基于卷积神经网络的铁路故障持续时间预测方法研究

Prediction method of railway faults duration based on convolutional neural network

随着铁路网络复杂程度的不断提高,铁路运营部门调度难度日益增加,亟须研究精准预测铁路故障持续时间的方法,从而提高铁路调度系统应对各类风险和事故的能力。文章基于“安监报1”的文本数据,结合Jieba分词、Word2vec词向量模型等自然语言处理技术,构建了一种基于卷积神经网络(CNN,Convolutional Neural Network)的铁路故障持续时间预测模型,并基于中国铁路沈阳局集团有限公司的实际生成数据进行试验。试验结果表明,本预测模型能够较为快速、准确地获取铁路故障持续时间及其概率分布,为列车的运行调整提供参考。

With the continuous increase in the complexity of railway networks,the scheduling difficulty of railway operation departments is increasing.It is urgent to study methods for accurately predicting the duration of railway failures,in order to improve the ability of railway dispatch systems to cope with various risks and accidents.This paper was based on the text data of"Safety Supervision Report 1",combined with natural language processing techniques such as Jieba word segmentation and Word2vec word vector model,to construct a railway fault duration prediction model based on Convolutional Neural Network(CNN).The model was tested based on actual generated data from China Railway Shenyang Group Co.Ltd.The experimental results show that this prediction model can quickly and accurately obtain the duration and probability distribution of railway faults,and provide reference for train operation adjustment.