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396 lines
18 KiB
396 lines
18 KiB
#coding:utf8
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import os, sys
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import io
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sys.stdout = io.TextIOWrapper(sys.stdout.buffer, encoding='utf8')
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cur_dir = os.path.dirname(os.path.abspath(__file__)) or os.getcwd()
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par_dir = os.path.abspath(os.path.join(cur_dir, os.path.pardir))
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sys.path.append(cur_dir)
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sys.path.append(par_dir)
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import json
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from django.http import HttpResponse
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from text_analysis.tools import to_kafka,tool
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from django.views.decorators.csrf import csrf_exempt
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from log_util.set_logger import set_logger
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logging=set_logger('logs/results.log')
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import traceback
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import queue
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import requests
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import time
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from datetime import datetime, timedelta
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from text_analysis.cusException import userFile_Exception,postFile_Exception,replyFile_Exception
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from text_analysis.tools.tool import parse_data
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import os
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import joblib
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from text_analysis.tools.db_pool import get_conn_pool
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#任务队列
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global task_queue
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task_queue = queue.Queue()
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global replyGraph
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replyGraph={}
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@csrf_exempt
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def robotIdentificationTopic(request):
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if request.method == 'POST':
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try:
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raw_data = json.loads(request.body)
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task_queue.put(raw_data)
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return HttpResponse(json.dumps({"code": 1, "msg": "请求正常！"}, ensure_ascii=False))
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except:
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logging.error(traceback.format_exc())
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return HttpResponse(json.dumps({"code": 0, "msg": "请求json格式不正确！"}, ensure_ascii=False))
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else:
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return HttpResponse(json.dumps({"code": 0, "msg": "请求方式错误，改为post请求"}, ensure_ascii=False))
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def predictTopic(user_file_result,post_file_result,task,dbConfig,taskId):
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try:
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# 识别结果返回值
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recognition_code = "0"
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# 用户数据
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res = {"successCode": "1", "errorLog": "", "results": {}}
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# 获取历史数据源
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all_result = task['data']
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user_data = []
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data={}
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#返回值需要的三个字段
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accountId=""
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nickName=""
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accountName=""
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# {"user_file": "9_获取用户信息", "post_file": "10_获取用户发帖信息"}
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if user_file_result:
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data['user_file'] = user_file_result
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logging.info('用户数据：{}'.format(data['user_file']))
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accountId = data["user_file"]["accountId"]
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nickName = data["user_file"]["nickName"]
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accountName = data["user_file"]["accountName"]
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else:
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data['user_file'] ={}
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raise userFile_Exception
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if post_file_result:
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data['post_file'] = post_file_result
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logging.info('帖子数据：{}'.format(data['post_file']))
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else:
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data['post_file'] = {}
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raise postFile_Exception
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try:
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user_data_otherInfo_1 = 0 if data["user_file"]["otherInfo"].strip() == "" else 1
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except:
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user_data_otherInfo_1 = 0
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try:
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user_data_nickName_2 = 0 if data["user_file"]["nickName"].strip() == "" else 1
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except:
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user_data_nickName_2 = 0
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try:
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user_data_likeCount_4 = int(data["user_file"]["likeCount"])
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except:
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user_data_likeCount_4 = 0
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try:
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user_data_postCount_5 = int(data["user_file"]["postCount"])
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except:
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user_data_postCount_5 = 0
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try:
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user_data_authentication_6 = int(data["user_file"]["authentication"])
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except:
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user_data_authentication_6 = 0
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user_data.extend(
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[user_data_otherInfo_1, user_data_nickName_2, user_data_likeCount_4,user_data_postCount_5, user_data_authentication_6])
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logging.info("用户数据处理完毕！-{}".format(user_data))
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# 帖子数据
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post_data = []
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if data["post_file"]=={}:
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post_data=[0,0,0,0,0,0,0,0]
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else:
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try:
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post_data_LikeCount_1 = int(data["post_file"]["LikeCount"])
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except:
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post_data_LikeCount_1 = 0
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try:
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post_data_ShareCount_2 = int(data["post_file"]["ShareCount"])
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except:
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post_data_ShareCount_2 = 0
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try:
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post_data_emotionCount_3 = int(data["post_file"]["emotionCount"])
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except:
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post_data_emotionCount_3 = 0
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try:
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post_data_CommentsCount_4 = int(data["post_file"]["CommentsCount"])
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except:
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post_data_CommentsCount_4 = 0
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try:
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post_data_length_5 = int(data["post_file"]["length"])
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except:
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post_data_length_5 = 0
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try:
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post_data_tags_6 = int(data["post_file"]["tags"])
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except:
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post_data_tags_6 = 0
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try:
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post_data_https_7 = int(data["post_file"]["https"])
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except:
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post_data_https_7 = 0
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try:
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post_data_diffdate_8 = int(data["post_file"]["diffdate"])
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except:
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post_data_diffdate_8 = 0
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post_data.extend(
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[post_data_LikeCount_1, post_data_ShareCount_2, post_data_emotionCount_3, post_data_CommentsCount_4,
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post_data_length_5, post_data_tags_6, post_data_https_7, post_data_diffdate_8])
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logging.info("帖子数据处理完毕！-{}".format(post_data))
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#关系数据
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reply_data_1 = [0, 0, 0, 0, 0]
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reply_data_2 = [0, 0]
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try:
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#先判断内存中是否有该专题图信息
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topicID=taskId
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if topicID not in list(replyGraph.keys()):
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reply_file=tool.mysqlData(dbConfig,topicID,logging)
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if reply_file:
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graph=tool.get_replyData(reply_file)
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replyGraph[topicID]=graph
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else:
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raise replyFile_Exception
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else:
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graph=replyGraph[topicID]
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userId=data["user_file"]["accountId"]
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if userId in list(graph.keys()):
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closeness_centrality=graph["userId"]["closeness_centrality"]
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pagerank=graph["userId"]["pagerank"]
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clustering=graph["userId"]["clustering"]
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in_degree=graph["userId"]["in_degree"]
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out_degree=graph["userId"]["out_degree"]
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reply_data_1=[closeness_centrality,pagerank,clustering,in_degree,out_degree]
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user_flag_infl=graph["userId"]["user_flag_infl"]
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user_flag_act=graph["userId"]["user_flag_act"]
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reply_data_2=[user_flag_infl,user_flag_act]
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replyGraph[topicID]["last_operation_time"]=datetime.now()
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except:
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logging.info("专题关系数据mysql获取失败！")
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logging.info(traceback.format_exc())
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logging.info("关系数据处理完毕！{}-{}".format(reply_data_1,reply_data_2))
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features = [user_data + reply_data_1 + post_data + reply_data_2]
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bot_user = joblib.load(cur_dir+"/model/bot_topic.pkl") # 加载训练好的模型
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result = bot_user.predict(features)
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recognition_code = str(result[0])
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res["results"]=str(result[0])
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results = {}
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# 用户id
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results['authorId'] = accountId
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# 用户昵称
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results['nickName'] = nickName
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# 用户账号
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results['accountName'] = accountName
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# 结束标识
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res['isLast'] = True
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# 数据类型 --目前只提供给图谱使用
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results['pageType'] = 'userAuthenPage'
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if recognition_code == '0':
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results['recognitionResult'] = '非机器人'
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results['recognitionCode'] = recognition_code
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elif recognition_code == '1':
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results['recognitionResult'] = '机器人'
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results['recognitionCode'] = recognition_code
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else:
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results['recognitionResult'] = '未知识别结果'
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results['recognitionCode'] = recognition_code
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results["isLast"]=1
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res['results'] = json.dumps(results)
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res["status"]=1
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res["message"]="成功"
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task["result"] = res
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logging.info("增加预测数据-{}".format(task))
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to_kafka.send_kafka(task, logging)
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except userFile_Exception:
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res = {"successCode": "0", "errorLog": "用户数据为空！", "results": {}}
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results={}
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results['authorId'] = ""
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results['nickName'] = ""
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results['accountName'] = ""
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results['recognitionResult'] = '用户数据为空'
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results["isLast"]=1
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res['results'] = json.dumps(results)
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res["status"]=2
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res["message"]="用户数据为空"
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task["result"] = res
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logging.info("该条请求用户数据为空-{}".format(task))
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to_kafka.send_kafka(task, logging)
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except postFile_Exception:
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res = {"successCode": "0", "errorLog": "帖子数据为空！", "results": {}}
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results={}
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results['authorId'] = accountId
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results['nickName'] = nickName
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results['accountName'] = accountName
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results['recognitionResult'] = '帖子数据为空'
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results["isLast"]=1
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res['results'] = json.dumps(results)
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res["status"]=2
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res["message"]="帖子数据为空"
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task["result"] = res
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logging.info("该条请求帖子数据为空-{}".format(task))
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to_kafka.send_kafka(task, logging)
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except replyFile_Exception:
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res = {"successCode": "0", "errorLog": "发帖和评论关系数据为空！", "results": {}}
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results={}
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results['authorId'] = accountId
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results['nickName'] = nickName
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results['accountName'] = accountName
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results['recognitionResult'] = '发帖和评论关系数据为空'
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results["isLast"]=1
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res['results'] = json.dumps(results)
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res["status"]=2
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res["message"]="发帖和评论关系数据为空"
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task["result"] = res
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logging.info("该条请求发帖和评论关系数据为空-{}".format(task))
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to_kafka.send_kafka(task, logging)
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except:
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res = {"successCode": "0", "errorLog": "", "results": {}}
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results = {}
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results['authorId'] = accountId
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results['nickName'] = nickName
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results['accountName'] = accountName
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results['recognitionResult'] = ""
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results["isLast"]=1
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res['results'] = json.dumps(results)
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res["status"]=2
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res["message"]="异常"
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task["result"] = res
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task["result"]["errorLog"] = traceback.format_exc()
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logging.info(traceback.format_exc())
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to_kafka.send_kafka(task, logging)
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def data_structure(dbConfig):
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'''
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所需计算数据入库
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:param dbConfig: 数据库连接信息
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:return:
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'''
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# 获取数据库连接
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sqlhelper = get_conn_pool(dbConfig['host'], dbConfig['port'], dbConfig['username'], dbConfig['password'],dbConfig['db'])
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#用户任务结构体缓存
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user_tasks = {}
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while True:
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if task_queue.qsize() > 0:
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try:
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task = task_queue.get()
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input = task['input']
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account = input['account']
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post = input['post']
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reply = input['reply']
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#判断数据类型
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data = task['data']
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page_type = None
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taskId = None
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app_data = None
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for data_str in data:
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try:
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app_data = json.loads(data[data_str])
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taskId = app_data['taskId']
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if "pageType" in app_data:
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page_type = app_data['pageType']
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break
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except:
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logging.error("正常判断，异常请忽略")
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if page_type == 'userInfoPage':
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#用户添加到缓存
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accountId = parse_data(task, account['accountId'])
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user_tasks[accountId] = task
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logging.info('成功添加用户缓存：{}'.format(accountId))
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#用户类型数据写入
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sql = "INSERT INTO `user_account`(`taskId`, `accountId`, `accountName`, `nickName`, `fansCount`, `likeCount`, `postCount`, `otherInfo`, `authentication`) VALUES (%s, %s, %s, %s, %s, %s, %s, %s, %s)"
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# 构造参数元组
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values = (
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parse_data(task, account['taskId']),
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parse_data(task, account['accountId']),
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parse_data(task, account['accountName']),
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parse_data(task, account['nickName']),
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parse_data(task, account['fansCount']),
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parse_data(task, account['likeCount']),
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parse_data(task, account['postCount']),
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parse_data(task, account['otherInfo']),
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parse_data(task, account['authentication'])
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)
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sqlhelper.insert(sql,values)
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elif page_type == 'storyDetailPage':
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#帖子类型数据写入
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sql = "INSERT INTO `user_post`(`taskId`, `postId`, `accountId`, `accountName`, `likeCount`, `emotionCount`, `commentsCount`, `shareCount`, `content`, `pubTime`, `crawlTime`) VALUES (%s, %s, %s, %s, %s, %s, %s, %s, %s, %s, %s)"
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# 构造参数元组
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values = (
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parse_data(task, post['taskId']),
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parse_data(task, post['postId']),
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parse_data(task, post['accountId']),
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parse_data(task, post['accountName']),
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parse_data(task, post['likeCount']),
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parse_data(task, post['emotionCount']),
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parse_data(task, post['commentsCount']),
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parse_data(task, post['shareCount']),
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parse_data(task, post['content']),
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parse_data(task, post['pubTime']),
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parse_data(task, post['crawlTime'])
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)
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sqlhelper.insert(sql,values)
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elif page_type == 'socialComment':
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#评论类型数据写入
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sql = "INSERT INTO `reply`(`taskId`, `ReviewerAccountId`, `ReviewerAccountName`, `postId`, `ShareCount`, `LikeCount`, `CommentCount`, `CommentTime`) VALUES (%s, %s, %s, %s, %s, %s, %s, %s)"
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# 构造参数元组
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values = (
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parse_data(task, reply['taskId']),
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parse_data(task, reply['reviewerAccountId']),
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parse_data(task, reply['reviewerAccountName']),
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parse_data(task, reply['postId']),
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parse_data(task, reply['shareCount']),
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parse_data(task, reply['likeCount']),
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parse_data(task, reply['commentsCount']),
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parse_data(task, reply['commentTime'])
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)
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sqlhelper.insert(sql,values)
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#判断是否是此次数据流的最后一条，最后一条直接触发用户的水军识别算法
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if 'isLast'in app_data:
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#获取用户相关的数据
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sql = "select accountId,accountName,nickName,fansCount,likeCount,postCount,otherInfo,authentication from user_account where taskId ='{}'".format(taskId)
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user_file_result = sqlhelper.queryAll(sql)
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if user_file_result:
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for user in user_file_result:
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try:
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# 获取帖子相关的数据
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sql = "SELECT CONVERT(COUNT(postId), CHAR(255)) AS count, CONVERT(AVG(likeCount), CHAR(255)) AS LikeCount, CONVERT(AVG(commentsCount), CHAR(255)) AS CommentsCount, CONVERT(AVG(shareCount), CHAR(255)) AS ShareCount, CONVERT(AVG(LENGTH(content)), CHAR(255)) AS length, CONVERT(AVG((LENGTH(content) - LENGTH(REPLACE(content, '#', ''))) / LENGTH('#')), CHAR(255)) AS tags, CONVERT(AVG((LENGTH(content) - LENGTH(REPLACE(content, 'https', ''))) / LENGTH('https')), CHAR(255)) AS https, CONVERT(AVG((LENGTH(content) - LENGTH(REPLACE(content, '@', ''))) / LENGTH('@')), CHAR(255)) AS at, CONVERT(MIN(TIMESTAMPDIFF(SECOND, pubTime, GREATEST(pubTime, crawlTime))), CHAR(255)) AS diffdate FROM user_post WHERE taskId = '{taskId}' and accountId = '{accountId}'".format(taskId=taskId,accountId=user['accountId'])
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post_file_result = sqlhelper.queryOne(sql)
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send_task = user_tasks[user['accountId']]
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predictTopic(user,post_file_result,send_task,dbConfig,taskId)
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except Exception as e:
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traceback.print_exc()
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logging.error("用户id：{}".format(user['accountId']))
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logging.error("用户缓存加载失败：{}".format(send_task))
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else:
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#清空用户任务缓存
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user_tasks.clear()
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except Exception as e:
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traceback.print_exc()
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else:
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# 暂无任务，进入休眠
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time.sleep(10)
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def replyGraphThread():
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'''
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判断话题是否结束，如果2个小时未访问话题，则删除该话题的图信息。
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:return:
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'''
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while True:
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try:
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if replyGraph!={}:
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# 获取当前时间
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current_time = datetime.now()
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for topicID in list(replyGraph.keys()):
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# 计算最后一次操作的时间与当前时间的差值
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time_difference = current_time - replyGraph[topicID]['last_operation_time']
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# 如果差值大于等于120分钟，则删除该话题图信息
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if time_difference >= timedelta(minutes=120):
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del replyGraph[topicID]
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except:
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logging.info(traceback.format_exc())
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finally:
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time.sleep(1800)
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