Unmanned aerial vehicle cluster networking security communication method, system, device and medium

Abstract

The invention provides an unmanned aerial vehicle cluster networking secure communication method, which comprises the following steps: acquiring real-time position information of each node in an unmanned aerial vehicle cluster, and calculating a relative distance between the nodes in combination with a preset communication range threshold; based on the communicable node pair set, constructing a communication topological graph of the unmanned aerial vehicle cluster by adopting a network topology analysis method based on a graph theory, and identifying a possible local congestion area in the network; in a local congestion area, dynamically increasing the interval between the nodes by adjusting the flight path of part of the nodes, and reducing the influence of channel competition and multipath effect; if the node distance is greater than a preset stability threshold value, a relay node is introduced to optimize a communication link, and continuity and reliability of data transmission are ensured; a clustering analysis method in a machine learning algorithm is adopted to perform grouping management on unmanned aerial vehicle clusters, the global configuration of networking intervals is optimized, and the overall performance of the network is improved.

Description

Technical Field

[0001] The present invention relates to the field of drone network information technology, and in particular to a drone cluster networking secure communication method, system, equipment and medium. Background Art

[0002] The core issue of network spacing technology in drone swarm networking security lies in ensuring communication link stability while avoiding network performance degradation caused by overcrowding or sparse node density. When the distance between drone nodes is too small, while communication signal strength is high, it is prone to signal interference. This is especially true in high-density swarm environments, where multipath effects and channel contention are significantly exacerbated, leading to increased data transmission delays and packet loss rates. Furthermore, overcrowding increases the complexity of the network topology, making route selection and maintenance more difficult and prone to local congestion, impacting overall network throughput.

[0003] On the other hand, if the distance between drone nodes is too large, while signal interference can be reduced, the stability of the communication link will be significantly reduced. Due to the limited communication range of drones, excessive distances may prevent some nodes from communicating directly, requiring them to rely on relay nodes for data transmission, which increases communication latency and energy consumption. Furthermore, long-distance communications are more susceptible to environmental factors (such as obstacles and weather conditions), causing signal attenuation or interruption, which in turn affects the reliability and real-time performance of the network.

[0004] Furthermore, the dynamic nature of drone swarms further complicates networking intervals. During mission execution, drones' flight paths and speeds can change frequently, causing the relative distances between nodes to fluctuate. This dynamic change requires networking interval technology to adapt to changes in network topology in real time. However, excessive adjustments can cause network oscillations, impacting communication continuity and stability.

[0005] Therefore, how to optimize the networking interval in a dynamic environment becomes a key technical problem. Summary of the Invention

[0006] In response to the above-mentioned defects or improvement needs of the prior art, the present invention provides a method, system, equipment and medium for secure communication in a drone cluster network, which at least solves one of the above-mentioned technical problems.

[0007] According to one aspect of the present invention, a method for secure communication in a drone cluster network is provided, the method specifically comprising:

[0008] S101 obtains the real-time location information of each node in the drone cluster and calculates the relative distance between nodes based on the preset communication range threshold;

[0009] S102 constructs a communication topology map of the drone cluster based on the set of communicable node pairs and identifies possible local congestion areas in the network;

[0010] S103, in the local congested area, dynamically increasing the intervals between nodes by adjusting the flight trajectories of some nodes;

[0011] In step S104, if the node distance is greater than a preset stability threshold, a relay node is introduced to optimize the communication link to ensure the continuity and reliability of data transmission.

[0012] As a preferred technical solution of the present invention, the method of obtaining the real-time location information of each node in the drone cluster and calculating the relative distance between the nodes in combination with a preset communication range threshold specifically includes:

[0013] Obtain the real-time location coordinates of all nodes in the drone cluster and establish a node location information database;

[0014] According to the node location information database, the Euclidean distance formula is used to calculate the relative distance between every two nodes to generate a distance matrix;

[0015] Read the preset communication distance threshold and compare and analyze the distance matrix with the communication threshold;

[0016] If the relative distance between two nodes is less than the communication distance threshold, the two nodes are determined to be in a communicative state, and a set of communicative node pairs is generated and stored in the database.

[0017] As a preferred technical solution of the present invention, the communication topology diagram of the drone cluster is constructed based on the set of communicable node pairs and the network topology analysis method based on graph theory is used to identify possible local congested areas in the network, including:

[0018] Starting from the set of communicable node pairs, a breadth-first search algorithm is used to construct a communication topology graph;

[0019] Based on the communication topology graph, the degree of each node is calculated and the number of node connections is determined;

[0020] Based on the node degree distribution, nodes with degree values ​​higher than a preset threshold are identified as potential high-load nodes;

[0021] Based on the location of high-load nodes and their connection relationships, local congestion areas in the network are determined.

[0022] As a preferred technical solution of the present invention, the method of dynamically increasing the interval between nodes by adjusting the flight trajectories of some nodes in a local congested area specifically includes:

[0023] The region is divided by density-based spatial clustering algorithm to obtain the boundary coordinates of the local domain;

[0024] Determine the spatial extent of the congested area according to the boundary coordinates, and extract the location information of all nodes in the congested area;

[0025] Calculate the current distance between nodes. If the current distance is less than the preset threshold, re-plan the node path.

[0026] The flight trajectory of the node is updated in real time according to the path replanning results, and the interval increase operation is performed.

[0027] As a preferred technical solution of the present invention, determining the spatial range of the congested area according to the boundary coordinates further includes:

[0028] Calculate the node channel occupancy rate within the boundary coordinates and obtain the evaluation value:

[0029]

[0030] Where η is the channel occupancy rate, T is the total observation time, and t k is the channel occupancy time of the kth time period, T k is the length of the kth time period, ω k is the weight coefficient, K is the number of time periods;

[0031] Determine the signal attenuation degree caused by the multipath effect according to the evaluation value and obtain a degree value;

[0032] If the degree value is higher than a preset threshold, the spatial extent of the congested area is determined;

[0033] As a preferred technical solution of the present invention, if the node distance is greater than a preset stability threshold, a relay node is introduced to optimize the communication link, specifically including:

[0034] Get the current distance between nodes and compare it with the preset stability threshold;

[0035] If the distance between nodes is greater than the preset stability threshold, the relay node is selected from the candidate node set;

[0036] According to the location information of the relay node, the signal attenuation model is used to calculate the communication link quality between the source node and the target node;

[0037] Use the shortest path algorithm to re-plan the communication paths of the source node, relay node and target node;

[0038] Update the location information of relay nodes in real time.

[0039] As a preferred technical solution of the present invention, cluster analysis is also used to group and manage the drone clusters and optimize the global configuration of the networking interval, specifically including:

[0040] Obtain the location information and communication capability data of the drone cluster and construct the feature vector of the cluster node;

[0041] The K-means clustering algorithm is used to group the drone clusters and divide the groups according to the similarity of the node feature vectors.

[0042] According to another aspect of the present invention, there is provided a secure communication device for drone cluster networking, characterized by comprising:

[0043] A node calculation module is configured to obtain real-time location information of each node in the drone cluster and calculate the relative distance between nodes based on a preset communication range threshold;

[0044] A network identification module is configured to construct a communication topology map of the drone cluster based on a set of communicative node pairs and identify possible local congested areas in the network;

[0045] a trajectory adjustment module configured to dynamically increase the interval between nodes by adjusting the flight trajectories of some nodes in the local congested area; and

[0046] The node optimization module is configured to introduce a relay node to optimize the communication link if the node distance is greater than a preset stability threshold, thereby ensuring the continuity and reliability of data transmission.

[0047] As a preferred technical solution of the present invention, it can also include a management optimization module, which uses the cluster analysis method in the machine learning algorithm to group and manage the drone cluster, optimize the global configuration of the networking interval, and improve the overall network performance.

[0048] According to another aspect of the present invention, there is provided a drone cluster networking security communication device, comprising 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 the steps of the method.

[0049] According to another aspect of the present invention, a storage medium is provided, characterized in that it stores a computer program executable by an access authentication device, and when the computer program runs on the access authentication device, the access authentication device executes the steps of the method.

[0050] In general, the above technical solutions conceived by the present invention have the following advantages compared with the prior art:

[0051] Beneficial effects:

[0052] The present invention discloses a method for adaptive optimization of a drone cluster communication network. By acquiring the position information of each node in the cluster in real time, the relative distance between nodes is calculated according to a preset communication range threshold, and the signal interference and communication stability risks are judged. When the node distance is less than the interference threshold, the density adjustment mechanism is triggered; when the distance is greater than the stability threshold, a relay node is introduced to optimize the communication link. The present invention uses graph theory methods to construct a communication topology map, identify local congested areas, and dynamically increase the node interval by adjusting the flight trajectory. At the same time, machine learning cluster analysis is used for group management to optimize the global configuration of networking intervals. This method can effectively reduce the impact of channel contention and multipath effects, ensure the continuity and reliability of data transmission, and improve the overall performance and adaptability of the drone cluster communication network. BRIEF DESCRIPTION OF THE DRAWINGS

[0053] Figure 1 This is a flow chart of an embodiment of a method for secure communication in a drone cluster network according to the present invention;

[0054] Figure 2 This is a flow chart of calculating the relative distance between nodes in one embodiment of a method for secure communication in a drone cluster network according to the present invention;

[0055] Figure 3 This is a schematic diagram of a process for dynamically increasing the interval between nodes in an embodiment of a method for secure communication in a drone cluster network according to the present invention;

[0056] Figure 4 This is a flow chart of introducing relay nodes to optimize communication links in an embodiment of a method for secure communication in a drone cluster network of the present invention. DETAILED DESCRIPTION

[0057] 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 with reference to the accompanying drawings and embodiments. 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.

[0058] The following will describe the technical solutions in the embodiments of the present invention in detail with reference to the accompanying drawings. It should be noted that the described embodiments are only part of the embodiments of the present invention.

[0059] like Figure 1 As shown, a method for secure communication in a drone cluster network according to this embodiment may specifically include:

[0060] Step S101: obtain the real-time location information of each node in the drone cluster, and calculate the relative distance between the nodes based on the preset communication range threshold.

[0061] Obtain the real-time location coordinates of all nodes in the drone cluster and establish a node location information database. Based on the node location information database, use the Euclidean distance formula to calculate the relative distance between each two nodes and generate a distance matrix.

[0062] The preset communication distance threshold is read and the distance matrix is ​​compared with the communication threshold. If the relative distance between two nodes is less than the communication distance threshold, the two nodes are considered to be in a communicative state. A set of communicative node pairs is generated and stored in the database.

[0063] For example, real-time location coordinates are obtained from the positioning devices of all nodes in the drone cluster, in longitude and latitude format. These coordinates are stored in a MySQL database, where the database table structure contains three fields: node number, longitude, and latitude. Based on the stored location coordinates, the relative distance between each two nodes is calculated using the Euclidean distance formula.

[0064]

[0065] In the above formula, d ij represents the Euclidean distance between node i and node j, n represents the spatial dimension, x ik represents the coordinate value of node i in the kth dimension, x jk Represents the coordinate value of node j in the kth dimension.

[0066] The calculated relative distances are stored in a two-dimensional array to form a distance matrix. A preset communication distance threshold is read from a configuration file, for example, 500 meters or another appropriate distance. Each element in the distance matrix is ​​compared with the communication distance threshold. If the distance is less than the threshold, the two nodes are considered to be in a communicative state. Node pairs determined to be communicable are stored in a Redis database, which uses a set data structure, with each set element representing a communicative node pair.

[0067] Step S102: Based on the set of communicative node pairs, a network topology analysis method based on graph theory is used to construct a communication topology map of the drone cluster and identify possible local congested areas in the network.

[0068] Starting from a set of communicable node pairs, a breadth-first search algorithm is used to construct a communication topology. Based on this communication topology, the degree of each node is calculated to determine the number of node connections. Furthermore, based on the node degree distribution, nodes with degree values ​​above a preset threshold are identified as potentially high-load nodes.

[0069] Based on the location of high-load nodes and their connection relationships, possible local congestion areas in the network are determined.

[0070] like Figure 2 As shown, starting from a set of communicable node pairs, a breadth-first search algorithm is used to construct a communication topology graph. In this embodiment, the breadth-first search algorithm is used to traverse all communicable nodes layer by layer starting from the starting node, recording the connection relationships between nodes to generate a communication topology graph. Furthermore, based on the communication topology graph, the degree of each node is calculated to determine the number of node connections.

[0071] Based on the node degree distribution, nodes with degree values ​​above a preset threshold are identified as potential high-load nodes. Based on the location of high-load nodes and their connection relationships, possible local congestion areas in the network are determined.

[0072] The breadth-first search algorithm is the foundation for constructing a communication topology. Starting from a starting node, it prioritizes nodes closest to the starting node. By traversing pairs of communicable nodes, it gradually constructs the communication network structure of the entire drone cluster. For example, given five drone nodes, the search begins with drone number 1, using the communicable node pairs stored in the database. First, it finds drones numbered 2 and 3, which are directly connected to it. It then continues searching for other nodes connected to drones numbered 2 and 3, ultimately forming a complete communication topology.

[0073] In the constructed communication topology, a node's degree represents the number of connections it has with other nodes. A node with a high degree value requires more connections to be maintained. For example, a node with a degree value of 4 indicates that it maintains connections with four other nodes. If the preset degree threshold is 3, nodes with a degree greater than 3 are marked as potentially high-load nodes.

[0074] Identifying high-load nodes is crucial for optimizing network performance. When a node needs to handle multiple communication connections simultaneously, data transmission congestion may occur. For example, in a drone swarm scenario, drone nodes in the central area often establish connections with multiple surrounding nodes, potentially reaching degrees of six or higher. This significantly increases the communication load on these nodes. Determining localized congestion areas requires considering the spatial distribution and connectivity of high-load nodes. For example, if there are multiple adjacent high-load nodes within a certain area, and these nodes have direct communication connections, this area is likely to experience communication congestion. For example, if within a 100-meter radius, there are three nodes with degrees exceeding 4, each with interconnected communication connections, this area is likely to become a network performance bottleneck. Analyzing the network topology and identifying potential congested areas can provide a basis for subsequent load balancing and routing optimization. For example, the spatial positioning of drones can be adjusted to distribute the communication pressure around high-load nodes to other nodes, or relay nodes can be added to the congested area to share the communication load. This network optimization approach based on topology analysis can effectively improve the communication efficiency and reliability of drone swarms.

[0075] Step S103: In the local congested area, the flight trajectories of some nodes are adjusted to dynamically increase the intervals between nodes, thereby reducing the impact of channel contention and multipath effects.

[0076] like Figure 1 and 3 As shown in the figure, a density-based spatial clustering algorithm is used to partition the region and obtain the boundary coordinates of the local domain. The spatial extent of the congested area is determined based on the boundary coordinates, and a node search algorithm is used to extract the location information of all nodes within the congested area. The current distance between nodes is calculated based on Euclidean distance. If the current distance is less than a preset threshold, the node path is replanned. Based on the path replanning results, the node's flight trajectory is updated in real time, and the interval is increased. The adjusted channel occupancy is evaluated based on the signal-to-interference-to-noise ratio to determine the degree of multipath attenuation. Interference between nodes is quantitatively assessed using an interference severity index, and the final trajectory optimization solution is output.

[0077] In this embodiment, a density-based spatial clustering algorithm is used to analyze node distribution density to achieve regional division. This algorithm groups nodes with similar density and close proximity into the same cluster, forming local regions. When multiple nodes in a drone cluster are concentrated in a small space, these areas are identified as potential congestion zones. For example, if eight drones are distributed within a 50-meter radius and their communication connections are dense, this area will be classified as a separate cluster.

[0078] Obtaining the boundary coordinates of clustered areas is key to determining the spatial extent of congested areas. By analyzing the spatial distribution of nodes, the geometric shape and extent of congested areas can be determined. In practical applications, boundary coordinates are typically represented as a set of three-dimensional coordinates of polygonal vertices. For example, the boundary of a congested area might consist of six vertex coordinates, forming an irregular hexagonal region. The node search algorithm traverses the space within the congested area and extracts the location information of all nodes.

[0079] In a congested area, each node has unique three-dimensional coordinates. Once the node locations are obtained, the Euclidean distance between them can be calculated. Assuming the safe distance threshold between two nodes is set to 10 meters, if the current distance is less than this value, path replanning is triggered.

[0080] Path replanning employs a heuristic search approach, evaluating movement costs and estimating distances to plan the optimal path for each node. During the planning process, consideration is not only given to increasing node spacing, but also to avoiding new congestion with other nodes. For example, if a node needs to move away from a congested area, a path can be planned that circumvents the dense area, increasing spatial separation while ensuring connectivity. In this embodiment, the A-star algorithm is preferably used for node path planning.

[0081] Channel occupancy is assessed based on the signal-to-interference-and-noise ratio (SIN), reflecting the quality of communication. Multipath effects can cause signals to propagate along different paths and overlap at the receiving end, resulting in signal distortion. In this embodiment, the SIN is used to evaluate the channel occupancy of nodes within the boundary coordinates, determining the degree of signal attenuation caused by multipath effects and determining the spatial extent of the congested area.

[0082] The signal-to-interference-noise ratio algorithm calculates the channel occupancy of nodes within the boundary coordinates and obtains the evaluation value.

[0083]

[0084] Where η is the channel occupancy rate, T is the total observation time, and t k is the channel occupancy time of the kth time period, T k is the length of the kth time period, ω_k is the weight coefficient, and K is the number of time periods.

[0085] Based on the evaluation value, the degree of signal attenuation caused by multipath effects is determined to obtain a degree value. If the degree value is higher than a preset threshold, the spatial extent of the congested area is determined to obtain a range value. Based on the range value, node location information within the boundary coordinates is extracted to obtain coordinate values. Based on the coordinate values, the Euclidean distance between nodes is calculated to obtain an interval value. If the interval value is lower than the preset threshold, the A-star algorithm is used to replan the node path to obtain a path value. The node's flight trajectory is updated in real time based on the path value, and the interval is increased.

[0086] For example, the signal-to-interference-and-noise ratio (SINR) assessment method compares received signal strength with background noise levels. In congested areas, when multiple nodes share the same channel, channel occupancy can increase dramatically. For example, at one point, channel occupancy in the congested area reached 85%, while in normal areas it was only 30%, indicating severe channel contention.

[0087] Multipath causes signals to travel through multiple paths before reaching the receiver. When signals propagate along different paths, phase differences due to varying path lengths create signal distortion. For example, within a congested area, the signal attenuation reaches 0.7, exceeding the preset threshold of 0.5. This requires spatial extent determination. When determining the spatial extent of the congested area, node density and communication connection strength must be considered. In real-world scenarios, a typical congested area may have an irregular shape, with its boundaries defined by multiple vertex coordinates. Suppose a congested area consists of eight vertex coordinates, forming a closed polygonal area. The node density within this area is significantly higher than that of the surrounding area. When acquiring node location information, each node has unique three-dimensional coordinates. By analyzing these coordinates, the actual distance between nodes can be calculated. If the distance between two nodes is only six meters, less than the safe threshold of ten meters, path planning is initiated.

[0088] Path planning uses a heuristic search method to determine the optimal path by evaluating movement costs and estimated distances. During the planning process, consideration is given not only to increasing the distance between nodes but also to avoiding creating new congestion with other nodes. For example, when a node's position needs to be adjusted, a detour can be planned that maintains network connectivity while increasing the distance to surrounding nodes.

[0089] Step S104: If the node distance is greater than a preset stability threshold, a relay node is introduced to optimize the communication link to ensure the continuity and reliability of data transmission.

[0090] like Figure 4 As shown, the current distance between nodes is obtained and compared with a preset stability threshold. If the distance between nodes is greater than the preset stability threshold, a relay node is selected from the candidate node set. Based on the relay node's location information, a signal attenuation model is used to calculate the quality of the communication link between the relay node and the source and destination nodes.

[0091] Comparing node separation distance with the stability threshold is fundamental to evaluating network communication quality. In drone communication networks, the stability threshold is typically determined based on the performance parameters of the communication equipment and environmental factors. For example, in urban environments, the stability threshold between two nodes might be set at 20 meters or another appropriate value to account for building obstruction and electromagnetic interference. When the actual separation distance exceeds this threshold, appropriate relay nodes must be selected to ensure communication quality.

[0092] The selection process for relay nodes requires comprehensive consideration of multiple factors. First, locational appropriateness: Relay nodes should be located appropriately between the source and destination nodes. Second, energy reserves: Relay nodes must have sufficient energy to support data forwarding. For example, in a scenario, there are three candidate relay nodes located 15 meters, 20 meters, and 25 meters from the source node, respectively. By calculating the link quality indicator, the node with the most appropriate distance is selected as the relay node.

[0093] Communication path replanning requires balancing multiple objectives. On the one hand, it's crucial to ensure the shortest possible path and minimize transmission delay. On the other hand, it's crucial to ensure link quality and avoid excessive signal attenuation. Link quality assessment models consider multiple metrics, including signal-to-noise ratio (SNR), bit error rate (BER), and latency. In practical evaluations, weighting factors can be set to integrate these metrics. For example, a link might have a SNR of 15 decibels, a bit error rate of 0.1%, and a latency of 10 milliseconds.

[0094] Quantitative evaluation results provide an intuitive reference for link optimization. By setting a scoring threshold, we can determine whether the optimization solution has achieved the expected results. This evaluation method objectively reflects the degree of improvement in communication performance and provides a basis for subsequent optimization adjustments. In practice, a complete link optimization process may require multiple rounds of iterations until the expected performance indicators are achieved.

[0095] In a preferred solution, the shortest path algorithm is used to re-plan the communication paths of the source node, relay node, and target node. Specifically, it can be:

[0096]

[0097] Where R(t) represents the optimal path value, α represents the node processing delay, β represents the transmission delay coefficient, d represents the distance between nodes, V represents the set of optional nodes, and s represents the source node.

[0098] A dynamic adjustment mechanism updates relay node location information in real time to ensure communication link stability. A link quality assessment model analyzes the quality of optimized communication links and determines the continuity of data transmission. Based on link reliability indicators, a quantitative assessment of the optimized communication link is performed, and a final link optimization solution is output.

[0099] Further preferably, step S105 may be included, i.e., using the cluster analysis method in the machine learning algorithm to group and manage the drone cluster, optimize the global configuration of the networking interval, and improve the overall network performance.

[0100] In this embodiment, the location information and communication capability data of the drone cluster are first obtained to construct the feature vectors of the cluster nodes. The K-means clustering algorithm is then used to group the drone cluster, dividing the groups based on the similarity of the node feature vectors.

[0101] The location information of a drone cluster includes longitude and latitude coordinates and flight altitude, while the communication capability data covers parameters such as signal strength, bandwidth, and latency. By establishing a feature vector, these attributes can be represented in a unified manner, facilitating subsequent analysis and processing. For example, the feature vector of a drone node may include its location coordinates [30° North Latitude, 120° East Longitude, Altitude 500 meters] and communication parameters [Signal Strength -40Db, Bandwidth 10Mb / s, Latency 20ms]. In practical applications, the construction of feature vectors needs to consider data standardization. Data of different dimensions may have dimensional differences and need to be normalized to make them comparable. For example, the location coordinates can be calculated as an offset relative to the center point of the mission area, while the communication parameters can be expressed as a ratio relative to the maximum value.

[0102] The purpose of clustering is to group drones with similar characteristics to facilitate coordinated control and task allocation. In the example of a drone swarm in an urban environment, assume there are twelve drones performing a surveillance mission, four equipped with high-definition cameras, four with infrared sensors, and four with communication relay equipment. Based on device characteristics and mission requirements, they can be divided into three functional groups. When using distance-based clustering, it is necessary to define an appropriate distance metric. For location information, Euclidean distance can be used; for communication capability, a comprehensive metric can be constructed. For example, if two nodes are 100 meters apart and their communication parameters differ by 20%, their feature similarity can be calculated through a weighted calculation. During the clustering process, the selection of the initial cluster center can affect the final result. It is recommended to select the node with the strongest communication capability as the initial center and gradually adjust the center until the clustering is stable. For example, in one clustering process, in the first round of iteration, three nodes with communication capability scores of 90, 85, and 75 were selected as centers. After five rounds of iteration, a stable clustering result was achieved. Evaluation of clustering results requires consideration of both intra-group similarity and inter-group difference. The feature vectors of nodes within a group should be similar, and the feature differences between different groups should be obvious. In practical applications, a threshold can be set to judge the grouping effect.

[0103] In another preferred embodiment, a drone cluster networking secure communication device is also provided, comprising:

[0104] A node calculation module is configured to obtain real-time location information of each node in the drone cluster and calculate the relative distance between nodes based on a preset communication range threshold;

[0105] A network identification module is configured to construct a communication topology map of the drone cluster based on a set of communicative node pairs and identify possible local congested areas in the network;

[0106] a trajectory adjustment module configured to dynamically increase the interval between nodes by adjusting the flight trajectories of some nodes in the local congested area; and

[0107] The node optimization module is configured to introduce a relay node to optimize the communication link if the node distance is greater than a preset stability threshold, thereby ensuring the continuity and reliability of data transmission.

[0108] Optionally, a management optimization module may be included, which uses cluster analysis methods in machine learning algorithms to group and manage drone clusters, optimize the global configuration of networking intervals, and improve the overall network performance.

[0109] Those skilled in the art will readily appreciate that the foregoing description is merely 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 shall be included within the scope of protection of the present invention.

Claims

1. A method for secure communication of drone cluster networking, characterized in that: include: S101 obtains the real-time location information of each node in the drone cluster and calculates the relative distance between nodes based on the preset communication range threshold; S102 constructs a communication topology map of the drone cluster based on the set of communicable node pairs and identifies possible local congestion areas in the network; S103, in the local congested area, dynamically increasing the intervals between nodes by adjusting the flight trajectories of some nodes; In step S104, if the node distance is greater than a preset stability threshold, a relay node is introduced to optimize the communication link to ensure the continuity and reliability of data transmission.

2. The method according to claim 1, characterized in that The method of obtaining the real-time location information of each node in the drone cluster and calculating the relative distance between the nodes in combination with a preset communication range threshold specifically includes: Obtain the real-time location coordinates of all nodes in the drone cluster and establish a node location information database; According to the node location information database, the Euclidean distance formula is used to calculate the relative distance between every two nodes to generate a distance matrix; Read the preset communication distance threshold and compare and analyze the distance matrix with the communication threshold; If the relative distance between two nodes is less than the communication distance threshold, the two nodes are determined to be in a communicative state, and a set of communicative node pairs is generated and stored in the database.

3. The method according to claim 1, characterized in that Based on the set of communicable node pairs, a network topology analysis method based on graph theory is used to construct a communication topology map of the drone cluster and identify possible local congested areas in the network, including: Starting from the set of communicable node pairs, a breadth-first search algorithm is used to construct a communication topology graph; Based on the communication topology graph, the degree of each node is calculated and the number of node connections is determined; Based on the node degree distribution, nodes with degree values ​​higher than a preset threshold are identified as potential high-load nodes; Based on the location of high-load nodes and their connection relationships, local congestion areas in the network are determined.

4. The method according to claim 1, wherein The method of dynamically increasing the intervals between nodes by adjusting the flight trajectories of some nodes in the local congested area specifically includes: The region is divided by density-based spatial clustering algorithm to obtain the boundary coordinates of the local domain; Determine the spatial extent of the congested area according to the boundary coordinates, and extract the location information of all nodes in the congested area; Calculate the current distance between nodes. If the current distance is less than the preset threshold, re-plan the node path. The flight trajectory of the node is updated in real time according to the path replanning results, and the interval increase operation is performed.

5. The method according to claim 4, characterized in that Determining the spatial range of the congested area according to the boundary coordinates further includes: Calculate the node channel occupancy rate within the boundary coordinates and obtain the evaluation value: Where η is the channel occupancy rate, T is the total observation time, and t k is the channel occupancy time of the kth time period, T k is the length of the kth time period, ω k is the weight coefficient, K is the number of time periods; Determine the signal attenuation degree caused by the multipath effect according to the evaluation value and obtain a degree value; If the degree value is higher than a preset threshold, the spatial extent of the congested area is determined.

6. The method according to claim 1, characterized in that If the node distance is greater than a preset stability threshold, a relay node is introduced to optimize the communication link, specifically including: Get the current distance between nodes and compare it with the preset stability threshold; If the distance between nodes is greater than the preset stability threshold, the relay node is selected from the candidate node set; According to the location information of the relay node, the signal attenuation model is used to calculate the communication link quality between the source node and the target node; Use the shortest path algorithm to re-plan the communication paths of the source node, relay node and target node; Update the location information of relay nodes in real time.

7. The method according to claim 1, characterized in that It also includes using cluster analysis to manage drone clusters in groups and optimize the global configuration of networking intervals, including: Obtain the location information and communication capability data of the drone cluster and construct the feature vector of the cluster node; The K-means clustering algorithm is used to group the drone clusters and divide the groups according to the similarity of the node feature vectors.

8. A secure communication device for drone cluster networking, characterized in that: include: A node calculation module is configured to obtain real-time location information of each node in the drone cluster and calculate the relative distance between nodes based on a preset communication range threshold; A network identification module is configured to construct a communication topology map of the drone cluster based on a set of communicative node pairs and identify possible local congested areas in the network; a trajectory adjustment module configured to dynamically increase the interval between nodes by adjusting the flight trajectories of some nodes in the local congested area; and The node optimization module is configured to introduce a relay node to optimize the communication link if the node distance is greater than a preset stability threshold, thereby ensuring the continuity and reliability of data transmission.

9. A drone cluster networking security communication device, characterized in that: The method comprises 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 the steps of the method according to any one of claims 1 to 7.

10. A storage medium, characterized in that: It stores a computer program executable by an access authentication device. When the computer program runs on the access authentication device, the access authentication device executes the steps of the method according to any one of claims 1 to 7.

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