feat: 热词热力图改为Treemap矩形堆砌布局，面积代表提及次数；赛道分析页面布局调整

backend/app.py

@@ -45,9 +45,11 @@ def now_iso() -> str:
 
 
 def db() -> sqlite3.Connection:
-    conn = sqlite3.connect(DB_PATH)
+    conn = sqlite3.connect(DB_PATH, timeout=10)
     conn.row_factory = sqlite3.Row
+    conn.execute("PRAGMA journal_mode = WAL")
     conn.execute("PRAGMA foreign_keys = ON")
+    conn.execute("PRAGMA busy_timeout = 5000")
     return conn
 
 
@@ -160,7 +162,7 @@ def dashboard():
                 "type": r["type"] or "经营干货",
                 "score": r["score"] or 0,
                 "summary": r["ai_summary"] or "",
-                "entities": json.loads(r["entities"]) if r["entities"] else [],
+                "entities": [b["name"] if isinstance(b, dict) else b for b in (json.loads(r["entities"]) if r["entities"] else [])],
             }
             for r in top_intel
         ],
@@ -251,7 +253,7 @@ def intel_list():
                 "summary": r["ai_summary"] or r["summary"] or "",
                 "keyPoints": json.loads(r["key_points"]) if r["key_points"] else [],
                 "score": r["score"] or 0,
-                "entities": json.loads(r["entities"]) if r["entities"] else [],
+                "entities": [b["name"] if isinstance(b, dict) else b for b in (json.loads(r["entities"]) if r["entities"] else [])],
                 "timeliness": r["timeliness"] or "中",
                 "tags": json.loads(r["tags"]) if r["tags"] else [],
                 "url": r["canonical_url"],
@@ -393,26 +395,41 @@ def brand_detail(brand_id: int):
 @app.get("/api/analysis")
 def analysis():
     with db() as conn:
-        # 赛道热度趋势：按月统计各赛道的平均评分
+        # 赛道热度趋势：按月统计各赛道的文章数和平均评分，热度=文章数×平均评分/10
         heat_rows = conn.execute(
             """
-            SELECT strftime('%m', a.published_at) AS month,
+            SELECT strftime('%Y-%m', a.published_at) AS month,
                    a.category,
-                   AVG(CAST(json_extract(x.result_json, '$.score') AS REAL)) AS heat
+                   COUNT(*) AS article_cnt,
+                   AVG(CAST(json_extract(x.result_json, '$.score') AS REAL)) AS avg_score
             FROM articles a
             LEFT JOIN ai_analyses x ON x.article_id = a.id AND x.analysis_type = 'editorial'
-            WHERE a.crawl_status = 'success' AND a.category IS NOT NULL
+            WHERE a.crawl_status = 'success' AND a.category IS NOT NULL AND a.category != ''
+              AND a.published_at >= date('now', '-12 months')
             GROUP BY month, a.category
             ORDER BY month
             """
         ).fetchall()
 
-        # 开关店对比（从文章中提取的品类趋势数据）
-        category_counts = conn.execute(
+        # 品牌门店规模（按赛道聚合真实门店总数）
+        brand_stores = conn.execute(
             """
-            SELECT a.category, COUNT(*) AS cnt
+            SELECT category, SUM(stores) AS total_stores, COUNT(*) AS brand_cnt
+            FROM brands WHERE category IS NOT NULL AND category != '' AND stores > 0
+            GROUP BY category
+            """
+        ).fetchall()
+
+        # 舆情分布（按赛道统计正面/负面/中性文章数）
+        sentiment_rows = conn.execute(
+            """
+            SELECT a.category,
+                   SUM(CASE WHEN json_extract(x.result_json, '$.sentiment') = 'positive' THEN 1 ELSE 0 END) AS positive_cnt,
+                   SUM(CASE WHEN json_extract(x.result_json, '$.sentiment') = 'negative' THEN 1 ELSE 0 END) AS negative_cnt,
+                   SUM(CASE WHEN json_extract(x.result_json, '$.sentiment') = 'neutral' OR json_extract(x.result_json, '$.sentiment') IS NULL THEN 1 ELSE 0 END) AS neutral_cnt
             FROM articles a
-            WHERE a.crawl_status = 'success' AND a.category IS NOT NULL
+            JOIN ai_analyses x ON x.article_id = a.id AND x.analysis_type = 'editorial'
+            WHERE a.crawl_status = 'success' AND a.category IS NOT NULL AND a.category != ''
             GROUP BY a.category
             """
         ).fetchall()
@@ -432,29 +449,55 @@ def analysis():
             """
         ).fetchone()
 
-        # 热词
-        tag_rows = conn.execute(
+        # 热词热力图：近7天每天各标签出现次数
+        tag_heatmap = conn.execute(
             """
-            SELECT value AS tag, COUNT(*) AS cnt
-            FROM ai_analyses, json_each(json_extract(result_json, '$.tags'))
-            GROUP BY value
-            ORDER BY cnt DESC
-            LIMIT 8
+            SELECT value AS tag,
+                   strftime('%Y-%m-%d', a.published_at) AS day,
+                   COUNT(*) AS cnt
+            FROM ai_analyses x
+            JOIN articles a ON a.id = x.article_id
+            , json_each(json_extract(x.result_json, '$.tags'))
+            WHERE a.published_at >= date('now', '-6 days')
+              AND value IS NOT NULL AND value != ''
+            GROUP BY tag, day
             """
         ).fetchall()
 
-    # 构建热度趋势数据
+        # 热词总量排行（用于选取 TOP 标签）
+        tag_total = conn.execute(
+            """
+            SELECT value AS tag, COUNT(*) AS cnt
+            FROM ai_analyses x
+            JOIN articles a ON a.id = x.article_id
+            , json_each(json_extract(x.result_json, '$.tags'))
+            WHERE a.published_at >= date('now', '-6 days')
+              AND value IS NOT NULL AND value != ''
+            GROUP BY value
+            ORDER BY cnt DESC
+            LIMIT 10
+            """
+        ).fetchall()
+
+    # 构建热度趋势数据（热度 = 文章数 × 平均评分 / 10）
     heat_trend = {}
     for r in heat_rows:
-        month_key = f"{r['month']}月" if r["month"] else "未知"
+        month_key = f"{r['month'][-2:]}月" if r["month"] else "未知"
         if month_key not in heat_trend:
             heat_trend[month_key] = {"month": month_key}
-        heat_trend[month_key][r["category"]] = round(r["heat"] or 0)
+        heat_val = round((r["article_cnt"] or 0) * (r["avg_score"] or 0) / 10)
+        heat_trend[month_key][r["category"]] = heat_val
 
-    # 构建开关店数据
-    open_close = [
-        {"category": r["category"], "新开": r["cnt"], "关闭": max(0, r["cnt"] - int(r["cnt"] * 0.85))}
-        for r in category_counts
+    # 构建品牌门店规模数据
+    brand_stores_data = [
+        {"category": r["category"], "stores": r["total_stores"] or 0, "brands": r["brand_cnt"]}
+        for r in brand_stores
+    ]
+
+    # 构建舆情分布数据
+    sentiment_data = [
+        {"category": r["category"], "正面": r["positive_cnt"] or 0, "负面": r["negative_cnt"] or 0, "中性": r["neutral_cnt"] or 0}
+        for r in sentiment_rows
     ]
 
     # 构建价格分布
@@ -468,11 +511,28 @@ def analysis():
         {"band": "150元以上", "pct": round((price_rows["band6"] or 0) * 100 / total_brands)},
     ]
 
+    # 构建热词热力图数据（用 UTC 日期与 SQLite date('now') 保持一致）
+    from datetime import timedelta, timezone
+    today_utc = datetime.now(timezone.utc).date()
+    days = [(today_utc - timedelta(days=i)).isoformat() for i in range(6, -1, -1)]
+    top_tags = [r["tag"] for r in tag_total]
+    # 构建矩阵 {tag: {day: cnt}}
+    matrix = {tag: {day: 0 for day in days} for tag in top_tags}
+    for r in tag_heatmap:
+        if r["tag"] in matrix and r["day"] in matrix[r["tag"]]:
+            matrix[r["tag"]][r["day"]] = r["cnt"]
+    hot_keywords = {
+        "days": days,
+        "tags": top_tags,
+        "matrix": matrix,
+    }
+
     return jsonify({
         "categoryHeatTrend": list(heat_trend.values()),
-        "openCloseByCategory": open_close,
+        "brandStoresByCategory": brand_stores_data,
+        "sentimentByCategory": sentiment_data,
         "priceBandData": price_bands,
-        "hotKeywords": [{"word": r["tag"], "change": min(r["cnt"] * 5, 50)} for r in tag_rows],
+        "hotKeywords": hot_keywords,
     })