The Application of a Marine Weather Data Reconstruction Model Based on Deep Super-Resolution in Ship Route Optimization

Accurate weather data are very important for the navigation of ships. However, due to the insufficient coverage of the maritime network, the high cost of satellite communication, and the limited bandwidth, it is difficult for ships to obtain high-resolution weather data during route planning. This challenge greatly limits the accuracy and effectiveness of ship navigation. To solve this problem, this paper proposes a marine weather data reconstruction model based on deep super-resolution. Firstly, the model uses a convolutional neural network to extract features from wind speed and wave height data. Secondly, the model uses SRResNet as the reconstruction framework and effectively captures the complex nonlinear feature relationship in weather data through the residual block structure to realize the fine reconstruction of low-resolution weather data. In addition, the attention mechanism is integrated into the model to dynamically adjust the weights of different weather features, which further enhances the attention to key features. The results show that the model has a good effect on the super-resolution reconstruction of weather data. The PSNR, SSIM, GMSD, and FSIM of wave height reconstruction are 49.73 dB, 0.9949, 0.0082, and 0.9999, respectively, and the PSNR, SSIM, GMSD, and FSIM of wind speed reconstruction are 41.52 dB, 0.9797, 0.0400, and 0.9997, respectively. Based on the reconstructed data, route planning can effectively reduce the navigation distance of the ship and avoid unnecessary detours, thus saving fuel consumption and reducing operating costs.