close()
+    except Exception as e:
+        logger.warning(f"从DB加载 china_pains 失败: {e}")
+    _cached_china_pains = DEFAULT_CHINA_PAINS
+    return _cached_china_pains
+
+
+def _get_china_pain(category: str) -> str:
+    pains = _load_china_pains()
+    return pains.get(category, "中国相关数据不足,需本土化验证")
 
 
 @dataclass
@@ -322,14 +363,8 @@ class SustainabilityCollector:
                 if not content:
                     content = title
 
-                # 关键词匹配(来源特定或全局)
-                keywords = source_keywords if source_keywords else [
-                    'sustainable', 'green', 'eco', 'circular', 'climate', 'carbon',
-                    'zero waste', 'renewable', 'recycle', '环保', '可持续', '碳中和',
-                    '循环经济', '零浪费', '低碳', '生态'
-                ]
-
                 search_text = (title + content).lower()
+                keywords = source_keywords if source_keywords else _load_rss_keywords()
                 if any(keyword.lower() in search_text for keyword in keywords):
                     articles.append({
                         'title': title,
@@ -445,6 +480,8 @@ class SustainabilityCollector:
             logger.warning("LLM不可用，跳过AI选题生成")
             return []
 
+        from prompt_loader import get_prompt, get_prompt_params
+
         existing = self._get_existing_titles()
         existing_hint = ""
         if existing:
@@ -461,28 +498,21 @@ class SustainabilityCollector:
         if cases:
             case_lines = [f"- {c.title[:40]}({c.category})" for c in cases[:5]]
             data_section += "\n采集案例：\n" + "\n".join(case_lines) + "\n"
+        if not data_section:
+            data_section = "（当前无实时采集数据，请基于你对中文互联网趋势的了解直接生成）"
 
         trend_context = self._get_trend_context()
 
-        prompt = f"""你是一个内容策略师。基于以下信息，为「{target_category}」类别生成一个高质量选题。
-
-{data_section if data_section else "（当前无实时采集数据，请基于你对中文互联网趋势的了解直接生成）"}
-{existing_hint}
-
-{trend_context}
-
-输出一个选题，格式JSON：
-{{{{
-  "title": "标题（20字内，含核心关键词）",
-  "core_concept": "核心观点（一句话）",
-  "audience_pain": "受众痛点",
-  "unique_angle": "差异化切入点",
-  "format": "内容形式（趋势洞察/实操指南/对比分析/案例解读）"
-}}}}
-只输出JSON。"""
+        prompt = get_prompt("topic_generate",
+            target_category=target_category,
+            data_section=data_section,
+            existing_hint=existing_hint,
+            trend_context=trend_context,
+        )
 
         try:
-            resp = call_llm(prompt, temperature=0.7)
+            params = get_prompt_params("topic_generate")
+            resp = call_llm(prompt, temperature=params.get("temperature", 0.6), max_tokens=params.get("max_tokens", 2000))
             resp = resp.strip()
             if resp.startswith("```"):
                 resp = resp.split("\n", 1)[1].rsplit("\n", 1)[0]
@@ -570,18 +600,8 @@ class SustainabilityCollector:
             # 实际应用中可用AI提取,这里用前100字符
             core_idea = content[:200] if len(content) > 200 else content
 
-            # 生成中国痛点(基于类别模板)
-            china_pains = {
-                "循环消费": "以旧换新流程繁琐、二手商品信任缺失、租赁市场不规范",
-                "低碳出行": "新能源车充电设施不足、城市规划不支持骑行、通勤距离长",
-                "干净饮食": "有机食品价格高、真伪难辨、外卖为主的生活方式难以改变",
-                "零浪费生活": "环保产品溢价高、可持续选择不便、漂绿营销难以分辨",
-                "绿色家电与节能": "绿色家电初期投入高、节能效果难量化、老旧小区改造难",
-                "碳普惠": "碳账户普及率低、减排量兑换吸引力不足、公众认知有限",
-                "环保科技产品": "绿色产品溢价68%难以承受、缺乏统一认证标准、担心漂绿",
-                "AI与效率": "AI工具选择困难、数据隐私担忧、学习成本高、实际效果难验证"
-            }
-            china_pain = china_pains.get(category, "中国相关数据不足,需本土化验证")
+# 生成中国痛点(基于类别模板，从DB读取pain_template)
+            china_pain = _get_china_pain(category)
 
             # 生成案例
             case = SustainabilityCase(