Unmanned system coordination control method, system and device based on particle swarm optimization algorithm, medium and product

Abstract

The invention discloses an unmanned system coordination control method, system and device based on a particle swarm optimization algorithm, a medium and a product, and relates to the technical field of unmanned systems.The method comprises the steps that an overall task of multiple unmanned systems is decomposed into multiple subtasks according to the initial state of a task target, determining a coupling relationship between the target function of each sub-task and the unmanned system; establishing a multi-subproblem coupling optimization model based on the coupling relationship between the target function of each subtask and the unmanned system; environment information is collected in real time, and the real-time state of the task target is determined; according to the environment information and the real-time state of the task target, the multi-subproblem coupling optimization model is updated in real time; solving the updated multi-subproblem coupling optimization model by adopting a particle swarm optimization algorithm to obtain a real-time task planning scheme; and performing coordination control on the plurality of unmanned systems based on the real-time task planning scheme. According to the invention, the coordination control capability and the task execution efficiency of the unmanned system are improved.

Description

Technical Field

[0001] The present application relates to the technical field of unmanned systems, and in particular to an unmanned system coordinated control method, system, equipment, medium and product based on a particle swarm optimization algorithm. Background Art

[0002] With the increasing application of unmanned systems (such as drones, unmanned vehicles and unmanned ships) in military, industrial, agricultural and disaster relief fields, how to effectively achieve coordinated control of unmanned systems has become a key research direction. The coordinated control of unmanned systems involves a variety of technologies, including path planning, task allocation, communication coordination and energy management.

[0003] At present, there are some traditional methods for realizing coordinated control of unmanned systems, such as centralized control-based scheduling algorithms, distributed control algorithms, and multi-agent coordinated control methods based on game theory. However, these methods have certain limitations when facing complex and changeable practical application scenarios. For example, the centralized control method requires a central control unit to uniformly schedule and control all unmanned systems. Although this method works well in small-scale unmanned systems, in large and widely distributed unmanned systems, its computational workload and communication burden are too large, which can easily become a bottleneck of the system. In addition, when the central control unit fails, the entire system will face the risk of paralysis. The distributed control method achieves the completion of the overall task through mutual communication and collaboration between multiple unmanned systems. Although this method is more scalable and robust in theory, it faces huge challenges in practical applications due to communication delays, unreliability, and dynamically changing environments between unmanned systems. Summary of the invention

[0004] The purpose of this application is to provide an unmanned system coordinated control method, system, equipment, medium and product based on a particle swarm optimization algorithm, which improves the coordinated control capability and task execution efficiency of the unmanned system by means of a multi-subproblem coupling optimization model updated in real time and solving it using a particle swarm optimization algorithm.

[0005] To achieve the above objectives, this application provides the following solutions:

[0006] In a first aspect, the present application provides an unmanned system coordinated control method based on a particle swarm optimization algorithm, comprising:

[0007] Decomposing the overall task of the multiple unmanned systems into multiple subtasks according to the initial state of the task objectives, and determining the coupling relationship between the objective function of each subtask and the unmanned system; the multiple subtasks include path planning and resource allocation;

[0008] A multi-sub-problem coupling optimization model is established based on the coupling relationship between the objective function of each sub-task and the unmanned system;

[0009] Collect environmental information in real time and determine the real-time status of mission objectives;

[0010] According to the real-time status of the environmental information and the task objectives, the multi-subproblem coupling optimization model is updated in real time;

[0011] The particle swarm optimization algorithm is used to solve the updated multi-subproblem coupling optimization model to obtain a real-time task planning scheme; the task planning scheme includes the path planning results and resource allocation results of each unmanned system;

[0012] Based on the real-time mission planning scheme, multiple unmanned systems are coordinated and controlled.

[0013] In a second aspect, the present application provides an unmanned system coordination control system based on a particle swarm optimization algorithm, comprising:

[0014] A task decomposition module is used to decompose the overall task of multiple unmanned systems into multiple subtasks according to the initial state of the task objectives, and determine the coupling relationship between the objective function of each subtask and the unmanned system;

[0015] A multi-sub-problem coupling optimization model establishment module, connected to the task decomposition module, is used to establish a multi-sub-problem coupling optimization model based on the coupling relationship between the objective function of each sub-task and the unmanned system;

[0016] The information collection and target determination module is used to collect environmental information in real time and determine the real-time status of the mission target;

[0017] A multi-subproblem coupling optimization model updating module is connected to the information collection and target determination module and the multi-subproblem coupling optimization model to establish a module connection, and is used to update the multi-subproblem coupling optimization model in real time according to the real-time status of the environmental information and the task target;

[0018] A task planning scheme acquisition module is connected to the multi-subproblem coupling optimization model update module and is used to solve the updated multi-subproblem coupling optimization model using a particle swarm optimization algorithm to obtain a real-time task planning scheme; the task planning scheme includes a path planning result and a resource allocation result for each unmanned system;

[0019] The collaborative control module is connected to the mission planning scheme acquisition module and is used to coordinate and control multiple unmanned systems based on the real-time mission planning scheme.

[0020] In a third aspect, the present application provides a computer device, comprising: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the above-mentioned unmanned system coordinated control method based on the particle swarm optimization algorithm.

[0021] In a fourth aspect, the present application provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the above-mentioned unmanned system coordinated control method based on the particle swarm optimization algorithm.

[0022] In a fifth aspect, the present application provides a computer program product, including a computer program, which, when executed by a processor, implements the above-mentioned unmanned system coordinated control method based on the particle swarm optimization algorithm.

[0023] According to the specific embodiments provided in this application, this application has the following technical effects:

[0024] The present application provides an unmanned system coordinated control method, system, equipment, medium and product based on a particle swarm optimization algorithm, which decomposes the overall task of multiple unmanned systems into multiple subtasks according to the initial state of the task objectives; the overall task decomposition method can ensure that the unmanned system can efficiently perform parallel processing when facing complex tasks. The multi-subproblem coupling optimization model is updated in real time through the real-time collection of environmental information and the real-time status of the task objectives, and the environmental factors and dynamic task objectives are considered to improve the reliability of the unmanned system in executing tasks. By using the particle swarm optimization algorithm to solve the updated multi-subproblem coupling optimization model, the efficiency of task planning is greatly improved, and the multi-subproblem coupling optimization model is combined to achieve efficient collaborative control of the unmanned system and improve the efficiency of task execution. BRIEF DESCRIPTION OF THE DRAWINGS

[0025] 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 will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0026] Figure 1 This is a flow chart of an unmanned system coordination control method based on a particle swarm optimization algorithm in one embodiment of the present application;

[0027] Figure 2 A schematic diagram of functional modules of an unmanned system coordination control system based on a particle swarm optimization algorithm provided in one embodiment of the present application;

[0028] Figure 3A schematic diagram of the structure of a computer device provided in one embodiment of the present application. DETAILED DESCRIPTION

[0029] The following will be combined with the drawings in the embodiments of the present application to clearly and completely describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0030] In order to make the purpose, features and advantages of the present application more obvious and easy to understand, the present application is further described in detail below in conjunction with the accompanying drawings and specific implementation methods.

[0031] In an exemplary embodiment, Figure 1 As shown, a method for coordinated control of an unmanned system based on a particle swarm optimization algorithm is provided, comprising the following steps 101 to 106. Among them:

[0032] Step 101, decompose the overall task of multiple unmanned systems into multiple subtasks according to the initial state of the task objectives, and determine the objective function of each subtask and the coupling relationship between the unmanned systems; the multiple subtasks include path planning and resource allocation.

[0033] Step 102: Establish a multi-sub-problem coupling optimization model based on the coupling relationship between the objective function of each sub-task and the unmanned system.

[0034] Step 103, collect environmental information in real time and determine the real-time status of the task target. This application uses multi-sensor data fusion technology to collect environmental information in real time and uses advanced machine learning algorithms to determine the real-time status of the task target. The machine learning algorithms include convolutional neural networks, recurrent neural networks, and reinforcement learning.

[0035] Step 104: updating the multi-subproblem coupling optimization model in real time according to the real-time status of the environmental information and the task target.

[0036] Step 105, using a particle swarm optimization algorithm to solve the updated multi-subproblem coupling optimization model to obtain a real-time mission planning solution; the mission planning solution includes a path planning result and a resource allocation result for each unmanned system.

[0037] Step 106: Coordinate and control the multiple unmanned systems based on the real-time mission planning scheme. Specifically, based on the real-time mission planning scheme, a three-stage variable neighborhood search algorithm is used to coordinate and control the multiple unmanned systems.

[0038] In another exemplary embodiment of the present application, a subtask is represented as a subproblem.

[0039] The multi-subproblem coupling optimization model is expressed as:

[0040] Among them, f i (x i ) is the objective function of the ith subproblem xi, i is the subproblem number, N is the total number of subproblems, g(s a ,s b ) is an unmanned system a and unmanned systems b The coupling cost between ab For the ath unmanned system s a and the bth unmanned system sb, R is the total number of unmanned systems, and a and b are the serial numbers of the unmanned systems.

[0041] For example: The objective function of path planning is: f i =αL i +βO i +γE i .

[0042] Constraints: L i ≤L max ,min(d k )≥d min ,E i ≤E max .

[0043] Among them, α, β, and γ are preset weight coefficients, α+β+γ=1, and the preset weight coefficients are used to balance the optimization priority of path length, obstacle avoidance cost, and energy consumption. i is the path length, O i is the obstacle avoidance cost, E i is the energy consumption, L max is the upper limit of the path length, d min is the safety distance threshold, E max is the upper limit of energy, min(d k ) is the minimum distance to obstacles.

[0044] The objective function of resource allocation is:

[0045] Constraints:

[0046] Among them, Q ir represents the demand for resource r by subproblem i, A ir represents the amount of resources actually allocated to subproblem i, T dis the latest completion time of the task, P r is the total upper limit of resource r, T i is the actual completion time of subproblem i.

[0047] In another exemplary embodiment of the present application, step 104 specifically includes:

[0048] The parameters of the objective function of each subtask are adjusted according to the environmental information; the environmental information includes terrain, climate, obstacles, etc.

[0049] For example, the influence of the terrain slope in the environmental information on the objective function of path planning is as follows: the greater the terrain slope s (unit: percentage), the greater the energy consumption weight coefficient γ increases proportionally: Among them, the default γ 0 =0.3,γ 0 is the initial weight coefficient of energy consumption. When the slope s of the terrain is 30%, γ=0.39, and the bottom slope path is preferred to reduce energy consumption.

[0050] The influence of the density of obstacles in the environmental information on the objective function of path planning is as follows: The higher the density of obstacles ρ (unit: pieces / square meter), the more linearly the weight coefficient β of the obstacle avoidance cost increases: β = β 0 +0.1ρ. Among them, the default β 0 =0.4,β 0 is the initial weight coefficient of obstacle avoidance cost, when ρ = 5 / m 2 When β=0.9, the path planning will give priority to bypassing dense obstacle areas.

[0051] The influence of climate conditions (wind speed) in environmental information on the objective function of path planning is as follows: The greater the wind speed v (unit: m / s), the greater the energy consumption weight coefficient γ increases in a square relationship: γ = γ 0 +0.01ν 2 Among them, when v = 10m / s, γ = 0.4, the high wind resistance area is automatically avoided.

[0052] The impact of path curvature in environmental information on the objective function of path planning is as follows: the larger the path curvature c (unit: 1 / meter), the lower the path length weight coefficient α in inverse proportion: Among them, the default α 0 =0.5,α 0 is the initial weight coefficient of the path length, when c = 2m -1 , α≈0.167, which allows the path to be appropriately extended to reduce the difficulty of turning.

[0053] The coupling relationship between multiple unmanned systems is adjusted according to the real-time status of the mission target. The present application uses advanced machine learning algorithms to determine the real-time status of the mission target. The machine learning algorithms include convolutional neural networks, recurrent neural networks, and reinforcement learning.

[0054] Specifically, when the state of the mission target changes, if unmanned system a is closest to the changed mission target, path planning and resource allocation are prioritized for unmanned system a, and the coupling relationship between unmanned system a and other unmanned systems is adjusted.

[0055] In another exemplary embodiment of the present application, the unmanned system coordinated control method based on particle swarm optimization algorithm further includes:

[0056] The execution status of multiple subtasks is monitored in real time to obtain feedback data, the task planning scheme is dynamically adjusted according to the feedback data, and the multiple unmanned systems are re-coordinated and controlled according to the adjusted task planning scheme.

[0057] Specifically, the task execution status of the unmanned system is monitored in real time, including path deviation, task progress, resource consumption, etc. If an abnormality is detected in the system, such as sensor failure, communication interruption, etc., the emergency response mechanism will be triggered to adjust or reallocate the task plan. Any abnormality will dynamically adjust the priority, resource allocation and execution order of the task according to the existing task progress, resource status and environmental changes. For example, when the execution of an unmanned system is delayed, the execution order of other unmanned systems will be adjusted or resources will be reallocated to ensure the completion of the overall task. Specific adjustment methods include: (1) If an unmanned system cannot be executed due to a failure, it can be reallocated to other unmanned systems. (2) According to the current unmanned system progress and environmental conditions, the order and duration of the unmanned system execution are adjusted to ensure that the subtasks can be completed efficiently.

[0058] Based on real-time feedback, this application will dynamically adjust task planning and control strategies to adapt to changes in the environment and objectives and ensure the continuous advancement of the task; after the task is completed, this application will also analyze the entire task execution process, identify existing deficiencies and perform corresponding optimizations to improve the execution efficiency of subsequent tasks.

[0059] In another exemplary embodiment of the present application, the unmanned system coordination control method based on the particle swarm optimization algorithm also includes: optimizing the task planning scheme using a multi-objective optimization algorithm to obtain an optimized task planning scheme, and re-coordinating and controlling multiple unmanned systems according to the optimized task planning scheme.

[0060] The multi-objective optimization algorithm can be a particle swarm optimization algorithm. The particle swarm optimization algorithm has the ability of global search and local optimization by simulating the information interaction between individuals in the group, and can quickly converge to the optimal solution set, thereby providing a reliable scheduling solution for the overall mission planning of the unmanned system.

[0061] The particle swarm searches the entire solution space by updating the particle flight speed and position. The update rules of the particle flight speed V and position X are:

[0062] V n (t+1)=ωV n (t)+c 1 r 1 (t)(p i -X n (t))+c 2 r 2 (t)(p g -X n (t)).

[0063] Among them, t represents the number of iterations, ω is the inertia weight, which is fixed or linearly reduced, and c 1 and c 2 is the acceleration constant, usually between 0 and 2. The initial X and V are randomly generated. n (t+1) is the flying speed of particle n at the t+1th iteration, V n (t) is the flying speed of particle n at the tth iteration, X n (t) is the position of particle n at the tth iteration, r 1 (t) and r 2 (t) are two independent generating functions at the t-th iteration, and the generating functions generate random numbers between (0, 1), p i is the personal optimal position of particle n, p g is the global optimal position, p i and p g Determined by the fitness function.

[0064] This application decomposes the overall task of multiple unmanned systems into multiple subtasks, performs path planning and resource allocation for multiple unmanned systems based on each subtask, and re-plans and allocates paths for each unmanned system according to the real-time target state and abnormal conditions such as unmanned system failures and communication failures, adjusts the priority of the unmanned system, and performs priority path planning and resource allocation for individual unmanned systems to cope with real-time target states and abnormal conditions. Through the cooperation of the multi-subproblem coupling optimization model and the particle swarm optimization algorithm, the re-planning of paths and the reallocation of resources are completed efficiently, improving the efficiency of multiple unmanned systems in performing the overall task.

[0065] Based on the same inventive concept, the embodiment of the present application also provides an unmanned system coordinated control system based on a particle swarm optimization algorithm for implementing the unmanned system coordinated control method based on a particle swarm optimization algorithm involved above. The implementation scheme for solving the problem provided by the system is similar to the implementation scheme recorded in the above method, so the specific limitations in one or more unmanned system coordinated control system embodiments based on a particle swarm optimization algorithm provided below can be referred to the limitations of the unmanned system coordinated control method based on a particle swarm optimization algorithm above, and will not be repeated here.

[0066] In an exemplary embodiment, Figure 2 As shown, an unmanned system coordinated control system based on particle swarm optimization algorithm is provided, including:

[0067] The task decomposition module 201 is used to decompose the overall task of multiple unmanned systems into multiple subtasks according to the initial state of the task objectives, and determine the coupling relationship between the objective function of each subtask and the unmanned system.

[0068] The multi-sub-problem coupling optimization model establishing module 202 is connected to the task decomposition module 201 and is used to establish a multi-sub-problem coupling optimization model based on the coupling relationship between the objective function of each sub-task and the unmanned system.

[0069] The information collection and target determination module 203 is used to collect environmental information in real time and determine the real-time status of the task target.

[0070] The multi-subproblem coupling optimization model updating module 204 is connected to the information collection and target determination module 203 and the multi-subproblem coupling optimization model establishing module 202, and is used to update the multi-subproblem coupling optimization model in real time according to the real-time status of the environmental information and task objectives.

[0071] The task planning scheme acquisition module 205 is connected to the multi-subproblem coupling optimization model updating module 204, and is used to use the particle swarm optimization algorithm to solve the updated multi-subproblem coupling optimization model to obtain a real-time task planning scheme; the task planning scheme includes the path planning results and resource allocation results of each unmanned system.

[0072] The collaborative control module 206 is connected to the mission planning scheme acquisition module 205 and is used to coordinate and control multiple unmanned systems based on the real-time mission planning scheme.

[0073] In an exemplary embodiment, a computer device is provided. The computer device may be a server or a terminal. The internal structure diagram thereof may be as follows: Figure 3As shown. The computer device includes a processor, a memory, an input / output interface (Input / Output, referred to as I / O) and a communication interface. Among them, the processor, the memory and the input / output interface are connected through a system bus, and the communication interface is connected to the system bus through the input / output interface. Among them, the processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program and a database. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The database of the computer device is used to store the initial state of the task target. The input / output interface of the computer device is used to exchange information between the processor and an external device. The communication interface of the computer device is used to communicate with an external terminal through a network connection. When the computer program is executed by the processor, a coordinated control method for an unmanned system based on a particle swarm optimization algorithm is implemented.

[0074] Those skilled in the art will understand that Figure 3 The structure shown in the figure is only a block diagram of a part of the structure related to the solution of the present application, and does not constitute a limitation on the computer device to which the solution of the present application is applied. The specific computer device may include more or fewer components than those shown in the figure, or combine certain components, or have a different arrangement of components. In an exemplary embodiment, a computer device is provided, including a memory and a processor, wherein a computer program is stored in the memory, and when the processor executes the computer program, the above-mentioned unmanned system coordinated control method based on the particle swarm optimization algorithm is implemented.

[0075] In an exemplary embodiment, a computer-readable storage medium is provided, storing a computer program, which, when executed by a processor, implements the above-mentioned unmanned system coordinated control method based on the particle swarm optimization algorithm.

[0076] In an exemplary embodiment, a computer program product is provided, including a computer program, which implements the above-mentioned unmanned system coordinated control method based on particle swarm optimization algorithm when executed by a processor.

[0077] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, stored data, displayed data, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties, and the collection, use and processing of relevant data must comply with relevant regulations.

[0078] Those of ordinary skill in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by instructing the relevant hardware through a computer program, and the computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to the memory, database or other medium used in the embodiments provided in the present application can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM may be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM).

[0079] The database involved in each embodiment provided in this application may include at least one of a relational database and a non-relational database. The non-relational database may include a distributed database based on blockchain, etc., but is not limited thereto. The processor involved in each embodiment provided in this application may be a general-purpose processor, a central processing unit, a graphics processor, a digital signal processor, a programmable logic device, a data processing logic device based on quantum computing, etc., but is not limited thereto.

[0080] The technical features of the above embodiments may be combined arbitrarily. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0081] This article uses specific examples to illustrate the principles and implementation methods of this application. The description of the above embodiments is only used to help understand the method and core ideas of this application. At the same time, for those skilled in the art, according to the ideas of this application, there will be changes in the specific implementation methods and application scope. In summary, the content of this specification should not be understood as limiting this application.

Claims

1. A coordinated control method for an unmanned system based on a particle swarm optimization algorithm, characterized in that: The unmanned system coordinated control method based on particle swarm optimization algorithm includes: Decomposing the overall task of the multiple unmanned systems into multiple subtasks according to the initial state of the task objectives, and determining the coupling relationship between the objective function of each subtask and the unmanned system; the multiple subtasks include path planning and resource allocation; A multi-sub-problem coupling optimization model is established based on the coupling relationship between the objective function of each sub-task and the unmanned system; Collect environmental information in real time and determine the real-time status of mission objectives; According to the real-time status of the environmental information and the task objectives, the multi-subproblem coupling optimization model is updated in real time; The particle swarm optimization algorithm is used to solve the updated multi-subproblem coupling optimization model to obtain a real-time task planning scheme; the task planning scheme includes the path planning results and resource allocation results of each unmanned system; Based on the real-time mission planning scheme, multiple unmanned systems are coordinated and controlled.

2. The unmanned system coordinated control method based on particle swarm optimization algorithm according to claim 1 is characterized in that: A subtask is represented as a subproblem; The multi-subproblem coupling optimization model is expressed as: Among them, f i (x i ) is the objective function of the ith subproblem xi, i is the subproblem number, N is the total number of subproblems, g(s a ,s b ) is an unmanned system a and unmanned systems b The coupling cost between ab For the ath unmanned system s a and the bth unmanned system sb, R is the total number of unmanned systems, and a and b are the serial numbers of the unmanned systems.

3. The unmanned system coordinated control method based on particle swarm optimization algorithm according to claim 1 is characterized in that: According to the real-time status of the environmental information and the task objectives, the multi-subproblem coupling optimization model is updated in real time, specifically including: Adjusting the parameters of the objective function of each subtask according to the environmental information; Adjust the coupling relationship between multiple unmanned systems according to the real-time status of the mission objectives.

4. The unmanned system coordinated control method based on particle swarm optimization algorithm according to claim 1 is characterized in that: The unmanned system coordinated control method based on particle swarm optimization algorithm also includes: The execution status of multiple subtasks is monitored in real time to obtain feedback data, the task planning scheme is dynamically adjusted according to the feedback data, and the multiple unmanned systems are re-coordinated and controlled according to the adjusted task planning scheme.

5. The unmanned system coordinated control method based on particle swarm optimization algorithm according to claim 1 is characterized in that: The unmanned system coordinated control method based on particle swarm optimization algorithm also includes: The mission planning scheme is optimized by using a multi-objective optimization algorithm to obtain an optimized mission planning scheme, and multiple unmanned systems are re-coordinated and controlled according to the optimized mission planning scheme.

6. The unmanned system coordinated control method based on particle swarm optimization algorithm according to claim 1 is characterized in that: Coordinated control of multiple unmanned systems based on the real-time mission planning scheme specifically includes: based on the real-time mission planning scheme, using a three-stage variable neighborhood search algorithm to coordinate control of multiple unmanned systems.

7. An unmanned system coordination control system based on particle swarm optimization algorithm, applying the unmanned system coordination control method based on particle swarm optimization algorithm according to any one of claims 1 to 6, characterized in that: The unmanned system coordination control system based on particle swarm optimization algorithm includes: A task decomposition module is used to decompose the overall task of multiple unmanned systems into multiple subtasks according to the initial state of the task objectives, and determine the coupling relationship between the objective function of each subtask and the unmanned system; A multi-sub-problem coupling optimization model establishment module, connected to the task decomposition module, is used to establish a multi-sub-problem coupling optimization model based on the coupling relationship between the objective function of each sub-task and the unmanned system; The information collection and target determination module is used to collect environmental information in real time and determine the real-time status of the mission target; A multi-subproblem coupling optimization model updating module is connected to the information collection and target determination module and the multi-subproblem coupling optimization model to establish a module connection, and is used to update the multi-subproblem coupling optimization model in real time according to the real-time status of the environmental information and the task target; A task planning scheme acquisition module is connected to the multi-subproblem coupling optimization model update module and is used to solve the updated multi-subproblem coupling optimization model using a particle swarm optimization algorithm to obtain a real-time task planning scheme; the task planning scheme includes a path planning result and a resource allocation result for each unmanned system; The collaborative control module is connected to the mission planning scheme acquisition module and is used to coordinate and control multiple unmanned systems based on the real-time mission planning scheme.

8. A computer device comprising: A memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the unmanned system coordinated control method based on the particle swarm optimization algorithm according to any one of claims 1 to 6.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the unmanned system coordinated control method based on the particle swarm optimization algorithm described in any one of claims 1 to 6 is implemented.

10. A computer program product, comprising a computer program, characterized in that When the computer program is executed by a processor, the unmanned system coordinated control method based on the particle swarm optimization algorithm described in any one of claims 1 to 6 is implemented.

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