Unmanned aerial vehicle equipment resource optimal configuration method and system

Abstract

The invention relates to the technical field of unmanned aerial vehicles, in particular to an unmanned aerial vehicle equipment resource optimal configuration method and system. The method comprises the steps that an unmanned aerial vehicle equipment resource optimization configuration model is established based on an objective function and constraint conditions, and the objective function is the lowest transportation cost and / or the shortest transportation time from the resource supply point to the unmanned aerial vehicle equipment place when the equipment resources needed by the unmanned aerial vehicle are transported; the constraint conditions comprise a transportation time limit constraint, an equipment resource stock constraint and an equipment resource consumption constraint; and solving the unmanned aerial vehicle equipment resource optimal configuration model according to a particle swarm algorithm to obtain an optimal scheme of equipment resource configuration of the unmanned aerial vehicle. According to the method, the optimal scheme of unmanned aerial vehicle equipment resource allocation can be obtained, and the reliability is high.

Description

Technical Field

[0001] The present invention generally relates to the field of unmanned aerial vehicle technology, and more specifically, to a method and system for optimizing the configuration of unmanned aerial vehicle equipment resources. Background Art

[0002] Resource allocation is an important part of equipment maintenance and support. Equipment support resource scheduling is the basic basis for the implementation of equipment maintenance and support operations. It requires the overall arrangement of a large amount of resources such as funds, venues, equipment, and personnel, which plays a vital role in quickly repairing faulty equipment and restoring the combat effectiveness of the army. Combat drones have a large mission range and a high probability of damage. When combat drones are damaged or encounter other situations during the execution of their missions, it is necessary to reasonably allocate resources to quickly repair faulty equipment, providing strong support for drones to travel again and improve their combat capabilities.

[0003] However, there is a large gap in the field of UAV equipment resource allocation. The general method is to transport the equipment resources needed for the UAV based on experience or prioritize from the nearest resource supply point. This method does not always get the best results and is relatively random. Optimizing the UAV equipment resource allocation method is an important support for obtaining the optimal aviation material and spare parts supply plan. Therefore, conducting research on the optimization method of UAV equipment support resource allocation has very important theoretical and practical application value. Summary of the invention

[0004] In order to solve the problem of low reliability of resource configuration of the above-mentioned UAV equipment, the present invention provides solutions in the following aspects.

[0005] In a first aspect, the present invention provides a method for optimizing the configuration of equipment resources for a drone, which is applied to obtaining equipment resources required for a drone from multiple resource supply points, comprising: establishing a drone equipment resource optimization configuration model based on an objective function and constraints, wherein the objective function is the minimum transportation cost and / or the shortest transportation time for transporting the equipment resources required for the drone from the resource supply point to the drone equipment site, and the constraints include transportation time constraints, equipment resource inventory constraints, and equipment resource consumption constraints; solving the drone equipment resource optimization configuration model according to a particle swarm algorithm to obtain the optimal solution for the equipment resource configuration of the drone.

[0006] Furthermore, before establishing the UAV equipment resource optimization configuration model, it includes: determining the objective function according to the transportation time and transportation cost of the equipment resources required to transport the UAV from the resource supply point to the UAV equipment site.

[0007] Furthermore, the objective function is:

[0008] λ1min 0＜i＜mmax 0＜j＜n t ij +λ2min 0＜i＜m,0＜j＜n m ij l ij ;

[0009] In the formula, t ij represents the transportation time of transporting the jth equipment resource from the i-th resource supply point to the drone equipment site, m ij represents the transportation cost of transporting the jth equipment resource from the i-th resource supply point to the drone equipment site, l ij It represents the quantity of the jth equipment resource provided by the i-th resource supply point to the UAV, m represents the number of resource supply points, n represents the number of equipment resources, λ1 represents the weight of the transportation time, and λ2 represents the weight of the transportation cost.

[0010] Furthermore, the conditional constraints are:

[0011]

[0012] In the formula, t ij represents the transportation time of transporting the jth equipment resource from the i-th resource supply point to the drone equipment site, l ijmax It indicates the maximum amount of equipment resources of the jth type that the i-th resource supply point can provide, l ij represents the number of j-th equipment resources provided by the i-th resource supply point to the drone, n represents the number of equipment resources, m represents the number of resource supply points, and p j represents the consumption rate of the j-th equipment resource of the drone itself, v j It represents the quantity of the jth equipment resource required by the UAV, and T0 represents the maximum time within the transportation time limit.

[0013] Furthermore, the update of the velocity and position of the particles in the particle swarm algorithm is:

[0014]

[0015] In the formula, represents the velocity of particle j after n+1 updates, represents the position of particle j after n+1 updates, p jd represents the local optimal position of particle j, p gd represents the global optimal position of particle j, r1 and r2 are constants between 0 and 1, c1 represents the self-update constant of particle j, c2 represents the social update constant of particle j, and ω represents the weight coefficient that changes dynamically over time.

[0016] Furthermore, the update of ω is:

[0017]

[0018] Where k represents the current iteration number of the particle, T represents the maximum number of steps allowed for the particle in the dynamic update process, ω1 represents the weight factor determined by optimization, and ω0 represents the weight factor before optimization.

[0019] Furthermore, the update of ω is:

[0020]

[0021] Where k represents the current iteration number of the particle, ω1 represents the weight factor determined by optimization, ω0 represents the weight factor before optimization, f1 represents the optimal fitness value in the particle optimization process, f2 represents the average fitness value of the particle in the optimization process, and δ is a constant that characterizes the precocity of the particle.

[0022] Furthermore, the updates of c1 and c2 are as follows:

[0023]

[0024] In the formula, c 1max 、c 1min represents the maximum and minimum values ​​of c1 in the particle swarm optimization process, c 2max 、c 2min They respectively represent the maximum and minimum values ​​of c2 in the particle swarm optimization process, k represents the current iteration number of the particle, and f(k,c,L) represents the modulo operation function directly related to the update constant c and the oscillation period L.

[0025] In a second aspect, the present invention provides a drone equipment resource optimization configuration system, comprising a processor and a memory, wherein the memory stores computer program instructions, and when the computer program instructions are executed by the processor, a drone equipment resource optimization configuration method described in the first aspect is implemented.

[0026] The beneficial effect of the present invention is that the method of the present invention can obtain the optimal solution for the configuration of UAV equipment resources by solving the UAV equipment resource optimization configuration model established by the particle swarm algorithm, avoiding the problems of randomness and low reliability caused by experience. Furthermore, by optimizing the weight coefficient, self-update constant and social update constant of the particle swarm algorithm, the problem of the basic particle swarm algorithm falling into the local optimum and giving up the update when solving the optimal solution for equipment resources is avoided, thereby ensuring that the optimal solution for the configuration of equipment resources obtained is the global optimal solution, and improving the reliability and accuracy of obtaining the optimal solution for the configuration of UAV equipment resources. BRIEF DESCRIPTION OF THE DRAWINGS

[0027] By reading the following detailed description with reference to the accompanying drawings, the above and other objects, features and advantages of the exemplary embodiments of the present invention will become readily understood. In the accompanying drawings, several embodiments of the present invention are shown in an exemplary and non-limiting manner, and the same or corresponding reference numerals represent the same or corresponding parts, wherein:

[0028] Figure 1 is a flow chart schematically illustrating a method for optimizing configuration of UAV equipment resources according to an embodiment of the present invention;

[0029] Figure 2 is a curve diagram schematically showing the change of the optimal fitness value of particles in the optimization process of the basic particle swarm algorithm according to an embodiment of the present invention;

[0030] Figure 3 is a curve diagram schematically showing the change of the optimal fitness value of particles in the optimization process of the improved particle swarm algorithm according to an embodiment of the present invention;

[0031] Figure 4 It is a structural block diagram schematically showing a system for optimizing and configuring resources of unmanned aerial vehicle equipment according to an embodiment of the present invention. DETAILED DESCRIPTION

[0032] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative work are within the scope of protection of the present invention.

[0033] The specific embodiments of the present invention are described in detail below with reference to the accompanying drawings.

[0034] Figure 1 It is a flowchart schematically illustrating a method for optimizing configuration of UAV equipment resources according to an embodiment of the present invention.

[0035] In a first aspect, the present invention provides a method for optimizing the allocation of UAV equipment resources, such as Figure 1 As shown, the method of the present invention comprises:

[0036] S101. Establish a UAV equipment resource optimization configuration model based on the objective function and constraint conditions.

[0037] It is understandable that when a UAV is damaged or encounters other problems during the mission, it is necessary to transport spare parts from multiple resource supply stations to the UAV equipment site in a timely manner to repair the damage or for other purposes, so as to provide strong support for the UAV to be deployed again and improve its combat capability. Among them, how to select appropriate resource supply points from multiple resource supply points to provide equipment resources to the UAV is a problem that needs to be solved, and the objective function provides a clear direction for solving this problem. The constraints are restrictions on the solution to the problem, ensuring that the optimal solution obtained by solving the problem can meet specific requirements and is feasible.

[0038] In one embodiment, the objective function may be determined based on the transportation cost and / or transportation time of the equipment resources required to transport the drone from multiple resource supply points to the drone equipment location. In this embodiment, the objective function determined is:

[0039] λ1min 0＜i＜m max 0＜j＜n t ij +λ2min 0＜i＜m,0＜j＜n m ij l ij ;

[0040] Where, t ij represents the transportation time of transporting the jth equipment resource from the i-th resource supply point to the drone equipment site, m ij represents the transportation cost of transporting the jth equipment resource from the i-th resource supply point to the drone equipment site, l ij It represents the quantity of the jth equipment resource provided by the i-th resource supply point to the UAV, m represents the number of resource supply points, n represents the number of equipment resources, λ1 represents the weight of the transportation time, and λ2 represents the weight of the transportation cost.

[0041] Among them, min 0＜i＜m max 0＜j＜n t ij It means that the maximum value of the transportation time from the resource supply point to the drone equipment location is minimized.

[0042] For example, the equipment resources required for transporting drones include Plan A (supply point 1 (3 days), supply point 2 (2 days), supply point 3 (4 days)), Plan B (supply point 1 (3 days), supply point 2 (2 days), supply point 4 (5 days)). The maximum transportation time corresponding to each supply point in Plan A is 4 days, and the maximum transportation time corresponding to each supply point in Plan B is 5 days. The minimum value between 4 days and 5 days is 4 days, so min 0＜u<m max 0＜j＜n t ij=4, that is, both Plan A and Plan B can transport the equipment resources required for the drone, but compared with Plan A, Plan B has a shorter transportation time and is more in line with the requirement of the shortest transportation time.

[0043] In one embodiment, the value range of λ1 and λ2 is between 0 and 1, and those skilled in the art can flexibly set the values ​​of λ1 and λ2 according to actual needs. For example, if only the shortest transportation time is required, λ1 can be set to 1 and λ2 can be set to 0; if only the lowest transportation cost is required, λ1 can be set to 0 and λ2 can be set to 1; if the transportation cost is mainly considered and the transportation time is sufficient, λ1 can be set to 0.2 and λ2 can be set to 0.8; if the transportation time is mainly considered, λ1 can be set to 0.6 and λ2 can be set to 0.4, etc.

[0044] By flexibly setting the values ​​of λ1 and λ2, those skilled in the art can accurately set the objective function according to actual needs, thereby accurately obtaining the optimal solution for the resource configuration of UAV equipment that better meets actual needs.

[0045] Furthermore, in this embodiment, the constraint conditions may include transportation time limit constraint, equipment resource inventory constraint and equipment resource consumption constraint. Specifically, the conditional constraints are:

[0046]

[0047] In the formula, l ijmax represents the maximum number of j types of equipment resources that the i-th resource supply point can provide to the drone, n represents the number of equipment resources, m represents the number of resource supply points, and p j represents the consumption rate of the j-th equipment resource of the drone itself, v j It represents the quantity of the jth equipment resource required by the UAV, and T0 represents the maximum time within the transportation time limit.

[0048] By considering the usage (consumption rate) of the equipment resources themselves, the problem of insufficient equipment resources transported from the resource supply point and the need for re-transportation is avoided, thereby ensuring that the optimal solution for obtaining drone equipment resources is more feasible, more accurate and reliable.

[0049] By setting constraints according to actual needs, the optimal solution for UAV equipment resource allocation is ensured to be feasible and reliable.

[0050] It can be understood that the objective function and constraints constitute the optimal configuration model of UAV equipment resources. By establishing the optimal configuration model of UAV equipment resources, the randomness of UAV equipment resource configuration can be reduced, and by considering transportation time and transportation cost, the waste of resources can be avoided, while also improving the efficiency of transportation equipment resources.

[0051] S102: solving the UAV equipment resource optimization configuration model according to the particle swarm algorithm to obtain the optimal solution for the equipment resource configuration of the UAV.

[0052] The particle swarm optimization algorithm is an optimization algorithm based on swarm intelligence. It searches for the optimal solution through collaboration and information sharing between individuals (particles, representing a potential solution to the problem) in the swarm. Specifically, each particle independently searches for the optimal solution in the search space, records it as the current individual extreme value, and shares the individual extreme value with other particles in the entire particle swarm. Then, the best individual extreme value is found from the shared individual extreme values ​​as the current global optimal solution of the entire particle swarm. Finally, each particle updates its position and speed according to its individual extreme value and the global optimal individual extreme value to obtain the optimal configuration plan for equipment resources.

[0053] Specifically, in this embodiment, the position and velocity of the particle are updated as follows:

[0054]

[0055] In the formula, represents the velocity of particle j after n+1 updates, represents the position of particle j after n+1 updates, p jd represents the local optimal position of particle j, p gd represents the global optimal position of particle j, r1 and r2 are constants between 0 and 1, c1 represents the self-update constant of particle j (also called learning factor), c2 represents the social update constant of particle j (also called learning factor), and ω represents the weight coefficient that changes dynamically over time (also called inertia factor).

[0056] Among them, when ω is large, it indicates that the particle is more active at this time and can quickly obtain the optimal value; when ω is small, it indicates that the particle is updating slowly at this time and it takes a long time to obtain the optimal value.

[0057] In one embodiment, the present invention proposes a basic particle swarm algorithm. In the basic particle swarm algorithm, in order to ensure that the particle swarm algorithm can quickly find the optimal value, ω can be dynamically updated in the following way:

[0058]

[0059] Where k represents the current iteration number of the particle, T represents the maximum number of steps allowed for the particle in the dynamic update process, ω1 represents the weight factor determined by optimization, and ω0 represents the weight factor before optimization.

[0060] Through the dynamic update of ω, the optimization conclusion can be searched quickly, but the conclusion may be a local optimal conclusion rather than a global optimal conclusion. Therefore, in order to avoid particles from falling into the local optimum and giving up dynamic update and optimization, the present invention also provides an improved particle swarm algorithm. In the improved particle swarm algorithm, ω can be dynamically updated in the following way:

[0061]

[0062] Where k represents the current iteration number of the particle, ω1 represents the weight factor determined by optimization, ω0 represents the weight factor before optimization, f1 represents the optimal fitness value in the particle optimization process, f2 represents the average fitness value of the particle in the optimization process, δ is a constant that characterizes the precocity of the particle, and the value range of δ is between 0 and 1. It is generally determined based on experience. For example, δ can be 0.3 or 0.5.

[0063] By improving and optimizing the dynamic update of ω, particles can correct ω in real time during the optimization process to avoid premature particle maturation (the phenomenon that particles gather near the local optimal solution too early and cannot continue to search for the global optimal solution), thereby avoiding the problem that the position and speed of particles fall into the local optimum and no longer update, thereby ensuring the accuracy and reliability of the optimal solution for the resource configuration of UAV equipment.

[0064] In one embodiment, the improved particle swarm algorithm further includes improving and optimizing the updates of c1 and c2. Specifically, in this embodiment, the updates of c1 and c2 are as follows:

[0065]

[0066] In the formula, c 1max 、c 1min represents the maximum and minimum values ​​of c1 in the particle swarm optimization process, c 2max 、c 2min They represent the maximum and minimum values ​​of c2 in the particle swarm optimization process, k represents the current iteration number of the particle, and f(k,c,L) represents the modulo operation function directly related to the update constant c (i.e. c1, c2) and the oscillation period L (i.e. L1, L2) to ensure that c1 and c2 always take values ​​according to the oscillation period, thereby avoiding the particles from falling into the local optimum prematurely.

[0067] In one embodiment, the calculation expression of f(k,c,L) is:

[0068]

[0069] Among them, L represents the oscillation period (L1, L2) corresponding to the dynamic update of constants c1 and c2, indicating that c1 and c2 will repeat values ​​according to the turbulence period; f1(k,c,L) is obtained by substituting k1, c1, and L1 into f(k,c,L). Similarly, f2(k,c,L) is obtained by substituting k2, c2, and L2 into f(k,c,L).

[0070] In summary, by improving ω, c1 and c2, we can avoid the problem of particles falling into local optimality and stopping updating during the optimization process, thereby ensuring that the optimal solution obtained by the particle swarm algorithm is the global optimal solution.

[0071] Furthermore, the accuracy and reliability of the optimal solution for the configuration of UAV equipment resources obtained by the method of the present invention can be verified by the following example. Specifically, in this example, there are 4 locations for supplying equipment support resources to combat UAVs. These 4 resource supply points can replenish 10 types of equipment support resources of the UAV system. The detailed configuration relationship is shown in Table 1.

[0072] Table 1 UAV system support resource allocation relationship table

[0073]

[0074] In addition, in order to verify the equipment support resource optimization configuration method proposed in the present invention, a transportation time and transportation cost table is also provided according to the optimization target based on transportation cost and transportation time proposed in the present invention, and the details are shown in Table 2.

[0075] Table 2 Transportation costs and transportation schedule of UAV system support resources

[0076]

[0077] In this example, after a certain damage, considering the actual consumption of support resources at the airport, the UAV system needs to replenish spare parts 4 (12 pieces), spare parts 6 (15 pieces), spare parts 8 (5 pieces) and spare parts 10 (15 pieces), and these UAV equipment support resources must be transported within 6 days (i.e., T0 is 6 days). Based on the above data, the particle swarm algorithm proposed in the present invention solves the UAV equipment resource optimization configuration model. In the process of solving the particle swarm algorithm, the change curve of the particle optimal fitness value is as follows: Figure 2 and Figure 3 As shown. Among them, Figure 2 is the optimal fitness value change curve of particles in the basic particle swarm algorithm. Figure 3 This is the curve of the optimal fitness value of particles in the improved particle swarm algorithm.

[0078] Depend on Figure 2It can be seen that the particles are almost stagnant when the iterative search is nearly 200 steps, and the basic particle swarm algorithm does not give the optimal configuration conclusion of equipment support resources, indicating that the particles have fallen into a local optimum; further, Figure 3 It can be seen that when the particle has been iteratively searching for nearly 600 steps, the optimal search conclusion is obtained, that is, the optimal solution for the configuration of UAV equipment resources is obtained. The optimal solution is: select resource supply point 1, resource supply point 3 and resource supply point 4 to provide equipment support resources for the UAV.

[0079] Therefore, through the above examples, it is verified that the method of the present invention can obtain the optimal solution for the resource configuration of UAV equipment, and the optimal solution has high reliability and accuracy, is a global optimal solution, and is feasible.

[0080] Figure 4 It is a structural block diagram schematically showing a UAV equipment resource optimization configuration system according to this embodiment.

[0081] In a second aspect, the present invention also provides a UAV equipment resource optimization configuration system. Figure 4 As shown, the system includes a processor and a memory, wherein the memory stores computer program instructions, and when the computer program instructions are executed by the processor, a method for optimizing the configuration of resources of unmanned aerial vehicle equipment according to the first aspect of the present invention is implemented.

[0082] The system also includes other components well known to those skilled in the art, such as a communication interface, whose configuration and functions are known in the art and thus will not be described in detail here.

[0083] In the present invention, the aforementioned memory may be any tangible medium containing or storing a program that may be used by or in combination with an instruction execution system, apparatus or device. For example, a computer-readable storage medium may be any suitable magnetic storage medium or magneto-optical storage medium, such as a resistive random access memory RRAM (Resistive Random Access Memory), a dynamic random access memory DRAM (Dynamic Random Access Memory), a static random access memory SRAM (Static Random-Access Memory), an enhanced dynamic random access memory EDRAM (Enhanced Dynamic Random Access Memory), a high-bandwidth memory HBM (High-Bandwidth Memory), a hybrid memory cube HMC (Hybrid Memory Cube), etc., or any other medium that may be used to store the required information and may be accessed by an application, a module, or both. Any such computer storage medium may be part of a device or accessible or connectable to a device. Any application or module described in the present invention may be implemented using computer-readable / executable instructions that may be stored or otherwise maintained by such a computer-readable medium.

[0084] In the description of this specification, “plurality” means at least two, such as two, three or more, etc., unless otherwise clearly and specifically defined.

[0085] Although this specification has shown and described a number of embodiments of the present invention, it will be apparent to those skilled in the art that such embodiments are provided by way of example only. Those skilled in the art will conceive of many modifications, changes and alternatives without departing from the ideas and spirit of the present invention. It should be understood that in the practice of the present invention, various alternatives to the embodiments of the present invention described herein may be employed.

Claims

1. A method for optimizing the configuration of UAV equipment resources, which is applied to obtain equipment resources required by UAVs from multiple resource supply points, characterized in that: include: Establishing a UAV equipment resource optimization configuration model based on the objective function and constraint conditions, wherein the objective function is the minimum transportation cost and / or the shortest transportation time of transporting equipment resources required for UAVs from multiple resource supply points to the UAV equipment location, and the constraint conditions include transportation time limit constraints, equipment resource inventory constraints, and equipment resource consumption constraints; The UAV equipment resource optimization configuration model is solved according to the particle swarm algorithm to obtain the optimal solution for the equipment resource configuration of the UAV.

2. The method for optimizing the allocation of UAV equipment resources according to claim 1, characterized in that: Before establishing the UAV equipment resource optimization configuration model, it includes: determining the objective function according to the transportation time and transportation cost of the equipment resources required for transporting the UAV from the resource supply point to the UAV equipment site.

3. The method for optimizing the allocation of UAV equipment resources according to claim 1 or 2, characterized in that: The objective function is: λ1min 0＜i＜m max 0＜j＜n t ij +λ2min 0＜i＜m,0＜j＜n m ij l ij ; Where, t ij represents the transportation time of transporting the jth equipment resource from the i-th resource supply point to the drone equipment site, m ij represents the transportation cost of transporting the jth equipment resource from the i-th resource supply point to the drone equipment site, l ij It represents the quantity of the jth equipment resource provided by the i-th resource supply point to the UAV, m represents the number of resource supply points, n represents the number of equipment resources, λ1 represents the weight of the transportation time, and λ2 represents the weight of the transportation cost.

4. The method for optimizing the allocation of UAV equipment resources according to claim 1, characterized in that: The conditional constraints are: Where, t ij represents the transportation time of transporting the jth equipment resource from the i-th resource supply point to the drone equipment site, l ijmax It indicates the maximum amount of equipment resources of the jth type that the i-th resource supply point can provide, l ij represents the number of j-th equipment resources provided by the i-th resource supply point to the drone, n represents the number of equipment resources, m represents the number of resource supply points, and p j represents the consumption rate of the j-th equipment resource of the drone itself, v j It represents the quantity of the jth equipment resource required by the UAV, and T0 represents the maximum time within the transportation time limit.

5. The method for optimizing the allocation of UAV equipment resources according to claim 1, characterized in that: The update of the velocity and position of particles in the particle swarm algorithm is: In the formula, represents the velocity of particle j after n+1 updates, represents the position of particle j after n+1 updates, p jd represents the local optimal position of particle j, p gd represents the global optimal position of particle j, r1 and r2 are constants between 0 and 1, c1 represents the self-update constant of particle j, c2 represents the social update constant of particle j, and ω represents the weight coefficient that changes dynamically over time.

6. The method for optimizing the allocation of UAV equipment resources according to claim 5, characterized in that: The update of ω is: Where k represents the current iteration number of the particle, T represents the maximum number of steps allowed for the particle in the dynamic update process, ω1 represents the weight factor determined by optimization, and ω0 represents the weight factor before optimization.

7. The method for optimizing the allocation of UAV equipment resources according to claim 5, characterized in that: The update of ω is: Where k represents the current iteration number of the particle, ω1 represents the weight factor determined by optimization, ω0 represents the weight factor before optimization, f1 represents the optimal fitness value in the particle optimization process, f2 represents the average fitness value of the particle in the optimization process, and δ is a constant that characterizes the precocity of the particle.

8. The method for optimizing the allocation of UAV equipment resources according to claim 5, characterized in that: The updates of c1 and c2 are: In the formula, c 1max 、c 1min represents the maximum and minimum values ​​of c1 in the particle swarm optimization process, c 2max 、c 2min They respectively represent the maximum and minimum values ​​of c2 in the particle swarm optimization process, k represents the current iteration number of the particle, and f(k,c,L) represents the modulo operation function directly related to the update constant c and the oscillation period L.

9. A drone equipment resource optimization configuration system, characterized in that: It includes a processor and a memory, wherein the memory stores computer program instructions, and when the computer program instructions are executed by the processor, a method for optimizing the configuration of unmanned aerial vehicle equipment resources according to claims 1-8 is implemented.

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