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fix: 修复章节内容分析导致伏笔回收数据重复问题

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mumu
2026-02-07 15:39:15 +08:00
parent 9ce866ffa5
commit f7eef4d89d
4 changed files with 96 additions and 26 deletions
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backend/app/services/foreshadow_service.py
+79 -17
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@@ -1211,6 +1211,15 @@ class ForeshadowService:
# 重新获取完整的伏笔对象
existing = await self.get_foreshadow(db, existing.get('id'))
# 检查伏笔是否已被回收（防止重复回收）
if existing:
if existing.status == "resolved" and existing.actual_resolve_chapter_number == chapter_number:
logger.info(f"ℹ️ 伏笔已在本章回收过，跳过: {existing.title}")
continue
elif existing.status == "resolved":
logger.warning(f"⚠️ 伏笔已在第{existing.actual_resolve_chapter_number}章回收，跳过: {existing.title}")
continue
# 执行回收
if existing and existing.status == "planted":
# 更新为已回收状态
@@ -1224,20 +1233,42 @@ class ForeshadowService:
existing.resolution_text = fs_data.get("content")
await db.flush()
await db.refresh(existing)
stats["resolved_count"] += 1
stats["updated_ids"].append(existing.id)
if matched_by_content:
stats["matched_by_content"] += 1
logger.info(f"✅ 自动回收伏笔: {existing.title} (ID: {existing.id})")
logger.info(f"✅ 自动回收伏笔: {existing.title} (ID: {existing.id}, status: {existing.status})")
# 从待匹配列表中移除已回收的伏笔
planted_foreshadows = [f for f in planted_foreshadows if f['id'] != existing.id]
elif existing:
logger.warning(f"⚠️ 伏笔状态不是planted，跳过回收: {existing.title} (status: {existing.status})")
else:
# 创建新回收记录（未能匹配到已埋入伏笔）
fs_title = fs_data.get("title", fs_data.get("content", "")[:30])
logger.warning(f"⚠️ 未能匹配到已埋入伏笔，创建新的回收记录: {fs_title}")
reference_chapter = fs_data.get("reference_chapter")
# 检查是否已存在相同的回收记录（防止重复创建）
duplicate_check = await db.execute(
select(Foreshadow).where(
and_(
Foreshadow.project_id == project_id,
Foreshadow.title == fs_title,
Foreshadow.actual_resolve_chapter_number == chapter_number,
Foreshadow.source_type == "analysis",
Foreshadow.status == "resolved"
)
)
)
duplicate_fs = duplicate_check.scalar_one_or_none()
if duplicate_fs:
logger.info(f"ℹ️ 已存在相同的回收记录，跳过: {fs_title}")
continue
logger.warning(f"⚠️ 未能匹配到已埋入伏笔，创建新的回收记录: {fs_title}")
new_resolved_foreshadow = Foreshadow(
id=str(uuid.uuid4()),
project_id=project_id,
@@ -1286,32 +1317,46 @@ class ForeshadowService:
fs_index = analysis_foreshadows.index(fs_data)
source_memory_id = f"analysis_{analysis_id}_{fs_index}"
# 检查是否已存在（可能已经通过 sync_from_analysis 创建）
# 检查是否已存在（防止重复分析创建重复记录）
existing_check = await db.execute(
select(Foreshadow).where(
and_(
Foreshadow.project_id == project_id,
Foreshadow.source_memory_id == source_memory_id
or_(
# 方式1：通过source_memory_id精确匹配
and_(
Foreshadow.project_id == project_id,
Foreshadow.source_memory_id == source_memory_id
),
# 方式2：通过标题+章节号匹配
and_(
Foreshadow.project_id == project_id,
Foreshadow.title == fs_title,
Foreshadow.plant_chapter_number == chapter_number,
Foreshadow.source_type == "analysis",
Foreshadow.status == "planted"
)
)
)
)
existing_fs = existing_check.scalar_one_or_none()
if existing_fs:
# 更新已存在的伏笔，避免重复创建
existing_fs.title = fs_title
existing_fs.content = fs_data.get("content", existing_fs.content)
existing_fs.strength = fs_data.get("strength", existing_fs.strength)
existing_fs.subtlety = fs_data.get("subtlety", existing_fs.subtlety)
existing_fs.hint_text = fs_data.get("keyword", existing_fs.hint_text)
existing_fs.target_resolve_chapter_number = fs_data.get("estimated_resolve_chapter", existing_fs.target_resolve_chapter_number)
# 确保source_memory_id是最新的
existing_fs.source_memory_id = source_memory_id
existing_fs.source_analysis_id = analysis_id
await db.flush()
logger.info(f"📝 更新已存在伏笔（避免重复）: {fs_title}")
logger.info(f"📝 更新已存在伏笔（避免重复）: {fs_title} (ID: {existing_fs.id})")
else:
# 创建新伏笔（使用统一的标识符格式）
# 🔧 修复Bug#7：如果AI没有填写estimated_resolve_chapter，提供合理的默认值
# 创建新伏笔
estimated_resolve = fs_data.get("estimated_resolve_chapter")
if estimated_resolve is None:
# 根据伏笔类型和长线属性计算默认回收章节
# 根据伏笔类型计算默认回收章节
if fs_data.get("is_long_term", False):
estimated_resolve = chapter_number + 15
else:
@@ -1455,11 +1500,12 @@ class ForeshadowService:
通过内容相似度匹配伏笔（备用机制）
匹配策略（按优先级）：
1. 标题完全匹配
1. 标题完全匹配（权重最高）
2. 标题部分匹配（包含关系）
3. 关键词匹配
4. 内容关键词匹配
5. 相关角色匹配 + 分类匹配
3. 标题关键词匹配（去除"回收"等后缀）
4. 关键词匹配
5. 内容关键词匹配
6. 相关角色匹配 + 分类匹配
Args:
resolved_fs_data: 分析结果中的回收伏笔数据
@@ -1479,6 +1525,14 @@ class ForeshadowService:
resolved_characters = set(resolved_fs_data.get("related_characters", []))
reference_chapter = resolved_fs_data.get("reference_chapter")
# 处理标题后缀（兜底机制）
resolved_title_clean = resolved_title
for suffix in ["回收", "揭示", "解答", "兑现"]:
if resolved_title.endswith(suffix):
resolved_title_clean = resolved_title[:-len(suffix)]
logger.debug(f"🔍 去除标题后缀: '{resolved_title}' -> '{resolved_title_clean}'")
break
best_match = None
best_score = 0.0
@@ -1490,17 +1544,25 @@ class ForeshadowService:
fs_characters = set(fs.get("related_characters", []))
fs_plant_chapter = fs.get("plant_chapter_number")
# 策略1: 标题完全匹配（最高分）
# 策略1: 标题匹配
if resolved_title and fs_title:
if resolved_title == fs_title:
score = 1.0
logger.debug(f"🎯 标题完全匹配: '{resolved_title}' == '{fs_title}'")
elif resolved_title_clean and resolved_title_clean == fs_title:
score = 0.95
logger.debug(f"🎯 清理标题匹配: '{resolved_title_clean}' == '{fs_title}'")
elif resolved_title in fs_title or fs_title in resolved_title:
# 标题包含关系
score = max(score, 0.8)
logger.debug(f"🔍 标题包含匹配: '{resolved_title}' <-> '{fs_title}'")
elif resolved_title_clean and (resolved_title_clean in fs_title or fs_title in resolved_title_clean):
score = max(score, 0.75)
logger.debug(f"🔍 清理标题包含匹配: '{resolved_title_clean}' <-> '{fs_title}'")
else:
# 计算标题词重叠
title_overlap = self._calculate_word_overlap(resolved_title, fs_title)
score = max(score, title_overlap * 0.7)
if title_overlap > 0.3:
logger.debug(f"📊 标题词重叠: overlap={title_overlap:.2f}")
# 策略2: 关键词匹配
if resolved_keyword and fs_content: