Risk diagnosis model for high-speed rail safety operation in big-data environment

Abstract

Aiming at the risk issue of high-speed rail (HSR) safety operation, big data technology and uncertain mathematical method are adopted to study it. Firstly, from the perspective of system science, the risk diagnosis mode of HSR safety operation is put forward, which mainly includes the operation environment diagnosis mode based on multivariate product, high-speed train diagnosis mode based on failure influence, staff diagnosis mode based on management conditions, track diagnosis mode based on probability safety, etc. And based on comprehensive analysis, the conventional risk diagnosis index system is constructed. Then the dynamic diagnosis index system based on principal component analysis is proposed, and the risk diagnosis model of HSR safety operation is established. The diagnosis model can quickly evaluate the operation situations of HSR, and the diagnosis results are conducive to grasping the situation of risk events quickly and accurately, so as to meet the timeliness requirements of emergency decision-making. Finally, to verify the effectiveness of this new model, the Beijing–Shanghai HSR is selected as a case study. The analysis results show that the diagnosis model can quickly diagnose the safety operation situation of HSR, simplify the evaluation process and improve the efficiency of the comprehensive evaluation of emergencies.