Distributed collaborative search task planning method and device
By dividing the target sea area into multiple areas in a distributed combat environment and generating bidding invitation applications, the problem of inefficient task allocation of search platforms in the existing technology is solved, and efficient target search and combat efficiency are achieved.
Patent Information
- Application Number
- CN202510039924.0
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-01-10
- Publication Date
- 2025-05-09
AI Technical Summary
The existing technology is difficult to achieve reasonable search platform task allocation in a distributed combat environment, resulting in low target search probability and accuracy.
By dividing the target sea area into multiple areas, each area corresponds to a search task, generating an auction invitation application and sending it to multiple search platforms, each platform can calculate the revenue function value according to its own situation and initiate an auction application, and determine the target search platform for each search task.
Reasonable dynamic allocation of tasks under multi-resource constraints has been achieved, target search probability and accuracy have been improved, the best search solution for each platform has been formed, and the overall combat effectiveness has been improved.
Smart Images
Figure CN119962892A_ABST
Abstract
Description
Technical Field
[0001] The present application belongs to the technical field of task planning, and more specifically, to a distributed collaborative search task planning method and device. Background Art
[0002] With the advancement of informatization, the platform-centered independent search has shifted to the network-centered distributed collaborative search. Traditional planned collaboration is increasingly unable to meet the requirements of complex collaborative operations. In a distributed combat environment, planned collaboration often has the characteristics of poor anti-destruction, long communication time, complex task allocation algorithm, and low overall efficiency. Especially in a bandwidth-constrained environment, the efficiency of collaborative search task planning will directly affect the entire Observe-Orient-Decide-Act (OODA) closed-loop time. Therefore, in a distributed multi-platform combat scenario, facing search platforms equipped with a variety of sensors such as radar, infrared, sonar, optoelectronics, and electronic reconnaissance, considering the constraints of tasks, resources, and time, how to reasonably allocate tasks to these search platforms to improve the probability and accuracy of target search, form the best search plan for each platform, and thus improve the overall combat effectiveness is a problem that needs to be considered. Summary of the invention
[0003] In view of the defects of the prior art, the purpose of this application is to provide a distributed collaborative search task planning method and device, which aims to solve the problem of low target search probability and accuracy caused by the difficulty in reasonable task allocation to each search platform in the prior art.
[0004] To achieve the above objectives, in a first aspect, the present application provides a distributed collaborative search task planning method, comprising:
[0005] The target sea area is divided into several areas according to operational requirements, and each area corresponds to a search mission;
[0006] Generate bidding invitation applications based on each search task;
[0007] Send the bidding invitation application to several search platforms, so that each search platform calculates the profit function value obtained by executing each search task according to the bidding invitation application, and initiates the bidding application;
[0008] According to the auction applications and profit function values received from each search platform, the target search platform for executing each search task is determined.
[0009] In some embodiments, generating a bidding invitation application according to each search task includes:
[0010] Generate an auction invitation application based on the task attribute parameters of each search task including task time limit, task requirements and task sea area parameters.
[0011] In some embodiments, according to the self-revenue function value carried in the auction application initiated by each search platform, the target search platform for executing each search task is determined, including:
[0012] For any search task:
[0013] The search platform with the largest profit function value obtained by performing the search task is taken as the target search platform.
[0014] In some embodiments, determining the target search platform for executing each search task respectively according to the self-profit function value carried in the auction application initiated by each search platform received, further includes:
[0015] Under the condition that each search task must be executed by a search platform and each search platform executes at most one search task, the target search platform that executes each search task is determined when the total benefit function value reaches the maximum value. The total benefit function value is determined based on the sum of the benefit function values obtained by each search platform when executing the search tasks.
[0016] In some embodiments, the value of the self-benefit function is determined based on the weight coefficient corresponding to the urgency of the search task, the theoretical benefit value of the search task, the difference in matching with the search task, the task risk of executing the search task, the waiting time for executing the search task, and the task time consuming for executing the search task.
[0017] In some embodiments, the above method further includes:
[0018] Contracts are granted to target search platforms that respectively perform each search task, so that the target search platform signs the contract and performs the search task.
[0019] In a second aspect, the present application provides a distributed collaborative search task planning device, comprising:
[0020] The division module is used to divide the target sea area into several areas according to combat requirements, and each area corresponds to a search mission;
[0021] A generation module is used to generate bidding invitation applications according to each search task;
[0022] A sending module is used to send the bidding invitation application to several search platforms, so that each search platform calculates the profit function value obtained by executing each search task according to the bidding invitation application and initiates the bidding application;
[0023] The planning module determines the target search platform to execute each search task according to the bidding applications and benefit function values initiated by each search platform.
[0024] In a third aspect, the present application provides an electronic device comprising: at least one memory for storing programs; and at least one processor for executing the programs stored in the memory. When the programs stored in the memory are executed, the processor is used to execute the distributed collaborative search task planning method described in the first aspect or any some embodiments of the first aspect.
[0025] In a fourth aspect, the present application provides a computer-readable storage medium, which stores a computer program. When the computer program runs on a processor, the processor executes the distributed collaborative search task planning method described in the first aspect or any embodiments of the first aspect.
[0026] In a fifth aspect, the present application provides a computer program product, which, when executed on a processor, enables the processor to execute the distributed collaborative search task planning method described in the first aspect or any embodiments of the first aspect.
[0027] In general, the above technical solutions conceived by this application have the following beneficial effects compared with the prior art:
[0028] The distributed collaborative search task planning method and device provided in this application, the task initiator gives each search platform a high degree of autonomy by sending a bidding invitation application to each search platform, so that each search platform can calculate the benefit function value obtained by executing each search task according to its own situation, and initiate an auction application. The task initiator determines the target search platform that executes each search task respectively according to the auction application initiated by each search platform, realizes the reasonable dynamic allocation of tasks under multiple resource constraints, improves the probability and accuracy of target search, and finally completes the search task in a specific area, realizes multi-task collaborative search task planning in distributed scenarios, forms the best search plan for each platform, and thus improves the overall combat effectiveness. BRIEF DESCRIPTION OF THE DRAWINGS
[0029] Figure 1 This is one of the flow charts of the distributed collaborative search task planning method provided in the embodiment of the present application;
[0030] Figure 2 It is a schematic diagram of the command center and search platform search task allocation of the contract auction algorithm provided in the embodiment of the present application;
[0031] Figure 3 This is the second flow chart of the distributed collaborative search task planning method provided in the embodiment of the present application;
[0032] Figure 4 It is a structural diagram of a distributed collaborative search task planning device provided in an embodiment of the present application;
[0033] Figure 5 It is a schematic diagram of the structure of an electronic device provided in an embodiment of the present application. DETAILED DESCRIPTION
[0034] In order to make the purpose, technical solution and advantages of the present application more clearly understood, the present application is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and are not used to limit the present application.
[0035] The term "and / or" in this article is a description of the association relationship of associated objects, indicating that there can be three relationships. For example, A and / or B can represent: A exists alone, A and B exist at the same time, and B exists alone. The symbol " / " in this article indicates that the associated objects are in an or relationship, for example, A / B means A or B.
[0036] In the embodiments of the present application, words such as "exemplary" or "for example" are used to indicate examples, illustrations or descriptions. Any embodiment or design described as "exemplary" or "for example" in the embodiments of the present application should not be interpreted as being more preferred or more advantageous than other embodiments or designs. Specifically, the use of words such as "exemplary" or "for example" is intended to present related concepts in a specific way.
[0037] In the description of the embodiments of the present application, unless otherwise specified, "several" means two or more than two. For example, several search platforms refer to two or more search platforms, etc.
[0038] The embodiment of the present application provides a distributed collaborative search task planning method, which is suitable for distributed scenarios with low bandwidth, weak communication, and distributed collaborative search task planning methods with or without human collaboration. It can effectively improve the probability and accuracy of target search, thereby improving the overall combat effectiveness. Each search platform has a high degree of autonomy, initiates transactions, participates in transactions, and determines transaction success information according to its own situation. By pursuing the optimal local indicators of the subsystem, all overall systems will reach a sub-optimal state. Thereby, dynamic allocation of tasks under multiple resource constraints is realized, and search tasks within a specific area are finally completed, and multi-task collaborative search task planning is realized in distributed scenarios, providing decision support for decision makers when planning tasks for deployment. The embodiments of the present application are described below in conjunction with the drawings in the embodiments of the present application.
[0039] See also Figure 1 , an embodiment of the present application provides a distributed collaborative search task planning method, which may include: steps 110 to 140.
[0040] The target sea area is divided into several areas according to operational requirements, and each area corresponds to a search mission;
[0041] Generate bidding invitation applications based on each search task;
[0042] Send the bidding invitation application to several search platforms, so that each search platform calculates the profit function value obtained by executing each search task according to the bidding invitation application, and initiates the bidding application;
[0043] The target search platforms for executing each search task are determined according to the profit function values carried in the bidding applications initiated by each search platform.
[0044] The distributed collaborative search task planning method provided in the embodiment of the present application is applicable to collaborative search task planning in a known area, and can assist decision makers in formulating multi-task collaborative search task planning schemes in distributed combat scenarios. It can also be extended and applied to the formulation and use of various military-civilian collaborative search task planning.
[0045] Please see further Figure 2 In the embodiment of the present application, the task initiator (command center) divides the target sea area into several areas according to the operational requirements, and each area corresponds to a search task. Among them, the target sea area is a specific sea area where a search task is required. In the embodiment of the present application, the initiator can be a surface ship.
[0046] The task initiator publishes the search task and packages the search task into a bidding invitation application.
[0047] Furthermore, in some embodiments, generating a bidding invitation application according to each search task may include:
[0048] Generate an auction invitation application based on the task attribute parameters of each search task including task time limit, task requirements and task sea area parameters.
[0049] In the embodiment of the present application, the bidding invitation application mainly includes task attribute parameters, which include the task theoretical benefit value of the search task, the search capability required for the search task, the strike capability required for the search task, the destruction capability required for the search task, the number of weapons required for the search task, the threat level of the search task, the task sea area coordinates of the search task, and the task time of the search task. The command center packages the above task attribute parameters and publishes them to other search platforms to initiate a bidding invitation application.
[0050] Take the nth search task as an example, its task attribute parameter Task n Specifically:
[0051]
[0052] in, represents the theoretical benefit value of the nth search task, represents the search capability required for the nth search task, represents the strike capability required for the nth search mission, represents the destructive capability required for the nth search mission, represents the number of weapons required for the nth search mission, represents the threat level of the nth search task, Indicates the coordinates of the mission sea area for the nth search mission, Indicates the task duration of the nth search task.
[0053] Each search platform that receives the bidding invitation application calculates the profit function value and participates in the bidding (auction). Each search platform that receives the bidding invitation application determines whether it meets the constraints (including distance constraints and time constraints), calculates the profit function value obtained by executing each search task if the constraints are met, and initiates a bidding application to the task initiator, and sends the profit function value back to the auction initiator.
[0054] The task initiator determines the target search platform to execute each search task based on the auction applications initiated by each search platform and the profit function value obtained by each search platform from executing each search task.
[0055] The distributed collaborative search task planning method provided in the embodiment of the present application is that the task initiator gives each search platform a high degree of autonomy by sending a bidding invitation application to each search platform, so that each search platform can calculate the profit function value obtained by executing each search task according to its own situation, and initiate a bidding application. The task initiator determines the target search platform that executes each search task respectively according to the bidding application initiated by each search platform received, realizes the reasonable dynamic allocation of tasks under multiple resource constraints, improves the probability and accuracy of target search, and finally completes the search task in a specific area, realizes multi-task collaborative search task planning in a distributed scenario, forms the best search plan for each platform, and thus improves the overall combat effectiveness.
[0056] Furthermore, in some embodiments, the value of the self-benefit function is determined based on the weight coefficient corresponding to the urgency of the search task, the theoretical benefit value of the search task, the degree of difference in matching with the search task, the task risk of executing the search task, the waiting time for executing the search task, and the task time for executing the search task.
[0057] In the embodiment of the present application, the revenue of each search platform (taking the Unmanned Surface Vessel (USV) as an example) performing the search mission mainly considers the capability matching principle, the minimum combat cost principle and the capability balance principle, so an auction revenue model will be established from these three aspects.
[0058] Assume that the parameters of the i-th USV are i as follows:
[0059] USV i = {V i Value ,V i scout ,V i attack ,V i eval ,V i arm ,V i speed ,V i coordinate}
[0060] Among them, V i Value represents the value of the i-th USV, V i scout represents the search capability of the i-th USV, V i attack represents the strike capability of the ith USV, V i eval represents the destructive capability of the ith USV, V i arm represents the number of weapons of the i-th USV, V i speed represents the driving speed of the i-th USV, V i coordinate Indicates the current coordinate position of the i-th USV.
[0061] The profit function of the i-th USV performing the n-th search task for:
[0062]
[0063] Among them, k1~k5 are weight coefficients defined by the command center according to the urgency of the search task, Value n (i) is the gross profit of the i-th USV performing the n-th search task, i.e., the theoretical profit value of the search task, Match n (i) is the matching difference between the i-th USV and the n-th search task, Risk n(i) Task risk for the i-th USV to perform the n-th search task, Wait n (i) is the waiting time for the i-th USV to perform the n-th search task, The task duration of performing the nth search task for the i-th USV.
[0064] Among them, Match n (i) can be calculated by the following formula:
[0065]
[0066] In the formula, mat n (i) is the number of attributes that the i-th USV attribute satisfies the n-th search task requirement, defined as:
[0067]
[0068] function is an indicative function.
[0069] The mission risk of the i-th USV performing the n-th search mission is Risk n (i) It is determined by the product of the value of the USV and the threat level of the nth search task, as shown in the following formula:
[0070]
[0071] Waiting time for the i-th USV to perform the n-th search task n (i) It is determined by the task sequence list of the USV. If the i-th USV is assigned to the n-th search task, the USV needs to put the n-th search task into its own task sequence list. The task time at the front of the task sequence list is the waiting time for the i-th USV to execute the n-th search task, as shown in the following formula:
[0072]
[0073] Where N i represents the task sequence list of the i-th USV itself, It represents the task time of task j in the task sequence list of the i-th USV itself.
[0074] The time taken by the i-th USV to perform the n-th search task is the time it takes for the USV to reach the mission area And the time required to complete the nth search task sum:
[0075]
[0076] In the formula, It represents the distance between the current coordinate position of the i-th USV and the area corresponding to the n-th search task.
[0077] Furthermore, in some embodiments, the above method may further include:
[0078] Contracts are granted to target search platforms that respectively perform each search task, so that the target search platform signs the contract and performs the search task.
[0079] In an embodiment of the present application, after the command center receives the bidding applications from all search platforms, it determines the search platform (i.e., the target search platform) to which the contract is awarded based on the bidding applications and the profit function values obtained by each search platform performing each search task.
[0080] The winning target search platform signs the contract and executes the search task. The winning target search platform receives the winning bid information from the command center, adds the search task to its own task manager, updates its own task sequence list, and continues to execute the relevant task contracts according to the task order in the task sequence list.
[0081] Furthermore, in some embodiments, determining the target search platform for executing each search task respectively according to the self-revenue function value carried in the received bidding application initiated by each search platform may include:
[0082] For any search task:
[0083] The search platform with the largest profit function value obtained by performing the search task is taken as the target search platform.
[0084] In the embodiment of the present application, the command center receives bidding applications initiated by various search platforms. The command center has two schemes to determine the target search platform to which the contract is awarded: one is to determine the target search platform to execute each search task in turn based on a greedy algorithm; the other is to determine all target search platforms to execute each search task at one time based on the idea of global optimization.
[0085] Assume that there are N search tasks in the search task list Task, namely Task1~Task N :
[0086] Task = {Task1,Task2,...,Task N}
[0087] There are M USVs participating in the auction, namely USV1~USV M :
[0088] USV={USV1,USV2,...,USV M}
[0089] Assume that N≤M.
[0090] Based on the greedy algorithm, the USVs that perform each search task are determined in turn:
[0091] First, select the USV with the largest profit function value for the first search task Task1, that is, the one that satisfies The USVs of i=1,.2,..,M, delete Task1 from the task list, delete the USV numbered g(1) from the USV list, and then use the same method to determine the USVs for each search task. The profit function value obtained by performing the first search task for the i-th USV.
[0092] Furthermore, in some embodiments, determining the target search platform for executing each search task respectively according to the self-profit function value carried in the received bidding application initiated by each search platform may also include:
[0093] Under the condition that each search task must be executed by a search platform and each search platform executes at most one search task, the target search platform that executes each search task is determined when the total benefit function value reaches the maximum value. The total benefit function value is determined based on the sum of the benefit function values obtained by each search platform when executing the search tasks.
[0094] In the embodiment of the present application, all target search platforms for executing each search task are determined at one time based on the idea of global optimization:
[0095] The total profit function value G determined by the sum of the profit function values obtained by each search platform performing the search task is used as the objective function. The constraints include that each search task must be performed by a search platform (such as USV), and each USV performs at most one search task, that is, each USV can win at most one search task.
[0096]
[0097] In the formula, A switch variable indicating whether the i-th USV wins the bid for the n-th search task, It means that the i-th USV won the bid for the n-th search task. It means that the i-th USV did not win the bid for the n-th search task.
[0098] In view of the characteristics of distributed collaborative search such as long communication time, complex task allocation algorithm, and low overall efficiency, this application comprehensively considers multiple constraints such as tasks, resources, and time, and utilizes the autonomy of the distributed search platform. This application proposes a distributed collaborative search task planning method, which realizes distributed collaborative search task planning suitable for distributed scenarios with low bandwidth, weak communication, and unmanned collaboration, and can effectively improve the target search probability and accuracy, thereby improving the overall combat effectiveness.
[0099] Please see further Figure 3 , the distributed collaborative search task planning method comprises:
[0100] According to operational requirements, the mission initiator (command center) divides the target sea area into regions, and each region corresponds to a search mission.
[0101] The task initiator (command center) publishes the search task. The command center packages the search task and generates an auction invitation application.
[0102] Each search platform that receives the auction invitation calculates the search revenue function and participates in the bidding. Each search platform that receives the auction task determines whether it meets the constraints (including distance constraints and time constraints), calculates the revenue function value obtained by executing each search task when the constraints are met, and sends the revenue function value back to the auction initiator.
[0103] The command center receives bidding applications from all search platforms and determines the target search platform to which the contract will be awarded based on the revenue function value of each search platform.
[0104] The winning target search platform signs a contract and carries out the search mission.
[0105] In the distributed collaborative search task planning method provided in the embodiment of the present application, each search platform has a high degree of autonomy, and initiates transactions, participates in transactions, and determines transaction success information according to its own situation. By pursuing the optimal local indicators of the subsystem, all overall systems will reach a sub-optimal state. This enables dynamic allocation of tasks under multiple resource constraints, and ultimately completes the search tasks within a specific area, realizes multi-task collaborative search task planning in distributed scenarios, improves the effectiveness of collaborative search task allocation, and provides decision support for decision makers when planning tasks.
[0106] The distributed collaborative search task planning device provided in the present application is described below. The distributed collaborative search task planning device described below and the distributed collaborative search task planning method described above can refer to each other.
[0107] See also Figure 4, an embodiment of the present application provides a distributed collaborative search task planning device, which may include: a division module 410, a generation module 420, a sending module 430 and a planning module 440.
[0108] A division module 410 is used to divide the target sea area into a number of areas according to combat requirements, each area corresponding to a search mission;
[0109] A generating module 420, for generating a bidding invitation application according to each search task;
[0110] The sending module 430 is used to send the bidding invitation application to several search platforms, so that each search platform calculates the profit function value obtained by executing each search task according to the bidding invitation application, and initiates the bidding application;
[0111] The planning module 440 determines the target search platform for executing each search task according to the received bidding applications and benefit function values initiated by each search platform.
[0112] In the distributed collaborative search task planning device provided in the embodiment of the present application, the task initiator gives each search platform a high degree of autonomy by sending a bidding invitation application to each search platform, so that each search platform can calculate the benefit function value obtained by executing each search task according to its own situation and initiate a bidding application. The task initiator determines the target search platform that executes each search task respectively according to the bidding application initiated by each search platform received, realizes the reasonable dynamic allocation of tasks under multiple resource constraints, improves the probability and accuracy of target search, and finally completes the search task in a specific area, realizes multi-task collaborative search task planning in a distributed scenario, forms the best search plan for each platform, and thus improves the overall combat effectiveness.
[0113] It can be understood that the detailed functional implementation of each of the above-mentioned units / modules can be found in the introduction of the aforementioned method embodiment, and will not be repeated here.
[0114] It should be understood that the above-mentioned device is used to execute the method in the above-mentioned embodiment. The implementation principle and technical effect of the corresponding program module in the device are similar to those described in the above-mentioned method. The working process of the device can refer to the corresponding process in the above-mentioned method, which will not be repeated here.
[0115] Based on the method in the above embodiment, the present application embodiment provides an electronic device, see Figure 5The electronic device may include: a processor (Processor) 510, a communication interface (CommunicationsInterface) 520, a memory (Memory) 530 and a communication bus 540, wherein the processor 510, the communication interface 520, and the memory 530 communicate with each other through the communication bus 540. The processor 510 may call the logic instructions in the memory 530 to execute the method in the above embodiment.
[0116] In addition, the logic instructions in the above-mentioned memory 530 can be implemented in the form of software functional units and can be stored in a computer-readable storage medium when sold or used as an independent product. Based on this understanding, the technical solution of the present application, or the part that contributes to the prior art, or the part of the technical solution, can be embodied in the form of a software product, which is stored in a storage medium and includes a number of instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present application.
[0117] Based on the method in the above embodiment, an embodiment of the present application provides a computer-readable storage medium, which stores a computer program. When the computer program runs on a processor, the processor executes the method in the above embodiment.
[0118] Based on the method in the above embodiment, an embodiment of the present application provides a computer program product. When the computer program product runs on a processor, the processor executes the method in the above embodiment.
[0119] It is understandable that the processor in the embodiment of the present application may be a central processing unit (CPU), or other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field programmable gate arrays (FPGA) or other programmable logic devices, transistor logic devices, hardware components or any combination thereof. The general-purpose processor may be a microprocessor or any conventional processor.
[0120] The method steps in the embodiments of the present application can be implemented by hardware or by a processor executing software instructions. The software instructions can be composed of corresponding software modules, and the software modules can be stored in random access memory (RAM), flash memory, read-only memory (ROM), programmable read-only memory (PROM), erasable programmable read-only memory (EPROM), electrically erasable programmable read-only memory (EEPROM), registers, hard disks, mobile hard disks, CD-ROMs, or any other form of storage medium known in the art. An exemplary storage medium is coupled to a processor so that the processor can read information from the storage medium and write information to the storage medium. Of course, the storage medium can also be a component of the processor. The processor and the storage medium can be located in an ASIC.
[0121] In the above embodiments, it can be implemented in whole or in part by software, hardware, firmware or any combination thereof. When implemented using software, it can be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions. When the computer program instructions are loaded and executed on a computer, the process or function described in the embodiment of the present application is generated in whole or in part. The computer may be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions may be stored in a computer-readable storage medium or transmitted through the computer-readable storage medium. The computer instructions may be transmitted from a website site, a computer, a server or a data center to another website site, a computer, a server or a data center by wired (e.g., coaxial cable, optical fiber, digital subscriber line (DSL)) or wireless (e.g., infrared, wireless, microwave, etc.) means. The computer-readable storage medium may be any available medium that a computer can access or a data storage device such as a server or a data center that includes one or more available media integrated. The available medium may be a magnetic medium (e.g., a floppy disk, a hard disk, a tape), an optical medium (e.g., a DVD), or a semiconductor medium (e.g., a solid-state drive (SSD)), etc.
[0122] It should be understood that the various numerical numbers involved in the embodiments of the present application are only used for the convenience of description and are not used to limit the scope of the embodiments of the present application.
[0123] It will be easily understood by those skilled in the art that the above description is only a preferred embodiment of the present application and is not intended to limit the present application. Any modifications, equivalent substitutions and improvements made within the spirit and principles of the present application shall be included in the scope of protection of the present application.
Claims
1. A distributed collaborative search task planning method, characterized in that: include: The target sea area is divided into several areas according to operational requirements, and each area corresponds to a search mission; Generate an auction invitation application based on each of the search tasks; Sending the bidding invitation application to several search platforms, so that each search platform calculates the profit function value obtained by executing each search task according to the bidding invitation application, and initiates the bidding application; According to the received bidding applications initiated by each of the search platforms and the profit function value, a target search platform for executing each of the search tasks is determined.
2. The distributed collaborative search task planning method according to claim 1, characterized in that: Generating a bidding invitation application according to each of the search tasks includes: The bidding invitation application is generated according to the task attribute parameters of each of the search tasks including the task time limit, task requirements and task sea area parameters.
3. The distributed collaborative search task planning method according to claim 1, characterized in that: The step of determining the target search platforms for executing the search tasks respectively according to the self-profit function values carried in the bidding applications initiated by the search platforms received comprises: For any search task: The search platform with the largest benefit function value obtained by executing the search task is used as the target search platform.
4. The distributed collaborative search task planning method according to claim 1, characterized in that: The step of determining the target search platforms for executing the search tasks respectively according to the self-profit function values carried in the received bidding applications initiated by the search platforms further includes: Under the condition that each search task must be executed by a search platform and each search platform executes at most one search task, the target search platform that executes each search task is determined to achieve the maximum value of the total benefit function. The total benefit function value is determined based on the sum of the benefit function values obtained by each search platform when executing the search task.
5. The distributed collaborative search task planning method according to claim 1, characterized in that: The self-benefit function value is determined based on the weight coefficient corresponding to the urgency of the search task, the theoretical benefit value of the search task, the difference in matching with the search task, the task risk of executing the search task, the waiting time for executing the search task, and the task time consuming for executing the search task.
6. The distributed collaborative search task planning method according to any one of claims 1 to 5, characterized in that: The method further comprises: Contracts are granted to target search platforms that respectively execute the search tasks, so that the target search platforms sign the contracts and execute the search tasks.
7. A distributed collaborative search task planning device, characterized in that: include: The division module is used to divide the target sea area into several areas according to combat requirements, and each area corresponds to a search mission; A generation module, used to generate an auction invitation application according to each of the search tasks; A sending module, used to send the bidding invitation application to several search platforms, so that each search platform calculates the profit function value obtained by executing each search task according to the bidding invitation application, and initiates the bidding application; The planning module determines the target search platform for executing each of the search tasks according to the received bidding applications initiated by each of the search platforms and the profit function value.
8. An electronic device, characterized in that: include: at least one memory for storing a computer program; At least one processor is used to execute the program stored in the memory. When the program stored in the memory is executed, the processor is used to execute the distributed collaborative search task planning method as described in any one of claims 1-6.
9. A computer-readable storage medium storing a computer program, characterized in that: When the computer program runs on a processor, the processor executes the distributed collaborative search task planning method as described in any one of claims 1-6.
10. A computer program product, characterized in that When the computer program product runs on a processor, the processor executes the distributed collaborative search task planning method as described in any one of claims 1-6.