Unmanned bee colony communication network evaluation method, device, equipment, medium and product

Abstract

The invention provides an unmanned bee colony communication network evaluation method and device, equipment, a medium and a product, and belongs to the field of unmanned bee colony communication, and the method comprises the steps: dividing a target bee colony task into a plurality of subtasks, determining communication network parameters corresponding to the subtasks, and distributing the subtasks and the communication network parameters to unmanned aerial vehicles in an unmanned bee colony; the task completion rate of the unmanned aerial vehicle under the communication network parameters is obtained, the task entropy of the target bee colony task under the communication network parameters is determined, and the task entropy is used for representing the completion degree of the target bee colony task; and determining the influence of the communication network parameters on the target bee colony task according to the task entropy to obtain an evaluation result of the communication network parameters. The task entropy of the target bee colony task is calculated according to the task completion rate of the unmanned aerial vehicles of the unmanned bee colony in the preset communication network parameters, so that the influence of the communication network parameters in the complex environment on the target bee colony task is judged, and the task completion effect of the unmanned bee colony is ensured.

Description

Technical Field

[0001] The present invention relates to the field of unmanned swarm communication, and particularly to a method, device, equipment, medium and product for evaluating an unmanned swarm communication network. Background Art

[0002] With the rapid development of unmanned aerial vehicle (UAV) technology and the new generation of communication technology, multiple small and medium-sized UAVs form an unmanned swarm as a group to perform tasks such as public safety, disaster management, and remote surveillance, effectively making up for the deficiencies of UAVs when performing tasks alone.

[0003] The existing task evaluation method for unmanned swarms is to evaluate the task completion rate of a single UAV, and then add up the task completion rates of all UAVs to obtain the total task completion rate, thereby realizing task evaluation. The evaluation method is relatively single, unable to reflect the impact of the communication network on the task completion rate, and is only applicable to a single task environment. If applied to a complex environment, the communication network is affected by the environment, which will have an adverse impact on the task completion effect of the unmanned swarm. Summary of the Invention

[0004] The present invention provides a method, device, equipment, medium and product for evaluating an unmanned swarm communication network, which solves the defect that the existing evaluation method is relatively single and unable to reflect the impact of the communication network on the task completion rate.

[0005] The present invention provides a method for evaluating an unmanned swarm communication network, including: Splitting a target swarm task into a number of subtasks and determining communication network parameters corresponding to the subtasks, and allocating the subtasks and the communication network parameters to UAVs in the unmanned swarm; Obtaining the task completion rate of the UAVs under the communication network parameters; According to the task completion rates of all the UAVs, determining the task entropy of the target swarm task under the communication network parameters, where the task entropy is used to characterize the completion degree of the target swarm task; According to the task entropy, determining the impact of the communication network parameters on the target swarm task to obtain an evaluation result of the communication network parameters.

[0006] As an embodiment, the step of determining the task entropy of the target swarm task under the communication network parameters according to the task completion rates of all the UAVs includes: Confirming the task contribution rate of each UAV according to the sum of the task completion rates of all the UAVs; Determining the task entropy of the target swarm task under the communication network parameters according to the task contribution rate.

[0007] As an embodiment, after determining the influence of the communication network parameters on the target swarm task according to the task entropy to obtain the evaluation result of the communication network parameters, the method further includes: If the evaluation result indicates that the communication network parameters have a negative impact on the target swarm task, optimize the communication network parameters according to the task entropy.

[0008] As an embodiment, optimizing the communication network parameters according to the task entropy includes: Determine the deviation value of the task entropy; Use the deviation value of the task entropy as an optimization parameter to optimize the communication network parameters.

[0009] As an embodiment, optimizing the communication network parameters according to the task entropy includes: Determine the deviation value of the task entropy and use the deviation value of the task entropy as an objective weight; Obtain the subjective weight determined by expert evaluation, and determine the comprehensive weight according to the objective weight and the subjective weight; Determine the optimization parameter according to the comprehensive weight to optimize the communication network parameters.

[0010] As an embodiment, after optimizing the communication network parameters according to the task entropy, the method further includes: Feed back the optimized communication network parameters to the corresponding drones, and return to execute the step of obtaining the task completion rate of the drones under the communication network parameters until the target swarm task is completed.

[0011] The present invention also provides an unmanned swarm communication network evaluation system, including: An initialization module, configured to split the target swarm task into several subtasks, determine the communication network parameters corresponding to the subtasks, and allocate the subtasks and the communication network parameters to the drones in the unmanned swarm; An acquisition module, configured to acquire the task completion rate of the drones under the communication network parameters; A determination module, configured to determine the task entropy of the target swarm task under the communication network parameters according to the task completion rates of all the drones, where the task entropy is used to characterize the completion degree of the target swarm task; An evaluation module, configured to determine the influence of the communication network parameters on the target swarm task according to the task entropy to obtain the evaluation result of the communication network parameters.

[0012] The present invention also provides an electronic device, including a memory, a processor, and a computer program stored on the memory and executable on the processor. When the processor executes the computer program, the method for evaluating an unmanned swarm communication network as described in any one of the above is implemented.

[0013] The present invention also provides a non-transitory computer-readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, the method for evaluating an unmanned swarm communication network as described in any one of the above is implemented.

[0014] The present invention also provides a computer program product, including a computer program. When the computer program is executed by a processor, the method for evaluating an unmanned swarm communication network as described in any one of the above is implemented.

[0015] The method, device, equipment, medium, and product for evaluating an unmanned swarm communication network provided by the present invention calculate the task entropy of a target swarm task according to the task completion rate of the unmanned aerial vehicles in the unmanned swarm under preset communication network parameters, and then determine the influence of the communication network parameters on the target swarm task, which can timely detect the influence of the communication network parameters on the task in a complex environment and avoid causing adverse effects. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] In order to more clearly illustrate the technical solutions in the present invention or the prior art, the following will briefly introduce the drawings required for use in the description of the embodiments or the prior art. Obviously, the drawings in the following description are some embodiments of the present invention. For those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative efforts.

[0017] Figure 1 It is a schematic diagram of the existing unmanned swarm task evaluation process.

[0018] Figure 2 It is one of the schematic diagrams of the process of the method for evaluating an unmanned swarm communication network provided by the present invention.

[0019] Figure 3 It is another schematic diagram of the process of the method for evaluating an unmanned swarm communication network provided by the present invention.

[0020] Figure 4 It is one of the schematic diagrams of the structure of the unmanned swarm communication network evaluation system provided by the present invention.

[0021] Figure 5 It is another schematic diagram of the structure of the unmanned swarm communication network evaluation system provided by the present invention.

[0022] Figure 6 It is a schematic diagram of the structure of the electronic device provided by the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0023] To make the objectives, technical solutions, and advantages of the present invention clearer, the technical solutions in the present invention will be clearly and completely described below in conjunction with the accompanying drawings in the present invention. Obviously, the described embodiments are some, but not all, of the embodiments of the present invention. All other embodiments obtained by those of ordinary skill in the art based on the embodiments in the present invention without creative efforts fall within the protection scope of the present invention.

[0024] Figure 1 is a schematic diagram of the existing unmanned swarm mission assessment process. As Figure 1 shown, the unmanned swarm is equipped with a wireless local area network chip supporting the 802.11 standard and uses the wireless local area network channel to achieve information exchange between the unmanned swarm and the ground receiving center. The ground receiving center is deployed with a mission assessment system, and the mission assessment system superimposes the mission completion rates of multiple unmanned aerial vehicles to determine the mission completion effect of the unmanned swarm.

[0025] Most of the communication protocols adopted by each unmanned aerial vehicle in the unmanned swarm are wireless local area network protocols, which are greatly affected by the environment. The method for evaluating the mission completion effect of the unmanned swarm is relatively single and cannot reflect the impact of wireless communication under the environment on mission completion. For this reason, the present invention proposes a method, device, equipment, medium, and product for evaluating the communication network of an unmanned swarm. According to the completion situations of multiple subtasks of the unmanned swarm mission, the mission information entropy is designed to evaluate the overall mission completion effect of the swarm, and the support effect of wireless communication is reflected in the mission completion effect of the unmanned swarm. The following will be described in detail in conjunction with the accompanying drawings.

[0026] Figure 2 is one of the schematic diagrams of the process of the method for evaluating the communication network of an unmanned swarm provided by the present invention. As Figure 2 shown, the present invention provides a method for evaluating the communication network of an unmanned swarm, including steps S100 - S400.

[0027] Step S100: Split the target swarm mission into several subtasks and determine the communication network parameters corresponding to the subtasks, and allocate the subtasks and the communication network parameters to the unmanned aerial vehicles in the unmanned swarm.

[0028] The target swarm mission can be tasks such as public safety, disaster management, remote surveillance, etc., which require all the unmanned aerial vehicles in the unmanned swarm to cooperate in execution. Let the target swarm mission be A, which is decomposed into different subtasks, and the subtasks are denoted as . Among them, n can be determined according to the specific process steps of the current number of unmanned aerial vehicles executing the target swarm mission.

[0029] Communication network parameters refer to the main network parameters (including but not limited to communication bandwidth, communication rate, transmission power, etc.) of the network capability indicators (such as network coverage, transmission delay, network reliability, network throughput, etc.) that support the swarm of drones to complete the target swarm mission. Let the network capability indicator be , and the communication network parameter be .

[0030] After determining the subtasks and communication network parameters, the subtasks and communication network parameters can be assigned to the drones. Before officially executing the mission, a mission rehearsal can be carried out. Through the mission rehearsal, it can be judged whether the communication network parameters will have an adverse impact on the target swarm mission. After the rehearsal is completed, if the communication network parameters do not have an adverse impact on the target swarm mission, the communication network parameters can be used as the initialization parameters for the swarm of drones to execute the mission, and the communication network parameters can be optimized and adjusted in real time when the swarm of drones executes the mission. If the communication network parameters do not have an adverse impact on the target swarm mission, the communication network parameters can be optimized and adjusted, and the optimized and adjusted communication network parameters can be used as the initialization parameters for the swarm of drones to execute the mission, and the communication network parameters can be optimized and adjusted in real time when the swarm of drones executes the mission.

[0031] Step S200, obtain the task completion rate of the drone under the communication network parameters.

[0032] Obtain the task completion rate of each drone in the swarm of drones under the constraint of the communication network parameters, denoted as the subtask The task completion rate under the constraint of the network capability indicator . .

[0033] Step S300, determine the task entropy of the target swarm mission under the communication network parameters according to the task completion rates of all the drones, and the task entropy is used to characterize the completion degree of the target swarm mission.

[0034] Step S400, determine the impact of the communication network parameters on the target swarm mission according to the task entropy to obtain the evaluation result of the communication network parameters.

[0035] It can be understood that according to the task completion rates of the drones in the swarm of drones under the preset communication network parameters, the present invention calculates the task entropy of the target swarm mission, and then determines the impact of the communication network parameters on the target swarm mission, which can timely discover the impact of the communication network parameters in a complex environment on the mission and avoid causing adverse effects.

[0036] Based on the above embodiments, as an optional embodiment, the determining the task entropy of the target swarm mission under the communication network parameters according to the task completion rates of all the drones includes steps S310 - S320.

[0037] Step S310: Confirm the task contribution rate of each UAV according to the sum of the task completion rates of all the UAVs.

[0038] Specifically, use the quotient of the task completion rate of a UAV and the sum of the task completion rates as the task contribution rate of this UAV. The calculation formula is as follows: where, is the task contribution rate of the i th UAV.

[0039] Step S320: Determine the task entropy of the target swarm task under the communication network parameters according to the task contribution rate.

[0040] Substitute the task contribution rate of the i th UAV into the calculation formula of entropy to obtain the evaluation index value of this UAV under the constraint of the network capability index . The combination of the evaluation index values of all UAVs gives the task entropy of the target swarm task under the communication network parameters.

[0041] The calculation formula of the evaluation index value is as follows: where, is the evaluation index value of the i th UAV under the constraint of the network capability index .

[0042] The calculation formula of the task entropy is as follows: where, is the task entropy, representing the task entropy of the target swarm task under the constraint of the network capability index .

[0043] It can be understood that the present invention calculates the task entropy of the target swarm task by using the task contribution rate of each UAV, which can more accurately measure the complexity and uncertainty of the task. A higher entropy means more complexity and possible situations of the task, and it is suitable for evaluating diverse and complex task environments.

[0044] Based on the above embodiments, as an optional embodiment, after determining the influence of the communication network parameters on the target swarm task according to the task entropy to obtain the evaluation result of the communication network parameters, it further includes: Step S500: If the evaluation result indicates that the communication network parameters have a negative impact on the target swarm task, optimize the communication network parameters according to the task entropy.

[0045] Specifically, a task entropy threshold can be set. If the task entropy is higher than the task entropy threshold, it is determined that the communication network parameters will have a negative impact on the target swarm task. The optimization parameters can be determined according to the task entropy, and the communication network parameters can be optimized using the optimization.

[0046] As an embodiment, optimizing the communication network parameters according to the task entropy includes step S511 - step S512.

[0047] Step S511, determining the deviation value of the task entropy.

[0048] The calculation formula for the deviation value of the task entropy is as follows: where, is the deviation value of the task entropy under the network capability index constraint.

[0049] Step S512, using the deviation value of the task entropy as the optimization parameter to optimize the communication network parameters. Specifically, the deviation value of the task entropy can be multiplied by the communication network parameters to achieve the optimization of the communication network parameters.

[0050] It can be understood that the present invention optimizes the communication network parameters using the task entropy to reduce the adverse impact of the communication network parameters on the target swarm task.

[0051] As an embodiment, optimizing the communication network parameters according to the task entropy includes step S521 - step S523.

[0052] Step S521, determining the deviation value of the task entropy and using the deviation value of the task entropy as the objective weight.

[0053] Step S522, obtaining the subjective weight determined by expert evaluation, and determining the comprehensive weight according to the objective weight and the subjective weight.

[0054] The subjective weight corresponding to the network capability index can be determined by expert subjective judgment, and then harmonic coefficients are set for the objective weight and the subjective weight respectively, and the comprehensive weight is calculated. The formula is as follows: where, is the comprehensive weight of the th drone under the network capability index i constraint, are the harmonic coefficients of the objective weight and the subjective weight respectively. In the initial case, , .

[0055] Step S523: Determine the optimization parameters according to the comprehensive weight to optimize the communication network parameters.

[0056] Specifically, the comprehensive task entropy can be obtained based on the comprehensive weight and the evaluation index value of each drone, and the comprehensive task entropy is used as the optimization parameter.

[0057] The calculation formula of the comprehensive task entropy is as follows: where is the comprehensive task entropy.

[0058] It can be understood that the present invention calculates the comprehensive task entropy that can evaluate the completion effect of the swarm task by using the completion situation of a series of subtasks that make up the target swarm task and combining the subjective judgment of experts, and can effectively evaluate and optimize the support of wireless communication for the target swarm task.

[0059] Based on the above embodiments, as an optional embodiment, after optimizing the communication network parameters according to the task entropy, step S600 is further included.

[0060] Step S600: Feed back the optimized communication network parameters to the corresponding drone, and return to execute the step of obtaining the task completion rate of the drone under the communication network parameters until the target swarm task is completed.

[0061] As Figure 3 shown, let the rate index in the communication network parameters be , that is, the communication rate of each drone in the unmanned swarm is . Taking the comprehensive task entropy as an example, the optimization of the communication network parameters is illustrated.

[0062] The optimized communication network parameters are as follows: where r is the optimized communication network parameter of the i th drone, and the optimized communication network parameters are fed back to the corresponding drone through the wireless channel.

[0063] Each drone uses the optimized communication rate value to complete its own bandwidth adjustment. Let the communication bandwidth of the i th drone in the unmanned swarm be . According to the optimized communication rate value , the formula for adjusting the communication bandwidth is as follows: where is the signal-to-noise ratio of the drone.

[0064] It can be understood that the present invention adopts a calculation method of task information entropy, combines the subjective preferences of evaluators, and forms an evaluation result of combat mission-capability-technology through weighted averaging of the contribution values of each communication index of the swarm, so as to evaluate the support effect of the current swarm communication network on combat missions. This evaluation result depends on weight information and index values. Then, through an index preprocessing method, each index value is transformed into a task contribution value in percentage, with the optimal result being 1 and the worst result being 0, thus transforming it into a score expression in percentage system. Finally, based on the task information entropy value, the optimization adjustment of communication parameters is completed, which can effectively improve the completion effect of the target swarm mission.

[0065] The following describes the unmanned swarm communication network evaluation system provided by the present invention. The unmanned swarm communication network evaluation system described below can be correspondingly referred to the unmanned swarm communication network evaluation method described above.

[0066] Figure 4 is one of the schematic structural diagrams of the unmanned swarm communication network evaluation system provided by the present invention. As Figure 4 shown, the present invention also provides an unmanned swarm communication network evaluation system, including the following modules.

[0067] An initialization module 410, configured to split a target swarm mission into several subtasks and determine communication network parameters corresponding to the subtasks, and allocate the subtasks and the communication network parameters to drones in the unmanned swarm; An acquisition module 420, configured to acquire the task completion rate of the drone under the communication network parameters; A determination module 430, configured to determine the task entropy of the target swarm mission under the communication network parameters according to the task completion rates of all the drones, where the task entropy is used to characterize the completion degree of the target swarm mission; An evaluation module 440, configured to determine the influence of the communication network parameters on the target swarm mission according to the task entropy to obtain an evaluation result of the communication network parameters.

[0068] As an embodiment, the determination module 430 is further configured to: Confirm the task contribution rate of each drone according to the sum of the task completion rates of all the drones; Determine the task entropy of the target swarm mission under the communication network parameters according to the task contribution rate.

[0069] As an embodiment, it further includes: An optimization module, configured to optimize the communication network parameters according to the task entropy if the evaluation result indicates that the communication network parameters have a negative impact on the target swarm task.

[0070] As an embodiment, the optimization module is further configured to: Determine the deviation value of the task entropy; Use the deviation value of the task entropy as an optimization parameter to optimize the communication network parameters.

[0071] As an embodiment, the optimization module is further configured to: Determine the deviation value of the task entropy and use the deviation value of the task entropy as an objective weight; Obtain a subjective weight determined by expert evaluation, and determine a comprehensive weight according to the objective weight and the subjective weight; Determine an optimization parameter according to the comprehensive weight to optimize the communication network parameters.

[0072] As an embodiment, it further includes: A feedback module, configured to feedback the optimized communication network parameters to the corresponding unmanned aerial vehicle, and return to execute the step of obtaining the task completion rate of the unmanned aerial vehicle under the communication network parameters until the target swarm task is completed.

[0073] Figure 5 It is the second schematic diagram of the structure of the unmanned swarm communication network evaluation system provided by the present invention. As Figure 5 shown, the unmanned swarm communication network evaluation system provided by the present invention includes a ground receiving center, a task evaluation center, and a network optimization center.

[0074] The ground receiving center is configured to split the target swarm task into several subtasks and determine the communication network parameters corresponding to the subtasks, allocate the subtasks and the communication network parameters to the unmanned aerial vehicles in the unmanned swarm, obtain the task completion rate of the unmanned aerial vehicles under the communication network parameters, and send the communication network parameters and the task completion rate to the task evaluation center.

[0075] The task evaluation center is configured to determine the task entropy of the target swarm task under the communication network parameters according to the task completion rates of all the unmanned aerial vehicles. The task entropy is used to characterize the completion degree of the target swarm task. According to the task entropy, determine the impact of the communication network parameters on the target swarm task to obtain the evaluation result of the communication network parameters.

[0076] The network optimization center is configured to optimize the communication network parameters according to the task entropy and feedback the optimized communication network parameters to the corresponding unmanned aerial vehicle.

[0077] It should be noted that the unmanned swarm communication network evaluation system provided by the present invention can execute the unmanned swarm communication network evaluation method described in any of the above embodiments during specific operation, and has technical effects corresponding to the method, which will not be elaborated in this embodiment.

[0078] Figure 6 An entity structure diagram of an electronic device is exemplified, as Figure 6 shown, the electronic device may include: a processor 610, a communication interface 620, a memory 630, and a communication bus 640. Among them, the processor 610, the communication interface 620, and the memory 630 complete mutual communication through the communication bus 640. The processor 610 can call the logical instructions in the memory 630 to execute the unmanned swarm communication network evaluation method, and the method includes: splitting a target swarm task into several subtasks and determining communication network parameters corresponding to the subtasks, and allocating the subtasks and the communication network parameters to the unmanned aerial vehicles in the unmanned swarm; obtaining the task completion rate of the unmanned aerial vehicles under the communication network parameters; determining the task entropy of the target swarm task under the communication network parameters according to the task completion rates of all the unmanned aerial vehicles, and the task entropy is used to characterize the completion degree of the target swarm task; determining the influence of the communication network parameters on the target swarm task according to the task entropy to obtain the evaluation result of the communication network parameters.

[0079] In addition, when the logical instructions in the above-mentioned memory 630 are implemented in the form of software functional units and sold or used as an independent product, they can be stored in a computer-readable storage medium. Based on such an understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, or a part of this technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium and includes several instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the methods described in various embodiments of the present invention. The foregoing storage medium includes: various media such as a USB flash drive, a mobile hard disk, a read-only memory (ROM, Read-Only Memory), a random access memory (RAM, Random Access Memory), a magnetic disk, or an optical disc that can store program codes.

[0080] On the other hand, the present invention also provides a computer program product, which includes a computer program. The computer program can be stored on a non-transitory computer-readable storage medium. When the computer program is executed by a processor, the computer can execute the unmanned swarm communication network evaluation method provided by the above-mentioned various methods. The method includes: splitting a target swarm task into several subtasks and determining communication network parameters corresponding to the subtasks, and allocating the subtasks and the communication network parameters to unmanned aerial vehicles in the unmanned swarm; obtaining the task completion rate of the unmanned aerial vehicles under the communication network parameters; determining the task entropy of the target swarm task under the communication network parameters according to the task completion rates of all the unmanned aerial vehicles, where the task entropy is used to characterize the completion degree of the target swarm task; and determining the influence of the communication network parameters on the target swarm task according to the task entropy to obtain an evaluation result of the communication network parameters.

[0081] In another aspect, the present invention also provides a non-transitory computer-readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, it implements the unmanned swarm communication network evaluation method provided by the above-mentioned various methods. The method includes: splitting a target swarm task into several subtasks and determining communication network parameters corresponding to the subtasks, and allocating the subtasks and the communication network parameters to unmanned aerial vehicles in the unmanned swarm; obtaining the task completion rate of the unmanned aerial vehicles under the communication network parameters; determining the task entropy of the target swarm task under the communication network parameters according to the task completion rates of all the unmanned aerial vehicles, where the task entropy is used to characterize the completion degree of the target swarm task; and determining the influence of the communication network parameters on the target swarm task according to the task entropy to obtain an evaluation result of the communication network parameters.

[0082] The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separated. The components shown as units may or may not be physical units, that is, they may be located in one place, or may be distributed to multiple network units. Some or all of the modules can be selected according to actual needs to achieve the purpose of the solution of this embodiment. Those of ordinary skill in the art can understand and implement it without creative efforts.

[0083] Through the description of the above embodiments, those skilled in the art can clearly understand that each embodiment can be implemented by means of software plus a necessary general hardware platform, and of course, it can also be implemented by hardware. Based on such an understanding, the above technical solution, in essence, or the part that contributes to the prior art can be embodied in the form of a software product. This computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, magnetic disk, optical disk, etc., and includes several instructions to enable a computer device (which can be a personal computer, a server, or a network device, etc.) to execute the methods described in each embodiment or some parts of the embodiments.

[0084] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that they can still modify the technical solutions recorded in the foregoing embodiments, or perform equivalent replacements for some of the technical features; and these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for evaluating an unmanned swarm communication network, characterized in that: include: Splitting the target swarm task into a plurality of subtasks and determining communication network parameters corresponding to the subtasks, and allocating the subtasks and the communication network parameters to drones in the drone swarm; Obtaining a mission completion rate of the UAV under the communication network parameters; Determine the task entropy of the target swarm task under the communication network parameters according to the task completion rate of all the drones, wherein the task entropy is used to characterize the completion degree of the target swarm task; According to the task entropy, the influence of the communication network parameters on the target swarm task is determined to obtain an evaluation result of the communication network parameters.

2. The unmanned bee colony communication network evaluation method according to claim 1, characterized in that: Determining the task entropy of the target swarm task under the communication network parameters according to the task completion rate of all the drones includes: Determine the mission contribution rate of each drone according to the sum of the mission completion rates of all the drones; According to the task contribution rate, the task entropy of the target swarm task under the communication network parameters is determined.

3. The unmanned bee colony communication network evaluation method according to claim 1, characterized in that: After determining the influence of the communication network parameter on the target swarm task according to the task entropy to obtain the evaluation result of the communication network parameter, the method further includes: If the evaluation result indicates that the communication network parameters have a negative impact on the target swarm task, the communication network parameters are optimized according to the task entropy.

4. The unmanned bee colony communication network evaluation method according to claim 3 is characterized in that: The optimizing the communication network parameters according to the task entropy includes: determining a deviation value of the task entropy; The communication network parameters are optimized by using the deviation value of the task entropy as an optimization parameter.

5. The unmanned bee colony communication network evaluation method according to claim 3, characterized in that: The optimizing the communication network parameters according to the task entropy includes: Determining a deviation value of the task entropy, and using the deviation value of the task entropy as an objective weight; Obtaining the subjective weight determined by the expert evaluation, and determining the comprehensive weight according to the objective weight and the subjective weight; An optimization parameter is determined according to the comprehensive weight so as to optimize the communication network parameter.

6. The unmanned bee colony communication network evaluation method according to any one of claims 3 to 5, characterized in that: After optimizing the communication network parameters according to the task entropy, the method further includes: The optimized communication network parameters are fed back to the corresponding UAV, and the step of obtaining the task completion rate of the UAV under the communication network parameters is returned to be executed until the target swarm task is completed.

7. An unmanned swarm communication network evaluation system, characterized in that: include: An initialization module, used to split the target swarm task into a plurality of subtasks and determine the communication network parameters corresponding to the subtasks, and distribute the subtasks and the communication network parameters to the drones in the drone swarm; An acquisition module, used to obtain the mission completion rate of the UAV under the communication network parameters; A determination module, used to determine the task entropy of the target swarm task under the communication network parameters according to the task completion rate of all the drones, wherein the task entropy is used to characterize the completion degree of the target swarm task; An evaluation module is used to determine the influence of the communication network parameters on the target swarm task according to the task entropy to obtain an evaluation result of the communication network parameters.

8. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein: When the processor executes the computer program, the unmanned swarm communication network evaluation method as described in any one of claims 1 to 6 is implemented.

9. A non-transitory computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the unmanned swarm communication network evaluation method as 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 swarm communication network evaluation method as described in any one of claims 1 to 6 is implemented.

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