Ship path planning method and system based on Beidou grid code

Abstract

The invention discloses a ship path planning method and system based on Beidou grid codes, and belongs to the technical field of path planning, and the method comprises the steps: obtaining planning data of a to-be-planned area of a ship, carrying out the priority level division of the ship, and combining a prior path, a priority level and a planning index, a grid chart based on Beidou grid codes is generated through a real environment Beidou grid code management module to perform global path planning, collision risk analysis is performed according to real-time sensing data of a navigation positioning system, an AIS, an electric gyrocompass, a log, waves and weather forecast, a path planning effect is evaluated according to task completion efficiency after planning, and the path planning efficiency is improved. According to the method, real-time sensing data such as port information, ship route information, route planning historical data and meteorological information of ships are analyzed and excavated, the route is recommended, the safety, economical efficiency and reliability of the route are improved, and the method is suitable for large-scale popularization and application. And the intelligent level and the operation efficiency of the ship are improved.

Description

Technical Field

[0001] The present invention relates to the field of path planning technology, and in particular to a ship path planning method and system based on Beidou grid code Background Art

[0002] Smart shipping is an inevitable trend in the development of water transportation in the future, and it is also a top priority for the development and research of my country's shipping industry. With the gradual recovery of shipping demand and the application of autonomous ships, the paths and navigation environment of ships are bound to become more complex and diverse. The significance of ship path planning is to ensure the safe and efficient transportation of ships and cargoes. Choosing the right route can reduce the risk of ships encountering natural disasters at sea, and can also reduce sailing time and costs.

[0003] Existing technologies mainly rely on global path planning, local path planning, or a combination of the two. Global path planning requires obtaining electronic charts and environmental charts and then using path planning algorithms to optimize the path. It is only suitable for relatively long-distance channel planning and dealing with static obstacles, and cannot work for emergencies (waves, marine animals, and the appearance of other ships); while existing local path planning on the sea surface mainly relies on algorithms such as A* and VFH* to process local paths and obstacles. It lacks a visual recognition system and cannot efficiently and accurately deal with complex situations. At the same time, traditional path planning algorithms often use longitude and latitude coordinates to represent spatial information. However, longitude and latitude are expressed in the form of degrees-minutes-seconds, which is complex to describe and not conducive to fast calculation. The longitude and latitude characterization of a planar airspace requires determining the longitude and latitude coordinates of several corner points and the center point of the airspace, which is not only cumbersome and complex but also requires verification of the accuracy of the calculation results. Beidou grid code uses an integer to represent a regional location. Compared with the grid system currently used in the United States (MGRS, USNG, GOOGLE, etc.), Beidou grid has technical advantages in key technical points such as inheritance of the existing spatial coordinate system, construction of the earth's global spatial stereo and encoding binary integers. It is mainly reflected in: 1) It maintains a high degree of consistency with the common longitude and latitude system of existing sea and land data, and can be directly converted to each other. 2) A seamless, non-overlapping, and mutually nested three-dimensional subdivision system with a globally unique code. 3) A geographic space coding algebraic calculation method based on a binary operation mechanism, with efficient binary computing capabilities, to achieve efficient coding operations for applications such as three-dimensional spatial information organization, storage, transmission, distribution, and services.

[0004] Therefore, in view of the development needs of ship navigation safety, navigation efficiency and intelligent ships, a ship path planning method based on Beidou grid code is proposed, which makes full use of the environmental information provided by electronic nautical charts. It integrates sea and land geographic spatial data with grid code information, and uses data mining technology to analyze historical data such as port information and ship information, as well as real-time perception data such as meteorological information. Routes are recommended according to different indicators to improve the safety, economy and reliability of routes, and further enhance the intelligence level and operational efficiency of ships in complex marine environments. Summary of the invention

[0005] In order to solve the above technical problems, a ship path planning method and system based on Beidou grid code are proposed.

[0006] The technical solution of the present invention is: a ship path planning method based on Beidou grid code, comprising the following steps:

[0007] S1. Obtaining planning data of the area to be planned for the ship;

[0008] The planning data includes the starting point, the end point, the ship type and size, and the set sailing depth of the ship;

[0009] According to the starting point and the end point, the data is converted into Beidou grid code data to determine the location information of the ship's navigation area;

[0010] Determining large-scale weather forecast data based on the starting point and the end point;

[0011] S2. Priority levels are divided according to different types of ships. When there are multiple ships at the same time, the ship with the highest priority level is given priority in planning the route.

[0012] S3. Combine the prior paths, priorities and planning indicators, and generate a Beidou grid code grid map based on the real environment Beidou grid code management module for global path planning;

[0013] The a priori path is a case library established based on historical route planning results and actual navigation routes, which depends on the number and accuracy of historical examples;

[0014] The grid diagram based on Beidou grid code = spatial Beidou grid code + Beidou segmentation time code + object + attribute + associated information;

[0015] The global path planning method adopts an improved A* algorithm based on the Beidou grid code grid map, which is based on priority levels and planning indicators;

[0016] Combining the prior path and global path planning, an initial global optimal path is obtained;

[0017] S4, navigation and positioning system, AIS, gyrocompass, speedometer, wave and weather forecast real-time perception data are transmitted to the local dynamic path planning and analysis module.

[0018] S5. The local dynamic path planning and analysis module determines the grid code level, quickly generates a local real-time grid map from the grid code management module, and performs collision risk analysis;

[0019] Based on the real-time collected data, combined with the real-world environment "Beidou grid code grid map" data, Beidou grid location code positioning matching is performed, and the local environment is optimized, adjusted and refined through the grid code level, and the entity type in the grid is identified to generate a "local real-time grid map" for collision risk analysis;

[0020] S6. Establish memory data, classify the danger level according to the memory data, and perform local path planning if there is a collision risk;

[0021] The memory data is the characteristics, location, movement status, and danger level of non-target objects that have been detected and avoided during path planning at sea. The non-target objects are objects at sea that have an impact on the path of their own mission or on their own safety.

[0022] Combined with the entity types in the local environment of the "local real-time grid map", real-time analysis is performed to determine whether the ship maintains a safe distance from the surrounding grids when moving in the local environment, and whether a collision will occur. If a collision occurs, combined with the "local real-time grid map", a local optimized path is generated and the memory data is updated;

[0023] S7. Evaluate the path planning effect according to the efficiency of task completion after planning, and adaptively adjust the grid code level according to different indicators such as planning effect or energy efficiency;

[0024] If the dangerous situation of the target does not improve, it is considered as poor planning, and the accuracy, i.e. the grid code level, is improved to seek better results in the next planning;

[0025] If the current planning task completion efficiency exceeds the average of the historical task completion efficiency, it is considered to be well planned and the grid code level is lowered; if the current planning task completion efficiency is lower than the average of the historical task completion efficiency, it is considered to be poorly planned and the accuracy, i.e., the grid code level, is increased;

[0026] If the indicators after planning do not meet the indicator requirements, it is considered that the planning effect is poor, and the accuracy, that is, the grid code level, is reduced.

[0027] Furthermore, the objects described in step S3 include static obstacles and dynamic obstacles. Static obstacles include danger zones, prohibited navigation zones, islands, areas that do not meet the set navigation depth of the ship, etc., and dynamic obstacles include ships and navigation marks.

[0028] Further, in step S3, the global path planning method of improving A* based on the Beidou grid code grid map, the evaluation function of the algorithm is f(n)=g(n)+P×h(n);

[0029] Where P = min(abs(x curr -x souce ),abs(abs(y curr -y souce ))+min(abs(x curr -x goal ),abs(abs(y curr -y gal ))

[0030] g(n) is the actual cost from the initial node to the current node, P is the interference value of the change of direction, and it is sailing in one direction as much as possible. The closer the point is to the starting point or the end point, the higher the priority. h(n) is the estimated cost of the grid code of the current ship position and the grid code of the target point. (x curr ,y curr ) is the current Beidou grid code, (x next ,y next ) is the Beidou grid code of the next node of the current node, (x goal ,y goal ) is the Beidou grid code of the target point;

[0031] The cost includes Euclidean distance, Manhattan distance, diagonal distance, and Chebyshev distance. This method uses Manhattan distance.

[0032] g(n)=abs(x curr -x next )+abs(y curr -y next )

[0033]

[0034] λ represents the threshold constant; ω1 and ω2 are weights, based on the original heuristic factor h(n). When it is higher than the threshold λ, the weight increases, and the algorithm searches faster; when it is lower than the threshold λ, the weight decreases, giving priority to the optimal path; the threshold is dynamically selected based on factors such as computer computing performance and map size, and the weight value is also adjusted based on the size and complexity of the set map; M is the number of static obstacles from the current node n to the target node, and N is the number of dynamic obstacles;

[0035] D(n) collision risk value:

[0036]

[0037] Where θ is the proportional coefficient, the closest distance between the current node n and the obstacle in the path is d, and D is the impact range of the obstacle collision risk;

[0038] a Safety (planning indicator) is converted into the normalized conversion coefficient of safety (planning indicator) cost.

[0039] Furthermore, the collision risk analysis in step S5 uses the collision risk value D(n)

[0040]

[0041] Furthermore, the memory data in step S6 is the characteristics, location, movement status, and danger level of non-target objects that have been detected and avoided during the path planning at sea. The non-target objects are objects at sea that have an impact on the path of the mission or on the safety of the mission itself.

[0042] Furthermore, step S6 divides the danger level according to the memory data, and specifically includes the following steps:

[0043] If a target terrain or object matching the memory data is detected, a higher processing priority is set;

[0044] Classify the danger level of objects that have threatened one's own safety in the memory data;

[0045] Classify the danger levels of the targets in the memory data that have been avoided during path planning;

[0046] If the avoided target is a stationary or slow-moving target, the memory path is combined to determine whether there is a conflict between the memory path and the current optimal global path. If there is a conflict, the avoided target is classified as the first danger level;

[0047] If the avoided target is in a moving state, the avoided target is classified as a second danger level.

[0048] Another aspect of the present invention provides a ship path planning system based on Beidou grid code, including: a priori path generation module, a Beidou grid code management module, a real-time data acquisition module, a local real-time grid map construction module, a path planning template, a path collision risk analysis module, and a user information interaction module.

[0049] (1) The prior path generation module extracts historical route planning results, historical ship operation data, AIS historical trajectory data, etc., performs digital and standardized data preprocessing, establishes an instance library through trajectory clustering and navigation experience, and obtains the prior paths of different types of ships.

[0050] (2) The Beidou grid code management module manages the basic data of the Beidou grid location code, encodes the real environment: ships, dangerous areas, prohibited navigation areas, islands, etc. based on the Beidou grid code, generates the "basic Beidou grid base map", i.e., the Beidou grid map data, and provides it to each module for query and call. The Beidou grid map = spatial Beidou grid code + Beidou segmentation time code + object + attribute + associated information.

[0051] (3) The real-time data acquisition module collects data from onboard equipment in real time, such as navigation and positioning system, AIS, gyrocompass, speedometer, wave and weather forecast, etc., for use in the path planning module.

[0052] (4) The local real-time grid map construction module includes performing Beidou grid location code positioning and matching based on the real-time collected data and the real environment grid map data, optimizing, adjusting and refining the local environment through the grid code hierarchy, identifying the entity type in the grid, and generating a "local real-time grid map" to pass to the path planning module.

[0053] (5) Path planning template, including global path planning module and local dynamic path planning analysis module:

[0054] The global path planning model optimizes and adjusts the environment based on the Beidou grid map data, combined with the prior paths, priority levels, and planning indicators through the grid code level, and generates a global path based on improved A* and other algorithms, taking into account the surface obstacles that have a greater impact on the global planning effect, such as danger zones, no-fly zones, islands, etc.;

[0055] The local dynamic path planning and analysis module optimizes and adjusts the environment according to the local real-time grid map data of the local real-time grid map construction module through the grid code level method, generates a local path based on the improved A* and other algorithms, and realizes path optimization in the local environment.

[0056] (6) The path collision risk analysis module includes combining entity types in the environment, calculating the closest distance between the current node in the path and the obstacle, and analyzing in real time whether the ship maintains a safe distance from surrounding obstacles when moving, and whether a collision will occur.

[0057] (7) A user information interaction module obtains planning data of the area to be planned, wherein the planning data includes a starting point, an end point, a ship type and size, and a set sailing depth of the ship; based on the starting point and the end point, the data is converted into Beidou grid code data to determine the location information of the ship's sailing area; based on the starting point and the end point, a large-scale weather forecast data is determined.

[0058] Beneficial effects of the present invention:

[0059] The present invention is based on the Beidou grid code technology system. In the ship path planning work, the historical navigation data such as the port information and ship information of the ship and the real-time perception data such as meteorological information are analyzed, and local path optimization is performed. The present invention adopts the Beidou grid code technology system, and utilizes the efficient binary computing power of the shaping code to realize the efficient coding operation of the application such as three-dimensional space information organization, storage, transmission, distribution, and service. The navigation path planning results and / or the actual navigation path are stored in the form of data as a path planning instance library, which is more accurate and close to the usual practice of the crew. From the actual point of view, it can be directly selected as a priori path to avoid disagreements with the ship driver and affect the operation of the ship. The present invention can recommend routes according to different indicators, provide safe, economical and effective solutions for the response to complex and urgent situations in maritime traffic, serve for navigation safety, and realize the intelligence and scientificization of navigation safety. BRIEF DESCRIPTION OF THE DRAWINGS

[0060] Figure 1 This is a flow chart of a ship path planning method based on Beidou grid code of the present invention.

[0061] Figure 2 This is a system composition diagram of a ship path planning system based on Beidou grid code in the present invention DETAILED DESCRIPTION

[0062] In order to make the purpose, technical solution and advantages of the embodiments of the present invention clearer, the technical solution in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0063] like Figure 1 , Figure 2 As shown, the present invention discloses a ship path planning method based on Beidou grid code, comprising the following steps:

[0064] S1. Obtaining planning data of the area to be planned for the ship;

[0065] The planning data includes the starting point, the end point, the ship type and size, and the set sailing depth of the ship;

[0066] According to the starting point and the end point, the data is converted into Beidou grid code data to determine the location information of the ship's navigation area;

[0067] Based on the starting point and the end point, large-scale weather forecast data is determined.

[0068] S2. Priority levels are divided according to different types of ships. When there are multiple ships at the same time, the ship with the highest priority level is given priority in planning the route.

[0069] S3. Combining the prior paths, priorities and planning indicators, the Beidou grid code management module in the real environment generates a Beidou grid code grid map for global path planning.

[0070] The a priori path is an instance library established based on historical route planning results and actual navigation routes, which depends on the number and accuracy of historical instances.

[0071] The grid diagram based on Beidou grid code=spatial Beidou grid code+Beidou segmentation time code+object+attribute+associated information.

[0072] The objects include static obstacles, dynamic obstacles, etc. Static obstacles include danger zones, no-fly zones, islands, areas that do not meet the set navigation depth of the ship, etc. Dynamic obstacles include ships, navigation marks, etc.

[0073] The global path planning method adopts an improved A* algorithm based on a Beidou grid code grid map, which is based on priority levels and planning indicators.

[0074] Improved A* algorithm evaluation function f(n) = g(n) + P × h(n)

[0075] Where P = min(abs(x curr -x souce ),abs(abs(y curr -y souce ))+min(abs(x curr -x goal ),abs(abs(y curr -y goal ))

[0076] g(n) is the actual cost from the initial node to the current node, P is the interference value of the change of direction, and the ship sails in one direction as much as possible. The closer the point is to the starting point or the end point, the higher the priority. h(n) is the estimated cost of the grid code of the current ship position and the grid code of the target point. The cost includes Euclidean distance, Manhattan distance, diagonal distance, and Chebyshev distance. This algorithm uses Manhattan distance.

[0077] g(n)=abs(x curr -n ext )+abs(y curr -y next )

[0078]

[0079] λ represents the threshold constant; ω1 and ω2 are weights, based on the original heuristic factor h(n). When it is higher than the threshold λ, the weight increases, and the algorithm searches faster; when it is lower than the threshold λ, the weight decreases, giving priority to the optimal path. The threshold is dynamically selected based on factors such as computer computing performance and map size, and the weight value is also adjusted based on the size and complexity of the set map. M is the number of static obstacles from the current node n to the target node, and N is the number of dynamic obstacles.

[0080] D(n) collision risk value:

[0081]

[0082] Where θ is the proportional coefficient, the closest distance between the current node n and the obstacle in the path is d, and D is the impact range of the obstacle collision risk.

[0083] aSafety (index) is converted to the normalized conversion coefficient of safety (index) cost.

[0084] (x curr ,y curr ) is the current Beidou grid code, (x next ,y next ) is the Beidou grid code of the next node of the current node, (x goal ,y goal ) is the Beidou grid code of the target point.

[0085] By integrating the prior path and global path planning, an initial global optimal path is obtained.

[0086] S4, navigation and positioning system, AIS, gyrocompass, speedometer, wave and weather forecast and other real-time perception data are transmitted to the local dynamic path planning and analysis module.

[0087] S5. The local dynamic path planning and analysis module determines the grid code level, quickly generates a local real-time grid map from the grid code management module, and performs collision risk analysis.

[0088] Based on the data collected in real time and combined with the "Beidou grid code-based grid map" data provided by the Beidou grid location code management module in the real environment, Beidou grid location code positioning and matching are carried out. Through the grid code hierarchy, the local environment is optimized, adjusted and refined, and the entity type in the grid is identified to generate a "local real-time grid map" for collision risk analysis.

[0089]

[0090] S6. Establish memory data and classify danger levels according to the memory data. If there is a risk of collision, perform local path planning based on the danger level.

[0091] The memory data is the characteristics, location, movement status, and danger level of non-target objects that have been detected and avoided during path planning at sea. The non-target objects are objects at sea that have an impact on the path of their own mission or on their own safety.

[0092] As a preferred technical solution, the specific steps include:

[0093] If a target terrain or object matching the memory data is detected, a higher processing priority is set;

[0094] Classify the danger level of objects that have threatened one's own safety in the memory data;

[0095] Classify the danger levels of the targets in the memory data that have been avoided during path planning;

[0096] If the avoided target is a stationary or slow-moving target, the memory path is combined to determine whether there is a conflict between the memory path and the current optimal global path. If there is a conflict, the avoided target is classified as the first danger level;

[0097] If the avoided target is in a moving state, the avoided target is classified as a second danger level.

[0098] Combined with the entity types in the local environment of the "local real-time grid map", real-time analysis is performed on whether the ship maintains a safe distance from the surrounding grids when moving in the local environment, and whether a collision will occur. If a collision occurs, combined with the "local real-time grid map", a local optimized path is generated and the memory data is updated.

[0099] S7. Evaluate the path planning effect according to the efficiency of task completion after planning, and adaptively adjust the grid code level according to different indicators such as planning effect or energy efficiency.

[0100] If the danger situation of the target does not improve, that is, the target danger level does not decrease, it is considered as poor planning, and the accuracy, that is, the grid code level, is improved to seek better results in the next planning;

[0101] If the current task completion efficiency exceeds the average of the historical task completion efficiencies, it is considered to be well planned and the grid code level is lowered. If the current task completion efficiency is lower than the average of the historical task completion efficiencies, it is considered to be poorly planned and the accuracy, i.e., the grid code level, is increased.

[0102] If the indicators after planning do not meet the indicator requirements, it is considered that the planning effect is poor, and the accuracy, that is, the grid code level, is reduced.

[0103] The present invention also provides a ship path planning system based on Beidou grid code, including: a priori path generation module, a Beidou grid code management module, a real-time data acquisition module, a local real-time grid map construction module, a path planning template, a path collision risk analysis module, and a user information interaction module.

[0104] (1) The prior path generation module extracts historical route planning results, historical ship operation data, AIS historical trajectory data, etc., performs digital and standardized data preprocessing, establishes an instance library through trajectory clustering and navigation experience, and obtains the prior paths of different types of ships.

[0105] (2) The Beidou grid code management module manages the basic data of the Beidou grid location code, encodes the real environment: ships, danger zones, prohibited navigation zones, islands, etc. based on the Beidou grid code, generates the "basic Beidou grid base map", i.e., the Beidou grid map data, and provides it to each module for query and call.

[0106] The Beidou grid map = spatial Beidou grid code + Beidou segmentation time code + object (static obstacles, dynamic obstacles such as ships, etc.) + attribute + associated information.

[0107] The obstacles are divided into three categories: point, line and surface. Point obstacles mainly include various isolated reefs, shipwrecks, navigation aids, shallow points, etc.; line obstacles mainly include safety depth contours, land boundaries, waterway boundaries, etc.; surface obstacles mainly include danger zones, no-fly zones, islands, etc.

[0108] (3) The real-time data acquisition module collects data from onboard equipment in real time, such as navigation and positioning system, AIS, gyrocompass, speedometer, wave and weather forecast, etc., for use in the path planning module.

[0109] (4) The local real-time grid map construction module includes performing Beidou grid location code positioning and matching based on the real-time collected data and the real environment grid map data, optimizing, adjusting and refining the local environment through the grid code hierarchy, and identifying the entity type in the grid, generating a "local real-time grid map" and passing it to the path planning module.

[0110] (5) The path planning template includes a global path planning module and a local dynamic path planning analysis module:

[0111] The global path planning model optimizes and adjusts the environment based on the Beidou grid map data of the Beidou grid code management module, combined with prior paths, priorities, and planning indicators through a grid code hierarchy, and generates a global path based on improved A* and other algorithms, taking into account surface obstacles that have a greater impact on the global planning effect, such as danger zones, no-fly zones, islands, etc.

[0112] The local dynamic path planning and analysis module optimizes and adjusts the environment according to the local real-time grid map data of the local real-time grid map construction module through the grid code level method, generates a local path based on the improved A* and other algorithms, and realizes path optimization in the local environment.

[0113] (6) The path collision risk analysis module includes combining entity types in the environment, calculating the closest distance between the current node in the path and the obstacle, and analyzing in real time whether the ship maintains a safe distance from surrounding obstacles when moving, and whether a collision will occur.

[0114] (7) A user information interaction module obtains planning data of the area to be planned, wherein the planning data includes a starting point, an end point, a ship type and size, and a set sailing depth of the ship; based on the starting point and the end point, the data is converted into Beidou grid code data to determine the location information of the ship's sailing area; based on the starting point and the end point, a large-scale weather forecast data is determined.

[0115] The present invention and its embodiments are described above, and such description is not restrictive. The drawings show only one embodiment of the present invention, and the actual structure is not limited thereto. In short, if ordinary technicians in the field are inspired by it, without departing from the purpose of the invention, they can design a structure and embodiment similar to the technical solution without creativity, which should belong to the protection scope of the present invention.

Claims

1. A ship path planning method based on Beidou grid code, characterized in that: The following steps are involved: S1. Obtain planning data of the area to be planned for the ship: the planning data includes the starting point, the end point, the ship type and size, and the set sailing depth of the ship; according to the starting point and the end point, convert them into Beidou grid code data to determine the location information of the ship's sailing area; according to the starting point and the end point, determine the large-scale weather forecast data; S2. Priority levels are divided according to different types of ships. When there are multiple ships at the same time, the ship with the highest priority level is given priority in planning the route; S3. Combine the prior paths, priorities and planning indicators, and generate a Beidou grid code grid map based on the real environment Beidou grid code management module for global path planning; The a priori path is an example library established based on historical route planning results and actual navigation routes, which depends on the number and accuracy of historical examples; the global path planning method adopts an improved A* algorithm based on the Beidou grid code grid map, which is based on priority levels and planning indicators; integrates the a priori path and global path planning to obtain an initial global optimal path; S4, the real-time perception data of the navigation and positioning system, AIS, gyrocompass, speedometer, wave and weather forecast are transmitted to the local dynamic path planning and analysis module; S5. The local dynamic path planning and analysis module determines the grid code level, quickly generates a local real-time grid map from the grid code management module, and performs collision risk analysis; based on the real-time collected data and the "grid map based on Beidou grid code" data of the real environment, the Beidou grid location code positioning and matching are performed, and the local environment is optimized, adjusted and refined through the grid code level, and the entity type in the grid is identified to generate a "local real-time grid map" for collision risk analysis; S6. Establish memory data, divide the danger level according to the memory data, and carry out local path planning if there is a collision risk; combine the entity types in the local environment of the "local real-time grid map" to analyze in real time whether the ship maintains a safe distance from the surrounding grid when moving in the local environment, and whether a collision will occur. If a collision occurs, combine the "local real-time grid map" to generate a local optimized path and update the memory data; S7. Evaluate the path planning effect according to the efficiency of task completion after planning, and adaptively adjust the grid code level according to the planning effect or the effect of different energy efficiency indicators.

2. A ship path planning method based on Beidou grid code according to claim 1, characterized in that: The Beidou grid code-based grid diagram described in step S3 = spatial Beidou grid code + Beidou segmentation time code + object + attribute + associated information.

3. A ship path planning method based on Beidou grid code according to claim 1, characterized in that: The improved A* algorithm based on the Beidou grid code grid map described in step S3, the algorithm evaluation function f(n)=g(n)+P×h(n), where P=min(abs(x curr -x souce ),abs(abs(y curr -y source ))+min(abs(x curr -x goal ),abs(abs(y curr -y goal )); g(n) is the actual cost from the initial node to the current node, P is the interference value of the change of direction, and it is sailing in one direction as much as possible. The closer the point is to the starting point or the end point, the higher the priority. h(n) is the estimated cost of the grid code of the current ship position and the grid code of the target point. (x curr ,y curr ) is the current Beidou grid code, (x next ,y next ) is the Beidou grid code of the next node of the current node, (x goal ,y goal ) is the Beidou grid code of the target point; The cost includes Euclidean distance, Manhattan distance, diagonal distance, and Chebyshev distance. This method uses Manhattan distance. g(n)=abs(x curr -x next )+abs(y curr -y next ) λ represents the threshold constant; ω1 and ω2 are weights, based on the original heuristic factor h(n). When it is higher than the threshold λ, the weight increases, and the algorithm searches faster; when it is lower than the threshold λ, the weight decreases, giving priority to the optimal path; the threshold is dynamically selected based on factors such as computer computing performance and map size, and the weight value is also adjusted based on the size and complexity of the set map; M is the number of static obstacles from the current node n to the target node, and N is the number of dynamic obstacles; D(n) collision risk value: Where θ is the proportional coefficient, the closest distance between the current node n and the obstacle in the path is d, and D is the impact range of the obstacle collision risk; a Safety (planning indicator) is converted into the normalized conversion coefficient of safety (planning indicator) cost.

4. A ship path planning method based on Beidou grid code according to claim 1, characterized in that: The memory data in step S6 is the characteristics, position, movement status, and danger level of non-target objects that have been detected and avoided during path planning at sea. The non-target objects are objects at sea that have an impact on the path of the mission itself or on its own safety.

5. A ship path planning method based on Beidou grid code according to claim 1, characterized in that: Step S6, dividing the danger level according to the memory data, includes the following steps: If a target terrain or object matching the memory data is detected, a higher processing priority is set; Classify the danger level of objects that have threatened one's own safety in the memory data; Classify the danger levels of the targets in the memory data that have been avoided during path planning: If the avoided target is a stationary or slow-moving target, the memory path is combined to determine whether there is a conflict between the memory path and the current optimal global path. If there is a conflict, the avoided target is classified as the first danger level; If the avoided target is in a moving state, the avoided target is classified as a second danger level.

6. A system for implementing the method according to any of the above claims, namely a ship path planning system based on Beidou grid code, characterized in that: include: The prior path generation module extracts historical route planning results, historical ship operation data, and historical AIS trajectory data, performs digital and standardized data preprocessing, and establishes a case library through trajectory clustering and navigation experience to obtain the prior paths of different types of ships; Beidou grid code management module manages the basic data of Beidou grid location code, encodes the real environment: ships, dangerous areas, no-fly zones, islands based on Beidou grid code, generates Beidou grid map data, and provides it to each module for query and call; Real-time data acquisition module, which collects data from onboard equipment in real time, such as navigation and positioning system, AIS, gyrocompass, speed log, wave and weather forecast, for use in the path planning module; The local real-time grid map construction module includes positioning and matching the Beidou grid location code based on the real-time collected data and the grid map data, optimizing, adjusting and refining the local environment through the grid code hierarchy, and identifying the entity type in the grid to generate a "local real-time grid map"; The path planning template includes a global path planning module and a local dynamic path planning analysis module. The global path planning module optimizes and adjusts the environment based on the Beidou grid map data, combined with the prior path, priority level, and planning indicators through the grid code level, generates a global path based on the improved A* and other algorithms, and considers the surface obstacles that have a greater impact on the global planning effect, such as danger zones, no-fly zones, and islands; the local dynamic path planning analysis module optimizes and adjusts the environment based on the local real-time grid map data of the local real-time grid map construction module through the grid code level, generates a local path based on the improved A* algorithm, and realizes path optimization in the local environment; The path collision risk analysis module combines the entity types in the environment and calculates the closest distance between the current node in the path and the obstacle to analyze in real time whether the ship maintains a safe distance from the surrounding obstacles and whether a collision will occur when moving; The user information interaction module obtains the planning data of the area to be planned, and the planning data includes the starting point, the end point, the ship type and size, and the set sailing depth of the ship; according to the starting point and the end point, it is converted into Beidou grid code data to determine the location information of the ship's sailing area; according to the starting point and the end point, the large-scale weather forecast data is determined.

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