-
{selectedYear} 年收视走势
-
- 拖拽底部滑块缩放 · 颜色只对收视份额
-
+ {/* ── 指标卡 — 5 列网格 ── */}
+
+ {/* 卡 1：平均份额 */}
+
+
+ {kpiMetrics.avgShare || '—'}
+
+
平均份额
+
+ 基础完成率 {kpiMetrics.baseRate}
+
+
+ {/* 卡 2：摸高目标完成率（四档动态效果） */}
+
+
+ {kpiMetrics.stretchPulseClass === 'analytics-kpi-card-excellent' && '🎉 '}
+ {kpiMetrics.stretchRate}
+
+
摸高目标完成率
+
+
+ {/* 卡 3：最高份额 */}
+
+
+ {kpiMetrics.maxShare || '—'}
+
+
最高份额
+
+ 第{kpiMetrics.maxShareEpisode?.episode_number}期{' '}
+ {truncateName(kpiMetrics.maxShareEpisode?.program_name)}
+
+
+
+ {/* 卡 4：最低份额 */}
+
+
+ {kpiMetrics.minShare || '—'}
+
+
最低份额
+
+ 第{kpiMetrics.minShareEpisode?.episode_number}期{' '}
+ {truncateName(kpiMetrics.minShareEpisode?.program_name)}
+
+
+
+ {/* 卡 5：达标期数 */}
+
+
+ {kpiMetrics.passCount}
+
+ /{kpiMetrics.count}
+
+
+
达标期数
+
+ 达标率 {kpiMetrics.passRate} ｜ 优秀{' '}
+ {kpiMetrics.excellentCount} 期
+
+
+
+
+ {/* ── 收视走势图（柱状图 + 折线图组合） ── */}
+
+
收视走势
-
-
-
+
+
+
+ )}
+
+
+ {/* ── AI 诊断报告占位 ── */}
+
+
AI 诊断报告
+
即将上线 - 基于双引擎模型的智能收视诊断
+
+
+ {/* ── 双引擎象限图 ── */}
+
+
双引擎象限分析
+
+
+
+ {/* ── 左列(季度+编导) + 右列(题材 Task3) ── */}
+
+
+
季度对比
+
+
+
+
编导对比
+
+
+
+ {/* 题材对比 — 右列通高 */}
+
+
+
题材对比
+
- 绿=未达基础目标（待提升）
-
-
-
- 蓝=未达摸高目标（达标）
-
-
-
- 红=超过摸高目标（优秀）
-
+
diff --git a/scripts/fix_label_mapping.py b/scripts/fix_label_mapping.py
new file mode 100644
index 0000000..1fe8947
--- /dev/null
+++ b/scripts/fix_label_mapping.py
@@ -0,0 +1,106 @@
+"""
+修复 AI 标签映射：ground-truth ep编号 ≠ Excel 播出期号
+用标题模糊匹配重新关联正确的 AI 标签
+"""
+
+import json
+import sys
+import os
+from difflib import SequenceMatcher
+
+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
+from sqlmodel import select
+
+project_root = os.path.join(os.path.dirname(__file__), '..')
+
+# 读 ground-truth
+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)
+
+gt_episodes = gt_data['episodes']
+
+# 读数据库
+session = next(get_session())
+db_episodes = session.exec(select(Episode).order_by(Episode.episode_number)).all()
+
+print(f"DB episodes: {len(db_episodes)}")
+print(f"GT episodes: {len(gt_episodes)}")
+print()
+
+# ── 标题相似度匹配 ──
+def title_similarity(a, b):
+ """计算两个标题的相似度"""
+ # 清理标题
+ clean_a = a.replace('"', '').replace('"', '').replace("'", '').replace('——', '').replace('：', '').replace(' ', '').replace('《', '').replace('》', '')
+ clean_b = b.replace('"', '').replace('"', '').replace("'", '').replace('——', '').replace('：', '').replace(' ', '').replace('《', '').replace('》', '')
+ return SequenceMatcher(None, clean_a, clean_b).ratio()
+
+# 对每个 DB episode，找最佳匹配的 GT episode
+matched = []
+used_gt = set()
+
+for db_ep in db_episodes:
+ best_score = 0
+ best_gt = None
+
+ for i, gt_ep in enumerate(gt_episodes):
+ if i in used_gt:
+ continue
+
+ gt_title = gt_ep.get('title', '')
+ score = title_similarity(db_ep.program_name, gt_title)
+
+ # 也检查份额是否匹配（辅助判断）
+ gt_share = gt_ep.get('share')
+ share_match = (gt_share is not None and db_ep.audience_share is not None
+ and abs(float(gt_share) - float(db_ep.audience_share)) < 0.01)
+
+ # 份额匹配加分
+ if share_match:
+ score += 0.3
+
+ if score > best_score:
+ best_score = score
+ best_gt = (i, gt_ep)
+
+ if best_gt and best_score > 0.3:
+ used_gt.add(best_gt[0])
+ matched.append((db_ep, best_gt[1], best_score))
+ status = 'OK' if best_score > 0.5 else 'WEAK'
+ print(f"[{status}] DB ep{db_ep.episode_number:02d} \"{db_ep.program_name[:15]}\" -> GT ep{best_gt[1]['ep']:02d} \"{best_gt[1]['title'][:15]}\" (score={best_score:.2f})")
+ else:
+ print(f"[MISS] DB ep{db_ep.episode_number:02d} \"{db_ep.program_name[:15]}\" -> NO MATCH")
+ matched.append((db_ep, None, 0))
+
+print(f"\nMatched: {sum(1 for _,gt,_ in matched if gt is not None)}/{len(db_episodes)}")
+
+# ── 确认后更新 ──
+print("\n--- Updating AI labels ---")
+updated = 0
+for db_ep, gt_ep, score in matched:
+ if gt_ep is None:
+ continue
+
+ db_ep.program_format = gt_ep.get('program_format')
+ db_ep.equipment_domain = gt_ep.get('equipment_domain')
+ db_ep.scene_tags = gt_ep.get('scene_tags')
+ db_ep.tech_tags = gt_ep.get('tech_tags')
+ db_ep.narrative_structure = gt_ep.get('narrative_structure')
+ db_ep.opening_hook = gt_ep.get('opening_hook')
+ db_ep.ai_label_confidence = 'reviewed'
+
+ session.add(db_ep)
+ updated += 1
+
+session.commit()
+print(f"Updated {updated} episodes")
+
+# ── 验证 ──
+print("\n--- Verification ---")
+db_episodes = session.exec(select(Episode).order_by(Episode.episode_number)).all()
+for ep in db_episodes:
+ print(f"ep{ep.episode_number:02d} | {ep.program_name[:18]:18s} | {ep.program_format or '-':8s} | {ep.narrative_structure or '-':6s} | {ep.opening_hook or '-'}")
diff --git a/scripts/import_real_episodes.py b/scripts/import_real_episodes.py
new file mode 100644
index 0000000..7b2c775
--- /dev/null
+++ b/scripts/import_real_episodes.py
@@ -0,0 +1,94 @@
+"""
+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 '-'}")