support text + image hybrid scenariors

config.py

@@ -150,7 +150,7 @@ A_STOCK_MONITOR_CONFIG = {
             "000333.SZ",
             "002230.SZ",
             "300308.SZ",
-            "002475.SZ"
+            "002475.SZ",
         ],
         "bars": ["1D", "1W", "1M"],
         "initial_date": "2015-01-01 00:00:00",
@@ -236,7 +236,7 @@ ITICK_API_KEY = "dfd4bc0caed148d6bc03b960224754ffb5356349e389431f828702b3a27e8a2
 TWITTER_CONFIG = {
     "keys": {
         "api_key": "FRfhlDHnmYc1PCCrVHZdWtqDENr2",
-        },
+    },
     "base_url": "https://api.scrapecreators.com/v1/twitter/user-tweets?handle={0}&trim=false",
     "monitor_accounts": [
         {"name": "realDonaldTrump"},
@@ -247,7 +247,18 @@ TWITTER_CONFIG = {
     ],
 }
 
-TRUTH_SOCIAL_API = {"api_key": "FRfhlDHnmYc1PCCrVHZdWtqDENr2", 
+TRUTH_SOCIAL_API = {
-"user_id": {"realDonaldTrump": "107780257626128497"}}
+    "api_key": "FRfhlDHnmYc1PCCrVHZdWtqDENr2",
+    "media_config": [
+        {
+            "media_name": "Truth Social",
+            "base_url": "https://api.scrapecreators.com/v1/truthsocial/user/posts",
+            "user_info": {
+                "WhiteHouse": {"id": "", "full_name": "白宫"},
+                "realDonaldTrump": {"id": "107780257626128497", "full_name": "川普"},
+            },
+        }
+    ],
+}
 
 ALI_API_KEY = "sk-216039fdd9ee4bc48667418b23e648d0"

core/media/truth_social_retriever.py

@@ -19,7 +19,8 @@ logger = logging.logger
 class TruthSocialRetriever:
     def __init__(self) -> None:
         self.api_key = TRUTH_SOCIAL_API.get("api_key", "")
-        self.user_info = TRUTH_SOCIAL_API.get("user_id", {})
+        self.media_config_list = TRUTH_SOCIAL_API.get("media_config", [])
+        # self.user_info = TRUTH_SOCIAL_API.get("user_id", {})
         mysql_user = COIN_MYSQL_CONFIG.get("user", "xch")
         mysql_password = COIN_MYSQL_CONFIG.get("password", "")
         if not mysql_password:
@@ -53,6 +54,10 @@ class TruthSocialRetriever:
         with open(image_post_instruction_file, "r", encoding="utf-8") as f:
             self.image_post_instruction = json.load(f)
         
+        text_image_post_instruction_file = r"./instructions/media_article_image_post_instructions.json"
+        with open(text_image_post_instruction_file, "r", encoding="utf-8") as f:
+            self.text_image_post_instruction = json.load(f)
+
     def get_user_id_from_page(self, handle="realDonaldTrump"):
         url = f"https://truthsocial.com/@{handle}"
         headers = {
@@ -90,89 +95,96 @@ class TruthSocialRetriever:
         """
         headers = {"x-api-key": self.api_key, "Content-Type": "application/json"}
 
-        for user_name, user_id in self.user_info.items():
+        for media_config in self.media_config_list:
-            params = {
+            media_name = media_config.get("media_name", "")
-                "handle": user_name,  # 用户名
+            logger.info(f"开始获取{media_name}的帖子")
-                "user_id": user_id,  # 可选，用户 ID
+            base_url = media_config.get("base_url", "")
-                "next_max_id": None,  # 分页时设置为上一次响应的 max_id
+            user_info = media_config.get("user_info", {})
-                "trim": "false",  # 保留完整内容
+            for user_name, user_details in user_info.items():
-            }
+                user_id = user_details.get("id", "")
+                user_full_name = user_details.get("full_name", "")
 
-            url = "https://api.scrapecreators.com/v1/truthsocial/user/posts"
+                params = {
-            logger.info(f"Searching contents for user: {user_name}")
+                    "handle": user_name,  # 用户名
-            try:
+                    "user_id": user_id,  # 可选，用户 ID
-                response = requests.get(url, headers=headers, params=params)
+                    "next_max_id": None,  # 分页时设置为上一次响应的 max_id
-                response.raise_for_status()  # 检查 HTTP 错误
+                    "trim": "false",  # 保留完整内容
-                data = response.json()
+                }
 
-                # 提取帖子列表（假设响应中 'posts' 是键，根据实际文档调整）
+                logger.info(f"Searching contents for user: {user_name}")
-                if limit is not None and isinstance(limit, int):
+                try:
-                    posts = data.get("posts", [])[:limit]
+                    response = requests.get(base_url, headers=headers, params=params)
-                else:
+                    response.raise_for_status()  # 检查 HTTP 错误
-                    posts = data.get("posts", [])
+                    data = response.json()
 
-                results = []
+                    # 提取帖子列表（假设响应中 'posts' 是键，根据实际文档调整）
-                if posts:
+                    if limit is not None and isinstance(limit, int):
-                    logger.info(f"获取{user_name}帖子: {len(posts)}条")
+                        posts = data.get("posts", [])[:limit]
-                    for post in posts:
-                        result = {}
-                        result["article_id"] = post.get("id")
-                        result["user_id"] = user_id
-                        result["user_name"] = user_name
-                        datetime_text = post.get("created_at")
-                        datetime_dict = self.transform_datetime(datetime_text)
-                        timestamp_ms = datetime_dict["timestamp_ms"]
-                        result["timestamp"] = timestamp_ms
-                        beijing_time_str = datetime_dict["beijing_time_str"]
-                        result["date_time"] = beijing_time_str
-                        result["text"] = post.get("text", "无内容")
-                        media_attachments = post.get("media_attachments", [])
-                        result["media_url"] = ""
-                        result["media_type"] = ""
-                        result["media_thumbnail"] = ""
-                        if media_attachments:
-                            for media_attachment in media_attachments:
-                                result["media_url"] = media_attachment.get("url")
-                                result["media_type"] = media_attachment.get("type")
-                                result["media_thumbnail"] = media_attachment.get(
-                                    "preview_url"
-                                )
-                                break
-                        results.append(result)
-                else:
-                    print("获取帖子失败，请检查 API 密钥或网络。")
-
-                if len(results) > 0:
-                    result_df = pd.DataFrame(results)
-                    result_df = self.remove_duplicate_posts(result_df)
-                    
-                    if len(result_df) > 0:
-                        result_df["analysis_result"] = ""
-                        result_df["analysis_token"] = 0
-                        result_df = self.send_wechat_message(result_df)
-                        result_df = result_df[
-                            [
-                                "article_id",
-                                "user_id",
-                                "user_name",
-                                "timestamp",
-                                "date_time",
-                                "text",
-                                "analysis_result",
-                                "analysis_token",
-                                "media_url",
-                                "media_type",
-                                "media_thumbnail",
-                            ]
-                        ]
-                        self.db_truth_social_content.insert_data_to_mysql(result_df)
-                        logger.info(f"已将{len(result_df)}条数据插入到数据库")
                     else:
-                        logger.info(f"没有数据需要插入到数据库和发送企业微信消息")
+                        posts = data.get("posts", [])
-            except requests.exceptions.RequestException as e:
+
-                print(f"请求错误: {e}")
+                    results = []
-            except json.JSONDecodeError as e:
+                    if posts:
-                print(f"JSON 解析错误: {e}")
+                        logger.info(f"获取{user_name}帖子: {len(posts)}条")
+                        for post in posts:
+                            result = {}
+                            result["article_id"] = post.get("id")
+                            result["user_id"] = user_id
+                            result["user_name"] = user_name
+                            datetime_text = post.get("created_at")
+                            datetime_dict = self.transform_datetime(datetime_text)
+                            timestamp_ms = datetime_dict["timestamp_ms"]
+                            result["timestamp"] = timestamp_ms
+                            beijing_time_str = datetime_dict["beijing_time_str"]
+                            result["date_time"] = beijing_time_str
+                            result["text"] = post.get("text", "")
+                            media_attachments = post.get("media_attachments", [])
+                            result["media_url"] = ""
+                            result["media_type"] = ""
+                            result["media_thumbnail"] = ""
+                            if media_attachments:
+                                for media_attachment in media_attachments:
+                                    result["media_url"] = media_attachment.get("url")
+                                    result["media_type"] = media_attachment.get("type")
+                                    result["media_thumbnail"] = media_attachment.get(
+                                        "preview_url"
+                                    )
+                                    break
+                            results.append(result)
+                    else:
+                        print("获取帖子失败，请检查 API 密钥或网络。")
+
+                    if len(results) > 0:
+                        result_df = pd.DataFrame(results)
+                        result_df = self.remove_duplicate_posts(result_df)
+                        
+                        if len(result_df) > 0:
+                            result_df["analysis_result"] = ""
+                            result_df["analysis_token"] = 0
+                            result_df = self.send_wechat_message(result_df, user_full_name)
+                            result_df = result_df[
+                                [
+                                    "article_id",
+                                    "user_id",
+                                    "user_name",
+                                    "timestamp",
+                                    "date_time",
+                                    "text",
+                                    "analysis_result",
+                                    "analysis_token",
+                                    "media_url",
+                                    "media_type",
+                                    "media_thumbnail",
+                                ]
+                            ]
+                            self.db_truth_social_content.insert_data_to_mysql(result_df)
+                            logger.info(f"已将{len(result_df)}条数据插入到数据库")
+                        else:
+                            logger.info(f"没有数据需要插入到数据库和发送企业微信消息")
+                except requests.exceptions.RequestException as e:
+                    print(f"请求错误: {e}")
+                except json.JSONDecodeError as e:
+                    print(f"JSON 解析错误: {e}")
 
     def send_message_by_json_file(self, json_file_name: str):
         with open(json_file_name, "r", encoding="utf-8") as f:
@@ -204,7 +216,7 @@ class TruthSocialRetriever:
             logger.error(f"删除重复的行失败: {e}")
         return result_df
 
-    def send_wechat_message(self, result_df: pd.DataFrame):
+    def send_wechat_message(self, result_df: pd.DataFrame, user_full_name: str):
         if self.wechat is None:
             logger.error("企业微信未初始化")
             return
@@ -213,7 +225,72 @@ class TruthSocialRetriever:
                 date_time = row["date_time"]
                 text = row["text"]
                 media_thumbnail = row["media_thumbnail"]
-                if media_thumbnail and len(media_thumbnail) > 0:
+                if len(text) > 0:
+                    if media_thumbnail and len(media_thumbnail) > 0:
+                        contents = []
+                        contents.append(f"## {user_full_name}推文")
+                        contents.append(text)
+                        contents.append(f"## 推文时间")
+                        contents.append(date_time)
+                        mark_down_text = "\n\n".join(contents)
+                        self.wechat.send_markdown(mark_down_text)
+                        response, image_path, base64_str, md5_str = self.wechat.send_image(media_thumbnail)
+                        image_format = "jpg"
+                        if image_path is not None and len(image_path) > 0:
+                            image_format = image_path.split(".")[-1]
+                            if image_format == "jpeg":
+                                image_format = "jpg"
+                        analysis_result, analysis_token = self.analyze_truth_social_content(
+                            text=mark_down_text,
+                            image_stream=base64_str,
+                            image_format=image_format,
+                            media_type="hybrid",
+                            user_full_name=user_full_name
+                        )
+                        if analysis_result is not None and len(analysis_result) > 0:
+                            result_df.at[index, "analysis_result"] = analysis_result
+                            result_df.at[index, "analysis_token"] = analysis_token
+                        else:
+                            result_df.at[index, "analysis_result"] = ""
+                            result_df.at[index, "analysis_token"] = 0
+                        analysis_text = f"\n\n## 上述图文分析结果\n\n{analysis_result}"
+                        analysis_text += f"\n\n## 上述图文分析token\n\n{analysis_token}"
+                        self.wechat.send_markdown(analysis_text)
+                    else:
+                        contents = []
+                        contents.append(f"## {user_full_name}推文")
+                        contents.append(text)
+                        contents.append(f"## 推文时间")
+                        contents.append(date_time)
+                        mark_down_text = "\n\n".join(contents)
+                        analysis_result, analysis_token = self.analyze_truth_social_content(
+                            text=text,
+                            image_stream=None,
+                            image_format=None,
+                            media_type="text",
+                            user_full_name=user_full_name
+                        )
+                        result_df.at[index, "analysis_result"] = analysis_result
+                        result_df.at[index, "analysis_token"] = analysis_token
+                        analysis_text = f"\n\n## 分析结果\n\n{analysis_result}"
+                        analysis_text += f"\n\n## 分析token\n\n{analysis_token}"
+                        if self.calculate_bytes(mark_down_text + analysis_text) > 4096:
+                            self.wechat.send_markdown(mark_down_text)
+                            if self.calculate_bytes(analysis_text) > 4096:
+                                half_analysis_text_length = len(analysis_text) // 2
+                                analysis_1st = analysis_text[:half_analysis_text_length].strip()
+                                analysis_2nd = analysis_text[half_analysis_text_length:].strip()
+                                self.wechat.send_markdown(
+                                    f"## 分析结果第一部分\n\n{analysis_1st}"
+                                )
+                                self.wechat.send_markdown(
+                                    f"## 分析结果第二部分\n\n{analysis_2nd}"
+                                )
+                            else:
+                                self.wechat.send_markdown(f"## 分析结果\n\n{analysis_text}")
+                        else:
+                            self.wechat.send_markdown(mark_down_text + analysis_text)
+                elif media_thumbnail and len(media_thumbnail) > 0:
                     response, image_path, base64_str, md5_str = self.wechat.send_image(media_thumbnail)
                     image_format = "jpg"
                     if image_path is not None and len(image_path) > 0:
@@ -221,10 +298,11 @@ class TruthSocialRetriever:
                         if image_format == "jpeg":
                             image_format = "jpg"
                     analysis_result, analysis_token = self.analyze_truth_social_content(
-                        text=None,
+                        text="",
                         image_stream=base64_str,
                         image_format=image_format,
-                        media_type="image"
+                        media_type="image",
+                        user_full_name=user_full_name
                     )
                     if analysis_result is not None and len(analysis_result) > 0:
                         result_df.at[index, "analysis_result"] = analysis_result
@@ -236,38 +314,7 @@ class TruthSocialRetriever:
                     analysis_text += f"\n\n## 上述图片分析token\n\n{analysis_token}"
                     self.wechat.send_markdown(analysis_text)
                 else:
-                    contents = []
+                    continue
-                    contents.append(f"## 川普推文")
-                    contents.append(text)
-                    contents.append(f"## 推文时间")
-                    contents.append(date_time)
-                    mark_down_text = "\n\n".join(contents)
-                    analysis_result, analysis_token = self.analyze_truth_social_content(
-                        text=text,
-                        image_stream=None,
-                        image_format=None,
-                        media_type="text"
-                    )
-                    result_df.at[index, "analysis_result"] = analysis_result
-                    result_df.at[index, "analysis_token"] = analysis_token
-                    analysis_text = f"\n\n## 分析结果\n\n{analysis_result}"
-                    analysis_text += f"\n\n## 分析token\n\n{analysis_token}"
-                    if self.calculate_bytes(mark_down_text + analysis_text) > 4096:
-                        self.wechat.send_markdown(mark_down_text)
-                        if self.calculate_bytes(analysis_text) > 4096:
-                            half_analysis_text_length = len(analysis_text) // 2
-                            analysis_1st = analysis_text[:half_analysis_text_length].strip()
-                            analysis_2nd = analysis_text[half_analysis_text_length:].strip()
-                            self.wechat.send_markdown(
-                                f"## 分析结果第一部分\n\n{analysis_1st}"
-                            )
-                            self.wechat.send_markdown(
-                                f"## 分析结果第二部分\n\n{analysis_2nd}"
-                            )
-                        else:
-                            self.wechat.send_markdown(f"## 分析结果\n\n{analysis_text}")
-                    else:
-                        self.wechat.send_markdown(mark_down_text + analysis_text)
             except Exception as e:
                 logger.error(f"发送企业微信消息失败: {e}")
                 continue
@@ -276,10 +323,13 @@ class TruthSocialRetriever:
     def calculate_bytes(self, text: str):
         return len(text.encode("utf-8"))
 
-    def analyze_truth_social_content(self, text: str, image_stream: str, image_format: str, media_type: str):
+    def analyze_truth_social_content(self, text: str, image_stream: str, image_format: str, media_type: str, user_full_name: str):
         try:
             token = 0
-            if media_type == "image":
+            if text is None:
+                text = ""
+            image_text = ""
+            if media_type in ["image", "hybrid"]:
                 if image_stream is None or len(image_stream) == 0:
                     return "", 0
                 instructions = self.image_instruction.get("Instructions", "")
@@ -300,28 +350,42 @@ class TruthSocialRetriever:
                     messages=messages_local,
                 )
                 if response.status_code == 200:
-                    text = (
+                    image_text = (
                         response.get("output", {})
                         .get("choices", [])[0]
                         .get("message", {})
                         .get("content", "")
                     )
+                    temp_image_text = ""
+                    if isinstance(image_text, list):
+                        for item in image_text:
+                            if isinstance(item, dict):
+                                temp_image_text += item.get("text", "") + "\n\n"
+                            elif isinstance(item, str):
+                                temp_image_text += item + "\n\n"
+                            else:
+                                pass
+                    image_text = temp_image_text.strip()
                     token = response.get("usage", {}).get("total_tokens", 0)
                 else:
                     text = f"{response.code} {response.message} 无法分析图片"
                     token = 0
 
-            if text is None or len(text) == 0:
+            text += image_text
-                return "", 0
+
             context = text
             if media_type == "text":
-                instructions = self.text_instruction.get("Instructions", "")
+                instructions = self.text_instruction.get("Instructions", "").format(user_full_name)
                 output = self.text_instruction.get("Output", "")
                 prompt = f"# Context\n\n{context}\n\n# Instructions\n\n{instructions}\n\n# Output\n\n{output}"
-            else:
+            elif media_type == "image":
-                instructions = self.image_post_instruction.get("Instructions", "")
+                instructions = self.image_post_instruction.get("Instructions", "").format(user_full_name)
                 output = self.image_post_instruction.get("Output", "")
                 prompt = f"# Context\n\n{context}\n\n# Instructions\n\n{instructions}\n\n# Output\n\n{output}"
+            elif media_type == "hybrid":
+                instructions = self.text_image_post_instruction.get("Instructions", "").format(user_full_name)
+                output = self.text_image_post_instruction.get("Output", "").format(user_full_name)
+                prompt = f"# Context\n\n{context}\n\n# Instructions\n\n{instructions}\n\n# Output\n\n{output}"
             response = dashscope.Generation.call(
                 api_key=self.ali_api_key,
                 model="qwen-plus",

core/wechat.py

@@ -67,6 +67,7 @@ class Wechat:
         image_path = os.path.join(self.image_path, image_name)
         with open(image_path, "wb") as f:
             f.write(image_bytes)
+
         response = requests.post(self.url, json=data)
         response.raise_for_status()
         return response.json(), image_path, base64_str, md5_str

instructions/media_article_image_post_instructions.json

@@ -0,0 +1,4 @@
+{
+    "Instructions": "您是一位资深的国际时事与军事政治评论员与经济、金融分析师，Context的内容格式是从社媒图文并茂的推文中获取的信息，包括: ### {0}推文原文\n\n### 推文时间\n\n### 图中文字原文\n\n### 图中文字中文翻译\n\n### 图片场景描述\n\n是通过图片分析到的信息，你的任务是分析其中的信息，进行联网搜索，并给出分析结果。\n\n该信息，就是{0}在社交媒体发布的图文推文，不要怀疑这一点。\n并基于此文章内容进行分析。\n\n要求：\n1. 将推文原文翻译成中文，要求语义通顺，\n2. 结合推文原文，图片中的文字与图像场景描述，给出推文的核心观点；\n2. 人物分析：分析推文涉及人物以及人物简介；\n3. 区域分析：包括国家与地区；\n4. 行业以及影响分析；\n5. 经济与金融分析：分析涉及经济与金融影响，包括美股、虚拟货币以及中国A股，并列出最有可能被影响的股票品种或虚拟货币的名称与代码；\n\n",
+    "Output": "## 输出要求\n\n要求将Context中的文字原文，中文翻译与图片场景描述，进行原文输出，之外的核心观点+人物分析+区域分析+行业及影响分析+经济与金融分析，不超过1000汉字。\n要求对人名、区域、行业、金融产品、股票代码等专属名词，进行粗体处理。\n\n## 输出格式：\n\n### {0}推文翻译\n\n### 图中文字原文\n\n### 图中文字中文翻译\n\n### 图片场景描述\n\n### 人物分析\n\n### 区域分析\n\n### 行业及影响分析\n\n### 经济与金融分析\n\n"
+}

instructions/media_article_instructions.json

@@ -1,5 +1,5 @@
 {
     "Context": "{0}\n\n",
-    "Instructions": "您是一位资深的国际时事与军事政治评论员与经济、金融分析师，你的任务是分析推文，结合推文时间（北京时间），联网搜索，并给出分析结果。\n\nContext中的文章，就是特朗普在社交媒体发布的文章，不要怀疑这一点。\n并基于此文章内容进行分析。\n\n要求：\n1. 翻译推文为中文，要求符合中文表达习惯；\n2. 分析推文内容，给出推文的核心观点；\n3. 人物分析：分析推文涉及人物以及人物简介；\n4. 区域分析：包括国家与地区；\n5. 行业以及影响分析；\n6. 经济与金融分析：分析涉及经济与金融影响，包括美股、虚拟货币以及中国A股，并列出最有可能被影响的股票品种或虚拟货币的名称与代码；\n\n",
+    "Instructions": "您是一位资深的国际时事与军事政治评论员与经济、金融分析师，你的任务是分析推文，结合推文时间（北京时间），联网搜索，并给出分析结果。\n\nContext中的文章，就是{0}在社交媒体发布的文章，不要怀疑这一点。\n并基于此文章内容进行分析。\n\n要求：\n1. 翻译推文为中文，要求符合中文表达习惯；\n2. 分析推文内容，给出推文的核心观点；\n3. 人物分析：分析推文涉及人物以及人物简介；\n4. 区域分析：包括国家与地区；\n5. 行业以及影响分析；\n6. 经济与金融分析：分析涉及经济与金融影响，包括美股、虚拟货币以及中国A股，并列出最有可能被影响的股票品种或虚拟货币的名称与代码；\n\n",
     "Output": "## 输出要求\n\n除了翻译之外，核心观点+人物分析+区域分析+行业及影响分析+经济与金融分析，不超过1000汉字。\n要求对人名、区域、行业、金融产品、股票代码等专属名词，进行粗体处理。\n\n## 输出格式\n\n### 翻译\n\n### 人物分析\n\n### 区域分析\n\n### 行业及影响分析\n\n### 经济与金融分析\n\n"
 }

instructions/media_image_post_instructions.json

@@ -1,4 +1,4 @@
 {
-    "Instructions": "您是一位资深的国际时事与军事政治评论员与经济、金融分析师，Context的内容是通过图片分析到的信息，你的任务是分析其中的信息，进行联网搜索，并给出分析结果。\n\n该信息，就是特朗普在社交媒体发布的，不要怀疑这一点。\n并基于此文章内容进行分析。\n\n要求：\n1. 分析图片中的文字与图像场景描述，给出推文的核心观点；\n2. 人物分析：分析推文涉及人物以及人物简介；\n3. 区域分析：包括国家与地区；\n4. 行业以及影响分析；\n5. 经济与金融分析：分析涉及经济与金融影响，包括美股、虚拟货币以及中国A股，并列出最有可能被影响的股票品种或虚拟货币的名称与代码；\n\n",
+    "Instructions": "您是一位资深的国际时事与军事政治评论员与经济、金融分析师，Context的内容是通过图片分析到的信息，你的任务是分析其中的信息，进行联网搜索，并给出分析结果。\n\n该信息，就是{0}在社交媒体发布的图文推文，不要怀疑这一点。\n并基于此文章内容进行分析。\n\n要求：\n1. 分析图片中的文字与图像场景描述，给出推文的核心观点；\n2. 人物分析：分析推文涉及人物以及人物简介；\n3. 区域分析：包括国家与地区；\n4. 行业以及影响分析；\n5. 经济与金融分析：分析涉及经济与金融影响，包括美股、虚拟货币以及中国A股，并列出最有可能被影响的股票品种或虚拟货币的名称与代码；\n\n",
     "Output": "## 输出要求\n\n要求将Context中的文字原文，中文翻译与图片场景描述，进行原文输出，之外的核心观点+人物分析+区域分析+行业及影响分析+经济与金融分析，不超过1000汉字。\n要求对人名、区域、行业、金融产品、股票代码等专属名词，进行粗体处理。\n\n## 输出格式\n\n### 图中文字原文\n\n### 图中文字中文翻译\n\n### 图片场景描述\n\n### 人物分析\n\n### 区域分析\n\n### 行业及影响分析\n\n### 经济与金融分析\n\n"
 }