Unmanned aerial vehicle communication relay method based on microwave energy efficiency optimization

Abstract

The invention relates to the technical field of wireless communication, in particular to an unmanned aerial vehicle communication relay method based on microwave energy efficiency optimization. Position data and wireless signal data of a ground base station and ground equipment as well as position data, flight state data, power endurance data and wireless signal data of an unmanned aerial vehicle are acquired, a signal hotspot map is drawn, power spectrum and power distribution optimization analysis is performed, signal quality is accurately evaluated, a reasonable unmanned aerial vehicle position is simulated, and the unmanned aerial vehicle position is simulated. Generating a topological graph to optimize signal coverage and energy efficiency; the simulation position communication quality optimization effect is evaluated, signal attenuation is calculated, wireless signal quality improvement is predicted, and a basis is provided for subsequent optimization; and finally, combining various data and prediction results, optimizing the target position of the unmanned aerial vehicle by using a machine learning algorithm, performing fine adjustment to obtain a scheduling scheme, screening a priority scheduling reference unmanned aerial vehicle, optimizing the scheduling efficiency, maximizing the system energy efficiency, and ensuring the communication stability.

Description

Technical Field

[0001] This invention relates to the field of wireless communication technology, specifically to a UAV communication relay method based on microwave energy efficiency optimization. Background Technology

[0002] In today's era of rapid technological advancement, wireless communication technology has become a core force driving social progress and industrial transformation. From everyday mobile communications and IoT applications to special scenarios such as emergency rescue and military operations, stable and efficient wireless communication networks are the key foundation for ensuring smooth information transmission and successful mission execution.

[0003] With their advantages of flexible deployment, rapid response, and ability to cover complex terrain, drones are increasingly widely used in the field of wireless communication. Especially in building temporary communication networks, enhancing signal coverage, and improving communication quality, drones play an irreplaceable role as relay nodes. For example, after a natural disaster, traditional communication infrastructure may suffer severe damage. Utilizing drones to quickly establish relay communication networks can promptly restore communication in disaster-stricken areas, providing strong support for rescue efforts.

[0004] However, in practical applications, there are still many challenges in how to scientifically and rationally deploy relay drones, optimize signal coverage, and improve the energy efficiency of communication systems. Existing wireless communication technologies and drone scheduling methods are gradually revealing some problems that urgently need to be solved when dealing with complex and ever-changing communication environments and diverse mission requirements.

[0005] To address the aforementioned issues, it is necessary to propose a UAV communication relay method based on microwave energy efficiency optimization. Summary of the Invention

[0006] The purpose of this invention is to solve the problems existing in the background technology and to propose a UAV communication relay method based on microwave energy efficiency optimization.

[0007] The objective of this invention can be achieved through the following technical solutions: A UAV communication relay method based on microwave energy efficiency optimization includes the following steps: Step 1: Wireless signal acquisition; Acquire location data and wireless signal data from ground base stations and ground equipment; acquire location data and power / endurance data from relay drones. Create a signal distribution heatmap based on the location data from the ground equipment and the wireless signal data.

[0008] Specifically, the location data of the ground base station is the coordinates of the ground base station, and the wireless signal data of the ground base station is the signal strength and power of the ground base station. The location data and wireless signal data of the ground equipment include: the coordinates of each ground equipment, wireless signal strength, wireless timing signal, communication bandwidth, and the distance from the coordinates of each ground equipment to the ground base station.

[0009] The relay drone location data includes the coordinates of each relay drone, and the relay drone power and endurance data includes the remaining battery power of each drone.

[0010] In a preferred embodiment of the present invention, a three-dimensional scatter plot is drawn based on the location data and wireless signal data of ground base stations and ground equipment to describe the spatial distribution of the ground equipment. The wireless signal data of each ground equipment is converted into grayscale values ​​in the three-dimensional scatter plot to describe the signal strength distribution of the ground equipment, resulting in a signal distribution heatmap.

[0011] Step 2: Energy efficiency optimization analysis; Power spectrum analysis and power allocation optimization analysis are performed on the wireless signal data of ground base stations and ground equipment to obtain signal quality assessment data for each ground device. Based on the signal distribution heatmap and signal quality assessment data, the deployment location of relay UAVs is simulated to obtain a relay link topology diagram representing the simulated location of the relay UAVs.

[0012] The specific process of performing power spectrum analysis and power allocation optimization analysis based on wireless signal data from ground equipment is as follows: Windowed short-time Fourier transforms are performed on the wireless timing signals of each ground device to obtain spectrum data. The carrier frequencies and portions with preset frequency component ranges of each ground device are extracted from the spectrum data to obtain the effective channel portion and the noise portion.

[0013] The signal-to-noise ratio (SNR) and channel capacity of each ground device are obtained by calculating the effective channel portion and the noise portion. The SNR and channel capacity are then recorded as signal quality evaluation data for each ground device.

[0014] The specific process of performing power allocation optimization calculations is as follows: Define the objective function for power allocation optimization: maximize the sum of the products of channel capacity and power of ground equipment.

[0015] The constraints for power allocation optimization include: Total power constraint: The total transmission power of all ground equipment and relay drones must not exceed the predetermined maximum power limit; Off-season power constraints: Under low load conditions, the total transmission power of all ground equipment and relay drones must not be lower than the predetermined off-season minimum power limit. QoS constraints: Ensure that the communication quality between each ground device and the relay UAV meets the set requirements. The ratio of the signal-to-noise ratio to the power of each ground device should be greater than the minimum threshold, and the ratio of the channel capacity to the power of each ground device should be greater than the minimum threshold.

[0016] The power of each ground device is solved based on the objective function and constraints of power allocation optimization.

[0017] As a preferred embodiment of the present invention, the specific process of simulating the deployment location of the relay UAV based on the signal distribution heatmap is as follows: Obtain the grayscale values ​​of each coordinate point in the signal distribution heatmap, and calculate the mean and standard deviation of all grayscale values. Obtain the grayscale value corresponding to the standard signal intensity, and denot it as the standard grayscale value.

[0018] Using the grayscale value at each coordinate point, the mean of all grayscale values, and the standard deviation as parameters, calculate the difference between the grayscale value and the standard deviation of the grayscale values ​​at each coordinate point to obtain Result 1. Divide the absolute value of Result 1 by the mean of the grayscale values ​​to obtain the first criterion.

[0019] Calculate the difference between the grayscale value of each coordinate point and the standard grayscale value to obtain result two. Divide result two by the standard grayscale value to obtain the second criterion.

[0020] The coordinates of the ground equipment with poor communication quality are marked as the first criterion being greater than the maximum threshold of the first criterion and the second criterion being less than the minimum threshold of the second criterion.

[0021] The minimum discrete envelope of the set of all minimum coordinate points corresponding to ground equipment with poor communication quality is calculated by using the convex hull algorithm, and several envelope regions are obtained.

[0022] If the total number of detected envelope regions exceeds the total number of relay drones, a drone shortage signal will be output.

[0023] Each envelope region is defined to have the following characteristics: Each envelope area needs to cover at least one ground device with poor communication quality; In the convex hull algorithm, the total number of envelope regions should be minimized, and the area of ​​each envelope region should be maximized. Each envelope region contains only the coordinates of ground devices marked as having poor communication quality; The minimum distance between each envelope region is greater than the minimum distance threshold.

[0024] Obtain the calculation results of the convex hull algorithm and extract the center coordinates of each envelope region as the simulated deployment coordinates of the relay UAV, thus obtaining the simulated position of the relay UAV. The center coordinates of each envelope region are recorded as relay points.

[0025] As a preferred embodiment of the present invention, the coordinates of each relay point are input into a three-dimensional coordinate system to generate a relay link topology diagram.

[0026] In the relay link topology diagram, relay points are marked according to the number of hops required to reach the ground base station coordinates. The center coordinates of the envelope area that can reach the ground base station coordinates with only one hop are marked as a one-hop relay point; the center coordinates of the envelope area that can reach the ground base station coordinates with at least two hops are marked as a two-hop relay point; the center coordinates of the envelope area that can reach the ground base station coordinates with at least three hops are marked as a three-hop relay point; and so on.

[0027] Step 3: Coordinated control of multiple UAV backhaul links; The optimization effect of ground equipment communication quality at each relay point in the simulated location of a relay UAV is evaluated. Relay signal attenuation at each relay point is calculated based on the relay link topology. The improvement of signal quality assessment data is predicted based on relay signal attenuation.

[0028] In a preferred embodiment of the present invention, the distances between each relay point and the distances from each relay point to the coordinates of the ground base station are calculated. Signal attenuation between relay points is calculated based on the distances between each relay point, and signal attenuation from one hop relay point to the ground base station is calculated based on the distance between one hop relay point and the ground base station.

[0029] As a preferred embodiment of the present invention, the improvement of signal quality assessment data based on relay signal attenuation prediction is specifically carried out as follows: The wireless timing signals of each ground device are updated based on relay signal attenuation. The spectrum data corresponding to the wireless timing signals of each ground device is acquired, and the effective channel portion is extracted from it.

[0030] The effective channel portion of each ground device is simulated by calculating the distance from each relay point to each ground device and the relay signal attenuation at each relay point, and the updated effective channel portion is obtained.

[0031] Substitute the updated effective channel portion back into step two to perform signal-to-noise ratio and channel capacity calculations, obtaining the updated signal-to-noise ratio and updated channel capacity.

[0032] The difference in signal-to-noise ratio and channel capacity before and after the update are calculated to obtain the improvement value of the measurement signal quality assessment data for each ground device.

[0033] Step 4: Relay scheme optimization; The system acquires environmental, location, flight status, and power / endurance data for relay drones and performs optimization calculations based on the predicted results improved by combining signal quality assessment data. It optimizes the specific target positions of each relay drone within its envelope area, fine-tuning the positions of all relay drones within their respective envelope areas to obtain the final relay scheduling scheme. Finally, it selects the priority drones for scheduling based on the relay drone scheduling order.

[0034] As a preferred embodiment of the present invention, a machine learning objective function is set to maximize the improvement value of each ground device in the predicted signal quality assessment data; The target position adjustment formula for machine learning is defined as follows: the adjustment values ​​of the relay UAVs in each envelope region based on the center coordinates of the envelope region are particleized to obtain a particle swarm representing the results of each iteration. Each particle in the particle swarm contains the adjustment values ​​of the center coordinates of each envelope region, including the x-component adjustment value, y-component adjustment value, and z-component adjustment value.

[0035] The final relay scheduling scheme is obtained by solving the condition that the iterative operation is greater than the maximum threshold or the particle swarm when the historical best position converges.

[0036] As a preferred embodiment of the present invention, the baseline UAV to be prioritized for scheduling is distinguished from the final relay scheduling scheme. The specific process is as follows: In the final relay scheduling scheme, the adjustment value of the relay UAV in each envelope region based on the center coordinate point of the envelope region is obtained, and it is added to the center coordinate point of each envelope region to obtain the final target position of the relay UAV in each envelope region.

[0037] Based on the final target location of the relay drones in each envelope area, nearby relay drones are recruited according to the minimum distance principle. If a relay drone is identified whose distance to its final target location is less than the minimum threshold, and the remaining battery power of the relay drone is greater than 80%, it is determined that the relay drone can continue to maintain relay operation without recharging after reaching the target location, and is capable of continuously providing a stable relay signal for the flight scheduling of other relay drones. This drone is then marked as the reference drone.

[0038] Step 5: Drone scheduling and execution; The baseline UAV and subsequent relay UAVs were dispatched to the target location in batches, with priority given to dispatching the baseline UAV. During the dispatching of all relay UAVs, the real-time communication quality of all UAVs was monitored, and the transmission power and signal frequency of the baseline UAV were adjusted based on the monitoring results to provide coordinate references and communication signals for the dispatching of all subsequent relay UAVs.

[0039] Compared with the prior art, the beneficial effects of the present invention are: 1. This invention collects data from multiple sources to create a signal distribution heatmap, performs power spectrum and power allocation optimization analysis on wireless signal data, accurately assesses signal quality, simulates reasonable UAV deployment locations, generates relay link topology maps, effectively optimizes signal coverage, and achieves energy efficiency optimization and improves resource utilization by optimizing the objective function and constraints of power allocation. 2. This invention evaluates the communication quality optimization effect of relay UAVs at simulated locations, calculates relay signal attenuation, and predicts the improvement in signal quality assessment data. By updating the wireless timing signal of ground equipment, simulating the effective channel portion of the gain, and recalculating the signal-to-noise ratio and channel capacity, the improvement value of signal quality assessment data is obtained, providing a basis for subsequent optimization and helping to further improve communication quality. 3. This invention combines prediction results from multiple data sources and signal quality assessment data, employs machine learning algorithms to optimize the specific target position of the UAV within the envelope area, and fine-tunes the position of the relay UAV to obtain the final relay scheduling scheme. By selecting a benchmark UAV for priority scheduling and using it as the core for scheduling, the UAV scheduling efficiency is optimized, system energy efficiency is maximized, and the stable provision of communication signals during the mission is ensured. Attached Figure Description

[0040] To facilitate understanding by those skilled in the art, the present invention will be further described below with reference to the accompanying drawings: Figure 1 This is a flowchart of the method of the present invention; Figure 2 This is a signal distribution hotspot diagram proposed in the embodiments of the present invention; Figure 3 This is a relay link topology diagram proposed in the embodiments of the present invention. Detailed Implementation

[0041] The technical solution of the present invention will be clearly and completely described below with reference to the embodiments. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0042] Please see Figure 1 As shown, a UAV communication relay method based on microwave energy efficiency optimization includes the following steps: Step 1: Wireless signal acquisition; Acquire location and wireless signal data from ground base stations and ground equipment; acquire wireless signal, location, and power / endurance data from relay drones. Create a signal distribution heatmap based on the location and wireless signal data from the ground equipment.

[0043] Specifically, the location data of the ground base station is the coordinates of the ground base station (x0, y0, z0), and the wireless signal data of the ground base station is the signal strength Q and power P of the ground base station. The location data and wireless signal data of the ground equipment include: the coordinates (xi, yi, zi) of each ground equipment i, the wireless signal strength Q (xi, yi, zi), the wireless timing signal Si(t), the communication bandwidth Bi, and the distance d (x, y, z) from the coordinates (x, y, z) of each ground equipment to the ground base station. Here, i is the ground equipment number, i = 1, 2, ..., n; n is the total number of ground equipment.

[0044] The location data of the relay drones includes the coordinates (xj, yj, zj) of each relay drone j; where j is the sequential number of the relay drone, j=1, 2, ..., m; and m is the total number of relay drones.

[0045] Among them, the power and endurance data of the relay drones includes the remaining power Ej of each relay drone j; Furthermore, a signal distribution heatmap is drawn based on the location data of the ground equipment and the wireless signal data. The specific process is as follows: Please see Figure 2 As shown, a three-dimensional scatter plot is drawn based on the coordinates (xi, yi, zi) of each ground device to describe the spatial distribution of the ground devices. The grayscale value of each coordinate point in the three-dimensional scatter plot is assigned according to the wireless signal strength of each ground device to obtain the signal distribution heat map.

[0046] Obtain the maximum and minimum wireless signal strength values ​​(MaxQ and MinQ) of each ground device, along with their corresponding coordinates.

[0047] Assign the coordinate point corresponding to the maximum wireless signal strength MaxQ to the gray value representing pure black: G(xi, yi, zi) = 0. Assign the coordinate point corresponding to the minimum wireless signal strength MinQ to the gray value representing pure white: G(xi, yi, zi = 255); Calculated by the difference between the maximum and minimum wireless signal strength:

[0048] Obtain the grayscale values ​​G(xi, yi, zi) of all ground device coordinates (xi, yi, zi) in the signal distribution hotspot map, except for the coordinate points corresponding to the maximum and minimum wireless signal strength, and assign all grayscale values ​​to their respective coordinate points in the signal distribution hotspot map.

[0049] Step 2: Energy efficiency optimization analysis; Power spectrum analysis and power allocation optimization analysis are performed on the wireless signal data of ground base stations and ground equipment to obtain signal quality assessment data for each ground device. Based on the signal distribution heatmap and signal quality assessment data, the deployment location of relay UAVs is simulated to obtain a relay link topology diagram representing the simulated location of the relay UAVs.

[0050] Based on the wireless signal data from ground equipment, power spectrum analysis and power allocation optimization analysis are performed. The specific process is as follows: The windowed short-time Fourier transform (STFT) is performed on the wireless timing signal Si(t) of each ground device i, and the Haining window function is introduced to suppress spectral leakage, thus obtaining the spectral data Si(f,t).

[0051] The formula for calculating the windowed short-time Fourier transform is:

[0052] in, For the Haining window function, and

[0053] Among them The preset window length for Haining windows, of which For the frequency index of the Haining window; Where f is the frequency component obtained after windowing short-time Fourier transform; Furthermore, the carrier frequency fi of each ground device is extracted from the spectrum data Si(f,t), and the portion with a preset frequency component range (fmin, fmax) is extracted from the spectrum data Si(f,t) to obtain the effective channel portion Si(f), where fmin and fmax are the preset upper and lower limits of the effective frequency, respectively. The noise component Ni(f) is obtained by extracting the portion with a preset frequency component range (f'min, f'max) from the spectral data Si(f,t). Here, f'min and f'max are the preset upper and lower limits of the noise frequency, respectively.

[0054] By preset formula

[0055] Calculate the signal-to-noise ratio SNRi and channel capacity Ci for each ground device i, where Bi is the communication bandwidth of each ground device i.

[0056] Record the signal quality assessment data for each ground device: {SNRi, Ci}.

[0057] Further, power allocation optimization calculations are performed, the specific process of which is as follows: Define the objective function for power allocation optimization:

[0058] Pi represents the power of each ground device.

[0059] The constraints for power allocation optimization include: Total power constraint: The total transmission power of all ground equipment and relay UAVs must not exceed the predetermined total power limit of the relay system, i.e.:

[0060] Where Pj is the power of each relay UAV j; and Pmax is the preset upper limit of the total power of the relay system. Off-season power constraints: Under low load conditions, the total transmission power of all ground equipment and relay drones must not be lower than the predetermined off-season minimum power limit, i.e.:

[0061] Pmin is the preset lower limit of total power during the off-season; QoS constraints: Ensure that the communication quality between each ground device and the relay UAV meets the set requirements. The quotient of the signal-to-noise ratio and power of each ground device should be greater than the minimum threshold, and the quotient of the channel capacity and power of each ground device should be greater than the minimum threshold.

[0062] in, The first threshold is preset. This is the preset second threshold.

[0063] The power Pi of each ground device i is solved based on the objective function and constraints of power allocation optimization.

[0064] Furthermore, the deployment location of the relay UAV is simulated based on the signal distribution heatmap. The specific process is as follows: Obtain the grayscale values ​​G(xi, yi, zi) of each coordinate point in the signal distribution heat map, and calculate the mean of all grayscale values. and standard deviation Obtain the grayscale value corresponding to the standard signal strength Q0, and denote it as the standard grayscale value G0.

[0065] Through the formula:

[0066] Calculate the difference between the gray value and the standard deviation of the gray value at each coordinate point to obtain Result 1. Divide the absolute value of Result 1 by the mean of the gray values ​​to obtain the first criterion e1i.

[0067] Calculate the difference between the grayscale value of each coordinate point and the standard grayscale value, and divide by the standard grayscale value. This yields the second criterion, e2i.

[0068] The coordinates (xi, yi, zi) where the first criterion is greater than the maximum threshold e1max of the first criterion and the second criterion is less than the minimum threshold e2min of the second criterion are marked as ground equipment with poor communication quality.

[0069] The minimum discrete envelope of the set of minimum coordinate points corresponding to ground equipment with poor communication quality is calculated by using the convex hull algorithm, resulting in several envelope regions k, k=1,2,...,K; K is the total number of envelope regions.

[0070] If the total number of relay drones in the envelope area K is greater than the total number of relay drones m, a drone shortage signal is output, informing the management personnel to allocate no less than Km relay drones to the areas where all ground equipment is located.

[0071] Each envelope region is defined to have the following characteristics: Each envelope area needs to cover at least one ground device with poor communication quality; In the convex hull algorithm, the total number of envelope regions should be minimized, and the area of ​​each envelope region should be maximized. Each envelope region contains only the coordinates of ground devices marked as having poor communication quality; The minimum distance between each envelope region is greater than the minimum distance threshold.

[0072] Obtain the calculation results of the convex hull algorithm and extract the center coordinates (xk, yk, zk) of each envelope region as the simulated deployment coordinates of the relay UAV, thus obtaining the simulated position of the relay UAV. The center coordinates (xk, yk, zk) of each envelope region are recorded as relay points.

[0073] Please see Figure 3 As shown, the coordinates of each relay point are input into a three-dimensional coordinate system to generate a relay link topology diagram.

[0074] In the relay link topology diagram, relay points are marked according to the number of hops required to reach the ground base station coordinates. The center coordinates of the envelope area that can reach the ground base station coordinates with only one hop are marked as a one-hop relay point; the center coordinates of the envelope area that can reach the ground base station coordinates with at least two hops are marked as a two-hop relay point; the center coordinates of the envelope area that can reach the ground base station coordinates with at least three hops are marked as a three-hop relay point; and so on.

[0075] Step 3: Coordinated control of multiple UAV backhaul links; The optimization effect of ground equipment communication quality at each relay point in the simulated location of a relay UAV is evaluated. Relay signal attenuation at each relay point is calculated based on the relay link topology. The improvement of signal quality assessment data is predicted based on relay signal attenuation.

[0076] Calculate the distance between each relay point and the distance from each relay point to the coordinates of the ground base station.

[0077] The signal attenuation between relay points is calculated based on the distance between each relay point, and the signal attenuation from a single relay point to the ground base station is calculated based on the distance between a single relay point and the ground base station.

[0078] The number of the one-hop relay point is denoted as . The number of the two-hop relay point is denoted as The number of the three-hop relay point is recorded as follows: And so on.

[0079] Based on the recursive formula for relay signal attenuation: Calculate the relay signal attenuation at each relay point, including the signal from the ground base station to each one-hop relay point. Relay signal attenuation All one-hop relay points to each two-hop relay point Relay signal attenuation ..., all r-1 hop relays to each r hop relay. Relay signal attenuation .

[0080] in, For each one-hop relay point The distance to the coordinates of the ground base station; where For each one-hop relay point To the second hop relay point The distance; among which For each r-hop relay point To the r-1 hop relay point The distance is r; r is the maximum number of hops for a relay point in the relay link topology.

[0081] in , ,..., These represent the total number of relay points from hop one to hop r.

[0082] Where f^ is the relay frequency and c is the speed of light.

[0083] Furthermore, the improvement of signal quality assessment data based on relay signal attenuation prediction is specifically achieved through the following process: The radio timing signal Si(t) of each ground device i is updated based on the relay signal attenuation. The spectrum data Si(f,t) corresponding to the radio timing signal Si(t) of each ground device i is obtained, and the effective channel portion Si(f) is extracted from it.

[0084] Calculate the distance from each relay point k to each ground device i using a preset formula: The effective channel portion is simulated and gained for each ground device i to obtain the updated effective channel portion. .

[0085] in, , ,..., Let i be the square of the distance from each ground device i to each one-hop relay point, two-hop relay point, ..., r-hop relay point.

[0086] Updated effective channel portion Substitute back to step two, expand the signal-to-noise ratio and channel capacity calculations, and obtain the updated signal-to-noise ratio. and updated channel capacity .

[0087] Calculate the difference in signal-to-noise ratio before and after the update: ; Calculate the difference in channel capacity before and after the update: .

[0088] Obtain the improvement values ​​of each ground device i with respect to the predicted signal quality assessment data: { , }

[0089] Step 4: Relay scheme optimization; The system acquires location, flight status, and power / endurance data of relay drones, and performs optimization calculations based on the predicted improvements derived from signal quality assessment data. It optimizes the specific target positions of each relay drone within its envelope area, fine-tuning the positions of all relay drones within their respective envelope areas to obtain the final relay scheduling scheme. Finally, it selects priority drones for scheduling based on the relay drone scheduling order.

[0090] Define the objective function for machine learning to maximize the improvement of each ground device i with respect to the measurement signal quality assessment data:

[0091] Set the target position adjustment formula for machine learning:

[0092] Where X is the particle swarm Xu = {x1, x2, x3, ..., xK} in the u-th iteration. x1, x2, x3, ..., xK are the adjustment values ​​of the relay UAV based on the center coordinates (xk, yk, zk) of each envelope region, including the x-component adjustment value, y-component adjustment value, and z-component adjustment value. u is the iteration number.

[0093] x1,x2,x3,...,xK={(△xk,△yk,△zk)}=(△x1,△y1,△z1),(△x2,△y2,△z2),..., (△xK,△yK,△zK).

[0094] Where △x1, △x2, ..., △xK are the x-component adjustment values ​​for each envelope region; Where △y1, △y2, ..., △yK are the y-component adjustment values ​​for each envelope region; Where △z1, △z2, ..., △zK are the z-component adjustment values ​​for each envelope region; Where w is the inertia weight, controlling the iteration speed of the particle swarm Xu; where c1 and c2 are learning factors, controlling the movement of particles towards the individual optimum and the global optimum. Where r1 and r2 are random numbers randomly generated within the preset range: [0, 1].

[0095] in This is the historically optimal position, meaning it meets the following conditions: The largest particle swarm optimization operation ever achieved through iterative computation; among which It is a locally optimal position, meaning that within the past N preset number of calculations, the following condition is met: The particle swarm with the largest iterative computation.

[0096] The solution is obtained through iterative calculations, satisfying the following conditions: The iterative calculation u must be greater than the maximum threshold umax, or the historical optimal position. The particle swarm Xu at convergence yields the final relay scheduling scheme Xu.

[0097] Furthermore, the baseline UAVs to be prioritized for scheduling are distinguished from the final relay scheduling scheme. The specific process is as follows: In the final relay scheduling scheme Xu, the adjustment values ​​(△xk, △yk, △zk) of the relay UAV based on the center coordinates of the envelope region in each envelope region are obtained, and these values ​​are added to the center coordinates (xk, yk, zk) of each envelope region k to obtain the final target position of the relay UAV in each envelope region k: (xk+△xk, yk+△yk, zk+△zk).

[0098] Based on the final target location of the relay drones in each region k, nearby relay drones are recruited according to the minimum distance principle. The relay drone j with the minimum distance to (xk+△xk, yk+△yk, zk+△zk) is scheduled to the final target location of the relay drones in each region k. If it is identified that the distance between relay drone j and its final target location (xk+△xk, yk+△yk, zk+△zk) is less than the minimum threshold, and the remaining battery power Ej of relay drone j is greater than 80%, then it is determined that relay drone j can continue to maintain relay operation without recharging after reaching the target location, and is capable of continuously providing a stable relay signal for the flight scheduling of other relay drones. It is then marked as the baseline drone.

[0099] It should be noted that the purpose of identifying the base drone is to optimize the scheduling efficiency of relay drones and maximize the system's energy efficiency. In multi-drone collaborative operations, the base drone plays a core role. It needs to have a long continuous flight time and low energy consumption to ensure that it can continuously provide communication signals to other relay drones during the mission, supporting them to arrive at their designated locations one after another.

[0100] Step 5: Drone scheduling and execution; According to the final relay scheduling plan, the relay UAVs are scheduled to be dispatched in batches, and the base UAVs and subsequent relay UAVs are dispatched to the target location in sequence. The base UAVs are dispatched to the target location first, and their coordinates are used for positioning, so as to provide communication signals and positioning coordinates for the subsequent scheduling of other relay UAVs.

[0101] It should be understood that the terms “comprising” and “including” used in this disclosure and claims indicate the presence of the described features, integrals, steps, operations, elements and / or components, but do not exclude the presence or addition of one or more other features, integrals, steps, operations, elements, components and / or collections thereof.

[0102] It should also be understood that the terminology used herein is for the purpose of describing particular embodiments only and is not intended to limit the disclosure. As used in this disclosure and claims, the singular forms “a,” “an,” and “the” are intended to include the plural forms unless the context clearly indicates otherwise. It should also be understood that the term “and / or” as used in this disclosure and claims means any combination and all possible combinations of one or more of the associated listed items, and includes such combinations; The preferred embodiments of the present invention disclosed above are merely illustrative of the invention. These preferred embodiments do not exhaustively describe all details, nor do they limit the invention to any specific implementation. Clearly, many modifications and variations can be made based on the content of this specification. This specification selects and specifically describes these embodiments to better explain the principles and practical applications of the invention, thereby enabling those skilled in the art to better understand and utilize the invention. The invention is limited only by the claims and their full scope and equivalents.

Claims

1. A UAV communication relay method based on microwave energy efficiency optimization, characterized in that, Includes the following steps: Step 1: Wireless signal acquisition; Acquire location data and wireless signal data from ground base stations and ground equipment; acquire location data and power / endurance data from relay drones; and draw signal distribution heatmaps based on the location data and wireless signal data from ground equipment. Step 2: Energy efficiency optimization analysis; Power spectrum analysis and power allocation optimization analysis are performed on the wireless signal data of ground base stations and ground equipment to obtain signal quality assessment data for each ground equipment; based on the signal distribution heat map and signal quality assessment data, the deployment location of relay UAVs is simulated to obtain a relay link topology map representing the simulated location of the relay UAVs; Step 3: Coordinated control of multiple UAV backhaul links; The communication quality optimization effect of ground equipment at each relay point in the simulated location of the relay UAV is evaluated; the relay signal attenuation at each relay point is calculated based on the relay link topology; and the signal quality evaluation data is improved based on the relay signal attenuation prediction. Step 4: Relay scheme optimization; Based on the prediction results of the improved signal quality assessment data, optimization calculations are performed to optimize the specific target position of each relay UAV in the envelope area. The positions of all relay UAVs in their respective envelope areas are fine-tuned to obtain the final relay scheduling scheme. The priority scheduling baseline drone is selected by allocating the scheduling order of relay drones. Step 5: Drone scheduling and execution; The baseline drone and subsequent relay drones were dispatched to the target location in batches, with priority given to dispatching the baseline drone to the target location.

2. The UAV communication relay method based on microwave energy efficiency optimization according to claim 1, characterized in that, The specific process of performing power spectrum analysis and power allocation optimization analysis is as follows: Windowed short-time Fourier transform is performed on the wireless timing signals of each ground device to obtain spectrum data; the carrier frequency and the part with a preset frequency component range of each ground device are extracted from the spectrum data to obtain the effective channel part and the noise part; The signal-to-noise ratio and channel capacity of each ground device are obtained by calculating the effective channel portion and the noise portion. The signal-to-noise ratio and channel capacity are recorded as signal quality assessment data for each ground device.

3. The UAV communication relay method based on microwave energy efficiency optimization according to claim 1, characterized in that, The specific process of performing power allocation optimization calculations is as follows: Define the objective function for power allocation optimization: maximize the sum of the products of channel capacity and power of ground equipment; The constraints for power allocation optimization include: Total power constraint: The total transmission power of all ground equipment and relay drones must not exceed the predetermined maximum power limit; Off-season power constraints: Under low load conditions, the total transmission power of all ground equipment and relay drones must not be lower than the predetermined off-season minimum power limit. QoS constraints: Ensure that the communication quality between each ground device and the relay UAV meets the set requirements. The ratio of the signal-to-noise ratio to the power of each ground device should be greater than the minimum threshold, and the ratio of the channel capacity to the power of each ground device should be greater than the minimum threshold. The power of each ground device is solved based on the objective function and constraints of power allocation optimization.

4. The UAV communication relay method based on microwave energy efficiency optimization according to claim 1, characterized in that, The specific process of simulating the deployment location of relay UAVs based on the signal distribution heatmap is as follows: Obtain the grayscale values ​​of each coordinate point in the signal distribution heat map, and calculate the mean and standard deviation of all grayscale values; obtain the grayscale value corresponding to the standard signal intensity, and record it as the standard grayscale value; Using the grayscale value of each coordinate point, the mean and standard deviation of all grayscale values ​​as parameters, calculate the difference between the grayscale value and the standard deviation of the grayscale value at each coordinate point to obtain Result 1; divide the absolute value of Result 1 by the mean grayscale value to obtain the first criterion; Calculate the difference between the gray value of each coordinate point and the standard gray value to obtain result two; divide result two by the standard gray value to obtain the second criterion; The coordinates of the ground equipment with poor communication quality are marked as the first criterion being greater than the maximum threshold of the first criterion and the second criterion being less than the minimum threshold of the second criterion.

5. The UAV communication relay method based on microwave energy efficiency optimization according to claim 4, characterized in that... ； The minimum discrete envelope of the set of all minimum coordinate points corresponding to ground equipment with poor communication quality is calculated by the convex hull algorithm, and several envelope regions are obtained. If the total number of detected envelope regions is greater than the total number of relay drones, then a drone shortage signal will be output. Each envelope region is defined to have the following characteristics: Each envelope area needs to cover at least one ground device with poor communication quality; In the convex hull algorithm, the total number of envelope regions should be minimized, and the area of ​​each envelope region should be maximized. Each envelope region contains only the coordinates of ground devices marked as having poor communication quality; The minimum distance between each envelope region is greater than the minimum distance threshold; Obtain the calculation results of the convex hull algorithm and extract the center coordinates of each envelope region as the simulated deployment coordinates of the relay UAV to obtain the simulated position of the relay UAV; record the center coordinates of each envelope region as the relay point.

6. The UAV communication relay method based on microwave energy efficiency optimization according to claim 5, characterized in that: Input the coordinates of each relay point into a three-dimensional coordinate system to generate a relay link topology diagram; In the relay link topology diagram, relay points are marked according to the number of hops required to reach the ground base station coordinates. The center coordinates of the envelope area that can reach the ground base station coordinates with only one hop are marked as a one-hop relay point; the center coordinates of the envelope area that can reach the ground base station coordinates with at least two hops are marked as a two-hop relay point; the center coordinates of the envelope area that can reach the ground base station coordinates with at least three hops are marked as a three-hop relay point; and so on.

7. The UAV communication relay method based on microwave energy efficiency optimization according to claim 1, characterized in that, The specific process for evaluating the effectiveness of communication quality optimization is as follows: Calculate the distance between each relay point and the distance from each relay point to the coordinates of the ground base station; The signal attenuation between relay points is calculated based on the distance between each relay point, and the signal attenuation from a one-hop relay point to a ground base station is calculated based on the distance between a one-hop relay point and the ground base station. The wireless timing signals of each ground device are updated based on the attenuation of the relay signal; Acquire the spectrum data corresponding to the wireless timing signals of each ground device, and extract the effective channel portion from them; The effective channel portion of each ground device is simulated by calculating the distance from each relay point to each ground device and the relay signal attenuation at each relay point, and the updated effective channel portion is obtained. Substitute the updated effective channel portion back into step two, and perform signal-to-noise ratio and channel capacity calculations to obtain the updated signal-to-noise ratio and updated channel capacity. The difference in signal-to-noise ratio and channel capacity before and after the update are calculated to obtain the improvement value of the measurement signal quality assessment data for each ground device.

8. The UAV communication relay method based on microwave energy efficiency optimization according to claim 1, characterized in that, The specific process for optimizing the target location of each relay UAV within the envelope area is as follows: Define the objective function for machine learning to maximize the improvement of each ground device i with respect to the measurement signal quality evaluation data; The target position adjustment formula for machine learning is defined as follows: the adjustment values ​​of relay UAVs in each envelope region based on the center coordinates of the envelope region are particleized to obtain a particle swarm representing the results of each iteration; each particle in the particle swarm contains the adjustment values ​​of the center coordinates of each envelope region, including the x-component adjustment value, y-component adjustment value and z-component adjustment value. The final relay scheduling scheme is obtained by solving the condition that the iterative operation is greater than the maximum threshold or the particle swarm when the historical best position converges.

9. The UAV communication relay method based on microwave energy efficiency optimization according to claim 1, characterized in that, The specific process for selecting the benchmark UAVs for priority scheduling is as follows: In the final relay scheduling scheme, the adjustment value of the relay UAV in each envelope area based on the center coordinate point of the envelope area is obtained, and it is added to the center coordinate point of each envelope area to obtain the final target position of the relay UAV in each envelope area. Based on the final target location of the relay drones in each envelope area, and according to the principle of minimum distance, nearby relay drones are recruited. If it is detected that the distance between the relay drone and its final target location is less than the minimum threshold, and the remaining battery power of the relay drone is greater than 80%, then it is determined that the relay drone can continue to maintain relay work without charging after reaching the target location, and is capable of continuously providing stable relay signals for the flight scheduling of other relay drones, and is marked as the reference drone.

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