A Cooperative Cluster Control Method, Device, Equipment and Storage Medium for Unmanned Aerial Vehicles

Abstract

The present invention provides a cooperative cluster control method, device, equipment and storage medium for unmanned aerial vehicles. In the initial stage, initial information of N unmanned aerial vehicles in the cluster is obtained, and the leading weight value of each unmanned aerial vehicle is calculated based on the initial information, wherein the initial information includes the ammunition load, combat radius and endurance time; then, the leading unmanned aerial vehicle is determined according to the leading weight value of each unmanned aerial vehicle, and the communication delay between the leading unmanned aerial vehicle and other unmanned aerial vehicles in the cluster is calculated; finally, other unmanned aerial vehicles in the cluster are clustered and grouped according to the communication delay, a secondary leading unmanned aerial vehicle is randomly selected from each group, and a communication routing table is generated and synchronized to each unmanned aerial vehicle in the cluster, wherein the secondary leading unmanned aerial vehicle is used to communicate with the unmanned aerial vehicles and the leading unmanned aerial vehicle in the group. The problem that the existing communication method between the leading unmanned aerial vehicle and the cluster is prone to cluster out-of-control is solved.

Description

Technical Field

[0001] The present invention relates to the field of unmanned aerial vehicles, and particularly to a cooperative cluster control method, device, equipment and storage medium for unmanned aerial vehicles. Background Art

[0002] The formation control of unmanned aerial vehicle clusters generally adopts the leader-follower method. It has the advantages of simplicity, high efficiency, high formation accuracy, etc. However, the traditional leader-follower method requires all unmanned aerial vehicles in the cluster to communicate directly with the leader unmanned aerial vehicle, resulting in excessive communication volume and increasing the system burden; and when the leader unmanned aerial vehicle fails, it is easy to cause the entire unmanned aerial vehicle cluster to get out of control.

[0003] In view of this, this application is proposed. Summary of the Invention

[0004] The present invention discloses a cooperative cluster control method, device, equipment and storage medium for unmanned aerial vehicles, aiming to solve the problem that the communication method between the existing leader unmanned aerial vehicle and the cluster is prone to cluster out-of-control.

[0005] The first embodiment of the present invention provides a cooperative cluster control method for unmanned aerial vehicles, including:

[0006] In the starting stage, obtain the initial information of N unmanned aerial vehicles in the cluster, and calculate the leading weight value of each unmanned aerial vehicle based on the initial information, where the initial information includes the ammunition load, combat radius and endurance time;

[0007] Determine the leading unmanned aerial vehicle according to the leading weight value of each unmanned aerial vehicle, and calculate the communication delay between the leading unmanned aerial vehicle and other unmanned aerial vehicles in the cluster;

[0008] Cluster and group other unmanned aerial vehicles in the cluster according to the communication delay, randomly select an unmanned aerial vehicle from each group as a secondary leading unmanned aerial vehicle, and generate a communication routing table and synchronize it to each unmanned aerial vehicle in the cluster, where the secondary leading unmanned aerial vehicle is used to communicate with the unmanned aerial vehicles and the leading unmanned aerial vehicle in the group.

[0009] Preferably, the determination of the leading unmanned aerial vehicle according to the leading weight value of each unmanned aerial vehicle is specifically:

[0010] Create a ballot box for each unmanned aerial vehicle, control each unmanned aerial vehicle in the cluster to vote for itself initially, and make each unmanned aerial vehicle transfer its own vote to other unmanned aerial vehicles, while receiving the votes of other unmanned aerial vehicles;

[0011] Compare the received vote weights, and modify the vote pointing weight to a higher-weighted unmanned aerial vehicle when the received vote weight is higher than its own weight;

[0012] Continuously count the voting situation. When more than half of the drones vote for the same drone, set that drone as the leading drone.

[0013] Preferably, clustering and grouping other drones in the cluster according to the communication delay is specifically as follows:

[0014] Fuse the communication delay time with the leading weight value of the drone to generate a weight array. Based on the weight array, execute the clustering and grouping algorithm, divide the drone cluster into k = (N - 1) / t groups, calculate the distance between drones using the Euclidean distance formula, and complete the clustering through an iterative optimization process, where t represents the expected number of drones in each group.

[0015] Preferably, the execution process of the clustering and grouping algorithm is as follows:

[0016] S21 Randomly select k initial centroid vectors , where each centroid vector represents the coordinates of the drone weight combination data in the multi-dimensional feature space;

[0017] S22 Calculate the distance between the weight array of each drone and each centroid vector using the Euclidean distance formula , and its calculation formula is:

[0018]

[0019] where, represents the value of drone i on the l-th feature, represents the value of the centroid vector on the l-th feature, and assign to the cluster that makes the smallest ;

[0020] S23 For each cluster , update the clustering center according to the following formula:

[0021]

[0022] where, represents the j-th clustering, which contains several data points , represents the total number of data points in the clustering , , represents the accumulation of all data points in the clustering , represents the new clustering center;

[0023] Repeat steps S22 and S23 until all centroid vectors no longer change, and then output the clustering grouping result.

[0024] Preferably, it further includes:

[0025] Guide the UAV cluster to perform intermittent heartbeat monitoring through the communication routing table, detect the status of the leading UAV, and when it is detected that the leading UAV is out of control or offline, immediately trigger the process of re-electing the leading UAV for the entire cluster.

[0026] The second embodiment of the present invention provides a collaborative cluster control device for UAVs, including:

[0027] A weight calculation unit, configured to obtain the initial information of N UAVs in the cluster at the starting stage, and calculate the leading weight value of each UAV based on the initial information, where the initial information includes the ammunition load, combat radius, and endurance time;

[0028] A communication delay calculation unit, configured to determine the leading UAV according to the leading weight value of each UAV, and calculate the communication delay between the leading UAV and other UAVs in the cluster;

[0029] A communication routing table generation unit, configured to perform clustering grouping on other UAVs in the cluster according to the communication delay, randomly select a UAV from each group as a secondary leading UAV, and generate a communication routing table and synchronize it to each UAV in the cluster, where the secondary leading UAV is used to communicate with the UAVs and the leading UAV within the group.

[0030] The third embodiment of the present invention provides a collaborative cluster control device for UAVs, characterized by including a memory and a processor, where a computer program is stored in the memory, and the computer program can be executed by the processor to implement a collaborative cluster control method for UAVs as described in any one of the above.

[0031] The fourth embodiment of the present invention provides a computer-readable storage medium, characterized by storing a computer program, and the computer program can be executed by the processor of the device where the computer-readable storage medium is located to implement a collaborative cluster control method for UAVs as described in any one of the above.

[0032] Based on a collaborative cluster control method, device, equipment and storage medium for unmanned aerial vehicles provided by the present invention, in the initial stage, initial information of N unmanned aerial vehicles in the cluster is obtained, and the leading weight value of each unmanned aerial vehicle is calculated based on the initial information, wherein the initial information includes the ammunition load, combat radius and endurance time; then, the leading unmanned aerial vehicle is determined according to the leading weight value of each unmanned aerial vehicle, and the communication delay between the leading unmanned aerial vehicle and other unmanned aerial vehicles in the cluster is calculated; finally, other unmanned aerial vehicles in the cluster are clustered and grouped according to the communication delay, a secondary leading unmanned aerial vehicle is randomly selected from each group, and a communication routing table is generated and synchronized to each unmanned aerial vehicle in the cluster, wherein the secondary leading unmanned aerial vehicle is used to communicate with the unmanned aerial vehicles and the leading unmanned aerial vehicle in the group. The problem that the existing communication method between the leading unmanned aerial vehicle and the cluster is prone to cluster out-of-control is solved. BRIEF DESCRIPTION OF THE DRAWINGS

[0033] Figure 1 is a schematic flowchart of a collaborative cluster control method for unmanned aerial vehicles provided by the first embodiment of the present invention;

[0034] Figure 2 is a schematic diagram of the unmanned aerial vehicle election process provided by the present invention;

[0035] Figure 3 is a schematic diagram of cluster routing communication provided by the present invention;

[0036] Figure 4 is a schematic diagram of modules of a collaborative cluster control device for unmanned aerial vehicles provided by the second embodiment of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0037] The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0038] For a better understanding of the technical solutions of the present invention, the embodiments of the present invention will be described in detail below with reference to the accompanying drawings.

[0039] It should be clear that the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0040] The terms used in the embodiments of the present invention are for the purpose of describing specific embodiments only and are not intended to limit the present invention. The singular forms "a", "the" and "said" used in the embodiments of the present invention and the appended claims are also intended to include the plural forms unless the context clearly dictates otherwise.

[0041] It should be understood that the term "and / or" used herein is merely a correlative relationship describing associated objects, indicating that three relationships may exist. For example, A and / or B may represent: A exists alone, A and B exist simultaneously, and B exists alone. In addition, the character " / " herein generally represents an "or" relationship between the associated objects before and after.

[0042] Depending on the context, the word "if" as used herein may be interpreted as "when" or "while" or "in response to determining" or "in response to detecting". Similarly, depending on the context, the phrase "if determined" or "if detected (stated condition or event)" may be interpreted as "when determined" or "in response to determining" or "when detecting (stated condition or event)" or "in response to detecting (stated condition or event)".

[0043] The "first / second" mentioned in the embodiments is only to distinguish similar objects and does not represent a specific order for the objects. It can be understood that the "first / second" can be interchanged in a specific order or sequence when allowed. It should be understood that the objects distinguished by the "first / second" can be interchanged appropriately so that the embodiments described herein can be implemented in an order other than those illustrated or described herein.

[0044] The following describes the specific embodiments of the present invention in detail with reference to the accompanying drawings.

[0045] The present invention discloses a collaborative cluster control method, device, equipment and storage medium for unmanned aerial vehicles, aiming to solve the problem that the communication method between the existing leading unmanned aerial vehicle and the cluster is prone to cluster out-of-control.

[0046] Please refer to Figure 1 , the first embodiment of the present invention provides a collaborative cluster control method for unmanned aerial vehicles, which can be executed by a collaborative cluster control device of the unmanned aerial vehicle (hereinafter referred to as the control device), and particularly, executed by one or more processors in the control device to at least achieve the following steps:

[0047] S101, in the initial stage, obtain the initial information of N unmanned aerial vehicles in the cluster, and calculate the leading weight value of each unmanned aerial vehicle based on the initial information, where the initial information includes the payload, combat radius and endurance time;

[0048] In this embodiment, the control device can be a terminal with data processing capabilities such as a server, a desktop computer, a laptop computer, etc., which can communicate with each drone in the cluster. The corresponding operating system and application software can be installed in the control device, and the functions required in this embodiment can be realized through the combination of the operating system and the application software.

[0049] It should be noted that in this embodiment, after the task is started, the control device first establishes a stable connection with each drone in the cluster through a pre-configured communication network, so as to collect the initial state data of each drone in real time. These data can include but are not limited to the ammunition load, combat radius, and endurance time.

[0050] The control device performs weighted summation on the ammunition load, combat radius, and endurance time of the drone according to a predetermined weight coefficient. The leading weight value of each drone can be calculated using the formula uav_i_leader_weight = uav_payload_am × 0.4 + uav_radius × 0.3 + uav_endurance × 0.3. So that the control device can quickly and accurately quantify the comprehensive combat capabilities of each drone.

[0051] In a possible implementation manner of the present invention, a dynamic adjustment mechanism is introduced into the initial weight calculation formula, so that the weight coefficients of the ammunition load, combat radius, and endurance time of the drone can adaptively change under different task scenarios or environmental conditions. For example, in long-distance reconnaissance or high-intensity tasks, the endurance time of the drone may be more critical. At this time, the control device can increase the weight coefficient of the endurance time from the original 0.3 to 0.5 or higher by real-time monitoring of the task requirements and environmental parameters, and at the same time reduce the weights of the ammunition load or combat radius accordingly to more accurately reflect the actual requirements of the current task for the combat capabilities of the drone. On the other hand, if the task requires high strike capabilities, the importance of the ammunition load may increase. At this time, the control device can dynamically increase the weight of the ammunition load, and in a specific environment, such as complex terrain or a crowded war zone, the influence of the combat radius may also need to be further amplified, so as to adjust to a higher weight coefficient.

[0052] S102. Determine the leading drone according to the leading weight value of each drone, and calculate the communication delay between the leading drone and other drones in the cluster;

[0053] Please combine Figure 2, in a specific implementation, the control device first requires each UAV to create an independent ballot box when starting up and vote for itself during initialization, that is, each UAV records its own leadership weight as the basis for voting in the ballot box. Subsequently, each UAV transmits its own voting information to all other UAVs in the cluster according to a pre-determined communication protocol, and at the same time receives voting data from other UAVs. Each UAV continuously detects whether there is a candidate with a higher weight by comparing its own held leadership weight with the voting weights obtained from other UAVs; once it is found that the weight of a UAV in the received voting information is higher than its own, it will automatically adjust the voting direction and turn its vote to the UAV with the higher weight.

[0054] It should be noted that the dynamic vote transfer mechanism enables each UAV to always maintain its recognition of the optimal leadership candidate in the cluster, thus forming a self-organizing voting network. The control device continuously counts and monitors the voting situations of all UAVs in the background, and when it detects that more than half of the UAVs have voted for the same UAV, it immediately confirms this UAV as the leadership UAV and triggers the subsequent communication route construction and task assignment processes. This enables the cluster to quickly and accurately elect the leadership UAV with the most comprehensive combat capabilities without a central node, effectively reducing the risks caused by single-point failures.

[0055] Furthermore, after the leadership UAV is elected, it immediately enters the calculation stage of the communication link routing table. First, the leadership UAV establishes a real-time connection with each UAV in the cluster through a pre-set communication protocol, and uses the method of multiple round-trip data transmissions to accurately measure the communication delays of each link, so as to obtain the delay time between each pair of UAVs. Subsequently, the leadership UAV fuses these real-time measured communication delay data with the previously calculated leadership weight values of each UAV to form a new weight array, which is represented in the form of uva_cluster_list=[(time(Ei_j),uav_i_leader_weight),(time(Ei_k),uav_k_leader_weight)], where time(Ei_j) represents the communication delay between UAV i and UAV j, and uav_i_leader_weight represents the comprehensive combat capabilities of UAV i. Through this fusion, the real-time communication status of each link is accurately reflected.

[0056] S103, cluster and group the other UAVs in the cluster according to the communication delays, randomly select one UAV from each group as the secondary leadership UAV, and generate a communication routing table and synchronize it to each UAV in the cluster, where the secondary leadership UAV is used to communicate with the UAVs within the group and the leadership UAV.

[0057] It should be noted that by calculating the communication delay between UAVs, the actual communication quality and distance between nodes can be intuitively reflected, which provides an important basis for optimizing the communication architecture of the entire cluster. In practical applications, UAV clusters are often distributed in a wide area, and the communication links between different nodes may be affected by factors such as physical distance, environmental interference, or signal occlusion, resulting in different delays. After fusing these delay data with the respective leading weights of the UAVs and performing clustering, UAVs with better communication conditions and faster response speeds can be effectively grouped into the same group. In this way, nodes within each group can exchange information and cooperate in a shorter time, while reducing the communication load with long distances and high delays in the entire cluster. After grouping, a secondary leading UAV can be selected within each group to centrally process the data within the group, realizing hierarchical management and distributed control, thereby reducing the overall communication burden while enhancing the robustness of the system in the face of single-point failures or link interruptions.

[0058] Specifically, please combine Figure 3 In practical applications, when the control device obtains the communication delay data of each UAV, it first fuses these delay data with the leading weight values pre-calculated for each UAV to generate a weight array that comprehensively reflects the communication status and combat capabilities. This weight array not only reflects the actual communication delay between UAVs but also incorporates their respective performance indicators. Next, the control device calculates the number of clustering groups k based on the total number of UAVs N in the cluster and the expected number t in each group, where k = (N - 1) / t, which can be determined by appropriate rounding.

[0059] During the clustering process, the control device first randomly selects k initial centroid vectors in the multi-dimensional feature space, and each centroid vector represents an initial central position of the UAV weight combination data. Subsequently, by calculating the Euclidean distance between the weight array of each UAV and each centroid vector, the calculation formula is , where represents the value of UAV i on the l-th feature, represents the value of centroid vector uⱼ on the l-th feature, and is assigned to the cluster that makes the smallest ,

[0060] The control device assigns each UAV to the cluster where the nearest centroid is located. Then, for the UAVs within each cluster, the update formula is used to recalculate the clustering center, where represents the j-th cluster, which contains several data points , represents the cluster The total number of data points in , indicating that the clusters accumulate all the data points in represents the new cluster center;

[0061] The control device continuously repeats the iterative process of calculating the Euclidean distance and updating the centroid until all centroid vectors tend to be stable and no longer change. Finally, the clustering grouping result is output. It can accurately divide the UAV groups under different communication conditions.

[0062] It should be noted that after the clustering grouping is completed, a UAV is randomly selected from each group as the secondary pilot, aiming to simplify the selection mechanism and balance the load within each group. Since the clustering process has divided the UAVs into several subsets that are relatively close in communication quality and combat capabilities based on multi-dimensional features such as communication delay and pilot weight, within each subset, the UAVs have a certain degree of homogeneity in overall performance. At this time, the method of random selection can avoid introducing additional computational complexity or additional selection criteria, while ensuring that each group is representative without the need for more complex sorting or comparison of the specific details of each UAV within the group. In addition, this random selection method also helps to prevent the communication load from being overly concentrated on a specific UAV, because in a dynamic environment, if the secondary pilot of a group fails due to a fault or other reasons, it is relatively simple and quick to randomly select a new representative.

[0063] Furthermore, the pilot UAV synchronizes the routing table to each UAV in the cluster through a pre-set reliable data transmission protocol. In this way, each UAV can obtain the latest communication path information and link status, so as to clarify the best path and method for communicating with other nodes. Synchronizing the communication routing table can not only eliminate the local communication interruption problem caused by a single node or link failure, but also enable each UAV to independently select the optimal transmission path according to a unified and global routing strategy when receiving task instructions or sensor data, minimizing the transmission delay and improving the reliability of information transmission. In addition, this global synchronization mechanism also provides the UAV cluster with a fast response and self-repair ability. When communication anomalies or pilot UAV failures occur, other UAVs can quickly adjust the communication path according to the locally stored routing table to ensure the stable operation of the entire cluster.

[0064] In a possible implementation manner of the present invention, it further includes:

[0065] Guiding the UAV cluster to perform intermittent heartbeat monitoring through the communication routing table, detecting the status of the pilot UAV. When it is detected that the pilot UAV is out of control or offline, immediately trigger the process of re-electing the pilot UAV for the entire cluster.

[0066] It should be noted that all drones form a network with real-time updates through the synchronized communication routing table. Each drone sends and receives heartbeat information at preset time intervals to monitor the operating status of the leading drone in real time. Specifically, the current leading drone needs to broadcast its own status information, including control indicators and communication quality parameters, within a fixed cycle, and transmit these heartbeat data to the entire cluster through the predetermined communication route. When other drones fail to receive the heartbeat data from the leading drone within the specified time window, or detect abnormal status (such as control failure, data anomaly, or significant signal attenuation) in the received heartbeat data, these drones will immediately report the abnormal information to neighboring nodes through the network and cross-verify the situations detected by each other. After global information sharing, when a certain proportion of drones in the cluster confirm that the leading drone is out of control or offline, the system will automatically trigger the re-election process. At this time, all drones quickly complete the re-election of the leading drone according to the predefined voting rules, and update the new communication routing table to ensure that the entire cluster can resume normal communication and command control in the shortest time.

[0067] Please refer to Figure 4 , the second embodiment of the present invention provides a collaborative cluster control device for drones, including:

[0068] A weight calculation unit 201, configured to obtain the initial information of N drones in the cluster at the starting stage, and calculate the leading weight value of each drone based on the initial information, where the initial information includes the payload, combat radius, and endurance time;

[0069] A communication delay calculation unit 202, configured to determine the leading drone according to the leading weight value of each drone, and calculate the communication delay between the leading drone and other drones in the cluster;

[0070] A communication routing table generation unit 203, configured to cluster and group other drones in the cluster according to the communication delay, randomly select a drone as the secondary leading drone from each group, and generate a communication routing table and synchronize it to each drone in the cluster, where the secondary leading drone is used to communicate with the drones and the leading drone in the group.

[0071] The third embodiment of the present invention provides a collaborative cluster control device for drones, which is characterized by including a memory and a processor, where the memory stores a computer program that can be executed by the processor to implement a collaborative cluster control method for drones as described in any one of the above.

[0072] The fourth embodiment of the present invention provides a computer-readable storage medium, which is characterized in that it stores a computer program, and the computer program can be executed by a processor of a device where the computer-readable storage medium is located to implement the collaborative cluster control method of an unmanned aerial vehicle as described in any one of the above.

[0073] Based on the collaborative cluster control method, device, equipment and storage medium of an unmanned aerial vehicle provided by the present invention, in the initial stage, initial information of N unmanned aerial vehicles in the cluster is obtained, and a leading weight value of each unmanned aerial vehicle is calculated based on the initial information, where the initial information includes the ammunition load, combat radius and endurance time; then, a leading unmanned aerial vehicle is determined according to the leading weight value of each unmanned aerial vehicle, and the communication delay between the leading unmanned aerial vehicle and other unmanned aerial vehicles in the cluster is calculated; finally, other unmanned aerial vehicles in the cluster are clustered and grouped according to the communication delay, a secondary leading unmanned aerial vehicle is randomly selected from each group, and a communication routing table is generated and synchronized to each unmanned aerial vehicle in the cluster, where the secondary leading unmanned aerial vehicle is used to communicate with the unmanned aerial vehicles and the leading unmanned aerial vehicle in the group. The problem that the existing communication method between the leading unmanned aerial vehicle and the cluster is prone to cluster out-of-control is solved.

[0074] Exemplarily, the computer program described in the third and fourth embodiments of the present invention can be divided into one or more modules, and the one or more modules are stored in the memory and executed by the processor to complete the present invention. The one or more modules can be a series of computer program instruction segments capable of completing specific functions, and the instruction segments are used to describe the execution process of the computer program in the device for implementing the collaborative cluster control method of an unmanned aerial vehicle. For example, the device described in the second embodiment of the present invention.

[0075] The so-called processor may be a central processing unit (CPU), or may also be other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor may be a microprocessor or the processor may also be any conventional processor, etc. The processor is the control center of the collaborative cluster control method of an unmanned aerial vehicle, and uses various interfaces and lines to connect the various parts of the method for implementing the collaborative cluster control method of an unmanned aerial vehicle.

[0076] The memory can be used to store the computer programs and / or modules. By running or executing the computer programs and / or modules stored in the memory, and by invoking the data stored in the memory, the processor can implement various functions of a collaborative cluster control method for an unmanned aerial vehicle. The memory mainly includes a program storage area and a data storage area. Among them, the program storage area can store an operating system, application programs required for at least one function (such as a sound playback function, a text conversion function, etc.); the data storage area can store data created according to the use of the mobile phone (such as audio data, text message data, etc.). In addition, the memory can include high-speed random access memory, and can also include non-volatile memory, such as a hard disk, a memory, a plug-in hard disk, a smart media card (SMC), a secure digital (SD) card, a flash card, at least one magnetic disk storage device, a flash memory device, or other volatile solid-state storage devices.

[0077] Among them, if the implemented module is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on such an understanding, to implement all or part of the processes in the above-mentioned embodiment methods of the present invention, it can also be completed by instructing relevant hardware through a computer program. The computer program can be stored in a computer-readable storage medium. When the computer program is executed by a processor, the steps of the above-mentioned various method embodiments can be implemented. Among them, the computer program includes computer program code, and the computer program code can be in the form of source code, object code, an executable file, or some intermediate form, etc. The computer-readable medium can include: any entity or device capable of carrying the computer program code, a recording medium, a USB flash drive, a mobile hard disk, a magnetic disk, an optical disc, a computer memory, a read-only memory (ROM), a random access memory (RAM), an electrical carrier signal, a telecommunication signal, and a software distribution medium, etc. It should be noted that the content included in the computer-readable medium can be appropriately increased or decreased according to the requirements of legislation and patent practice in the jurisdiction. For example, in some jurisdictions, according to legislation and patent practice, the computer-readable medium does not include electrical carrier signals and telecommunication signals.

[0078] It should be noted that the device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or 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. In addition, in the accompanying drawings of the device embodiments provided by the present invention, the connection relationships between the modules indicate that they have communication connections, which can be specifically implemented as one or more communication buses or signal lines. Those of ordinary skill in the art can understand and implement it without creative efforts.

[0079] As described above, the above is only the preferred specific implementation manner of the present invention, but the protection scope of the present invention is not limited thereto. Any changes or substitutions that can be easily thought of by those skilled in the art within the technical scope disclosed by the present invention should be covered by the protection scope of the present invention. Therefore, the protection scope of the present invention should be subject to the protection scope of the claims.

Claims

1. A collaborative cluster control method for an unmanned aerial vehicle, characterized in that Including: In the initial stage, obtain the initial information of N drones in the cluster, and calculate the leader weight value of each drone based on the initial information. Specifically: create a ballot box for each drone, control each drone in the cluster to vote for itself initially, and make each drone pass its own vote to other drones while receiving the votes of other drones; compare the received vote weights, and modify the vote pointing to the drone with a higher weight when the received vote weight is higher than its own weight; Continuously count the voting situation. When more than half of the drones vote for the same drone, set that drone as the leading drone. Among them, the initial information includes the payload, combat radius, and endurance time. The initial weight calculation formula is uav_i_leader_weight = uav_payload_am×0.4 + uav_radius×0.3 + uav_endurance×0.

3. A dynamic adjustment mechanism is introduced in the initial weight calculation formula, so that the weight coefficients of the payload, combat radius, and endurance time of the drone can adaptively change under different mission scenarios or environmental conditions; Determine the leading drone according to the leader weight value of each drone, and calculate the communication delay between the leading drone and other drones in the cluster; Cluster and group other drones in the cluster according to the communication delay. Specifically: fuse the communication delay time with the leader weight value of the drone to generate a weight array, execute a clustering and grouping algorithm based on the weight array, divide the drone cluster into k = (N - 1) / t groups, calculate the distance between drones using the Euclidean distance formula, and complete the clustering through an iterative optimization process, where t represents the expected number of drones in each group; randomly select a drone from each group as the secondary leading drone, and generate a communication routing table and synchronize it to each drone in the cluster. The secondary leading drone is used to communicate with the drones and the leading drone within the group.

2. The collaborative cluster control method for an unmanned aerial vehicle according to claim 1, wherein The execution process of the clustering and grouping algorithm is: S21 Randomly select k initial centroid vectors , where each centroid vector represents the coordinates of the UAV weight combination data in the multi-dimensional feature space; S22 weights array of each drone Calculate its distance from each centroid vector using the Euclidean distance formula The distance between , and its calculation formula is: Among them, represents the value of the drone i on the l-th feature, represents the centroid vector on the l-th feature, and is assigned to the cluster that makes the smallest ; S23 For each cluster , update the cluster center according to the following formula : Among them, represents the j-th cluster, which contains a number of data points , represents the total number of data points in the cluster , represents adding up all the data points in the cluster , represents the new cluster center; Repeat steps S22 and S23 until all centroid vectors no longer change and output the clustering and grouping result.

3. A collaborative cluster control method for an unmanned aerial vehicle according to claim 1, characterized in that Also including: Guide the drone cluster to perform intermittent heartbeat monitoring through the communication routing table, detect the status of the leading drone. When it is detected that the leading drone is out of control or offline, immediately trigger the process of re-electing the leading drone for the entire cluster.

4. A collaborative cluster control device for an unmanned aerial vehicle, characterized in that, Including: A weight calculation unit, which is used to obtain the initial information of N drones in the cluster in the initial stage and calculate the leader weight value of each drone based on the initial information. Specifically, it is used to: create a ballot box for each drone, control each drone in the cluster to vote for itself initially, and make each drone pass its own vote to other drones while receiving the votes of other drones; compare the received vote weights, and modify the vote pointing to the drone with a higher weight when the received vote weight is higher than its own weight; Continuously count the voting situation. When more than half of the drones vote for the same drone, set that drone as the leading drone. Among them, the initial information includes the ammunition load, combat radius, and endurance time. The initial weight calculation formula is uav_i_leader_weight = uav_payload_am × 0.4 + uav_radius × 0.3 + uav_endurance × 0.

3. A dynamic adjustment mechanism is introduced into the initial weight calculation formula so that the weight coefficients of the ammunition load, combat radius, and endurance time of the drone can adaptively change under different mission scenarios or environmental conditions; A communication delay calculation unit, configured to determine the leading drone according to the leading weight value of each drone, and calculate the communication delay between the leading drone and other drones in the cluster; A communication routing table generation unit, configured to cluster and group other drones in the cluster according to the communication delay. Specifically, it is used to: fuse the communication delay time with the leading weight value of the drone to generate a weight array, execute a clustering grouping algorithm based on the weight array, divide the drone cluster into k = (N - 1) / t groups, calculate the distance between drones using the Euclidean distance formula, and complete clustering through an iterative optimization process, where t represents the expected number of drones in each group; randomly select a drone from each group as the secondary leading drone, and generate a communication routing table and synchronize it to each drone in the cluster. Among them, the secondary leading drone is used to communicate with the drones and the leading drone within the group.

5. A collaborative cluster control device for an unmanned aerial vehicle, characterized in that, It includes a memory and a processor. The memory stores a computer program, and the computer program can be executed by the processor to implement a collaborative cluster control method for a drone as described in any one of claims 1 to 3.

6. A computer-readable storage medium, characterized in that, A computer program is stored, and the computer program can be executed by the processor of the device where the computer-readable storage medium is located to implement a collaborative cluster control method for a drone as described in any one of claims 1 to 3.

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