[1]陈俐冰[],何容[],邱林[],等. 电力客服中心用户行为分析研究与实现[J].计算机技术与发展,2017,27(02):116-119.
　CHEN Li-bing[],HE Rong[],QIU Lin[],et al. Research and Implementation of User Behaviors Analysis of Electric Power Customer Service Center[J].,2017,27(02):116-119.
点击复制

 电力客服中心用户行为分析研究与实现()
分享到：

《计算机技术与发展》[ISSN:1006-6977/CN:61-1281/TN]

卷:
27
期数:
2017年02期
页码:
116-119
栏目:
应用开发研究
出版日期:
2017-02-10

文章信息/Info

Title:
 Research and Implementation of User Behaviors Analysis of Electric Power Customer Service Center
文章编号:
1673-629X（2017)02-0116-04
作者:
 陈俐冰[1]; 何容[1]; 邱林[1]; 王颖[1]; 孙天昊[2]
1. 国网重庆市电力公司客户服务中心；2.重庆大学计算机学院
Author(s):
 CHEN Li-bing[1]; HE Rong[1]; QIU Lin[1]; WANG Ying[1]; SUN Tian-hao[2]
关键词:
 ; 大数据; 电力大数据; 数据挖掘; 用户行为分析
Keywords:
 ; big data; power big data; data mining; user behavior analysis
分类号:
TP39
文献标志码:
A
摘要:
 目前电力客服中心已经开展了多元化服务渠道为客户提供全方位的服务,但缺少对各渠道的深度挖掘和分析.针对这种情况,提出基于大数据进行电力客服中心用户行为分析,将为多个电力客户服务渠道建立智能在线监测分析系统.首先进行分布式多线程的数据采集,然后基于大数据从多维度进行用户在线行为分析,包括统计分析和聚类分析等.其中统计分析包括服务渠道指标、服务功能总指标、单个服务功能指标、客户访问时间指标、客户区域分布等,聚类分析采用K-means聚类根据忠诚度、使用频度、贡献度三个指标进行用户细分挖掘分析.实现的系统能够获得各渠道的客户行为特性,为企业全面掌握各服务渠道提供了支撑,有助于提供更为智能便捷的个性化服务.
Abstract:
 At present,the electric power customer service center has already carried out the diversified service channels to provide customers with a full range of services,but the lack of depth mining and analysis for each channel.In view of this,the user behaviors analysis of electric power customer service center is conducted based on big data.An intelligent online monitoring and analysis system is set up for multiple power customer service channels.First it collects data using distributed multi thread,then makes online analysis of user behavior with multiple dimensions based on big data,including statistical analysis and cluster analysis.Statistical analysis contains service channel index,service function total indexes,service function single index,customer access time index,customer area distribution,etc..Cluster analysis uses K-means clustering to analyze user segmentation according to loyalty,frequency and contribution.The system implemented can get the customer behavior characteristics of each channel,provide support for enterprises to fully grasp the various service channels and help to give more intelligent and convenient personalized service.

相似文献/References:

[1]严霄凤,张德馨.大数据研究[J].计算机技术与发展,2013,(04):168.
　YAN Xiao-feng,ZHANG De-xin.Big Data Research[J].,2013,(02):168.
[2]张志宏,吴庆波,邵立松,等.基于飞腾平台TOE协议栈的设计与实现[J].计算机技术与发展,2014,24(07):1.
　ZHANG Zhi-hong,WU Qing-bo,SHAO Li-song,et al. Design and Implementation of TCP/IP Offload Engine Protocol Stack Based on FT Platform[J].,2014,24(02):1.
[3]梁文快,李毅. 改进的基因表达算法对航班优化排序问题研究[J].计算机技术与发展,2014,24(07):5.
　LIANG Wen-kuai,LI Yi. Research on Optimization of Flight Scheduling Problem Based on Improved Gene Expression Algorithm[J].,2014,24(02):5.
[4]黄静,王枫,谢志新,等. EAST文档管理系统的设计与实现[J].计算机技术与发展,2014,24(07):13.
　HUANG Jing,WANG Feng,XIE Zhi-xin,et al. Design and Implementation of EAST Document Management System[J].,2014,24(02):13.
[5]侯善江[],张代远[][][]. 基于样条权函数神经网络P2P流量识别方法[J].计算机技术与发展,2014,24(07):21.
　HOU Shan-jiang[],ZHANG Dai-yuan[][][]. P2P Traffic Identification Based on Spline Weight Function Neural Network[J].,2014,24(02):21.
[6]李璨,耿国华,李康,等. 一种基于三维模型的文物碎片线图生成方法[J].计算机技术与发展,2014,24(07):25.
　LI Can,GENG Guo-hua,LI Kang,et al. A Method of Obtaining Cultural Debris’ s Line Chart Based on Three-dimensional Model[J].,2014,24(02):25.
[7]翁鹤,皮德常. 混沌RBF神经网络异常检测算法[J].计算机技术与发展,2014,24(07):29.
　WENG He,PI De-chang. Chaotic RBF Neural Network Anomaly Detection Algorithm[J].,2014,24(02):29.
[8]刘茜[],荆晓远[],李文倩[],等. 基于流形学习的正交稀疏保留投影[J].计算机技术与发展,2014,24(07):34.
　LIU Qian[],JING Xiao-yuan[,LI Wen-qian[],et al. Orthogonal Sparsity Preserving Projections Based on Manifold Learning[J].,2014,24(02):34.
[9]尚福华,李想,巩淼. 基于模糊框架-产生式知识表示及推理研究[J].计算机技术与发展,2014,24(07):38.
　SHANG Fu-hua,LI Xiang,GONG Miao. Research on Knowledge Representation and Inference Based on Fuzzy Framework-production[J].,2014,24(02):38.
[10]叶偲,李良福,肖樟树. 一种去除运动目标重影的图像镶嵌方法研究[J].计算机技术与发展,2014,24(07):43.
　YE Si,LI Liang-fu,XIAO Zhang-shu. Research of an Image Mosaic Method for Removing Ghost of Moving Targets[J].,2014,24(02):43.
[11]王雷,陈彦先,袁哲,等. 面向预拌混凝土行业的云计算[J].计算机技术与发展,2014,24(08):14.
　WANG Lei,CHEN Yan-xian,YUAN Zhe JI Xu. Research on Cloud Computing for Ready-mixed Concrete Industry[J].,2014,24(02):14.
[12]金宗泽,冯亚丽,文必龙,等. 大数据分析流程框架的研究[J].计算机技术与发展,2014,24(08):117.
　JIN Zong-ze,FENG Ya-l,WEN Bi-long,et al. Research on Framework of Big Data Analytic Process[J].,2014,24(02):117.
[13]张也弛,周文钦,石润华. 一种面向云的大数据完整性检测协议[J].计算机技术与发展,2014,24(09):68.
　ZHANG Ye-chi,ZHOU Wen-qin,SHI Run-hua. A Big Data Integrity Checking Protocol for Cloud[J].,2014,24(02):68.
[14]谢怡,王航,刘新瀚,等. 大数据环境下数据读取关键技术研究[J].计算机技术与发展,2015,25(02):113.
　XIE Yi,WANG Hang,LIU Xin-han,et al. Research on Data Reading Techniques Based on Big Data Environment[J].,2015,25(02):113.
[15]付燕平,罗明宇,刘其军. 大数据三维模型快速显示技术研究[J].计算机技术与发展,2015,25(05):87.
　FU Yan-ping,LUO Ming-yu,LIU Qi-jun. Research on Fast Display Technology for Big Data Three-dimensional Model[J].,2015,25(02):87.
[16]赵震,任永昌. 大数据时代基于云计算的电子政务平台研究[J].计算机技术与发展,2015,25(10):145.
　ZHAO Zhen,REN Yong-chang. Research on E-government Platform Based on Cloud Computing in Big Data Era[J].,2015,25(02):145.
[17]胡存刚,程莹. 基于粒子群算法的大数据智能搜索引擎的研究[J].计算机技术与发展,2015,25(12):14.
　HU Cun-gang,CHENG Ying. Research on Big Data Intelligent Search Engine Based on PSO[J].,2015,25(02):14.
[18]肖洁,袁嵩,谭天. 大数据时代数据隐私安全研究[J].计算机技术与发展,2016,26(05):91.
　XIAO Jie,YUAN Song,TAN Tian. Research on Data Privacy in Big Data Age[J].,2016,26(02):91.
[19]郭先超,林宗缪,姚文勇. 互联网+质量检测平台设计[J].计算机技术与发展,2016,26(05):120.
　GUO Xian-chao,LIN Zong-miao,YAO Wen-yong. Design of Platform for Internet+ Quality Inspection[J].,2016,26(02):120.
[20]程艳云,张守超,杨杨. 基于大数据的时间序列异常点检测研究[J].计算机技术与发展,2016,26(05):139.
　CHENG Yan-yun,ZHANG Shou-chao,YANG Yang. Research on Time Series Outlier Detection Based on Big Data[J].,2016,26(02):139.

更新日期/Last Update: 2017-05-11