## 1 Introduction  
With the development of the economy, maritime transportation has become one of the most important modes of transportation in the global logistics network. As a key link in international trade, maritime transport not only undertakes the task of transporting commodities but also connects the world's major economies [1, 2, 3]. As one of the key factors affecting ship transportation, weather directly affects the economy and safety of ships. Through the real-time acquisition and processing of weather information, ships can better cope with bad weather and optimize route planning, thereby improving the safety and efficiency of navigation [4, 5, 6, 7]. Therefore, the accurate analysis of weather data is very important for ensuring navigation safety and improving the economic benefits of ships. At present, many scholars have studied this field. For example, Du et al. [8] introduced a refined three-dimensional dynamic programming approach to optimize ship routing. This method plans the ship's course and speed according to weather conditions and limiting factors and generates an optimal solution for ship route planning. However, the optimization results of this method are greatly affected by the grid's discrete step size. Zhao [9] proposed a multi-objective optimal ship navigation planning model. The experimental results showed that the algorithm can achieve safe, efficient, and economical navigation goals. However, the model does not take visibility and other ship factors into account when calculating navigation risk. Nunez et al. [10] introduced a stochastic ship routing model that leverages a conditional risk value metric to guide an enhanced continuous belief tree search algorithm in identifying safe and fuel-efficient long-distance routes. Charalambopoulos et al. [11] proposed an improved PRM algorithm, and the effectiveness of the proposed algorithm was proved by the simulation of the Aegean Islands and the Mediterranean. However, the ship speed considered in this model is only affected by the waves, and the input characteristics are relatively simple. Wu et al. [12] developed a Transformer-based time series model to reconstruct spatio-temporally discrete weather forecast data, aiming to enhance voyage optimization accuracy. The reconstructed forecasts were validated against observed weather data, confirming their reliability and precision. However, the model does not take into account the problems of ship stall and fuel consumption. Kaklis et al. [13] presented a navigation planning approach aimed at delivering optimal route suggestions for vessels. Experimental comparisons with a leading routing method demonstrated that it could reduce voyage length, leading to cost savings for the maritime industry. However, the cost function designed by this method is relatively simple and lacks important factors such as weather and fuel consumption. Guzelbulut et al. [14] used artificial neural networks to establish an energy model based on environmental conditions, ship routes, and ship speeds. Findings indicated that the method helped lower energy usage throughout a ship's operational lifespan. However, the model lacks consideration of the dynamic environment, which may reduce the practicability of the model. Pennino et al. [15] proposed an adaptive weather routing model based on Dijkstra's shortest-path algorithm. The results of the study showed that high ship seaworthiness can be achieved without excessively increasing the length of the route and the duration of the voyage. However, this method requires further research on the incidence of selected seakeeping standards in route evaluation. It can be seen that many studies have effectively improved the effectiveness of ship route planning. However, there are still some limitations in the existing research. For example, 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 forecast data during route planning.