A method, system and storage medium for planning spectrum resources of clustered drone swarms

Abstract

The present application discloses a method, system and storage medium for planning spectrum resources of clustered drone swarms. The method includes: preprocessing the actual information of communication scenarios and drone nodes to obtain communication scenario attribute parameter information and drone node attribute parameter information; using the K-Means algorithm to perform initial formation of the drone swarm based on the communication scenario attribute parameter information and the drone node attribute parameter information, and obtaining the initial clustering result and initial spectrum allocation result of the drone swarm; inputting the initial clustering result and the initial spectrum allocation result into the simulated annealing-ant colony clustering algorithm, and outputting the target clustering result and target spectrum allocation result of the drone swarm. By clustering the drone swarm, the present invention can reduce communication interference between drones, improve the stability and reliability of communication links, optimize drone clustering and spectrum allocation, and improve the utilization rate of spectrum resources and the reliability of drone swarm communication.

Description

Technical Field

[0001] The present application relates to the technical field of spectrum planning, and more specifically, to a spectrum resource planning method, system and storage medium for a clustered drone swarm. Background Art

[0002] With the rapid development of modern drone swarm communication technology and the increasing shortage of electromagnetic spectrum resources, cognitive radio spectrum sharing technology has become one of the important methods to solve the shortage of spectrum resources. To plan the spectrum resources of drone swarms, it is necessary to form a drone swarm. At present, the main control methods of drone swarm formation include decentralized control method, distributed control method, centralized control method and clustering control method. Among the clustering control methods, the classic drone swarm clustering methods mainly include Low Energy Adaptive Clustering Hierarchy (LEACH) algorithm, GAF (Geographical Adaptive Fidelity) clustering algorithm and minimum ID (Identification) clustering algorithm.

[0003] In the existing technology, the dynamic clustering mechanism and load balancing method, combined with the political optimization algorithm (PO) and Shannon entropy function, can solve the fault tolerance and traffic peak problems in the cluster, thereby improving data transmission efficiency and balancing load. The clustering method based on the binary whale optimization algorithm and the routing selection algorithm based on the multi-objective ant colony optimization algorithm effectively reduce energy consumption, extend network survival time, and improve data transmission efficiency. The resource allocation algorithm based on the physical layer security transmission theory aims to maximize the network confidentiality energy efficiency, comprehensively considers the user's confidentiality rate and transmission power constraints, and introduces the concept of network confidentiality energy efficiency.

[0004] However, although the PO algorithm can provide high accuracy, its convergence speed is not ideal due to the large amount of calculation. The efficiency and adaptability of the clustering method based on the binary whale optimization algorithm and the optimization algorithm based on the multi-objective ant colony need to be improved, and the cost of obtaining the location information of the drone nodes is high. The resource allocation algorithm based on the physical layer security transmission theory is highly complex and does not consider active attacks and other security threats. Most of the current drone swarm clustering algorithms consider node energy and network reliability, and do not consider the planning of spectrum resources by drone swarms under the spectrum sharing mechanism. Summary of the invention

[0005] In response to at least one defect or improvement need in the prior art, the present invention provides a clustered drone swarm spectrum resource planning method, system and storage medium, which are used to solve the problem that most drone swarm clustering algorithms in the prior art consider node energy and network reliability, but do not consider the planning of spectrum resources for drone swarms under a spectrum sharing mechanism.

[0006] To achieve the above object, according to a first aspect of the present invention, a method for planning spectrum resources for a clustered drone swarm is provided, comprising:

[0007] Preprocess the actual information of the communication scene and the drone node to obtain the communication scene attribute parameter information and the drone node attribute parameter information;

[0008] According to the communication scenario attribute parameter information and the drone node attribute parameter information, the K-Means algorithm is used to perform the initial formation of the drone swarm, and the initial clustering result and initial spectrum allocation result of the drone swarm are obtained;

[0009] The initial clustering results and initial spectrum allocation results are input into the simulated annealing-ant colony clustering algorithm, and the target clustering results and target spectrum allocation results of the drone swarm are obtained as output.

[0010] In a possible implementation, the K-Means algorithm is used to perform initial formation of the drone swarm according to the communication scenario attribute parameter information and the drone node attribute parameter information, and the initial clustering result and initial spectrum allocation result of the drone swarm are obtained, which also includes:

[0011] Perform iterative clustering on the drone nodes until the preset iteration termination condition is reached to obtain the initial drone cluster and initial cluster head;

[0012] Allocate frequency channels to the initial cluster head through cluster head information of the ground base station and the initial UAV cluster;

[0013] Different frequency channels are allocated to other UAV nodes in the same UAV cluster according to the frequency channel of the initial cluster head.

[0014] In a possible implementation, the UAV nodes are iteratively clustered until a preset iteration termination condition is reached to obtain an initial UAV cluster and an initial cluster head, and further includes:

[0015] Initialize the spatial position of the drone nodes and randomly select several nodes from the drone nodes as the initial clustering centers;

[0016] According to the Euclidean distance between other UAV nodes and the initial cluster center, other UAV nodes are assigned to the cluster where the initial cluster center with the smallest Euclidean distance is located to obtain multiple UAV clusters;

[0017] Iterative clustering is performed based on the attribute parameter information of all drone nodes in the drone cluster until the cluster center of the generated drone cluster no longer changes or reaches a preset number of iterations, and the cluster center is used as the initial cluster head of the initial drone cluster.

[0018] In a possible implementation, allocating a frequency channel to the initial cluster head through cluster head information of the ground base station and the initial UAV cluster group also includes:

[0019] Allocating different frequency channels to the initial cluster head whose distance from the ground base station is less than a preset distance;

[0020] The initial cluster head allocated with a frequency channel allocates a frequency channel different from its own frequency channel to the initial cluster head which is closest in space and not allocated with a frequency channel, until all the initial cluster heads are allocated with frequency channels.

[0021] In a possible implementation, different frequency channels are allocated to other drone nodes in the same drone cluster according to the frequency channel of the initial cluster head, and further includes:

[0022] When all the initial cluster heads are assigned frequency channels, the initial cluster heads assigned frequency channels will allocate a frequency channel different from their own frequency channel and the frequency channels of other initial cluster heads to the drone nodes in their respective drone clusters one by one.

[0023] In a possible implementation, the initial clustering result and the initial spectrum allocation result are input into a simulated annealing-ant colony clustering algorithm, and the target clustering result and the target spectrum allocation result of the drone swarm are output, which also includes:

[0024] Project the nodes in the drone cluster onto a preset grid plane according to the initial clustering results and the initial spectrum allocation results;

[0025] Randomly project the initialized ant nodes onto the preset grid plane where the drone nodes are located;

[0026] Spectrum resources are iteratively allocated to the drone cluster based on the positions of ant nodes on a preset grid plane and the positions of drone nodes on a preset grid plane.

[0027] In a possible implementation, iteratively allocating spectrum resources to a drone cluster based on positions of ant nodes on a preset grid plane and positions of drone nodes on a preset grid plane also includes:

[0028] If there is no drone node at the location of the ant node, the ant node will be randomly moved according to the preset step size;

[0029] If there is a drone node at the location of the ant node, traverse the neighborhood of the drone node and calculate the probability that the ant node drops the drone node;

[0030] If there is no location to drop the drone node, the ant node will put the drone node back to the initial position of the drone node on the preset grid plane.

[0031] In a possible implementation, iteratively allocating spectrum resources to a drone cluster based on positions of ant nodes on a preset grid plane and positions of drone nodes on a preset grid plane also includes:

[0032] The temperature of the ant node is updated after each iteration. When the updated temperature of the ant node is less than the preset termination temperature, the iteration is terminated.

[0033] According to a second aspect of the present invention, a clustered UAV swarm spectrum resource planning system is also provided, comprising:

[0034] An information acquisition module is configured to pre-process the actual information of the communication scene and the drone node to obtain the communication scene attribute parameter information and the drone node attribute parameter information;

[0035] The initial formation module is configured to perform initial formation of the drone swarm using the K-Means algorithm according to the communication scenario attribute parameter information and the drone node attribute parameter information, and obtain the initial clustering result and initial spectrum allocation result of the drone swarm;

[0036] The spectrum planning module is configured to input the initial clustering results and the initial spectrum allocation results into the simulated annealing-ant colony clustering algorithm, and output the target clustering results and the target spectrum allocation results of the drone swarm.

[0037] According to the third aspect of the present invention, there is also provided a spectrum resource planning device for a clustered drone swarm, which includes at least one processing unit and at least one storage unit, wherein the storage unit stores a computer program, and when the computer program is executed by the processing unit, the processing unit executes any step of the above-mentioned spectrum resource planning method for a clustered drone swarm.

[0038] According to the fourth aspect of the present invention, there is also provided a storage medium storing a computer program executable by an access authentication device. When the computer program runs on the access authentication device, the access authentication device executes any step of the above-mentioned method for clustered drone swarm spectrum resource planning.

[0039] In general, the above technical solutions conceived by the present invention can achieve the following beneficial effects compared with the prior art:

[0040] The present invention provides a clustered UAV swarm spectrum resource planning method, which extracts key communication scenario attribute parameters and UAV node attribute parameter information by preprocessing the actual information of the communication scene and the UAV node, which helps to improve the accuracy of resource planning. The K-Means algorithm is used for initial formation, which has high efficiency in processing large-scale data and can quickly narrow the search space. According to the simulated annealing-ant colony clustering algorithm, limited spectrum resources can be more effectively utilized, spectrum conflicts and interference can be reduced, and the utilization rate of spectrum resources and the reliability of UAV swarm communication can be improved. The spectrum resource planning of UAV swarm under the clustered architecture can not only quickly realize the clustering of UAV swarm, effectively improve the overall frequency efficiency of UAV swarm, but also improve the global search capability of the planning method. Through reasonable clustering and spectrum allocation, it can ensure that the UAV swarm maintains efficient communication links and data transmission capabilities during the collaborative work process, which helps to improve the collaborative work efficiency of the UAV swarm. BRIEF DESCRIPTION OF THE DRAWINGS

[0041] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings required for use in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0042] Figure 1 A schematic diagram of a flow chart of an embodiment of a method for planning spectrum resources of a clustered UAV group provided by the present invention;

[0043] Figure 2 The present invention provides Figure 1 A schematic diagram of a flow chart of an embodiment of step S102;

[0044] Figure 3 The present invention provides Figure 2 A schematic diagram of a flow chart of an embodiment of step S201;

[0045] Figure 4 The present invention provides Figure 1 A schematic flow chart of an embodiment of step S103;

[0046] Figure 5 The present invention provides Figure 4 A schematic flow chart of an embodiment of step S403;

[0047] Figure 6 A schematic diagram of the structure of an embodiment of a clustered UAV swarm spectrum resource planning device provided by the present invention;

[0048] Figure 7A schematic diagram of the structure of a clustered UAV swarm spectrum resource planning device provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0049] In order to make the purpose, technical solutions and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention. In addition, the technical features involved in the various embodiments of the present invention described below can be combined with each other as long as they do not conflict with each other.

[0050] The terms "first", "second", "third", etc. in the specification and claims of this application and the above-mentioned drawings are used to distinguish different objects, rather than to describe a specific order. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions. For example, a process, method, system, product or device that includes a series of steps or units is not limited to the listed steps or units, but optionally includes steps or units that are not listed, or optionally includes other steps or units inherent to these processes, methods, products or devices.

[0051] The present invention provides a method, system and storage medium for spectrum resource planning of a clustered UAV swarm, which are described below respectively.

[0052] See also Figure 1 , Figure 1 A schematic flow chart of an embodiment of a method for planning spectrum resources of a clustered drone swarm provided by the present invention. In a specific embodiment of the present invention, a method for planning spectrum resources of a clustered drone swarm is disclosed, including:

[0053] S101, preprocessing the actual information of the communication scene and the drone node to obtain the communication scene attribute parameter information and the drone node attribute parameter information;

[0054] S102, using the K-Means algorithm to perform initial formation of the drone swarm according to the communication scenario attribute parameter information and the drone node attribute parameter information, and obtaining the initial clustering result and initial spectrum allocation result of the drone swarm;

[0055] S103, inputting the initial clustering result and the initial spectrum allocation result into the simulated annealing-ant colony clustering algorithm, and outputting the target clustering result and the target spectrum allocation result of the drone swarm.

[0056] In the above embodiment, relevant data of the communication scene is collected, including but not limited to topography, obstacle distribution, weather conditions, etc., as well as detailed attribute parameters of the drone node, such as location information (longitude, latitude, altitude), speed, flight direction, communication capability (transmitting power, receiving sensitivity), battery capacity and current remaining power, task priority, etc.

[0057] By integrating and analyzing the above data, data processing techniques (such as data cleaning and normalization) are used to extract the attribute parameter information of the communication scenario, including the path loss index, shadow effect standard deviation, additive white Gaussian noise power and variance of the Rayleigh fading envelope in the Rayleigh fading model (collectively referred to as the statistical parameter information of the Rayleigh fading model), etc. The attribute parameter information of the UAV node mainly includes the number of nodes, node location, transmit power, receive power, antenna gain, available frequency channels, etc.

[0058] According to the communication scene attribute parameters (such as terrain complexity, obstacle density) and the attribute parameters of the drone nodes (such as communication distance, mission requirements), the distance from each drone node to each center point is iteratively calculated and assigned to the cluster to which the nearest center point belongs. The center point position of each cluster is continuously updated until the center point position no longer changes significantly or reaches the preset number of iterations, at which time the initial clustering result of the drone group is obtained. Combined with the clustering results, spectrum resources are preliminarily allocated to each cluster to form the initial spectrum allocation result.

[0059] The initial clustering results and initial spectrum allocation results obtained in S102 are used as input, and the simulated annealing algorithm is introduced to enhance the global search capability. By probabilistically accepting poor solutions to jump out of the local optimum, the global optimal solution is gradually approached. On the basis of simulated annealing, the ant colony algorithm is combined for detailed search to simulate the process of ants finding the shortest path through pheromones. The drone nodes are regarded as ants, the clustering results are regarded as paths, and the spectrum allocation is regarded as resource allocation on the path. By continuously updating the pheromone concentration (i.e., the degree of clustering and spectrum allocation), the ants (drone nodes) are guided to gather towards a better solution (clustering and spectrum allocation scheme). After multiple rounds of iterations, until the termination condition is met (such as reaching the maximum number of iterations and the quality of the solution is no longer significantly improved), the target clustering results and target spectrum allocation results of the drone swarm are finally output. These results optimize the use efficiency of spectrum resources while ensuring the communication quality and improve the collaborative operation capability of the drone swarm.

[0060] The communication scenario attribute parameter information includes the statistical parameter information of the Rayleigh fading model. In the model, the probability density of the signal envelope conforms to the Rice distribution, and its probability density function is expressed as follows:

[0061] ;

[0062] in, represents the signal envelope, represents the signal envelope variance, represents the sight distance component, represents the first kind of zero-order modified Bessel function. When When , that is, when there is no line-of-sight link, Rayleigh distribution can be obtained. At this time, the channel fading model is a Rayleigh fading model, and its probability density function is expressed as:

[0063] ;

[0064] Among them, when the signal envelope obeys Rayleigh distribution, the square envelope obeys exponential distribution, and its probability density function is expressed as:

[0065] ;

[0066] The large-scale channel fading model of UAV swarm communication is established as:

[0067] ;

[0068] in, is the UAV transmission power, is the received power, Indicates the reference distance The path loss is is the path loss exponent, is the distance from the transmitter to the receiver, is a log-normally distributed random variable with zero mean and standard deviation Represents the power change caused by the shadow effect.

[0069] Among them, the number and location of drone nodes conform to the Poisson cluster process, which Including the parent process and the child process, first through the Poisson point process The parent process that generates the Poisson cluster process is when a circle with these points as the center and a radius of Multiple points are randomly generated uniformly within the circle, and their probability density function is expressed as follows:

[0070] ;

[0071] in, is the coordinate of the sub-process point relative to the parent process point, is the Euclidean distance from the child process point to the parent process point. In the random variable All values , each value conforms to the Poisson distribution in a certain area, that is:

[0072] ;

[0073] in, is the number of points of the subprocess, Poisson point process density.

[0074] Compared with the prior art, the present embodiment provides a clustered UAV swarm spectrum resource planning method, which extracts key communication scenario attribute parameters and UAV node attribute parameter information by preprocessing the actual information of the communication scene and the UAV node, which helps to improve the accuracy of resource planning. The K-Means algorithm is used for initial formation, which has high efficiency in processing large-scale data and can quickly narrow the search space. According to the simulated annealing-ant colony clustering algorithm, limited spectrum resources can be more effectively utilized, spectrum conflicts and interference can be reduced, and the utilization rate of spectrum resources and the reliability of UAV swarm communication can be improved. The spectrum resource planning of UAV swarm under the clustered architecture can not only quickly realize the clustering of UAV swarm, effectively improve the overall frequency efficiency of UAV swarm, but also improve the global search capability of the planning method. Through reasonable clustering and spectrum allocation, it can ensure that the UAV swarm maintains efficient communication links and data transmission capabilities during the collaborative work process, which helps to improve the collaborative work efficiency of the UAV swarm.

[0075] See also Figure 2 , Figure 2 The present invention provides Figure 1 In some embodiments of the present invention, the K-Means algorithm is used to perform initial formation of the drone swarm according to the communication scenario attribute parameter information and the drone node attribute parameter information to obtain the initial clustering result and initial spectrum allocation result of the drone swarm, and further includes:

[0076] S201, performing iterative clustering processing on the drone nodes until a preset iteration termination condition is reached to obtain an initial drone cluster and an initial cluster head;

[0077] S202, allocating a frequency channel to the initial cluster head through cluster head information of the ground base station and the initial UAV cluster;

[0078] S203. Allocate different frequency channels to other drone nodes in the same drone cluster according to the frequency channel of the initial cluster head.

[0079] In the above embodiment, each iteration recalculates the center point of each cluster and reallocates the drone nodes to each cluster according to the new center point. This process continues until the preset iteration termination condition is met. When the iteration termination condition is met, the initial drone clusters and the cluster head of each cluster are obtained. The cluster head is selected as the drone node with the highest communication capability or the most central position in the cluster so that it can effectively communicate with other cluster heads and ground base stations.

[0080] The ground base station will allocate a unique frequency channel to the cluster head according to the cluster head information of the initial UAV cluster. In the process of allocating frequency channels, multiple factors need to be considered, such as the availability of frequency channels, interference between adjacent cluster heads, and communication requirements.

[0081] The drone cluster head allocates different frequency channels to other drone nodes in the same drone cluster according to the allocated frequency channels. It is necessary to ensure that the drone nodes in the same cluster do not interfere with each other and use the available spectrum resources as efficiently as possible.

[0082] See also Figure 3 , Figure 3 The present invention provides Figure 2 In some embodiments of the present invention, the process of performing iterative clustering on the drone nodes until a preset iteration termination condition is reached to obtain an initial drone cluster and an initial cluster head also includes:

[0083] S301, initializing the spatial position of the drone nodes, and randomly selecting several nodes from the drone nodes as initial clustering centers;

[0084] S302, according to the Euclidean distance between other UAV nodes and the initial cluster center, allocating other UAV nodes to the cluster where the initial cluster center with the smallest Euclidean distance is located to obtain multiple UAV clusters;

[0085] S303, performing iterative clustering based on the attribute parameter information of all drone nodes in the drone cluster until the cluster center of the generated drone cluster no longer changes or reaches a preset number of iterations, and using the cluster center as the initial cluster head of the initial drone cluster.

[0086] In the above embodiment, the drone node attribute parameter information includes the spatial position of the drone node, and specifically includes coordinate data such as longitude, latitude and altitude, which determines the relative position and distance between drone nodes. A random selection method is used to select several nodes from all drone nodes as initial clustering centers.

[0087] After determining the initial cluster centers, the Euclidean distances between other drone nodes and these initial cluster centers are calculated. Based on the calculated Euclidean distances, each drone node is assigned to the cluster where the initial cluster center closest to it is located, and a preliminary drone cluster division is obtained.

[0088] In each iteration, the cluster center of each drone cluster is recalculated according to the mean of the attributes of all nodes in each drone cluster in the drone cluster obtained in step S302 until the preset iteration termination condition is met. The preset iteration termination condition is that the cluster center of the generated drone cluster no longer changes or reaches the preset number of iterations. When the preset iteration termination condition is met, a stable drone cluster division and initial cluster head are obtained.

[0089] In some embodiments of the present invention, allocating a frequency channel to an initial cluster head through cluster head information of a ground base station and an initial UAV cluster group also includes:

[0090] Allocating different frequency channels to the initial cluster head whose distance from the ground base station is less than a preset distance;

[0091] The initial cluster head allocated with a frequency channel allocates a frequency channel different from its own frequency channel to the initial cluster head which is closest in space and not allocated with a frequency channel, until all the initial cluster heads are allocated with frequency channels.

[0092] In the above embodiment, the ground base station needs to measure the communication distance between each initial cluster head, which is achieved by wireless signal propagation time, GPS positioning data or other distance measurement technology. In order to ensure communication quality and reduce interference, a preset distance threshold needs to be set, and the preset distance threshold is determined based on the characteristics of the communication system (such as signal attenuation, noise level, etc.). For initial cluster heads whose distance from the ground base station is less than the preset distance, the ground base station will allocate different frequency channels to them according to the available frequency channels to ensure that cluster heads in close proximity do not use the same frequency channel, thereby reducing potential communication interference.

[0093] The initial cluster heads whose communication distance with the ground base station is greater than the preset distance threshold have not been assigned frequency channels. For each initial cluster head that has been assigned a frequency channel, it will check the surrounding cluster heads that have not been assigned channels and select the one with the closest spatial distance. The cluster head that has been assigned a channel will select a channel different from the frequency channel currently used by itself from the available frequency channels and assign it to the nearest unassigned cluster head, ensuring that the communication between adjacent cluster heads will not interfere with each other. This process will be recursively executed until all initial cluster heads have been assigned frequency channels. Each recursion will dynamically select new channels to be assigned to unassigned cluster heads based on the current status of the assigned channels.

[0094] In some embodiments of the present invention, different frequency channels are allocated to other drone nodes in the same drone cluster according to the frequency channel of the initial cluster head, further comprising:

[0095] When all the initial cluster heads are assigned frequency channels, the initial cluster heads assigned frequency channels will allocate a frequency channel different from their own frequency channel and the frequency channels of other initial cluster heads to the drone nodes in their respective drone clusters one by one.

[0096] In the above embodiment, for each initial cluster head, according to its allocated frequency channel, a frequency channel that is different from the cluster head frequency channel and all other initial cluster head frequency channels is screened out from the remaining available frequency channels and allocated to other drone nodes in the cluster.

[0097] It should be noted that the initial cluster head needs to allocate frequency channels to each drone node in the cluster one by one, and it needs to ensure that each drone node is allocated a unique frequency channel that does not conflict with other drone nodes in or outside the cluster. After each allocation, the frequency channel information of the drone node needs to be recorded to ensure the accuracy of subsequent allocations.

[0098] After completing the frequency channel allocation for all drone nodes, a comprehensive verification can be performed to ensure that each drone node has been assigned a valid, non-conflicting frequency channel. If any conflicts or incorrect allocations are found, adjustments need to be made immediately.

[0099] In a preferred embodiment of the present invention, the specific process of using the K-Means algorithm to perform initial formation of a drone group is as follows:

[0100] 1) Initialize the spatial position of the drone node.

[0101] 2) Randomly select from each drone node Cluster Centers .

[0102] 3) Calculate the remaining drone nodes Get here Cluster Centers Euclidean distance, and for each drone node arrive The distances between cluster centers are sorted and then assigned to the cluster where the nearest cluster center is located.

[0103] 4) Calculate the new cluster center of each drone cluster. At this time, the new cluster center is the mean of the attributes of all nodes in each drone cluster and is a virtual cluster head.

[0104] 5) Repeat steps 2-3 until the cluster center of each cluster is The iteration ends when there is no more change or the maximum number of iterations is reached.

[0105] 6) Calculate each node in each cluster To cluster center The distance is sorted and the nearest drone node is placed Update to the final cluster head.

[0106] 7) The ground base station randomly allocates frequency channels to the neighboring cluster head nodes: Starting from the base station, different frequency channels are allocated to the neighboring cluster head nodes.

[0107] 8) Cluster head nodes randomly assign frequency channels to cluster head nodes: Cluster head nodes that are assigned frequency channels assign a frequency channel different from the frequency channel on which their own signals are received to the nearest cluster head node that is not assigned a frequency channel.

[0108] 9) The cluster head node randomly assigns frequency channels to cluster member nodes: traverse all cluster head nodes and assign a frequency channel to the cluster member nodes that is different from the frequency channel used by the cluster head to receive cluster head or ground base station signals and the frequency channel used to transmit signals to other cluster heads, and ensure that the frequency channels occupied by cluster member nodes receiving signals in the same cluster are different.

[0109] Remaining drone nodes With cluster center The Euclidean distance formula between is expressed as follows:

[0110] ;

[0111] in, For drone nodes No. properties, is the cluster center The properties, .

[0112] After obtaining the initial drone cluster, the new cluster center position is calculated as follows:

[0113] ;

[0114] in, For the The number of all nodes in a cluster, express The cluster where it is located.

[0115] See also Figure 4 , Figure 4 The present invention provides Figure 1The flowchart of an embodiment of step S103 in the embodiment of the present invention includes: in some embodiments of the present invention, the initial clustering result and the initial spectrum allocation result are input into the simulated annealing-ant colony clustering algorithm, and the target clustering result and the target spectrum allocation result of the drone group are output, and the following steps are also included:

[0116] S401, projecting the nodes in the drone cluster onto a preset grid plane according to the initial clustering result and the initial spectrum allocation result;

[0117] S402, randomly projecting the initialized ant nodes to the preset grid plane where the drone nodes are located;

[0118] S403: Iteratively allocate spectrum resources to the drone cluster based on the positions of the ant nodes on the preset grid plane and the positions of the drone nodes on the preset grid plane.

[0119] In the above embodiment, first, according to the initial clustering results and spectrum allocation results obtained in the above steps, each node in the drone cluster is mapped to a pre-set two-dimensional grid plane. Each drone node has a corresponding position coordinate on this grid, which reflects their relative position relationship in space and provides a basis for subsequent clustering and spectrum allocation.

[0120] Initialize a certain number of ant nodes, which represent the search agents in the algorithm and are used to find the optimal solution in the solution space. Each ant node is also randomly projected onto the preset grid plane where the drone node mentioned above is located. The position of the ant nodes is random, which ensures the diversity of the algorithm in the search space and helps to find the global optimal solution.

[0121] After both ant nodes and drone nodes are projected onto the grid plane, the algorithm begins to iteratively allocate spectrum resources based on their positional relationship. This process follows the working principle of the ant colony algorithm, that is, ant nodes select paths according to certain probabilistic rules (here, paths can be understood as the connection or clustering between drone nodes) and update pheromones (representing the quality of the path or the quality of spectrum allocation).

[0122] Specifically, the "distance" or "similarity" between the current location of each ant node and the location of the surrounding drone nodes is calculated, and based on this information and the initial spectrum allocation, a decision is made as to whether to reallocate spectrum resources. This process is repeated many times, and each iteration gradually converges to a better clustering and spectrum allocation solution based on the search and selection of ant nodes and the update of pheromones.

[0123] See also Figure 5 , Figure 5 The present invention provides Figure 4In some embodiments of the present invention, iteratively allocating spectrum resources to the drone cluster based on the positions of the ant nodes in the preset grid plane and the positions of the drone nodes in the preset grid plane also includes:

[0124] S501, if there is no drone node at the location of the ant node, move the ant node randomly according to a preset step length;

[0125] S502: If there is a drone node at the location of the ant node, traverse the neighborhood of the drone node and calculate the probability that the ant node drops the drone node;

[0126] S503: If there is no location to put down the drone node, the ant node puts the drone node back to the initial position of the drone node on the preset grid plane.

[0127] In the above embodiment, in each iteration, if there is no drone node at the current location of the ant node (that is, the location is empty or does not belong to the effective coverage range of any drone node), it will move randomly according to the preset step size, so that the ant node can traverse the entire grid plane and explore possible solution spaces. Random movement ensures the search range of the algorithm and helps to discover new and better clustering and spectrum allocation schemes.

[0128] If there is a drone node at the current location of the ant node, the algorithm will traverse the neighborhood of the drone node (i.e., the grid points within a certain range around it), and for each grid point in the neighborhood, the algorithm will calculate the probability that the ant node will drop (or associate) the drone node at that point. The result of the probability calculation will determine whether the ant node chooses to drop the drone node at the neighborhood point, thus affecting the results of clustering and spectrum allocation.

[0129] After traversing the neighborhood and trying to drop the drone node, if the ant node does not find a suitable location to drop the drone node (that is, all the attempted locations do not meet the conditions, or the drone node is dropped but does not meet the subsequent optimization requirements), the ant node will put the drone node back to its initial position on the preset grid plane. This step is a fallback mechanism of the algorithm, which ensures that the algorithm does not lose the initial valid information during the search process, and also helps the algorithm to re-explore these locations in subsequent iterations.

[0130] It should be noted that in the above process, each step is accompanied by information updates and feedback. For example, when an ant node successfully drops a drone node, the spectrum resource usage at that location is updated, which may affect the subsequent decisions of other ant nodes. Similarly, when an ant node fails to drop a drone node, it will also adjust its subsequent action strategy based on the current state.

[0131] In some embodiments of the present invention, iteratively allocating spectrum resources to a drone cluster based on positions of ant nodes on a preset grid plane and positions of drone nodes on a preset grid plane further includes:

[0132] The temperature of the ant node is updated after each iteration. When the updated temperature of the ant node is less than the preset termination temperature, the iteration is terminated.

[0133] In the above embodiment, after each iteration, the temperature of the ant node is updated. Here, "temperature" is a parameter in the simulated annealing algorithm, which is used to measure the quality of the current solution or the activity of the search. The temperature update can be based on a variety of factors, such as the gap between the current solution and the optimal solution, the number of iterations, the efficiency of the allocation of spectrum resources, etc. When the updated ant node temperature is less than the preset termination temperature, it is considered that the algorithm has converged to a sufficiently good solution, or the search space has been fully explored, and the iteration is terminated.

[0134] It should be noted that the choice of the preset termination temperature needs to be adjusted according to the specific problem to ensure that the algorithm can find a better solution within a reasonable time and will not miss a better solution due to premature termination. During the iteration process, it may be necessary to combine other strategies (such as local search, heuristic rules, etc.) to further improve the performance and efficiency of the algorithm.

[0135] In a preferred embodiment of the present invention, the specific process of obtaining the target clustering result and the target spectrum allocation result of the drone group by using the simulated annealing-ant colony clustering algorithm is as follows:

[0136] 1) Number of ants 、 Ant step length , initial temperature , termination temperature , cooling rate Perform initial settings.

[0137] 2) Project the drone nodes onto a 20×10 two-dimensional grid plane based on their clusters and assigned frequency channels.

[0138] 3) The ants are randomly projected onto the 20×10 two-dimensional grid plane where the drone nodes are located.

[0139] 4) Start iteration, traverse each ant, and determine whether there is a drone node at the ant’s location. If there is no drone node at the ant’s location, follow the ant’s step length. Perform random movement; if there is a drone node at the ant’s location, traverse the neighborhood of the drone node and calculate the probability that the ant will drop the drone node , until the ant puts down the drone node. If there is no place for the drone node to be put down, the ant will put the drone node back to its original position. After each iteration, update the temperature. Stop iteration when .

[0140] Among them, the update temperature formula is expressed as follows:

[0141] ;

[0142] in, is the temperature at the next iteration.

[0143] Among them, the probability formula for ants to drop drone nodes is It is expressed as follows:

[0144] ;

[0145] in, is a random number between 0 and 1, Spectral utility function representing the new solution for the UAV swarm formation.

[0146] Among them, the spectrum utility function of the drone swarm in step It is expressed as follows:

[0147] ;

[0148] in, is the working bandwidth of all drone nodes, is the total spectrum efficiency of the drone swarm downlink, is the variance of the total downlink throughput of the drone swarm.

[0149] Among them, the total spectrum efficiency of the drone swarm downlink in step , which is expressed as follows:

[0150] ;

[0151] in, , , It represents the access relationship among the cluster head node, cluster member nodes and ground base station, namely:

[0152] ;

[0153] , , It represents the frequency channel allocation scheme among cluster head nodes, cluster member nodes and ground base stations, namely:

[0154] ;

[0155] , , Indicates that the drone node uses the frequency channel The signal-to-interference-plus-noise ratio (SINR) when receiving the signal is:

[0156] ;

[0157] in, , , Indicates that the drone node uses the frequency channel The power of the co-channel interference signal received when receiving the signal, assuming that the UAV communication signal will not leak adjacent channels during the propagation process, that is, do not consider the cross-frequency interference, only consider the interference on the same channel, that is, co-channel interference, can be expressed as:

[0158] ;

[0159] in, Representation Node Frequency Channel Receiving Node The received signal power when transmitting the signal, Representation Node Frequency Channel Receiving Node The Gaussian white noise power when transmitting the signal, Representation Node Frequency Channel Receiving Node The antenna gain when transmitting the signal, Indicates a ground base station.

[0160] Among them, the variance of the total downlink throughput of the drone swarm is , which is expressed as follows:

[0161] ;

[0162] in, Indicates that the node adopts Frequency Channel Receiving Node The channel bandwidth occupied when transmitting signals, throughput is the sum of the throughput of all drone nodes and base stations, expressed as follows:

[0163] ;

[0164] is the mean throughput, expressed as follows:

[0165] .

[0166] In order to better implement the clustered UAV swarm spectrum resource planning method in the embodiment of the present invention, based on the clustered UAV swarm spectrum resource planning method, correspondingly, please refer to Figure 6 , Figure 6 This is a schematic diagram of the structure of an embodiment of a clustered UAV swarm spectrum resource planning device provided by the present invention. The embodiment of the present invention provides a clustered UAV swarm spectrum resource planning device 600, including:

[0167] An information acquisition module 610 is configured to pre-process the actual information of the communication scene and the drone node to obtain the communication scene attribute parameter information and the drone node attribute parameter information;

[0168] An initial formation module 620 is configured to perform an initial formation of the drone swarm using a K-Means algorithm according to the communication scenario attribute parameter information and the drone node attribute parameter information, and obtain an initial clustering result and an initial spectrum allocation result of the drone swarm;

[0169] The spectrum planning module 630 is configured to input the initial clustering results and the initial spectrum allocation results into the simulated annealing-ant colony clustering algorithm, and output the target clustering results and the target spectrum allocation results of the drone swarm.

[0170] It should be noted here that: the device 600 provided in the above embodiment can implement the technical solutions described in the above method embodiments. The specific implementation principles of the above modules or units can refer to the corresponding contents in the above method embodiments, which will not be repeated here.

[0171] See also Figure 7 , Figure 7 A schematic diagram of the structure of a clustered drone swarm spectrum resource planning device provided by an embodiment of the present invention. Based on the above-mentioned clustered drone swarm spectrum resource planning method, the present invention also provides a clustered drone swarm spectrum resource planning device, which can be a computing device such as a mobile terminal, a desktop computer, a notebook, a PDA, and a server. The clustered drone swarm spectrum resource planning device 700 includes a processor 710, a memory 720, and a display 730. Figure 7 Only some components of the clustered drone swarm spectrum resource planning device are shown, but it should be understood that it is not required to implement all of the shown components, and more or fewer components may be implemented instead.

[0172] In some embodiments, the memory 720 may be an internal storage unit of the clustered unmanned aerial vehicle swarm spectrum resource planning device 700, such as a hard disk or memory of the clustered unmanned aerial vehicle swarm spectrum resource planning device 700. In other embodiments, the memory 720 may also be an external storage device of the clustered unmanned aerial vehicle swarm spectrum resource planning device 700, such as a plug-in hard disk, a smart memory card (Smart Media Card, SMC), a secure digital (Secure Digital, SD) card, a flash card (Flash Card), etc. equipped on the clustered unmanned aerial vehicle swarm spectrum resource planning device 700. Further, the memory 720 may also include both an internal storage unit and an external storage device of the clustered unmanned aerial vehicle swarm spectrum resource planning device 700. The memory 720 is used to store application software and various types of data installed in the clustered unmanned aerial vehicle swarm spectrum resource planning device 700, such as a program code for installing the clustered unmanned aerial vehicle swarm spectrum resource planning device 700. The memory 720 may also be used to temporarily store data that has been output or is to be output. In one embodiment, a clustered drone swarm spectrum resource planning program 740 is stored in the memory 720, and the clustered drone swarm spectrum resource planning program 740 can be executed by the processor 710, thereby realizing the clustered drone swarm spectrum resource planning method of each embodiment of the present application.

[0173] In some embodiments, the processor 710 can be a central processing unit (CPU), a microprocessor or other data processing chip, used to run the program code stored in the memory 720 or process data, such as executing a clustered drone swarm spectrum resource planning method.

[0174] In some embodiments, the display 730 may be an LED display, a liquid crystal display, a touch-sensitive liquid crystal display, an OLED (Organic Light-Emitting Diode) touch device, etc. The display 730 is used to display information on the clustered drone swarm spectrum resource planning device 700 and to display a visual user interface. The components 710-730 of the clustered drone swarm spectrum resource planning device 700 communicate with each other via a system bus.

[0175] In one embodiment, when the processor 710 executes the clustered drone swarm spectrum resource planning program 740 in the memory 720, the steps in the above clustered drone swarm spectrum resource planning method are implemented.

[0176] This embodiment further provides a computer-readable storage medium, on which a clustered drone swarm spectrum resource planning program is stored. When the clustered drone swarm spectrum resource planning program is executed by a processor, the following steps are implemented:

[0177] Preprocess the actual information of the communication scene and the drone node to obtain the communication scene attribute parameter information and the drone node attribute parameter information;

[0178] According to the communication scenario attribute parameter information and the drone node attribute parameter information, the K-Means algorithm is used to perform the initial formation of the drone swarm, and the initial clustering result and initial spectrum allocation result of the drone swarm are obtained;

[0179] The initial clustering results and initial spectrum allocation results are input into the simulated annealing-ant colony clustering algorithm, and the target clustering results and target spectrum allocation results of the drone swarm are obtained as output.

[0180] In summary, the present invention provides a clustered UAV swarm spectrum resource planning method, which extracts key communication scenario attribute parameters and UAV node attribute parameter information by preprocessing the actual information of the communication scene and the UAV node, which helps to improve the accuracy of resource planning. The K-Means algorithm is used for initial formation, which has high efficiency in processing large-scale data and can quickly narrow the search space. According to the simulated annealing-ant colony clustering algorithm, limited spectrum resources can be more effectively utilized, spectrum conflicts and interference can be reduced, and the utilization rate of spectrum resources and the reliability of UAV swarm communication can be improved. The spectrum resource planning of the UAV swarm under the clustered architecture can not only quickly realize the clustering of the UAV swarm, effectively improve the overall frequency efficiency of the UAV swarm, but also improve the global search capability of the planning method. Through reasonable clustering and spectrum allocation, it can ensure that the UAV swarm maintains efficient communication links and data transmission capabilities during the collaborative work process, which helps to improve the collaborative work efficiency of the UAV swarm.

[0181] The present application also provides a computer-readable storage medium on which a computer program is stored, and when the program is executed by a processor, the steps of the above method are implemented. The computer-readable storage medium may include, but is not limited to, any type of disk, including a floppy disk, an optical disk, a DVD, a CD-ROM, a micro drive, and a magneto-optical disk, a ROM, a RAM, an EPROM, an EEPROM, a DRAM, a VRAM, a flash memory device, a magnetic card or an optical card, a nanosystem (including a molecular memory IC), or any type of medium or device suitable for storing instructions and / or data.

[0182] It should be noted that, for the aforementioned method embodiments, for the sake of simplicity, they are all expressed as a series of action combinations, but those skilled in the art should be aware that the present application is not limited by the described order of actions, because according to the present application, certain steps can be performed in other orders or simultaneously. Secondly, those skilled in the art should also be aware that the embodiments described in the specification are all preferred embodiments, and the actions and modules involved are not necessarily required by the present application.

[0183] In the above embodiments, the description of each embodiment has its own emphasis. For parts that are not described in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0184] In the several embodiments provided in the present application, it should be understood that the disclosed devices can be implemented in other ways. For example, the device embodiments described above are only schematic, such as the division of the units, which is only a logical function division. There may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some service interfaces, and the indirect coupling or communication connection of devices or units can be electrical or other forms.

[0185] 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 on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0186] In addition, each functional unit in each embodiment of the present application may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of software functional units.

[0187] If the integrated unit 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 memory. Based on this understanding, the technical solution of the present application, or the part that contributes to the prior art, or all or part of the technical solution can be embodied in the form of a software product. The computer software product is stored in a memory, including a number of instructions to enable a computer device (which can be a personal computer, server or network device, etc.) to execute all or part of the steps of the method described in each embodiment of the present application. The aforementioned memory includes: U disk, read-only memory (ROM), random access memory (RAM), mobile hard disk, disk or optical disk and other media that can store program codes.

[0188] A person skilled in the art may understand that all or part of the steps in the various methods of the above embodiments may be completed by instructing related hardware through a program, and the program may be stored in a computer-readable memory, which may include: a flash drive, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk, etc.

[0189] The above is only an exemplary embodiment of the present disclosure, and the scope of the present disclosure cannot be limited thereto. That is, any equivalent changes and modifications made according to the teachings of the present disclosure are still within the scope of the present disclosure. After considering the specification and practicing the disclosure here, those skilled in the art will easily think of the implementation scheme of the present disclosure. This application is intended to cover any modification, use or adaptation of the present disclosure, which follows the general principles of the present disclosure and includes common knowledge or customary technical means in the technical field not recorded in the present disclosure. The description and examples are regarded as exemplary only, and the scope and spirit of the present disclosure are defined by the claims.

[0190] The technical features of the above embodiments may be combined arbitrarily. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0191] It will be easily understood by those skilled in the art that the above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions and improvements made within the spirit and principles of the present invention should be included in the protection scope of the present invention.

Claims

1. A method for planning spectrum resources of clustered drone swarms, characterized in that: include: Preprocess the actual information of the communication scene and the drone node to obtain the communication scene attribute parameter information and the drone node attribute parameter information; According to the communication scenario attribute parameter information and the drone node attribute parameter information, the drone swarm is initially formed using the K-Means algorithm to obtain an initial clustering result and an initial spectrum allocation result of the drone swarm; Inputting the initial clustering result and the initial spectrum allocation result into a simulated annealing-ant colony clustering algorithm, and outputting a target clustering result and a target spectrum allocation result of the drone swarm; The step of inputting the initial clustering result and the initial spectrum allocation result into a simulated annealing-ant colony clustering algorithm and outputting a target clustering result and a target spectrum allocation result of the drone swarm further includes: Projecting the nodes in the drone cluster onto a preset grid plane according to the initial clustering result and the initial spectrum allocation result; Randomly projecting the initialized ant nodes to the preset grid plane where the drone nodes are located; Iteratively allocating spectrum resources to the drone cluster based on the positions of the ant nodes on the preset grid plane and the positions of the drone nodes on the preset grid plane; The iterative allocation of spectrum resources to the drone cluster based on the position of the ant node on the preset grid plane and the position of the drone node on the preset grid plane further includes: If there is no drone node at the location of the ant node, the ant node is randomly moved according to the preset step size; If there is a drone node at the location of the ant node, traverse the neighborhood of the drone node and calculate the probability that the ant node drops the drone node; If there is no location to drop the drone node, the ant node puts the drone node back to the initial position of the drone node on the preset grid plane.

2. The method for planning spectrum resources of clustered drone swarms according to claim 1, characterized in that: The method of performing an initial formation of the drone swarm using the K-Means algorithm according to the communication scenario attribute parameter information and the drone node attribute parameter information to obtain an initial clustering result and an initial spectrum allocation result of the drone swarm also includes: Perform iterative clustering on the drone nodes until the preset iteration termination condition is reached to obtain the initial drone cluster and initial cluster head; Allocate frequency channels to the initial cluster head through cluster head information of the ground base station and the initial UAV cluster; Different frequency channels are allocated to other UAV nodes in the same UAV cluster according to the frequency channel of the initial cluster head.

3. The method for planning spectrum resources of clustered drone swarms as claimed in claim 2, characterized in that: The iterative clustering process is performed on the drone nodes until a preset iteration termination condition is reached to obtain an initial drone cluster and an initial cluster head, and further includes: Initialize the spatial position of the drone nodes and randomly select several nodes from the drone nodes as the initial clustering centers; According to the Euclidean distance between other UAV nodes and the initial cluster center, other UAV nodes are assigned to the cluster where the initial cluster center with the smallest Euclidean distance is located to obtain multiple UAV clusters; Iterative clustering is performed based on the attribute parameter information of all drone nodes in the drone cluster until the cluster center of the generated drone cluster no longer changes or reaches a preset number of iterations, and the cluster center is used as the initial cluster head of the initial drone cluster.

4. The method for planning spectrum resources of clustered drone swarms as claimed in claim 2, characterized in that: The method of allocating a frequency channel to the initial cluster head through the cluster head information of the ground base station and the initial UAV cluster group also includes: Allocating different frequency channels to the initial cluster head whose distance from the ground base station is less than a preset distance; The initial cluster head allocated with a frequency channel allocates a frequency channel different from its own frequency channel to the initial cluster head which is closest in space and not allocated with a frequency channel, until all the initial cluster heads are allocated with frequency channels.

5. The method for planning spectrum resources of clustered drone swarms as claimed in claim 4, characterized in that: The method allocates different frequency channels to other drone nodes in the same drone cluster according to the frequency channel of the initial cluster head, and further includes: When all the initial cluster heads are assigned frequency channels, the initial cluster heads assigned frequency channels will allocate a frequency channel different from their own frequency channel and the frequency channels of other initial cluster heads to the drone nodes in their respective drone clusters one by one.

6. The method for planning spectrum resources of clustered drone swarms as claimed in claim 1, characterized in that: The iterative allocation of spectrum resources to the drone cluster based on the position of the ant node on the preset grid plane and the position of the drone node on the preset grid plane also includes: The temperature of the ant node is updated after each iteration. When the updated temperature of the ant node is less than the preset termination temperature, the iteration is terminated.

7. A clustered UAV swarm spectrum resource planning system, characterized in that: include: An information acquisition module is configured to pre-process the actual information of the communication scene and the drone node to obtain the communication scene attribute parameter information and the drone node attribute parameter information; An initial formation module, which is configured to perform initial formation of the drone swarm using a K-Means algorithm according to the communication scenario attribute parameter information and the drone node attribute parameter information, and obtain an initial clustering result and an initial spectrum allocation result of the drone swarm; A spectrum planning module, which is configured to input the initial clustering result and the initial spectrum allocation result into a simulated annealing-ant colony clustering algorithm, and output a target clustering result and a target spectrum allocation result of the drone swarm; The step of inputting the initial clustering result and the initial spectrum allocation result into a simulated annealing-ant colony clustering algorithm and outputting a target clustering result and a target spectrum allocation result of the drone swarm further includes: Projecting the nodes in the drone cluster onto a preset grid plane according to the initial clustering result and the initial spectrum allocation result; Randomly projecting the initialized ant nodes to the preset grid plane where the drone nodes are located; Iteratively allocating spectrum resources to the drone cluster based on the positions of the ant nodes on the preset grid plane and the positions of the drone nodes on the preset grid plane; The iterative allocation of spectrum resources to the drone cluster based on the position of the ant node on the preset grid plane and the position of the drone node on the preset grid plane further includes: If there is no drone node at the location of the ant node, the ant node is randomly moved according to the preset step size; If there is a drone node at the location of the ant node, traverse the neighborhood of the drone node and calculate the probability that the ant node drops the drone node; If there is no location to drop the drone node, the ant node puts the drone node back to the initial position of the drone node on the preset grid plane.

8. A storage medium, characterized in that: It stores a computer program that can be executed by an access authentication device. When the computer program runs on the access authentication device, the access authentication device executes the steps of the clustered drone swarm spectrum resource planning method according to any one of claims 1 to 6.

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