## 1 Introduction
This limitation makes it impossible for ships to obtain accurate weather information in real time, which may lead to an increase in navigation distance. To solve this problem, in this paper, weather data recovery is carried out by combining super-resolution reconstruction technology and an attention mechanism to improve the accuracy and real-time performance of route planning, thereby reducing shipping costs and improving navigation efficiency. Super-resolution reconstruction is a technique for enhancing image resolution, commonly applied in computer vision and image processing. By combining multiple low-resolution images and using advanced algorithms, this technique can generate images with higher resolution than the original image, revealing more details and structures [16; 17; 18]. Super-resolution reconstruction not only improves the clarity of the image but also plays an important role in medical imaging, remote sensing, video surveillance, and other fields [19; 20; 21; 22]. Liu et al. [23] introduced a unified framework leveraging deep learning-based super-resolution, which adapted cutting-edge natural image super-resolution techniques for MRI reconstruction. Experimental results demonstrated that this approach outperforms traditional methods in terms of reconstruction quality. Xi [24] employed a deep autoencoder-based learning strategy to enhance the reconstruction algorithm, achieving an improved super-resolution performance through a refined network model. However, this method lacks the process of model convergence, which can easily lead to model underfitting. He et al. (2018) proposed a feature distillation-based reconstruction framework, which was shown to more effectively recover image features. Shao et al. (2018) developed an inverse synthetic aperture radar (ISAR) imaging method tailored for complex maneuvering ship targets. Simulation results indicate that the proposed algorithm addresses the trade-off between high-resolution imaging and motion error complexity under challenging sea conditions. However, the reconstruction effect of this method is low in multi-target and multi-static situations. Ying et al. (2018) designed a multi-granularity feature enhancement network (MFENet) to mitigate the decline in detection accuracy caused by sea fog interference in visible light images. However, due to changes in environmental factors, the image reconstructed by this method is unstable, which is likely to affect the robustness of the detection results. Additionally, Han et al. (2018) presented a dynamic multi-target tracking algorithm by integrating YOLOv7 with DeepSORT, with simulation outcomes showing a 9.4% improvement in the mAP50-95 compared to the baseline YOLOv7. However, the number of ships used in the model is small, and it is impossible to carry out multiple ship identification and tracking tests. Secondly, the attention mechanism has also been applied in many different fields. Liu et al. (2019) introduced a deep learning model incorporating a weighted pure attention mechanism, which demonstrated notable improvements in predictive accuracy through experimental validation. However, the model uses less evaluation methods, which cannot fully verify the validity of the model. Li (2018) developed a novel hybrid attention mechanism along with a high-resolution representation network utilizing efficient channel attention. Results indicated that this model delivers superior segmentation performance compared to similar approaches. However, this method lacks the input of noise data, which may affect the robustness of the model. Kardakis et al. (2018) applied an attention-based recurrent neural network (RNN) to emotion analysis, showing that attention fusion substantially enhances the predictive capabilities of deep neural networks. Li et al. (2019) proposed a segmentation and grasp detection network leveraging a dual-attention mechanism, achieving a high grasp success rate on a custom-built robotic arm platform. Yang et al.