Abnormal interference prediction of railway signal transmission nodes based on deep learning
In response to the prediction error caused by frequency fluctuations in railway signal transmission,this article proposes a deep learning based anomaly interference prediction method.This method comprehensively obtains node interference data,extracts key features using data processing techniques,and reflects signal anomalies.Based on deep learning algorithms,construct an interference signal anomaly detection framework to automatically learn and recognize abnormal patterns.Through real-time data input,the framework can quickly output prediction results and accurately predict abnormal interference.Experimental results have shown that the average prediction error distance of this method is 50.5 kHz,which is more than 85 kHz lower than the control group.It achieves accurate prediction of abnormal interference in railway signal transmission nodes and provides strong guarantees for the safe and stable operation of railways.
deep learning modelrailway signaltransmission nodeabnormal interference predictiontime domain skewness
万方数据
维普
