A method, system, device and storage medium for planning maritime emergency rescue routes

Abstract

This invention discloses a method, system, equipment, and storage medium for planning maritime emergency rescue routes. By dividing the national sea area into a basic grid and storing the grid's accessibility, water depth, and navigable surface information, it can quickly distinguish between navigable and impassable areas, optimize the planning process, and reduce resource waste. Utilizing the Dixtra algorithm, it can comprehensively consider multiple factors and plan an optimal coarse path from the starting point to the destination. After identifying path gaps, the algorithm is used again to analyze and refine the optimal path, filling in the gaps and ensuring that rescue personnel reach the target directly. At the same time, it can identify and automatically remove path segments intersecting with impassable surfaces, preventing troops from accidentally entering dangerous areas. By identifying gaps and planning refined paths, the continuity and integrity of the rescue path are ensured, reducing the risk of rescue failure due to path problems. In addition, precise planning can reduce unnecessary detours and stops, ensuring the smooth progress of rescue operations.

Description

Technical Field

[0001] This invention relates to the field of rescue route planning technology, and in particular to a method, system, equipment and storage medium for planning maritime emergency rescue routes. Background Technology

[0002] Against the backdrop of the continuous growth of global maritime activities, maritime safety issues have become an increasingly important focus. As an important means of responding to maritime emergencies and protecting the lives and property of personnel, the scientific nature and efficiency of maritime emergency rescue operations are crucial. However, current maritime emergency rescue route planning methods have significant defects: (1) Insufficient planning accuracy: Traditional maritime emergency rescue route planning methods are often based on relatively coarse geographical information, such as dividing the entire sea area into large blocks and planning routes based on these blocks. This method ignores the complex navigation conditions in the sea area (such as water depth changes, seabed topography, etc.), which may result in the planned route not being optimal, and even in some cases, it may even be inaccurate. (1) Impassable; (2) Poor adaptability: Existing planning methods are inadequate when facing dynamic and changing marine environments. For example, when new impassable areas (such as military exercise areas, warning areas, etc.) appear in the sea area, traditional planning methods often cannot update the path in time, which leads to the obstruction of rescue operations; (3) Algorithm efficiency problem: Traditional path planning algorithms have a huge amount of computation when dealing with large-scale sea areas, resulting in excessively long planning time, which cannot meet the timeliness requirements of emergency rescue; (4) Lack of intelligence: Existing planning methods mostly rely on manual judgment and decision-making, and lack intelligent auxiliary means. This not only increases the difficulty and complexity of planning, but may also lead to the subjectivity and uncertainty of planning results. Summary of the Invention

[0003] In view of this, the present invention proposes a method, system, equipment and storage medium for planning maritime emergency rescue routes, which can improve the scientificity and efficiency of maritime emergency rescue route planning.

[0004] The technical solution of this invention is implemented as follows:

[0005] A method for planning maritime emergency rescue routes, specifically including:

[0006] The entire sea area of ​​the country is divided into basic grids. The grid information includes whether it is passable, water depth, and passable surface. Among them, some grids are passable and some are not, which are set as suspicious grids.

[0007] Receive latitude and longitude information of the starting point and ending point from the front end of the rescue personnel;

[0008] Based on the latitude and longitude information of the starting point and the ending point, the optimal coarse path from the starting point to the ending point is analyzed using the Dixtra algorithm.

[0009] Determine whether the optimal coarse path intersects with an impassable surface; if so, obtain the set of path segments that need to be removed.

[0010] Subtract the set of path segments to be removed from the set of optimal coarse paths to obtain the set of passable path segments;

[0011] Identify path gaps in the set of passable path segments;

[0012] The optimal refined path is analyzed using the Dixtra algorithm in the path gaps;

[0013] If the solution set of the optimal refined path is empty, then the optimal coarse path is the final rescue path; otherwise, the optimal refined path and the optimal coarse path are concatenated to obtain the final rescue path.

[0014] As a further optional scheme of the aforementioned maritime emergency rescue route planning method, the step of using the Dixtro algorithm to analyze the optimal coarse path from the starting point to the ending point based on the latitude and longitude information of the starting point and the ending point specifically includes:

[0015] Based on the latitude and longitude information of the starting and ending points, corresponding subgrids are divided in the basic grid;

[0016] The optimal coarse path from the starting cell to the ending cell is analyzed using the Dixtra algorithm in the subgrid.

[0017] If there is no solution for the coarse path in the optimal coarse path, then expand the grid range and use the Dixtra algorithm again to find the optimal coarse path.

[0018] As a further optional solution to the aforementioned maritime emergency rescue route planning method, the step of using the Dixtro algorithm in the sub-grid to analyze the optimal coarse path from the starting cell to the ending cell specifically includes:

[0019] Based on the latitude and longitude of the starting and ending points passed in from the front end, subgrid regions containing the starting and ending points are divided in the basic grid;

[0020] Within the divided sub-grid regions, the Dixtra algorithm is applied to calculate the shortest path length to all other grid cells, with the starting grid cell as the source point.

[0021] From the shortest paths obtained through calculation, the path that reaches the destination cell is selected as the optimal coarse path.

[0022] As a further optional scheme of the aforementioned maritime emergency rescue route planning method, the step of applying the Dixtra algorithm within the divided sub-grid area, using the starting grid as the source point, to calculate the shortest path length to all other grids, specifically includes:

[0023] Step S11: Within the divided sub-grid area, with the starting grid cell as the source point, initialize a distance array. The distance array records the shortest path length from the source point to all other grid cells. Initially, set the distance from the source point to itself to 0, and the distance from the source point to other grid cells to infinity.

[0024] Step S12: Create an unvisited set and add all cells except the source point to the set;

[0025] Step S13: Select the cell closest to the source point from the unvisited set as the current cell, and remove it from the unvisited set;

[0026] Step S14: Update the shortest path length from the adjacent cells of the current cell to the source point. If the path from the current cell to the adjacent cell is shorter than the previously recorded path, update the shortest path length of the adjacent cell and set its parent node to the current cell.

[0027] Repeat steps S13 and S14 until the unvisited set is empty. At this point, the distance array records the shortest path length from the source point to all cells.

[0028] As a further optional solution to the aforementioned maritime emergency rescue route planning method, the step of identifying path gaps in the set of passable path segments specifically includes:

[0029] The location of the gap is determined based on the intersection of the optimal coarse path and the impassable surface;

[0030] The region containing path gaps is meshed to obtain path gap mesh cells that are smaller than the sub-mesh.

[0031] As a further optional solution to the aforementioned maritime emergency rescue route planning method, the step of using the Dixtra algorithm to analyze the optimal refined path in the path gaps specifically includes:

[0032] Step S21: Initialize the distance values ​​of all path gap mesh cells smaller than the sub-mesh, set the distance value of the starting mesh cell to 0, and set the distance values ​​of other mesh cells to infinity;

[0033] Step S22: Establish a priority queue to store the grid cells to be processed, with the starting grid cell added to the queue as the first element;

[0034] Step S23: Take the grid cell with the smallest distance value from the priority queue as the grid cell to be processed, and update the distance values ​​of its adjacent grid cells.

[0035] Step S24: If the distance value of an adjacent grid cell is updated to be smaller, add it to the priority queue;

[0036] Repeat steps S23 and S24 until the priority queue is empty or the distance value of the endpoint grid cell is updated to a finite value;

[0037] By recording the predecessor grid cells of each grid cell, the optimal refinement path is formed by tracing back from the end grid cell to the starting grid cell.

[0038] As a further optional solution to the aforementioned maritime emergency rescue route planning method, the step of concatenating the optimal refined path and the optimal coarse path to obtain the final rescue path specifically includes:

[0039] Define the endpoints of each optimal refinement path, where each endpoint includes a start point and an end point;

[0040] In the optimal coarse path, find the position corresponding to the endpoint of the optimal refined path;

[0041] Compare the relationship between the endpoints of the optimal refined path and the corresponding positions of the optimal coarse path, and match the connection points between the optimal refined path and the optimal coarse path;

[0042] Based on the matching results, the refined path fragments and the coarse path fragments are spliced ​​together to form a complete path.

[0043] A maritime emergency rescue route planning system, comprising:

[0044] The grid division module is used to divide the national sea area into basic grids and store the accessibility status, water depth and accessible surface information of each grid. Among them, some grids that contain both accessible and inaccessible areas are set as suspicious grids.

[0045] The information receiving module is used to receive the latitude and longitude information of the starting point and the destination transmitted from the front end of the rescue personnel;

[0046] The coarse path analysis module uses the Dixtra algorithm to analyze the optimal coarse path from the starting point to the ending point based on the received latitude and longitude information of the starting point and the ending point.

[0047] The path verification module is used to determine whether the optimal coarse path intersects with an impassable surface; if so, it determines the set of path segments that need to be removed.

[0048] The passable path filtering module subtracts the set of path segments to be removed from the optimal coarse path set to obtain the passable path segment set.

[0049] The gap recognition module identifies path gaps in the set of passable path segments;

[0050] The path refinement module uses the Dixtra algorithm to analyze the identified path gaps and find the optimal refined path.

[0051] The path splicing module splices the optimal refined path with the optimal coarse path when the solution set of the optimal refined path is not empty, to obtain the final rescue path; when the solution set of the optimal refined path is empty, the optimal coarse path is determined as the final rescue path.

[0052] The output module is used to output the final rescue route.

[0053] A computing device includes a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the steps of any of the above-described maritime emergency rescue route planning methods.

[0054] A computer-readable storage medium storing a computer program that, when executed by a processor, implements the steps of any of the above-described maritime emergency rescue route planning methods.

[0055] The beneficial effects of this invention are as follows: By dividing the entire national sea area into a basic grid and storing the grid's accessibility, water depth, and navigable surface information, navigable and impassable areas can be quickly identified, thus avoiding wasted time and resources during the planning process. The Dixtra algorithm is used to analyze the optimal coarse path from the starting point to the endpoint. This algorithm considers multiple factors to ensure that the selected path is optimal overall. After identifying path gaps, the Dixtra algorithm can be further used to analyze the optimal refined path, thereby filling in the gaps in the path and ensuring that rescue personnel can successfully reach the target location. It can determine whether the optimal rough path intersects with impassable surfaces and automatically remove intersecting path segments, thereby preventing rescuers from getting lost or encountering danger in impassable areas. By identifying path gaps and planning the optimal refined path, it can ensure the continuity and integrity of the rescue path, reducing the risk of rescue failure due to path interruption or missing parts. By accurately planning the rescue path, it can reduce unnecessary detours and stops for rescuers during navigation, thereby saving time and fuel costs. At the same time, it can also rationally allocate rescue resources according to the actual situation of the sea area and rescue needs, ensuring the smooth progress of rescue operations. Attached Figure Description

[0056] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0057] Figure 1 This is a flowchart illustrating a maritime emergency rescue route planning method according to the present invention.

[0058] Figure 2 This is a schematic diagram of the composition of a maritime emergency rescue route planning system according to the present invention. Detailed Implementation

[0059] The technical solutions in the embodiments of the present invention will be clearly and completely described below. Obviously, the described embodiments are only a part of the embodiments of the present invention, and not all of them. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0060] refer to Figures 1 to 2 A method for planning maritime emergency rescue routes, specifically including:

[0061] The entire sea area of ​​the country is divided into basic grids. The grid information includes whether it is passable, water depth, and passable surface. Among them, some grids are passable and some are not, which are set as suspicious grids.

[0062] Receive latitude and longitude information of the starting point and ending point from the front end of the rescue personnel;

[0063] Based on the latitude and longitude information of the starting point and the ending point, the optimal coarse path from the starting point to the ending point is analyzed using the Dixtra algorithm.

[0064] Determine whether the optimal coarse path intersects with an impassable surface; if so, obtain the set of path segments that need to be removed.

[0065] Subtract the set of path segments to be removed from the set of optimal coarse paths to obtain the set of passable path segments;

[0066] Identify path gaps in the set of passable path segments;

[0067] The optimal refined path is analyzed using the Dixtra algorithm in the path gaps;

[0068] If the solution set of the optimal refined path is empty, then the optimal coarse path is the final rescue path; otherwise, the optimal refined path and the optimal coarse path are concatenated to obtain the final rescue path.

[0069] In this embodiment, by dividing the entire national sea area into a basic grid and storing the grid's accessibility, water depth, and navigable surface information, navigable and impassable areas can be quickly identified, thus avoiding wasted time and resources during the planning process. The Dixtra algorithm is used to analyze the optimal coarse path from the starting point to the endpoint. This algorithm considers multiple factors to ensure that the selected path is optimal overall. After identifying path gaps, the Dixtra algorithm can be further used to analyze the optimal refined path, thereby filling in the gaps in the path and ensuring that rescue personnel can successfully reach the target location. The system can determine whether the optimal coarse path intersects with impassable surfaces and automatically remove intersecting path segments, thereby preventing rescuers from getting lost or encountering danger in impassable areas. By identifying path gaps and planning the optimal refined path, the continuity and integrity of the rescue path can be ensured, reducing the risk of rescue failure due to path interruption or missing parts. By accurately planning the rescue path, unnecessary detours and stops during the voyage can be reduced, thereby saving time and fuel costs. At the same time, it can also rationally allocate rescue resources according to the actual situation of the sea area and rescue needs, ensuring the smooth progress of rescue operations.

[0070] It should be noted that the side length of each basic grid is set to 0.02 degrees. This is to refine the division of the sea area, which will facilitate subsequent path planning and analysis.

[0071] Preferably, the step of analyzing the optimal coarse path from the starting point to the ending point using the Dixtro algorithm based on the latitude and longitude information of the starting point and the ending point specifically includes:

[0072] Based on the latitude and longitude information of the starting and ending points, corresponding subgrids are divided in the basic grid;

[0073] The optimal coarse path from the starting cell to the ending cell is analyzed using the Dixtra algorithm in the subgrid.

[0074] If there is no solution for the coarse path in the optimal coarse path, then expand the grid range and use the Dixtra algorithm again to find the optimal coarse path.

[0075] In this embodiment, by dividing the base grid into corresponding sub-grids based on the latitude and longitude information of the starting and ending points, precise path planning can be performed within a relatively small spatial range. This increases the accuracy of path planning and ensures that the planned path is closer to the actual marine environment and conditions. When the initially planned optimal coarse path is unsolvable, this technical solution can flexibly expand the grid range and re-plan the path. This flexibility ensures that a feasible rescue path can still be found in complex marine environments. Using the Dixtra algorithm for path planning within the sub-grids can efficiently find the optimal coarse path from the starting grid to the ending grid. The Dixtra algorithm, as a classic path planning algorithm, is a valuable tool for finding optimal coarse paths. The path planning algorithm features high computation speed and high path quality, making it suitable for time-sensitive scenarios such as maritime emergency rescue. By first planning a coarse path and then gradually refining it, it reduces the computational load of directly applying complex algorithms globally, thus improving overall computational efficiency. This technical solution can handle situations containing suspicious grids (i.e., grids that are partially passable and partially impassable). By expanding the grid range and replanning the path, it increases adaptability to complex marine environments. When encountering impassable areas (such as land, islands, military exercise areas, and warning zones), it can automatically avoid these areas and replan a feasible rescue path, demonstrating good robustness.

[0076] It should be noted that the side length of each subgrid is 0.02 degrees.

[0077] Preferably, the step of using the Dixtro algorithm in the sub-grid to analyze the optimal coarse path from the starting cell to the ending cell specifically includes:

[0078] Based on the latitude and longitude of the starting and ending points passed in from the front end, subgrid regions containing the starting and ending points are divided in the basic grid;

[0079] Within the divided sub-grid regions, the Dixtra algorithm is applied to calculate the shortest path length to all other grid cells, with the starting grid cell as the source point.

[0080] From the shortest paths obtained through calculation, the path that reaches the destination cell is selected as the optimal coarse path.

[0081] In this embodiment, applying the Dixtra algorithm within a sub-grid region efficiently calculates the shortest path from the starting cell to all other cells. Because the sub-grid area is relatively small, the computational load and time complexity of the algorithm are reduced, thus improving the efficiency of path planning. By selecting the shortest path to the destination cell as the optimal coarse path, the accuracy and optimality of the path are ensured. The Dixtra algorithm itself guarantees finding the shortest path from the source point to all other points; therefore, applying this algorithm within a sub-grid region can accurately find the optimal path from the starting point to the destination. This technical solution can adapt to different starting points... The changes in the positions of the starting and ending points are mitigated by dividing the sub-grid regions based on the latitude and longitude information of the starting and ending points input from the front end. Therefore, regardless of how the positions of the starting and ending points change, the sub-grid regions can be re-divided to adapt to these changes and corresponding path planning can be performed. This technical solution can be extended and optimized as needed. For example, more environmental factors (such as wind fields and ocean currents) can be introduced as constraints for path planning, or the algorithm itself can be optimized to improve computational efficiency and accuracy. These extensions and optimizations can be based on the existing technical solution without requiring major changes to the overall architecture.

[0082] Preferably, within the divided sub-grid region, the Dixtra algorithm is applied to calculate the shortest path length to all other grid cells, using the starting grid cell as the source point. Specifically, this includes:

[0083] Step S11: Within the divided sub-grid area, with the starting grid cell as the source point, initialize a distance array. The distance array records the shortest path length from the source point to all other grid cells. Initially, set the distance from the source point to itself to 0, and the distance from the source point to other grid cells to infinity.

[0084] Step S12: Create an unvisited set and add all cells except the source point to the set;

[0085] Step S13: Select the cell closest to the source point from the unvisited set as the current cell, and remove it from the unvisited set;

[0086] Step S14: Update the shortest path length from the adjacent cells of the current cell to the source point. If the path from the current cell to the adjacent cell is shorter than the previously recorded path, update the shortest path length of the adjacent cell and set its parent node to the current cell.

[0087] Repeat steps S13 and S14 until the unvisited set is empty. At this point, the distance array records the shortest path length from the source point to all cells.

[0088] In this embodiment, by progressively expanding the shortest path, Dixtra's algorithm ensures that the shortest path from the starting cell to all other cells is found. This is crucial in rescue route planning because time is of the essence, and the shortest path means a faster rescue response. The algorithm can handle grid areas with different traversability conditions (such as impassable areas like water depth, land, and islands). By initializing the distance array and progressively updating the shortest path length, the algorithm can flexibly adapt to complex geographical environments and rescue needs. The algorithm can intelligently identify and remove impassable path segments, thereby avoiding wasting computational resources on invalid paths. This helps to make quick decisions in emergency situations and improve rescue efficiency. The data structures such as the unvisited set and distance array, as well as the strategy of progressively updating the shortest path length, enable the algorithm to flexibly adjust when faced with impassable factors (such as land, islands, and military exercise areas) and find feasible alternative paths.

[0089] Preferably, identifying path gaps in the set of passable path segments specifically includes:

[0090] The location of the gap is determined based on the intersection of the optimal coarse path and the impassable surface;

[0091] The region containing path gaps is meshed to obtain path gap mesh cells that are smaller than the sub-mesh.

[0092] In this embodiment, by identifying the intersection of the optimal coarse path with impassable surfaces, the location of gaps in the path can be accurately determined. This is a crucial step in path planning because gaps may represent impassable areas or areas that require specific strategies (such as detours) to pass through. Further finer mesh segmentation of the areas containing gaps allows for analysis of the passage conditions within the gaps, helping to discover small passages or obstacles that might be overlooked in the coarse mesh, thereby improving the accuracy of path planning. By accurately identifying path gaps and performing targeted mesh segmentation, the algorithm can reduce unnecessary computation, which helps to find the optimal path faster with limited computing resources, thus improving rescue efficiency. Precise path planning and optimization ensure that rescue teams can quickly reach the target area along the optimal path, thereby increasing the success rate of rescue operations.

[0093] Preferably, the step of using the Dixtro algorithm to analyze the optimal refinement path in the path gaps specifically includes:

[0094] Step S21: Initialize the distance values ​​of all path gap mesh cells smaller than the sub-mesh, set the distance value of the starting mesh cell to 0, and set the distance values ​​of other mesh cells to infinity;

[0095] Step S22: Establish a priority queue to store the grid cells to be processed, with the starting grid cell added to the queue as the first element;

[0096] Step S23: Take the grid cell with the smallest distance value from the priority queue as the grid cell to be processed, and update the distance values ​​of its adjacent grid cells.

[0097] Step S24: If the distance value of an adjacent grid cell is updated to be smaller, add it to the priority queue;

[0098] Repeat steps S23 and S24 until the priority queue is empty or the distance value of the endpoint grid cell is updated to a finite value;

[0099] By recording the predecessor grid cells of each grid cell, the optimal refinement path is formed by tracing back from the end grid cell to the starting grid cell.

[0100] In this embodiment, by using the Dixtra algorithm in path gaps, the optimal refined path from the starting grid cell to the ending grid cell (or the nearest point within reach) can be accurately calculated. This ensures the accuracy and reliability of the rescue path in complex environments. Path gaps often contain complex terrain and obstacles, such as narrow passages, shoals, and reefs. The Dixtra algorithm, by progressively expanding the shortest path, can flexibly adapt to these complex terrains and find feasible optimal paths. Using a priority queue to store the grid cells to be processed ensures that the grid cell with the smallest current distance value is processed each time, thereby greatly improving the computational efficiency of the algorithm. This helps to reduce computational burden within limited computing resources. The algorithm quickly finds the optimal path under limited resources. By recording the predecessor grid cells of each grid cell and forming the optimal refined path during backtracking, it avoids repeatedly calculating path lengths and fragments, further improving the resource utilization efficiency of the algorithm. Furthermore, by recording the predecessor grid cells of each grid cell and forming the optimal refined path during backtracking, the path can be presented visually, helping rescue personnel to intuitively understand the path situation and make more accurate decisions. The optimal refined path not only provides rescue personnel with clear action guidelines but also serves as a basis for decision support, such as selecting the optimal path among multiple rescue paths and assessing the risks and costs of different paths.

[0101] Preferably, the step of concatenating the optimal refined path and the optimal coarse path to obtain the final rescue path specifically includes:

[0102] Define the endpoints of each optimal refinement path, where each endpoint includes a start point and an end point;

[0103] In the optimal coarse path, find the position corresponding to the endpoint of the optimal refined path;

[0104] Compare the relationship between the endpoints of the optimal refined path and the corresponding positions of the optimal coarse path, and match the connection points between the optimal refined path and the optimal coarse path;

[0105] Based on the matching results, the refined path fragments and the coarse path fragments are spliced ​​together to form a complete path.

[0106] In this embodiment, by clearly defining the endpoints (including the start and end points) of each optimal refined path and finding the corresponding positions of these endpoints in the optimal coarse path, the accuracy and integrity of the path splicing are ensured. This endpoint matching method avoids breakage or misalignment of the path during splicing, thus ensuring the continuity and feasibility of the rescue path. When comparing the relationship between the endpoints of the optimal refined path and the corresponding positions of the optimal coarse path, the path can be further optimized by matching the connection points between the optimal refined path and the optimal coarse path. This optimization not only considers the shortest distance of the path but also the smoothness and feasibility of the path. At the same time, since the refined path is the optimal solution obtained by Dijkstra's algorithm in the path gaps, the spliced ​​path still maintains high efficiency overall. This technical solution also has a certain degree of flexibility and scalability. When encountering new impassable areas or changes in navigation conditions, the rescue path can be replanned by adjusting the basic grid division, modifying the parameters of Dijkstra's algorithm, or adding new influencing factors (such as wind fields, ocean currents, etc.). This flexibility makes this technical solution applicable to different types of rescue missions and different marine environments.

[0107] A maritime emergency rescue route planning system, comprising:

[0108] The grid division module is used to divide the national sea area into basic grids and store the accessibility status, water depth and accessible surface information of each grid. Among them, some grids that contain both accessible and inaccessible areas are set as suspicious grids.

[0109] The information receiving module is used to receive the latitude and longitude information of the starting point and the destination transmitted from the front end of the rescue personnel;

[0110] The coarse path analysis module uses the Dixtra algorithm to analyze the optimal coarse path from the starting point to the ending point based on the received latitude and longitude information of the starting point and the ending point.

[0111] The path verification module is used to determine whether the optimal coarse path intersects with an impassable surface; if so, it determines the set of path segments that need to be removed.

[0112] The passable path filtering module subtracts the set of path segments to be removed from the optimal coarse path set to obtain the passable path segment set.

[0113] The gap recognition module identifies path gaps in the set of passable path segments;

[0114] The path refinement module uses the Dixtra algorithm to analyze the identified path gaps and find the optimal refined path.

[0115] The path splicing module splices the optimal refined path with the optimal coarse path when the solution set of the optimal refined path is not empty, to obtain the final rescue path; when the solution set of the optimal refined path is empty, the optimal coarse path is determined as the final rescue path.

[0116] The output module is used to output the final rescue route.

[0117] A computing device includes a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the steps of any of the above-described maritime emergency rescue route planning methods.

[0118] A computer-readable storage medium storing a computer program that, when executed by a processor, implements the steps of any of the above-described maritime emergency rescue route planning methods.

[0119] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the scope of protection of the present invention.

Claims

1. A method for planning maritime emergency rescue routes, characterized in that, Specifically include: The entire sea area of ​​the country is divided into basic grids. The grid information includes whether it is passable, water depth, and passable surface. Among them, some grids are passable and some are not, which are set as suspicious grids. Receive latitude and longitude information of the starting point and ending point from the front end of the rescue personnel; Based on the latitude and longitude information of the starting point and the ending point, the optimal coarse path from the starting point to the ending point is analyzed using the Dixtra algorithm. Determine whether the optimal coarse path intersects with an impassable surface; if so, obtain the set of path segments that need to be removed. Subtract the set of path segments to be removed from the set of optimal coarse paths to obtain the set of passable path segments; Identify path gaps in the set of passable path segments; The optimal refined path is analyzed using the Dixtra algorithm in the path gaps; If the solution set of the optimal refined path is empty, then the optimal coarse path is the final rescue path; otherwise, the optimal refined path and the optimal coarse path are concatenated to obtain the final rescue path.

2. The maritime emergency rescue route planning method according to claim 1, characterized in that, The step of using the Dixtra algorithm to analyze the optimal coarse path from the starting point to the ending point based on the latitude and longitude information of the starting point and the ending point specifically includes: Based on the latitude and longitude information of the starting and ending points, corresponding subgrids are divided in the basic grid; The optimal coarse path from the starting cell to the ending cell is analyzed using the Dixtra algorithm in the subgrid. If there is no solution for the coarse path in the optimal coarse path, then expand the grid range and use the Dixtra algorithm again to find the optimal coarse path.

3. The maritime emergency rescue route planning method according to claim 2, characterized in that, The process of using the Dixtro algorithm in the sub-grid to analyze the optimal coarse path from the starting cell to the ending cell specifically includes: Based on the latitude and longitude of the starting and ending points passed in from the front end, subgrid regions containing the starting and ending points are divided in the basic grid; Within the divided sub-grid regions, the Dixtra algorithm is applied to calculate the shortest path length to all other grid cells, with the starting grid cell as the source point. From the shortest paths obtained through calculation, the path that reaches the destination cell is selected as the optimal coarse path.

4. The maritime emergency rescue route planning method according to claim 3, characterized in that, Within the divided sub-grid regions, the Dixtra algorithm is applied to calculate the shortest path length to all other grid cells, using the starting grid cell as the source point. Specifically, this includes: Step S11: Within the divided sub-grid area, with the starting grid cell as the source point, initialize a distance array. The distance array records the shortest path length from the source point to all other grid cells. Initially, set the distance from the source point to itself to 0, and the distance from the source point to other grid cells to infinity. Step S12: Create an unvisited set and add all cells except the source point to the set; Step S13: Select the cell closest to the source point from the unvisited set as the current cell, and remove it from the unvisited set; Step S14: Update the shortest path length from the adjacent cells of the current cell to the source point. If the path from the current cell to the adjacent cell is shorter than the previously recorded path, update the shortest path length of the adjacent cell and set its parent node to the current cell. Repeat steps S13 and S14 until the unvisited set is empty. At this point, the distance array records the shortest path length from the source point to all cells.

5. The maritime emergency rescue route planning method according to claim 4, characterized in that, The process of identifying path gaps in the set of passable path segments specifically includes: The location of the gap is determined based on the intersection of the optimal coarse path and the impassable surface; The region containing path gaps is meshed to obtain path gap mesh cells that are smaller than the sub-mesh.

6. The maritime emergency rescue route planning method according to claim 5, characterized in that, The process of using the Dixtro algorithm to analyze the optimal refinement path in the path gaps specifically includes: Step S21: Initialize the distance values ​​of all path gap mesh cells smaller than the sub-mesh, set the distance value of the starting mesh cell to 0, and set the distance values ​​of other mesh cells to infinity; Step S22: Establish a priority queue to store the grid cells to be processed, with the starting grid cell added to the queue as the first element; Step S23: Take the grid cell with the smallest distance value from the priority queue as the grid cell to be processed, and update the distance values ​​of its adjacent grid cells. Step S24: If the distance value of an adjacent grid cell is updated to be smaller, add it to the priority queue; Repeat steps S23 and S24 until the priority queue is empty or the distance value of the endpoint grid cell is updated to a finite value; By recording the predecessor grid cells of each grid cell, the optimal refinement path is formed by tracing back from the end grid cell to the starting grid cell.

7. The maritime emergency rescue route planning method according to claim 6, characterized in that, The process of concatenating the optimal refined path with the optimal coarse path to obtain the final rescue path specifically includes: Define the endpoints of each optimal refinement path, where each endpoint includes a start point and an end point; In the optimal coarse path, find the position corresponding to the endpoint of the optimal refined path; Compare the relationship between the endpoints of the optimal refined path and the corresponding positions of the optimal coarse path, and match the connection points between the optimal refined path and the optimal coarse path; Based on the matching results, the refined path fragments and the coarse path fragments are spliced ​​together to form a complete path.

8. A maritime emergency rescue route planning system, characterized in that, include: The grid division module is used to divide the national sea area into basic grids and store the accessibility status, water depth and accessible surface information of each grid. Among them, some grids that contain both accessible and inaccessible areas are set as suspicious grids. The information receiving module is used to receive the latitude and longitude information of the starting point and the destination transmitted from the front end of the rescue personnel; The coarse path analysis module uses the Dixtra algorithm to analyze the optimal coarse path from the starting point to the ending point based on the received latitude and longitude information of the starting point and the ending point. The path verification module is used to determine whether the optimal coarse path intersects with an impassable surface; if so, it determines the set of path segments that need to be removed. The passable path filtering module subtracts the set of path segments to be removed from the optimal coarse path set to obtain the passable path segment set. The gap recognition module identifies path gaps in the set of passable path segments; The path refinement module uses the Dixtra algorithm to analyze the identified path gaps and find the optimal refined path. The path splicing module splices the optimal refined path with the optimal coarse path when the solution set of the optimal refined path is not empty, to obtain the final rescue path; when the solution set of the optimal refined path is empty, the optimal coarse path is determined as the final rescue path. The output module is used to output the final rescue route.

9. A computing device, characterized in that, The method includes a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the steps of the maritime emergency rescue route planning method according to any one of claims 1-7.

10. A computer-readable storage medium, characterized in that, The storage medium stores a computer program, which, when executed by a processor, implements the steps of the maritime emergency rescue route planning method according to any one of claims 1-7.

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