MA-NINN: Prediction of Civil Aviation Data Network Transmission Delay Using Multi-Head Attention Physics-Informed Neural Networks

This article proposes a prediction model that combines network physical characteristics and deep learning methods to solve the problem of insufficient round-trip time (RTT) prediction accuracy caused by complex dynamic characteristics in civil aviation business data networks. This model is based on a physical information neural network framework, which embeds domain knowledge such as delay load relationships, burst traffic attenuation patterns, and inverse RTT window constraints into a long short-term memory (LSTM) network. In addition, the multi constraint loss function enhances the adaptability of the model to complex network activities. Moreover, in order to overcome the limitations of LSTM networks in modeling long-range dependencies, a multi head attention (MA) mechanism was implemented to capture long-term temporal dependencies in parallel, thereby improving the model’s ability to capture long-range time step correlations. Benchmarking and extension tests were conducted on four civil aviation business data network datasets. The experimental results show that compared with the baseline model, the proposed model significantly improves the root mean square error (RMSE), mean absolute error (MAE), and mean absolute percentage error (MAPE) for bidirectional network communication RTT prediction tasks. The study has verified that combining network physical attribute constraints with attention mechanisms can effectively improve the accuracy of transmission delay prediction, providing an effective method for effective traffic prediction in highly dynamic civil aviation network environments.