Large unmanned aerial vehicle communication system, communication system resource classification method and unmanned aerial vehicle

Abstract

The invention relates to the technical field of unmanned aerial vehicle communication, in particular to a large unmanned aerial vehicle communication system, a communication system resource classification method and an unmanned aerial vehicle, the large unmanned aerial vehicle communication system comprises a data collection module, a resource classification module, a hierarchical structure model construction and application module and a resource analysis module, wherein the data collection module obtains communication resource data and current environment data; the resource classification module generates a hierarchical resource list by using a hierarchical clustering algorithm; the hierarchical structure model construction and application module is combined with current environment data to construct a model and calculate a resource weight; and the resource analysis module carries out analysis according to the weight priority and obtains a system overall score. The modular design improves the accuracy of resource allocation and the environmental adaptability, provides a scientific basis for decision making, significantly improves the performance and reliability of the unmanned aerial vehicle communication system in a complex environment, and realizes the intelligentization and high efficiency of resource management.

Description

Technical Field

[0001] The present invention relates to the field of unmanned aerial vehicle communication technology, and in particular to a large-scale unmanned aerial vehicle communication system, a communication system resource classification method, and a unmanned aerial vehicle. Background Art

[0002] Traditional UAV communication systems often face many technical challenges when facing complex and changing communication environments.

[0003] First, resource management lacks a hierarchical and structured approach, which makes it difficult to fully grasp the relationship and importance between various communication resources.

[0004] Secondly, the impact of environmental factors has not been fully considered, making it difficult for the system to adapt to the complexity of actual communication scenarios. In addition, the lack of clear priority guidance in the resource analysis process may result in key resources not being evaluated in a timely and effective manner. At the same time, resource evaluation results are often isolated and lack a comprehensive scoring mechanism to reflect the performance of the overall communication system.

[0005] These problems limit the accuracy and adaptability of traditional systems in resource allocation and management, making it difficult to effectively respond to rapidly changing communication needs. In addition, insufficient decision support and a lack of intuitive and quantitative performance indicators affect the efficiency of system optimization and management. These technical problems combined limit the performance and reliability of traditional UAV communication systems in complex environments, making it difficult to meet the growing demand for efficient and flexible communications. Summary of the invention

[0006] The purpose of the present invention is to provide a large-scale unmanned aerial vehicle communication system, a communication system resource classification method and an unmanned aerial vehicle to solve the problems raised in the above-mentioned background technology.

[0007] To achieve the above-mentioned purpose, one of the purposes of the present invention is that a large-scale unmanned aerial vehicle communication system includes a data collection module, a resource classification module, a hierarchical model construction and application module and a resource analysis module, wherein: The data collection module collects communication resource data and current environment data; The resource classification module classifies the communication resource data using a hierarchical clustering algorithm to generate a hierarchical resource list, wherein the hierarchical resource list includes different types of resources; The hierarchical structure model construction and application module constructs a hierarchical structure model based on the current environment data and the hierarchical resource list using the hierarchical analysis method; and calculates the weights of different types of resources in the hierarchical resource list based on the constructed hierarchical structure model; The resource analysis module performs resource analysis on different types of resources in the hierarchical resource list in order from large to small according to the weights of different types of resources, obtains analysis results of different types of resources, and performs weighted summation of the analysis results of different types of resources according to the weights to obtain an overall score, which is output to the front end.

[0008] As a further improvement of the present technical solution, the data collection module collects communication resource data and current environment data, wherein the communication resource data includes but is not limited to hardware resources, software resources, spectrum resources and data link resources; the current environment data includes but is not limited to electromagnetic environment, meteorological conditions and geographical location.

[0009] As a further improvement of the technical solution, the resource classification module classifies the communication resource data using a hierarchical clustering algorithm to generate a structured resource list. The hierarchical clustering algorithm specifically includes: For each type of communication resource data, key features are extracted and the extracted features are standardized to form an N-dimensional feature vector, where the total number of resource items in the communication resource data; According to the N-dimensional feature vector of each type of communication resource data, the distance between any two types of communication resource data is calculated to construct an N×N similarity matrix, wherein the distance calculation includes but is not limited to Euclidean distance and Manhattan distance; Each type of communication resource data is regarded as a separate cluster, and according to the similarity matrix, the two clusters with the highest similarity are found, the two clusters are merged, the similarity matrix is ​​updated, and this step is repeated until the preset number of clusters is reached to generate a clustering result; A hierarchical tree is constructed based on the clustering results, where each node represents a communication resource category and the leaf nodes are specific resource items in the communication resource category; A descriptive label is assigned to each non-leaf node in the hierarchical tree. The label is determined based on the common characteristics of the resource items in the category, and the tree structure is converted into a hierarchical resource list. The hierarchical resource list includes resource categories and specific resource items.

[0010] As a further improvement of the technical solution, the hierarchical model construction and application module includes a hierarchical model construction unit, which constructs a hierarchical model based on current environment data and a hierarchical resource list using a hierarchical analysis method, specifically including: Obtain current environmental data and use machine learning methods to identify the factors that have the greatest impact on communication effectiveness; quantify each identified key factor and convert it into a value between 0 and 1, where the quantification method includes but is not limited to linear mapping, nonlinear function and discrete classification; Based on the quantified key environmental factors and the hierarchical resource list, a hierarchical model is established, wherein the hierarchical model includes a top layer, a middle layer and a bottom layer, wherein the top layer is the overall goal, the middle layer uses the quantified key environmental factors as influencing factors, and the bottom layer uses the resource categories in the hierarchical resource list.

[0011] As a further improvement of the present technical solution, the hierarchical model construction and application module includes a hierarchical model application unit, which calculates the weights of different types of resources in the hierarchical resource list according to the constructed hierarchical model, specifically including: Each pair of environmental factors in the middle layer of the hierarchical model is compared pairwise to construct a judgment matrix A, and the comparison is made using a 1-9 scale, where 1 indicates equal importance and 9 indicates the most important; Use the eigenvalue method to calculate the maximum eigenvalue λmax of the judgment matrix A and the corresponding eigenvector W, normalize the eigenvector W, and obtain the weight vector W_env of the environmental factors; Calculate the consistency index CI = (λmax-n) / (n-1), where n is the number of environmental factors, and calculate the consistency ratio CR = CI / RI, where RI is the random consistency index. If CR < 0.1, it is judged to have acceptable consistency, and the relative importance of resource categories is evaluated to obtain the weight matrix B of each resource category relative to each environmental factor; otherwise, reconstruct the judgment matrix A; According to the different types of resources in the hierarchical resource list, the comprehensive contribution of the top level in the hierarchical model is calculated as the final weight vector, according to W_end=B*W_env, where W_end is the final weight vector of the resource category.

[0012] As a further improvement of the technical solution, the resource analysis module includes a primary analysis unit, which performs resource analysis on the hierarchical resource list in descending order according to the weights of different types of resources, and obtains analysis results of different types of resources, specifically including: The final weight vector W_end of the resource categories calculated by the hierarchical model application unit is sorted in descending order, and a resource type index list corresponding to the weight is created; Based on the sorted weights, determine the priority of the hierarchical resource list for resource analysis, and prepare corresponding analysis methods and evaluation indicators for each resource type.

[0013] As a further improvement of the present technical solution, the resource analysis module includes a secondary analysis unit, which standardizes the evaluation indicators of each resource type calculated in the primary analysis unit; for each resource type, the standardized analysis result is multiplied by the corresponding weight to obtain an overall score for reflecting the performance of the entire communication system, and the overall score is output to the front end.

[0014] The second object of the present invention is to provide a large-scale unmanned aerial vehicle communication system resource classification method, comprising the following method steps: S1. Collect communication resource data and current environment data; S2. Classify the communication resource data using a hierarchical clustering algorithm to generate a hierarchical resource list, wherein the hierarchical resource list includes different types of resources; S3. Based on the current environment data and the hierarchical resource list, a hierarchical structure model is constructed using the hierarchical analysis method; based on the constructed hierarchical structure model, the weights of different types of resources in the hierarchical resource list are calculated; S4. According to the weights of different types of resources, in order from large to small, perform resource analysis on different types of resources in the hierarchical resource list to obtain analysis results of different types of resources. According to the weights, perform weighted summation on the analysis results of different types of resources to obtain an overall score, which is then output to the front end.

[0015] A third object of the present invention is to provide a drone of a large drone communication system, comprising a processor and a memory storing a computer program.

[0016] Compared with the prior art, the present invention has the following beneficial effects: 1. The large-scale UAV communication system, communication system resource classification method and UAV classify the communication resource data by using the hierarchical clustering algorithm to generate a hierarchical resource list, which lays a structured foundation for subsequent analysis; this hierarchical resource list not only provides a clear classification of resources, but also retains the hierarchical relationship between resources; then, the system cleverly combines the current environmental data with this hierarchical resource list, and uses the hierarchical analysis method to construct a hierarchical structure model that comprehensively considers environmental factors and resource distribution; this method enables the model to more accurately reflect the complexity of the actual communication scenario; based on this model, the system further calculates the weights of different types of resources in the hierarchical resource list, and this process not only considers the importance of the resources themselves, but also incorporates the influence of environmental factors. Through this multiple application, the hierarchical resource list has become a key link connecting the original data, environmental factors and resource importance, greatly improving the accuracy and adaptability of resource allocation and management, so that the system can better cope with the complex and changing communication environment.

[0017] 2. After completing the weight calculation of different types of resources in the hierarchical resource list, the system further exerts the value of these weight data; first, the system sorts the resources from large to small according to the calculated weights, which provides a clear priority guide for subsequent resource analysis; then, the system strictly follows this priority order and conducts in-depth analysis of different types of resources in turn, ensuring that the most important resources are given priority and full evaluation; this weight-based analysis order not only improves the analysis efficiency, but also enables the focus to be placed on the most critical resources within a limited time; then, the system innovatively combines the analysis results of various types of resources with their corresponding weights, and obtains a comprehensive overall score by weighted summation; this score is output to the front end as a quantitative indicator of the performance of the entire communication system for reference by decision makers; this method of multiple application weights enables the system to comprehensively and prominently evaluate the resource status, while providing an intuitive and comprehensive performance indicator, greatly improving the scientificity and efficiency of decision-making, and providing strong data support for the optimization and management of large-scale UAV communication systems. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] Figure 1 It is a schematic diagram of the overall module unit of the present invention; Figure 2 It is a schematic diagram of the overall method flow of the present invention.

[0019] In the figure: 100, data collection module; 200, resource classification module; 300, hierarchical model construction and application module; 301, hierarchical model construction unit; 302, hierarchical model application unit; 400, resource analysis module; 401, primary analysis unit; 402, secondary analysis unit. DETAILED DESCRIPTION

[0020] The following will be combined with the accompanying drawings in the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0021] Unless otherwise specified, the terms (including technical terms) used herein have the commonly understood meanings to those skilled in the art. In addition, it is understood that the terms defined in commonly used dictionaries should be understood to have the same meanings as those in the context of the relevant fields, and should not be understood as idealized or overly formal meanings.

[0022] The following are some definitions of terms: Hierarchical resource list: refers to the hierarchical resource list generated after classifying communication resource data through a hierarchical clustering algorithm. It contains resource categories and specific resource items at different levels, forming a tree structure; for example, the top layer in the tree structure may be a large resource category (such as hardware resources, software resources, etc.), the next layer is a specific category (such as antennas, processors, etc.), and the bottom layer is a specific resource item (such as bandwidth, CPU speed, etc.).

[0023] Hierarchical model: It is a model built based on current environmental data and a hierarchical resource list using the hierarchical analysis method. The model includes the top layer (overall goal, such as optimal communication effect), the middle layer (quantified key environmental factors) and the bottom layer (resource categories in the hierarchical resource list). It is used to analyze the impact of environmental factors on different resource categories and provide a basis for calculating resource weights.

[0024] The weight of different types of resources: refers to the relative importance of each resource category to the overall goal calculated through the hierarchical model. These weights reflect the contribution of different types of resources to the performance of the communication system in the current environment. The larger the weight value, the more important the resource is in the current situation. Priority should be given to analysis and optimization.

[0025] Next, see Figure 1-Figure 2 One of the purposes of this embodiment is to provide a large-scale UAV communication system, including a data collection module 100, a resource classification module 200, a hierarchical model construction and application module 300 and a resource analysis module 400.

[0026] The data collection module 100 collects communication resource data and current environment data, wherein the communication resource data includes but is not limited to hardware resources (such as hardware parameters such as antennas, processors, storage devices, etc.), software resources (such as communication protocols, algorithms, operating systems, etc. software information), spectrum resources (such as available frequency bands, bandwidths, etc.) and data link resources (such as the status and performance parameters of various communication links), etc.; the current environment data includes but is not limited to electromagnetic environment (such as signal-to-noise ratio, interference sources, etc.), meteorological conditions (such as visibility, wind speed, temperature, etc.) and geographic location (such as GPS coordinates, altitude, distance from the ground station, etc.), etc.

[0027] The resource classification module 200 classifies the communication resource data using a hierarchical clustering algorithm to generate a structured resource list. The hierarchical clustering algorithm specifically includes: For each type of communication resource data, extract key features (such as processing power, bandwidth, power consumption, etc.), and standardize the extracted features to form an N-dimensional feature vector; According to the N-dimensional feature vector of each type of communication resource data, the distance between any two types of communication resource data is calculated to construct an N×N similarity matrix (N is the total number of resource items), where the distance calculation includes but is not limited to Euclidean distance and Manhattan distance; Each type of communication resource data is regarded as a separate cluster, and according to the similarity matrix, the two clusters with the highest similarity are found, the two clusters are merged, the similarity matrix is ​​updated, and this step is repeated until the preset number of clusters is reached to generate a clustering result; A hierarchical tree is constructed based on the clustering results, where each node represents a communication resource category and the leaf node is a specific resource item in the communication resource category. For example, the communication resource category is spectrum resources and the specific resource item is bandwidth. A descriptive label is assigned to each non-leaf node (i.e., each category) in the hierarchical tree. The label is determined based on the common characteristics of the resource items in the category, and the tree structure is converted into a hierarchical resource list, which includes resource categories and specific resource items.

[0028] The hierarchical structure model construction unit 301 in the hierarchical structure model construction and application module 300 constructs a hierarchical structure model according to the current environment data and the hierarchical resource list using the hierarchical analysis method, which specifically includes: Obtain current environmental data, use machine learning methods (such as principal component analysis PCA) to identify the factors that have the greatest impact on communication effects, for example, identify signal quality, environmental complexity, and temperature impact as key factors; quantify each identified key factor and convert it into a value between 0 and 1, where the quantification method includes but is not limited to linear mapping (such as linearly mapping the signal-to-noise ratio from dB to a range of 0-1), nonlinear functions (such as using a sigmoid function to map temperature impact to 0-1), and discrete classification (such as mapping the environment type (urban, suburban, rural) to a specific value); Based on the quantified key environmental factors and the hierarchical resource list, a hierarchical model is established, where the hierarchical model includes a top layer, a middle layer and a bottom layer, where the top layer is the overall goal (such as optimal communication effect), the middle layer uses the quantified key environmental factors as influencing factors, and the bottom layer uses the resource categories in the hierarchical resource list.

[0029] The hierarchical model application unit 302 in the hierarchical model construction and application module 300 calculates the weights of different types of resources in the hierarchical resource list according to the constructed hierarchical model, specifically including: Each pair of environmental factors in the middle layer of the hierarchical model is compared pairwise to construct a judgment matrix A, and the comparison is made using a 1-9 scale, where 1 indicates equal importance and 9 indicates the most important; Use the eigenvalue method to calculate the maximum eigenvalue λmax of the judgment matrix A and the corresponding eigenvector W, normalize the eigenvector W, and obtain the weight vector W_env of the environmental factors; Calculate the consistency index CI = (λmax-n) / (n-1), where n is the number of environmental factors, and calculate the consistency ratio CR = CI / RI, where RI is the random consistency index. If CR < 0.1, it is judged to have acceptable consistency, and the relative importance of resource categories is evaluated to obtain the weight matrix B of each resource category relative to each environmental factor; otherwise, reconstruct the judgment matrix A; According to the different types of resources in the hierarchical resource list, the comprehensive contribution of the top level in the hierarchical model is calculated as the final weight vector, according to W_end=B*W_env, where W_end is the final weight vector of the resource category.

[0030] The primary analysis unit 401 in the resource analysis module 400 performs resource analysis on the hierarchical resource list in descending order according to the weights of the different types of resources, and obtains analysis results of the different types of resources, specifically including: The final weight vectors W_end of the resource categories calculated by the hierarchical model application unit 302 are sorted in descending order to create a resource type index list corresponding to the weights; According to the sorted weights, determine the priority of the hierarchical resource list for resource analysis, and prepare corresponding analysis methods and evaluation indicators for each resource type, such as: Hardware resource analysis: evaluate processor performance (such as CPU speed, number of cores), analyze storage capacity and read / write speed, evaluate antenna parameters (such as gain, directivity), and calculate hardware resource utilization; Software resource analysis: evaluate the efficiency and applicability of communication protocols, analyze the complexity and execution time of algorithms, evaluate the stability and resource management capabilities of the operating system, and check the compatibility of software versions; Spectrum resource analysis: calculate the total bandwidth of available frequency bands, analyze spectrum utilization efficiency, evaluate frequency band interference, and consider the possibility of spectrum sharing; Data link resource analysis: Evaluate the throughput of each communication link, analyze link stability and reliability, calculate link utilization, and evaluate link latency and jitter.

[0031] The secondary analysis unit 402 in the resource analysis module 400 performs weighted summation on the analysis results of different types of resources according to the weights, and outputs the result to the front end; the evaluation index of each resource type calculated in the primary analysis unit 401 is standardized; for each resource type, the standardized analysis result is multiplied by the corresponding weight to obtain an overall score to reflect the performance of the entire communication system, and the overall score is output to the front end for further maintenance by management personnel.

[0032] The second purpose of this embodiment is to provide a large-scale unmanned aerial vehicle communication system resource classification method, including the following method steps: S1. Collect communication resource data and current environment data; S2. Classify the communication resource data using a hierarchical clustering algorithm to generate a hierarchical resource list, wherein the hierarchical resource list includes different types of resources; S3. Based on the current environment data and the hierarchical resource list, a hierarchical structure model is constructed using the hierarchical analysis method; based on the constructed hierarchical structure model, the weights of different types of resources in the hierarchical resource list are calculated; S4. According to the weights of different types of resources, in order from large to small, perform resource analysis on different types of resources in the hierarchical resource list to obtain analysis results of different types of resources. According to the weights, perform weighted summation on the analysis results of different types of resources to obtain an overall score, which is then output to the front end.

[0033] The third purpose of this embodiment is to provide a drone of a large drone communication system, including a processor and a memory storing a computer program, and the processor implements the drone communication system provided by the above embodiments when executing the computer program.

[0034] The above shows and describes the basic principles, main features and advantages of the present invention. Those skilled in the art should understand that the present invention is not limited by the above embodiments. The above embodiments and descriptions are only preferred examples of the present invention and are not intended to limit the present invention. Without departing from the spirit and scope of the present invention, the present invention may have various changes and improvements, which fall within the scope of the present invention. The scope of protection of the present invention is defined by the attached claims and their equivalents.

Claims

1. Large UAV communication system, characterized by: The system comprises a data collection module (100), a resource classification module (200), a hierarchical structure model construction and application module (300) and a resource analysis module (400), wherein: The data collection module (100) collects communication resource data and current environment data; The resource classification module (200) classifies the communication resource data using a hierarchical clustering algorithm to generate a hierarchical resource list, wherein the hierarchical resource list includes different types of resources; The hierarchical structure model construction and application module (300) constructs a hierarchical structure model based on current environment data and the hierarchical resource list using the hierarchical analysis method; and calculates the weights of different types of resources in the hierarchical resource list based on the constructed hierarchical structure model; The resource analysis module (400) performs resource analysis on different types of resources in the hierarchical resource list in order of weight from large to small according to the weights of the different types of resources, obtains analysis results of the different types of resources, and performs weighted summation of the analysis results of the different types of resources according to the weights to obtain an overall score, which is then output to the front end.

2. The large-scale UAV communication system according to claim 1, characterized in that: The data collection module (100) collects communication resource data and current environment data, wherein the communication resource data includes but is not limited to hardware resources, software resources, spectrum resources and data link resources; the current environment data includes but is not limited to electromagnetic environment, meteorological conditions and geographical location.

3. The large-scale UAV communication system according to claim 1, characterized in that: The resource classification module (200) classifies the communication resource data using a hierarchical clustering algorithm to generate a structured resource list. The hierarchical clustering algorithm specifically includes: For each type of communication resource data, key features are extracted and the extracted features are standardized to form an N-dimensional feature vector, where the total number of resource items in the communication resource data; According to the N-dimensional feature vector of each type of communication resource data, the distance between any two types of communication resource data is calculated to construct an N×N similarity matrix, wherein the distance calculation includes but is not limited to Euclidean distance and Manhattan distance; Each type of communication resource data is regarded as a separate cluster, and according to the similarity matrix, the two clusters with the highest similarity are found, the two clusters are merged, the similarity matrix is ​​updated, and this step is repeated until the preset number of clusters is reached to generate a clustering result; A hierarchical tree is constructed based on the clustering results, where each node represents a communication resource category and the leaf nodes are specific resource items in the communication resource category; A descriptive label is assigned to each non-leaf node in the hierarchical tree. The label is determined based on the common characteristics of the resource items in the category, and the tree structure is converted into a hierarchical resource list. The hierarchical resource list includes resource categories and specific resource items.

4. The large-scale UAV communication system according to claim 3, characterized in that: The hierarchical structure model construction and application module (300) comprises a hierarchical structure model construction unit (301), wherein the hierarchical structure model construction unit (301) constructs a hierarchical structure model according to current environment data and a hierarchical resource list by using a hierarchical analysis method, specifically comprising: Obtain current environmental data and use machine learning methods to identify the factors that have the greatest impact on communication effectiveness; quantify each identified key factor and convert it into a value between 0 and 1, where the quantification method includes but is not limited to linear mapping, nonlinear function and discrete classification; Based on the quantified key environmental factors and the hierarchical resource list, a hierarchical model is established, wherein the hierarchical model includes a top layer, a middle layer and a bottom layer, wherein the top layer is the overall goal, the middle layer uses the quantified key environmental factors as influencing factors, and the bottom layer uses the resource categories in the hierarchical resource list.

5. The large-scale UAV communication system according to claim 4, characterized in that: The hierarchical model construction and application module (300) comprises a hierarchical model application unit (302), wherein the hierarchical model application unit (302) calculates weights of different types of resources in the hierarchical resource list according to the constructed hierarchical model, specifically comprising: Each pair of environmental factors in the middle layer of the hierarchical model is compared pairwise to construct a judgment matrix A, and the comparison is made using a 1-9 scale, where 1 indicates equal importance and 9 indicates the most important; Use the eigenvalue method to calculate the maximum eigenvalue λmax of the judgment matrix A and the corresponding eigenvector W, normalize the eigenvector W, and obtain the weight vector W_env of the environmental factors; Calculate the consistency index CI = (λmax-n) / (n-1), where n is the number of environmental factors, and calculate the consistency ratio CR = CI / RI, where RI is the random consistency index. If CR < 0.1, it is judged to have acceptable consistency, and the relative importance of resource categories is evaluated to obtain the weight matrix B of each resource category relative to each environmental factor; otherwise, reconstruct the judgment matrix A; According to the different types of resources in the hierarchical resource list, the comprehensive contribution of the top level in the hierarchical model is calculated as the final weight vector, according to W_end=B*W_env, where W_end is the final weight vector of the resource category.

6. The large-scale UAV communication system according to claim 5, characterized in that: The resource analysis module (400) comprises a primary analysis unit (401), wherein the primary analysis unit (401) performs resource analysis on the hierarchical resource list in descending order according to the weights of the different types of resources, and obtains analysis results of the different types of resources, specifically including: The final weight vectors W_end of the resource categories calculated by the hierarchical model application unit (302) are sorted in descending order, and a resource type index list corresponding to the weights is created; Based on the sorted weights, determine the priority of the hierarchical resource list for resource analysis, and prepare corresponding analysis methods and evaluation indicators for each resource type.

7. The large-scale UAV communication system according to claim 6, characterized in that: The resource analysis module (400) comprises a secondary analysis unit (402), wherein the secondary analysis unit (402) performs standardization processing on the evaluation index of each resource type calculated in the primary analysis unit (401); For each resource type, the standardized analysis result is multiplied by the corresponding weight to obtain an overall score that reflects the performance of the entire communication system, and the overall score is output to the front end.

8. A method for classifying resources of a large unmanned aerial vehicle communication system using any one of claims 1 to 7, characterized in that: The method comprises the following steps: S1. Collect communication resource data and current environment data; S2. Classify the communication resource data using a hierarchical clustering algorithm to generate a hierarchical resource list, wherein the hierarchical resource list includes different types of resources; S3. Based on the current environment data and the hierarchical resource list, a hierarchical structure model is constructed using the hierarchical analysis method; based on the constructed hierarchical structure model, the weights of different types of resources in the hierarchical resource list are calculated; S4. According to the weights of different types of resources, in order from large to small, perform resource analysis on different types of resources in the hierarchical resource list to obtain analysis results of different types of resources. According to the weights, perform weighted summation on the analysis results of different types of resources to obtain an overall score, which is then output to the front end.

9. A drone of a large drone communication system, comprising a processor and a memory storing a computer program, characterized in that: When the processor executes the computer program, the drone communication system according to any one of claims 1 to 7 is implemented.

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