Intelligent Railway Technology Station Safety Evaluation Methods Based on BP Neural Network Improved by Sparrow Search Algorithm
In order to improve the safety of railway technology stations,an intelligent railway technology station safety evaluation method that integrates subjective and objective evaluation methods,sparrow search algorithm,and back propagation(BP)neural network model was proposed,which could effectively solve the safety evaluation problem of production operations at railway technology stations.The entropy weight method was used to determine attribute weights,and a corresponding comprehensive safety evaluation model was established to generate training and testing samples for the BP neural network,realizing intelligent safety evaluation of the railway technology stations.In view of the randomness and uncertainty in selecting key parameters of the BP neural network,the sparrow search algorithm was used to globally optimize and solve the key parameters of the network,further improving its accuracy.The feasibility and effectiveness of the designed method were verified through testing and analysis,providing new ideas for optimizing the safety management of railway technology stations.
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维普
