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

Automobile Applied Technology ›› 2024, Vol. 49 ›› Issue (19): 40-45.DOI: 10.16638/j.cnki.1671-7988.2024.019.008

• Intelligent Connected Vehicle • Previous Articles    

Intelligent Connected Vehicle In-vehicle Service Ecology Framework Planning and Development Proposals

NI Baochener1 , XU Jianhan2 , CHEN Jinwen1 , HE Qiuyao1 , LIN Yuxin1   

  1. 1.School of Business Administration, Northeastern University; 2.School of Software, Northeastern University
  • Published:2024-10-10
  • Contact: NI Baochener

智能网联汽车车载服务生态的架构规划 与发展建议

倪宝琛儿 1,徐健涵 2,陈锦雯 1,何秋瑶 1,林予歆 1   

  1. 1.东北大学 工商管理学院;2.东北大学 软件学院
  • 通讯作者: 倪宝琛儿
  • 作者简介:倪宝琛儿(2004-),女,研究方向为智能网联汽车、深度学习、多模态知识图谱,E-mail:2868232189@qq.com。

Abstract: A good intelligent connected vehicle vehicle service ecosystem can not only promote the healthy development of the intelligent connected vehicle vehicle service market, but also provide more development momentum and cash opportunities for the vehicle service research and development industry, and ultimately provide the market with better quality vehicle service products. Based on the current situation of the continuous development of intelligent connected vehicle vehicle services, this paper analyzes its market foundation and development problems in detail, and proposes solutions for building an intelligent connected vehicle services business ecosystem. It includes establishing the business ecological concept of vehicle service, adopting K-means++ clustering algorithm and convolutional neural network (CNN) portrait as its main application technology, maintaining market order, and building business models.

Key words: Intelligent connected vehicles; In-vehicle service; Commercial ecology; Portrait of the owner; K-means++; Convolutional neural network

摘要: 良好的智能网联汽车车载服务生态不仅可以促进智能网联汽车车载服务市场健康发展, 也可以为车载服务研发行业提供更多发展动力与变现机遇,最终能够为市场提供更优质的车 载服务产品。文章基于智能网联汽车车载服务不断发展的现状,详细分析了其市场基础与发 展问题,并提出构建智能网联汽车车载服务商业生态的解决方式,包括建立车载服务商业生 态理念、采用 K-means++聚类算法与卷积神经网络(CNN)画像作为其主要应用技术、维持 市场秩序、构建商业模式等。

关键词: 智能网联汽车;车载服务;商业生态;车主画像;K-means++;卷积神经网络