修改文章图片匹配逻辑：合并文章查询、按科室缩小图片范围、按挂载次数排序、按图片类型分类处理
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@@ -79,7 +79,7 @@ class ArticleImageMatcher:
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def get_articles_with_tags(self, limit: int = BATCH_SIZE) -> List[Dict]:
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"""
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从ai_article_tags表获取需要匹配图片的文章
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从ai_articles表获取需要匹配图片的文章
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Returns:
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包含文章ID、标签等信息的列表
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@@ -91,55 +91,32 @@ class ArticleImageMatcher:
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# 查询有标签但未匹配图片的文章
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sql = """
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SELECT
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at.id,
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at.article_id,
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at.coze_tag
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FROM ai_article_tags at
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WHERE at.coze_tag IS NOT NULL
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AND at.coze_tag != ''
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a.id as article_id,
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a.title,
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a.content,
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a.coze_tag,
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a.department
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FROM ai_articles a
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WHERE a.coze_tag IS NOT NULL
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AND a.coze_tag != ''
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AND NOT EXISTS (
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SELECT 1 FROM ai_article_images ai
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WHERE ai.article_id = at.article_id
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WHERE ai.article_id = a.id
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)
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AND at.article_id IN (
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SELECT id FROM ai_articles
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WHERE status = 'pending_review'
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)
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ORDER BY at.id DESC
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AND a.status = 'pending_review'
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ORDER BY a.id DESC
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LIMIT %s
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"""
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cursor.execute(sql, (limit,))
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results = cursor.fetchall()
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# 为每个结果添加文章标题和内容
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processed_results = []
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for row in results:
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# 查询文章标题和内容
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article_sql = """
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SELECT title, content
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FROM ai_articles
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WHERE id = %s
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"""
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cursor.execute(article_sql, (row['article_id'],))
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article_data = cursor.fetchone()
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if article_data:
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processed_row = {
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'id': row['id'],
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'article_id': row['article_id'],
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'coze_tag': row['coze_tag'],
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'title': article_data['title'],
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'content': article_data['content']
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}
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processed_results.append(processed_row)
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if processed_results:
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logger.info(f"查询到 {len(processed_results)} 篇需要匹配图片的文章")
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self.log_to_database('INFO', f"查询到需要匹配图片的文章", f"数量: {len(processed_results)}")
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if results:
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logger.info(f"查询到 {len(results)} 篇需要匹配图片的文章")
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self.log_to_database('INFO', f"查询到需要匹配图片的文章", f"数量: {len(results)}")
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else:
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logger.info("未查询到需要匹配图片的文章")
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return processed_results
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return results
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finally:
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connection.close()
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except Exception as e:
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@@ -159,7 +136,8 @@ class ArticleImageMatcher:
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connection = self.db_manager.get_connection()
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try:
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with connection.cursor(pymysql.cursors.DictCursor) as cursor:
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# 查询状态为generate且附加文章数量小于5的图片
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# 查询状态为generate且附加文章数量小于5的图片（不使用JOIN）
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# 包含image_source字段用于区分实拍图和模板图
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sql = """
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SELECT
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it.id,
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@@ -173,11 +151,14 @@ class ArticleImageMatcher:
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it.keywords_name,
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it.department_id,
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it.department_name,
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it.image_attached_article_count
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it.image_attached_article_count,
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it.image_source
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FROM ai_image_tags it
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INNER JOIN ai_images i ON it.image_id = i.id
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WHERE it.image_attached_article_count < 5
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AND i.status = 'generate'
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AND it.image_id IN (
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SELECT id FROM ai_images
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WHERE status = 'generate'
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)
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ORDER BY it.image_attached_article_count ASC, it.id DESC
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"""
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cursor.execute(sql)
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@@ -763,6 +744,7 @@ class ArticleImageMatcher:
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article_title = article_data.get('title', '')
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article_content = article_data.get('content', '')
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coze_tag = article_data.get('coze_tag', '')
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article_department = article_data.get('department', '')
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try:
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# 解析文章标签
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@@ -771,15 +753,46 @@ class ArticleImageMatcher:
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logger.warning(f"文章 {article_id} 没有有效标签，跳过")
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return False
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logger.info(f"开始为文章 {article_id} 匹配图片 - 标题: {article_title}, 标签: {article_tags}")
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logger.info(f"开始为文章 {article_id} 匹配图片 - 标题: {article_title}, 标签: {article_tags}, 科室: {article_department}")
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# 根据文章科室缩小图片范围
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department_filtered_images = []
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for img in available_images:
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# 优先匹配科室相同的图片
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if article_department and img.get('department_name', '').lower() == article_department.lower():
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department_filtered_images.append(img)
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# 如果没有匹配科室的图片，则使用所有图片
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if not department_filtered_images:
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department_filtered_images = available_images
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# 根据图片类型（实拍图/模板图）进行分类处理
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# 根据image_source字段：1=clean_images(模板图), 2=Flower_character(实拍图)
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actual_photos = [] # 实拍图
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template_photos = [] # 模板图
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for img in department_filtered_images:
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image_source = img.get('image_source', 1) # 默认为模板图
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if image_source == 2: # 实拍图
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actual_photos.append(img)
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else: # 模板图
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template_photos.append(img)
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# 按照挂载次数排序，优先选择挂载次数少的图片
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actual_photos.sort(key=lambda x: x['image_attached_article_count'])
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template_photos.sort(key=lambda x: x['image_attached_article_count'])
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# 合并图片列表，优先使用实拍图，然后是模板图
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filtered_images = actual_photos + template_photos
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best_match = None
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best_score = 0.0
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# 遍历可用图片，找到最佳匹配
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for image_data in available_images:
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# 遍历筛选后的可用图片，找到最佳匹配
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for image_data in filtered_images:
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image_tags = [image_data['tag_name']]
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image_keywords = image_data.get('keywords_name', '')
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image_department = image_data.get('department_name', '')
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# 调用通义千问评估匹配度
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is_match, score = self.call_qwen_for_matching(
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@@ -787,7 +800,7 @@ class ArticleImageMatcher:
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)
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# 记录评估结果
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logger.info(f"文章 {article_id} vs 图片 {image_data['image_id']} - 匹配: {is_match}, 分数: {score}")
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logger.info(f"文章 {article_id} vs 图片 {image_data['image_id']} - 匹配: {is_match}, 分数: {score}, 科室: {image_department}")
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# 更新最佳匹配
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if is_match and score > best_score and score >= MATCH_THRESHOLD:
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