High-speed railway communication tower monitoring and intelligent prediction system based on iTransformer

High-speed railways are important national transportation infrastructure that significantly promotes the development of the national economy. The wireless communication between high-speed trains and the ground relies on trackside communication towers built along the railway line. With the rapid development of next-generation railway mobile communication technologies such as 5G-R, the demand for constructing high-speed railway communication towers is increasing. The stability of these towers plays an increasingly critical role in the safe operation of high-speed railways, making it necessary to deploy a tower monitoring system for real-time monitoring of tower status and the attitude of antennas on the towers. This paper studies the design scheme of a high-speed railway communication tower monitoring system, conducts intelligent analysis and early warning using real-time data collected by various sensors in the tower monitoring system, and proposes an iTransfomer model that combines LSTM and a differential self-attention mechanism by comparing multiple advanced time-series prediction and analysis models. Experiments show that the model achieves good performance in both prediction accuracy and operation speed, providing reliable technical support for the safety early warning and intelligent operation and maintenance of high-speed railway communication towers.