Method, device and equipment for synchronously planning meteorological route and navigational speed of ship and medium

Abstract

The invention relates to a ship meteorological course and speed synchronous planning method, device, equipment and medium, and belongs to the technical field of ship operation management.The method comprises the steps that ship navigation data and marine meteorological historical data are obtained, and a marine meteorological discrete grid is established based on the ship navigation data and the marine meteorological historical data; constructing a double-layer mapping swarm intelligent optimization algorithm based on the marine meteorological discrete grid; and mapping and optimizing in a decision layer and an information layer of the double-layer mapping swarm intelligence optimization algorithm according to the target function and the constraint condition, and adjusting the course angle and the navigational speed. According to the ship meteorological course and speed synchronous planning method designed in the invention, fuel consumption and pollution emission can be reduced, and the ship navigation efficiency can be improved.

Description

Technical Field

[0001] The present invention relates to the technical field of ship operation management, and in particular to a method, device, equipment and medium for synchronous planning of ship weather routes and speeds. Background Art

[0002] With the rapid development of the global shipping industry, the energy consumption and pollution emissions generated by ships during navigation are becoming increasingly serious.

[0003] Traditional ship route and speed planning methods mainly rely on pre-set fixed routes and speeds. This method cannot adapt to the complex and changing marine environment and cannot dynamically adjust routes and speeds to cope with real-time weather changes. There are problems such as low ship navigation efficiency, high fuel consumption, and serious pollution.

[0004] Therefore, how to solve the problem of synchronizing the route and speed of a ship during actual navigation is a technical problem that needs to be solved urgently. Summary of the invention

[0005] In view of this, it is necessary to provide a method, device, equipment and medium for synchronous planning of ship weather routes and speeds, so as to improve ship navigation efficiency and reduce fuel consumption and pollution emissions.

[0006] In order to achieve the above object, in a possible implementation, the present invention provides a method for synchronously planning a weather route and a speed, comprising: Acquire ship navigation data and marine meteorological historical data, and establish a marine meteorological discrete grid based on the ship navigation data and the marine meteorological historical data; Constructing a double-layer mapping group intelligent optimization algorithm based on the marine meteorological discrete grid; Mapping and optimization are performed in the decision layer and information layer of the double-layer mapping group intelligent optimization algorithm according to the objective function and the constraint conditions to adjust the heading angle and the speed.

[0007] In a possible implementation, the establishing of a marine meteorological discrete grid based on the ship navigation data and the marine meteorological historical data includes: Determine a ship navigation area in the ship navigation data, divide the ship navigation area into equally spaced grids, and divide the route between waypoints using the spatial resolution of the marine meteorological historical data, wherein the marine meteorological historical data includes meteorological data, sea condition data, and water depth data; The marine meteorological historical data and the ship navigation data are integrated based on spatial and temporal attributes to obtain the marine meteorological discrete grid.

[0008] In a possible implementation, the constructing of a double-layer mapping swarm intelligent optimization algorithm based on the ocean meteorological discrete grid includes: Establishing a decision layer based on the heading angle and the speed in the marine meteorological discrete grid; Establishing an information layer based on the knowledge point concentration and guidance information in the marine meteorological discrete grid; The double-layer mapping group intelligent optimization algorithm is established by utilizing the decision layer and the information layer.

[0009] In a possible implementation, the process of constructing the knowledge point concentration includes: Construct a knowledge point concentration network in the information layer based on the current environmental information and navigation status of the ship; Introducing a gravitational field mechanism to iterate the knowledge point concentration network to obtain the knowledge point concentration of the decision layer; The process of constructing the guidance information includes: The estimated cost of the alternative decision plan to the target port is calculated, and the estimated cost is calculated using a guidance strategy to obtain guidance information.

[0010] In a possible implementation, the objective function includes the ship's navigation cost; The constraint conditions include a first constraint that the ship sails within a preset water depth, a second constraint that the ship arrives at the destination port at a predetermined time, and a third constraint that the ship's turning angle is within a preset angle.

[0011] In a possible implementation, mapping and optimizing in the decision layer and information layer of the dual-layer mapping group intelligent optimization algorithm according to the objective function and the constraint conditions to adjust the heading angle and the speed include: Initialize relevant parameters, place each individual at the initial port, and generate multiple candidate speed points, wherein the relevant parameters include the number of individuals, the concentration of knowledge points, the importance factor of the guidance information, the volatility factor of the knowledge points, and the maximum number of iterations; Calculate the state probability transfer matrix of any individual, determine the next waypoint of any individual according to the state probability transfer matrix, and repeat the above process until arriving at the target port; Obtaining the objective function value of any individual, and updating a pre-established knowledge point concentration network based on the marine meteorological historical data and the objective function value of any individual; If the current number of iterations is greater than the maximum number of iterations, the heading angle and speed corresponding to the current state are output.

[0012] In a possible implementation, calculating the state transition probability matrix includes: The individual state transition probability is calculated based on the knowledge point concentration and the guidance information, and the state transition probability matrix is ​​constructed using the individual state transition probability.

[0013] In a second aspect, the present invention further provides a device for synchronously planning a weather route and a speed, comprising: A data acquisition module, used to acquire ship navigation data and marine meteorological historical data, and establish a marine meteorological discrete grid based on the ship navigation data and the marine meteorological historical data; An algorithm building module, used for building a double-layer mapping group intelligent optimization algorithm based on the marine meteorological discrete grid; The route speed optimization module is used to map and optimize in the decision layer and information layer of the double-layer mapping group intelligent optimization algorithm according to the objective function and constraint conditions, and adjust the heading angle and speed.

[0014] In a third aspect, the present invention further provides an electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the steps in the above-mentioned method for synchronously planning weather routes and speeds when executing the computer program.

[0015] In a fourth aspect, the present invention further provides a computer storage medium, wherein the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the steps in the above-mentioned method for synchronous planning of weather routes and speeds are implemented.

[0016] The beneficial effects of the present invention are as follows: the present invention ensures the accuracy of data and the scientific nature of decision-making by utilizing high-resolution marine meteorological discrete grids, combining historical meteorological data and ship navigation information, improving the accuracy of route and speed planning, enabling ships to follow the optimal path more accurately, reducing deviation and errors, and improving the safety and reliability of navigation. By utilizing a double-layer mapping group intelligent optimization algorithm, by dynamically mapping and optimizing between the decision layer and the information layer, the coordinated planning of routes and speeds is achieved, adapting to the complex and changeable marine environment, thereby significantly improving the navigation efficiency of ships, and reducing fuel consumption and pollution emissions. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative work.

[0018] Figure 1 A schematic flow chart of an embodiment of a method for synchronously planning a weather route and a speed provided by the present invention; Figure 2 for Figure 1 Step 103 is a method flow chart of an embodiment; Figure 3A schematic diagram of the structure of an embodiment of a device for synchronously planning weather routes and ship speeds provided by the present invention; Figure 4 A schematic diagram of the structure of an electronic device provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0019] 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.

[0020] In the description of the embodiments of the present invention, unless otherwise specified, "multiple" means two or more than two. "And / or" describes the association relationship of associated objects, indicating that three relationships may exist. For example, "A and / or B" may mean: A exists alone, A and B exist at the same time, and B exists alone.

[0021] The descriptions of "first", "second", etc. involved in the embodiments of the present invention are only for descriptive purposes and cannot be understood as indicating or implying their relative importance or implicitly indicating the number of the indicated technical features. Therefore, the technical features defined as "first" and "second" may explicitly or implicitly include at least one of the features.

[0022] Reference to an "embodiment" herein means that a particular feature, structure, or characteristic described in conjunction with the embodiment may be included in at least one embodiment of the present invention. The appearance of the phrase in various places in the specification does not necessarily refer to the same embodiment, nor is it an independent or alternative embodiment that is mutually exclusive with other embodiments. It is explicitly and implicitly understood by those skilled in the art that the embodiments described herein may be combined with other embodiments.

[0023] The specific embodiments are described in detail below: See also Figure 1 , Figure 1 A schematic flow chart of an embodiment of a method for synchronously planning a weather route and a speed provided by the present invention. A specific embodiment of the present invention discloses a method for synchronously planning a weather route and a speed, comprising: Step 101: Obtain ship navigation data and marine meteorological historical data, and establish a marine meteorological discrete grid based on the ship navigation data and the marine meteorological historical data; Step 102: constructing a double-layer mapping group intelligent optimization algorithm based on the marine meteorological discrete grid; Step 103: Mapping and optimization are performed in the decision layer and information layer of the double-layer mapping group intelligent optimization algorithm according to the objective function and the constraint conditions to adjust the heading angle and the speed.

[0024] The present invention ensures the accuracy of data and the scientific nature of decision-making by utilizing high-resolution marine meteorological discrete grids, combining historical meteorological data and ship navigation information, improving the accuracy of route and speed planning, enabling ships to follow the optimal path more accurately, reducing deviation and errors, and improving the safety and reliability of navigation. By utilizing a double-layer mapping group intelligent optimization algorithm, through dynamic mapping and optimization between the decision layer and the information layer, the coordinated planning of routes and speeds is achieved, adapting to the complex and changeable marine environment, thereby significantly improving the navigation efficiency of ships and reducing fuel consumption and pollution emissions.

[0025] In one embodiment of the present invention, establishing a marine meteorological discrete grid based on ship navigation data and marine meteorological historical data includes: Determine the ship navigation area in the ship navigation data, divide the ship navigation area into equally spaced grids, and divide the route between waypoints using the spatial resolution of the marine meteorological historical data, wherein the marine meteorological historical data includes meteorological data, sea condition data, and water depth data; The marine meteorological historical data and ship navigation data are integrated based on spatial and temporal attributes to obtain the marine meteorological discrete grid.

[0026] Among them, meteorological data and sea state data can be obtained from the European Center for Medium-Term Weather Forecasts and the Copernicus Marine Environment Observation Service Center, and water depth data can be obtained from the NOAA digital bathymetric database. Then, based on the ship's navigation area, it is divided into grids with equal spacing r, and the spatial resolution of the historical marine meteorological data is divided into waypoints with a spacing of 0.25°. to The route between them generates multiple nodes, called slave checkpoints, obtains their coordinates, integrates all information through spatial and temporal attributes and puts them into the corresponding grid. Obtain the marine meteorological discrete grid.

[0027] A double-layer mapping swarm intelligent optimization algorithm is constructed based on marine meteorological discrete grids, including: Establish a decision layer based on the heading angle and speed in the marine meteorological discrete grid; Establish information layers based on knowledge point concentration and guidance information in marine meteorological discrete grids; A double-layer mapping swarm intelligent optimization algorithm is established using the decision layer and the information layer.

[0028] An information layer is established based on the knowledge point concentration and guidance information, and the individual state transition probability matrix is ​​calculated to select the optimal path from the alternative decision set to determine the next waypoint; Through the double-layer mapping group intelligent optimization algorithm, the navigation decisions in the decision-making layer are mapped to the real-time meteorological and ocean environmental data in the information layer, and the environmental change information fed back by the information layer is used to dynamically adjust the heading angle and speed in the decision-making layer to achieve the optimization of meteorological routes and meteorological speeds in complex ocean environments.

[0029] Specifically, a two-layer mapping swarm intelligent optimization algorithm is constructed through the marine meteorological discrete grid, and the decision layer is established according to the decision variables heading angle and speed, which can be expressed as: .

[0030] in, Each element in corresponds to a possible waypoint. Since the destination port is determined, the waypoint to The heading angle is also fixed, so .

[0031] In one embodiment of the present invention, the process of constructing the knowledge point concentration includes: Construct a knowledge point concentration network in the information layer based on the current environmental information and navigation status of the ship; Introduce the gravitational field mechanism to iterate the knowledge point concentration network and obtain the knowledge point concentration of the decision layer; The process of constructing guidance information includes: Calculate the estimated cost of alternative decision-making plans to the target port, use the guidance strategy to calculate the estimated cost, and obtain guidance information.

[0032] It is understandable that the knowledge point concentration network is constructed by comprehensively considering the environmental adaptability and the current navigation status: that is, combining past successful experiences to guide current decisions to adapt to new environmental information, and comprehensively weighing factors such as cost, safety and navigation time, formulating the update rules of knowledge point concentration, and introducing the gravitational field mechanism to enhance the attractiveness of effective nodes to individuals; among them, in the first Iteration, individual Corresponding waypoints The resulting gravitational force can be expressed as: ,in, is a natural constant; Represents two-dimensional coordinates; is the strength factor, and λ is the adaptation factor.

[0033] It should be noted that only feasible individuals can generate gravitational fields to exclude interference from infeasible individuals. The strength of the gravitational field of feasible individuals can be expressed as: ,in, is a constant, is the knowledge point strength constant; Is an individual At the cost of the current iteration; superimposing the gravitational fields generated by all individual corresponding waypoints, the corresponding scalar gravitational field can be obtained. The scalar gravitational field can be expressed as: , according to the update rule, in Knowledge point graph network at iteration It can be expressed as: Based on the knowledge point graph network in the information layer, the method of calculating the knowledge point concentration in the decision layer can be expressed as: .in, is a mapping function, defined as a sigmoid function, which transforms the knowledge point concentration range in the knowledge point graph network into the interval [0,1]. The above transformation can effectively avoid the knowledge point concentration of different alternatives being too small or too large, thereby causing the algorithm to fall into a local optimum; is corresponding to Alternative waypoints, calculate the next waypoint of the ship It can be expressed as: , , .in, is the decision variable, is the selected ship speed, is the voyage interval time, is the selected ship heading; the updated information layer knowledge point graph network can be expressed as: The calculation of pheromone in the decision layer depends on the update of the pheromone graph network corresponding to the information layer, and its update method can be expressed as: , ,in, is the volatility factor of the adaptive knowledge point; is the adaptive knowledge point residual factor.

[0034] It can be understood that by calculating the estimated cost of alternative decision-making solutions to the target port, using the guidance strategy to calculate the guidance information, introducing the adaptive adjustment factor, dynamically adjusting the concentration of historical knowledge points and the weight of guidance information to adapt to different navigation conditions and environmental changes; the improved guidance information can be expressed as: , .in, Indicates the corresponding waypoint from the current solution Estimated cost to destination port, is the distance weight coefficient, is the adaptive adjustment factor, which obeys the deformed normal distribution function. is the number of iterations of the current algorithm, is the maximum number of algorithm iterations.

[0035] In one embodiment of the present invention, the objective function includes the ship navigation cost; The constraints include a first constraint that the ship sails within a preset water depth, a second constraint that the ship arrives at the destination port at a scheduled time, and a third constraint that the ship's turning angle is within a preset angle.

[0036] It can be understood that, in one embodiment of the present invention, taking the navigation cost as an example, the objective function can be expressed as: , calculate the objective function ship navigation cost, where, is from to The fuel consumption function indicates that the ship to Depending on the weather data, the ship will encounter different weather and sea conditions when choosing different heading angles and speeds. The first constraint for ships to sail in waters that meet the water depth requirements can be expressed as: ,in, From waypoint to The minimum water depth value in the environmental information corresponding to the segment, and The second constraint that the ship arrives at the destination port at the scheduled time can be expressed as: , among which, the former The flight time of each leg is fixed. , No. When there are multiple legs, the time it takes for the ship to arrive at the destination port may be less than , so the calculation is done in the form of "distance / speed". The third constraint that the ship's turning angle will not be too large can be expressed as: , in, is the maximum steering angle; it is ensured that at the current waypoint, there is only one heading angle and speed combination selected to reach the next waypoint. The constraint can be expressed as: ,Regulation A 0-1 variable can be represented as: .

[0037] In one embodiment of the present invention, see Figure 2 , Figure 2 for Figure 1 Step 103 is a flow chart of a method in an embodiment, comprising: Step 1031: Initialize relevant parameters, place each individual at the initial port, and generate multiple candidate speed points, wherein the relevant parameters include the number of individuals, the concentration of knowledge points, the importance factor of the guidance information, the volatility factor of the knowledge points, and the maximum number of iterations; Step 1032: Calculate the state probability transfer matrix of any individual, determine the next waypoint of any individual according to the state probability transfer matrix, and repeat the above process until arriving at the target port; Step 1033: Obtain the objective function value of any individual, and update the pre-established knowledge point concentration network based on the marine meteorological historical data and the objective function value of any individual; Step 1034: If the current number of iterations is greater than the maximum number of iterations, output the heading angle and speed corresponding to the current state.

[0038] Specifically, all individuals are placed in the initial port, and the number of iterations is The direction angles of 0°-360° are divided into 5° intervals to generate a series of alternative heading angles, and the intervals of minimum speed and maximum speed are divided into 0.5 knot intervals to generate a series of alternative speed points; By combining the number of individuals, knowledge point concentration factor and guidance information factor, as shown in Table 1: Table 1: Parameter candidate values ​​and optimal values ​​based on the two-layer mapping swarm intelligent optimization algorithm

[0039] Based on the comprehensive knowledge point graph network, the state probability transfer matrix of a specific individual is calculated, and the next waypoint of the individual is determined according to the roulette method. The above process is repeated until the target port is reached. Based on the marine environment data and the objective function values ​​of all individuals, the scalar gravitational field is constructed by combining the established knowledge point graph network and the update rules, and the knowledge point graph network is updated. ;judge If it is greater than the maximum number of iterations, the algorithm ends, and by comparing the navigation costs of different schemes, the scheme with the lowest cost is selected, and the heading angle and speed combination scheme corresponding to the scheme is output to achieve coordinated planning of weather routes and weather speeds.

[0040] In one embodiment of the present invention, the calculation method of the individual state transition probability matrix includes: The individual state transition probability is calculated based on the knowledge point concentration and guidance information, and the state transition probability matrix is ​​constructed using the individual state transition probability.

[0041] It can be understood that the calculation of state transition probability mainly depends on the knowledge point concentration and guidance information. The calculation of state transition probability can be expressed as:

[0042] in, For individuals In the The corresponding state transition probability at the iteration is, and They are The iteration starts from the current waypoint The knowledge point concentration and guidance information to the corresponding alternative waypoints, and They are the knowledge point concentration factor and the guidance information factor respectively.

[0043] State transition probability matrix It can be expressed as: .

[0044] In order to better implement the weather route and speed synchronization planning method in the embodiment of the present invention, based on the weather route and speed synchronization planning method, correspondingly, Figure 3 As shown, the embodiment of the present invention further provides a weather route and speed synchronization planning device, the weather route and speed synchronization planning device 300 includes: The data acquisition module 301 is used to acquire ship navigation data and marine meteorological historical data, and establish a marine meteorological discrete grid based on the ship navigation data and the marine meteorological historical data; Algorithm construction module 302, used to construct a double-layer mapping group intelligent optimization algorithm based on marine meteorological discrete grids; The route speed optimization module 303 is used to map and optimize in the decision layer and information layer of the double-layer mapping group intelligent optimization algorithm according to the objective function and constraint conditions, and adjust the heading angle and speed.

[0045] The weather route and speed synchronization planning device 300 provided in the above embodiment can implement the technical solution described in the above weather route and speed synchronization planning method embodiment. The specific implementation principles of the above modules or units can refer to the corresponding contents in the above weather route and speed synchronization planning method embodiment, which will not be repeated here.

[0046] like Figure 4 As shown, the present invention also provides an electronic device 400. The electronic device 400 includes a processor 401, a memory 402 and a display 403. Figure 4 Only some components of the electronic device 400 are shown, but it should be understood that it is not required to implement all of the components shown, and more or fewer components may be implemented instead.

[0047] In some embodiments, the processor 401 may be a central processing unit (CPU), a microprocessor or other data processing chip, and is used to run program codes or process data stored in the memory 402, such as the weather route and speed synchronization planning method of the present invention.

[0048] In some embodiments, processor 401 may be a single server or a server group. The server group may be centralized or distributed. In some embodiments, processor 401 may be local or remote. In some embodiments, processor 401 may be implemented in a cloud platform. In one embodiment, the cloud platform may include a private cloud, a public cloud, a hybrid cloud, a community cloud, a distributed cloud, an internal cloud, a multi-cloud, etc., or any combination thereof.

[0049] In some embodiments, the memory 402 may be an internal storage unit of the electronic device 400, such as a hard disk or memory of the electronic device 400. In other embodiments, the memory 402 may also be an external storage device of the electronic device 400, such as a plug-in hard disk, a smart media card (SMC), a secure digital (SD) card, a flash card, etc., equipped on the electronic device 400.

[0050] Furthermore, the memory 402 may include both an internal storage unit of the electronic device 400 and an external storage device. The memory 402 is used to store application software installed in the electronic device 400 and various data.

[0051] In some embodiments, the display 403 may be an LED display, a liquid crystal display, a touch-sensitive liquid crystal display, an OLED (Organic Light-Emitting Diode) touch device, etc. The display 403 is used to display information of the electronic device 400 and to display a visual user interface. The components 401-403 of the electronic device 400 communicate with each other via a system bus.

[0052] In some embodiments, when the processor 401 executes the weather route and speed synchronization planning program in the memory 402, the following steps may be implemented: Obtain ship navigation data and marine meteorological historical data, and establish marine meteorological discrete grids based on the ship navigation data and marine meteorological historical data; A double-layer mapping swarm intelligent optimization algorithm is constructed based on marine meteorological discrete grids; According to the objective function and constraints, mapping and optimization are performed in the decision layer and information layer of the double-layer mapping group intelligent optimization algorithm to adjust the heading angle and speed.

[0053] It should be understood that: when the processor 401 executes the weather route and speed synchronization planning program in the memory 402, in addition to the above functions, other functions can also be implemented. For details, please refer to the description of the corresponding method embodiment above.

[0054] Furthermore, the embodiment of the present invention does not specifically limit the type of the electronic device 400 mentioned, and the electronic device 400 may be a portable electronic device such as a mobile phone, a tablet computer, a personal digital assistant (PDA), a wearable device, a laptop computer, etc. Exemplary embodiments of portable electronic devices include but are not limited to portable electronic devices equipped with IOS, Android, Microsoft or other operating systems. The above-mentioned portable electronic devices may also be other portable electronic devices, such as a laptop computer with a touch-sensitive surface (e.g., a touch panel). It should also be understood that in some other embodiments of the present invention, the electronic device 400 may not be a portable electronic device, but a desktop computer with a touch-sensitive surface (e.g., a touch panel).

[0055] Accordingly, an embodiment of the present application also provides a computer-readable storage medium, which is used to store computer-readable programs or instructions. When the program or instructions are executed by a processor, it can implement the steps or functions in the weather route and speed synchronization planning method provided in the above-mentioned method embodiments.

[0056] 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.

[0057] 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 method for synchronously planning weather routes and speeds, characterized in that: include: Acquire ship navigation data and marine meteorological historical data, and establish a marine meteorological discrete grid based on the ship navigation data and the marine meteorological historical data; Constructing a double-layer mapping group intelligent optimization algorithm based on the marine meteorological discrete grid; Mapping and optimization are performed in the decision layer and information layer of the double-layer mapping group intelligent optimization algorithm according to the objective function and the constraint conditions to adjust the heading angle and the speed.

2. The method for synchronously planning weather routes and speed according to claim 1, characterized in that: The establishing of a marine meteorological discrete grid based on the ship navigation data and the marine meteorological historical data comprises: Determine a ship navigation area in the ship navigation data, divide the ship navigation area into equally spaced grids, and divide the route between waypoints using the spatial resolution of the marine meteorological historical data, wherein the marine meteorological historical data includes meteorological data, sea condition data, and water depth data; The marine meteorological historical data and the ship navigation data are integrated based on spatial and temporal attributes to obtain the marine meteorological discrete grid.

3. The method for synchronously planning weather routes and speed according to claim 1, characterized in that: The dual-layer mapping swarm intelligent optimization algorithm based on the marine meteorological discrete grid comprises: Establishing a decision layer based on the heading angle and the speed in the marine meteorological discrete grid; Establishing an information layer based on the knowledge point concentration and guidance information in the marine meteorological discrete grid; The double-layer mapping group intelligent optimization algorithm is established by utilizing the decision layer and the information layer.

4. The method for synchronously planning weather routes and speed according to claim 3 is characterized in that: The process of constructing the knowledge point concentration includes: Construct a knowledge point concentration network in the information layer based on the current environmental information and navigation status of the ship; Introducing a gravitational field mechanism to iterate the knowledge point concentration network to obtain the knowledge point concentration of the decision layer; The process of constructing the guidance information includes: The estimated cost of the alternative decision plan to the target port is calculated, and the estimated cost is calculated using a guidance strategy to obtain guidance information.

5. The method for synchronously planning weather routes and speed according to claim 1, characterized in that: The objective function includes the ship's navigation cost; The constraint conditions include a first constraint that the ship sails within a preset water depth, a second constraint that the ship arrives at the destination port at a predetermined time, and a third constraint that the ship's turning angle is within a preset angle.

6. The method for synchronously planning weather routes and speed according to claim 5, characterized in that: The mapping and optimization are performed in the decision layer and the information layer of the double-layer mapping group intelligent optimization algorithm according to the objective function and the constraint conditions to adjust the heading angle and the speed, including: Initialize relevant parameters, place each individual at the initial port, and generate multiple candidate speed points, wherein the relevant parameters include the number of individuals, the concentration of knowledge points, the importance factor of the guidance information, the volatility factor of the knowledge points, and the maximum number of iterations; Calculate the state probability transfer matrix of any individual, determine the next waypoint of any individual according to the state probability transfer matrix, and repeat the above process until arriving at the target port; Obtaining the objective function value of any individual, and updating a pre-established knowledge point concentration network based on the marine meteorological historical data and the objective function value of any individual; If the current number of iterations is greater than the maximum number of iterations, the heading angle and speed corresponding to the current state are output.

7. The method for synchronously planning weather routes and speed according to claim 6, characterized in that: Calculate the state transition probability matrix, including: The individual state transition probability is calculated based on the knowledge point concentration and the guidance information, and the state transition probability matrix is ​​constructed using the individual state transition probability.

8. A device for synchronously planning weather routes and ship speeds, characterized in that: include: A data acquisition module, used to acquire ship navigation data and marine meteorological historical data, and establish a marine meteorological discrete grid based on the ship navigation data and the marine meteorological historical data; An algorithm building module, used for building a double-layer mapping group intelligent optimization algorithm based on the marine meteorological discrete grid; The route speed optimization module is used to map and optimize in the decision layer and information layer of the double-layer mapping group intelligent optimization algorithm according to the objective function and constraint conditions, and adjust the heading angle and speed.

9. An electronic device, characterized in that: comprising a memory and a processor, wherein: The memory is used to store programs; The processor is coupled to the memory and is used to execute the program stored in the memory to implement the steps in the method for synchronously planning weather routes and speeds as described in any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that: Used to store computer-readable programs or instructions, which, when executed by a processor, can implement the steps in the method for synchronous planning of weather routes and speeds as described in any one of claims 1 to 7 above.

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