基于G1-CRITIC-云模型的城市新能源汽车充电站安全韧性评价

"Public Security + Artificial Intelligence" Special Issue:Safety Resilience Evaluation of Urban New Energy Vehicle Charging Stations Based on the G1-CRITIC-Cloud Model

  • 摘要: 在“双碳”战略的政策驱动下,新能源电动汽车产业呈现规模化发展态势,充电站作为关键基础设施,其安全运行直接关系到社会公共安全。为此本研究结合充电站运行特征界定安全韧性概念,搭建“四阶段-三维空间”分析框架,筛选26项核心指标建立评价体系;融合G1序关系分析法与CRITIC法完成组合赋权,引入云模型量化评价中的模糊性与随机性,构建兼具主客观特性与不确定性表征的安全韧性测度模型,并以焦作市9座典型充电站开展实证分析。结果表明,焦作市充电站安全韧性呈“阶梯式分布”,部分站点达优秀等级,且抵御、响应阶段韧性普遍优于恢复、适应阶段;物理防护、消防设施等基础指标及应急响应效率是各站点的共性障碍,不同类型站点还存在场景化短板。研究构建的评价模型可为充电站安全韧性提升提供精准的量化评估工具,也为新能源充电基础设施安全管理提供了系统方法论。

     

    Abstract: Driven by the policy of the “dual carbon” strategy, the new energy electric vehicle industry is witnessing large-scale development. As critical infrastructure, the safe operation of charging stations is directly related to social public security. To this end, this study defines the concept of safety resilience in combination with the operational characteristics of charging stations, constructs a “four-stage and three-dimensional space” analytical framework, and selects 26 core indicators to establish an evaluation system. It integrates the G1 rank correlation analysis method and the CRITIC method to realize combined weighting, and introduces the cloud model to quantify the fuzziness and randomness in the evaluation, thus building a safety resilience measurement model that embodies both subjective and objective characteristics and uncertainty representation. An empirical analysis is conducted on 9 typical charging stations in Jiaozuo City. The results show that the safety resilience of charging stations in Jiaozuo City presents a “stepwise distribution”, with some stations reaching the excellent level; in addition, the resilience in the resistance and response stages is generally better than that in the recovery and adaptation stages. Basic indicators such as physical protection and fire-fighting facilities, as well as emergency response efficiency, are common obstacles for all stations, and different types of stations also have scenario-specific shortcomings. The evaluation model constructed in this study can provide an accurate quantitative assessment tool for improving the safety resilience of charging stations, and also offer a systematic methodology for the safety management of new energy charging infrastructure.

     

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