融合改进决策树算法的轨道交通运维数据分析与优化技术OA

With the improvement of the intelligent level of urban rail transit network,the analysis and mining technology for massive operation and maintenance data provides a new way to improve operation and maintenance efficiency and ensure operation safety.Based on the vehicle operation,maintenance and status data collected by the metro vehicle intelligent analysis system,a method of large data analysis and strategy optimization for rail transit operation and maintenance is proposed by integrating multiple improved decision tree algorithms.According to the characteristics of rail transit operation and maintenance data,the classical decision tree algorithm is improved in aspects of pruning,feature selection and integration,and the composite decision tree model is constructed by means of the fusion strategy,which can realize based accurate prediction of vehicle faults,root cause analysis and health status assessment.The experimental testing results on the simulation self-built dataset show that,in comparison with existing methods,the proposed algorithm can significantly improve both fault diagnosis accuracy and operational maintenance strategy generation efficiency.The fault diagnosis accuracy can reach 95.2%,and the generation efficiency of operational maintenance strategies is increased by 18.5%.