A data communication method for ground-to-air collaborative networking of unmanned aerial vehicles

Abstract

The present invention relates to the field of data communication technology, specifically disclosing a data communication method for ground-to-air collaborative networking of unmanned aerial vehicles (UAVs), comprising the following steps: marking a UAV in a UAV cluster that is used to perform a preset task as a first UAV, converting data collected by the first UAV into a signal that is recorded as a target signal; obtaining a line connecting the first UAV and a ground center as a target line, and determining a communication link for the first UAV based on the target line, wherein the communication link is based on the composition of the UAVs; ensuring that the UAVs on the communication link are relatively stationary relative to the first UAV, transmitting the target signal to the ground center via the communication link, and the ground center performing frequency compensation on the received target signal. The present invention can reduce the impact of the Doppler effect on data transmission quality.

Description

Technical Field

[0001] The present invention relates to the field of data communication technology, and in particular to a data communication method for ground-to-air collaborative networking of unmanned aerial vehicles (UAVs). Background Art

[0002] The drone ground-to-air collaborative networking is a network architecture that enables the ground network and the aerial drone system to cooperate with each other and complement each other's advantages. The drone not only exists as a separate information collection and transmission platform, but can also form a collaborative overall network with the ground station and other drones to achieve fast, efficient and stable information transmission.

[0003] The Doppler effect refers to the phenomenon in which the frequency of waves (such as sound waves and light waves) received by an observer changes when there is relative motion between the wave source and the observer. In drone-to-ground collaborative networking, the relative motion between drones and ground stations or other platforms while in mid-flight can cause frequency shifts in wireless communication signals, affecting data communication quality. Summary of the Invention

[0004] The purpose of the present invention is to provide a data communication method for ground-to-air collaborative networking of unmanned aerial vehicles to solve the following technical problems:

[0005] In the ground-to-air collaborative networking of drones, since the drones are flying in the air, there is relative motion between them and the ground station or other platforms, which will cause frequency deviation of the wireless communication signal and affect the quality of data communication.

[0006] The purpose of the present invention can be achieved through the following technical solutions:

[0007] A method for ground-to-air collaborative networking data communication of unmanned aerial vehicles, comprising the following steps:

[0008] The drone in the drone cluster used to perform the preset task is marked as a first drone, and the signal obtained by converting the data collected by the first drone is recorded as a target signal;

[0009] Obtaining a line between the first UAV and a ground center, recorded as a target line, and determining a communication link of the first UAV based on the target line, wherein the communication link is based on the composition of the UAVs;

[0010] The UAV and the first UAV on the communication link are kept relatively stationary, the target signal is transmitted to a ground center through the communication link, and the ground center performs frequency compensation on the received target signal.

[0011] As a further solution of the present invention, determining the communication link of the first UAV based on the target line includes:

[0012] Step 1: Use the first UAV as a component machine, draw a circle with the component machine as the center and the preset optimal communication distance D as the radius, and mark the UAVs within the circle as candidate machines;

[0013] Obtain the distance between the projection point of the candidate aircraft on the target line and the first UAV, record it as the selection distance, and use the candidate aircraft corresponding to the largest selection distance as the new component aircraft A;

[0014] Step 2: With the component machine A as the center of the circle, repeat step 1 to obtain a new component machine, and repeat the above steps until the distance between a new component machine and the ground center is less than the optimal communication distance, and all the candidate machines form the communication link.

[0015] As a further solution of the present invention, the process of keeping the UAV and the first UAV on the communication link relatively stationary includes:

[0016] Marking factors that affect the speed of the drone as target factors, collecting the target factors of the first drone, inputting them into a pre-trained speed decay prediction model, outputting a speed decay value, setting the speed of the first drone to a preset value V, and subtracting the speed decay value from the preset value V to obtain a predicted speed V0 of the first drone;

[0017] Mark the UAV on the communication link as a target UAV, obtain the speed attenuation value V1 of the target UAV, and set the speed of the target UAV to V1+V0;

[0018] Ensure that the target UAV and the first UAV move in the same direction, so that the UAV and the first UAV on the communication link are relatively stationary.

[0019] As a further solution of the present invention, the process of pre-training the speed decay prediction model includes:

[0020] Acquire a data set, wherein the data set stores target factors with labeled speed attenuation values, and divide the data set into a training set and a validation set according to a preset ratio;

[0021] A speed decay prediction model is established based on deep learning, and the speed decay prediction model is trained and verified based on the training set and the verification set to obtain a pre-trained speed decay prediction model.

[0022] As a further solution of the present invention, the process of transmitting the target signal to the ground center via the communication link further includes the following steps:

[0023] The last UAV on the communication link is used as the second UAV. After receiving the target signal, the second UAV preprocesses the target signal, and the preprocessing includes adaptive filtering and frequency precompensation. The second UAV transmits the preprocessed target signal to the ground center.

[0024] As a further solution of the present invention, the process of transmitting the target signal to the ground center via the communication link further includes the following steps:

[0025] When the distance between the second UAV and the ground center is equal to 0.95D, if the first UAV has not yet completed the task, a new UAV is started to join the UAV cluster, and when the distance between the second UAV and the ground center is equal to D, the communication link is updated.

[0026] As a further solution of the present invention: the process of determining the communication link of the first UAV based on the target line further includes:

[0027] When UAV i is part of m communication links and m>n, where n is a preset number, the following steps are performed:

[0028] The m communication links corresponding to the drone i are used as pending links, and the distance between the drone i and a third drone is obtained and recorded as the sorting distance. The third drone is the drone that is adjacent to the drone i in the pending links.

[0029] The pending links are sorted in ascending order according to the size of the sorting distance, and the drone i is used as a component of the first n pending links in the sorting.

[0030] As a further solution of the present invention: after the communication links of all the first UAVs are determined, the UAVs that do not belong to any of the communication links return to the ground center and enter a dormant state.

[0031] The beneficial effects of the present invention are as follows: In this solution, by selecting the first drone in the drone cluster to specifically perform the preset task without participating in the optimization of the communication link, the energy consumption of the first drone is minimized, ensuring that it can concentrate resources to successfully complete the core task; at the same time, other drones build a communication link around the target line between the first drone and the ground center and are selected step by step based on the optimal communication distance. This method not only ensures that data will not jump excessively and the link will be unstable during transmission, but also avoids communication delays caused by frequent switching; on this basis, the speed of each drone on the communication link remains relatively static with the first drone. Thanks to the flight speed attenuation compensation setting achieved by the pre-trained speed attenuation prediction model, this relative staticness not only makes the data transmission process more stable and continuous, but also significantly reduces the Doppler effect caused by the relative speed difference and reduces the impact of frequency offset during communication; in addition, when the transmission is about to end, if the first drone has not completed the task, a new drone will be automatically started to join the cluster to take over the link end, thereby maintaining the ideal communication distance and further reducing the interference caused by sudden Doppler frequency shift; Before transmitting the target signal to the ground center, the last UAV on the link performs adaptive filtering and frequency pre-compensation. This pre-compensation measure, combined with the ground center's final frequency compensation, effectively offsets most frequency offsets caused by the flight environment, airflow disturbances, or a small amount of residual relative velocity, further reducing the negative impact of the Doppler effect on communication quality. Furthermore, when updating the communication link, the second UAV changes—that is, the one that pre-processes the target signal—to prevent excessive energy consumption by a particular UAV. To prevent a single UAV from being reused in multiple communication links and causing excessive resource consumption, a priority selection mechanism is implemented when the same UAV belongs to multiple communication links simultaneously, thereby rationally allocating communication tasks and improving the reliability and coordination of the overall network. All UAVs not belonging to any communication link return to the ground center and enter a dormant state, ensuring the effective utilization and expansion of system resources. In summary, the above method not only achieves efficient and robust link communication between UAVs and the ground center, but also significantly reduces the negative impact of the Doppler effect while reducing the energy consumption of the first UAV and ensuring the successful completion of the mission, thereby ensuring data transmission quality and flight efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0032] The present invention will be further described below with reference to the accompanying drawings.

[0033] Figure 1 It is a flow chart of a method for ground-to-air collaborative networking data communication for unmanned aerial vehicles of the present invention. DETAILED DESCRIPTION

[0034] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. 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 making any creative efforts shall fall within the scope of protection of the present invention.

[0035] See also Figure 1 As shown, the present invention is a UAV ground-to-air collaborative networking data communication method, comprising the following steps:

[0036] The drone in the drone cluster used to perform the preset task is marked as a first drone, and the signal obtained by converting the data collected by the first drone is recorded as a target signal;

[0037] Obtaining a line between the first UAV and a ground center, recorded as a target line, and determining a communication link of the first UAV based on the target line, wherein the communication link is based on the composition of the UAVs;

[0038] In a preferred embodiment of the present invention, determining the communication link of the first UAV based on the target line includes:

[0039] Step 1: Use the first UAV as a component machine, draw a circle with the component machine as the center and the preset optimal communication distance D as the radius, and mark the UAVs within the circle as candidate machines;

[0040] Obtain the distance between the projection point of the candidate aircraft on the target line and the first UAV, record it as the selection distance, and use the candidate aircraft corresponding to the largest selection distance as the new component aircraft A;

[0041] Step 2: With the component machine A as the center of the circle, repeat step 1 to obtain a new component machine, and repeat the above steps until the distance between a new component machine and the ground center is less than the optimal communication distance, and all the candidate machines form the communication link;

[0042] It should be noted that, first, the three-dimensional spatial coordinates of all UAVs and the position information of the first UAV and the ground center are obtained in advance, and the line between the first UAV and the ground center is defined as the target line; then, taking the first UAV as the initial component, based on the three-dimensional coordinates of its position, a spherical area (or a circular area if in a plane scene) is constructed with the preset optimal communication distance D as the radius, and all UAVs with coordinates within the area are marked as candidate aircraft; for each candidate aircraft, its projection point relative to the target line is calculated, and the distance between the projection point and the position of the first UAV is measured, which is the selection distance; among all the candidate aircraft, the UAV with the largest distance is selected as the new component aircraft A, which means that it is on the target line longer than other candidate aircraft. It can extend further, thus helping to reduce the number of hops of subsequent communication nodes and improve link stability. Then, the coordinates of the new component machine A are used as the new sphere center, and the above steps are repeated: the spherical area is reconstructed with the same optimal communication distance D, the next batch of candidate machines are found, the projection points are calculated, and the maximum selection distance is selected to obtain a new component machine. This process is repeated step by step, approaching the ground center step by step until, in a certain iteration, the distance between the new component machine and the ground center is less than the optimal communication distance D, indicating that the communication link can reach the ground center directly from the first UAV and no further extension is required. In this process, each selected component machine, together with the candidate machines generated in the middle, forms a continuous UAV relay link in the selection order, realizing segmented and gradually extended network coverage.

[0043] In a preferred embodiment of this embodiment, the process of determining the communication link of the first UAV based on the target line further includes:

[0044] When UAV i is part of m communication links and m>n, where n is a preset number, the following steps are performed:

[0045] The m communication links corresponding to the drone i are used as pending links, and the distance between the drone i and a third drone is obtained and recorded as the sorting distance. The third drone is the drone that is adjacent to the drone i in the pending links.

[0046] Sort the pending links in ascending order according to the size of the sorting distance, and the drone i is regarded as a component of the first n pending links in the sorting;

[0047] It's worth noting that to prevent the overutilization of the same drone, which could lead to uneven resource consumption or excessive communication burdens, when a drone i is detected as belonging to multiple communication links exceeding a pre-set threshold n, it is preferentially retained. Specifically, all communication links associated with drone i are identified, denoted as m, and considered as pending links. Next, for each pending link, the system calculates a ranking distance based on the distance between drone i and its previous neighbor (referred to as the third drone) in that link. This previous neighbor is defined as the node preceding i along the target line between the first drone and the ground center when establishing the communication link. For a given link, if the third drone is j, the ranking distance is the three-dimensional Euclidean distance between i and j, or another appropriate measure. The system then ranks these m pending links in ascending order based on their corresponding ranking distances. Generally speaking, a smaller distance indicates a more compact and stable communication configuration for that link, and also implies that i's geographic location and topological role in that link are more critical. Based on this idea, the system will retain i as a component of the top n links and eliminate i from the lower-ranked links, thereby ensuring that in the overall communication design of the drone swarm, i only appears on the n most prioritized links.

[0048] For example, suppose drone i currently belongs to five pending links (m=5) and the preset threshold n=3. After calculation, the ranking distances of the five links are 2, 7, 8, 10, and 15, respectively. The system will retain the three links with the smallest ranking distances (2, 7, and 8) for i, and remove the two links with ranking distances of 10 and 15. In actual operation, the removed links will try to find alternative drones or re-plan nodes to ensure communication continuity, while drone i can continue to perform its communication function on the most favorable and stable links.

[0049] In another preferred embodiment of the present invention, after determining the communication links of all the first UAVs, the UAVs that do not belong to any of the communication links return to the ground center and enter a dormant state;

[0050] It is important to note that this avoids wasting energy and spectrum resources due to ineffective waiting in the drone cluster, while also reducing airspace occupancy and collision risks, leaving sufficient resources and space for subsequent missions or newly emerging first drone communication needs. If a new communication link or mission does arise, the ground center only needs to wake up the dormant backup drone and repeat the previous communication link construction and screening process;

[0051] The UAV and the first UAV on the communication link are kept relatively stationary, the target signal is transmitted to a ground center through the communication link, and the ground center performs frequency compensation on the received target signal.

[0052] It is worth noting that after receiving and down-converting the digital baseband signal, the ground center typically combines a preset pilot sequence or known training symbols to estimate frequency deviations such as Doppler shift. This deviation is first roughly detected through correlation operations or matched filtering, and the detection results are fed into a numerically controlled oscillator or digital mixing module for coarse compensation, essentially returning the carrier center frequency to its ideal position. Subsequently, closed-loop methods such as decision feedback or phase-locked loops are used for refined tracking and phase synchronization to further suppress residual frequency deviation and ensure the accuracy of subsequent demodulation or decoding. If the environment changes drastically during communication or the Doppler shift fluctuates significantly over time, adaptive filtering or iterative update algorithms are used to continuously adjust the compensation parameters, thereby maintaining low frequency deviation and high demodulation reliability even in dynamic flight environments.

[0053] In another preferred embodiment of the present invention, the process of keeping the UAV and the first UAV on the communication link relatively stationary includes:

[0054] Marking factors that affect the speed of the drone as target factors, collecting the target factors of the first drone, inputting them into a pre-trained speed decay prediction model, outputting a speed decay value, setting the speed of the first drone to a preset value V, and subtracting the speed decay value from the preset value V to obtain a predicted speed V0 of the first drone;

[0055] Mark the UAV on the communication link as a target UAV, obtain the speed attenuation value V1 of the target UAV, and set the speed of the target UAV to V1+V0;

[0056] Ensure that the target UAV and the first UAV move in the same direction, so that the UAV and the first UAV on the communication link are relatively stationary.

[0057] In a preferred embodiment of the present invention, the process of pre-training the speed attenuation prediction model includes:

[0058] Acquire a data set, wherein the data set stores target factors with labeled speed attenuation values, and divide the data set into a training set and a validation set according to a preset ratio;

[0059] Establishing a speed decay prediction model based on deep learning, training and validating the speed decay prediction model based on the training set and the validation set to obtain a pre-trained speed decay prediction model;

[0060] It is worth noting that during the model pre-training phase, it is necessary to prepare historical or experimental data on various factors (target factors) that affect the speed of the drone. For example, this can include various environmental and mission-related factors such as wind speed, wind direction, altitude, temperature and humidity, body load, engine or motor power loss, and annotate each data sample with its corresponding speed attenuation value. After cleaning and standardizing these raw data, they are divided into training and validation sets. Then, a suitable deep learning or machine learning model (such as one based on a multi-layer perceptron, LSTM network, or other neural network structure) is selected to iteratively train the training set. The model accuracy and generalization ability are evaluated on the validation set. If the accuracy meets the requirements, a pre-trained speed attenuation prediction model is obtained.

[0061] In the actual deployment phase, assume there is such a scenario: the first drone F (performing the core mission) and several target drones B, C, etc. that form the communication link need to fly together to achieve data relay and transmission. To keep B and C stationary relative to F during flight, thereby avoiding excessive relative displacement and Doppler effect caused by speed differences, the following steps can be followed: During flight, the ground control center or onboard system obtains the current environmental parameters and internal status of each drone in real time, such as A's altitude, wind speed and direction, ambient temperature, payload weight, engine performance degradation, and battery charge. These key speed-related values are input as target factors into a pre-trained speed decay prediction model to obtain the speed decay value of drone F (denoted as F1). For the first drone F, an ideal operating speed V is usually given in advance (such as the cruising speed required for a certain mission). After obtaining F's speed decay value F1, F can be directly subtracted from V to obtain the predicted speed V0 of the first drone, which is the speed that can be maintained under the current target factors (the target factor affects the speed, and the speed is set to V, but in reality it may only reach V0). For the other drones B and C that form the communication link, their respective target factors need to be collected and input into the speed decay prediction model to obtain their respective decay values (denoted as B1 and C1, respectively). Since B and C do not need to bear the load of performing core tasks, their attenuation values are often different from F. In order to achieve relative stillness with F1, in theory, it is sufficient to make the actual speed of B and C consistent with the actual speed of F. In practice, it can be set according to the method of "B's speed = V0 + B1" to keep B and F at the same speed (the same applies to C). In addition to matching the speed scalars, it is also necessary to ensure that the heading of each target drone is consistent with that of A. This requires synchronous adjustment of the attitude and route planning in the onboard controller or autopilot. For example, after receiving the "Follow F" command on the flight control system, B and C can automatically correct their azimuth and heading angles to keep them on the same route or parallel route as F, without obvious yaw or side deviation, thereby maintaining relative stillness with F for a long time.

[0062] In a preferred embodiment of the present invention, the process of transmitting the target signal to the ground center via the communication link further includes the following steps:

[0063] The last UAV on the communication link is used as a second UAV. After receiving the target signal, the second UAV pre-processes the target signal, wherein the pre-processing includes adaptive filtering and frequency pre-compensation. The second UAV transmits the pre-processed target signal to the ground center.

[0064] It is worth noting that adaptive filtering focuses on real-time suppression of random noise or interference in the air environment. It can dynamically adjust filtering parameters based on the collected noise characteristics to reduce clutter during signal transmission. Frequency pre-compensation makes preliminary corrections to the carrier frequency based on the detected offset at the air end, so that the signal arriving at the ground center has characteristics closer to the ideal frequency point. In this way, the ground center can eliminate most of the interference and offset when performing detailed frequency compensation and demodulation, thereby improving compensation accuracy and reducing the overall bit error rate of the system.

[0065] In a preferred embodiment of this invention, the process of transmitting the target signal to the ground center via the communication link further includes the following steps:

[0066] When the distance between the second UAV and the ground center is equal to 0.95D, if the first UAV has not yet completed the mission, a new UAV is started to join the UAV cluster, and when the distance between the second UAV and the ground center is equal to D, the communication link is updated;

[0067] It is understandable that when the target signal is transmitted step by step along the communication link to the last drone (i.e., the second drone), the second drone will first pre-process the received signal before sending the pre-processed signal to the ground center. In specific implementation, the second drone can have a built-in adaptive filtering and frequency pre-compensation function module, which dynamically adjusts the filter coefficients to maximize the signal-to-noise ratio by detecting the noise characteristics of the incoming signal or the offset between it and the carrier reference frequency in real time, and performs appropriate pre-compensation based on the monitored frequency deviation, thereby effectively reducing the impact of negative factors such as environmental noise and residual Doppler frequency shift during the communication process on signal integrity. After the pre-processing is completed, the second drone will transmit the filtered and frequency-corrected target signal to the ground center in digital or analog form. The ground center can also cooperate to perform further frequency compensation or filtering after finally receiving the signal to ensure the demodulation accuracy and reliability of the data. In addition, during the transmission process, if the distance between the second UAV and the ground center approaches 0.95D (where D represents the optimal communication distance), the system will detect whether the first UAV has completed its mission. If the first UAV has not yet completed its mission, a new UAV will be started from the idle or backup UAV pool to join the cluster in order to maintain the integrity of the communication link in the future; when the second UAV continues to approach the ground center and reaches D, the system will trigger an update operation on the existing communication link, that is, without affecting the ongoing data transmission, the newly added UAV will take over or be incorporated into the communication link, so that the entire link can continue to maintain a high-quality signal channel and relatively stable communication coverage even if the first UAV has not yet completed its core mission, thereby avoiding data interruption or reliability degradation caused by too short a distance or too much attenuation.

[0068] The above is a detailed description of an embodiment of the present invention. However, the content described is only a preferred embodiment of the present invention and should not be considered to limit the scope of the present invention. All equivalent changes and improvements made within the scope of the present invention should still fall within the scope of the patent coverage of the present invention.

Claims

1. A UAV ground-to-air collaborative networking data communication method, characterized in that: The following steps are involved: The drone in the drone cluster used to perform the preset task is marked as a first drone, and the signal obtained by converting the data collected by the first drone is recorded as a target signal; Obtaining a line between the first UAV and a ground center, recorded as a target line, and determining a communication link of the first UAV based on the target line, wherein the communication link is obtained based on the component machine; The process of obtaining the component aircraft includes: taking the first UAV as the component aircraft, determining a new component aircraft based on the determined component aircraft and the target line, until the distance between a new component aircraft and the ground center is less than a preset optimal communication distance; Keeping the other components on the communication link relatively still with the first UAV, transmitting the target signal to a ground center via the communication link, and the ground center performing frequency compensation on the received target signal.

2. The method for ground-to-air collaborative networking data communication of UAVs according to claim 1, characterized in that: Determining a communication link of the first UAV based on the target line includes: Step 1: Draw a sphere with the first UAV as the component machine, the component machine as the center, and the preset optimal communication distance D as the radius, and mark the UAVs in the UAV cluster within the sphere as candidate machines; Obtain the distance between the projection point of the candidate aircraft on the target line and the first UAV, record it as the selection distance, and use the candidate aircraft corresponding to the largest selection distance as the new component aircraft A; Step 2: With the component machine A as the center of the sphere, repeat step 1 to obtain a new component machine, and repeat the above steps until the distance between a new component machine and the ground center is less than the optimal communication distance. All the component machines are connected in sequence in the order in which they were selected to form a communication link.

3. The method for ground-to-air collaborative networking data communication of UAVs according to claim 1, characterized in that: The process of keeping the other component machines on the communication link relatively stationary with the first UAV includes: Marking factors that affect the speed of the drone as target factors, collecting the target factors of the first drone, inputting them into a pre-trained speed decay prediction model, outputting a speed decay value, setting the speed of the first drone to a preset value V, and subtracting the speed decay value from the preset value V to obtain a predicted speed V0 of the first drone; Mark the other components on the communication link as target UAVs, obtain the speed attenuation value V1 of the target UAV, and set the speed of the target UAV to V1+V0; Ensure that the target UAV and the first UAV move in the same direction, and achieve that the other component machines on the communication link are relatively stationary with the first UAV.

4. The method for ground-to-air collaborative networking data communication of UAVs according to claim 3, characterized in that: The process of pre-training the speed decay prediction model includes: Acquire a data set, wherein the data set stores target factors with labeled speed attenuation values, and divide the data set into a training set and a validation set according to a preset ratio; A speed decay prediction model is established based on deep learning, and the speed decay prediction model is trained and verified based on the training set and the verification set to obtain a pre-trained speed decay prediction model.

5. The method for ground-to-air collaborative networking data communication of UAVs according to claim 1, characterized in that: The process of transmitting the target signal to the ground center via the communication link further includes the following steps: The last component machine on the communication link is used as the second UAV. After receiving the target signal, the second UAV preprocesses the target signal, and the preprocessing includes adaptive filtering and frequency precompensation. The second UAV transmits the preprocessed target signal to the ground center.

6. The method for ground-to-air collaborative networking data communication of UAVs according to claim 5, characterized in that: The process of transmitting the target signal to the ground center via the communication link further includes the following steps: When the distance between the second UAV and the ground center is equal to 0.95D, if the first UAV has not yet completed the task, a new UAV is started to join the UAV cluster, and when the distance between the second UAV and the ground center is equal to D, the communication link is updated, where D represents the preset optimal communication distance.

7. The method for ground-to-air collaborative networking data communication of UAVs according to claim 2, characterized in that: The process of determining the communication link of the first UAV based on the target line further includes: When component machine i is a component of m communication links and m>n, n being a preset number, the following steps are performed: The m communication links corresponding to component machine i are taken as pending links, and the distance between component machine i and the third UAV is obtained and recorded as the sorting distance. The third UAV is the previous component machine adjacent to component machine i in the pending links; The pending links are sorted in ascending order according to the size of the sorting distance, and the component machine i is used as a component of the first n pending links in the sorting.

8. The method for ground-to-air collaborative networking data communication of UAVs according to claim 1, characterized in that: After the communication links of all the first drones are determined, the drones in the drone cluster that do not belong to any of the communication links return to the ground center and enter a dormant state.

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