基于知识图谱的轨道交通运营风险事件智能分析研究

Intelligent Analysis of Rail Transit Operation Risk Events Based on Knowledge Graph

目前城市轨道交通运营风险事件频发,复杂的内部结构和网络化运营模式使得各风险之间动态关联,致险因素不易识别和管理;风险管理知识碎片化,分散存储在各种资料中,相关管理者不易获取管理经验。为解决以上问题,基于人工智能领域的网络爬虫技术和知识图谱技术,从各城市的政府官网、专业网站以及百度新闻网页获取2000—2022年间的549起城市轨道交通运营风险事件资料,使用Neo4j图数据库构建城市轨道交通运营风险事件知识图谱,实现对风险事件数据的可视化展示与智能分析。通过对风险事件开展四类分类查询、三类统计分析及两类关联路径问答研究,对以往风险事件进行智能分析,为后续类似风险事件提供事前防范或者事后处置的管理经验。研究表明:在城市轨道交通领域应用知识图谱,可以保障风险事件信息持续存储功能及效率,优化风险事件统计途径及可视化效果,实现事件关联关系灵活化智能分析,可为城市轨道交通运营期间风险的前馈控制和反馈响应提供管理经验和参考建议。

At present,urban rail transit operation risk events occur frequently.The complex internal structure and network operation mode make the risks dynamically linked,and the risk factors difficult to be identified and managed.Risk management knowledge is fragmented and stored in various materials,witch makes it difficult for relevant managers to obtain management experience.In order to solve the above problems,549 urban rail transit operational risk events data are collected from the official government websites,professional websites of each city and Baidu News website from 2000 to 2022 based on the web crawler and knowledge graph technology in the field of artificial intelligence.The Neo4j diagram database is used to build knowledge graph and realize the visual display and management of risk event data.By conducting classified inquiry,statistical analysis and related path Q&A research on risk events,analysis of past risk events,management experience is provided for the prevention or disposal of subsequent similar risk events in advance.The research results show that the application of knowledge graph in the field of urban rail transit can guarantee the continuous storage function and efficiency of risk events,optimize the statistical approach and visualization effect of risk events,and realize the flexible analysis of event correlation.The research results can provide management experience and reference suggestions for the feedforward control and feedback response of risk during the operation of urban rail transit.