湖南电力 ›› 2023, Vol. 43 ›› Issue (5): 151-154.doi: 10.3969/j.issn.1008- 0198.2023.05.022

• 经验与探讨 • 上一篇    

基于jieba中文分词的电力客户精准分类方法

高攀, 李飞, 彭远豪, 张璨辉, 彭海君   

  1. 国网湖南省电力有限公司供电服务中心(计量中心),湖南 长沙 410116
  • 收稿日期:2023-04-17 修回日期:2023-08-07 出版日期:2023-10-25 发布日期:2023-11-03
  • 通信作者: 高攀(1992),男,通信作者,工程师,研究方向为客户关系管理、客户服务、服务渠道运营。
  • 作者简介:李飞(1989),女,工程师,研究方向为客户关系管理、客户服务、服务渠道运营。彭远豪(1988),男,工程师,研究方向为客户关系管理、客户服务、服务渠道运营。张璨辉(1987),女,副高级工程师,研究方向为客户关系管理、客户服务、服务渠道运营。彭海君(1979) ,男,工程师,研究方向为客户关系管理、客户服务、服务渠道运营。

Accurate Classification Method of Power Customers Based on Jieba Chinese Word Segmentation

GAO Pan, LI Fei, PENG Yuanhao, ZHANG Canhui, PENG Haijun   

  1. State Grid Hunan Electric Power Company Limited Power Supply Service Center (Metrology Center), Changsha 410116, China
  • Received:2023-04-17 Revised:2023-08-07 Online:2023-10-25 Published:2023-11-03

摘要: 针对电力营销中基础数据中的客户细分,提出一种基于jieba中文分词实现大客户精准分类的方法。首先构建包含客户基本类别的自定义字典,利用jieba分词对文本数据完成分词;其次,基于分词结果中的高频词和关键词,分析统计部分分类规律、构建分类特征库,将分类特征库作为神经网络预训练模型的输入,训练客户分类的神经网络模型,最终输出电力客户的精准分类结果。该方法解决电力系统数据库中用户类别不清晰或分类方法过于复杂的问题,为电力公司制定客户差异化服务提供基础。

关键词: 客户分类, 中文分词, jieba, 神经网络

Abstract: Aiming at the customer segmentation in the basic data of electric power marketing, an innovative method is proposed to achieve accurate classification of major customers based on jieba Chinese word segmentation. A self-defined dictionary containing the basic categories of customers is constructed, and jieba is used to complete the segmentation of the text data. The classification feature database is built based on the classification rules of high-frequency words and keywords in the word segmentation results. The classification feature database is used as the input of the neural network pre-training model to train the neural network model of customer classification, and the accurate classification results of power customers are finally output. This method solves the problem of unclear user category or too complicated classification method in the current power system database and provides the basis for the power company to develop differentiated customer service.

Key words: customer classification, Chinese word segmentation, jieba, neural network

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