Weather and historical data integrated ship route planning method and system

Abstract

The invention relates to a ship route planning method and system integrating meteorological data and historical data, belongs to the technical field of ship route planning, and solves the problem of low feasibility of route planning caused by unreasonable space grid division and no consideration of meteorological data in the prior art. Comprising the steps that after historical route data are collected, a space diagram grid is constructed through downsampling and clustering; according to a to-be-planned route and ship information, space nodes corresponding to all the necessary passing points in the space diagram grids are obtained to serve as all the path points; predicting meteorological data and sea condition data of each space node in the space diagram grid, reconstructing the space diagram grid in combination with ship information, and calculating the weight of each edge in the reconstructed space diagram grid; and on the basis of the reconstructed space diagram grid, obtaining an optimal path passing through each path point according to the sequence of each path point and the weight of each edge, and then replacing each path point in the optimal path with each necessary point to obtain a planned route path. And safe and reliable ship route planning is realized.

Description

Technical Field

[0001] The present invention relates to the technical field of ship route planning, and in particular to a ship route planning method and system integrating meteorological and historical data. Background Art

[0002] Path planning technology, as a core component of intelligent decision-making, has been widely used and matured in many fields such as land logistics, automobile navigation systems, and aircraft route planning. These fields have achieved the optimal choice of paths through the integration of efficient algorithms and real-time data, greatly improving transportation efficiency and safety. However, in the vast field of ocean transportation, despite significant technological advances, route planning still faces many unique challenges and is far from the ideal state of automation and intelligence.

[0003] The formulation of ship navigation plans has always relied on the captain's rich experience and deep understanding of the complex marine environment, supplemented by weather forecasts and navigation advice. Although this traditional model has its rationality, it also exposes obvious limitations: on the one hand, for non-fixed routes or newly opened waterways, the captain may find it difficult to avoid potential risk points due to lack of direct experience; on the other hand, information with extremely high timeliness (such as sudden weather changes, new port openings, regulatory updates, etc.) is difficult to incorporate into the plan in a timely manner, resulting in frequent adjustments to the route during execution, affecting navigation efficiency and safety.

[0004] Among the existing automatic planning schemes, some divide the vast ocean into several navigation blocks and set up turning points between these blocks as connecting hubs to achieve segmented optimization of routes; this scheme is difficult to ensure the optimality of the routes on a global scale. Especially when facing long-distance, cross-seasonal navigation missions, route selection is closely related to seasonal changes, and the directions and paths are often very different. In some schemes, the spatial graph grid division is too fine. Although it can improve the optimization accuracy, it will greatly increase the number of grid points, resulting in a sharp increase in the algorithm running time and affecting decision-making efficiency. None of the existing schemes take meteorological data into consideration, which may lead to encountering bad weather during actual navigation, thereby increasing navigation safety risks and affecting operational benefits. Summary of the invention

[0005] In view of the above analysis, an embodiment of the present invention aims to provide a ship route planning method and system that integrates meteorological and historical data, so as to solve the problem that the existing spatial grid division is unreasonable and does not consider meteorological data, resulting in low feasibility of route planning.

[0006] On the one hand, an embodiment of the present invention provides a ship route planning method integrating meteorological and historical data, comprising the following steps:

[0007] After collecting historical route data, a spatial graph grid is constructed through downsampling and clustering;

[0008] According to the route to be planned and the ship information, the spatial nodes corresponding to each necessary point in the spatial graph grid are obtained as each waypoint; according to the sailing time of the route to be planned, the meteorological data and sea condition data of each spatial node in the spatial graph grid are predicted, and the spatial graph grid is reconstructed in combination with the ship information, and the weight of each edge in the reconstructed spatial graph grid is calculated;

[0009] Based on the reconstructed spatial graph grid, the optimal path passing through each waypoint is obtained according to the order of each waypoint and the weight of each edge, and then each necessary point replaces each waypoint in the optimal path to obtain the planned route path.

[0010] Based on the further improvement of the above method, after collecting historical route data, the spatial graph grid is constructed through downsampling and clustering, including:

[0011] After sorting the turning points of each flight segment in the historical route data by time, the key turning points of each flight segment are sampled using the Douglas-Peucker algorithm;

[0012] All key turning points are clustered by spatial density clustering algorithm to obtain multiple classes and noise points; the center point of each class is used as the spatial node of the spatial graph grid;

[0013] According to the spatial nodes, noise points and historical route data, the edges between the spatial nodes are constructed; and the spatial graph grid is obtained according to the spatial nodes and edges.

[0014] Based on the further improvement of the above method, the edges between spatial nodes are constructed according to spatial nodes, noise points and historical route data, including:

[0015] Sort the spatial nodes and noise points by flight segments and time, and take out two adjacent spatial nodes in turn. If there is no noise point between the two spatial nodes, an edge between the two spatial nodes is established when there is historical route data between the classes to which the two spatial nodes belong; if there is a noise point between the two spatial nodes, an edge between the two spatial nodes is established only when the distance or time difference between the two spatial nodes is within the threshold range.

[0016] Based on the further improvement of the above method, the spatial nodes corresponding to each necessary point in the spatial graph grid are obtained by calculating the great circle route distance between each necessary point and each spatial node in the spatial graph grid, and taking the spatial node corresponding to the smallest great circle route distance.

[0017] Based on the further improvement of the above method, the spatial graph grid is reconstructed, including:

[0018] If the water depth of a spatial node in the predicted spatial graph grid is less than the sum of the estimated draft and the surplus water depth in the ship information, the spatial node and the edges connected to it are deleted;

[0019] If the predicted wind speed or wave height of a spatial node in the spatial graph grid exceeds the wind and wave resistance level in the ship information, the spatial node and the edges connected to it are deleted.

[0020] Based on the further improvement of the above method, the weight of each edge in the reconstructed spatial graph grid is obtained by calculating the respective influencing factors according to the predicted wind speed, wind direction, wave height and surge direction of the spatial nodes at both ends of each edge, taking the average respectively, and then weighting it with the normalized great circle route distance between the spatial nodes.

[0021] Based on the further improvement of the above method, the wind speed influence factor and wave height influence factor are calculated by the following formula:

[0022]

[0023]

[0024] in, represents the wind speed influence factor of spatial node i, represents the wave height influence factor of spatial node i, B wS and B wH Represent the wind speed threshold and wave height threshold respectively, and They represent the predicted wind speed and wave height of spatial node i respectively.

[0025] Based on the further improvement of the above method, the wind direction influence factor and surge direction influence factor are calculated by the following formula:

[0026]

[0027] in, represents the wind direction influence factor of spatial node i, represents the influencing factor of the inrush direction of spatial node i, θ i Represents the angle between the navigation direction and the predicted wind direction of spatial node i; σ i It represents the angle between the navigation direction and the predicted flow direction of spatial node i.

[0028] Based on the further improvement of the above method, the optimal path passing through each waypoint is obtained according to the order of each waypoint and the weight of each edge. It starts from the starting point of each waypoint, and uses the dynamic programming algorithm to solve the optimal path of each segment for each two adjacent waypoints according to the weight of each edge, until the end point of each waypoint, and finally splices the optimal path segments in order to obtain the entire optimal path.

[0029] On the other hand, an embodiment of the present invention provides a ship route planning system integrating meteorological and historical data, comprising:

[0030] The spatial graph grid construction module is used to construct the spatial graph grid through downsampling and clustering after collecting historical route data;

[0031] The spatial graph grid reconstruction module is used to obtain the spatial nodes corresponding to each necessary point in the spatial graph grid according to the route to be planned and the ship information, as each waypoint; according to the sailing time of the route to be planned, the meteorological data and sea condition data of each spatial node in the spatial graph grid are predicted, and the spatial graph grid is reconstructed in combination with the ship information, and the weight of each edge in the reconstructed spatial graph grid is calculated;

[0032] The route path planning module is used to obtain the optimal path passing through each waypoint according to the order of each waypoint and the weight of each edge based on the reconstructed spatial graph grid, and then replace the waypoints in the optimal path with each necessary point to obtain the planned route path.

[0033] Compared with the prior art, the present invention can achieve at least one of the following beneficial effects:

[0034] 1. By constructing a spatial graph grid through downsampling and clustering, the route planning space is effectively represented with a smaller number of nodes. This not only significantly reduces the computing resources and time cost required for model operation, but also minimizes resource consumption while maintaining high-precision route planning capabilities.

[0035] 2. Based on the route and ship information to be planned, the spatial graph grid constructed by deeply integrating historical route data is reconstructed closely in line with the weather and sea conditions data at the sailing time, and the latest edge weights are calculated according to the dynamic cost function to generate safe and efficient route recommendations, thereby realizing flexible adjustment of route planning strategies.

[0036] 3. It integrates real-time weather and sea conditions with historical data, and is not limited by the scope of the sea area or the sailing distance. It can handle both short-distance shuttle and long-distance voyage with ease, breaking through the traditional method's excessive reliance on historical data.

[0037] In the present invention, the above-mentioned technical solutions can also be combined with each other to achieve more preferred combination solutions. Other features and advantages of the present invention will be described in the subsequent description, and some advantages can become obvious from the description, or can be understood by practicing the present invention. The purpose and other advantages of the present invention can be realized and obtained through the contents particularly pointed out in the description and the drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0038] The accompanying drawings are only used for the purpose of illustrating specific embodiments and are not to be considered as limiting the present invention. In the entire drawings, the same reference symbols represent the same components;

[0039] Figure 1 This is a flow chart of a ship route planning method integrating meteorological and historical data in Example 1 of the present invention;

[0040] Figure 2 This is a schematic diagram of the structure of a ship route planning system that integrates meteorological and historical data in Example 2 of the present invention. DETAILED DESCRIPTION

[0041] The preferred embodiments of the present invention are described in detail below in conjunction with the accompanying drawings, wherein the accompanying drawings constitute a part of this application and are used together with the embodiments of the present invention to illustrate the principles of the present invention, but are not used to limit the scope of the present invention.

[0042] Example 1

[0043] A specific embodiment of the present invention discloses a ship route planning method integrating meteorological and historical data, such as Figure 1 As shown, the following steps are included:

[0044] S1. After collecting historical route data, a spatial graph grid is constructed through downsampling and clustering.

[0045] It should be noted that historical route data is collected from multiple data sources such as the automatic identification system AIS and the global positioning system GPS. The historical route may only be a partial segment of the complete route; the historical route data includes but is not limited to: MMSI (Maritime Mobile Service Identify), IMO (International Maritime Organization, an identification code issued by the International Maritime Organization), ship type, timestamp of each turning point, longitude and latitude, and draft, etc.

[0046] Furthermore, based on the collected historical route data, a spatial graph grid is constructed through downsampling and clustering, including:

[0047] ① After sorting the turning points of each segment in the historical route data by time, the key turning points of each segment are sampled using the Douglas-Peucker algorithm.

[0048] The historical route data is split according to the ship's MMSI and different segments, sorted by the timestamp of the turning points in each segment, and then downsampled using the Douglas-Peucker algorithm, a shape-based downsampling algorithm that significantly reduces the amount of data, extracts the key turning points of each segment, and retains the shape characteristics of the route.

[0049] When downsampling, the distances from each turning point on the segment to the line connecting the first and last points are calculated, and these distances are compared with the set distance threshold. If the calculated distance is greater than the distance threshold, the turning point is retained and the segment is divided into two parts for further processing; if the calculated distance is less than or equal to the distance threshold, the turning point is discarded. The size of the distance threshold directly affects the degree of simplification of the curve; the smaller the threshold, the more points are retained and the smoother the segment curve; the larger the threshold, the fewer points are retained and the sharper the segment curve.

[0050] ② Cluster all key turning points through spatial density clustering algorithm to obtain multiple classes and noise points; the center point of each class is used as the spatial node of the spatial graph grid;

[0051] Summarize all the key turning points obtained by downsampling, and use the spatial density clustering algorithm according to the longitude and latitude coordinates. This embodiment uses the DBSCAN algorithm for clustering, which marks all key turning points into three types: core points, boundary points, and noise points. Boundary points belong to the same class as the core points within the neighborhood radius, and noise points do not belong to any class. The center point of each class finally obtained is the average longitude and latitude of all key turning points in the class.

[0052] The center point of each class is used as the spatial node of the spatial graph grid. The density and distribution of spatial nodes in coastal areas, complex sea areas and vast sea areas are obviously different.

[0053] ③ According to the spatial nodes, noise points and historical route data, construct the edges between the spatial nodes; obtain the spatial graph grid based on the spatial nodes and edges.

[0054] Specifically, the spatial nodes and noise points are sorted by flight segments and time, and two adjacent spatial nodes are taken out in turn. If there is no noise point between the two spatial nodes, then when there is historical route data between the classes to which the two spatial nodes belong, that is, there is a historical route record between any spatial node in the class and any spatial node in the adjacent class, an edge between the two spatial nodes is established; if there is a noise point between the two spatial nodes, then the edge between the two spatial nodes is established only when the distance or time difference between the two spatial nodes is within the threshold range.

[0055] Preferably, the corresponding historical route data is recorded and stored for each edge, and statistical features are calculated as edge features to assist decision making. Exemplarily, the statistical features include: travel frequency, time distribution, seasonal distribution, draft distribution, fuel consumption statistics, and ship type.

[0056] In this step, a spatial graph grid is constructed by downsampling and clustering the historical route data, thereby achieving effective representation of the route planning space with a smaller number of nodes. This not only significantly reduces the computing resources and time cost required for model operation, but also minimizes resource consumption while maintaining high-precision route planning capabilities. Moreover, as an empirical database, the spatial graph grid can execute step S1 again to update the spatial graph grid data as the historical route data is updated.

[0057] S2. According to the route and ship information to be planned, the spatial nodes corresponding to each necessary point in the spatial graph grid are obtained as the waypoints; according to the departure time of the route to be planned, the meteorological data and sea condition data of each spatial node in the spatial graph grid are predicted, and the spatial graph grid is reconstructed in combination with the ship information, and the weight of each edge in the reconstructed spatial graph grid is calculated.

[0058] It should be noted that the route and ship information to be planned include: departure port, arrival port, transit port, stopover time at transit port, strait, sailing time, estimated draft and surplus water depth, etc. Among them, the longitude and latitude of the departure port, arrival port, transit port and strait are the necessary points of the route to be planned, and the departure port and arrival port are the starting point and end point of the necessary points.

[0059] Based on the geographical location of the transit port and the transit strait, according to the longitude and latitude of the transit port and the longitude and latitude of both ends of the transit strait, the great circle route distance between the transit port and the transit strait and the starting point and the end point is calculated, and the navigation order of each must-pass point is sorted according to the distance.

[0060] Furthermore, the spatial nodes corresponding to each necessary point in the spatial graph grid are obtained by calculating the great circle route distances between each necessary point and each spatial node in the spatial graph grid, and taking the spatial node corresponding to the smallest great circle route distance.

[0061] According to the sailing time of the planned route, the weather and sea condition forecast interface is called to predict the weather and sea condition data of each spatial node in the spatial graph grid, and the spatial graph grid is reconstructed, including:

[0062] If the water depth of a spatial node in the predicted spatial graph grid is less than the sum of the estimated draft and the surplus water depth in the ship information, the spatial node and the edges connected to it are deleted;

[0063] If the predicted wind speed or wave height of a spatial node in the spatial graph grid exceeds the wind and wave resistance level in the ship information, the spatial node and the edges connected to it are deleted.

[0064] The edge weights between spatial nodes in the reconstructed spatial graph grid are defined as the cost of navigation. A variety of influencing factors are dynamically calculated based on the predicted meteorological and sea conditions data of the spatial nodes, and the distance between nodes is weighted to construct a dynamic cost function to calculate the edge weights.

[0065] Specifically, the weight of each edge in the reconstructed spatial graph grid is calculated based on the predicted wind speed, wind direction, wave height and surge direction of the spatial nodes at both ends of each edge, and then averaged, and then weighted with the normalized great circle route distance between the spatial nodes. The formula is as follows:

[0066]

[0067] Among them, Cost i,j represents the edge weight between spatial nodes i and j, and Respectively represent the wind speed influence factors of spatial nodes i and j, and Respectively represent the wind direction influencing factors of spatial nodes i and j, and Respectively represent the wave height influence factors of spatial nodes i and j, and Respectively represent the influencing factors of the inrush direction of spatial nodes i and j, represents the normalized great circle route distance between spatial nodes i and j; w1, w2, w3, w4, w5 represent the weights of wind speed influence factor, wind direction influence factor, wave height influence factor, surge direction influence factor and great circle route distance respectively. Each weight is set based on the actual risk-taking capacity and needs.

[0068] Furthermore, the various influencing factors of the spatial nodes at both ends of each edge in the spatial graph grid and the normalized great circle route distance between the spatial nodes are calculated by the following formula:

[0069]

[0070] Among them, B wS and B wH Represent the wind speed threshold and wave height threshold respectively, and Respectively represent the predicted wind speed and wave height of spatial node i; θ i Represents the angle between the navigation direction and the predicted wind direction of spatial node i; σ i represents the angle between the navigation direction and the predicted flow direction of spatial node i; θ i and σ i The reference direction is due north; R represents the radius of the earth. and Respectively represent the latitude of spatial nodes i and j, λ i and λ j denote the longitude of spatial nodes i and j respectively, arccos(·) denotes the inverse cosine function, and Normalized(·) denotes the normalized function.

[0071] It can be seen from the above formula that when the predicted wind speed or wave height of a spatial node exceeds the corresponding threshold, the spatial node may not be suitable for navigation. This effect can be amplified by the fourth power operation, and the power index can be adjusted according to the actual situation. When the angle between the navigation direction and the predicted wind direction and surge direction of the spatial node is in the range of [100°, 170°], the navigation speed and navigation efficiency are not high. By setting the influence factor to 1, the cost of the spatial node is increased; when the angle is in the range of [10°, 80°], it is conducive to navigation. By setting the influence factor to -1, the cost of the spatial node is reduced.

[0072] S3. Based on the reconstructed spatial graph grid, after obtaining the optimal path passing through each waypoint according to the order of each waypoint and the weight of each edge, each necessary point replaces each waypoint in the optimal path to obtain the planned route path.

[0073] It should be noted that the optimal path through each waypoint is obtained according to the order of each waypoint and the weight of each edge. It starts from the starting point of each waypoint, and uses the dynamic programming algorithm to solve the optimal path of each segment for each two adjacent waypoints according to the weight of each edge, until the end point of each waypoint, and finally splices the optimal path segments in order to obtain the entire optimal path. The dynamic programming algorithm uses the A* algorithm or the Dijkstra algorithm, and the optimal path solved is the path with the minimum navigation cost.

[0074] Finally, the necessary points of the route to be planned are used to replace the waypoints in the optimal path to obtain the planned route path.

[0075] During navigation along the planned route, the weather and sea condition forecast interface is continuously and regularly called to evaluate the weather and sea condition data of each turning point on the route. If the water depth, wind speed or wave height does not meet the requirements for safe passage, the spatial graph grid reconstruction of step S2 and the acquisition of the optimal path of step S3 are executed to optimize the unexecuted route and ensure navigation safety.

[0076] Compared with the prior art, the present embodiment provides a ship route planning method that integrates meteorological and historical data. It constructs a spatial graph grid by downsampling and clustering, and realizes an effective representation of the route planning space with a smaller number of nodes. It not only significantly reduces the computing resources and time cost required for model operation, but also minimizes resource consumption while maintaining high-precision route planning capabilities. It deeply integrates the spatial graph grid constructed by historical route data based on the route and ship information to be planned, and reconstructs the meteorological and sea conditions data that closely fits the sailing time, and calculates the latest edge weights according to the dynamic cost function to generate safe and efficient route suggestions, so as to realize flexible adjustment of route planning strategies. It integrates real-time meteorological and sea conditions with historical data, and is not limited by the scope of the sea area or the sailing distance. Whether it is short-distance shuttle or ocean voyage, it can cope with it easily, breaking through the excessive reliance of traditional methods on historical data.

[0077] Example 2

[0078] Another embodiment of the present invention discloses a ship route planning system integrating meteorological and historical data, thereby realizing a ship route planning method integrating meteorological and historical data in embodiment 1. Figure 2 As shown, the specific implementation of each module refers to the corresponding description in Example 1. The system includes:

[0079] A spatial graph grid construction module 101 is used to construct a spatial graph grid by downsampling and clustering after collecting historical route data;

[0080] The spatial graph grid reconstruction module 102 is used to obtain the spatial nodes corresponding to each necessary point in the spatial graph grid as each waypoint according to the route to be planned and the ship information; predict the meteorological data and sea condition data of each spatial node in the spatial graph grid according to the sailing time of the route to be planned, reconstruct the spatial graph grid in combination with the ship information, and calculate the weight of each edge in the reconstructed spatial graph grid;

[0081] The route path planning module 103 is used to obtain the optimal path passing through each waypoint according to the order of each waypoint and the weight of each edge based on the reconstructed spatial graph grid, and then replace each waypoint in the optimal path with each necessary point to obtain the planned route path.

[0082] Since the ship route planning system integrating meteorological and historical data in this embodiment and the ship route planning method integrating meteorological and historical data in the above embodiment can be mutually referenced, it is a repeated description here, so it will not be repeated here. Since the principles of this system embodiment and the above method embodiment are the same, this system embodiment also has the corresponding technical effects of the above method embodiment.

[0083] Those skilled in the art will appreciate that all or part of the processes of the above-mentioned embodiments can be implemented by instructing related hardware through a computer program, and the program can be stored in a computer-readable storage medium, wherein the computer-readable storage medium is a disk, an optical disk, a read-only storage memory, or a random access memory, etc.

[0084] The above description is only a preferred specific implementation manner of the present invention, but the protection scope of the present invention is not limited thereto. Any changes or substitutions that can be easily conceived by any technician familiar with the technical field within the technical scope disclosed by the present invention should be covered within the protection scope of the present invention.

Claims

1. A ship route planning method integrating meteorological and historical data, characterized in that: The following steps are involved: After collecting historical route data, a spatial graph grid is constructed through downsampling and clustering; According to the route to be planned and the ship information, the spatial nodes corresponding to each necessary point in the spatial graph grid are obtained as each waypoint; according to the sailing time of the route to be planned, the meteorological data and sea condition data of each spatial node in the spatial graph grid are predicted, and the spatial graph grid is reconstructed in combination with the ship information, and the weight of each edge in the reconstructed spatial graph grid is calculated; Based on the reconstructed spatial graph grid, the optimal path passing through each waypoint is obtained according to the order of each waypoint and the weight of each edge, and then each necessary point replaces each waypoint in the optimal path to obtain the planned route path.

2. The ship route planning method based on comprehensive meteorological and historical data according to claim 1 is characterized in that: After collecting the historical route data, a spatial graph grid is constructed by downsampling and clustering, including: After sorting the turning points of each flight segment in the historical route data by time, the key turning points of each flight segment are sampled using the Douglas-Peucker algorithm; All key turning points are clustered by spatial density clustering algorithm to obtain multiple classes and noise points; the center point of each class is used as the spatial node of the spatial graph grid; According to the spatial nodes, noise points and historical route data, the edges between the spatial nodes are constructed; and the spatial graph grid is obtained according to the spatial nodes and edges.

3. The ship route planning method based on comprehensive meteorological and historical data according to claim 2 is characterized in that: The method of constructing edges between spatial nodes according to spatial nodes, noise points and historical route data includes: Sort the spatial nodes and noise points by flight segments and time, and take out two adjacent spatial nodes in turn. If there is no noise point between the two spatial nodes, an edge between the two spatial nodes is established when there is historical route data between the classes to which the two spatial nodes belong; if there is a noise point between the two spatial nodes, an edge between the two spatial nodes is established only when the distance or time difference between the two spatial nodes is within the threshold range.

4. The ship route planning method based on integrated meteorological and historical data according to claim 1, characterized in that: The spatial nodes corresponding to each necessary point in the spatial graph grid are obtained by calculating the great circle route distances between each necessary point and each spatial node in the spatial graph grid, and obtaining the spatial node corresponding to the smallest great circle route distance.

5. The ship route planning method based on integrated meteorological and historical data according to claim 1, characterized in that: Reconstructing the spatial graph grid includes: If the water depth of a spatial node in the predicted spatial graph grid is less than the sum of the estimated draft and the surplus water depth in the ship information, the spatial node and the edges connected to it are deleted; If the predicted wind speed or wave height of a spatial node in the spatial graph grid exceeds the wind and wave resistance level in the ship information, the spatial node and the edges connected to it are deleted.

6. The ship route planning method based on integrated meteorological and historical data according to claim 1, characterized in that: The weight of each edge in the reconstructed spatial graph grid is obtained by calculating the respective influencing factors based on the predicted wind speed, wind direction, wave height and surge direction of the spatial nodes at both ends of each edge, taking the average respectively, and then weighting it with the normalized great circle route distance between the spatial nodes.

7. The ship route planning method based on integrated meteorological and historical data according to claim 6 is characterized in that: The wind speed influence factor and wave height influence factor are calculated by the following formula: in, represents the wind speed influence factor of spatial node i, represents the wave height influence factor of spatial node i, B wS and B wH Represent the wind speed threshold and wave height threshold respectively, and They represent the predicted wind speed and wave height of spatial node i respectively.

8. The ship route planning method based on integrated meteorological and historical data according to claim 6, characterized in that: The wind direction influence factor and surge direction influence factor are calculated by the following formula: in, represents the wind direction influence factor of spatial node i, represents the influencing factor of the inrush direction of spatial node i, θ i Represents the angle between the navigation direction and the predicted wind direction of spatial node i; σ i It represents the angle between the navigation direction and the predicted flow direction of spatial node i.

9. The ship route planning method based on integrated meteorological and historical data according to claim 6, characterized in that: The method of obtaining the optimal path passing through each waypoint according to the weight of each edge in the order of each waypoint is to start from the starting point of each waypoint, and use a dynamic programming algorithm to solve the optimal path of each segment for each two adjacent waypoints according to the weight of each edge, until the end point of each waypoint, and finally splice the optimal path segments in order to obtain the entire optimal path.

10. A ship route planning system integrating meteorological and historical data, characterized in that: include: The spatial graph grid construction module is used to construct the spatial graph grid through downsampling and clustering after collecting historical route data; The spatial graph grid reconstruction module is used to obtain the spatial nodes corresponding to each necessary point in the spatial graph grid as each waypoint according to the route to be planned and the ship information; predict the meteorological data and sea condition data of each spatial node in the spatial graph grid according to the sailing time of the route to be planned, reconstruct the spatial graph grid in combination with the ship information, and calculate the weight of each edge in the reconstructed spatial graph grid; The route path planning module is used to obtain the optimal path passing through each waypoint according to the order of each waypoint and the weight of each edge based on the reconstructed spatial graph grid, and then replace the waypoints in the optimal path with each necessary point to obtain the planned route path.

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