Maintaining the time synchronization of the train control system,as the key system of high-speed railways,is important for the safety of train operation.Aiming at the problem of inaccurate estimation of master-slave clock offset caused by time-varying uplink and downlink transmission delays and random clock jumps in existing time synchronization methods,a time synchronization method was proposed for high-speed railway based on differential autoregressive integrat-ed moving average model and temporal convolutional network(ARIMA-TCN)hybrid model.Firstly,the mathematical theory and practical observation model for high-speed railway clocks were established according to the asymmetry ratio of uplink and downlink transmission rates.Then,the jump outliers were identified and processed using the PauTa criterion to complete the preprocessing of the actual time series.Next,the ARIMA model was used to smooth out the noise jitter caused by the uncertain delays in the time series to obtain a stable time series.Finally,the attention enhanced TCN model was used to predict and compensate the clock offset.Through the experimental simulation,the influence of the lo-cation of the base station,the distance between the base stations and the train speed on the time synchronization of the high-speed railways was analyzed.The experimental results show that the compensated root mean square error of the pro-posed method is 75％less than that of the ordinary least squares method,44.4％less than that of the maximum likeli-hood estimation method,and 16.7％less than that of the back propagation neural network method,verifying the lower synchronization error and higher synchronization accuracy of the proposed method.
关键词
时间同步/精确时钟协议/差分自回归移动平均模型/注意力增强时域卷积网络/时间补偿
Key words
time synchronization/precision time protocol/differential autoregressive integrated moving average model/at-tention enhancement temporal convolutional network/time compensation
维普
万方数据