【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。更多还原

【Abstract】 A text mining based identification 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 identified. The analysis result was intuitively and clearly displayed by using the visualization technology of word cloud. The proposed model was experimentally verified by using 4 662 records of railway infrastructure quality problems in Lanzhou Railway Administration between January 2012 to April 2016.更多还原