Unmanned cluster search and rescue task planning method, system and device and storage medium

Abstract

The invention discloses an unmanned cluster search and rescue task planning method, system and device and a storage medium, and relates to the technical field of search and rescue, and the method comprises the steps: analyzing a multi-source distress signal of a region to be searched and rescued, and determining accident point information; dynamically updating the target discovery probability of each grid sub-region in the to-be-searched-and-rescued region based on the environment feature information and the target moving feature information of the to-be-searched-and-rescued region; determining a current task planning strategy based on the type number of the unmanned equipment in the unmanned cluster and the accident point information; and based on the current task planning strategy and the target discovery probability of the grid sub-region, determining a region search and rescue priority list of each unmanned device, and performing task allocation on the unmanned cluster. Through the above mode, task planning and path distribution are uniformly carried out on multiple types of unmanned equipment in combination with the environment characteristics and the target movement characteristics, accident points are rapidly covered, the search and rescue efficiency is improved, and the success rate of search and rescue tasks is improved.

Description

Technical Field

[0001] The present application relates to the field of maritime search and rescue technology, and in particular to unmanned swarm search and rescue mission planning methods, systems, equipment and storage media. Background Art

[0002] In recent years, maritime search and rescue technology has received extensive attention in terms of ensuring the safety of personnel and property. Unmanned equipment has the potential to perform patrol and rescue missions in a large area of ​​sea with its fast and flexible mobility and relatively low operating costs. However, current maritime search and rescue work mainly relies on human search or single equipment assistance. This method has the problems of low efficiency, untimely response and limited coverage, and it is difficult to meet the search and rescue needs in modern complex maritime environments.

[0003] At present, the maritime search technology based on unmanned swarms has not yet been realized. Although single equipment such as drones and unmanned ships have been applied in some fields, they can only provide limited support and have not formed a complete system for the coordinated operation of multiple unmanned equipment. In addition, the relevant search and rescue mission planning and allocation algorithms are almost blank, and no task optimization method specifically for heterogeneous unmanned swarms in dynamic maritime environments has been developed.

[0004] Maritime search is highly complex and requires comprehensive consideration of multiple dynamic and environmental factors. On the one hand, the target in distress continues to drift under the influence of wind, waves and tides, and its position changes unpredictably over time; on the other hand, special terrains such as shallow sea islands and areas with complex currents further increase the challenges of search and rescue missions. These characteristics require that search and rescue mission planning must not only efficiently handle the problems of multiple accident points and multi-equipment coordination, but also have the ability to respond to dynamic environments in real time.

[0005] The above contents are only used to assist in understanding the technical solution of the present invention and do not constitute an admission that the above contents are prior art. Summary of the invention

[0006] The main purpose of this application is to provide an unmanned swarm search and rescue mission planning method, system, device and storage medium, aiming to solve the technical problem in the prior art that the mission planning scheme of unmanned swarms in complex search and rescue scenarios has poor search and rescue efficiency, affecting the success rate of search and rescue missions.

[0007] To achieve the above objectives, the present application provides an unmanned swarm search and rescue mission planning method, the method comprising: Analyze the multi-source distress signals in the search and rescue area to determine the accident point information; Dynamically updating the target discovery probability of each grid sub-area in the search and rescue area based on the environmental feature information and target movement feature information of the search and rescue area; Determining a current mission planning strategy based on the number and types of unmanned equipment in the unmanned cluster and the accident point information; Determine a regional search and rescue priority list for each unmanned equipment based on the current mission planning strategy and the target discovery probability of the grid sub-area; Tasks are allocated based on the regional search and rescue priority list, scene adaptability and execution cost of each unmanned equipment to determine the target search and rescue area, execution sequence and search and rescue path of each unmanned equipment.

[0008] In one embodiment, the accident point information includes at least the number of accident points and the scene type of the accident point, and the step of analyzing the multi-source distress signals in the search and rescue area to determine the accident point information includes: Extracting spatiotemporal features from multi-source distress signals in the area to be searched and rescued; Inputting the spatiotemporal characteristics into an accident point quantity recognition model for recognition, and determining the number of accident points; Extracting scene features from multi-source distress signals in the area to be searched and rescued; The scene features are input into the accident point scene classification model for classification to determine the accident point scene type.

[0009] In one embodiment, the step of determining the current mission planning strategy based on the number of types of unmanned equipment in the unmanned cluster and the accident point information includes: When the number of types of the unmanned equipment is greater than or equal to a preset type number threshold, determining that the current task planning strategy is a task planning strategy based on a competition mechanism; When the number of types of the unmanned equipment is less than a preset type number threshold and the accident point information does not conform to a multi-accident point scenario, determining that the current task planning strategy is a task planning strategy based on probability sorting; When the number of types of the unmanned equipment is less than a preset type number threshold and the accident point information conforms to a multi-accident point scenario, it is determined that the current task planning strategy is a task planning strategy based on probability and speed sorting.

[0010] In one embodiment, the step of determining the regional search and rescue priority list of each unmanned equipment based on the current mission planning strategy and the target discovery probability of the grid sub-area includes: When the current mission planning strategy is a mission planning strategy based on probability and speed sorting, obtaining a corresponding relationship between the target discovery probability, the area distance, the speed of the unmanned equipment and the search and rescue priority; Determine the search and rescue priority of the unassigned area corresponding to each unassigned area based on the target discovery probability of the unassigned area in the grid sub-area, the speed of the unmanned equipment, the area distance between the unmanned equipment and the unassigned area, and the corresponding relationship; The search and rescue priorities of the unassigned areas corresponding to each unmanned equipment are sorted in descending order to obtain a regional search and rescue priority list of each unmanned equipment.

[0011] In one embodiment, the step of determining the regional search and rescue priority list of each unmanned equipment based on the current mission planning strategy and the target discovery probability of the grid sub-area includes: When the current mission planning strategy is a mission planning strategy based on a competition mechanism, obtaining a corresponding relationship between the target discovery probability, the area distance, the speed of the unmanned equipment and the search and rescue priority; Determine the search and rescue priority of the grid sub-area corresponding to each unmanned equipment based on the target discovery probability of the grid sub-area, the speed of the unmanned equipment, the area distance between the unmanned equipment and the grid sub-area, and the corresponding relationship; The search and rescue priorities of the grid sub-areas corresponding to each unmanned equipment are sorted in descending order to obtain a regional search and rescue priority list of each unmanned equipment.

[0012] In one embodiment, the step of determining the regional search and rescue priority list of each unmanned equipment based on the current mission planning strategy and the target discovery probability of the grid sub-area includes: When the current task planning strategy is a task planning strategy based on probability sorting, the target discovery probability of the unassigned area in the grid sub-area is used as the search and rescue priority of the corresponding unassigned area; The search and rescue priorities of the unassigned areas corresponding to each unmanned equipment are sorted in descending order to obtain a regional search and rescue priority list of each unmanned equipment.

[0013] In one embodiment, the step of performing task allocation based on the regional search and rescue priority list, scene adaptability and execution cost of each unmanned equipment, and determining the target search and rescue area, execution sequence and search and rescue path of each unmanned equipment includes: Based on the regional search and rescue priority list, mapping the grid sub-areas into a task set, and determining the weight of each task in the task set; Constructing a task allocation variable, and constructing a comprehensive benefit function of the unmanned cluster based on the task allocation variable, the weight of the task, the scene adaptability, the execution cost, and the balance coefficient; Based on the comprehensive benefit function and the corresponding constraints, the optimal allocation strategy is solved to obtain the target search and rescue area, execution sequence and search and rescue path of each unmanned equipment.

[0014] In addition, to achieve the above purpose, the present application also proposes an unmanned swarm search and rescue mission planning system, which includes: The scene recognition module is used to analyze the multi-source distress signals in the search and rescue area and determine the accident point information; A probability calculation module, used for dynamically updating the target discovery probability of each grid sub-area in the area to be searched and rescued based on the environmental feature information and target movement feature information of the area to be searched and rescued; A strategy selection module, used to determine the current mission planning strategy based on the number of types of unmanned equipment in the unmanned cluster and the accident point information; A mission planning module, configured to determine a regional search and rescue priority list for each unmanned equipment based on the current mission planning strategy and the target discovery probability of the grid sub-area; The task planning module is also used to allocate tasks based on the regional search and rescue priority list, scene adaptability and execution cost of each unmanned equipment, and determine the target search and rescue area, execution sequence and search and rescue path of each unmanned equipment.

[0015] In addition, to achieve the above-mentioned purpose, the present application also proposes an unmanned cluster search and rescue mission planning device, which includes: a memory, a processor, and a computer program stored in the memory and executable on the processor, and the computer program is configured to implement the steps of the unmanned cluster search and rescue mission planning method as described above.

[0016] In addition, to achieve the above-mentioned purpose, the present invention also proposes a storage medium, which is a computer-readable storage medium. A computer program is stored on the storage medium. When the computer program is executed by a processor, the steps of the unmanned cluster search and rescue mission planning method as described above are implemented.

[0017] In addition, to achieve the above-mentioned purpose, the present application also provides a computer program product, which includes a computer program. When the computer program is executed by a processor, it implements the steps of the unmanned cluster search and rescue mission planning method as described above.

[0018] The present application provides an unmanned cluster search and rescue task planning method, which analyzes multi-source distress signals in a search and rescue area to determine accident point information; dynamically updates the target discovery probability of each grid sub-area in the search and rescue area based on the environmental feature information and target movement feature information of the search and rescue area; determines the current task planning strategy based on the number of types of unmanned equipment in the unmanned cluster and the accident point information; determines the regional search and rescue priority list of each unmanned equipment based on the current task planning strategy and the target discovery probability of the grid sub-area; allocates tasks based on the regional search and rescue priority list of each unmanned equipment, the scene adaptability and the execution cost, and determines the target search and rescue area, execution sequence and search and rescue path of each unmanned equipment. The environmental characteristics and target movement characteristics are updated in real time, and combined with the planning strategies in different scenarios, the task planning and path allocation of multiple types of unmanned equipment are unified. The speed, remaining energy and environmental adaptability of each equipment can be comprehensively considered, and multiple accident points, different scenarios, and the dynamic changes of the environment and distressed targets can be considered. The optimal or near-optimal task planning scheme can be calculated in real time to achieve rapid coverage of multiple accident points and timely response to emergencies, improve the search and rescue efficiency and accuracy in complex search and rescue scenarios, and improve the success rate of search and rescue missions. It solves the technical problem that the task planning scheme of unmanned clusters in complex search and rescue scenarios has poor search and rescue efficiency, which affects the success rate of search and rescue missions. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the present application and, together with the description, serve to explain the principles of the present application.

[0020] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, for ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative labor.

[0021] Figure 1 This is a flow chart of the first embodiment of the unmanned swarm search and rescue mission planning method of the present application; Figure 2 A schematic diagram of target discovery probability calculation for the unmanned swarm search and rescue mission planning method provided in Example 1 of the present application; Figure 3 A schematic diagram of convolution kernel coefficient calculation for the unmanned cluster search and rescue mission planning method provided in Example 1 of the present application; Figure 4 This is a flow chart of the second embodiment of the unmanned swarm search and rescue mission planning method of the present application; Figure 5 A brief flowchart of the unmanned swarm search and rescue mission planning method provided in Example 2 of the present application; Figure 6 This is a schematic diagram of the module structure of the unmanned cluster search and rescue mission planning system according to an embodiment of the present application; Figure 7 This is a schematic diagram of the device structure of the hardware operating environment involved in the unmanned cluster search and rescue mission planning method in the embodiment of the present application.

[0022] The realization of the purpose, functional features and advantages of this application will be further explained in conjunction with embodiments and with reference to the accompanying drawings. DETAILED DESCRIPTION

[0023] It should be understood that the specific embodiments described herein are only used to explain the technical solutions of the present application and are not used to limit the present application.

[0024] In order to better understand the technical solution of the present application, a detailed description will be given below in conjunction with the accompanying drawings and specific implementation methods.

[0025] The main solutions of the embodiments of the present application are: analyzing multi-source distress signals in the search and rescue area to determine the accident point information; dynamically updating the target discovery probability of each grid sub-area in the search and rescue area based on the environmental feature information and target movement feature information of the search and rescue area; determining the current task planning strategy based on the number and types of unmanned equipment in the unmanned cluster and the accident point information; determining the regional search and rescue priority list for each unmanned equipment based on the current task planning strategy and the target discovery probability of the grid sub-area; allocating tasks based on the regional search and rescue priority list of each unmanned equipment, the scene adaptability and the execution cost, and determining the target search and rescue area, execution sequence and search and rescue path for each unmanned equipment.

[0026] The present application provides a solution, which updates environmental characteristics and target movement characteristics in real time, and combines planning strategies in different scenarios to uniformly perform task planning and path allocation for multiple types of unmanned equipment. It can comprehensively consider the speed, remaining energy and environmental adaptability of each equipment, and consider multiple accident points, different scenarios, and the dynamic changes of the environment and distressed targets, and calculate the optimal or near-optimal task planning scheme in real time, thereby achieving rapid coverage of multiple accident points and timely response to emergencies, improving the search and rescue efficiency and accuracy in complex search and rescue scenarios, and improving the success rate of search and rescue missions. It solves the technical problem that the task planning scheme of unmanned clusters in complex search and rescue scenarios has poor search and rescue efficiency, which affects the success rate of search and rescue missions.

[0027] It should be noted that the execution subject of this embodiment can be a computing service device with data processing, network communication and program running functions, such as a tablet computer, a personal computer, a mobile phone, etc., or an electronic device capable of realizing the above functions, an unmanned cluster search and rescue mission planning device, etc., and this embodiment does not specifically limit this. The following takes the unmanned cluster search and rescue mission planning device as an example to illustrate this embodiment and the following embodiments.

[0028] In addition, the unmanned swarm is a collaborative operation system composed of various types of unmanned equipment, which can be applied to various search and rescue scenarios, such as deserts, seas, ruins, etc. This embodiment is described using the sea search and rescue scenario as an example.

[0029] The present application embodiment provides a method for planning an unmanned swarm search and rescue mission. Figure 1 , Figure 1 This is a flow chart of the first embodiment of the unmanned swarm search and rescue mission planning method of the present application.

[0030] In this embodiment, the unmanned swarm search and rescue mission planning method includes steps S10 to S50: Step S10, analyzing the multi-source distress signals in the search and rescue area to determine the accident point information; It should be noted that the search and rescue area is the area where the unmanned swarm is needed for search and rescue, that is, the area where the target in distress may be located, which can be one area at sea or multiple areas at sea, and there is no specific limitation on this. Among them, the target in distress is the target that encounters an emergency and needs rescue, which can be a ship or a person in distress, and there is no specific limitation on this.

[0031] In addition, it should be noted that multi-source distress signals are distress signals received in the area to be searched and rescued. Distress signals usually include two categories, namely active distress signals and passive discovery signals. Active distress signals are data sent by the target in distress through a distress device (such as an SOS beacon, a radio station, etc.), and passive discovery signals are data generated when a suspicious target or abnormal situation is automatically identified by a patrol or monitoring device (such as a drone patrol video, sonar detection, shore camera, radar, etc.). Since there are many ways to generate distress signals, and different methods generate different data, it can be considered that the distress signal is multi-source. Generally speaking, the area to be searched and rescued can be determined based on the received distress signal, and a suitable range is divided as the area to be searched and rescued according to the location where the distress signal is sent.

[0032] It is understandable that the accident point is the place where the distress situation occurs, which can be considered as the place / location where the distress signal is sent. The accident point information is the relevant information of the accident point, including at least the number of accident points and the scene type of the accident point. The number of accident points is the number of accident points. If the number of accident points is 1, it is usually considered to be a single accident point. If the number of accident points is greater than 1, it is usually considered to be multiple accident points. It can be seen that the number of accident points can be divided into two cases: single accident point and multiple accident points. The accident point scene type is the category of the scene of the accident point, for example: sea surface (Sea Surface), shallow underwater (ShallowUnderwater), island (Island), etc., and there is no specific limitation on this.

[0033] It should be understood that this embodiment analyzes the received multi-source distress signals to determine the number of accident points and the scene types of the accident points, thereby determining the search and rescue environment, and providing an accurate basis for environmental perception and accident point distribution for subsequent path planning and unmanned equipment scheduling.

[0034] Step S20, dynamically updating the target discovery probability of each grid sub-area in the search and rescue area based on the environmental feature information and target movement feature information of the search and rescue area; It should be noted that the members of the unmanned swarm (MoUS) may include various types of unmanned equipment, such as unmanned aerial vehicles (UAVs), unmanned ships (USVs), robot dogs (DOGs), unmanned underwater vehicles (UUVs), etc. All members may perform search and rescue missions individually or in any combination. In this embodiment, the unmanned equipment includes at least one of unmanned aerial vehicles, unmanned ships, robot dogs, and unmanned underwater vehicles. The unmanned swarm may be a single type of unmanned equipment, such as a swarm of unmanned aerial vehicles, or may be multiple (two or more) types of unmanned equipment, such as a swarm of unmanned aerial vehicles and unmanned ships. The specific unmanned equipment used shall be determined according to the actual situation and is not limited thereto.

[0035] It is understandable that the unmanned boat can be used as a relay charging platform for drones and a launch and recovery center for unmanned submersibles (unmanned submersibles are activated only when shallow underwater scenes are identified), and can also independently perform sea surface search and rescue missions; drones can conduct large-scale patrol reconnaissance in cooperative mode; robot dogs and unmanned submersibles can search island areas and shallow underwater areas respectively, forming a three-dimensional collaborative network with drones and unmanned boats; robot dogs are launched and recovered by drones (the robot dog is activated only when island scenes are identified), and the two can conduct large-scale patrol searches in cooperative mode. It can be seen that drones, unmanned boats, unmanned submersibles and robot dogs have different operating modes: drones are suitable for aerial reconnaissance of sea surfaces / islands, with fast patrol speeds but limited endurance; unmanned boats have slightly slower maneuverability and can be used as launch / charging platforms; unmanned submersibles have underwater sonar search functions, can use sonar to detect distressed targets in shallow waters, and need to be launched by unmanned boats; robot dogs have strong search capabilities on the ground of islands and need to be launched by drones.

[0036] Compared with using only a single type of unmanned equipment, the use of unmanned swarms can more effectively bring into play the characteristics of different types of unmanned equipment and avoid the limitations of a single type of unmanned equipment in search and rescue. For example, the speed of unmanned ships is relatively slow, but through collaborative search with drones, the speed advantage of drones can be fully utilized to expand the search and rescue range of unmanned ships and improve search efficiency; drones have limited endurance, and unmanned ships can serve as charging platforms for drones. Before the drone runs out of power, it will fly to the nearest unmanned ship for charging, thereby making up for the defect of insufficient range of drones and enabling them to conduct long-term searches in designated sea areas.

[0037] In the specific implementation, the feasibility and dependency relationship between unmanned equipment are determined according to the number of accident points and the type of accident scene, so as to determine the type and quantity of unmanned equipment required, and then decide on the unmanned cluster used for search and rescue.

[0038] It should be noted that the environmental characteristic information, i.e., the environmental characteristics of the area to be searched and rescued, includes at least terrain information, wind and wave information, tidal information, and ocean current information. The terrain information may include information such as the sea surface range, island terrain, and shallow underwater terrain. In addition to wind and wave information, tidal information, and ocean current information, other sea condition information may also be provided, and this embodiment does not specifically limit this. Since the target in distress may be attached to floating objects or lifeboats, the search and rescue target is usually mobile, and the motion trajectory will be similar to that of floating objects. The target movement characteristic information, i.e., the movement characteristics of the target in distress in the area to be searched and rescued, includes at least the speed of the target in distress itself and the convolution kernel parameters after being affected by the ocean current, tide, and island blocking effects. These data can be used as the initial possible diffusion direction and range of the target in distress.

[0039] In addition, it should be noted that the area to be searched and rescued can be divided into a plurality of grid units, each of which is a sub-area of ​​the area to be searched and rescued, that is, a grid sub-area. The size of the grid sub-area is set according to the actual situation and is not specifically limited. The target discovery probability (P-DSRT) is the probability of the distress target appearing, that is, the probability of discovering the distress target, recorded as .

[0040] It is understandable that after receiving the initial distress signal, there may be a probability of finding the target in distress in multiple grid sub-areas. However, as time goes by, the target discovery probability of the grid sub-areas will continue to spread around the initial position. In the initial state, the target discovery probability of the location where the initial distress signal is sent is the largest. The longer the search and rescue time, the more likely the target in distress will appear at a location farther away from the location where the initial distress signal is sent. At this time, the change trend of the target discovery probability shows the characteristics of spreading outward. When the unmanned cluster completes the search of a certain grid sub-area, the target discovery probability of the area will become smaller. However, since the target in distress may move, the target discovery probability of the surrounding area will transfer to the area that has been searched, that is, the target discovery probability of the surrounding area will become smaller, and the target discovery probability of the area that has been searched will become larger.

[0041] It should be understood that the location of the target in distress may change over time. It is in line with the movement logic of the target in distress to regard the search and rescue area as a discrete space and use the grid dynamics model to simulate the diffusion of the probability of the target in distress being found in the discrete space. The target discovery probability of a certain grid sub-area will evolve according to the target discovery probability of the area at the previous moment and the target discovery probability of the surrounding area, so as to calculate the target discovery probability of any grid sub-area at any time, and the target discovery probability of the area will also be affected by the passing of unmanned clusters. In discrete space, the target discovery probability will change over time according to the direction in which the target in distress may move, which is similar to convolution calculation. Reference Figure 2 , combining the environmental feature information and the target movement feature information, through the iterative calculation of the convolution kernel and the grid dynamics model, the target appearance probability of the grid sub-region at the next moment is obtained. The calculation formula of the target discovery probability of the grid sub-area at a certain moment is as follows:

[0042] In the formula, express The time coordinate is The target detection probability of the grid sub-area is Represents the convolution kernel coefficient, reflecting the distress target from the adjacent grid sub-region Drift to the current grid sub-region The probability ratio is the probability that the target in distress moves to the periphery. In addition, on the boundary The relationship between the target discovery probability of the grid sub-area at the moment is: .

[0043] It can be understood that, assuming the side length of each grid sub-region is , the external environmental factors make the vector of the distress target fixed and moving , the vector of random movement of the target in distress is ,consider At a radius of The target discovery probability of the grid sub-area is . refer to Figure 3 , the probability that the target in distress moves to the surrounding area is a vector The area of ​​the circle and the corresponding grid sub-area (rectangle) overlapped is the convolution kernel coefficient. The target discovery probability of the central area at the next moment is affected by the target discovery probability of the nearby area. The probability of the distressed target moving to the periphery is calculated using the following formula:

[0044] In the formula, Indicates the probability that the target in distress moves to the surrounding area, that is, the convolution kernel coefficient, , are the horizontal and vertical coordinates of the area where the target in distress will move to at the next moment relative to the area at the current moment. It should be noted that if the randomly moving vector When the length is larger, a larger convolution kernel is needed to represent it; if the randomly moving vector When the length is small, the side length of the discrete space needs to be reduced accordingly ; If the vector It is not evenly distributed in the circle. The volume of the area corresponding to the convolution kernel is calculated by integration at different positions as the probability of the distressed target moving to the periphery. When the convolution kernel size is When , indicating that the target in distress will not move.

[0045] When an unmanned device passes through a certain grid sub-area, it will detect the target with a certain probability, thereby reducing the residual probability of the grid sub-area. At this time, the calculation relationship of the target detection probability can be simplified to:

[0046] In the formula, is the probability that the unmanned equipment can detect the target, The coordinates for the next moment are The target detection probability of the grid sub-area is for The time coordinate is The probability of finding a target in a grid sub-area. When , it means that as long as unmanned equipment passes by, the distressed target can be found. At this time, it is only necessary to make the paths of all unmanned equipment cover the search and rescue area.

[0047] In actual application, the environmental feature information and target movement feature information are updated in real time, and the target detection probability of each grid sub-area is continuously corrected to enable more accurate mission planning. In addition, the grid sub-areas can be colored, with darker colors for high target detection probability and lighter colors for low target detection probability.

[0048] Step S30, determining a current task planning strategy based on the number of types of unmanned equipment in the unmanned cluster and the accident point information; It should be noted that the task planning strategy is the task planning algorithm, and the current task planning strategy is the task planning algorithm currently used. In this embodiment, a variety of task planning strategies are set, including at least a task planning strategy based on probability sorting (algorithm 1), a task planning strategy based on probability and speed sorting (algorithm 2), and a task planning strategy based on a competition mechanism (algorithm 3).

[0049] It is understandable that different scenarios use different task planning strategies. Algorithm 1 is applicable to single accident points and homogeneous unmanned clusters. It is characterized by fast calculation speed and is used for emergency response. Algorithm 2 is applicable to multiple accident points and homogeneous unmanned clusters. It takes into account the equipment speed and arrival time for optimization. When two or more categories of unmanned equipment are started, Algorithm 3 is automatically specified for task allocation and path planning. Algorithm 3 combines cooperation and competition mechanisms to achieve efficient coverage of multiple accident points under the complex coupling relationship between unmanned ships, drones, robot dogs and unmanned submarines.

[0050] In a feasible implementation, step S30 may include: when the number of types of the unmanned equipment is greater than or equal to a preset type number threshold, determining that the current task planning strategy is a task planning strategy based on a competition mechanism; when the number of types of the unmanned equipment is less than the preset type number threshold and the accident point information does not conform to a multi-accident point scenario, determining that the current task planning strategy is a task planning strategy based on probability sorting; when the number of types of the unmanned equipment is less than the preset type number threshold and the accident point information conforms to a multi-accident point scenario, determining that the current task planning strategy is a task planning strategy based on probability and speed sorting.

[0051] It should be noted that the number of types of unmanned equipment is the number of categories of unmanned equipment in the unmanned cluster. For example, if the unmanned cluster is a drone cluster, that is, the unmanned equipment in the unmanned cluster is a drone, then the number of types is 1. If the unmanned cluster is a drone cluster and an unmanned ship, that is, the unmanned equipment in the unmanned cluster is a drone and an unmanned ship, then the number of types is 2. The preset type number threshold is the threshold of the set type number, and the value is usually 2. If the number of equipment types of unmanned equipment is greater than or equal to 2, heterogeneous unmanned equipment is used at this time, that is, multiple types of unmanned equipment are used, and a task planning strategy based on a competition mechanism is adopted. If the number of types of unmanned equipment is less than 2, homogeneous unmanned equipment is used at this time, that is, a single type of unmanned equipment is used, and further judgment is made based on the accident point information.

[0052] It can be understood that if the number of story points indicates that the current situation is a single story point, it does not meet the multiple accident point scenario. If the number of story points indicates that the current situation is multiple story points, it meets the multiple accident point scenario. If the number of unmanned equipment types is less than 2 and the current situation is a single story point, a task planning strategy based on probability sorting is adopted. If the number of unmanned equipment types is less than 2 and the current situation is multiple story points, a task planning strategy based on probability and speed sorting is adopted.

[0053] Step S40, determining a regional search and rescue priority list for each unmanned equipment based on the current mission planning strategy and the target discovery probability of the grid sub-area; In a feasible implementation, step S40 may include steps A11-A12: Step A11, when the current task planning strategy is a task planning strategy based on probability sorting, the target discovery probability of the unassigned area in the grid sub-area is used as the search and rescue priority of the corresponding unassigned area; It should be noted that the search and rescue priority is the calculated priority of each grid sub-area. When using a mission planning strategy based on probability sorting, the search and rescue priority of a grid sub-area is the target discovery probability of the grid sub-area.

[0054] It can be understood that the core idea of ​​Algorithm 1 is to allocate areas that have not been searched and rescued and have the maximum target detection probability to the unmanned equipment in the unmanned cluster, so as to detect the distressed target in time and avoid the probability diffusion caused by the target movement.

[0055] Step A12, sorting the search and rescue priorities of the unassigned areas corresponding to the unmanned equipment in descending order to obtain a regional search and rescue priority list for each unmanned equipment.

[0056] It should be noted that the unallocated area is an area that has not been allocated to unmanned equipment for search and rescue. The regional search and rescue priority list is a list formed by sorting different areas according to the search and rescue priority. In this embodiment, the unallocated areas are sorted in descending order according to the search and rescue priority, thereby forming a regional search and rescue priority list for each unmanned equipment.

[0057] In the specific implementation, first find the probability according to the target All grid sub-areas are sorted, and then the unassigned areas with the maximum target discovery probability are allocated to the idle unmanned equipment in the unmanned cluster.

[0058] The advantage of Algorithm 1 is that it can determine in real time the target search and rescue area that the unmanned cluster should go to next, thereby forming a coherent search and rescue path, while the traditional intelligent algorithm needs to iterate multiple rounds to determine the best path. Since only the target discovery probability between grid sub-areas needs to be compared, the calculation speed is relatively fast. However, since the search and rescue priority is only determined by the size of the target discovery probability, the distance between the assigned unmanned equipment and the area is not considered, which may cause the unmanned equipment to waste travel time when going to the target search and rescue area, and may cause resource waste such as overlapping planned paths within the unmanned cluster. Among them, the target search and rescue area is the area currently assigned to each unmanned device that needs to be searched and rescued.

[0059] In another feasible implementation, step S40 may include steps B11 to B13: Step B11, when the current mission planning strategy is a mission planning strategy based on probability and speed sorting, obtaining a corresponding relationship between the target discovery probability, the area distance, the speed of the unmanned equipment and the search and rescue priority; It is understandable that the core idea of ​​Algorithm 2 is to comprehensively consider the time required to reach the target search and rescue area and the target detection probability. For each unmanned equipment waiting for a task to be assigned, it can be assigned to an area with the shortest arrival time and the highest target detection probability.

[0060] It should be noted that the corresponding relationship between the target detection probability, regional distance, speed of the unmanned equipment and the search and rescue priority is the calculation formula of the search and rescue priority, as shown below:

[0061] In the formula, Indicates Unmanned equipment at all times The corresponding coordinates are The search and rescue priority of the grid sub-area, Indicates at time The coordinates are The target detection probability of the grid sub-area is Indicates Unmanned equipment at all times Go to the coordinates of The distance between the grid sub-areas, that is, at the time No. The unmanned equipment and coordinates are The area distance between the grid sub-areas, Indicates The speed of an unmanned device, Indicates Unmanned equipment at all times Go to the coordinates of The time required for a grid sub-region.

[0062] Step B12, determining the search and rescue priority of the unassigned area corresponding to each unmanned equipment based on the target discovery probability of the unassigned area in the grid sub-area, the speed of the unmanned equipment, the area distance between the unmanned equipment and the unassigned area, and the corresponding relationship; It can be understood that the target discovery probability of the unallocated area in the grid sub-area, the speed of the unmanned equipment, and the area distance between the unmanned equipment and the unallocated area are substituted into the above corresponding relationship to calculate the search and rescue priority of the unallocated area.

[0063] Step B13, sorting the search and rescue priorities of the unassigned areas corresponding to the unmanned equipment in descending order to obtain a regional search and rescue priority list for each unmanned equipment.

[0064] In this embodiment, the search and rescue priorities of the unassigned areas are sorted in descending order, thereby forming a regional search and rescue priority list for each unmanned equipment.

[0065] It should be understood that according to the calculated search and rescue priority , dispatch no. The unmanned equipment is heading to the coordinates By searching the unallocated areas, it is possible to reach the area with the highest probability of finding the target in the shortest time, thereby finding the target in distress earlier.

[0066] Algorithm 2 comprehensively considers the speed of the unmanned equipment, the distance between the unmanned equipment and the target search and rescue area, and the target detection probability in the target search and rescue area. Therefore, in a multi-accident rescue mission, even if the target detection probability of some locations is reduced, the unmanned equipment currently searching and rescuing will not be immediately sent to other accident sites. This can prevent the unmanned equipment from moving back and forth between multiple distant accident sites, reduce the overlap of paths between unmanned equipment in the unmanned cluster, and save travel time.

[0067] In another feasible implementation, step S40 may include steps C11 to C13: Step C11, when the current mission planning strategy is a mission planning strategy based on a competition mechanism, obtaining a corresponding relationship between the target discovery probability, the area distance, the speed of the unmanned equipment and the search and rescue priority; It should be noted that Algorithm 3 introduces a competition mechanism between different unmanned equipment based on Algorithm 2, that is, multiple unmanned equipment plans search behaviors in real time according to their own speed and target discovery probability in the grid sub-area. The core idea is to give full play to the advantages of the unmanned cluster's high speed and high search efficiency, reduce the long-term occupation of the search and rescue area by unmanned equipment with slower speed and longer arrival time, and avoid the situation where other nearby unmanned equipment cannot assist in the search.

[0068] It can be understood that when using a mission planning strategy based on a competition mechanism, the calculation method of the search and rescue priority is the same as that of the mission planning strategy based on probability and speed sorting.

[0069] Step C12, determining the search and rescue priority of the grid sub-area corresponding to each unmanned equipment based on the target discovery probability of the grid sub-area, the speed of the unmanned equipment, the area distance between the unmanned equipment and the grid sub-area, and the corresponding relationship; It can be understood that the target discovery probability of the grid sub-area, the speed of the unmanned equipment, and the regional distance between the unmanned equipment and the grid sub-area are substituted into the above corresponding relationship to calculate the search and rescue priority of the grid sub-area.

[0070] Step C13, sorting the search and rescue priorities of the grid sub-areas corresponding to each unmanned equipment in descending order to obtain a regional search and rescue priority list of each unmanned equipment.

[0071] In this embodiment, the search and rescue priorities of all grid sub-areas are sorted in descending order, thereby forming a regional search and rescue priority list for each unmanned equipment.

[0072] It is understandable that when assigning tasks, Algorithm 3 no longer only considers idle unmanned equipment, but also unmanned equipment that is performing tasks can participate in task allocation (including unmanned equipment that is heading to the target search and rescue area and searching the target area). Algorithm 3 still uses the task planning framework of Algorithm 2, but when the unmanned equipment is heading to the original target search and rescue area, if it finds that the search and rescue priority of a grid sub-area is greater than the value of the original target search and rescue area, the unmanned equipment will change the target search and rescue area. Tasks can be assigned to unmanned equipment in real time, rather than at the moment when the unmanned equipment completes the search and rescue task in a certain area, which is conducive to avoiding the search and rescue area being occupied by unmanned equipment with lower speeds. Especially when there are unmanned equipment with different speeds in the unmanned cluster, this algorithm is conducive to the effective use of resources.

[0073] It should be understood that after the unmanned equipment completes the previous round of search tasks, all algorithms will assign it the highest priority area. Algorithms 1 and 2 will search for the highest priority area from the unassigned area, but Algorithm 3 will search in all grid sub-areas. Algorithms 2 and 3 have different sorting rules for areas than Algorithm 1. Algorithm 1 will directly compare the target discovery probability of each grid sub-area. , Algorithm 2 and Algorithm 3 will compare the search and rescue priorities ,After sorting by the algorithm, the area with the highest value will be searched first.

[0074] When the competition mechanism is triggered, if the drone cooperates with the unmanned ship for search and rescue, when the unmanned ship is closer to the target and has sufficient resources, the corresponding search task can be directly assigned to the unmanned ship to execute; if the robot dog cooperates with the drone for search and rescue, when the robot dog is closer to the target and has sufficient resources, the corresponding search task can be directly assigned to the robot dog to execute.

[0075] In this embodiment, the optimal planning algorithm is flexibly selected in the search and rescue needs of single or multiple types of unmanned equipment. When two or more types of unmanned equipment are detected to participate in the search and rescue at the same time, the task planning algorithm based on the competition mechanism (algorithm 3) is automatically specified, making full use of the advantages of unmanned ships as control centers and offshore relay platforms, as well as the complementarity of drones, robot dogs, and unmanned submersibles, to achieve three-dimensional, all-round and efficient search and rescue of the sea surface, islands and reefs, and shallow sea environments.

[0076] Step S50, based on the regional search and rescue priority list, scene adaptability and execution cost of each unmanned equipment, task allocation is performed to determine the target search and rescue area, execution sequence and search and rescue path of each unmanned equipment.

[0077] In a feasible implementation, step S50 may include: based on the regional search and rescue priority list, mapping the grid sub-area into a task set, and determining the weight of each task in the task set; constructing a task allocation variable, and determining the comprehensive benefit function of the unmanned cluster based on the task allocation variable, the weight of the task, the scene adaptability, the execution cost, and the balance coefficient; based on the comprehensive benefit function and the corresponding constraints, solving the optimal allocation strategy to obtain the target search and rescue area, execution sequence, and search and rescue path of each unmanned equipment.

[0078] It should be noted that according to the regional search and rescue priority list, the grid sub-regions are mapped to task sets, and the search and rescue priorities calculated by the three task planning strategies for the grid sub-regions are converted into task weights. , used to indicate unmanned equipment Whether to execute the task assigned to you Combining the scene adaptability of the unmanned equipment, the weight of the task, the execution cost of the unmanned equipment and the balance coefficient, the profit function is constructed as follows:

[0079] In the formula, is the profit function, and is the balance coefficient, is the scene adaptability, is the execution cost (such as distance, time, energy consumption, etc.), is the weight of the task. Thus, the overall benefit function, that is, the comprehensive benefit function, can be obtained as follows:

[0080] In the formula, is the profit function, Assign variables to tasks, is the comprehensive income function.

[0081] It is understandable that when constraints, dependencies and resource limits are met (such as equipment life not exceeding a threshold ), maximize the comprehensive benefit function and solve the optimal allocation strategy (optimal task allocation plan) to determine the target search and rescue area and execution sequence of each unmanned equipment, and plan the search and rescue path.

[0082] Among them, the constraints for unmanned equipment to perform tasks are as follows: DOG performs island tasks and UAVs must perform them at the same time. ; UUV needs USV to launch, ; UAV needs USV resupply in sea scenarios, ; For infeasible scenarios (such as DOG cannot be executed underwater), you can directly command Or make the profit minimum.

[0083] It should be understood that the basic parameters of drones, unmanned ships, robot dogs and unmanned underwater vehicles can be obtained in advance, such as speed, endurance, load conditions, etc.

[0084] Furthermore, the graphical user interface (GUI) can be used to display the current position, mission status, and path planning results of each unmanned equipment, and the target discovery probability of the grid sub-area can be marked in the form of a heat map. When two or more types of unmanned equipment are detected to be enabled, the GUI automatically displays that "Algorithm 3" has been selected. Through the GUI, users can view the switching logic between the unmanned ship as a relay center and performing search and rescue missions. In addition, users can intervene in the GUI to manually change the scheduling of unmanned equipment or force the activation of specific algorithms.

[0085] This embodiment provides an unmanned cluster search and rescue task planning method, which analyzes multi-source distress signals in the search and rescue area to determine the accident point information; dynamically updates the target discovery probability of each grid sub-area in the search and rescue area based on the environmental feature information and target movement feature information of the search and rescue area; determines the current task planning strategy based on the number of types of unmanned equipment in the unmanned cluster and the accident point information; determines the regional search and rescue priority list of each unmanned equipment based on the current task planning strategy and the target discovery probability of the grid sub-area; and performs task allocation based on the regional search and rescue priority list, scene adaptability and execution cost of each unmanned equipment, and determines the target search and rescue area, execution sequence and search and rescue path of each unmanned equipment. The environmental features and target movement features are updated in real time, and combined with the planning strategies in different scenarios, the task planning and path allocation of multiple types of unmanned equipment are unified, which can comprehensively consider the speed, remaining energy and environmental adaptability of each equipment, consider multiple accident points, different scenarios and the dynamic changes of the environment and the target in distress, and calculate the optimal or near-optimal task planning scheme in real time, so as to achieve rapid coverage of multiple accident points and timely response to emergencies, improve the search and rescue efficiency and accuracy in complex search and rescue scenarios, and improve the success rate of search and rescue tasks.

[0086] Based on the first embodiment of the present application, in the second embodiment of the present application, the same or similar contents as those in the above-mentioned embodiment 1 can refer to the above introduction, and will not be repeated later. Figure 4 , step S10 may include steps S101 to S104: Step S101, extracting spatiotemporal features from multi-source distress signals in the area to be searched and rescued; It is understandable that spatiotemporal features are extracted simultaneously from multi-source distress signals (radar, radio, sonar, images, etc.).

[0087] Step S102, inputting the spatiotemporal characteristics into an accident point quantity recognition model for recognition to determine the number of accident points; It should be noted that the accident point quantity recognition model is a model that can recognize the number of accident points. This embodiment uses a trained deep learning model as the accident point quantity recognition model. The model is based on a convolutional neural network (CNN), and CNN can fuse these spatiotemporal features.

[0088] It can be understood that the accident point quantity recognition model includes convolutional layer, pooling layer and fully connected layer. Assume that the input data set is , after the convolution layer and the pooling layer, the feature vector is obtained , and then the classification prediction of the number of accident points is performed through the fully connected layer, and the output is a single accident point or multiple accident points, as shown below:

[0089] In the formula, is the identification result of the number of accident points, is the activation function, and Respectively, categories The weights and biases of is the index of the accident point quantity category, is the feature vector. If it is determined to be multiple accident points, all scattered coordinate information will be marked and the location of each accident point will be processed later.

[0090] Step S103, extracting scene features from the multi-source distress signals in the area to be searched and rescued; It should be noted that after determining the number of accident points, the scene-related features, namely the scene features, are obtained by extracting features from multi-source distress signals such as radar echoes, underwater cameras, and satellite images.

[0091] Step S104: input the scene features into the accident point scene classification model for classification to determine the accident point scene type.

[0092] It should be noted that the accident point scene classification model is a model that can classify and identify the accident point scene type. This embodiment uses a trained deep learning model as the accident point scene classification model, and the model combines a convolutional neural network with an attention mechanism.

[0093] It can be understood that the accident scene classification model includes multiple layers of convolutional layers, pooling layers, attention layers, and fully connected layers. Assume that the initial feature vector obtained after the operation of multiple layers of convolutional layers and pooling layers is , and then generate the key feature vector through the attention layer , and then classify it through the fully connected layer to output which of the three types of scenes the accident point belongs to: sea surface, shallow sea surface, and island, as shown below:

[0094] In the formula, is the classification result of the accident scene type, is the activation function, and Respectively, categories The weights and biases of is the index of the accident scene type, is the initial feature vector, is the key feature vector. After completing the preliminary judgment of the scene type, the accident point coordinates and scene type labels are combined to use clustering algorithms such as DBSCAN or K-means to identify the accident point clusters in the same area and calibrate their attributes. For example, if the center of a cluster is within the island boundary, it is marked as IslandCluster; if an underwater characteristic signal is identified, it is marked as ShallowUnderwaterCluster.

[0095] This embodiment provides an unmanned cluster search and rescue task planning method, which extracts spatiotemporal features from multi-source distress signals in the search and rescue area; inputs the spatiotemporal features into the accident point quantity recognition model for recognition, and determines the number of accident points; extracts scene features from the multi-source distress signals in the search and rescue area; inputs the scene features into the accident point scene classification model for classification, and determines the scene type of the accident point. The deep learning model is used to automatically identify the number of accident points and classify the scene types of accident points on the input data, and the manual input method is replaced by the automatic judgment mode, so as to automatically generate the scene configuration, and provide accurate basis for environmental perception and accident point distribution for subsequent path planning and unmanned equipment scheduling, thereby improving the efficiency and accuracy of search and rescue.

[0096] For example, in order to help understand the implementation process of the unmanned cluster search and rescue mission planning method obtained by combining this embodiment with the above-mentioned embodiment 2, please refer to Figure 5 , Figure 5 A brief flowchart of an unmanned swarm search and rescue mission planning method is provided, specifically: The first step is data input. Input the basic parameters of the drone, unmanned ship, unmanned submarine, and robot dog (speed, endurance, load, etc.), as well as the size of the grid in the search and rescue area, the convolution kernel, the target movement feature vector, etc.; pre-allocate the initial accident point probability distribution, and if there are multiple accident points, they can be mapped to multiple grid cells.

[0097] The second step is grid dynamics calculation. The convolution kernel coefficients are updated according to factors such as ocean currents, wind direction, random drift, etc. If the equipment search is successful, the probability of the corresponding grid decreases, and iterates according to the time step or event-driven mode.

[0098] In the third step, multiple algorithms are used to prioritize the grids. Algorithms 1, 2, or 3 are automatically selected according to different scenarios (single / multiple accident points, homogeneous / heterogeneous equipment) to obtain a grid priority list or value index.

[0099] The fourth step is to generate dispatch tasks and multi-equipment allocation. According to the priority list, map the grid or grid cluster to a task set and set the task weight; solve or iterate the task allocation variables and output the specific execution sequence and path of each equipment; if Algorithm 3 allows competition for orders, dynamic scheduling between equipment can also be triggered during execution.

[0100] Step 5: Simulation and visualization. In multi-agent simulation modeling (e.g. using AnyLogic platform), the probability evolution of grid dynamics and the behavior state diagram of drones / unmanned ships / unmanned underwater vehicles / robot dogs are realized; the GUI displays the grid probability distribution, equipment position and remaining endurance, algorithm allocation results and competition process in real time, facilitating user interaction and solution evaluation.

[0101] It should be noted that the above examples are only used to understand the present application and do not constitute a limitation on the unmanned cluster search and rescue mission planning method of the present application. More simple transformations based on this technical concept are all within the scope of protection of the present application.

[0102] This application also provides an unmanned cluster search and rescue mission planning system, please refer to Figure 6 , the unmanned swarm search and rescue mission planning system includes: A scene recognition module 10 is used to analyze the multi-source distress signals in the search and rescue area and determine the accident point information; A probability calculation module 20, configured to dynamically update the target discovery probability of each grid sub-area in the search and rescue area based on the environmental feature information and target movement feature information of the search and rescue area; A strategy selection module 30, for determining a current mission planning strategy based on the number of types of unmanned equipment in the unmanned cluster and the accident point information; A mission planning module 40, configured to determine a regional search and rescue priority list for each unmanned equipment based on the current mission planning strategy and the target discovery probability of the grid sub-area; The task planning module 40 is also used to allocate tasks based on the regional search and rescue priority list, scene adaptability and execution cost of each unmanned equipment, and determine the target search and rescue area, execution sequence and search and rescue path of each unmanned equipment.

[0103] In a feasible implementation manner, the scene recognition module 10 is further used to extract spatiotemporal features from multi-source distress signals in the search and rescue area; Inputting the spatiotemporal characteristics into an accident point quantity recognition model for recognition, and determining the number of accident points; Extracting scene features from multi-source distress signals in the area to be searched and rescued; The scene features are input into the accident point scene classification model for classification to determine the accident point scene type.

[0104] In a feasible implementation manner, the strategy selection module 30 is further used to determine that the current task planning strategy is a task planning strategy based on a competition mechanism when the number of types of the unmanned equipment is greater than or equal to a preset type number threshold; When the number of types of the unmanned equipment is less than a preset type number threshold and the accident point information does not conform to a multi-accident point scenario, determining that the current task planning strategy is a task planning strategy based on probability sorting; When the number of types of the unmanned equipment is less than a preset type number threshold and the accident point information conforms to a multi-accident point scenario, it is determined that the current task planning strategy is a task planning strategy based on probability and speed sorting.

[0105] In a feasible implementation manner, the mission planning module 40 is further used to obtain the corresponding relationship between the target discovery probability, the area distance, the speed of the unmanned equipment and the search and rescue priority when the current mission planning strategy is a mission planning strategy based on probability and speed sorting; Determine the search and rescue priority of the unassigned area corresponding to each unassigned area based on the target discovery probability of the unassigned area in the grid sub-area, the speed of the unmanned equipment, the area distance between the unmanned equipment and the unassigned area, and the corresponding relationship; The search and rescue priorities of the unassigned areas corresponding to each unmanned equipment are sorted in descending order to obtain a regional search and rescue priority list of each unmanned equipment.

[0106] In a feasible implementation manner, the mission planning module 40 is further used to obtain the corresponding relationship between the target discovery probability, the area distance, the speed of the unmanned equipment and the search and rescue priority when the current mission planning strategy is a mission planning strategy based on a competition mechanism; Determine the search and rescue priority of the grid sub-area corresponding to each unmanned equipment based on the target discovery probability of the grid sub-area, the speed of the unmanned equipment, the area distance between the unmanned equipment and the grid sub-area, and the corresponding relationship; The search and rescue priorities of the grid sub-areas corresponding to each unmanned equipment are sorted in descending order to obtain a regional search and rescue priority list of each unmanned equipment.

[0107] In a feasible implementation manner, the mission planning module 40 is further configured to use the target discovery probability of the unassigned area in the grid sub-area as the search and rescue priority of the corresponding unassigned area when the current mission planning strategy is a mission planning strategy based on probability sorting; The search and rescue priorities of the unassigned areas corresponding to each unmanned equipment are sorted in descending order to obtain a regional search and rescue priority list of each unmanned equipment.

[0108] In a feasible implementation manner, the task planning module 40 is further used to map the grid sub-area into a task set based on the regional search and rescue priority list, and determine the weight of each task in the task set; Constructing a task allocation variable, and determining a comprehensive benefit function of the unmanned cluster based on the task allocation variable, the weight of the task, the scene adaptability, the execution cost, and the balance coefficient; Based on the comprehensive benefit function and the corresponding constraints, the optimal allocation strategy is solved to obtain the target search and rescue area, execution sequence and search and rescue path of each unmanned equipment.

[0109] The unmanned cluster search and rescue mission planning system provided by the present application adopts the unmanned cluster search and rescue mission planning method in the above embodiment, which can solve the technical problem that the search and rescue efficiency of the unmanned cluster mission planning scheme in complex search and rescue scenarios is poor, affecting the success rate of the search and rescue mission. Compared with the prior art, the beneficial effects of the unmanned cluster search and rescue mission planning system provided by the present application are the same as the beneficial effects of the unmanned cluster search and rescue mission planning method provided by the above embodiment, and other technical features of the unmanned cluster search and rescue mission planning system are the same as the features disclosed in the above embodiment method, which will not be repeated here.

[0110] The present application provides an unmanned cluster search and rescue mission planning device, which includes: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor so that the at least one processor can execute the unmanned cluster search and rescue mission planning method in the above-mentioned embodiment one.

[0111] Reference below Figure 7 , which shows a schematic diagram of the structure of an unmanned cluster search and rescue mission planning device suitable for implementing the embodiment of the present application. The unmanned cluster search and rescue mission planning device in the embodiment of the present application may include but is not limited to mobile terminals such as mobile phones, laptop computers, digital broadcast receivers, PDAs (Personal Digital Assistants), PADs (Portable Application Descriptions), PMPs (Portable Media Players), vehicle-mounted terminals (such as vehicle-mounted navigation terminals), etc., and fixed terminals such as digital TVs, desktop computers, etc. Figure 7 The unmanned cluster search and rescue mission planning device shown is merely an example and should not bring any limitation to the functions and scope of use of the embodiments of the present application.

[0112] like Figure 7As shown, the unmanned cluster search and rescue mission planning device may include a processing system 1001 (such as a central processing unit, a graphics processor, etc.), which can perform various appropriate actions and processes according to the program stored in the ROM (Read Only Memory) 1002 or the program loaded from the storage system 1003 to the RAM (Random Access Memory) 1004. Various programs and data required for the operation of the unmanned cluster search and rescue mission planning device are also stored in the RAM 1004. The processing system 1001, the ROM 1002, and the RAM 1004 are connected to each other via a bus 1005. The input / output (I / O) interface 1006 is also connected to the bus. Typically, the following systems may be connected to the I / O interface 1006: an input system 1007 including, for example, a touch screen, a touchpad, a keyboard, a mouse, an image sensor, a microphone, an accelerometer, a gyroscope, etc.; an output system 1008 including, for example, a liquid crystal display (LCD), a speaker, a vibrator, etc.; a storage system 1003 including, for example, a magnetic tape, a hard disk, etc.; and a communication system 1009. The communication system 1009 may allow the unmanned cluster search and rescue mission planning device to communicate wirelessly or wired with other devices to exchange data. Although the figure shows an unmanned cluster search and rescue mission planning device with various systems, it should be understood that it is not required to implement or have all the systems shown. More or fewer systems may be implemented or have alternatively.

[0113] In particular, according to the embodiments disclosed in the present application, the process described above with reference to the flowchart can be implemented as a computer software program. For example, the embodiments disclosed in the present application include a computer program product, which includes a computer program carried on a computer-readable medium, and the computer program includes a program code for executing the method shown in the flowchart. In such an embodiment, the computer program can be downloaded and installed from a network through a communication system, or installed from a storage system 1003, or installed from a ROM 1002. When the computer program is executed by the processing system 1001, the above-mentioned functions defined in the method of the embodiment disclosed in the present application are executed.

[0114] The unmanned cluster search and rescue mission planning device provided by the present application adopts the unmanned cluster search and rescue mission planning method in the above embodiment, which can solve the technical problem that the search and rescue efficiency of the unmanned cluster mission planning scheme in complex search and rescue scenarios is poor, affecting the success rate of the search and rescue mission. Compared with the prior art, the beneficial effects of the unmanned cluster search and rescue mission planning device provided by the present application are the same as the beneficial effects of the unmanned cluster search and rescue mission planning method provided by the above embodiment, and the other technical features in the unmanned cluster search and rescue mission planning device are the same as the features disclosed in the method of the previous embodiment, which will not be repeated here.

[0115] It should be understood that the various parts disclosed in this application can be implemented by hardware, software, firmware or a combination thereof. In the description of the above embodiments, specific features, structures, materials or characteristics can be combined in any one or more embodiments or examples in a suitable manner.

[0116] The above are only specific implementations of the present application, but the protection scope of the present application is not limited thereto. Any technician familiar with the technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be based on the protection scope of the claims.

[0117] The present application provides a computer-readable storage medium having computer-readable program instructions (ie, computer programs) stored thereon, and the computer-readable program instructions are used to execute the unmanned cluster search and rescue mission planning method in the above-mentioned embodiment.

[0118] The computer-readable storage medium provided in the present application may be, for example, a USB flash drive, but is not limited to electrical, magnetic, optical, electromagnetic, infrared, or semiconductor systems, systems or devices, or any combination of the above. More specific examples of computer-readable storage media may include, but are not limited to: an electrical connection with one or more wires, a portable computer disk, a hard disk, a random access memory (RAM: Random Access Memory), a read-only memory (ROM: Read Only Memory), an erasable programmable read-only memory (EPROM: Erasable Programmable Read Only Memory or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM: CD-Read Only Memory), an optical storage device, a magnetic storage device, or any suitable combination of the above. In this embodiment, the computer-readable storage medium may be any tangible medium containing or storing a program, which may be used by or in combination with an instruction execution system, system or device. The program code contained on the computer-readable storage medium may be transmitted using any appropriate medium, including but not limited to: wires, optical cables, RF (Radio Frequency: Radio Frequency), etc., or any suitable combination of the above.

[0119] The above-mentioned computer-readable storage medium may be included in the unmanned swarm search and rescue mission planning device; or it may exist independently without being assembled into the unmanned swarm search and rescue mission planning device.

[0120] The above-mentioned computer-readable storage medium carries one or more programs. When the above-mentioned one or more programs are executed by the unmanned cluster search and rescue task planning device, the unmanned cluster search and rescue task planning device: analyzes the multi-source distress signals in the search and rescue area to determine the accident point information; dynamically updates the target discovery probability of each grid sub-area in the search and rescue area based on the environmental characteristic information and target movement characteristic information of the search and rescue area; determines the current task planning strategy based on the number of types of unmanned equipment in the unmanned cluster and the accident point information; determines the regional search and rescue priority list of each unmanned equipment based on the current task planning strategy and the target discovery probability of the grid sub-area; performs task allocation based on the regional search and rescue priority list of each unmanned equipment, the scene adaptability and the execution cost, and determines the target search and rescue area, execution sequence and search and rescue path of each unmanned equipment.

[0121] Computer program code for performing the operations of the present application may be written in one or more programming languages ​​or a combination thereof, including object-oriented programming languages ​​such as Java, Smalltalk, C++, and conventional procedural programming languages ​​such as "C" or similar programming languages. The program code may be executed entirely on the user's computer, partially on the user's computer, as a separate software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer through any type of network, including a local area network (LAN) or a wide area network (WAN), or may be connected to an external computer (e.g., via the Internet using an Internet service provider).

[0122] The flow chart and block diagram in the accompanying drawings illustrate the possible architecture, function and operation of the system, method and computer program product according to various embodiments of the present application. In this regard, each square box in the flow chart or block diagram can represent a module, a program segment or a part of a code, and the module, the program segment or a part of the code contains one or more executable instructions for realizing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the square box can also occur in a sequence different from that marked in the accompanying drawings. For example, two square boxes represented in succession can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each square box in the block diagram and / or flow chart, and the combination of the square boxes in the block diagram and / or flow chart can be implemented with a dedicated hardware-based system that performs a specified function or operation, or can be implemented with a combination of dedicated hardware and computer instructions.

[0123] The modules involved in the embodiments described in this application may be implemented by software or hardware, wherein the name of the module does not constitute a limitation on the unit itself in some cases.

[0124] The readable storage medium provided by the present application is a computer-readable storage medium, which stores computer-readable program instructions (i.e., computer programs) for executing the above-mentioned unmanned cluster search and rescue mission planning method, and can solve the technical problem that the search and rescue efficiency of the mission planning scheme of the unmanned cluster in complex search and rescue scenarios is poor, which affects the success rate of the search and rescue mission. Compared with the prior art, the beneficial effects of the computer-readable storage medium provided by the present application are the same as the beneficial effects of the unmanned cluster search and rescue mission planning method provided by the above-mentioned embodiment, and will not be repeated here.

[0125] The present application also provides a computer program product, including a computer program, which implements the steps of the unmanned cluster search and rescue mission planning method as described above when the computer program is executed by a processor.

[0126] The computer program product provided by this application can solve the technical problem that the search and rescue efficiency of the unmanned swarm mission planning scheme in complex search and rescue scenarios is poor, which affects the success rate of the search and rescue mission. Compared with the prior art, the beneficial effects of the computer program product provided by this application are the same as the beneficial effects of the unmanned swarm search and rescue mission planning method provided by the above embodiment, and will not be repeated here.

[0127] The above are only some embodiments of the present application, and are not intended to limit the patent scope of the present application. All equivalent structural changes made using the contents of the present application specification and drawings under the technical concept of the present application, or direct / indirect application in other related technical fields are included in the patent protection scope of the present application.

Claims

1. A method for planning an unmanned swarm search and rescue mission, characterized in that: The method comprises: Analyze the multi-source distress signals in the search and rescue area to determine the accident point information; Dynamically updating the target discovery probability of each grid sub-area in the search and rescue area based on the environmental feature information and target movement feature information of the search and rescue area; Determine a current mission planning strategy based on the number and types of unmanned equipment in the unmanned cluster and the accident point information; Determine a regional search and rescue priority list for each unmanned equipment based on the current mission planning strategy and the target discovery probability of the grid sub-area; Tasks are allocated based on the regional search and rescue priority list, scene adaptability and execution cost of each unmanned equipment to determine the target search and rescue area, execution sequence and search and rescue path of each unmanned equipment.

2. The method according to claim 1, characterized in that The accident point information at least includes the number of accident points and the scene type of the accident point. The step of analyzing the multi-source distress signals in the search and rescue area to determine the accident point information includes: Extracting spatiotemporal features from multi-source distress signals in the area to be searched and rescued; Inputting the spatiotemporal characteristics into an accident point quantity recognition model for recognition, and determining the number of accident points; Extracting scene features from multi-source distress signals in the area to be searched and rescued; The scene features are input into the accident point scene classification model for classification to determine the accident point scene type.

3. The method according to claim 1, characterized in that The step of determining the current task planning strategy based on the number of types of unmanned equipment in the unmanned cluster and the accident point information comprises: When the number of types of the unmanned equipment is greater than or equal to a preset type number threshold, determining that the current task planning strategy is a task planning strategy based on a competition mechanism; When the number of types of the unmanned equipment is less than a preset type number threshold and the accident point information does not conform to a multi-accident point scenario, determining that the current task planning strategy is a task planning strategy based on probability sorting; When the number of types of the unmanned equipment is less than a preset type number threshold and the accident point information conforms to a multi-accident point scenario, it is determined that the current task planning strategy is a task planning strategy based on probability and speed sorting.

4. The method according to claim 1, characterized in that The step of determining the regional search and rescue priority list of each unmanned equipment based on the current mission planning strategy and the target discovery probability of the grid sub-area includes: When the current mission planning strategy is a mission planning strategy based on probability and speed sorting, obtaining a corresponding relationship between the target discovery probability, the area distance, the speed of the unmanned equipment and the search and rescue priority; Determine the search and rescue priority of the unassigned area corresponding to each unassigned area based on the target discovery probability of the unassigned area in the grid sub-area, the speed of the unmanned equipment, the area distance between the unmanned equipment and the unassigned area, and the corresponding relationship; The search and rescue priorities of the unassigned areas corresponding to each unmanned equipment are sorted in descending order to obtain a regional search and rescue priority list of each unmanned equipment.

5. The method according to claim 1, characterized in that The step of determining the regional search and rescue priority list of each unmanned equipment based on the current mission planning strategy and the target discovery probability of the grid sub-area includes: When the current mission planning strategy is a mission planning strategy based on a competition mechanism, obtaining a corresponding relationship between the target discovery probability, the area distance, the speed of the unmanned equipment and the search and rescue priority; Determine the search and rescue priority of the grid sub-area corresponding to each unmanned equipment based on the target discovery probability of the grid sub-area, the speed of the unmanned equipment, the area distance between the unmanned equipment and the grid sub-area, and the corresponding relationship; The search and rescue priorities of the grid sub-areas corresponding to each unmanned equipment are sorted in descending order to obtain a regional search and rescue priority list of each unmanned equipment.

6. The method according to claim 1, characterized in that The step of determining the regional search and rescue priority list of each unmanned equipment based on the current mission planning strategy and the target discovery probability of the grid sub-area includes: When the current task planning strategy is a task planning strategy based on probability sorting, the target discovery probability of the unassigned area in the grid sub-area is used as the search and rescue priority of the corresponding unassigned area; The search and rescue priorities of the unassigned areas corresponding to each unmanned equipment are sorted in descending order to obtain a regional search and rescue priority list of each unmanned equipment.

7. The method according to any one of claims 1 to 6, characterized in that The steps of allocating tasks based on the regional search and rescue priority list, scene adaptability and execution cost of each unmanned equipment, and determining the target search and rescue area, execution sequence and search and rescue path of each unmanned equipment include: Based on the regional search and rescue priority list, mapping the grid sub-areas into a task set, and determining the weight of each task in the task set; Constructing a task allocation variable, and determining a comprehensive benefit function of the unmanned cluster based on the task allocation variable, the weight of the task, the scene adaptability, the execution cost, and the balance coefficient; Based on the comprehensive benefit function and the corresponding constraints, the optimal allocation strategy is solved to obtain the target search and rescue area, execution sequence and search and rescue path of each unmanned equipment.

8. An unmanned swarm search and rescue mission planning system, characterized in that: The system comprises: The scene recognition module is used to analyze the multi-source distress signals in the search and rescue area and determine the accident point information; A probability calculation module, used for dynamically updating the target discovery probability of each grid sub-area in the area to be searched and rescued based on the environmental feature information and target movement feature information of the area to be searched and rescued; A strategy selection module, used to determine the current mission planning strategy based on the number of types of unmanned equipment in the unmanned cluster and the accident point information; A mission planning module, configured to determine a regional search and rescue priority list for each unmanned equipment based on the current mission planning strategy and the target discovery probability of the grid sub-area; The task planning module is also used to allocate tasks based on the regional search and rescue priority list, scene adaptability and execution cost of each unmanned equipment, and determine the target search and rescue area, execution sequence and search and rescue path of each unmanned equipment.

9. An unmanned swarm search and rescue mission planning device, characterized in that: The device comprises: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the computer program is configured to implement the steps of the unmanned swarm search and rescue mission planning method as described in any one of claims 1 to 7.

10. A storage medium, characterized in that: The storage medium is a computer-readable storage medium, and a computer program is stored on the storage medium. When the computer program is executed by a processor, the steps of the unmanned cluster search and rescue mission planning method as described in any one of claims 1 to 7 are implemented.

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