工业技术经济2026，Vol.45Issue(7)：55-69,15.DOI:10.3969/j.issn.1004-910X.2026.07.005
基于贝叶斯网络的数据跨境流动风险识别与评估
Risk Identification and Assessment of Cross-border Data Flow Based on Bayesian Network
摘要
Abstract
To reduce the risks associated with cross-border data flows in the electric vehicle industry,this study identifies key risk factors and develops a Bayesian network model for risk assessment.Based on collected cases of cross-border data violations and a review of relevant literature,grounded theory was applied to identify risk factors related to cross-border data flows in the elec-tric vehicle sector,resulting in the extraction of fifteen major risk factors.An Interpretive Structural Model(ISM)was then em-ployed to organize these factors into five hierarchical levels,and the resulting hierarchical structure was further transformed into a Bayesian network structure.Using the constructed Bayesian network,the collected data were input into the GeNIe software for pa-rameter learning,and the probability distribution of different risk levels for cross-border data flows in the electric vehicle industry was obtained.Subsequently,diagnostic analysis,sensitivity analysis,and maximum causal chain analysis were conducted based on the Bayesian network model to identify the key and sensitive factors leading to cross-border data flow risks.Based on the analysis results,corresponding risk prevention and control strategies were proposed,providing a scientific basis and analytical tool for cross-border data risk management in the electric vehicle industry.关键词
电动汽车产业/数据跨境流动/数据安全风险评估/扎根理论/贝叶斯网络/风险因素识别/解释结构模型/风险传导路径Key words
electric vehicle industry/cross-border data flow/data security risk assessment/grounded theory/bayesian network/identification of risk factors/Interpretive Structural Model/risk transmission path分类
管理科学引用本文复制引用
熊励,马闻婧..基于贝叶斯网络的数据跨境流动风险识别与评估[J].工业技术经济,2026,45(7):55-69,15.基金项目
国家社会科学基金后期资助项目"数据和模型驱动的跨境电商与数据流动发展及安全研究"(项目编号:24FGLB114). （项目编号:24FGLB114）
