feat(p1): 运营数据接入 + 归因分析 + Know-how自动更新

Phase 3 反馈进化闭环: 1. DataSourceService (运营数据接入): - 新增 datasource-service.js: 抽象适配器模式 - MockAdapter: 模拟运营数据 (开发调试) - FileAdapter: 从本地 JSON 文件读取数据 - 可扩展: 预留 WechatAdapter/DouyinAdapter 接口 - API: GET /data/metrics, /data/trending, /data/attribution 2. 归因分析引擎: - analyzeAttribution(): 自动对比两个时间段指标 - 计算变化百分比和趋势方向 - 自动提取显著变化维度并生成洞察文本 - API: GET /api/company/:id/data/attribution 3. Know-how 自动更新 (memory-service.js): - 新增 updateAgentKnowhow() 函数 - autoReflect 产出的 patterns/lessons 自动写回 SKILL.md - 在 SKILL.md 末尾维护'经验教训(自动沉淀)'段落 - 保留最近 20 条记录，自动清理过旧条目 - 闭环: Agent执行 → 反思 → 经验写入SKILL → 下次读取改进行为

feat(p1): 运营数据接入 + 归因分析 + Know-how自动更新
Phase 3 反馈进化闭环: 1. DataSourceService (运营数据接入): - 新增 datasource-service.js: 抽象适配器模式 - MockAdapter: 模拟运营数据 (开发调试) - FileAdapter: 从本地 JSON 文件读取数据 - 可扩展: 预留 WechatAdapter/DouyinAdapter 接口 - API: GET /data/metrics, /data/trending, /data/attribution 2. 归因分析引擎: - analyzeAttribution(): 自动对比两个时间段指标 - 计算变化百分比和趋势方向 - 自动提取显著变化维度并生成洞察文本 - API: GET /api/company/:id/data/attribution 3. Know-how 自动更新 (memory-service.js): - 新增 updateAgentKnowhow() 函数 - autoReflect 产出的 patterns/lessons 自动写回 SKILL.md - 在 SKILL.md 末尾维护'经验教训(自动沉淀)'段落 - 保留最近 20 条记录，自动清理过旧条目 - 闭环: Agent执行 → 反思 → 经验写入SKILL → 下次读取改进行为
yibo
1 parent 67b54882
Showing 3 changed files with 362 additions and 0 deletions
server/routes/company.js
server/services/datasource-service.js
server/services/memory-service.js
@@ -339,4 +339,56 @@ router.delete(&#39;/:companyId/schedule/slots/:slotId&#39;, (req, res) =&gt; {
   res.json({ success: true });
 });
  
+// ═══════════════════════════════════════
+//  DataSource (运营数据接入)
+// ═══════════════════════════════════════
+
+const { DataSourceService } = require('../services/datasource-service');
+const dataSourceService = new DataSourceService();
+
+// GET /api/company/:companyId/data/metrics — 获取运营指标
+router.get('/:companyId/data/metrics', async (req, res) => {
+  try {
+    const { source = 'mock', startDate, endDate } = req.query;
+    const sd = startDate || new Date(Date.now() - 7 * 86400000).toISOString().slice(0, 10);
+    const ed = endDate || new Date().toISOString().slice(0, 10);
+    const data = await dataSourceService.fetchMetrics(source, { startDate: sd, endDate: ed });
+    res.json(data);
+  } catch (err) {
+    res.status(400).json({ error: err.message });
+  }
+});
+
+// GET /api/company/:companyId/data/trending — 获取热点趋势
+router.get('/:companyId/data/trending', async (req, res) => {
+  try {
+    const { source = 'mock', platform = 'weibo', count = '10' } = req.query;
+    const data = await dataSourceService.fetchTrending(source, { platform, count: parseInt(count, 10) });
+    res.json(data);
+  } catch (err) {
+    res.status(400).json({ error: err.message });
+  }
+});
+
+// GET /api/company/:companyId/data/attribution — 归因分析
+router.get('/:companyId/data/attribution', async (req, res) => {
+  try {
+    const { source = 'mock' } = req.query;
+    const now = new Date();
+    const currentEnd = now.toISOString().slice(0, 10);
+    const currentStart = new Date(now.getTime() - 7 * 86400000).toISOString().slice(0, 10);
+    const previousEnd = new Date(now.getTime() - 7 * 86400000).toISOString().slice(0, 10);
+    const previousStart = new Date(now.getTime() - 14 * 86400000).toISOString().slice(0, 10);
+
+    const analysis = await dataSourceService.analyzeAttribution(
+      source,
+      { startDate: currentStart, endDate: currentEnd },
+      { startDate: previousStart, endDate: previousEnd }
+    );
+    res.json(analysis);
+  } catch (err) {
+    res.status(400).json({ error: err.message });
+  }
+});
+
 module.exports = router;
+/**
+ * DataSourceService — 运营数据接入抽象层
+ *
+ * 提供标准化的数据源接口，支持多平台运营数据接入。
+ * 每个数据源实现 DataSourceAdapter 接口:
+ *   - fetchMetrics(dateRange) → 获取核心指标
+ *   - fetchContent(contentId) → 获取内容详情
+ *   - fetchTrending(params) → 获取热点/趋势
+ *
+ * 已实现的 Adapter:
+ *   - MockAdapter: 本地模拟数据 (开发调试用)
+ *   - FileAdapter: 从本地文件读取数据 (CSV/JSON)
+ *
+ * 可扩展:
+ *   - WechatAdapter: 微信公众号后台数据
+ *   - DouyinAdapter: 抖音数据
+ *   - CustomAPIAdapter: 自定义 REST API
+ */
+
+const fs = require('fs');
+const path = require('path');
+
+const DATA_DIR = path.resolve(__dirname, '../../data');
+
+// ════════════════════════════════════════
+//  DataSource Adapter 基类
+// ════════════════════════════════════════
+
+class DataSourceAdapter {
+  constructor(config = {}) {
+    this.config = config;
+    this.name = 'base';
+  }
+
+  /** @returns {Promise<any>} */
+  async fetchMetrics(dateRange) { return null; }
+  /** @returns {Promise<any>} */
+  async fetchContent(contentId) { return null; }
+  /** @returns {Promise<any>} */
+  async fetchTrending(params) { return null; }
+}
+
+// ════════════════════════════════════════
+//  Mock Adapter — 模拟数据 (开发调试用)
+// ════════════════════════════════════════
+
+class MockAdapter extends DataSourceAdapter {
+  constructor(config) {
+    super(config);
+    this.name = 'mock';
+  }
+
+  async fetchMetrics({ startDate, endDate }) {
+    const days = [];
+    const start = new Date(startDate);
+    const end = new Date(endDate);
+    for (let d = new Date(start); d <= end; d.setDate(d.getDate() + 1)) {
+      days.push({
+        date: d.toISOString().slice(0, 10),
+        views: Math.floor(Math.random() * 10000 + 500),
+        likes: Math.floor(Math.random() * 500 + 20),
+        shares: Math.floor(Math.random() * 100 + 5),
+        comments: Math.floor(Math.random() * 200 + 10),
+        followers: Math.floor(Math.random() * 50 + 1),
+        revenue: Math.round(Math.random() * 5000) / 100,
+      });
+    }
+    return {
+      source: 'mock',
+      period: { startDate, endDate },
+      dailyMetrics: days,
+      summary: {
+        totalViews: days.reduce((s, d) => s + d.views, 0),
+        totalLikes: days.reduce((s, d) => s + d.likes, 0),
+        avgDailyViews: Math.round(days.reduce((s, d) => s + d.views, 0) / days.length),
+      },
+    };
+  }
+
+  async fetchTrending({ platform = 'weibo', count = 10 }) {
+    const topics = [
+      'AI 革命新进展', '年轻人消费降级', '职场新趋势', '健康科技创新',
+      '城市生活方式', '教育改革方向', '新能源汽车竞争', '社交媒体新玩法',
+      '房市最新政策', '投资理财策略', '远程办公生态', '文化IP跨界',
+    ];
+    return {
+      source: 'mock',
+      platform,
+      timestamp: new Date().toISOString(),
+      topics: topics.slice(0, count).map((t, i) => ({
+        rank: i + 1,
+        title: t,
+        hotness: Math.floor(Math.random() * 1000000 + 100000),
+        category: ['tech', 'lifestyle', 'finance', 'culture'][i % 4],
+      })),
+    };
+  }
+}
+
+// ════════════════════════════════════════
+//  File Adapter — 从本地文件读取数据
+// ════════════════════════════════════════
+
+class FileAdapter extends DataSourceAdapter {
+  constructor(config) {
+    super(config);
+    this.name = 'file';
+    this.dataDir = config.dataDir || path.join(DATA_DIR, '_datasources');
+  }
+
+  async fetchMetrics({ startDate, endDate }) {
+    const metricsPath = path.join(this.dataDir, 'metrics.json');
+    if (!fs.existsSync(metricsPath)) return { source: 'file', error: 'metrics.json not found' };
+    const allData = JSON.parse(fs.readFileSync(metricsPath, 'utf-8'));
+
+    // 按日期过滤
+    const filtered = (allData.dailyMetrics || [])
+      .filter(d => d.date >= startDate && d.date <= endDate);
+
+    return {
+      source: 'file',
+      period: { startDate, endDate },
+      dailyMetrics: filtered,
+      summary: {
+        totalViews: filtered.reduce((s, d) => s + (d.views || 0), 0),
+        totalLikes: filtered.reduce((s, d) => s + (d.likes || 0), 0),
+        avgDailyViews: filtered.length > 0
+          ? Math.round(filtered.reduce((s, d) => s + (d.views || 0), 0) / filtered.length)
+          : 0,
+      },
+    };
+  }
+
+  async fetchTrending({ platform = 'local', count = 10 }) {
+    const trendingPath = path.join(this.dataDir, `trending_${platform}.json`);
+    if (!fs.existsSync(trendingPath)) {
+      // fallback to generic trending.json
+      const genericPath = path.join(this.dataDir, 'trending.json');
+      if (!fs.existsSync(genericPath)) return { source: 'file', error: 'trending data not found' };
+      return JSON.parse(fs.readFileSync(genericPath, 'utf-8'));
+    }
+    return JSON.parse(fs.readFileSync(trendingPath, 'utf-8'));
+  }
+}
+
+// ════════════════════════════════════════
+//  DataSourceService — 统一管理入口
+// ════════════════════════════════════════
+
+class DataSourceService {
+  constructor() {
+    this.adapters = new Map();
+    // 默认注册 Mock 适配器
+    this.register('mock', new MockAdapter());
+    this.register('file', new FileAdapter());
+  }
+
+  /**
+   * 注册数据源适配器
+   */
+  register(name, adapter) {
+    this.adapters.set(name, adapter);
+  }
+
+  /**
+   * 获取数据源
+   */
+  getAdapter(name) {
+    return this.adapters.get(name);
+  }
+
+  /**
+   * 获取指标数据 (自动选择适配器)
+   * @param {string} source - 'mock' | 'file' | 'wechat' | ...
+   * @param {object} params - { startDate, endDate }
+   */
+  async fetchMetrics(source, params) {
+    const adapter = this.adapters.get(source);
+    if (!adapter) throw new Error(`DataSource adapter "${source}" not found`);
+    return adapter.fetchMetrics(params);
+  }
+
+  /**
+   * 获取热点趋势
+   */
+  async fetchTrending(source, params) {
+    const adapter = this.adapters.get(source);
+    if (!adapter) throw new Error(`DataSource adapter "${source}" not found`);
+    return adapter.fetchTrending(params);
+  }
+
+  /**
+   * 归因分析 — 对比两个时间段的指标变化
+   *
+   * @param {string} source
+   * @param {object} current - { startDate, endDate } 当前周期
+   * @param {object} previous - { startDate, endDate } 上一周期
+   * @returns {object} 归因分析结果
+   */
+  async analyzeAttribution(source, current, previous) {
+    const currentData = await this.fetchMetrics(source, current);
+    const previousData = await this.fetchMetrics(source, previous);
+
+    const cs = currentData.summary;
+    const ps = previousData.summary;
+
+    const changes = {};
+    for (const key of Object.keys(cs)) {
+      const curr = cs[key] || 0;
+      const prev = ps[key] || 0;
+      const delta = prev > 0 ? ((curr - prev) / prev * 100).toFixed(1) : 'N/A';
+      changes[key] = {
+        current: curr,
+        previous: prev,
+        changePercent: delta,
+        trend: curr > prev ? '📈 上升' : curr < prev ? '📉 下降' : '➡️ 持平',
+      };
+    }
+
+    // 找出最显著的变化维度
+    const significantChanges = Object.entries(changes)
+      .filter(([_, v]) => v.changePercent !== 'N/A' && Math.abs(parseFloat(v.changePercent)) > 10)
+      .sort((a, b) => Math.abs(parseFloat(b[1].changePercent)) - Math.abs(parseFloat(a[1].changePercent)));
+
+    return {
+      source,
+      currentPeriod: current,
+      previousPeriod: previous,
+      changes,
+      insights: significantChanges.map(([key, val]) =>
+        `${key}: ${val.trend} ${val.changePercent}% (${val.previous} → ${val.current})`
+      ),
+      recommendation: significantChanges.length > 0
+        ? `主要变化维度: ${significantChanges[0][0]} (${significantChanges[0][1].changePercent}%)`
+        : '各项指标基本持平，无显著变化',
+    };
+  }
+}
+
+module.exports = { DataSourceService, DataSourceAdapter, MockAdapter, FileAdapter };
@@ -721,6 +721,9 @@ function autoReflect(agentId, convId, meta, output) {
       // 更新 memory.md 的"已验证模式"和"失败教训"段
       updateMemoryMdSections(agentId, reflection);
  
+      // ── Know-how 自动更新: 将经验教训写回 SKILL.md ──
+      updateAgentKnowhow(agentId, reflection);
+
       console.log(`[Memory] 🪞 反思完成: ${agentId} (${reflection.patterns.length} 模式, ${reflection.lessons.length} 教训${reflection.plan ? ', +1 方案固化' : ''})`);
     }
  
@@ -986,6 +989,73 @@ function generateDailyReport(agentId) {
   }
 }
  
+// ════════════════════════════════════════
+//  9. Phase 3: Know-how 自动更新
+// ════════════════════════════════════════
+
+/**
+ * 将反思产出的经验教训写回 Agent 的 SKILL.md
+ * 在 SKILL.md 末尾维护一个 "## 经验教训 (自动沉淀)" 段落
+ *
+ * @param {string} agentId
+ * @param {object} reflection - { patterns: string[], lessons: string[] }
+ */
+function updateAgentKnowhow(agentId, reflection) {
+  try {
+    if (!reflection.patterns.length && !reflection.lessons.length) return;
+
+    const agentDir = getAgentDir(agentId);
+
+    // 查找 SKILL.md（可能在多个路径）
+    const skillPaths = [
+      path.join(agentDir, '.claude', 'skills', 'main_skill', 'SKILL.md'),
+      path.join(agentDir, 'SKILL.md'),
+    ];
+    let skillPath = null;
+    for (const sp of skillPaths) {
+      if (fs.existsSync(sp)) { skillPath = sp; break; }
+    }
+    if (!skillPath) return; // 没有 SKILL.md，跳过
+
+    let content = fs.readFileSync(skillPath, 'utf-8');
+    const date = new Date().toISOString().substring(0, 10);
+
+    // 构建新条目
+    const entries = [];
+    for (const p of reflection.patterns) {
+      entries.push(`- ✅ [${date}] ${p}`);
+    }
+    for (const l of reflection.lessons) {
+      entries.push(`- ⚠️ [${date}] ${l}`);
+    }
+    const newBlock = entries.join('\n');
+
+    // 查找或创建 "经验教训" 段
+    const SECTION_HEADER = '## 经验教训 (自动沉淀)';
+    if (content.includes(SECTION_HEADER)) {
+      // 追加到已有段落
+      content = content.replace(
+        new RegExp(`(${SECTION_HEADER.replace(/[()]/g, '\\$&')}\\n)([\\s\\S]*?)(?=\\n## |$)`),
+        (match, header, existing) => {
+          const trimmed = existing.trim();
+          // 限制最大条目数（保留最近 20 条）
+          const lines = (trimmed + '\n' + newBlock).split('\n').filter(l => l.startsWith('- '));
+          const limited = lines.slice(-20).join('\n');
+          return `${header}${limited}\n`;
+        }
+      );
+    } else {
+      // 新增段落
+      content += `\n\n${SECTION_HEADER}\n\n> 以下经验由系统自动从历史对话中提取沉淀，帮助你避免重复犯错。\n\n${newBlock}\n`;
+    }
+
+    fs.writeFileSync(skillPath, content, 'utf-8');
+    console.log(`[Memory] 📚 Know-how 已更新: ${agentId} SKILL.md (+${entries.length} 条经验)`);
+  } catch (err) {
+    console.error(`[Memory] ⚠️ Know-how 更新失败:`, err.message);
+  }
+}
+
 module.exports = {
   // Phase 1
   saveSessionSummary,