基于知识图谱的轨道交通运营风险事件智能分析研究OA北大核心

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.