主办:陕西省汽车工程学会
ISSN 1671-7988  CN 61-1394/TH
创刊:1976年

汽车实用技术 ›› 2023, Vol. 48 ›› Issue (19): 194-200.DOI: 10.16638/j.cnki.1671-7988.2023.019.038

• 综述 • 上一篇    

基于大数据分析的新能源汽车画像研究进展

王泽兴,韩博洋*,蔺会光,吕冯婧,张 炀   

  1. 国家新能源汽车技术创新中心
  • 出版日期:2023-10-15 发布日期:2023-10-15
  • 通讯作者: 韩博洋
  • 作者简介:王泽兴(1983-),男,博士,高级工程师,研究方向为新能源汽车、汽车数字化,E-mail:wangzexing@nevc. com.cn。 通信作者:韩博洋(1998-),男,硕士,研究方向为计算科学、虚拟现实,E-mail:hanboyang@nevc.com.cn。

Progress of Research on New Energy Vehicle Portraits Based on Big Data Analysis

WANG Zexing, HAN Boyang* , LIN Huiguang, LV Fengjing, ZHANG Yang   

  1. National New Energy Vehicle Technology Innovation Center
  • Online:2023-10-15 Published:2023-10-15
  • Contact: HAN Boyang

摘要: 大数据技术与新能源汽车的融合是未来发展的必然趋势,在软件和场景定义汽车的大 背景下,明确用户群体,构建清晰的产品画像,已成为汽车企业在数字化转型期的发展趋势 之一。文章首先分析了常用大数据处理框架的技术特征,对大数据基本处理流程及其对应原 理进行了梳理,同时阐述了大数据统计分析的方法及过程。其次结合大数据分析技术分别梳 理了用户特征分析、用户及产品画像挖掘的相关新能源汽车画像研究及应用,并对基于 K-means 聚类的汽车用户及产品画像实例进行了分析。最后,对大数据分析技术在新能源汽 车画像研究的应用前景进行了总结,并提出了部分思考及未来展望。

关键词: 新能源汽车;用户画像挖掘;大数据分析;K-means 聚类

Abstract: The integration of big data technology and new energy vehicles is the inevitable trend of the future. In the context of software and scenario-defined vehicles, clarifying user profiles and establishing clear product portraits have become one of the development trends for automotive companies in the phase of digital transformation. Firstly, an analyse of the technical features of commonly used big data processing frameworks, the basic processing flow of big data and its corresponding principles are sorted out, and the methods and processes of statistical analysis of big data are described. Secondly, combined with big data analysis technology, the paper respectively sorts out the research and application of new energy vehicle portrait of user characteristics analysis and user and product portrait mining, and analyzes the examples of vehicle users and product portrait based on K-means clustering. Finally, the application prospect of big data analysis technology in new energy vehicle profiling research is summarized and some reflections and future prospects are put forward.

Key words: New energy vehicle; User persona mining; Big data analysis; K-means clustering