基于大模型混合算法的站台门设备状态预测方法研究

Study on Prediction Method of Platform Door Equipment State Based on Large Model Hybrid Algorithm

地铁站台门设备关键状态量的监测为设备状态预警提供了重要依据。根据免疫算法及小波神经网络的基本原理,对设备状态预测进行优化,提出大模型混合算法的站台门设备状态预测算法。结合国内外城市轨道交通机电类设备健康运营现状,对基于大模型混合算法(免疫算法+小波神经网络)的站台门设备健康管理系统进行验证,结果表明此算法预测设备状态误差小、理论值准确。

The monitoring of the key status of metro station door equipment provides an important basis for early warning of equipment status.The basic principles of artificial immune algorithm and wavelet neural network are introduced,the equipment state prediction is optimized,and a large model hybrid algorithm for platform door equipment state prediction algorithm is proposed.Combined with the domestic and foreign urban rail transit electromechanical equipment health operation status,the platform door equipment health management system based on the large model hybrid algorithm(immune algorithm+wavelet neural network)was verified.The results show that the algorithm predicts the equipment state error is small and the theoretical value is accurate.