一种基于文本挖掘的铁路基础设施设备风险隐患识别模型  

Text mining based identification model for railway infrastructure risk

作　　者：李擎[1] 张秋艳[1] 白磊[1,2] LI Qing;ZHANG Qiuyan;BAI Lei(School of Trafc and Transportation, Beijing Jiaotong University, Beijing 100044, China;Beijing E-Hualu Information Technology Co. Ltd., Beijing 100043, China)

机构地区：[1]北京交通大学交通运输学院,北京100044 [2]北京易华录信息技术股份有限公司,北京100043

出　　处：《铁路计算机应用》2018年第2期1-4,共4页Railway Computer Application

基　　金：国家自然科学基金(51578057);中国铁路总公司重点课题(2017T003-C)

摘　　要：提出一种基于文本挖掘的铁路基础设施设备风险隐患识别模型,该模型采用基于层叠隐马尔科夫的分词算法对长文本形式的设备质量问题数据进行分词处理,在此基础上,统计每类词出现的频度,识别铁路基础设施设备管理风险隐患,利用词云图可视化技术,对分析结果进行直观、清晰地展示。作者选取了兰州铁路局2012年1月~2016年4月期间4 662条工务、电务和供电专业的铁路基础设施设备质量问题数据,验证了模型的有效性。A text mining based identifcation model for railway infrastructure risk was proposed in this paper. The model used segmentation algorithm based on Cascaded Hidden Markov Model （CHMM） to deal with data in the form of long text, which recorded railway infrastructure quality problems. Then, the word frequency was calculated and the railway infrastructure management risk was identifed. The analysis result was intuitively and clearly displayed by using the visualization technology of word cloud. The proposed model was experimentally verifed by using 4 662 records of railway infrastructure quality problems in Lanzhou Railway Administration between January 2012 to April 2016.

关 键 词：铁路基础设施 风险隐患 文本挖掘 

分 类 号：U216.42[交通运输工程—道路与铁道工程] TP39[自动化与计算机技术—计算机应用技术]