## 5 Conclusions  
This paper proposes a marine weather data reconstruction model based on deep super-resolution. The model uses convolutional neural networks to extract features from wind speed and wave height data. Secondly, the model takes 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. However, there are still some limitations in this study. First of all, the ship's route planning is a multi-factor comprehensive decision-making process, which is not only affected by wind speed and wave height but also includes multi-source environmental and operational factors such as water flow, sea ice, and safety regulations. Secondly, limited by experimental resources and computational conditions, the experimental area selected in this study is relatively small, which may affect the generalization ability of the model. Although this study has made some progress in the reconstruction of high-resolution weather fields, there are still some directions worthy of further improvement. First, the current model is only trained and validated in specific sea areas, and subsequent studies will be extended to areas with different climatic conditions and hydrological characteristics to evaluate the generalization ability of the model. Secondly, limited by the amount of data available, the amount of data currently used is small. In the future, we will consider introducing larger data sets and further analyze the robustness performance of the model under small-sample conditions. Finally, although this study preliminarily demonstrates the application potential of reconstructed data in route planning, it does not integrate a complete route optimization module. In the future, the coupling mechanism between high-resolution reconstructed data and a dynamic route optimization algorithm will be further explored to improve its practicability and decision support ability.  
The authors confirm that the contributions to this paper are as follows: conceptualization, S.L.; methodology, S.L.; software, S.L.; validation, J.Y. and Z.W.; formal analysis, J.Y.; investigation, J.Y.; resources, Z.W.; data curation, J.Y.; writing--original draft preparation, S.L.; writing--review and editing, Z.W.; visualization, Z.W.; supervision, Z.W. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Excellent Young Teacher Project of Jiangsu Qinglan Project grant number [013103024]. This research was funded by the Educational Science Project of China Transportation Education Research Association grant number [JT2024 YB258]. This research was funded by the Youth Special Project of Jiangsu Province's Education Science grant number [C/2024/02/50].  
Restrictions apply to the availability of these data. Data were obtained from ECMWF and are available [[https://cds.climate.copernicus.eu](https://cds.climate.copernicus.eu)]([https://cds.climate.copernicus.eu](https://cds.climate.copernicus.eu)) with the permission of ECMWF.  
The authors declare no conflicts of interest.