| | |
|---|
| | | import logging |
|---|
| | | import time |
|---|
| | | from concurrent.futures import ThreadPoolExecutor |
|---|
| | | import threading |
|---|
| | |
|---|
| | | # 加载集合 |
|---|
| | | self.collection = Collection(name="smartobject") |
|---|
| | | self.collection.load() |
|---|
| | | if config.get('cuda') == None or config.get('cuda') == '0': |
|---|
| | | if config.get('cuda') is None or config.get('cuda') == '0': |
|---|
| | | self.device = f"cuda" |
|---|
| | | else: |
|---|
| | | self.device = f"cuda:{config.get('cuda')}" |
|---|
| | | self.model_pool = [] |
|---|
| | | self.lock_pool = [threading.Lock() for _ in range(int(config.get("threadnum")))] |
|---|
| | | for i in range(int(config.get("threadnum"))): |
|---|
| | | self.lock_pool = [threading.Lock() for _ in range(int(config.get("qwenwarning")))] |
|---|
| | | for i in range(int(config.get("qwenwarning"))): |
|---|
| | | model = AutoModelForVision2Seq.from_pretrained( |
|---|
| | | config.get("qwenaddr"), |
|---|
| | | device_map=self.device, |
|---|
| | |
|---|
| | | |
|---|
| | | def tark_do(self,res,ragurl,rag_mode,max_tokens): |
|---|
| | | try: |
|---|
| | | # 1. 从集合A获取向量和元数据 |
|---|
| | | is_waning = 0 |
|---|
| | | is_desc = 2 |
|---|
| | | # 生成图片描述 |
|---|
| | | ks_time = datetime.now() |
|---|
| | | desc_time = datetime.now() - ks_time |
|---|
| | | current_time = datetime.now() |
|---|
| | | |
|---|
| | | risk_description = "" |
|---|
| | | suggestion = "" |
|---|
| | | # 调用规则匹配方法,判断是否预警 |
|---|
| | | is_waning = self.image_rule(res) |
|---|
| | | # 如果预警,则生成隐患描述和处理建议 |
|---|
| | | if is_waning == 1: |
|---|
| | | # 获取规章制度数据 |
|---|
| | | filedata = self.get_filedata(res['waning_value'],res['suggestion'], ragurl) |
|---|
| | | # 生成隐患描述 |
|---|
| | | risk_description = self.image_rule_chat_with_detail(filedata, res['waning_value'], ragurl,rag_mode,max_tokens) |
|---|
| | | # 生成处理建议 |
|---|
| | | suggestion = self.image_rule_chat_suggestion(filedata, res['waning_value'], ragurl,rag_mode,max_tokens) |
|---|
| | | #self.logger.info(f"{res['video_point_id']}执行完毕：{res['id']}:是否预警{is_waning},安全隐患：{risk_description}\n处理建议：{suggestion}") |
|---|
| | | # 数据组 |
|---|
| | | self.logger.info(f"预警规则规则规则is_waning：{is_waning}") |
|---|
| | | #更新数据的预警结果与数据预警状态 |
|---|
| | | data = { |
|---|
| | | "event_level_id": res['event_level_id'], # event_level_id |
|---|
| | | "event_level_name": res['event_level_name'], # event_level_id |
|---|
| | |
|---|
| | | "video_point_id": res['video_point_id'], # video_point_id |
|---|
| | | "video_point_name": res['video_point_name'], |
|---|
| | | "is_waning": is_waning, |
|---|
| | | "is_desc": 1, |
|---|
| | | "is_desc": 5, #改为已经预警 |
|---|
| | | "zh_desc_class": res['zh_desc_class'], # text_vector |
|---|
| | | "bounding_box": res['bounding_box'], # bounding_box |
|---|
| | | "task_id": res['task_id'], # task_id |
|---|
| | |
|---|
| | | # 保存到milvus |
|---|
| | | image_id = self.collection.insert(data).primary_keys |
|---|
| | | res['id'] = image_id[0] |
|---|
| | | # 图片描述生成成功 |
|---|
| | | desc = self.image_desc(res) |
|---|
| | | if desc: |
|---|
| | | is_desc = 2 |
|---|
| | | else: |
|---|
| | | is_desc = 3 |
|---|
| | | # 数据组 |
|---|
| | | data = { |
|---|
| | | "event_level_id": res['event_level_id'], # event_level_id |
|---|
| | | "event_level_name": res['event_level_name'], # event_level_id |
|---|
| | | "rule_id": res["rule_id"], |
|---|
| | | "video_point_id": res['video_point_id'], # video_point_id |
|---|
| | | "video_point_name": res['video_point_name'], |
|---|
| | | "is_waning": is_waning, |
|---|
| | | "is_desc": is_desc, |
|---|
| | | "zh_desc_class": desc, # text_vector |
|---|
| | | "bounding_box": res['bounding_box'], # bounding_box |
|---|
| | | "task_id": res['task_id'], # task_id |
|---|
| | | "task_name": res['task_name'], # task_id |
|---|
| | | "detect_id": res['detect_id'], # detect_id |
|---|
| | | "detect_time": res['detect_time'], # detect_time |
|---|
| | | "detect_num": res['detect_num'], |
|---|
| | | "waning_value": res['waning_value'], |
|---|
| | | "image_path": res['image_path'], # image_path |
|---|
| | | "image_desc_path": res['image_desc_path'], # image_desc_path |
|---|
| | | "video_path": res['video_path'], |
|---|
| | | "text_vector": res['text_vector'], |
|---|
| | | "risk_description": risk_description, |
|---|
| | | "suggestion": suggestion, |
|---|
| | | "knowledge_id": res['knowledge_id'] |
|---|
| | | } |
|---|
| | | self.collection.delete(f"id == {res['id']}") |
|---|
| | | # 保存到milvus |
|---|
| | | image_id = self.collection.insert(data).primary_keys |
|---|
| | | data = { |
|---|
| | | "id": str(image_id[0]), |
|---|
| | | "video_point_id": res['video_point_id'], |
|---|
| | | "video_path": res["video_point_name"], |
|---|
| | | "zh_desc_class": desc, |
|---|
| | | "detect_time": res['detect_time'], |
|---|
| | | "image_path": f"{res['image_path']}", |
|---|
| | | "task_name": res["task_name"], |
|---|
| | | "event_level_name": res["event_level_name"], |
|---|
| | | "rtsp_address": f"{res['video_path']}" |
|---|
| | | } |
|---|
| | | # 调用rag |
|---|
| | | asyncio.run(self.insert_json_data(ragurl, data)) |
|---|
| | | rag_time = datetime.now() - current_time |
|---|
| | | self.logger.info(f"{res['video_point_id']}执行完毕：{image_id}运行结束总体用时:{datetime.now() - ks_time},图片描述用时{desc_time},RAG用时{rag_time}") |
|---|
| | | if is_waning == 1: |
|---|
| | | self.logger.info(f"{res['video_point_id']}执行完毕：{image_id},图片描述：{desc}\n隐患：{risk_description}\n建议：{suggestion}") |
|---|
| | | self.logger.info(f"{res['video_point_id']}预警执行完毕：{image_id}运行结束总体用时:{datetime.now() - ks_time}") |
|---|
| | | return None |
|---|
| | | except Exception as e: |
|---|
| | | self.logger.info(f"线程：执行模型解析时出错::{e}") |
|---|
| | | return 0 |
|---|
| | | |
|---|
| | | def image_desc(self, res_data): |
|---|
| | | try: |
|---|
| | | model, lock = self._acquire_model() |
|---|
| | | |
|---|
| | | image = Image.open(res_data['image_desc_path']).convert("RGB").resize((600, 600), Image.Resampling.LANCZOS) |
|---|
| | | messages = [ |
|---|
| | | { |
|---|
| | | "role": "user", |
|---|
| | | "content": [ |
|---|
| | | { |
|---|
| | | "type": "image", |
|---|
| | | }, |
|---|
| | | {"type": "text", "text": "请详细描述图片中的目标信息及特征。返回格式为整段文字描述"}, |
|---|
| | | ], |
|---|
| | | } |
|---|
| | | ] |
|---|
| | | # Preparation for inference |
|---|
| | | text = self.processor.apply_chat_template( |
|---|
| | | messages, add_generation_prompt=True |
|---|
| | | ) |
|---|
| | | inputs = self.processor( |
|---|
| | | text=[text], |
|---|
| | | images=[image], |
|---|
| | | padding=True, |
|---|
| | | return_tensors="pt", |
|---|
| | | ) |
|---|
| | | inputs = inputs.to(model.device) |
|---|
| | | with torch.inference_mode(), torch.amp.autocast(device_type=self.device, dtype=torch.float16): |
|---|
| | | outputs = model.generate(**inputs,max_new_tokens=200,do_sample=False,num_beams=1,temperature=None,top_p=None,top_k=1,use_cache=True,repetition_penalty=1.0) |
|---|
| | | generated_ids = outputs[:, len(inputs.input_ids[0]):] |
|---|
| | | image_text = self.processor.batch_decode( |
|---|
| | | generated_ids, skip_special_tokens=True, clean_up_tokenization_spaces=True |
|---|
| | | ) |
|---|
| | | image_des = (image_text[0]).strip() |
|---|
| | | #self.logger.info(f"{res_data['video_point_id']}:{res_data['id']}:{res_data['detect_time']}:{image_des}") |
|---|
| | | return image_des |
|---|
| | | except Exception as e: |
|---|
| | | self.logger.info(f"线程：执行图片描述时出错:{e}") |
|---|
| | | finally: |
|---|
| | | # 4. 释放模型 |
|---|
| | | self._release_model(model) |
|---|
| | | torch.cuda.empty_cache() |
|---|
| | | |
|---|
| | | def image_rule(self, res_data): |
|---|
| | | self.logger.info(f"预警规则规则规则等级分类就是打裂缝多少积分") |
|---|
| | | try: |
|---|
| | | model, lock = self._acquire_model() |
|---|
| | | image = Image.open(res_data['image_desc_path']).convert("RGB").resize((600, 600), Image.Resampling.LANCZOS) |
|---|
| | |
|---|
| | | "role": "user", |
|---|
| | | "content": [ |
|---|
| | | {"type": "image", "image": image}, |
|---|
| | | {"type": "text", "text": f"图片中是否有{res_data['waning_value']}?请回答yes或no"}, |
|---|
| | | {"type": "text", "text": f"请检测图片中{res_data['waning_value']}?请回答yes或no"}, |
|---|
| | | ], |
|---|
| | | } |
|---|
| | | ] |
|---|
| | |
|---|
| | | ) |
|---|
| | | |
|---|
| | | image_des = (image_text[0]).strip() |
|---|
| | | return image_des |
|---|
| | | upper_text = image_des.upper() |
|---|
| | | self.logger.info(f"预警规则规则规则：{upper_text}") |
|---|
| | | if "YES" in upper_text: |
|---|
| | | return 1 |
|---|
| | | else: |
|---|
| | | return 0 |
|---|
| | | except Exception as e: |
|---|
| | | self.logger.info(f"线程：执行图片描述时出错:{e}") |
|---|
| | | return 0 |
|---|
| | | finally: |
|---|
| | | # 4. 释放模型 |
|---|
| | | self._release_model(model) |
|---|
| | | torch.cuda.empty_cache() |
|---|
| | | |
|---|
| | | def get_rule(self,ragurl): |
|---|
| | | try: |
|---|
| | | rule_text = None |
|---|
| | | search_data = { |
|---|
| | | "collection_name": "smart_rule", |
|---|
| | | "query_text": "", |
|---|
| | | "search_mode": "hybrid", |
|---|
| | | "limit": 100, |
|---|
| | | "weight_dense": 0.7, |
|---|
| | | "weight_sparse": 0.3, |
|---|
| | | "filter_expr": "", |
|---|
| | | "output_fields": ["text"] |
|---|
| | | } |
|---|
| | | response = requests.post(ragurl + "/search", json=search_data) |
|---|
| | | results = response.json().get('results') |
|---|
| | | rule_text = "" |
|---|
| | | ruleid = 1 |
|---|
| | | for rule in results: |
|---|
| | | if rule['score'] >= 0: |
|---|
| | | rule_text = rule_text + str(ruleid) + ". " + rule['entity'].get('text') + ";\n" |
|---|
| | | ruleid = ruleid + 1 |
|---|
| | | # self.logger.info(len(rule_text)) |
|---|
| | | else: |
|---|
| | | self.logger.info(f"线程：执行获取规则时出错:{response}") |
|---|
| | | return rule_text |
|---|
| | | except Exception as e: |
|---|
| | | self.logger.info(f"线程：执行获取规则时出错:{e}") |
|---|
| | | return None |
|---|
| | | |
|---|
| | | def image_rule_chat(self, image_des,rule_text, ragurl, rag_mode,max_tokens): |
|---|
| | | try: |
|---|
| | | content = ( |
|---|
| | | f"图片描述内容为：\n{image_des}\n规则内容：\n{rule_text}。\n请验证图片描述中是否有不符合规则的内容，不进行推理和think。返回结果格式为[xxx符合的规则id]，如果没有返回[]") |
|---|
| | | #self.logger.info(len(content)) |
|---|
| | | search_data = { |
|---|
| | | "prompt": "", |
|---|
| | | "messages": [ |
|---|
| | | { |
|---|
| | | "role": "user", |
|---|
| | | "content": content |
|---|
| | | } |
|---|
| | | ], |
|---|
| | | "llm_name": rag_mode, |
|---|
| | | "stream": False, |
|---|
| | | "gen_conf": { |
|---|
| | | "temperature": 0.7, |
|---|
| | | "max_tokens": max_tokens |
|---|
| | | } |
|---|
| | | } |
|---|
| | | response = requests.post(ragurl + "/chat", json=search_data) |
|---|
| | | results = response.json().get('data') |
|---|
| | | ret = re.sub(r'<think>.*?</think>', '', results, flags=re.DOTALL) |
|---|
| | | ret = ret.replace(" ", "").replace("\t", "").replace("\n", "") |
|---|
| | | #self.logger.info(f"{rule_text}:{ret}") |
|---|
| | | is_waning = 0 |
|---|
| | | if len(ret) > 2: |
|---|
| | | is_waning = 1 |
|---|
| | | return is_waning |
|---|
| | | except Exception as e: |
|---|
| | | self.logger.info(f"线程：执行规则匹配时出错:{image_des, rule_text, ragurl, rag_mode,e}") |
|---|
| | | return None |
|---|
| | | |
|---|
| | | # 隐患描述 |
|---|
| | | def image_rule_chat_with_detail(self,filedata, rule_text, ragurl, rag_mode,max_tokens): |
|---|
| | | # API调用 |
|---|
| | | content = ( |
|---|
| | | f"规章制度为：[{filedata}]\n违反内容为：[{rule_text}]\n请查询违反内容在规章制度中的安全隐患，不进行推理和think，返回简短的文字信息") |
|---|
| | | # self.logger.info(len(content)) |
|---|
| | | search_data = { |
|---|
| | | "prompt": "", |
|---|
| | | "messages": [ |
|---|
| | | { |
|---|
| | | "role": "user", |
|---|
| | | "content": content |
|---|
| | | } |
|---|
| | | ], |
|---|
| | | "llm_name": rag_mode, |
|---|
| | | "stream": False, |
|---|
| | | "gen_conf": { |
|---|
| | | "temperature": 0.7, |
|---|
| | | "max_tokens": max_tokens |
|---|
| | | } |
|---|
| | | } |
|---|
| | | #self.logger.info(content) |
|---|
| | | response = requests.post(ragurl + "/chat", json=search_data) |
|---|
| | | # 从json提取data字段内容 |
|---|
| | | ret = response.json()["data"] |
|---|
| | | # 移除<think>标签和内容 |
|---|
| | | ret = re.sub(r'<think>.*?</think>', '', ret, flags=re.DOTALL) |
|---|
| | | # 字符串清理,移除空格,制表符,换行符,星号 |
|---|
| | | ret = ret.replace(" ", "").replace("\t", "").replace("\n", "").replace("**","") |
|---|
| | | #print(f"安全隐患:{ret}") |
|---|
| | | return ret |
|---|
| | | #处理建议 |
|---|
| | | def image_rule_chat_suggestion(self,filedata, rule_text, ragurl, rag_mode,max_tokens): |
|---|
| | | # 请求内容 |
|---|
| | | content = ( |
|---|
| | | f"规章制度为：[{filedata}]\n违反内容为：[{rule_text}]\n请查询违反内容在规章制度中的处理建议，不进行推理和think，返回简短的文字信息") |
|---|
| | | response = requests.post( |
|---|
| | | # ollama地址 |
|---|
| | | url=f"{ragurl}/chat", |
|---|
| | | json={ |
|---|
| | | # 指定模型 |
|---|
| | | "llm_name": rag_mode, |
|---|
| | | "messages": [ |
|---|
| | | {"role": "user", "content": content} |
|---|
| | | ], |
|---|
| | | "stream": False, # 关闭流式输出 |
|---|
| | | "gen_conf": { |
|---|
| | | "temperature": 0.7, |
|---|
| | | "max_tokens": max_tokens |
|---|
| | | } |
|---|
| | | } |
|---|
| | | ) |
|---|
| | | # 从json提取data字段内容 |
|---|
| | | ret = response.json()["data"] |
|---|
| | | # 移除<think>标签和内容 |
|---|
| | | ret = re.sub(r'<think>.*?</think>', '', ret, flags=re.DOTALL) |
|---|
| | | # 字符串清理,移除空格,制表符,换行符,星号 |
|---|
| | | ret = ret.replace(" ", "").replace("\t", "").replace("\n", "").replace("**","") |
|---|
| | | #print(f"处理建议:{ret}") |
|---|
| | | return ret |
|---|
| | | |
|---|
| | | # RAG服务发送请求,获取知识库内容 |
|---|
| | | def get_filedata(self, searchtext,filter_expr, ragurl): |
|---|
| | | search_data = { |
|---|
| | | # 知识库集合 |
|---|
| | | "collection_name": "smart_knowledge", |
|---|
| | | # 查询文本 |
|---|
| | | "query_text": searchtext, |
|---|
| | | # 搜索模式 |
|---|
| | | "search_mode": "hybrid", |
|---|
| | | # 最多返回结果 |
|---|
| | | "limit": 10, |
|---|
| | | # 调密向量搜索权重 |
|---|
| | | "weight_dense": 0.9, |
|---|
| | | # 稀疏向量搜索权重 |
|---|
| | | "weight_sparse": 0.1, |
|---|
| | | # 空字符串 |
|---|
| | | "filter_expr": f"docnm_kwd in {filter_expr}", |
|---|
| | | # 只返回 text 字段 |
|---|
| | | "output_fields": ["text"] |
|---|
| | | } |
|---|
| | | #print(search_data) |
|---|
| | | # 向 ragurl + "/search" 端点发送POST请求 |
|---|
| | | response = requests.post(ragurl + "/search", json=search_data) |
|---|
| | | # 从响应中获取'results'字段 |
|---|
| | | results = response.json().get('results') |
|---|
| | | # 初始化 text |
|---|
| | | text = "" |
|---|
| | | # 遍历所有结果规则(rule)，将每条规则的'entity'中的'text'字段取出. |
|---|
| | | for rule in results: |
|---|
| | | text = text + rule['entity'].get('text') + ";\n" |
|---|
| | | #print(text) |
|---|
| | | return text |
|---|
| | | |
|---|
| | | async def insert_json_data(self, ragurl, data): |
|---|
| | | try: |
|---|
| | | data = {'collection_name': "smartrag", "data": data, "description": ""} |
|---|
| | | requests.post(ragurl + "/insert_json_data", json=data, timeout=(0.3, 0.3)) |
|---|
| | | #self.logger.info(f"调用录像服务:{ragurl, data}") |
|---|
| | | except Exception as e: |
|---|
| | | #self.logger.info(f"{self._thread_name}线程：调用录像时出错:地址：{ragurl}：{e}") |
|---|
| | | return |
|---|
| | | |
|---|
| | | def _release_semaphore(self, future): |
|---|
| | | self.semaphore.release() |
|---|
| | | #self.logger.info(f"释放线程 (剩余空闲: {self.semaphore._value}/{self.max_workers})") |
|---|
| | |
|---|
| | | self.lock_pool[i].release() |
|---|
| | | break |
|---|
| | | |
|---|
| | | |
|---|
| | | def remove_duplicate_lines(self,text): |
|---|
| | | seen = set() |
|---|
| | | result = [] |
|---|
| | | for line in text.split('。'): # 按句号分割 |
|---|
| | | if line.strip() and line not in seen: |
|---|
| | | seen.add(line) |
|---|
| | | result.append(line) |
|---|
| | | return '。'.join(result) |
|---|
| | | def remove_duplicate_lines_d(self,text): |
|---|
| | | seen = set() |
|---|
| | | result = [] |
|---|
| | | for line in text.split(','): # 按句号分割 |
|---|
| | | if line.strip() and line not in seen: |
|---|
| | | seen.add(line) |
|---|
| | | result.append(line) |
|---|
| | | return '。'.join(result) |
|---|
| | | def remove_duplicate_lines_n(self,text): |
|---|
| | | seen = set() |
|---|
| | | result = [] |
|---|
| | | for line in text.split('\n'): # 按句号分割 |
|---|
| | | if line.strip() and line not in seen: |
|---|
| | | seen.add(line) |
|---|
| | | result.append(line) |
|---|
| | | return '。'.join(result) |
|---|
| | | |
|---|
