Multipoint Heave Motion Prediction Method for Ships Based on the PSO-TGCN Model

During ship operations, frequent heave movements can pose significant challenges to the overall safety of the ship and completion of cargo loading. The existing heave compensation systems suffer from issues such as dead zones and control system time lags, which necessitate the development of reasonable prediction models for ship heave movements. In this paper, a novel model based on a time graph convolutional neural network algorithm and particle swarm optimization algorithm (PSO-TGCN) is proposed for the first time to predict the multipoint heave movements of ships under different sea conditions. To enhance the dataset's suitability for training and reduce interference, various filter algorithms are employed to optimize the dataset. The training process utilizes simulated heave data under different sea conditions and measured heave data from multiple points. The results show that the PSO-TGCN model predicts the ship swaying motion in different sea states after 2 s with 84.7% accuracy, while predicting the swaying motion in three different positions. By performing a comparative study, it was also found that the present method achieves better performance that other popular methods. This model can provide technical support for intelligent ship control, improve the control accuracy of intelligent ships, and promote the development of intelligent ships.