Railway Communication QoS Alarm Mechanism Based on XGBoost-Informer Model and Multi-source Data
Addressing the challenges of massive real-time monitoring data and the lack of effective intelligent monitoring and alarm mechanisms in railway communication services,it is crucial to explore efficient analysis of multi-source railway monitoring data and establish an intelligent network service quality prediction and alarm mechanism to support ef-ficient railway operations and safety.In this paper,by performing data alignment,cleaning,and feature selection from multi-source railway monitoring data,an XGBoost-Informer fusion model was established for training.The XGBoost model was employed to identify the cross-sectional classification relationship between data features and target values for coarse-grained prediction.Then,the Informer model was used to refine the longitudinal temporal relationships between preceding and subsequent data,resulting in predictions of latency,signal strength,and service quality during train oper-ation,so as to trigger early alarms for anticipated anomalies,and classify alarms based on the predicted network features.The proposed algorithm model achieves a coarse-grained classification accuracy of 96.7％and an accuracy of 98.7％af-ter refinement when applied to multi-source railway monitoring data.Comparative analysis with other classification models shows that the proposed model has a root mean square error of 0.113 and a mean absolute error of 0.003,outperforming other models with significant improvements and demonstrating a good fitting effect.This mechanism enhances the process-ing efficiency of monitoring data while ensuring railway operation safety,and provides predictive insights into network status trends,which are highly valuable for enhancing on-the-way monitoring and intelligent maintenance in the next generation of railways.
railway communicationintelligent operation and maintenancemonitorXGBoostInformerpredictive alarm
万方数据
维普
