## 4 Data Reconstruction and Route Optimization  
The experiment was conducted on a Windows platform with an i5-13490F CPU and an NVIDIA(r) GeForce(r) RTX 4060 Ti GPU. The development environment was Spyder, with the framework implemented in Python 3.12.4. The deep learning setup included Python 3.11.7, Spyder IDE 5.4.3, PyTorch 1.12.1 + cu117, CUDA 12.2, and cuDNN 8.7.0.0. The area of the experiment was (130.25\({}^{\circ}\) W-118.25\({}^{\circ}\) W, 19.75\({}^{\circ}\) N-31.75\({}^{\circ}\) N). The experimental data were wind speed and wave height data at 1 January 2021 00:00 UTC-31 December 2022 23:00. There were 8760 images with a resolution of 24 \(\times\) 24 in each group, 60% of which were used as a training set, 20% as a validation set, and 20% as a test set. In the process of model training, the calculation of loss value is very important, including training loss and verification loss. To ensure model stability in practical applications, these losses must converge gradually to guarantee good generalization. In this experiment, \(L_{1}\) loss is used to measure the absolute error between the predicted and true values, as shown in Equation (16). \[L_{1}=\frac{1}{n}{\sum\limits_{i=1}^{n}}|y_{i}^{*}-y_{i}| \tag{16}\]  
where \(y_{i}^{*}\) is the predicted value of the model, \(y_{i}\) is the true value of the model, and \(n\) is the total sample. The learning rate is set to 0.001, the optimizer is Adam, and the number of iterations is set to 100 times. The loss value of the model is calculated according to Equation (4), and the loss function curve is constructed, as shown in Figures 13 and 14. Figure 13: Wind speed loss curve. \[GMSD=\sqrt{\frac{1}{n}\sum_{i=1}^{n}{(\frac{2G_{r}(i)G_{d}(i)+c}{G_{r}(i)^{2}+G_{d}( i)^{2}+c}-\mu_{GMS})^{2}}} \tag{19}\]  
where \(G_{r}(i)\) and \(G_{d}(i)\) are the gradient amplitudes of the reference image and the reconstructed image at pixel \(i\), respectively. \(c\) is the stability constant (prevents the denominator from being zero); \(\mu_{G\mathrm{MS}}\) is the average value of the GMS similarity graph; \(n\) is the total number of pixels. \[FSIM=\frac{\sum_{i\in\Omega}S_{PC}(i)\cdot W(i)}{\sum_{i\in\Omega}W(i)} \tag{20}\]  
where \(S_{PC}(i)\) is the feature similarity of the \(i\)-th pixel, \(W(i)\) is the weight of the \(i\)-th pixel, and \(\Omega\) is the image domain. Figure 15: Comparison of wind speed reconstruction results. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & & **PSNR** & **SSIM** & **GMSD** & **FSIM** \\ \hline \multirow{6}{*}{Wave Height} & SRCBAM & 49.73 dB & 0.9949 & 0.0082 & 0.9999 \\  & 95\% Confidence Interval & 49.53–49.92 & 0.9947–0.9952 & 0.0078–0.0086 & 0.9999, 0.9999 \\  & SRResNet & 48.13 dB & 0.9947 & 0.0194 & 0.9999 \\  & 95\% Confidence Interval & 47.96–48.29 & 0.9944–0.9950 & 0.0191–0.0198 & 0.9999, 0.9999 \\  & SRNet & 48.18 dB & 0.9948 & 0.0121 & 0.9999 \\  & 95\% Confidence Interval & 47.96–48.39 & 0.9946–0.9950 & 0.0116–0.0125 & 0.9999–0.9999 \\  & SRCNN & 48.11 dB & 0.9943 & 0.0159 & 0.9999 \\  & 95\% Confidence Interval & 47.91–48.30 & 0.9941–0.9945 & 0.0154–0.0164 & 0.9999–0.9999 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Comparison of evaluation results—wave height. Figure 16: Comparison of wave height reconstruction results. As shown in Tables 2-4, compared with other algorithms, the Super-Resolution Convolutional Block Attention Module (SRCBAM) shows the best reconstruction effect on all four evaluation indicators. Compared with SRResNet, the PSNR of wave height of the SRCBAM is increased by 0.6, the SSIM is increased by 0.0002, and the GMSD is increased by 0.0112. The PSNR of wind speed of the SRCBAM is increased by 0.4, the SSIM is increased by 0.0005, and the GMSD is increased by 0.0006. And, compared with other models, the t-values of the significance test are all less than 0.05, indicating that the results of the model are statistically significant and the differences are real, not accidental.