A swarm drone networking communication protocol method and system

Abstract

The present application relates to the technical field of wireless communication networks for unmanned aerial vehicles, and specifically to a communication protocol method and system for a swarm unmanned aerial vehicle network, the method comprising: collecting the spatial coordinates of each unmanned aerial vehicle in the swarm unmanned aerial vehicle network at each moment; obtaining a maneuvering energy consumption index according to the fluctuation of the distance between the spatial coordinates of each unmanned aerial vehicle at all adjacent moments, and the continuous change of the spatial coordinates at all moments; obtaining a real-time energy consumption rate according to the clustering of the unmanned aerial vehicle spatial coordinates and the maneuvering energy consumption index; obtaining an energy consumption impact factor according to the difference between the predicted result of the real-time energy consumption rate and the real-time energy consumption rate at the current moment; comprehensively considering the distance between each unmanned aerial vehicle and all communication base stations in the unmanned aerial vehicle network coverage area at the current moment and the energy consumption impact factor, obtaining a potential impact index, and selecting a cluster head node in the communication protocol. The present application can improve the communication efficiency of unmanned aerial vehicle networking.

Description

Technical Field

[0001] The present application relates to the technical field of wireless communication networks for unmanned aerial vehicles, and in particular to a communication protocol method and system for networking of swarm unmanned aerial vehicles. Background Art

[0002] Swarm drone networking technology is an emerging drone application model that achieves efficient mission execution and data transmission through collaborative work and communication between multiple drones. This technology has important application value in the fields of military, emergency response, intelligent traffic management, agricultural detection, etc. Drones are used as network nodes, and wireless communication equipment modules are used to achieve autonomous management and adaptation to dynamic network topology changes.

[0003] Effective communication is the basis and guarantee for realizing the coordinated tasks of drone clusters. At present, communication protocol algorithms are often used to reduce energy consumption by round-by-round communication and rotating cluster heads. The traditional LEACH protocol selects cluster heads by generating random numbers. However, in actual applications, the energy consumption of drones is uneven due to the influence of drone flight trajectories and spatial position distribution. If a drone with too fast energy consumption is randomly selected as a cluster head node, the energy depletion of the node will be accelerated, reducing the efficiency of network communication. Summary of the invention

[0004] In order to solve the above technical problems, the purpose of this application is to provide a swarm drone networking communication protocol method and system, and the technical solutions adopted are as follows:

[0005] In a first aspect, an embodiment of the present application provides a swarm drone networking communication protocol method, the method comprising the following steps:

[0006] S1, collects the spatial coordinates of each drone in the swarm drone network at each moment;

[0007] S2, according to the fluctuation of the distance between the spatial coordinates of each UAV at all adjacent moments, the cumulative change rate of the speed of each UAV at the current moment is obtained; according to the continuous change of the spatial coordinates of each UAV at all moments, the frequent coefficient of path change of the UAV at the current moment is obtained; the maneuvering energy consumption index of the UAV at the current moment is obtained by combining the cumulative change rate of the speed of each UAV at the current moment and the frequent coefficient of path change;

[0008] S3, according to the clustering of the spatial coordinates of all drones at each moment and the maneuvering energy consumption index, the real-time energy consumption rate of each drone at each moment is obtained; according to the difference between the predicted results of the real-time energy consumption rate of each drone at the current moment and the preset number of moments before and the real-time energy consumption rate at the current moment, the energy consumption influencing factor of each drone at the current moment is obtained;

[0009] S4, comprehensively considers the distance between each UAV and all communication base stations in the UAV network coverage area at the current moment and the energy consumption influencing factor, and obtains the potential impact index of each UAV at the current moment; selects the cluster head node in the communication protocol according to the potential impact index of all UAVs at the current moment.

[0010] Furthermore, the method for obtaining the cumulative change rate of the speed of each drone at the current moment includes:

[0011] For each drone, the sum of the distances between the spatial coordinates of the drone at all adjacent moments from the initial moment to the current moment is calculated as the flight distance of the drone at the current moment;

[0012] For each moment other than the initial moment, the ratio of the Euclidean distance between the spatial coordinates of each moment and the previous moment to the preset time interval is taken as the flight speed at each moment; wherein the flight speed at the initial moment is 0;

[0013] The standard deviation of the flight speed at all times before the current moment is calculated; the product of the standard deviation and the flight speed at the current moment is used as the cumulative change rate of the speed of each UAV at the current moment.

[0014] Furthermore, the method for obtaining the frequent coefficient of the path change of the UAV at the current moment includes:

[0015] Perform curve fitting on the spatial coordinates of each UAV at all times to obtain the trajectory curve of each UAV;

[0016] Calculate the curvature and torsion of the point on the trajectory curve of the drone that is closest to the spatial coordinates at the current moment; take the sum of the absolute value of the curvature and the absolute value of the torsion as the trajectory change of the drone at the current moment; calculate the cumulative sum of the trajectory changes at the current moment and all previous moments as the path change frequency coefficient of the drone at the current moment.

[0017] Furthermore, the method for obtaining the maneuvering energy consumption index of the UAV at the current moment includes:

[0018] The product of the UAV's path change frequency coefficient and the cumulative speed change rate at the current moment is taken as the UAV's maneuvering energy consumption index at the current moment.

[0019] Furthermore, according to the clustering of the spatial coordinates of all drones at each moment and the maneuvering energy consumption index, the real-time energy consumption rate of each drone at each moment is obtained, including:

[0020] Perform density peak clustering on the spatial coordinates of all drones at each moment to obtain the local density and relative distance of each drone at each moment;

[0021] The local density, relative distance and maneuvering energy consumption index of each UAV at each moment are combined to obtain the real-time energy consumption rate of each UAV at each moment.

[0022] Furthermore, the real-time energy consumption rate of each UAV at each moment is obtained by integrating the local density, relative distance and maneuvering energy consumption index of each UAV at each moment, including: recording the real-time energy consumption rate of the i-th UAV at the j-th moment as , ; In the formula, represents the maneuvering energy consumption index of the i-th UAV at the j-th moment, They represent the local density and relative distance of the i-th UAV at the j-th moment respectively.

[0023] Furthermore, the method for obtaining the energy consumption influencing factor of each UAV at the current moment includes:

[0024] For each drone, a prediction algorithm is used to predict the real-time energy consumption rate at the current moment and a preset number of moments before it, and the predicted value of the energy consumption rate at the next moment after the current moment is obtained; the absolute value of the difference between the real-time energy consumption rate at the current moment and the predicted value of the energy consumption rate at the next moment is used as the energy consumption influencing factor of each drone at the current moment.

[0025] Furthermore, the method for obtaining the potential impact index of each drone at the current moment includes:

[0026] Calculate the mean of the Euclidean distances between each UAV and all communication base stations in the UAV network coverage area at the current moment; multiply the normalized value of the mean by the energy consumption impact factor of each UAV at the current moment as the potential impact index of each UAV at the current moment.

[0027] Further, the cluster head node in the communication protocol is selected according to the potential impact index of all drones at the current moment, including:

[0028] In the communication protocol, all drones are arranged in ascending order according to their potential influence index at the last moment of the previous round of clustering process to obtain a drone selection sequence; drones with a preset proportion in the drone selection sequence are used as cluster head nodes for this round of communication.

[0029] In a second aspect, an embodiment of the present application also provides a swarm drone networking communication protocol system, comprising a memory, a processor, and a computer program stored in the memory and running on the processor, wherein the processor implements the steps of any one of the above-mentioned swarm drone networking communication protocol methods when executing the computer program.

[0030] This application has at least the following beneficial effects:

[0031] This application analyzes the problem that in actual application, the energy consumption of drones is uneven due to the influence of the flight trajectory and spatial position distribution of drones, which in turn reduces the efficiency of network communication. First, the complex characteristics of the drone's flight distance, speed and trajectory are analyzed to obtain the maneuvering energy consumption index, accurately obtaining the impact of the motion state of each drone on its mission path on energy consumption; further analyzing the impact of the spatial distribution of drones on communication efficiency, combining the spatial position distribution characteristics between swarm drones, calculating the energy consumption impact factor, and accurately obtaining the energy consumption characteristics of each drone during the communication process; finally, based on the potential change degree of real-time energy consumption rate and the distance characteristics between drones and base stations, the potential impact index of energy consumption on communication is calculated, and the energy retention capacity of the drone as the cluster head at the current moment is accurately evaluated, and the cluster head node of the swarm drone is further selected for drone communication, thereby improving the efficiency of network communication. BRIEF DESCRIPTION OF THE DRAWINGS

[0032] In order to more clearly illustrate the technical solutions and advantages in the embodiments of the present application or the prior art, the drawings required for use in the embodiments or the prior art descriptions are 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 creative work.

[0033] Figure 1 A flowchart of a swarm drone networking communication protocol method provided in one embodiment of the present application;

[0034] Figure 2 A schematic diagram of a network node structure provided for an embodiment of the present application. DETAILED DESCRIPTION

[0035] The following will be combined with the drawings in the embodiments of the present application to clearly and completely describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. In the absence of conflict, the embodiments of the present application and the technical features in the embodiments can be combined with each other. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0036] Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs.

[0037] The following is a detailed description of a specific solution of a swarm drone networking communication protocol method and system provided by the present application in conjunction with the accompanying drawings.

[0038] See also Figure 1 , which shows a flow chart of a swarm drone networking communication protocol method provided by an embodiment of the present application, the method comprising the following steps:

[0039] S1, collects the spatial coordinates of each drone in the swarm drone network at each moment.

[0040] A specific implementation scenario of the embodiment of the present application is a communication scenario between drones and base stations in a swarm drone network; during the swarm drone execution process, the drones and base stations form a communication network. Each drone is a node, and these nodes are randomly deployed in the network. The network node structure is as follows: Figure 2 As shown, the triangle in the figure is a communication base station, the solid point in the figure is the cluster head node, and the hollow point is the member node in the cluster. The member node communicates with the cluster head node, and the cluster head node communicates with the base station.

[0041] At present, communication protocols are often used to collect and integrate drone data and communicate with base stations. The principle of the communication protocol is that in each round of communication, a cluster head node is first generated by election. The cluster head node collects and integrates the data of all member nodes in the cluster, and then communicates with the base station.

[0042] This application takes into account the influence of the spatial distribution state and energy consumption of drones on the selection of cluster head nodes. This application uses the sensors carried by drones to obtain the spatial coordinates of each moment in real time, where the mission start time is taken as the initial time and the collection time interval is 0.01 seconds. According to the spatial coordinates of the drone, the flight distance, speed and trajectory of the drone are analyzed, and the energy consumption of the drone is calculated to select the appropriate cluster head node.

[0043] S2, according to the fluctuation of the distance between the spatial coordinates of each UAV at all adjacent moments, the cumulative change rate of the speed of each UAV at the current moment is obtained; according to the continuous change of the spatial coordinates of each UAV at all moments, the frequent coefficient of path change of the UAV at the current moment is obtained; the maneuvering energy consumption index of the UAV at the current moment is obtained by combining the cumulative change rate of the speed and the frequent coefficient of path change of each UAV at the current moment.

[0044] In the initial stage of swarm drone networking communication, the battery energy of each drone is close. As time goes by, the remaining energy of each node changes greatly due to different tasks performed and different distances between the node and the cluster head. It is possible that a node with lower energy is selected as the cluster head, causing the network to crash prematurely. During the operation of swarm drones, energy consumption mainly occurs in the flight mission payload and communication system. Therefore, a more suitable cluster head node can be selected by analyzing the energy consumption status of the drone.

[0045] First, from the perspective of flight mission payload. During the execution of a swarm of drones, some drones need to fly more frequently, which results in greater energy consumption. The longer the flight distance and the more complex the flight trajectory, for example, when the flight trajectory is changed frequently, more electrical energy is consumed to overcome the inertia during the flight, resulting in greater energy consumption.

[0046] Based on the above characteristics, it is necessary to analyze the flight trajectory of the drone in detail to explore the impact of trajectory changes on the selection of cluster head nodes in networking communication. Specifically, considering the impact of the drone's flight distance and flight speed on energy consumption, the sum of the Euclidean distances between the spatial coordinates of all adjacent moments from the initial moment to the current moment is calculated as the flight distance of the drone at the current moment. Since the time intervals between the acquisition of two adjacent spatial coordinates are consistent, for each moment other than the initial moment, the ratio of the Euclidean distance between each moment and the spatial coordinates of the previous moment to the preset time interval is used as the flight speed at each moment. Among them, the flight speed at the initial moment is 0. Since the energy consumption is faster when the speed changes frequently, this embodiment calculates the standard deviation of the flight speed at all moments before the current moment; the product of the standard deviation and the flight speed at the current moment is used as the cumulative change rate of the drone's speed at the current moment. The larger the cumulative change rate of speed, the longer the flight distance of the drone and the faster the speed change, and the more complex the flight mission of the drone.

[0047] Furthermore, the complex characteristics of the UAV flight trajectory are analyzed. The more frequent the turns and changes in direction during the flight of the UAV, the faster the energy consumption of the UAV will be. First, the spatial coordinates of each UAV at all times are curve fitted to obtain the trajectory curve of each UAV. Among them, the coordinate system used for the spatial coordinates is: a spatial rectangular coordinate system with the base station position as the origin o, the horizontal plane as the xoy plane, and the direction perpendicular to the horizontal plane as the positive direction of the z-axis. In this spatial coordinate system, the spatial coordinates at all times are curve fitted by polynomial fitting technology. In this embodiment, the polyfit function in MATLAB is used to obtain the corresponding fitting curve equation.

[0048] Therefore, the curvature and torsion of the point on the trajectory curve of the drone that is closest to the spatial coordinates at the current moment are calculated. The curvature describes the degree of bending of a point in the drone's flight trajectory, and the torsion describes the degree of distortion of a point in the drone's flight trajectory in space when it leaves the plane. The sum of the absolute value of the curvature and the absolute value of the torsion is taken as the change in the drone's trajectory at the current moment; the cumulative sum of the change in the trajectory at the current moment and all previous moments is calculated as the frequent coefficient of the drone's path change at the current moment. The larger the frequent coefficient of the path change, the more complex the change in the drone's flight trajectory.

[0049] Therefore, the change of speed under the UAV flight path and the complexity of the flight trajectory are comprehensively considered to evaluate the maneuvering energy consumption of the UAV. Specifically, the product of the UAV's path change frequency coefficient and the cumulative speed change rate at the current moment is used as the UAV's maneuvering energy consumption index. The larger the path change frequency coefficient, the more complex the flight trajectory is, and the more energy the UAV needs to consume to change direction. The larger the cumulative speed change rate, the greater the energy consumption of the UAV, and the larger the UAV's maneuvering energy consumption index, indicating that the UAV's flight trajectory during this time has a greater impact on the remaining energy.

[0050] S3, based on the clustering of the spatial coordinates of all UAVs at each moment and the maneuvering energy consumption index, obtain the real-time energy consumption rate of each UAV at each moment; based on the difference between the predicted results of the real-time energy consumption rate of each UAV at the current moment and a preset number of moments before and the real-time energy consumption rate at the current moment, obtain the energy consumption influencing factor of each UAV at the current moment.

[0051] During the communication process, if the distribution density of drones is low, that is, the distance between drones is far, then in order to maintain communication, the drones may need to increase the transmission power, which will lead to increased energy consumption. The selection of the LEACH protocol cluster head is random. If the drone node with low distribution density is used as the cluster head, the energy consumption generated by the communication will be further increased.

[0052] Therefore, this embodiment first uses a clustering algorithm to obtain the distribution density characteristics of drones at each moment. Specifically, the spatial coordinates of all drones at each moment are clustered to obtain the local density and relative distance of each drone at each moment; it should be noted that the DPC density peak clustering algorithm is used in this embodiment to cluster the spatial coordinates.

[0053] The input of the DPC density peak clustering algorithm is the spatial coordinates of all drones at the current moment, and the cutoff distance is set to 4% of the total data. The local density and relative distance of each drone's corresponding spatial position data are output. The local density reflects the number of drones in the local range, and the relative distance reflects the spatial distance between the drone and the nearest high-density drone. The smaller the local density, the fewer other drones around the drone, and the larger the relative distance, the more likely the drone is to be far away from the high-density drone area.

[0054] Generally speaking, if the drone is in a high-density distribution range, the change of its flight trajectory is relatively simple, that is, there are few frequent turns and changes of direction, and the energy consumption is relatively slow. Therefore, the influence of the flight path on energy consumption can be combined to analyze the interference of the distribution density of the swarm drones on data communication at each moment. Specifically, the local density, relative distance and maneuvering energy consumption index of each drone at each moment are combined to obtain the real-time energy consumption rate of each drone at each moment;

[0055] As an embodiment of the present application, the calculation formula of the real-time energy consumption rate is:

[0056]

[0057] In the formula, is the real-time energy consumption rate of the i-th UAV at the j-th moment, represents the maneuvering energy consumption index of the i-th UAV at the j-th moment, They represent the local density and relative distance of the ith UAV at the jth moment respectively. The value 1 is a constant greater than 0 to prevent the denominator from being 0.

[0058] The smaller the local density and the larger the relative distance, the more likely the drone is to stay away from the high-density drone area, and the need to increase the transmission power, resulting in increased energy consumption. At this time, the larger the maneuvering energy consumption index is, the more complex the flight trajectory of the drone is, the greater the impact on energy consumption, the larger the real-time energy consumption rate is, and the faster the drone consumes energy at that moment.

[0059] Furthermore, the degree of change of the remaining energy of each drone is inconsistent due to the influence of the flight state and distribution density. In order to analyze the potential change of the remaining energy of the drone, the real-time energy consumption rate of each drone is predicted in this embodiment, and the potential change characteristics of the remaining energy are analyzed based on the predicted data.

[0060] Specifically, for each drone, a prediction algorithm is used to predict the real-time energy consumption rate at the current moment and the preset number of moments before it, and the predicted value of the energy consumption rate at the next moment after the current moment is obtained; the absolute value of the difference between the real-time energy consumption rate at the current moment and the predicted value of the energy consumption rate at the next moment is used as the energy consumption influencing factor of the drone at the current moment. The preset number can be, for example, 9.

[0061] This embodiment uses the EMA exponential moving average algorithm to predict the real-time energy consumption rate. The input of EMA is the real-time energy consumption rate at the current moment and the 9 moments before it, and the weight parameter is set to 0.6. The output is the predicted value of the energy consumption rate at the next moment after the current moment. The greater the difference between the predicted value of the energy consumption rate and the real-time energy consumption rate, the greater the potential change in the energy reduction of the drone. It should be noted that this application uses the original algorithm for cluster selection in the first round of communication in the communication protocol, and the data of the first 10 moments are within the time period of the first round, so the data of the first 10 moments do not participate in the above prediction calculation.

[0062] S4, comprehensively considers the distance between each UAV and all communication base stations in the UAV network coverage area at the current moment and the energy consumption influencing factor, and obtains the potential impact index of each UAV at the current moment; selects the cluster head node in the communication protocol according to the potential impact index of all UAVs at the current moment.

[0063] When performing their missions, swarm drones may need to reach farther locations because they need to cover a wider area. The traditional LEACH protocol may select drones located at edge locations as cluster head nodes. These edge drones are far away from the communication base station, and the range of the cluster they cover is small when they serve as cluster heads, so the energy consumption of transmitting data will be too high.

[0064] Therefore, the mean of the Euclidean distances between each drone and all communication base stations in the drone network coverage area at the current moment is calculated; the product of the normalized value of the mean and the energy consumption impact factor of each drone at the current moment is used as the potential impact index of each drone at the current moment. This embodiment uses Z-score technology for normalization.

[0065] If the normalized result of the Euclidean distance between the drone and the base station at the current moment is larger, it means that the drone is farther away from the base station than other drones, and the energy consumption for data transmission with the base station when it serves as the cluster head is greater; the larger the energy consumption impact factor, the greater the potential change in the energy of the drone, and the larger the potential impact index obtained, indicating that the drone has a greater energy consumption and the lower the possibility of selecting it as the cluster head.

[0066] In the LEACH protocol, the communication process is divided into multiple rounds, each of which includes a clustering process and a data transmission process. This embodiment calculates the potential impact index of each drone's energy consumption on communication by analyzing the flight path of the drone and the distribution characteristics between drones. This value reflects the energy consumption state of the drone at the current moment. The larger the potential impact index, the more obvious its energy consumption, and the less suitable the drone is for communicating as the cluster head node of the LEACH protocol.

[0067] Therefore, in each round of clustering, drones with low potential impact index are selected as cluster heads to extend the network life of swarm drone networking communication. Specifically, in the communication protocol, all drones are arranged in ascending order according to their potential impact index at the last moment of the previous round of clustering process to obtain a drone selection sequence; the first 10% of drones in the drone selection sequence are selected as cluster head nodes for this round of communication, so as to carry out drone networking communication.

[0068] Based on the same inventive concept as the above method, an embodiment of the present application also provides a swarm drone networking communication protocol system, including a memory, a processor, and a computer program stored in the memory and running on the processor, and when the processor executes the computer program, the steps of any one of the above-mentioned swarm drone networking communication protocol methods are implemented.

[0069] Through the above description of the implementation method in combination with the accompanying drawings, technical personnel in the relevant field can understand that for the convenience and simplicity of description, only the division of the above-mentioned functional modules is used as an example. In actual applications, the above-mentioned functions can be assigned to different functional modules as needed, that is, the internal structure of the device can be divided into different functional modules to complete all or part of the functions described above.

[0070] The above contents are only specific implementation methods of the present application, but the protection scope of the present application is not limited thereto. Any technician familiar with the technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be covered by the protection scope of the present application.

Claims

1. A swarm drone networking communication protocol method, characterized in that: The method comprises the following steps: S1, collects the spatial coordinates of each drone in the swarm drone network at each moment; S2, according to the fluctuation of the distance between the spatial coordinates of each UAV at all adjacent moments, the cumulative change rate of the speed of each UAV at the current moment is obtained; according to the continuous change of the spatial coordinates of each UAV at all moments, the frequent coefficient of path change of the UAV at the current moment is obtained; the maneuvering energy consumption index of the UAV at the current moment is obtained by combining the cumulative change rate of the speed of each UAV at the current moment and the frequent coefficient of path change; S3, according to the clustering of the spatial coordinates of all drones at each moment and the maneuvering energy consumption index, the real-time energy consumption rate of each drone at each moment is obtained; according to the difference between the predicted results of the real-time energy consumption rate of each drone at the current moment and the preset number of moments before and the real-time energy consumption rate at the current moment, the energy consumption influencing factor of each drone at the current moment is obtained; S4, comprehensively considering the distance between each UAV and all communication base stations in the UAV network coverage area at the current moment and the energy consumption impact factor, obtain the potential impact index of each UAV at the current moment; select the cluster head node in the communication protocol according to the potential impact index of all UAVs at the current moment; The method for obtaining the maneuvering energy consumption index of the UAV at the current moment includes: The product of the UAV's path change frequency coefficient and the cumulative speed change rate at the current moment is used as the UAV's maneuvering energy consumption index at the current moment; According to the clustering of the spatial coordinates of all drones at each moment and the maneuvering energy consumption index, the real-time energy consumption rate of each drone at each moment is obtained, including: Perform density peak clustering on the spatial coordinates of all drones at each moment to obtain the local density and relative distance of each drone at each moment; The local density, relative distance and maneuvering energy consumption index of each UAV at each moment are combined to obtain the real-time energy consumption rate of each UAV at each moment. The real-time energy consumption rate of each UAV at each moment is obtained by integrating the local density, relative distance and maneuvering energy consumption index of each UAV at each moment, including: recording the real-time energy consumption rate of the i-th UAV at the j-th moment as , ; In the formula, represents the maneuvering energy consumption index of the i-th UAV at the j-th moment, They represent the local density and relative distance of the i-th UAV at the j-th moment respectively.

2. A swarm drone networking communication protocol method as claimed in claim 1, characterized in that: The method for obtaining the cumulative change rate of the speed of each drone at the current moment includes: For each drone, the sum of the distances between the spatial coordinates of the drone at all adjacent moments from the initial moment to the current moment is calculated as the flight distance of the drone at the current moment; For each moment other than the initial moment, the ratio of the Euclidean distance between the spatial coordinates of each moment and the previous moment to the preset time interval is taken as the flight speed at each moment; wherein the flight speed at the initial moment is 0; The standard deviation of the flight speed at all times before the current moment is calculated; the product of the standard deviation and the flight speed at the current moment is used as the cumulative change rate of the speed of each UAV at the current moment.

3. A swarm drone networking communication protocol method as claimed in claim 1, characterized in that: The method for obtaining the path change frequency coefficient of the UAV at the current moment includes: Perform curve fitting on the spatial coordinates of each UAV at all times to obtain the trajectory curve of each UAV; Calculate the curvature and torsion of the point on the trajectory curve of the drone that is closest to the spatial coordinates at the current moment; take the sum of the absolute value of the curvature and the absolute value of the torsion as the trajectory change of the drone at the current moment; calculate the cumulative sum of the trajectory changes at the current moment and all previous moments as the path change frequency coefficient of the drone at the current moment.

4. A swarm drone networking communication protocol method as claimed in claim 1, characterized in that: The method for obtaining the energy consumption influencing factor of each UAV at the current moment includes: For each drone, a prediction algorithm is used to predict the real-time energy consumption rate at the current moment and a preset number of moments before it, and the predicted value of the energy consumption rate at the next moment after the current moment is obtained; the absolute value of the difference between the real-time energy consumption rate at the current moment and the predicted value of the energy consumption rate at the next moment is used as the energy consumption influencing factor of each drone at the current moment.

5. A swarm drone networking communication protocol method as claimed in claim 1, characterized in that: The method for obtaining the potential impact index of each drone at the current moment includes: Calculate the mean of the Euclidean distances between each UAV and all communication base stations in the UAV network coverage area at the current moment; multiply the normalized value of the mean by the energy consumption impact factor of each UAV at the current moment as the potential impact index of each UAV at the current moment.

6. A swarm drone networking communication protocol method as claimed in claim 1, characterized in that: The method of selecting the cluster head node in the communication protocol according to the potential impact index of all drones at the current moment includes: In the communication protocol, all drones are arranged in ascending order according to their potential influence index at the last moment of the previous round of clustering process to obtain a drone selection sequence; drones with a preset proportion in the drone selection sequence are used as cluster head nodes for this round of communication.

7. A swarm drone networking communication protocol system, comprising a memory, a processor, and a computer program stored in the memory and running on the processor, characterized in that: When the processor executes the computer program, the steps of a swarm drone networking communication protocol method as described in any one of claims 1-6 are implemented.

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