#!/usr/bin/env python3 # -*- coding: utf-8 -*- """ 文章与图片智能挂靠脚本 根据文章标签匹配ai_image_tags表中的图片，使用大模型进行处理， 如果挂靠失败或没有相同标签的图片，则使用Gemini生成图片 """ import json import os import re import requests import csv import pymysql from typing import List, Dict, Tuple, Optional from collections import defaultdict from database_config import db_manager from log_config import setup_logger import time import random from requests.adapters import HTTPAdapter from urllib3.util.retry import Retry def get_articles_with_tags_from_db() -> List[Dict]: """ 从数据库获取文章及其标签 Returns: 包含文章信息的字典列表 """ # 设置日志记录器 logger = setup_logger('article_matching', 'logs/article_matching.log', 'logs/article_matching_error.log') articles = [] try: # 查询审核通过的文章，包含内容和标签 sql = """ SELECT id, title, content, coze_tag FROM ai_articles WHERE status = 'approved' ORDER BY id """ logger.info("开始查询审核通过的文章数据...") results = db_manager.execute_query(sql) if not results: logger.warning("没有找到状态为approved的文章") print("没有找到状态为approved的文章") return articles logger.info(f"查询到 {len(results)} 条审核通过的文章") print(f"查询到 {len(results)} 条审核通过的文章") for row in results: article_id, title, content, coze_tag = row # 解析标签 tags = [] if coze_tag: try: # 尝试解析JSON格式的标签 tags_data = json.loads(coze_tag) if isinstance(tags_data, list): tags = tags_data elif isinstance(tags_data, dict): # 如果是字典格式，提取值 tags = list(tags_data.values()) if isinstance(list(tags_data.values())[0], list) else list(tags_data.values()) else: # 如果是字符串，尝试按逗号分割 tags = [tag.strip() for tag in str(tags_data).split(',') if tag.strip()] except json.JSONDecodeError: # 如果不是JSON格式，按逗号分割 tags = [tag.strip() for tag in str(coze_tag).split(',') if tag.strip()] articles.append({ 'id': article_id, 'title': title, 'content': content, 'tags': tags }) except Exception as e: logger.error(f"从数据库获取文章数据时发生错误: {e}", exc_info=True) print(f"从数据库获取文章数据时发生错误: {e}") raise return articles def get_images_by_tags_from_db(tags: List[str] = [], used_counts: Dict[str, int] = {}) -> List[Dict]: """ 从数据库根据标签获取图片 Args: tags: 标签列表 used_counts: 已使用次数的字典，key为图片ID，value为使用次数 Returns: 包含图片信息的字典列表 """ if not tags: return [] # 设置日志记录器 logger = setup_logger('article_matching', 'logs/article_matching.log', 'logs/article_matching_error.log') images = [] try: # 查询符合条件的图像标签数据 sql = """ SELECT id, image_id, image_name, image_url, tag_name, keywords_name, department_name, image_attached_article_count FROM ai_image_tags WHERE image_attached_article_count < 5 ORDER BY id """ logger.info("开始查询符合条件的图像标签数据...") results = db_manager.execute_query(sql) if not results: logger.warning("没有找到符合条件的图像标签数据 (image_attached_article_count < 5)") print("没有找到符合条件的图像标签数据 (image_attached_article_count < 5)") return images logger.info(f"查询到 {len(results)} 条符合条件的图像标签数据") print(f"查询到 {len(results)} 条符合条件的图像标签数据") for row in results: ( image_id, db_image_id, image_name, image_url, tag_name, keywords_name, department_name, base_count ) = row # 检查图片的附加文章数量是否小于5，考虑已使用次数 used_count = used_counts.get(str(image_id), 0) total_count = base_count + used_count if total_count >= 5: continue # 检查标签是否匹配 if any(tag.lower() in tag_name.lower() for tag in tags): images.append({ 'id': str(image_id), 'image_id': db_image_id, 'image_name': image_name, 'image_url': image_url, 'tag_name': tag_name, 'keywords_name': keywords_name, 'department_name': department_name, 'base_count': base_count }) except Exception as e: logger.error(f"从数据库获取图片数据时发生错误: {e}", exc_info=True) print(f"从数据库获取图片数据时发生错误: {e}") raise print(f"从数据库找到 {len(images)} 张符合条件的匹配图片") return images def call_qwen_model(article: Dict, image_urls: List[str]) -> bool: """ 调用通义千问大模型进行文章与图片挂靠评估 Args: article: 文章信息 image_urls: 图片URL列表 Returns: 挂靠是否成功 """ # 通义千问API配置 api_key = "sk-e6a38204022a4b538b8954f0584712af" api_url = "https://dashscope.aliyuncs.com/api/v1/services/aigc/text-generation/generation" # 构建请求内容 content = f""" 请评估以下文章与图片的匹配度： 文章标题: {article['title']} 文章内容: {article['content'][:500]}... # 限制内容长度 图片URLs: {', '.join(image_urls)} 请判断这些图片是否适合用于这篇文章。如果匹配度高，请回复"匹配成功"；如果匹配度低，请回复"匹配失败"。 """ headers = { 'Authorization': f'Bearer {api_key}', 'Content-Type': 'application/json' } payload = { "model": "qwen-max", # 或其他合适的模型 "input": { "messages": [ { "role": "user", "content": content } ] }, "parameters": { "temperature": 0.7 } } try: response = requests.post(api_url, headers=headers, json=payload) if response.status_code == 200: result = response.json() # 解析响应，判断匹配结果 if 'output' in result and 'text' in result['output']: response_text = result['output']['text'].lower() # 根据响应内容判断是否匹配 if '匹配成功' in response_text or '是的' in response_text or '合适' in response_text: print(f"通义千问评估结果: 匹配成功 - 文章 '{article['title']}'") return True else: print(f"通义千问评估结果: 匹配失败 - 文章 '{article['title']}'") return False else: print(f"通义千问API响应格式异常: {result}") return False else: print(f"通义千问API调用失败: {response.status_code} - {response.text}") # API调用失败时，仍然尝试匹配，这里返回False触发图片生成 return False except Exception as e: print(f"调用通义千问API时发生错误: {e}") # 发生错误时，返回False以触发图片生成 return False def insert_generated_image_to_db(image_name: str, image_url: str, article_tags: List[str]) -> Optional[Dict]: """ 将Gemini生成的图片信息插入数据库 Args: image_name: 图片文件名，如 "1755310671174988.png" image_url: 图片URL路径，如 "20250816/1755310671174988.png" article_tags: 文章标签列表，用于查询department和keywords Returns: 包含插入信息的字典：{ 'tag_image_id': tag_image_id, 'image_id': image_id, 'image_url': image_url, 'image_thumb_url': image_thumb_url, 'keywords_id': keywords_id, 'keywords_name': keywords_name, 'department_id': department_id, 'department_name': department_name } """ connection = db_manager.get_connection() if connection is None: print("无法连接到数据库") return None try: with connection.cursor(pymysql.cursors.DictCursor) as cursor: # 1. 根据文章标签查询ai_image_tags表，获取department和keywords信息 if article_tags: # 使用第一个标签查询 query = """ SELECT department_name, keywords_name, department_id, keywords_id, tag_id FROM ai_image_tags WHERE tag_name = %s LIMIT 1 """ cursor.execute(query, (article_tags[0],)) tag_info = cursor.fetchone() if tag_info: department = tag_info['department_name'] keywords = tag_info['keywords_name'] department_id = tag_info['department_id'] keywords_id = tag_info['keywords_id'] tag_id = tag_info['tag_id'] tag_name = article_tags[0] else: # 如果没有找到，使用默认值 department = "AI生成" keywords = "AI图片" department_id = 1 keywords_id = 1 tag_id = 1 tag_name = article_tags[0] if article_tags else "AI生成" else: # 没有标签，使用默认值 department = "AI生成" keywords = "AI图片" department_id = 1 keywords_id = 1 tag_id = 1 tag_name = "AI生成" # 2. 插入ai_images表 insert_image_query = """ INSERT INTO ai_images (image_name, image_url, image_thumb_url, department, keywords, image_type, upload_user_id, status) VALUES (%s, %s, %s, %s, %s, %s, %s, %s) """ cursor.execute(insert_image_query, ( image_name, image_url, '', # image_thumb_url department, keywords, 'medical', # image_type 1, # upload_user_id（默认用户ID） 'active' # status )) image_id = cursor.lastrowid print(f"图片信息已插入ai_images表，image_id: {image_id}") # 3. 插入ai_image_tags表 insert_tag_query = """ INSERT INTO ai_image_tags (image_id, image_name, image_url, image_thumb_url, tag_id, tag_name, keywords_id, keywords_name, department_id, department_name, image_source, created_user_id, image_attached_article_count) VALUES (%s, %s, %s, %s, %s, %s, %s, %s, %s, %s, %s, %s, %s) """ cursor.execute(insert_tag_query, ( image_id, image_name, image_url, '', # image_thumb_url tag_id, tag_name, keywords_id, keywords, department_id, department, 3, # image_source: 3表示AI生成 1, # created_user_id 0 # image_attached_article_count )) tag_image_id = cursor.lastrowid print(f"图片标签信息已插入ai_image_tags表，tag_image_id: {tag_image_id}") # 提交事务 connection.commit() # 返回包含所有需要信息的字典 return { 'tag_image_id': tag_image_id, 'image_id': image_id, 'image_url': image_url, 'image_thumb_url': '', 'keywords_id': keywords_id, 'keywords_name': keywords, 'department_id': department_id, 'department_name': department } except Exception as e: print(f"插入图片信息到数据库失败: {e}") connection.rollback() return None finally: connection.close() def insert_article_image_relation(article_id: int, image_id: int, image_url: str, image_thumb_url: str, tag_image_id: int, keywords_id: int, keywords_name: str, department_id: int, department_name: str, image_source: int = 0) -> Optional[int]: """ 将文章与图片的关联信息插入ai_article_images表 Args: article_id: 文章ID image_id: 图片ID（ai_images表的id） image_url: 图片URL image_thumb_url: 缩略图URL tag_image_id: 图片标签ID（ai_image_tags表的id） keywords_id: 关键词ID keywords_name: 关键词名称 department_id: 部门ID department_name: 部门名称 image_source: 图片来源（0表示默认） Returns: 插入的ai_article_images表的ID """ connection = db_manager.get_connection() if connection is None: print("无法连接到数据库") return None try: with connection.cursor(pymysql.cursors.DictCursor) as cursor: # 1. 查询当前文章下已有图片的最大sort_order query_max_sort = """ SELECT COALESCE(MAX(sort_order), 0) as max_sort_order FROM ai_article_images WHERE article_id = %s """ cursor.execute(query_max_sort, (article_id,)) result = cursor.fetchone() max_sort_order = result['max_sort_order'] if result else 0 new_sort_order = max_sort_order + 1 print(f"文章 {article_id} 当前最大sort_order: {max_sort_order}, 新图片sort_order: {new_sort_order}") # 2. 插入ai_article_images表 insert_query = """ INSERT INTO ai_article_images (article_id, image_id, image_url, image_thumb_url, image_tag_id, sort_order, keywords_id, keywords_name, department_id, department_name, image_source) VALUES (%s, %s, %s, %s, %s, %s, %s, %s, %s, %s, %s) """ cursor.execute(insert_query, ( article_id, image_id, image_url, image_thumb_url, tag_image_id, new_sort_order, keywords_id, keywords_name, department_id, department_name, image_source )) article_image_id = cursor.lastrowid print(f"文章图片关联信息已插入ai_article_images表，id: {article_image_id}") # 提交事务 connection.commit() return article_image_id except Exception as e: print(f"插入文章图片关联信息失败: {e}") connection.rollback() return None finally: connection.close() def generate_image_with_gemini(prompt: str, article_tags: List[str], article_id: int) -> str: """ 使用Gemini生成图片并上传到服务器 Args: prompt: 图片生成提示词 article_tags: 文章标签列表，用于查询department和keywords article_id: 文章ID，用于关联图片 Returns: 上传后的图片URL """ # 导入必要的库 try: from google import genai from google.genai import types from google.genai.client import HttpOptions except ImportError: print("错误：未安装google-genai库，请运行 'pip install google-genai' 进行安装") raise client = genai.Client(http_options=HttpOptions(base_url="https://work.poloapi.com"), api_key="sk-V4tPnDgzFPa7nxWrvKnNJsW8ZcBXXPuGmjfgvPVRnwpHoeob") print(f"正在调用Gemini API生成图片，提示词: {prompt[:50]}...") # 生成内容 response = client.models.generate_content( model="gemini-3-pro-image-preview", contents=[prompt], ) # 检查是否有候选答案 if not response.candidates: raise Exception("Gemini API未返回任何候选答案") # 处理响应 - 遍历第一个候选答案的内容部分 candidate = response.candidates[0] if not candidate.content or not candidate.content.parts: raise Exception("Gemini API返回的候选答案中没有内容部分") for part in candidate.content.parts: if hasattr(part, 'text') and part.text is not None: print(f"Gemini响应文本: {part.text}") elif hasattr(part, 'inline_data') and part.inline_data is not None: image_data = part.inline_data if image_data.data is not None: # 生成唯一的文件名（基于时间戳） import time import os from datetime import datetime timestamp_ms = int(time.time() * 1000) # 毫秒级时间戳 image_filename = f"{timestamp_ms}.png" today_date = datetime.now().strftime("%Y%m%d") image_url_path = f"{today_date}/{image_filename}" temp_filename = f"temp_generated_image_{timestamp_ms}.png" # 保存图片数据到临时文件 with open(temp_filename, 'wb') as f: f.write(image_data.data) print(f"Gemini生成图片成功: {temp_filename}") # 先将图片信息插入数据库，获取相关信息 image_info = insert_generated_image_to_db(image_filename, image_url_path, article_tags) if not image_info: raise Exception("插入图片信息到数据库失败") print(f"图片信息已插入数据库，tag_image_id: {image_info['tag_image_id']}, image_id: {image_info['image_id']}") # 使用tag_image_id上传图片到服务器 uploaded_url = upload_image_to_server(temp_filename, image_info['tag_image_id']) # 将文章与图片的关联信息插入ai_article_images表 article_image_id = insert_article_image_relation( article_id=article_id, image_id=image_info['image_id'], image_url=image_info['image_url'], image_thumb_url=image_info['image_thumb_url'], tag_image_id=image_info['tag_image_id'], keywords_id=image_info['keywords_id'], keywords_name=image_info['keywords_name'], department_id=image_info['department_id'], department_name=image_info['department_name'], image_source=0 # 默认值 ) if article_image_id: print(f"文章图片关联信息已创建，ai_article_images.id: {article_image_id}") # 删除临时文件 os.remove(temp_filename) print(f"图片已上传到服务器: {uploaded_url}") # 返回上传后的图片URL return uploaded_url # 如果没有返回图片数据，抛出异常 raise Exception("Gemini API未返回有效的图片数据") def upload_image_to_server(image_path: str, tag_image_id: int) -> str: """ 上传图片到服务器 Args: image_path: 本地图片路径 tag_image_id: 图片标签ID Returns: 服务器上的图片URL """ import requests import json # 登录获取JWT token base_url = "http://47.99.184.230:8324" # 使用外网API地址 jwt_token = login_and_get_jwt_token(base_url) if not jwt_token: raise Exception("获取JWT token失败，无法上传图片") # 准备上传请求 upload_url = f"{base_url}/api/images/upload" headers = { 'Authorization': f'Bearer {jwt_token}', } # 读取图片文件 with open(image_path, 'rb') as image_file: files = {'file': image_file} data = {'tag_image_id': tag_image_id} # 添加必传参数 response = requests.post(upload_url, headers=headers, files=files, data=data) print(f"图片上传响应状态码: {response.status_code}") print(f"图片上传响应内容: {response.text}") if response.status_code == 200: result = response.json() if result.get('code') == 200: # 返回服务器上的图片URL return result['data']['http_image_url'] else: raise Exception(f"图片上传失败: {result.get('message', '未知错误')}") else: raise Exception(f"图片上传请求失败，状态码: {response.status_code}, 响应: {response.text}") def login_and_get_jwt_token(base_url: str) -> Optional[str]: """ 登录获取JWT token """ login_url = f"{base_url}/api/auth/login" login_data = { "username": "user010", # 使用固定的账号 "password": "@5^2W6R7" } print(f"尝试登录: {login_data['username']}") print(f"登录URL: {login_url}") try: response = requests.post(login_url, json=login_data, headers={'Content-Type': 'application/json'}) print(f"响应状态码: {response.status_code}") if response.status_code == 200: result = response.json() if result.get('code') == 200: jwt_token = result['data']['token'] print("JWT token获取成功") return jwt_token else: print(f"登录失败: {result.get('message', '未知错误')}") return None else: print(f"登录请求失败: {response.status_code}") return None except Exception as e: print(f"登录异常: {e}") return None def batch_publish_articles(base_url: str, jwt_token: str, article_ids: List[int]) -> bool: """ 批量提交文章到/api/articles/batch-publish-auto接口 """ try: print(f"开始批量提交 {len(article_ids)} 篇文章到batch-publish-auto接口") # 构建批量发布数据 publish_data = { "article_ids": article_ids } print(f"准备批量提交的数据: {json.dumps(publish_data, ensure_ascii=False)}") # 发送请求 upload_url = f"{base_url}/api/articles/batch-publish-auto" headers = { 'Authorization': f'Bearer {jwt_token}', 'Content-Type': 'application/json', 'Accept': 'application/json' } response = requests.post(upload_url, json=publish_data, headers=headers) print(f"批量提交响应状态码: {response.status_code}") if response.status_code == 200: try: result = response.json() print(f"批量提交响应内容: {result}") # 根据接口实际返回格式判断成功 if result.get('code') == 200: data = result.get('data', {}) published_count = data.get('published_count', 0) failed_count = data.get('failed_count', 0) success_msg = f"批量提交成功，发布: {published_count}篇，失败: {failed_count}篇" print(success_msg) return True else: print(f"批量提交失败: {result.get('message', '未知错误')}") return False except json.JSONDecodeError as e: print(f"解析批量提交响应失败: {e}") return False elif response.status_code == 401: # Token过期 print("收到401错误，JWT token可能已过期") return False else: print(f"批量提交请求失败，状态码: {response.status_code}") return False except Exception as e: print(f"批量提交异常: {e}") return False def process_single_article(article, used_image_counts, match_results): """ 处理单个文章与图片的匹配和挂靠 Args: article: 单个文章数据 used_image_counts: 图片使用计数 match_results: 匹配结果列表 Returns: 是否处理成功 """ print(f"\n处理文章: {article['title']} (ID: {article['id']})") # 根据文章标签获取匹配的图片（考虑已使用次数） matched_images = get_images_by_tags_from_db(article['tags'], used_image_counts) if matched_images: print(f"找到 {len(matched_images)} 张符合条件的匹配图片") # 按基础使用次数排序，优先使用基础计数较低的图片 matched_images.sort(key=lambda x: x['base_count']) matched = False for img in matched_images: # 提取图片URL并添加前缀 image_url = "http://images11.bxmkb.cn/Images/" + img['image_url'] if image_url: # 确保图片URL存在 # 调用通义千问大模型进行挂靠评估 match_success = call_qwen_model(article, [image_url]) if match_success: print(f"文章与图片挂靠成功: {article['title']}") # 更新图片使用次数 used_image_counts[img['id']] += 1 # 记录匹配结果 match_results.append({ '文章ID': article['id'], '文章标题': article['title'], '文章内容': article['content'][:100] + '...' if len(article['content']) > 100 else article['content'], # 限制内容长度 '标签': ', '.join(article['tags']), '匹配的图片URL': image_url, '图片ID': img['id'], '图片名称': img['image_name'], '图片标签': img['tag_name'], '图片关键词': img['keywords_name'], '图片部门': img['department_name'], '匹配状态': '成功' }) return True if not matched: print(f"文章未能与任何图片成功匹配，使用Gemini生成图片: {article['title']}") # 使用文章标题和标签生成提示词 prompt = f"与'{article['title']}'相关的插图，标签: {', '.join(article['tags'])}" generated_image_url = generate_image_with_gemini(prompt, article['tags'], article['id']) print(f"生成的图片URL: {generated_image_url}") # 记录生成图片的结果 match_results.append({ '文章ID': article['id'], '文章标题': article['title'], '文章内容': article['content'][:100] + '...' if len(article['content']) > 100 else article['content'], '标签': ', '.join(article['tags']), '匹配的图片URL': generated_image_url, '图片ID': 'N/A', '图片名称': 'Generated', '图片标签': 'N/A', '图片关键词': 'N/A', '图片部门': 'N/A', '匹配状态': '生成图片' }) return True else: print(f"没有找到符合条件的匹配图片，使用Gemini生成图片: {article['title']}") # 使用文章标题和标签生成提示词 prompt = f"与'{article['title']}'相关的插图，标签: {', '.join(article['tags'])}" generated_image_url = generate_image_with_gemini(prompt, article['tags'], article['id']) print(f"生成的图片URL: {generated_image_url}") # 记录生成图片的结果 match_results.append({ '文章ID': article['id'], '文章标题': article['title'], '文章内容': article['content'][:100] + '...' if len(article['content']) > 100 else article['content'], '标签': ', '.join(article['tags']), '匹配的图片URL': generated_image_url, '图片ID': 'N/A', '图片名称': 'Generated', '图片标签': 'N/A', '图片关键词': 'N/A', '图片部门': 'N/A', '匹配状态': '生成图片' }) return True def process_article_image_matching(test_mode=False, test_count=None): """ 处理文章与图片的匹配和挂靠 Args: test_mode: 是否为测试模式 test_count: 测试文章数量（仅在测试模式下使用） """ # 用于跟踪每张图片的使用次数 used_image_counts = defaultdict(int) # 存储匹配结果 match_results = [] try: # 根据模式决定获取哪些文章 articles = get_articles_with_tags_from_db() if not articles: print("没有找到文章") return # 如果是测试模式，只取前test_count条数据 if test_mode: if test_count is None: test_count = 3 # 默认测试前3条 articles = articles[:test_count] print(f"测试模式：处理前 {len(articles)} 篇文章") success_count = 0 generated_count = 0 # 收集所有处理后的文章ID用于发布 processed_article_ids = [] for article in articles: if process_single_article(article, used_image_counts, match_results): success_count += 1 processed_article_ids.append(article['id']) else: print(f"处理文章 {article['id']} 失败") # 将匹配结果写入CSV文件 output_csv = 'article_image_match_results.csv' with open(output_csv, 'w', newline='', encoding='utf-8-sig') as csvfile: fieldnames = [ '文章ID', '文章标题', '文章内容', '标签', '匹配的图片URL', '图片ID', '图片名称', '图片标签', '图片关键词', '图片部门', '匹配状态' ] writer = csv.DictWriter(csvfile, fieldnames=fieldnames) writer.writeheader() for result in match_results: writer.writerow(result) if not test_mode: print(f"\n处理完成! 成功挂靠: {success_count} 篇, 生成图片: {generated_count} 篇") print(f"匹配结果已保存至: {output_csv}") # 如果有处理过的文章，将它们提交到发布接口 if processed_article_ids: print(f"\n开始发布处理过的 {len(processed_article_ids)} 篇文章...") # 登录获取JWT token base_url = "http://47.99.184.230:8324" # 使用外网API地址 jwt_token = login_and_get_jwt_token(base_url) if jwt_token: # 批量发布文章 if batch_publish_articles(base_url, jwt_token, processed_article_ids): print(f"成功发布 {len(processed_article_ids)} 篇文章") else: print("批量发布失败") else: print("获取JWT token失败，无法发布文章") else: print("\n没有处理过的文章，跳过发布步骤") else: print(f"\n测试模式完成! 处理了 {len(articles)} 篇文章，成功挂靠: {success_count} 篇, 生成图片: {len([r for r in match_results if r['匹配状态'] == '生成图片'])} 篇") print(f"处理结果已保存至: {output_csv}") except Exception as e: print(f"处理文章图片匹配时发生错误: {e}") raise if __name__ == "__main__": import sys print("开始处理文章与图片的智能挂靠...") # 检查命令行参数 if len(sys.argv) > 1: if sys.argv[1] == "--test" and len(sys.argv) > 2: # 测试模式：处理前N篇文章 test_count = int(sys.argv[2]) print(f"启动测试模式，处理前 {test_count} 篇文章") process_article_image_matching(test_mode=True, test_count=test_count) elif sys.argv[1] == "--test" and len(sys.argv) == 2: # 提示用户输入要测试的文章数量 test_count_input = input("请输入要测试的文章数量 (默认3): ") test_count = int(test_count_input) if test_count_input.strip().isdigit() else 3 print(f"启动测试模式，处理前 {test_count} 篇文章") process_article_image_matching(test_mode=True, test_count=test_count) else: print("使用方法:") print(" 正常模式: python match_article_images.py") print(" 测试模式: python match_article_images.py --test [文章ID]") else: # 正常模式：处理所有文章 process_article_image_matching()