基于拓扑网络的广义城轨信号系统关键组分识别方法  

Identification Method of Key Components in Generalized Urban Rail Signaling System Based on Topology Network

作　　者：王璐 郝婼妍 张余豪 李承叡 王艳辉[2,3,5,6,7] WANG Lu;HAO Ruoyan;ZHANG Yuhao;LI Chengrui;WANG Yanhui(Beijing Mass Transit Railway Operation Corporation Limited,Beijing 100044,China;State Key Laboratory of Advanced Rail Autonomous Operation,Beijing Jiaotong University,Beijing 100044,China;School of Traffic and Transportation,Beijing Jiaotong University,Beijing 100044,China;Beijing MTR Construction Administration Corporation,Beijing 100068,China;Beijing Urban Traffic Information Intelligent Sensing and Service Engineering Technology Research Center,Beijing Jiaotong University,Beijing 100044,China;Transportation Industry R&D Center for Urban Rail Transit Operation Safety Management Technology and Equipment,Beijing Jiaotong University,Beijing 100044,China;Key Laboratory of Railway Industry for Operational Active Safety Assurance and Risk Prevention and Control,Beijing 100044,China)

机构地区：[1]北京市地铁运营有限公司,北京100044 [2]北京交通大学,先进轨道交通自主运行全国重点实验室,北京100044 [3]北京交通大学交通运输学院,北京100044 [4]北京市轨道交通建设管理有限公司,北京100068 [5]北京交通大学,北京市城市交通信息智能感知与服务工程技术研究中心,北京100044 [6]北京交通大学,城市轨道交通运营安全管理技术及装备交通运输行业研发中心,北京100044 [7]运营主动安全保障与风险防控铁路行业重点实验室,北京100044

出　　处：《铁路技术创新》2023年第3期33-41,共9页Railway Technical Innovation

基　　金：国家重点研发计划项目(2020YFB1600701)。

摘　　要：信号系统作为城市轨道交通系统运营的大脑,其故障问题是导致城轨运营事故发生的关键因素之一。然而1个事故或者故障的发生往往不是单一的组分出现问题,而是多个组分间相互影响、互相作用的结果。因此其可靠性和安全性可能会受到系统关键组分特性的显著影响。通过调研分析广义城轨信号系统的构成、运营安全特性及影响要素集,并结合城轨信号系统组分间关系,构建了城轨信号系统拓扑网络模型,并利用改进的有限状态机对其进行优化,得到城轨信号系统安全特征拓扑网络模型。基于隐马尔可夫模型的风险文本抽取算法对其关键组分进行识别,最终得到111个信号系统关键组分,并与信号系统国家级风险文件中设备相关风险点清单进行对比,发现覆盖率较高,证明该研究提出的信号系统关键组分辨识方法合理有效。As the brain of urban rail transit system operation,the fault of signaling system is one of the key factors leading to urban rail operation accidents.However,an accident or fault is often not a problem of a single component,but the result of interaction between multiple components.Therefore,its reliability and safety may be significantly affected by the characteristics of key components of the system.By investigating and analyzing the composition,operation safety characteristics and operation safety influencing factor set of generalized urban rail signaling system,in combination with the relationship between components of urban rail signaling system,a topology network model of urban rail signaling system is constructed,which is optimized by using an improved finite state machine to obtain the safety characteristic topology network model of urban rail signaling system.The risk text extraction algorithm based on Hidden Markov Model is used to identify its key components and 111 key components of the signaling system are obtained finally.Compared with the list of equipment-related risk points in the national risk documents of the signaling system,it is found that the coverage rate is high,which proves that the identification method for key components of the signaling system proposed in this study is reasonable and effective.

关 键 词：城市轨道交通 信号系统 拓扑网络 运营安全 节点重要度 隐马尔可夫模型 

分 类 号：U284[交通运输工程—交通信息工程及控制] U239.5[交通运输工程—道路与铁道工程]