计算机与现代化

• 人工智能 • 上一篇    下一篇

基于深度神经网络的电力客户诉求预判

  

  1. (1.河海大学计算机与信息学院,江苏南京211100;2.国网江苏省电力有限公司营销服务中心,江苏南京210019)
  • 收稿日期:2019-08-28 出版日期:2020-05-20 发布日期:2020-05-21
  • 作者简介:彭路(1995-),男,江苏南通人,硕士研究生,研究方向:机器学习在电力行业的应用,E-mail: 812234987@qq.com; 朱君(1988-),女,工程师,硕士,研究方向:电力营销客户服务大数据,营销数据管理,E-mail: 325661099@qq.com; 邹云峰(1977-),男,高级工程师,硕士,研究方向:电力营销信息化,大数据分析,E-mail: 13814099766@163.com。
  • 基金资助:
    国家重点研发计划项目(2017YFC0405805); 国网江苏省电力有限公司科技项目(J2018020)

Prediction of Power Customer Demands Based on Deep Neural Network

  1. (1. College of Computer and Information, Hohai University, Nanjing 211100, China;
    2. Marketing Service Center, State Grid Jiangsu Electric Power Co., Ltd., Nanjing 210019, China)
  • Received:2019-08-28 Online:2020-05-20 Published:2020-05-21

摘要: 电力企业的客户服务关系到客户的切身利益和企业的经营效益,提升客服系统对电力客户诉求预判的分析与理解能力是改善电力行业客服质量的重要途径之一。为高效、针对性地解决电力客户集中需求,做到“先于客户所想”,本文以深度神经网络技术为基础,针对电力领域改进传统的中文文本分词技术以及特征提取方法,给出电力客户诉求预判的方法和流程,并通过实验验证。本文提出的方法可快速精准地对电力客户服务工单文本进行分类,挖掘出隐藏的客户用电诉求,将服务由被动变主动,第一时间解决电力客户潜在诉求。

关键词: 深度神经网络, 客户诉求预判, 电力客户服务工单, 文本分类

Abstract: Customer service of electric power enterprises is related to the vital interests of customers and the business benefits of enterprises. Improving the analyzing and understanding ability of the customer service system for group customers’ electricity consumption problems is one of the important ways to improve the quality of customer service for power industry. In order to solve the concentrated demand of power customers efficiently and pertinently, and achieve “before the customers think”, based on the deep neural network technology, this paper improves the traditional Chinese text segmentation technology and feature extraction method in the field of power, gives the method and flow of power customer demand pre-judgment, and verifies it through experiments. The proposed method can quickly and accurately classify the texts of power customer service order and excavate hidden customer power problems, which changes the service from passive to active and solves the potential demands of power customers at the first time.

Key words: deep neural network, customer demands forecast, power customer service order, text classification

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