Unmanned aerial vehicle formation adjustment method and device, electronic equipment and storage medium

Abstract

The application provides a UAV formation adjustment method and device, electronic equipment and storage medium, the UAV formation adjustment method comprises: obtaining UAV formation information; using a genetic algorithm to encode the UAV formation to obtain a UAV sequence code; using a variable step size discretization neighborhood search algorithm to receive the target optimal position of the receiver; using an OODA coupling interaction strategy to determine the adjustment scheme of the UAV formation according to the current position and the target optimal position of the UAV in the UAV formation; using the genetic algorithm to perform at least one of gene mutation and cross transformation on the adjustment scheme, and performing position adjustment of the UAV formation for a preset number of times, so that each UAV in the UAV formation reaches the target optimal position, and a formation result is obtained. The application has the beneficial effects of improving the search efficiency of the UAV for the optimal position and improving the positioning accuracy of the UAV formation.

Description

Technical Field

[0001] This invention relates to the field of unmanned aerial vehicle (UAV) technology, and in particular to a method, apparatus, electronic device, and storage medium for adjusting UAV formations. Background Technology

[0002] When drone swarms are flying in formation, they should maintain electromagnetic silence as much as possible to avoid external interference and minimize the emission of electromagnetic signals. To maintain formation, a passive azimuth positioning method is proposed to adjust the drones' positions. This involves several drones in the formation transmitting signals, while the remaining drones passively receive them, extracting directional information for positioning. Each drone in the formation has its own unique identifier. Each drone receives only the angular information between signal sources and the identifiers of any known signal sources. Therefore, each drone in the formation needs to adjust its position through specific signal transmission and reception to maintain the appropriate formation.

[0003] In drone formation flying, the establishment of a communication network between drones plays a decisive role in maintaining formation and is crucial for coordinating and directing drone movements during flight. Common formation control strategies include the lead-follow method, the virtual structure method, and behavior-based methods.

[0004] The lead-follow method, also known as the master-slave method, is based on the idea of ​​pre-designating a specific drone in the drone formation to lead the formation and act as the "leader," while the other drones become "wingmen." They determine their own flight strategies only based on their relative position to the "leader." However, this method suffers from the problems of error transmission and accumulation, heavily relies on the "leader," and has poor overall anti-interference capabilities.

[0005] The core idea of ​​the virtual structure method is to treat the entire drone formation as a virtual rigid body. The entire formation uses certain specific points on this virtual rigid body (usually a virtual assembly center) as the "virtual leader," with other drones acting as "wingmen," continuously tracking the "virtual leader" during formation flight. This method eliminates the need for an actual leader, effectively avoiding formation failure caused by the failure of the "leader." However, it also requires the entire formation to maintain near-rigid-body motion during flight, placing high demands on coordination and cooperation among the drones, thus compromising reliability. Summary of the Invention

[0006] The main objective of this invention is to provide a method, device, electronic device, and storage medium for adjusting UAV formations, thereby improving the anti-interference capability and positioning accuracy of UAV formation adjustments.

[0007] Another aspect of the present invention provides a method for adjusting drone formations, comprising:

[0008] According to the drone formation request, obtain drone formation information, which includes drone number, drone identity, and drone position. The drone identity includes transmitter and receiver. The transmitter is used to identify the drone that transmits the signal, and the receiver is used to identify the drone that receives the signal from the transmitter. The drone position is used to identify the drone's heading angle.

[0009] The drone formation is encoded using a genetic algorithm to obtain drone sequence codes, which are used to characterize the drone sorting codes when the drone formation is adjusted in a single position.

[0010] The target optimal position of the receiver is determined using a variable step-size discretized neighborhood search algorithm.

[0011] Based on the current position of the drones in the drone formation and the optimal position of the target, the adjustment scheme of the drone formation is determined by using the OODA coupling interaction strategy, the drone sequence encoding, and the optimal position of the target;

[0012] A genetic algorithm is used to perform at least one of gene mutation and crossover transformation on the adjustment scheme, and the position adjustment of the UAV formation is performed a preset number of times so that each UAV in the UAV formation reaches the target optimal position, and the formation result is obtained.

[0013] According to the aforementioned drone formation adjustment method, obtaining drone formation information includes:

[0014] The drone obtains its own azimuth angle and uses a three-source positioning method to determine the drone's position. The three-source positioning method includes determining the position of all drones based on two or three signal sources whose positions have been determined and have no positional deviation.

[0015] Based on the target formation of the drone formation, drones with no positional deviation are selected from the drone formation as the transmitter, and drones with positional deviation are selected as the receiver.

[0016] According to the aforementioned UAV formation adjustment method, the UAV formation is encoded using a genetic algorithm to obtain UAV sequence codes, including:

[0017] The transmitter and the receiver are binary encoded, where 1 represents the transmitter and 0 represents the transmitter. The transmitter and the receiver are used as genes in a single formation adjustment encoding to obtain the UAV sequence encoding, wherein the sum of the genes in the binary encoding is less than or equal to 2.

[0018] According to the aforementioned UAV formation adjustment method, the target optimal position of the receiver, which employs a variable step-size discretized neighborhood search algorithm, includes:

[0019] A discretized grid is constructed, and the receiver performs a neighborhood search in the discretized grid. When the receiver is adjusted once, the discretized grid is re-divided to obtain a set of discretized points and a first solution.

[0020] Calculate the actual orientation angle between each point in the discretized point set and the receiver, evaluate the actual orientation angle using an evaluation function, and obtain the second solution;

[0021] Repeat the above location search and all receiver position adjustments to obtain the target optimal location, wherein the first solution and the second solution are used to characterize the receiver position.

[0022] According to the aforementioned drone formation adjustment method, the evaluation function includes:

[0023] Based on the actual azimuth information of the transmitter and the receiver, and the azimuth information at the target's optimal position, it is determined that the receiver should move towards the target's optimal position, where the evaluation function is:

[0024]

[0025] The evaluation function is used to characterize the minimum value of the weighted sum of squared angle deviations for points found in the neighborhood, where i and j represent the receiver and transmitter indices, respectively, i∈{1,2,3,…m}, j∈{1,2,3,…n}, and m and n represent the total number of receivers and transmitters, respectively, where {res1,res2,…,res...} m} represents the set of receiver sequence numbers, {lau1,lau2,…,lau2} n} represents the set of transmitter serial numbers. Indicates the actual direction angle information. The azimuth angle information for the target's optimal position is α, where α and α′ represent the angles measured when the UAV receives signals at the target's optimal position and current position, respectively.

[0026] According to the aforementioned UAV formation adjustment method, based on the current positions of the UAVs in the UAV formation and the optimal position of the target, an OODA coupling interaction strategy, UAV sequence encoding, and a determination of the UAV formation adjustment scheme are employed, including:

[0027] The receiver obtains the current azimuth information based on its current position, and moves the receiver continuously within the surrounding area to detect the actual azimuth angle of the receiver relative to the transmitter in real time. Based on the adjusted data, the receiver compares the actual azimuth angle with the expected azimuth angle to obtain the target optimal position with the smallest deviation. The receiver's current position is then adjusted to the target optimal position to obtain the adjustment scheme of the UAV formation.

[0028] The adjustment scheme is adjusted using a fitness function, where the fitness function is f().

[0029]

[0030]

[0031] Where chroms is the adjustment scheme, d is the reciprocal square of the sum of squared residuals between the UAV's adjusted position and the target's optimal position, used as the fitness function, (x if ,y if (x) represents the adjusted position after the adjustment plan. ib ,y ib ) represents the optimal position of the target, and t represents the total number of drones in the drone formation.

[0032] According to the aforementioned drone formation adjustment method, a genetic algorithm is used to perform at least one of gene mutation and crossover transformation on the adjustment scheme, and the drone formation is adjusted in position a preset number of times, including:

[0033] Construct a first population based on a genetic algorithm, wherein the population includes a first chromosome, a population size, a maximum number of iterations, and a mutation probability, wherein the mutation probability is obtained by mutation of the gene, wherein the first chromosome is used to characterize the adjustment scheme, and the first chromosome includes a first gene, wherein the first gene is used to characterize the encoding.

[0034] The fitness function is used to evaluate the first chromosome to obtain the fitness value of each first chromosome, and the first maximum fitness value is determined.

[0035] The roulette wheel algorithm is used to select the population, and the selected chromosomes are subjected to the crossover process to obtain a second individual and a second population containing the second individual.

[0036] The genes in the chromosomes of the second population are randomly mutated according to the mutation probability to obtain the second gene;

[0037] Calculate the fitness value of each second chromosome in the second population to determine the second fitness value. Compare the first maximum fitness value with the second fitness value. If the first maximum fitness value is less than the second fitness value, all second chromosomes with the second fitness value are included in the third population. Otherwise, no update is performed.

[0038] Repeat the processing for the maximum number of iterations to complete the preset number of position adjustments for the drone formation.

[0039] Embodiments of the present invention also disclose a drone formation adjustment device, comprising:

[0040] The first module is used to obtain drone formation information according to drone formation requests. The drone formation information includes drone number, drone identity and drone position. The drone identity includes transmitter and receiver. The transmitter is used to identify the drone that transmits the signal. The receiver is used to identify the drone that receives the signal from the transmitter. The virtual leader is used to identify the transmitter that is unique and has no position deviation. The drone position is used to identify the drone's heading angle.

[0041] The second module is used to encode the drone formation using a genetic algorithm to obtain drone sequence codes. The drone sequence codes are used to characterize the drone sorting codes when the drone formation is adjusted in a single position.

[0042] The third module is used to locate the target optimal position of the receiver using a variable step-size discretized neighborhood search algorithm.

[0043] The fourth module is used to determine the adjustment scheme of the UAV formation based on the current position of the UAVs in the UAV formation and the optimal position of the target, using the OODA coupling interaction strategy, the UAV sequence encoding, and the optimal position of the target;

[0044] The fifth module is used to perform at least one of gene mutation and crossover transformation on the adjustment scheme using a genetic algorithm, and to perform a preset number of position adjustments on the UAV formation so that each UAV in the UAV formation reaches the target optimal position, thereby obtaining the formation result.

[0045] Another aspect of the present invention provides an electronic device, including a processor and a memory;

[0046] The memory is used to store programs;

[0047] The processor executes the program to implement the method as described above.

[0048] This invention also discloses a computer program product or computer program, which includes computer instructions stored in a computer-readable storage medium. A processor of a computer device can read the computer instructions from the computer-readable storage medium and execute the computer instructions, causing the computer device to perform the methods described above.

[0049] The beneficial effects of this invention are as follows: The three-source localization method determines the position information of a UAV formation. By considering whether the UAVs in the formation have positional deviations, and within a limited range of correct deviation points, the unknown source number can be directly determined by the angle range, eliminating the need for extensive geometric calculations and reducing UAV resource consumption. The variable-step-size discretization neighborhood search algorithm balances algorithm effectiveness and complexity, effectively simulating the adjustment process of the UAV receiving the signal, thus improving the efficiency of the UAV in searching for the optimal position. A method for adjusting the position based on the evaluation function of the actual and desired orientation angles is proposed using an OODA coupling interaction strategy. By changing the UAVs transmitting and receiving signals, mutual error correction between UAVs is effectively achieved, enabling the UAVs to finally find a suitable position and improving the accuracy of UAV localization. Attached Figure Description

[0050] The above and / or additional aspects and advantages of the present invention will become apparent and readily understood from the description of the embodiments taken in conjunction with the following drawings, in which:

[0051] Figure 1 This is a schematic diagram of the drone formation adjustment system according to an embodiment of the present invention.

[0052] Figure 2 This is a schematic diagram of the drone formation adjustment process according to an embodiment of the present invention.

[0053] Figure 3 This is a schematic diagram of a circular formation according to an embodiment of the present invention.

[0054] Figure 4 This is a schematic diagram of chromosome encoding according to an embodiment of the present invention.

[0055] Figure 5 This is a schematic diagram of the variable step size discretization neighborhood search process according to an embodiment of the present invention.

[0056] Figure 6 This is a schematic diagram of the algorithm flow for variable step size discretized neighborhood search according to an embodiment of the present invention.

[0057] Figure 7 This is a schematic diagram of the discretized grid according to an embodiment of the present invention.

[0058] Figure 8This is a schematic diagram illustrating the variation of the discretized grid side length in an embodiment of the present invention.

[0059] Figure 9 This is a schematic diagram of a single OODA optimization loop according to an embodiment of the present invention.

[0060] Figure 10 This is a schematic diagram of the optimization process based on genetic algorithms according to an embodiment of the present invention.

[0061] Figure 11 This is a schematic diagram of gene mutation in an embodiment of the present invention.

[0062] Figure 12 This is a schematic diagram of cross-interchange in an embodiment of the present invention.

[0063] Figure 13 This is a schematic diagram of the drone adjustment process according to an embodiment of the present invention.

[0064] Figure 14 This is a schematic diagram illustrating the positional changes of each UAV when the desired points are aggregated to a single point, according to an embodiment of the present invention.

[0065] Figure 15 This is a schematic diagram of the polar diameter ρ variation curves of various UAVs in embodiments of the present invention.

[0066] Figure 16 This is a schematic diagram of the polar angle θ variation curves of various UAVs in embodiments of the present invention.

[0067] Figure 17 This is the optimal fitness iteration graph of the genetic algorithm in an embodiment of the present invention.

[0068] Figure 18 This is a schematic diagram illustrating the optimal encoding scheme of an embodiment of the present invention.

[0069] Figure 19 This is a schematic diagram of the drone attribute radar according to an embodiment of the present invention.

[0070] Figure 20 This is a schematic diagram of the cone-shaped UAV formation and coordinates according to an embodiment of the present invention.

[0071] Figure 21 This is a schematic diagram of a drone that violates the elements of an embodiment of the present invention.

[0072] Figure 22 This is a schematic diagram of the UAV collision value radar according to an embodiment of the present invention.

[0073] Figure 23 This is a schematic diagram of the optimal formation of the lead aircraft group according to an embodiment of the present invention.

[0074] Figure 24 This is a schematic diagram of the drone formation adjustment device according to an embodiment of the present invention. Detailed Implementation

[0075] The embodiments of the present invention are described in detail below, examples of which are shown in the accompanying drawings. Throughout the description, the same or similar reference numerals denote the same or similar elements or elements having the same or similar functions. In the following description, suffixes such as "module," "part," or "unit" used to denote elements are used only for the purpose of illustrative purposes and have no inherent meaning. Therefore, "module," "part," or "unit" can be used interchangeably. Terms such as "first," "second," etc., are used only to distinguish technical features and should not be construed as indicating or implying relative importance, or implicitly indicating the number of indicated technical features, or implicitly indicating the sequential relationship of the indicated technical features. In this subsequent description, the consecutive reference numerals for method steps are for ease of review and understanding. Adjusting the implementation order of steps, in conjunction with the overall technical solution of the present invention and the logical relationship between the various steps, will not affect the technical effect achieved by the technical solution of the present invention. The embodiments described below with reference to the accompanying drawings are exemplary and are only used to explain the present invention, and should not be construed as limiting the present invention.

[0076] Parameter explanation:

[0077] Table 1 Parameter Description Table

[0078]

[0079] Reference Figure 1 ,in Figure 1This is a schematic diagram of a drone formation adjustment system according to an embodiment of the present invention. It includes a drone formation 100, wherein the drone formation 100 includes multiple drones 200, and each drone 200 includes a signal transceiver 210, a processor 220, and a memory 230. The signal transceiver 210, processor 220, and memory 230 of the drone formation 100 perform the following processing according to the target drone formation: Based on a drone formation request, obtain drone formation information, including drone number, drone identity, and drone position. Drone identity includes transmitter and receiver; transmitter identifies the drone transmitting a signal, receiver identifies the drone receiving the transmitter's signal, and drone position identifies the drone's location. The direction angle is determined; a genetic algorithm is used to encode the UAV formation to obtain UAV sequence codes, which are used to characterize the UAV ordering during a single position adjustment of the UAV formation; a variable step size discretized neighborhood search algorithm is used to detect the target optimal position of the receiver; based on the current position of the UAVs in the UAV formation and the target optimal position, an OODA coupled interaction strategy, UAV sequence codes, and the target optimal position are used to determine the adjustment scheme of the UAV formation; a genetic algorithm is used to perform at least one of gene mutation and crossover transformation on the adjustment scheme, and the UAV formation is adjusted a preset number of times to make each UAV in the UAV formation reach the target optimal position, thus obtaining the formation result.

[0080] In some embodiments, the transmitter and receiver are both drones in a drone swarm.

[0081] In some embodiments, the drone number refers to the sequence number of the drone in the drone formation. For example, the nine drones are numbered FY01 to FY09.

[0082] In some implementations, the drone sequence code represents the order in which all drones are adjusted during a single adjustment of their positions, such as FY01-FY02-FY03…-FY09.

[0083] In some embodiments, the genetic algorithm encodes the drone sequence as a chromosome, and the genes in each chromosome represent the adjustment of a drone. The adjustment of the drone formation at the position a preset number of times is an array composed of multiple chromosomes.

[0084] Reference Figure 2 ,in Figure 2 This is a schematic diagram of the drone formation adjustment process according to an embodiment of the present invention, which includes, but is not limited to, steps S100 to S500:

[0085] S100: Based on the drone formation request, obtain drone formation information, which includes drone number, drone identity, and drone position. Drone identity includes transmitter and receiver. Transmitter is used to identify the drone that transmits signals, receiver is used to identify the drone that receives signals from the transmitter, and drone position is used to identify the drone's heading angle.

[0086] In some embodiments, the present invention adjusts the formation of UAVs in a passive (without external transmission source) manner. Therefore, the UAVs obtain their own azimuth angles and use a three-transmission source positioning method to determine the UAV positions. The three-transmission source positioning method includes determining the positions of all UAVs based on two or three signal sources whose positions have been determined and have no positional deviation; and selecting UAVs with no positional deviation from the UAV formation as transmitters and UAVs with positional deviations as receivers, based on the target formation of the UAV formation.

[0087] In some embodiments, reference Figure 3 The diagram shows a circular formation of drones. The drone formation consists of one drone in the center and nine drones evenly distributed around a circle. For a drone with a certain position deviation, the positioning or adjustment should be completed under the following conditions: (1) The central drone and two drones with certain positions on the circle transmit signals; (2) The central drone, one drone with a certain position on the circle, and at least one drone on the circle but with an uncertain position transmit signals; (3) The central drone and at most three drones that are not necessarily on the circle but have certain numbers, i.e., have certain approximate positions, transmit signals.

[0088] For (1), according to the inscribed angle theorem, when the inscribed angles corresponding to the same arc or equal arcs are equal, the arc lengths corresponding to the equal inscribed angles are also equal. So when a drone receives a signal α at a certain angle, if the position of the transmitting source is known, which in this problem means that the number is known and the position is without deviation, then the drone must be located on the circle with the two transmitting sources as chords and the inscribed angle as α. Now, apart from the drone FY00 at the center of the circle, the other two known drones that transmit the signal are on the circumference. After transmitting the signal, there are two definite chords and the inscribed angles corresponding to the chords. In this way, two circles can be drawn on the plane. The two circles have two intersection points, one of which is the center of the circle where FY00 is located, and the other intersection point is the position of the drone.

[0089] For (2), based on (1), a model has been obtained of the UAV's position obtained by transmitting signals from three determined locations. In (2), two locations are known, and the third location is unknown. The number of the third transmitter can be determined based on the angle information of the received signal. Once the number of the third transmitter is determined, the location of the UAV receiving the signal can be obtained based on the model in (1).

[0090] Regarding (3), based on the initial position data of the UAVs, it is noted that only UAVs FY00 and FY01 have no position deviation, while the actual positions of the other UAVs differ from their expected positions. Therefore, during each adjustment, FY00 and FY01 are always tasked with transmitting signals to constrain the position of the receiver through their azimuth angles, thus preventing the position error from continuously expanding due to the cumulative error effect, and enabling the other UAVs to return to their expected positions more quickly. Since the information that the receiving UAVs can receive is only azimuth angle information.

[0091] S200 uses a genetic algorithm to encode the drone formation, resulting in drone sequence codes. These drone sequence codes are used to characterize the drone sorting codes during a single position adjustment of the drone formation.

[0092] In some embodiments, reference Figure 4 The chromosome coding diagram shown is based on Figure 2 The drone formation shown corresponds to an 8-bit binary code (single adjustment code) for each adjustment, representing the status of drones FY02-FY09. Drones with a code of 1 are responsible for signal transmission, and drones with a code of 0 are responsible for signal reception. Considering that a maximum of three drones can perform a transmission mission on a circle, and FY01 must perform a transmission mission, for a single adjustment gene, the code x must satisfy... i The sum does not exceed 2, where i is the drone number to be adjusted, and its mathematical model is expressed as follows:

[0093] gene = (x i i∈{1,2,3,…8}

[0094]

[0095] Each adjustment encoding is used as a gene in the genetic algorithm. Each scheme uses 30 adjustments (adj_time = 30). Therefore, the chromosome array can be represented as chrom = {gene1, gene2, ..., gene}. 30}

[0096] The S300 uses a variable step-size discretized neighborhood search algorithm to determine the target optimal position of the receiver.

[0097] In some embodiments, reference Figure 5 The schematic diagram of the variable step-size discretization neighborhood search process shown includes, but is not limited to, S310 to S330:

[0098] S310, construct a discretized grid, perform a neighborhood search on the receiver in the discretized grid, and when the receiver performs a single adjustment, re-divide the discretized grid to obtain the set of discretized points and the first solution;

[0099] S320: Calculate the actual azimuth angle between each point in the discretized point set and the receiver, evaluate the actual azimuth angle using an evaluation function, and obtain the second solution;

[0100] S330, repeat the above position search and all receiver position adjustments to obtain the target optimal position, where the first solution and the second solution are used to characterize the receiver position.

[0101] In some embodiments, reference Figure 6 The schematic diagram of the algorithm flow for variable step size discretization neighborhood search shown includes steps (1) to (5):

[0102] Step (1), refer to Figure 7 The discretized mesh diagram shown has an initial discretization interval of diff_size, a discretized mesh size of w×h, a step size variation coefficient within a single adjustment of inter_rate, a step size variation depth within a single adjustment of inter_depth, a step size variation coefficient between each adjustment of outer_rate, a total number of adjustments of adj_time, and the initial solution is the current UAV initial position (x). k ,y k The single adjustment counter inter_step = 0, and the total number of adjustments counter adj_step = 0.

[0103] Step (2), refer to Figure 8 The diagram shown illustrates the variation of the discretized grid side length. If the UAV is adjusted to be a receiver in this adjustment, then the current solution (x) k ,y k Centered on ), with the discretization interval diff_size * inter_rate inter_step *outer_rate adj_step Given the side length of each small square within the grid, a discretized grid is created, resulting in a set of discretized points {(x... ij ,y ij ),i∈{1,2,…,w+1},j∈{1,2,…,h+1}}.

[0104] Step (3): If the UAV is adjusted to be a receiver, calculate the actual direction angle for each point in the discrete discrimination point set, select the point that minimizes the evaluation function, and take this position as the new solution (x). k ,y k ).

[0105] Step (4): inter_step += 1. If inter_step < inter_depth at this time, return to Step 2. Otherwise, adj_step += 1, inter_step = 0. If adj_step < adj_time at this time, wait until all receivers have completed the reception angle adjustment, then enter the next adjustment and return to Step (2). Otherwise, proceed to Step (5).

[0106] Step (5): Obtain the position (x kf , y kf ) of the UAV after adj_time adjustments.

[0107] In some embodiments, the position of the UAV is further adjusted to the optimal based on an evaluation function based on the direction angle deviation. The evaluation function based on the direction angle deviation includes:

[0108] Taking the receiver number as res and the transmitter number on the circumference as lau, the actual direction angle information α' received by the receiver at this time, with the receiver as the angular vertex and the transmitter on the circumference and the central transmitter as the two side vertices res,lau , and at the same time calculate the direction angle information α at the expected position res,lau . The goal of a single adjustment is to make the actual direction angle closer to the expected direction angle.

[0109] For a single adjustment, for the receiver number set RES = {res1, res2,..., res m} in receivers FY02 - FY09 and the transmitter number set LAU = {lau1, lau2,..., lau n}, for receiver res i , find a point in its neighborhood such that at this point, the evaluation function (weighted sum of squared angle deviations) is minimized:[[ID=X]] [[ID=Y]]

[0110] [[ID=Z]] [[ID=A]] [[ID=B]]

[0111] This evaluation function not only ensures that the transmitters at positions FY00 and FY01 without deviation have a relatively large weight for the receiver evaluation function, so as to promote the polar radius of the UAV relative to FY00 to approach the expected radius during the entire optimization process, but also allows FY02 - FY09 to correct each other's positions with the replacement of the single - adjustment coding, enabling the 8 UAVs to be gradually and evenly distributed on the circumference according to the requirements. At this time, from the perspective of the receiver, since the adjustment time for each time is negligible, it only needs to search within a certain range around it, compare according to the obtained direction information, and find the optimal position that minimizes the evaluation function in the current state. *The content in tags -

[0111] cannot be translated as they seem to be specific code - like notations without clear semantic meaning in this context. If there is more context available, a more accurate translation might be possible.*

[0112] In some embodiments, the method further includes evaluating the adjustment effect of the adjustment scheme for finding the optimal location of the target using a fitness function, including:

[0113] Adjust the chroms scheme to obtain the adjusted positions of all drones {(x if ,y if )}i∈{0,1,2,…,9}, obtain its relationship with the ideal position {(x ib ,y ib The sum of squared residuals compared to i∈{0,1,2,…,9}

[0114]

[0115] This paper selects the square of the sum of the squared residuals of all UAVs after adjustment, and the square of the reciprocal thereto, as the fitness function:

[0116]

[0117] The smaller the deviation between the adjusted position and the ideal position of the drone, the better the adjustment effect and the larger the fitness function.

[0118] S400 determines the adjustment scheme of the UAV formation based on the current position of the UAVs in the formation and the optimal position of the target, using an OODA coupling interaction strategy, UAV sequence encoding, and the optimal position of the target.

[0119] In some embodiments, reference Figure 9 The diagram shows a single OODA optimization loop.

[0120] The only information a receiving drone can receive is its azimuth angle. By adjusting the signal transmission and reception sequence T, the drones can make multiple adjustments based on the comparison between their actual and desired azimuth angles, thereby achieving the desired formation position. The desired azimuth angle information is shown in the table below.

[0121] Table 2 Desired Azimuth Angle

[0122]

[0123] In some embodiments, OODA includes:

[0124] Observe: The receiver acquires the current orientation angle information.

[0125] Orientation involves continuously moving the receiver within its surrounding area to continuously detect the actual azimuth angle of the receiver relative to the transmitter.

[0126] Decision: Based on the adjusted data, the receiver compares the actual azimuth angle with the desired azimuth angle to determine the position P where the deviation is minimized.

[0127] Action (Act): The receiver position is adjusted to position P.

[0128] Reference Figure 9 When the transmitter's OODA loop is interrupted, the receiver's OODA loop continues to operate normally, adjusting according to the azimuth angle and serial number information transmitted by the transmitter. As the UAV's signal transmission and reception status changes, each UAV's OODA loop transitions between interrupted and active states, enabling the UAVs to interact via signals and creating tight coupling between the various components of their respective OODA loops.

[0129] S500 employs a genetic algorithm to perform at least one of the following processes on the adjustment scheme: gene mutation and crossover transformation. It also performs a preset number of position adjustments on the drone formation to ensure that each drone in the formation reaches the target optimal position, thus obtaining the formation result.

[0130] In some embodiments, reference Figure 10 The schematic diagram of the optimization process based on the genetic algorithm shown includes, but is not limited to, steps S510 to S560:

[0131] S510, construct the first population based on the genetic algorithm, wherein the population includes the first chromosome, population size, maximum number of iterations and mutation probability, wherein the mutation probability is obtained through gene mutation, wherein the first chromosome is used to represent the adjustment scheme, the first chromosome includes the first gene, and the first gene is used to represent the encoding.

[0132] In some embodiments, reference Figure 11 The diagram shown illustrates gene mutations. Individual genes within chromosomes (chroms) undergo random mutations based on a mathematical model of gene coding. Mutations between different genes are independent, and the probability of a single gene mutation is P. v .

[0133] S520, evaluate the first chromosome using a fitness function to obtain the fitness value of each first chromosome, and determine the first maximum fitness value.

[0134] S530 uses the roulette wheel algorithm to select the population, and performs crossover on the selected chromosomes to obtain a second individual and a second population containing the second individual.

[0135] In some embodiments, reference Figure 12 The diagram shown illustrates the crossing over process, where gene segments are exchanged between different chromosomes in a population, with genes as the basic unit, to produce new chromosomes in the offspring.

[0136] S540, the genes in the chromosomes of the second population are randomly mutated according to the mutation probability to obtain the second gene.

[0137] S550: Calculate the fitness value of each second chromosome in the second population, determine the second fitness value, compare the first maximum fitness value with the second fitness value. If the first maximum fitness value is less than the second fitness value, all second chromosomes with the second fitness value are included in the third population; otherwise, no update is performed.

[0138] S560 repeatedly executes the processing for the maximum number of iterations to complete the preset number of position adjustments for the drone formation.

[0139] In some embodiments, the multiple adjustments to the UAV's transmission and reception states utilize the fact that FY00 and FY01 are in the correct positions. Guided by the azimuth angle under the ideal state, a scheme for UAV state transformation is formulated, enabling the receiver to find the optimal point in the neighborhood for movement simply by evaluating the deviation between the actual azimuth angle and the ideal azimuth angle, ultimately completing the formation adjustment.

[0140] The specific steps of solving the genetic algorithm include:

[0141] Step 1: Determine the parameters: initial population size S = 200, maximum number of iterations G = 20, mutation probability P. v =0.01, let the optimal individual chromosome be gbest.

[0142] Step 2: Use the fitness function to evaluate the chromosomes of individuals in the population, calculate the fitness value of each individual, and save the chromosome code of the individual with the highest fitness value in this generation as lbest.

[0143] Step 3: Select the population according to the roulette wheel algorithm.

[0144] Step 4: Crossover is performed on the selected individuals to obtain new individuals, update the population, and provide new gene combinations for the population.

[0145] Step 5, based on the mutation probability P v =0.01 Randomly mutates the genes on chromosomes in the population, providing new genes to the population.

[0146] Step 6: The newly generated population replaces the original population. The fitness value f(lbest) of the best individual in this generation is compared with the fitness value f(gbest) of the overall best individual. If f(lbest) > f(gbest), then lbest is used to replace gbest; otherwise, no update is performed.

[0147] Step 7: Increment the current iteration count by 1. If the current iteration count is less than the maximum iteration count, return to step 2; otherwise, the algorithm ends.

[0148] In some embodiments, the signal transmission and reception adjustment sequence T is obtained, and after iterative operation using a genetic algorithm, it is referenced. Figure 13 The optimal fitness iteration graph of the genetic algorithm is shown in Tables 3 and 4. The final adjusted position is compared with the expected final position. It can be seen that the deviation between the actual final position and the expected final position is negligible.

[0149] Table 3. Actual Final Location of the UAV

[0150]

[0151] Table 4

[0152]

[0153] In some embodiments, the present invention also provides a drone adjustment process, and draws schematic diagrams of the adjustment process for each drone under this scheme, wherein... Figure 13 This is a diagram illustrating the drone adjustment process. Figure 14 This is a diagram illustrating the positional changes of each drone when all desired points are aggregated to a single point. Figure 15 This is a schematic diagram of the polar radius ρ variation curves for each UAV. Figure 16 Schematic diagram of the polar angle θ variation curves for each UAV.

[0154] Under the optimal strategy, the line graph showing the changes in the polar radius ρ and polar angle θ of each UAV on the circumference as the number of iterations increases is as follows: Figure 15 and 16 As shown

[0155] As the number of adjustments increases, the polar radius and polar angle of each UAV fluctuate but gradually approach the desired value. The trend of the logarithmic value of its fitness function with the number of adjustments is as follows: Figure 17 The graph showing the optimal fitness iteration of the genetic algorithm indicates that the fitness function gradually increases with the number of adjustments. This can be seen from the graph. Figure 15 and Figure 16 When the total number of adjustments to the plan reaches about 20, the positions of each drone have reached a relatively stable value, and at this point, further adjustments are no longer necessary.

[0156] refer to Figure 18 The diagram showing the optimal coding scheme and Figure 19The diagram of the UAV attribute radar shown illustrates how the attribute radar maps of UAVs FY02-FY09 are adjusted. In this embodiment of the invention, it can be observed that UAVs with larger initial position deviations, such as FY08 and FY09, undertake fewer overall launch tasks, while UAVs with smaller initial position deviations, such as FY02, undertake more overall launch tasks. This not only gives FY08 and FY09 more adjustment opportunities but also reduces their interference with the positioning of other UAVs, which has a significant advantage in improving final adaptability.

[0157] In some embodiments, an optimal crew determination model and UAV position adjustment scheme are also disclosed, wherein the formation flight control strategy needs to meet the following conditions:

[0158] (1) Completeness of elements: The receiving UAV can determine the position of the "virtual alpha aircraft" it needs to follow based on the direction information and number information sent by the transmitting UAV. This target is proposed in response to the basic positioning requirements of this question.

[0159] (2) Adjusting anti-interference capability: If a drone in the drone group that transmits a signal loses control due to interference or other force majeure, as many drones as possible can take over the transmission of the signal (i.e., can meet the element completeness requirements with other drones) to improve the adjustability of the entire formation against external interference while maintaining radio silence as much as possible. This objective is proposed to address the shortcomings of the poor anti-interference capability of the pilot-follower method.

[0160] (3) Positioning robustness: If the position of the UAV transmitting the signal deviates slightly, the impact on the positioning of the UAV receiving the signal is limited to a small range, and the error will not increase significantly.

[0161] For condition (1), refer to Figure 20 The cone-shaped UAV formation and coordinates are based on the completeness of the elements constrained by the radio silence premise and positioning requirements in the embodiment of the present invention. The UAV swarm receiving signals must receive directional information transmitted by at least three UAVs transmitting signals. In combination with the requirement of radio silence as much as possible, the embodiment of the present invention selects three UAVs transmitting signals to form the lead UAV group in the formation flight process.

[0162] It can be noted that, such as Figure 21 The drones that violate the constraints shown are FY11, FY07, FY04, FY02, and FY01. If three of these drones are selected as the lead group, the other drones on that line will not be able to determine their correct positions based on the angle information (the range of drone positions that meet the angle constraints is a straight line).

[0163] Therefore, in this embodiment of the invention, the number of drones in the pilot drone group is 3, and these 3 drones cannot be located on the same straight line as other drones, so as to prevent the corresponding receiving drones on the same straight line from being unable to determine their own position.

[0164] Secondly, based on the adjustment of the anti-disturbance target constraint model using the evaluation function, when a member of the lead drone crew in a drone swarm fails, the probability of conflict with the previous drone after replacement should be minimized. To achieve this goal, this paper proposes a conflict warning judgment criterion, referencing the completeness target constraint model: for drones i and j, when acting as members of the lead drone crew, if there are two or more other drones in the same formation along the same line, there is a risk of conflict, and the corresponding conflict warning is set to 1; otherwise, the corresponding conflict warning is set to 0. A conflict warning judgment array Tr is obtained according to this rule. 15×15 The collision value for each drone is obtained sequentially.

[0165]

[0166] The radar chart showing the collision values ​​for each UAV is as follows: Figure 22 The radar chart showing drone collision values ​​is shown below:

[0167] In this embodiment of the invention, if the lead aircraft group FYS = (i,j,k), then the sum of its conflict values ​​can be obtained.

[0168] T con =Con i +Con j +Con k

[0169] refer to Figure 23 The diagram shows the optimal formation of the lead aircraft group, where FY13, FY04, and FY06 are transmitters. The three UAVs found in this embodiment of the invention, based on satisfying the element completeness objective constraint model, ensure that T... con Minimization. Based on element completeness and adjustment immunity, the embodiment of the present invention obtains the optimal pilot group FYS. best =(FY04,FY06,FY13).

[0170] Finally, a positioning robustness target constraint model based on Monte Carlo simulation was used to determine the optimal formation of the lead aircraft crew, and the entire formation was tested in FYS. best Sensitivity under navigation was analyzed to assess the robustness of the drone swarm flying in formation.

[0171] In this embodiment of the invention, the Monte Carlo method can be used for statistical analysis of sensitivity. This embodiment of the invention assumes FYS... bestThe deviation of each UAV follows a two-dimensional normal distribution. The simulation is repeated 1000 times. The positions of other UAVs that receive the signal are recorded after adjustment (according to the OODA coupled interactive UAV position adjustment strategy). The circular probability error (CEP) of these positions is evaluated to determine whether it is within the acceptable error range for aircraft formation flight.

[0172] Figure 24 This is a diagram of a drone formation adjustment and analysis device according to an embodiment of the present invention. The device includes a first module 2410, a second module 2420, a third module 2430, a fourth module 2440, and a fifth module 2450;

[0173] The system comprises five modules: The first module retrieves drone formation information based on a drone formation request. This information includes drone ID, drone identity, and drone position. Drone identity includes transmitter and receiver; transmitter represents the drone transmitting a signal, receiver represents the drone receiving the transmitter's signal, and a virtual leader represents a unique transmitter with no positional deviation. Drone position represents the drone's heading angle. The second module encodes the drone formation using a genetic algorithm to obtain drone sequence codes, which represent the drone ordering during a single position adjustment. The third module uses a variable-step-size discretized neighborhood search algorithm to determine the receiver's target optimal position. The fourth module determines the drone formation adjustment scheme based on the drones' current and target optimal positions, employing an OODA coupling interaction strategy, drone sequence codes, and the target optimal position. The fifth module uses a genetic algorithm to perform at least one of gene mutation and crossover transformations on the adjustment scheme and executes a preset number of position adjustments to ensure each drone in the formation reaches its target optimal position, thus obtaining the formation result.

[0174] Exemplarily, with the cooperation of the first, second, third, fourth, and fifth modules in the device, the embodiment device can implement any of the aforementioned drone formation adjustment methods. Specifically, based on a drone formation request, drone formation information is obtained. This information includes drone number, drone identity, and drone position. The drone identity includes a transmitter and a receiver; the transmitter identifies the drone transmitting a signal, and the receiver identifies the drone receiving the signal from the transmitter. The drone position represents the drone's heading angle. A genetic algorithm is then used to encode the drone formation to obtain drone sequence codes. The UAV sequence code is used to characterize the UAV sorting code when performing a single position adjustment on the UAV formation; the target optimal position of the receiver is obtained by using a variable step size discretized neighborhood search algorithm; based on the current position of the UAVs in the UAV formation and the target optimal position, the adjustment scheme of the UAV formation is determined by using an OODA coupling interaction strategy, the UAV sequence code, and the target optimal position; a genetic algorithm is used to perform at least one of gene mutation and crossover transformation on the adjustment scheme, and the position adjustment of the UAV formation is performed a preset number of times to make each UAV in the UAV formation reach the target optimal position, thus obtaining the formation result. The beneficial effects of this invention are as follows: The three-source localization method determines the position information of a UAV formation. By considering whether the UAVs in the formation have positional deviations, and within a limited range of correct deviation points, the unknown source number can be directly determined by the angle range, eliminating the need for extensive geometric calculations and reducing UAV resource consumption. The variable-step-size discretization neighborhood search algorithm balances algorithm effectiveness and complexity, effectively simulating the adjustment process of the UAV receiving the signal, thus improving the efficiency of the UAV in searching for the optimal position. A method for adjusting the position based on the evaluation function of the actual and desired orientation angles is proposed using an OODA coupling interaction strategy. By changing the UAVs transmitting and receiving signals, mutual error correction between UAVs is effectively achieved, enabling the UAVs to finally find a suitable position and improving the accuracy of UAV localization.

[0175] This invention also provides an electronic device, which includes a processor and a memory;

[0176] The memory stores the program;

[0177] The processor executes a program to perform the aforementioned drone formation adjustment method; the electronic device has the function of carrying and running the drone formation adjustment software system provided in the embodiments of the present invention, such as a personal computer, minicomputer, mainframe, workstation, network or distributed computing environment, standalone or integrated computer platform, or communicating with charged particle tools or other imaging devices, etc.

[0178] This invention also provides a computer-readable storage medium storing a program that is executed by a processor to implement the UAV formation adjustment method described above.

[0179] In some alternative embodiments, the functions / operations mentioned in the block diagrams may not occur in the order shown in the operation diagrams. For example, depending on the functions / operations involved, two consecutively shown blocks may actually be executed substantially simultaneously, or the blocks may sometimes be executed in reverse order. Furthermore, the embodiments presented and described in the flowcharts of this invention are provided by way of example to provide a more comprehensive understanding of the technology. The disclosed methods are not limited to the operations and logic flows presented herein. Alternative embodiments are contemplated in which the order of various operations is altered and sub-operations described as part of a larger operation are executed independently.

[0180] This invention also discloses a computer program product or computer program, which includes computer instructions stored in a computer-readable storage medium. A processor of a computer device can read the computer instructions from the computer-readable storage medium and execute the computer instructions, causing the computer device to perform the aforementioned UAV formation adjustment method.

[0181] Furthermore, although the invention has been described in the context of functional modules, it should be understood that, unless otherwise stated, one or more of the described functions and / or features may be integrated into a single physical device and / or software module, or one or more functions and / or features may be implemented in a separate physical device or software module. It is also understood that a detailed discussion of the actual implementation of each module is unnecessary for understanding the invention. Rather, given the properties, functions, and internal relationships of the various functional modules in the apparatus disclosed herein, the actual implementation of the module will be understood within the scope of conventional skill of an engineer. Therefore, those skilled in the art can implement the invention as set forth in the claims using ordinary techniques without excessive experimentation. It is also understood that the specific concepts disclosed are merely illustrative and not intended to limit the scope of the invention, which is determined by the full scope of the appended claims and their equivalents.

[0182] If the aforementioned functions are implemented as software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this invention, essentially, or the part that contributes to the prior art, or a portion of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0183] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as a sequenced list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by, or in conjunction with, an instruction execution system, apparatus, or device (such as a computer-based system, a processor-included system, or other system that can fetch and execute instructions from, an instruction execution system, apparatus, or device). For the purposes of this specification, "computer-readable medium" can be any means that can contain, store, communicate, propagate, or transmit programs for use by, or in conjunction with, an instruction execution system, apparatus, or device.

[0184] More specific examples of computer-readable media (a non-exhaustive list) include: electrical connections (electronic devices) having one or more wires, portable computer disk drives (magnetic devices), random access memory (RAM), read-only memory (ROM), erasable and editable read-only memory (EPROM or flash memory), fiber optic devices, and portable optical disc read-only memory (CDROM). Furthermore, computer-readable media can even be paper or other suitable media on which the program can be printed, since the program can be obtained electronically, for example, by optically scanning the paper or other medium, followed by editing, interpreting, or otherwise processing as necessary, and then stored in computer memory.

[0185] It should be understood that various parts of the present invention can be implemented in hardware, software, firmware, or a combination thereof. In the above embodiments, multiple steps or methods can be implemented in software or firmware stored in memory and executed by a suitable instruction execution system. For example, if implemented in hardware, as in another embodiment, it can be implemented using any one or a combination of the following techniques known in the art: discrete logic circuits having logic gates for implementing logical functions on data signals, application-specific integrated circuits (ASICs) having suitable combinational logic gates, programmable gate arrays (PGAs), field-programmable gate arrays (FPGAs), etc.

[0186] In the description of this specification, references to terms such as "one embodiment," "some embodiments," "example," "specific example," or "some examples," etc., indicate that a specific feature, structure, material, or characteristic described in connection with that embodiment or example is included in at least one embodiment or example of the invention. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples.

[0187] Although embodiments of the invention have been shown and described, those skilled in the art will understand that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the claims and their equivalents.

[0188] The above is a detailed description of the preferred embodiments of the present invention, but the present invention is not limited to the embodiments described. Those skilled in the art can make various equivalent modifications or substitutions without departing from the spirit of the present invention, and these equivalent modifications or substitutions are all included within the scope defined by the claims of this application.

Claims

1. A method for adjusting unmanned aerial vehicle (UAV) formations, characterized in that, include: According to the drone formation request, obtain drone formation information, which includes drone number, drone identity, and drone position. The drone identity includes transmitter and receiver. The transmitter is used to identify the drone that transmits the signal, and the receiver is used to identify the drone that receives the signal from the transmitter. The drone position is used to identify the drone's heading angle. The drone formation is encoded using a genetic algorithm to obtain drone sequence codes, which are used to characterize the drone sorting codes when the drone formation is adjusted in a single position. The target optimal position of the receiver using a variable step-size discretized neighborhood search algorithm includes: constructing a discretized grid; performing a neighborhood search on the receiver within the discretized grid; re-dividing the discretized grid during a single adjustment of the receiver to obtain a set of discretized points and a first solution; calculating the actual orientation angle between each point in the set of discretized points and the receiver; evaluating the actual orientation angle using an evaluation function to obtain a second solution; and repeating the above position search and all position adjustments of the receiver to obtain the target optimal position, wherein the first and second solutions are used to characterize the position of the receiver. The evaluation function includes: Based on the actual azimuth information of the transmitter and the receiver, and the azimuth information at the target's optimal position, it is determined that the receiver should move towards the target's optimal position, where the evaluation function is: The evaluation function is used to characterize the minimum weighted sum of squared angle deviations found in the neighborhood, where i and j represent the receiver and transmitter indices, respectively. , m and n represent the total number of receivers and the total number of transmitters, respectively. For the receiver sequence number set, For the set of transmitter serial numbers, Indicates the actual direction angle information. The orientation angle information for the optimal position of the target. and These represent the angles measured when the UAV target is at its optimal position and when it receives signals at its current position, respectively. Based on the current positions of the drones in the drone formation and the optimal target position, an adjustment scheme for the drone formation is determined using an OODA coupling interaction strategy, drone sequence encoding, and the optimal target position. This includes: obtaining the current azimuth information based on the receiver's current position, continuously moving the receiver within its surrounding area to continuously detect the actual azimuth angle of the receiver relative to the transmitter; comparing the actual azimuth angle with the desired azimuth angle using the adjusted data to obtain the optimal target position with the smallest deviation; adjusting the receiver's current position to the optimal target position to obtain the adjustment scheme for the drone formation; and applying a fitness function to the adjustment scheme, where the fitness function is... , in, To adjust the strategy, d is the reciprocal square of the sum of squared residuals between the UAV's adjusted position and the target's optimal position, used as the fitness function. To adjust the position of the plan after the adjustment, Let t be the target's optimal position, and t be the total number of drones in the drone formation. A genetic algorithm is used to perform at least one of gene mutation and crossover transformation on the adjustment scheme, and the position adjustment of the UAV formation is performed a preset number of times so that each UAV in the UAV formation reaches the target optimal position, and the formation result is obtained.

2. The method for adjusting drone formations according to claim 1, characterized in that, The acquisition of drone formation information includes: The drone obtains its own azimuth angle and uses a three-source positioning method to determine the drone's position. The three-source positioning method includes determining the position of all drones based on two or three signal sources whose positions have been determined and have no positional deviation. Based on the target formation of the drone formation, drones with no positional deviation are selected from the drone formation as the transmitter, and drones with positional deviation are selected as the receiver.

3. The method for adjusting drone formations according to claim 1, characterized in that, The process of encoding the drone formation using a genetic algorithm to obtain drone sequence codes includes: The transmitter and the receiver are binary encoded, where 1 represents the transmitter and 0 represents the transmitter. The transmitter and the receiver are used as genes in a single formation adjustment encoding to obtain the UAV sequence encoding, wherein the sum of the genes in the binary encoding is less than or equal to 2.

4. The method for adjusting drone formations according to claim 1, characterized in that, The step of employing a genetic algorithm to perform at least one of gene mutation and crossover transformations on the adjustment scheme, and performing a preset number of position adjustments on the UAV formation, includes: Construct a first population based on a genetic algorithm, wherein the population includes a first chromosome, a population size, a maximum number of iterations, and a mutation probability, wherein the mutation probability is obtained by mutation of the gene, wherein the first chromosome is used to characterize the adjustment scheme, and the first chromosome includes a first gene, wherein the first gene is used to characterize the encoding. The fitness function is used to evaluate the first chromosome to obtain the fitness value of each first chromosome, and the first maximum fitness value is determined. The roulette wheel algorithm is used to select the population, and the selected chromosomes are subjected to the crossover process to obtain a second individual and a second population containing the second individual. The genes in the chromosomes of the second population are randomly mutated according to the mutation probability to obtain the second gene; Calculate the fitness value of each second chromosome in the second population to determine the second fitness value. Compare the first maximum fitness value with the second fitness value. If the first maximum fitness value is less than the second fitness value, all second chromosomes with the second fitness value are included in the third population. Otherwise, no update is performed. Repeat the processing for the maximum number of iterations to complete the preset number of position adjustments for the drone formation.

5. A drone formation adjustment device applying the drone formation adjustment method as described in any one of claims 1-4, characterized in that, include: The first module is used to obtain drone formation information according to drone formation requests. The drone formation information includes drone number, drone identity, and drone position. The drone identity includes transmitter and receiver. The transmitter is used to identify the drone that transmits the signal, and the receiver is used to identify the drone that receives the signal from the transmitter. The virtual leader is used to identify the transmitter that is unique and has no positional deviation. The drone position is used to identify the drone's heading angle. The second module is used to encode the drone formation using a genetic algorithm to obtain drone sequence codes. The drone sequence codes are used to characterize the drone sorting codes when the drone formation is adjusted in a single position. The third module is used to locate the target optimal position of the receiver using a variable step-size discretized neighborhood search algorithm. The fourth module is used to determine the adjustment scheme of the UAV formation based on the current position of the UAVs in the UAV formation and the optimal position of the target, using the OODA coupling interaction strategy, the UAV sequence encoding, and the optimal position of the target; The fifth module is used to perform at least one of gene mutation and crossover transformation on the adjustment scheme using a genetic algorithm, and to perform a preset number of position adjustments on the UAV formation so that each UAV in the UAV formation reaches the target optimal position, thereby obtaining the formation result.

6. An electronic device, characterized in that, Including the processor and memory; The memory is used to store programs; The processor executes the program to implement the UAV formation adjustment method as described in any one of claims 1-4.

7. A computer-readable storage medium, characterized in that, The storage medium stores a program that is executed by a processor to implement the UAV formation adjustment method as described in any one of claims 1-4.

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