feat: 收视分析看板前端 L1-L4 实现 + 25期真实数据导入

收视分析页面完整实现：指标卡（含四档动画）、走势折线图（dataZoom滑块+确认按钮）、季度/编导/题材对比（双列布局）、双引擎象限图（题材热度×叙事结构散点）。导入25期真实收视数据及AI标签，修复侧边栏fixed定位和滚轮冲突。 Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
2026-07-03 17:48:38 +08:00
parent 8d880f06cf
commit f37679530a
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+"""
+一次性脚本：清除测试数据，导入 25 期真实收视数据 + 回填 AI 标签
+数据来源：
+  - 收视数据：ai-labeling/example/2026收视update.xlsx（已导出为 _tmp_excel.json）
+  - AI 标签：ai-labeling/benchmark-set/ground-truth.json（v0.6.0，制片人审定）
+"""
+
+import json
+import sys
+import os
+from datetime import date
+
+# 加 backend 到 path
+sys.path.insert(0, os.path.join(os.path.dirname(__file__), '..', 'backend'))
+
+from app.db.session import get_session
+from app.models.episode import Episode
+from app.models.user import User  # 注册 User 表元数据，FK 解析需要
+from sqlmodel import select, delete
+
+# ── 1. 读数据源 ──
+project_root = os.path.join(os.path.dirname(__file__), '..')
+
+with open(os.path.join(project_root, '_tmp_excel.json'), 'r', encoding='utf-8') as f:
+    excel_rows = json.load(f)
+
+with open(os.path.join(project_root, 'ai-labeling', 'benchmark-set', 'ground-truth.json'), 'r', encoding='utf-8') as f:
+    gt_data = json.load(f)
+
+# ground-truth 按 ep 编号索引
+gt_map = {ep['ep']: ep for ep in gt_data['episodes']}
+
+print(f"Excel: {len(excel_rows)} rows")
+print(f"Ground-truth: {len(gt_map)} episodes (v{gt_data['version']})")
+
+# ── 2. 构造 Episode 对象 ──
+new_episodes = []
+for row in excel_rows:
+    # 解析期号："第1期" -> 1
+    ep_num_str = row['ep'].replace('第', '').replace('期', '')
+    ep_num = int(ep_num_str)
+
+    # 解析日期："2026 01 06" -> date(2026, 1, 6)
+    parts = row['date'].split()
+    air = date(int(parts[0]), int(parts[1]), int(parts[2]))
+
+    # 编导名（去空格）
+    editor_name = row['editor'].replace(' ', '').replace('　', '')
+
+    # 从 ground-truth 取 AI 标签
+    gt = gt_map.get(ep_num, {})
+
+    ep = Episode(
+        episode_number=ep_num,
+        program_name=row['title'],
+        air_date=air,
+        editor_id=None,          # 软引用，暂不关联 user id
+        editor_name_snapshot=editor_name,
+        audience_share=row['share'],
+        audience_rating=row['rating'],
+        # AI 标签
+        program_format=gt.get('program_format'),
+        equipment_domain=gt.get('equipment_domain'),
+        scene_tags=gt.get('scene_tags'),
+        tech_tags=gt.get('tech_tags'),
+        narrative_structure=gt.get('narrative_structure'),
+        opening_hook=gt.get('opening_hook'),
+        ai_label_confidence='reviewed' if gt else None,
+    )
+    new_episodes.append(ep)
+
+# ── 3. 执行：清旧 + 插新 ──
+session = next(get_session())
+
+# 删除所有旧数据
+old_count = len(session.exec(select(Episode)).all())
+session.exec(delete(Episode))
+print(f"Deleted {old_count} old test episodes")
+
+# 插入新数据
+for ep in new_episodes:
+    session.add(ep)
+
+session.commit()
+print(f"Inserted {len(new_episodes)} real episodes")
+
+# ── 4. 验证 ──
+all_eps = session.exec(select(Episode).order_by(Episode.air_date)).all()
+print(f"\nVerification: {len(all_eps)} episodes in DB")
+labeled = sum(1 for e in all_eps if e.program_format is not None)
+print(f"With program_format: {labeled}/{len(all_eps)}")
+print()
+for e in all_eps:
+    print(f"  ep{e.episode_number:02d} | {str(e.air_date)} | {e.audience_share} | {e.editor_name_snapshot} | {e.program_format or '-'}")