新能源汽车供应链的关键风险节点识别方法

doi:10.11896/jsjkx.221100052

计算机科学 ›› 2023, Vol. 50 ›› Issue (6A): 221100052-7.doi: 10.11896/jsjkx.221100052

新能源汽车供应链的关键风险节点识别方法

杨小博^1,2,3,4,5,6, 高海伟⁷, 刘天越^1,2,3,4,5,6, 郭炳晖^1,2,3,4,5,6

1 北京航空航天大学数学科学学院数学信息与行为教育部重点实验室北京 100191;
2 北京航空航天大学人工智能研究院北京 100191;
3 软件开发环境国家重点实验室北京 100191;
4 未来区块链与隐私计算高精尖中心北京100191;
5 中关村实验室北京 100094;
6 鹏城实验室广东深圳 518055;
7 北京交通大学机械与电子控制工程学院北京 100044

出版日期:2023-06-10 发布日期:2023-06-12
通讯作者: 刘天越(liutianyue97@163.com)
作者简介:(yangxb@buaa.edu.cn)
基金资助:
国家重点研发计划项目(2021ZD0201302);国家自然科学基金(U20B2053);广东省重点领域研发计划项目(2021B0101420003)

Key Risk Node Identification Methods in New Energy Vehicle Supply Chain

YANG Xiaobo^1,2,3,4,5,6, GAO Haiwei⁷, LIU Tianyue^1,2,3,4,5,6, GUO Binghui^1,2,3,4,5,6

1 Key Laboratory of Mathematics, lnformatics, Behavioral Semantics, School of Mathematical Sciences, Beihang University, Beijing 100191, China;
2 Institute of Artificial Intelligence, Beihang University, Beijing 100191, China;
3 State Key Laboratory of Software Development Enviroment,Beijing 100191,China;
4 Beijing Advanced Innovation Center for Future Blockchain and Privacy Computing,Beijing 100191,China;
5 Zhongguancun Laboratory,Beijing 100094,China;
6 Peng Cheng Laboratory,Shenzhen,Guangdong 518055,China;
7 School of Mechanical,Electronic and Control Engineering,Beijing Jiaotong University,Beijing 100044,China

Online:2023-06-10 Published:2023-06-12
About author:YANG Xiaobo,born in 1994,Ph.D candidate,is a member of China Computer Federation.His main research interests include data science and complex networks. LIU Tianyue,born in 1997,Ph.D candidate,is a member of China Computer Federation.Her main research interests include supply chain finance and privacy computing.
Supported by:
National Key R&D Program of China(2021ZD0201302),National Natural Science Foundation of China(U20B2053) and Key R&D Program of Guangdong Province,China(2021B0101420003).

摘要/Abstract

摘要： 以在新能源汽车行业中具有典型分析价值的特斯拉和小鹏汽车的公开数据为对象,分别构建了供应链网络模型,并针对供应链网络结构关联特征开展多维度融合的风险传播关键节点识别方法研究。首先,引入网络中心性指标,分析计算得到了中心性指标下的关键节点企业。同时,考虑到汽车供应链风险系统性传播的特点,引入风险免疫传播模型对网络中影响系统性风险传播的关键节点进行判定。最后,对两个网络分别进行级联失效模型分析,选出级联失效模型下在失效发生时对网络影响较大的关键节点。通过多维度的关键节点分析,发现新能源汽车供应链网络中对风险传播有很强影响力的关键节点不仅包含传统意义上的电池等核心企业,而且包含了具有行业隐形冠军属性的配件企业。通过提出的结构和传播属性综合分析方法,可以很好地发现新能源汽车供应链网络中潜在的隐性关键风险控制节点,具有很好的实践应用价值。

关键词: 汽车供应链, 风险传播, 关键节点识别

Abstract: In this paper,the open data of Tesla and XPENG,which have typical analytical value in the new energy automobile industry,are respectively used to construct networksof supply chain.And the key node identification method of risk transmission based on multi-dimensional fusion is studied according to the structural correlation characteristics of the network of supply chain.Firstly,the network centrality characteristics are introduced to analyze and calculate the key node.At the same time,considering the characteristics of systemic risk transmission in supply chain of automotive,the risk immune transmission model is introduced to determine the key nodes.Finally,the cascading failure model of the two networks is analyzed respectively,and the key nodes that have strong impact on network failure are selected.Through multi-dimensional key node analysis,it is found that the key nodes with strong impact include not only core enterprises such as batteries in the traditional sense,but also accessory enterprises with invisible leading position.Therefore,through the comprehensive analysis method of structure and transmission attribute proposed in this paper,the potential hidden key risk control nodes in the network of supply chain of new energy vehicles can be well found,which has good practical application value.

Key words: Automobile supply chain, Risk transmission, Key node identification

中图分类号:

杨小博, 高海伟, 刘天越, 郭炳晖. 新能源汽车供应链的关键风险节点识别方法[J]. 计算机科学, 2023, 50(6A): 221100052-7. https://doi.org/10.11896/jsjkx.221100052

YANG Xiaobo, GAO Haiwei, LIU Tianyue, GUO Binghui. Key Risk Node Identification Methods in New Energy Vehicle Supply Chain[J]. Computer Science, 2023, 50(6A): 221100052-7. https://doi.org/10.11896/jsjkx.221100052

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新能源汽车供应链的关键风险节点识别方法

Key Risk Node Identification Methods in New Energy Vehicle Supply Chain

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