Safety evaluation and risk reasoning of metro station based on IFPN

Abstract In order to realize the accurate evaluation of the safety status of metro station and the high-efficiency identification of the risk cause, an improved fuzzy petri net is introduced to describe the safety risk propagation process of the metro station. The normal gray cloud whiten weight model combined with whitening weight function in gray clustering system and cloud theory is introduced, and the uncertainty of the initial token of place is comprehensively described. The Apriori algorithm is used to extract the transition confidence and weight according to the strong association rules between places, and an IFPN analysis model to support the evolution of the safety state of metro station is established. Through the forward reasoning, the interpretation of the risk propagation path is realized, and the safety status of the metro station is solved. Through the reverse reasoning, the risk is identified and the risk place that has the greatest impact on the safety status of metro station is investigated. Finally, the model is reasoned with an example to verify the effectiveness of the evaluation method.