Phase adjustment method and device of antenna, equipment and medium

Abstract

The invention discloses an antenna phase adjustment method and device, equipment and a medium, and is applied to the technical field of antennas. The antenna phase adjustment method comprises the steps that an initial population set is generated, the position matrix of each individual in the population set is in one-to-one correspondence with antennas in an antenna array, the numerical value of one position in the position matrix is the numerical value of one gene in the individual, and the genes are in one-to-one correspondence with antenna units in the antennas; performing iterative operation on the initial population set according to a particle swarm algorithm and a genetic algorithm to obtain a position matrix of optimal individuals in the population set; and adjusting the phase of the antenna according to the position matrix of the optimal individual. Through the scheme of the invention, the problems of difficulty in antenna calibration, low accuracy, time consumption and the like in related technologies about antenna calibration are solved, the time overhead of antenna calibration is reduced, and the accuracy of antenna calibration and the antenna performance are improved.

Description

Technical Field

[0001] The present invention relates to the field of antenna technology, and in particular to a method, device, equipment and medium for adjusting the phase of an antenna. Background Art

[0002] In the field of low-orbit satellite communications, due to the fast movement speed, time-varying position and large number of satellites, there are high requirements for the antenna design of ground communication terminals. Compared with the traditional mechanical scanning antenna, the antenna scanning has no inertia and the beam can be flexibly changed, which can meet the communication requirements of low-orbit satellites. The liquid crystal antenna is an array antenna based on a liquid crystal phase shifter. Its basic principle is to use the dielectric anisotropy change generated by the liquid crystal when different bias voltages are applied to achieve the purpose of phase shifting. However, the liquid crystal antenna is a passive device and there is a problem of difficulty in calibrating the array surface. When calibrating the liquid crystal antenna, a single probe is generally used to collect its voltage-phase shift (Voltage-Phase, referred to as V_Phase) relationship curve, and then the array is matched to optimize the antenna performance. However, the liquid crystal phase shifter cannot be switched on and off separately, so the measurement result obtained by the above method is the result of the mutual coupling and superposition of each unit, which leads to a large gap between the antenna performance after matching the V_Phase relationship curve and the theoretical value. In addition, measuring the V_Phase relationship curve requires voltage-by-voltage and channel-by-channel measurement, which has a high labor cost for phased arrays with a large number of channels. Summary of the invention

[0003] The present invention aims to solve at least one of the technical problems existing in the related technologies regarding antenna calibration, and proposes a phase adjustment method, device, equipment and medium for an antenna.

[0004] An aspect of an embodiment of the present disclosure provides a method for adjusting the phase of an antenna, which specifically includes the following steps:

[0005] Generate an initial population set, wherein a position matrix of each individual in the population set corresponds one-to-one to an antenna in an antenna array, a value of a position in the position matrix is ​​a value of a gene in the individual, and the gene corresponds one-to-one to an antenna unit in the antenna; the number of antennas in the antenna array and the number of individuals in the population are both X, and X is greater than or equal to 2;

[0006] Iterative operation is performed on the initial population set to obtain a position matrix of the optimal individual in the population set; wherein the step of iterative operation is performed on the initial population set to obtain a position matrix of the optimal individual in the population set comprises:

[0007] For a third population set obtained after each iteration of the initial population set, the position matrix of X individuals in the third population set is updated based on the particle swarm algorithm to obtain a first population set; a crossover calculation is performed on the first population set based on the genetic algorithm to obtain a second population set; a mutation calculation is performed on the second population set based on the genetic algorithm to obtain a third population set;

[0008] Determining the fitness values ​​of the X individuals in this iterative operation according to the position matrix of the X individuals in the third population set, and determining the optimal individual based on the fitness values ​​of the X individuals;

[0009] Determine whether this iterative operation meets the termination condition; if the termination condition is met, terminate the iteration and output the position matrix corresponding to the optimal individual in the third population set; if the termination condition is not met, return to the third population set obtained after each iteration of the initial population set, and update the position matrix of X individuals in the third population set based on the particle swarm algorithm; the termination condition includes that the number of iterations reaches a preset maximum number of iterations or the fitness value corresponding to the optimal individual reaches a preset maximum fitness value;

[0010] The phase of the antenna is adjusted according to the position matrix of the optimal individual.

[0011] In some embodiments, after the step of generating an initial population set, the method further comprises:

[0012] Voltage is applied to the antenna array based on the position matrix of the X individuals in the initial population set to obtain an initial fitness value set; a value in the fitness value set corresponds to an electrical property of the antenna, and the electrical property includes a level.

[0013] In some implementations, the step of updating the position matrix of the X individuals in the third population set based on the particle swarm algorithm includes:

[0014] For the jth individual in the population, the velocity matrix of the k+1th iterative operation is obtained based on the velocity matrix and position matrix obtained by the kth iterative operation of the jth individual, the optimal position matrix of the jth individual in the position matrices obtained by the 1st to kth iterative operations, and the optimal position matrix of the population in the position matrices obtained by the 1st to kth iterative operations, and the position matrix of the k+1th iterative operation is obtained based on the velocity matrix of the k+1th iterative operation and the position matrix obtained by the kth iterative operation; j is 1~X.

[0015] In some embodiments, the step of obtaining a velocity matrix for the k+1th iterative operation based on the velocity matrix and position matrix obtained by the kth iterative operation of the jth individual, the optimal position matrix of the jth individual in the position matrices obtained by the 1st to kth iterative operations, and the optimal position matrix of the population in the position matrices obtained by the 1st to kth iterative operations, and obtaining a position matrix for the k+1th iterative operation based on the velocity matrix for the k+1th iterative operation and the position matrix obtained by the kth iterative operation includes:

[0016] The velocity matrix obtained by the k-th iteration operation of the j-th individual is updated according to the velocity update algorithm in the particle swarm algorithm to obtain the velocity matrix of the individual in the k+1-th iteration operation. The velocity update algorithm is:

[0017] v(j k+1 )=w*v(j k )+c1*rand1*(selfbest-pop(j k ))+c2*rand2*(popbest-pop(j k ));

[0018] Among them, v(j k+1 ) represents the velocity matrix of the jth individual in the k+1th iteration operation, v(j k ) represents the velocity matrix obtained by the k-th iteration of the j-th individual, pop(j k ) represents the position matrix of the j-th individual obtained by the k-th iteration operation, rand1 and rand2 represent two random numbers between 0 and 1, selfbest represents the optimal position matrix of the j-th individual in the historical iteration operation, and popbest represents the optimal position matrix of the population in the historical iteration operation;

[0019] The position matrix obtained by the k-th iteration operation of the j-th individual is updated according to the position update algorithm of the particle swarm algorithm to obtain the position matrix of the k+1-th iteration operation of the j-th individual. The position update algorithm is:

[0020] pop(j k+1 )=pop(j k )+v(j k+1 );

[0021] Among them, pop(j k+1 ) represents the position matrix of the jth individual in the k+1th iteration operation.

[0022] In some embodiments, the step of performing crossover calculation on the first population set based on a genetic algorithm includes:

[0023] Select a number of individuals that need to be crossed from the population; for the i-th individual among the several individuals that need to be crossed, perform a crossover calculation on the position matrix of the i-th individual that needs to be crossed in the first population set according to the position matrix of the i-th individual that needs to be crossed in the first population set and the position matrix of the i+1-th individual that needs to be crossed in the first population set, where i is 1 to Y, Y is greater than or equal to 2, and Y≤X.

[0024] In some embodiments, the step of performing crossover calculation on the position matrix of the i-th individual that needs to be crossed in the first population set according to the position matrix of the i-th individual that needs to be crossed in the first population set and the position matrix of the i+1-th individual that needs to be crossed in the first population set includes:

[0025] According to the crossover algorithm in the genetic algorithm, a crossover calculation is performed on the position matrix of the i-th individual that needs to be crossed in the first population set and the position matrix of the i+1-th individual that needs to be crossed in the first population set to obtain the position matrix of the i-th individual that needs to be crossed in the second population set. The crossover algorithm is:

[0026] pop′(i)=rand3*pop(i)+(1-rand3)*pop(i+1), if i+1>popsize, i+1 is replaced by 1;

[0027] Among them, pop′(i) represents the position matrix of the i-th individual that needs to be crossed in the second population set, pop(i) represents the position matrix of the i-th individual that needs to be crossed in the first population set, pop(i+1) represents the position matrix of the i+1-th individual that needs to be crossed in the first population set, rand3 represents a random number between 0 and 1, and popsize represents the size of the population.

[0028] In some embodiments, the step of selecting a number of individuals to be crossed from the population includes:

[0029] The number of individuals in the population that need to be crossovered is determined according to a preset crossover probability, and a number of individuals that need to be crossovered are randomly selected from the population according to the number of individuals that need to be crossovered.

[0030] In some implementations, the step of performing mutation calculation on the second population set based on the genetic algorithm includes:

[0031] A number of genes that need to be mutated are selected from each individual of the population; for the mth gene that needs to be mutated of the hth individual, a mutation calculation is performed on the mth gene among the genes that need to be mutated of the hth individual according to a preset gene threshold, where m is greater than or equal to 1, and h is 1 to X.

[0032] In some embodiments, the step of calculating the variation of the mth gene among the genes to be mutated of the hth individual according to a preset gene threshold comprises:

[0033] The mutation calculation is performed on the mth gene that needs to be mutated in the hth individual according to the mutation algorithm in the genetic algorithm, and the mutation algorithm is:

[0034] pop(h,m)=rand4*popmax;

[0035] Where pop(h,m) represents the mth gene of the hth individual, rand4 represents a random number between 0 and 1, popmax is the preset gene threshold, indicating the maximum value of each gene, and h ranges from 1 to X.

[0036] In some embodiments, the step of selecting a plurality of genes to be mutated from each individual in the population comprises:

[0037] The number of genes that need to be mutated in each individual is determined according to a preset mutation probability, and a number of genes that need to be mutated are selected from the individual according to the number of genes that need to be mutated.

[0038] In some implementations, the step of determining the fitness values ​​of the X individuals in this iterative operation according to the position matrix of the X individuals in the third population set includes:

[0039] Voltage is applied to the individual according to the position matrix of the individual in the third population set to obtain the fitness value of the individual in this iterative operation.

[0040] In some implementations, determining whether the current iteration operation meets the termination condition; if the termination condition is met, terminating the iteration and outputting the position matrix corresponding to the optimal individual in the third population set; if the termination condition is not met, returning to the third population set obtained after each iteration of the initial population set, and updating the position matrix of X individuals in the third population set based on the particle swarm algorithm, comprises:

[0041] Determine whether the fitness value corresponding to the optimal individual of this iterative calculation reaches the preset maximum fitness value;

[0042] If the fitness value corresponding to the optimal individual reaches the preset maximum fitness value, the iteration is terminated, and the position matrix corresponding to the optimal individual in the third population set is output;

[0043] If the fitness value corresponding to the optimal individual does not reach the preset maximum fitness value, determining whether the number of iterations reaches the preset maximum number of iterations;

[0044] If the number of iterations reaches a preset maximum number of iterations, the iteration is terminated, and a position matrix corresponding to the optimal individual in the third population set is output;

[0045] If the number of iterations does not reach the preset maximum number of iterations, returning to the third population set obtained after each iteration of the initial population set, and updating the position matrix of X individuals in the third population set based on the particle swarm algorithm.

[0046] Another aspect of the embodiments of the present disclosure further provides a phase adjustment device for an antenna, including:

[0047] A generation module, wherein the generation module is configured to generate an initial population set, wherein a position matrix of each individual in the population set corresponds one-to-one to an antenna in an antenna array, a value of a position in the position matrix is ​​a value of a gene in the individual, and the gene corresponds one-to-one to an antenna unit in the antenna; the number of antennas in the antenna array and the number of individuals in the population are both X, and X is greater than or equal to 2;

[0048] An iterative operation module, wherein the iterative operation module is configured to perform iterative operation on the initial population set to obtain a position matrix of the optimal individual in the population set; wherein the iterative operation module includes an operation module, a judgment module and a driving module,

[0049] The operation module is configured to update the position matrix of X individuals in the third population set based on the particle swarm algorithm for the third population set obtained after each iteration of the initial population set to obtain a first population set; perform crossover calculation on the first population set based on the genetic algorithm to obtain a second population set; perform mutation calculation on the second population set based on the genetic algorithm to obtain a third population set;

[0050] The driving module is configured to determine the fitness values ​​of the X individuals in this iterative operation according to the position matrix of the X individuals in the third population set, and determine the optimal individual based on the fitness values ​​of the X individuals;

[0051] The judgment module is configured to judge whether the current iteration operation reaches the termination condition; if the termination condition is reached, the iteration is terminated, and the position matrix corresponding to the optimal individual in the third population set is output; if the termination condition is not reached, the third population set obtained after each iteration of the initial population set is returned, and the position matrix of the X individuals in the third population set is updated based on the particle swarm algorithm; the termination condition includes that the number of iterations reaches a preset maximum number of iterations or the fitness value corresponding to the optimal individual reaches a preset maximum fitness value;

[0052] The phase adjustment module is configured to adjust the phase of the antenna according to the position matrix of the optimal individual.

[0053] According to another aspect of the embodiments of the present disclosure, there is provided an electronic device, comprising: at least one processor; and a memory, wherein the memory stores a computer program executable on the processor, and the computer program implements the steps of the above method when executed by the processor.

[0054] According to another aspect of the embodiments of the present disclosure, a storage medium is provided, which stores a computer program that implements the above method steps when executed by a processor. BRIEF DESCRIPTION OF THE DRAWINGS

[0055] In order to more clearly illustrate the technical solutions in the embodiments of the present invention or related technologies regarding antenna calibration, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other embodiments can be obtained based on these drawings without paying any creative work.

[0056] Figure 1 A schematic diagram of applying power to a liquid crystal phased array antenna to achieve phase change;

[0057] Figure 2 A system diagram of antenna phase calibration provided in an embodiment of the present disclosure;

[0058] Figure 3 A flow chart of a method for adjusting the phase of an antenna provided in an embodiment of the present disclosure;

[0059] Figure 4 A flow chart of a population iteration operation method provided by an embodiment of the present disclosure;

[0060] Figure 5 A flowchart of a method for determining whether iteration is terminated during a population iterative operation provided by an embodiment of the present disclosure;

[0061] Figure 6A structural diagram of a phase adjustment device for an antenna provided in an embodiment of the present disclosure. DETAILED DESCRIPTION

[0062] In order to make the objectives, technical solutions and advantages of the present invention more clearly understood, the embodiments of the present disclosure are further described in detail below in combination with specific embodiments and with reference to the accompanying drawings.

[0063] It should be noted that all expressions using "first" and "second" in the embodiments of the present disclosure are for distinguishing two non-identical entities with the same name or non-identical parameters. It can be seen that "first" and "second" are only for the convenience of expression and should not be understood as limitations on the embodiments of the present disclosure. The subsequent embodiments will not explain this one by one.

[0064] The embodiments of the present disclosure may be applied to a liquid crystal phased array antenna, which is an antenna based on a liquid crystal phase shifter. Figure 1 The figure is a schematic diagram of applying power to a liquid crystal phased array antenna to achieve phase change. Figure 1 As shown in the figure, the basic principle of liquid crystal phased array antenna is to use the dielectric anisotropy change of liquid crystal when different bias voltages are applied to achieve the purpose of phase shift. However, liquid crystal phased array antenna is a passive device, and there is a problem of difficulty in array calibration. Measuring V_Phase channel by channel is not only labor-intensive but also prone to errors, making it difficult to maximize the antenna performance.

[0065] The present invention combines genetic algorithm and particle swarm algorithm to perform phase-only adjustment on the liquid crystal phase shifter and optimize the antenna pattern performance, so as to solve the problem that mechanical calibration of the antenna array requires a lot of manpower and the calibration efficiency and accuracy cannot effectively meet the calibration requirements.

[0066] According to a first aspect of the embodiments of the present disclosure, a method for adjusting the phase of an antenna is proposed.

[0067] Figure 2 A schematic diagram of antenna phase calibration provided in an embodiment of the present disclosure; Figure 3 A flow chart of a method for adjusting the phase of an antenna provided in an embodiment of the present disclosure; Figure 4 A flow chart of a population iteration operation method provided by an embodiment of the present disclosure; Figure 5 A flowchart of a method for determining whether an iteration is terminated during a population iteration operation provided by an embodiment of the present disclosure. Figures 2 to 5 , the method comprises the following steps:

[0068] S10, generating an initial population set, wherein the position matrix of each individual in the population set corresponds one-to-one to an antenna in the antenna array, the value of a position in the position matrix is ​​the value of a gene in the individual, and the gene corresponds one-to-one to the antenna unit in the antenna; the number of antennas in the antenna array and the number of individuals in the population are both X, and X is greater than or equal to 2.

[0069] This step S10 is performed by the optimizer. Specifically, before generating the initial population set, the iteration parameters can be set first. The iteration parameters may include: general parameters and antenna parameters. Among them, the general parameters include the population size popsize, the maximum value of each gene of each individual in the population popmax, the maximum number of iterations epoch_th, and the iteration performance threshold F_th. The antenna parameters include: frequency f, unit horizontal spacing dx, unit vertical spacing dy, horizontal unit number M, and vertical unit number N. After setting the iteration parameters, the population is initialized, that is, an initial population set with a size of popsize and uniform distribution in (0, popmax) is generated. The initial population set contains a position matrix of X individuals generated randomly, and the position matrix is ​​also a power-on matrix. The value of a position in the position matrix corresponds to the power-on voltage of an antenna. A voltage can be applied to an individual according to a position matrix to obtain the electrical performance of the individual under the position matrix. The electrical performance includes but is not limited to level, signal strength, gain, bandwidth, etc. In the embodiment of the present disclosure, the electrical performance of the antenna is only represented by the level.

[0070] S20, performing iterative operations on the initial population set to obtain a position matrix of the optimal individual in the population set.

[0071] Specifically, the initial population set can be iteratively calculated through an intelligent optimization algorithm to continuously adjust and update the power-on voltage of each antenna unit of each antenna in the antenna array, and finally the optimal solution for antenna performance is obtained, avoiding the measurement of the V_Phase curve, saving a lot of manpower costs, and improving the accuracy of the antenna phase pattern debugging, thereby improving the electrical performance of the antenna.

[0072] The step S20 is performed by the optimizer, the driver, and the antenna pattern acquisition module. The antenna pattern acquisition module includes a probe and a VNA. The following is an example of an iterative operation to illustrate the embodiment of the present disclosure. It should be understood that the example described here is only used to illustrate and explain the present invention, and is not used to limit the present invention.

[0073] The optimizer performs an iterative operation and outputs the position matrix of each individual in the population output by the iterative operation to the driver. After the driver receives the position matrix, it loads the power-on voltage to the antenna according to the position matrix. The value of each position in the position matrix corresponds to the voltage value of the antenna unit in an antenna. After the antenna receives the power-on voltage of each antenna unit, it outputs a beam to the receiver. The receiver obtains the electrical performance of the antenna based on the beam and sends the level to the optimizer. In the example disclosed in the present invention, the electrical performance is a level. The receiver receives the beam through a probe and sends the level to the optimizer. The optimizer uses the level as the fitness value of the individual obtained by this iterative operation. In the example disclosed in the present invention, only one antenna is provided for receiving the power-on voltage of the driver. In the actual process, a corresponding number of antennas can be set based on the antenna scale. Thus, the fitness values ​​of all individuals in the cluster can be obtained at one time. Of course, other numbers of antennas less than the antenna scale can also be set to obtain the fitness values ​​of all individuals in the cluster in multiple times. In the disclosed embodiment, the antenna is a liquid crystal phased array antenna (Liquid Crystal Phased Antenna), which includes M×N antenna units, where M represents the number of horizontal units, and N represents the number of vertical units, and both M and N are greater than or equal to 1.

[0074] In some examples, when a power-on voltage is applied to the liquid crystal phased array antenna to obtain the electrical performance of the liquid crystal phased array antenna, the liquid crystal phased array antenna, the probe and the collector are all in a microwave darkroom, and the electrical performance of the liquid crystal phased array antenna is measured in the microwave darkroom. Specifically, the electrical performance of the antenna can be measured in a microwave darkroom using the far-field method, the approach field method and the compact field method. Among them, the compact field reduces the area occupied by the test site and avoids the errors caused by the near-far field conversion by using lenses, reflective surface antennas and the like, and the test process is more stable. In the example disclosed in the present invention, the performance optimization of liquid crystal phased array antennas, especially reflective surface phased array antennas, is achieved by combining the combined algorithm of the particle swarm algorithm and the genetic algorithm with the microwave darkroom and the complementary fusion of software and hardware.

[0075] In some examples, the intelligent optimization algorithm may include one or more of a particle swarm algorithm, a genetic algorithm, a bee colony algorithm, etc. In the disclosed embodiment, only the iterative operation of the initial population set in combination with the particle swarm algorithm and the genetic algorithm is described, but it is not limited thereto.

[0076] In some examples, step S20 specifically includes steps S21 to S23.

[0077] S21. For a third population set obtained after each iteration of the initial population set, the position matrix of X individuals in the third population set is updated based on the particle swarm algorithm to obtain the first population set; a crossover calculation is performed on the first population set based on the genetic algorithm to obtain the second population set; and a mutation calculation is performed on the second population set based on the genetic algorithm to obtain the third population set.

[0078] This step S21 is performed by the optimizer. Specifically, the genetic algorithm draws on the natural selection and genetic evolution mechanism of nature, and obtains individuals with good shapes through the hybridization and mutation behavior of individuals. The particle swarm algorithm draws on the foraging behavior of bird flocks. Birds constantly change their speed and flight direction during the foraging process, record their own optimal position and learn the optimal position of the population, and continuously iterate their flights to find the optimal solution. By combining the particle swarm algorithm and the genetic algorithm for the optimization of the antenna pattern, the disadvantages of using a single algorithm can be avoided, and the performance of the liquid crystal phased array antenna can be converged to a preset state at a relatively fast speed.

[0079] The particle swarm algorithm is a mutual learning algorithm between particles, which can optimize the antenna performance to a good level more quickly. However, in the later stage of optimization, the particle motion state tends to be consistent, which may lead to the problem of falling into the local optimum. The genetic algorithm retains the diversity of the population in the process of crossover and mutation. When solving optimization problems, the upper limit of the optimal solution is high, but the number of iterations is large. By combining the two algorithms, the particle swarm algorithm guides the evolution direction, and the genetic algorithm enhances the local search ability and retains a certain population diversity, which avoids the single algorithm from falling into the local optimum or failing to converge in the later stage of optimization.

[0080] In some examples, for the third population set obtained after each iteration of the initial population set, the position matrix of X individuals in the third population set is updated based on the particle swarm algorithm to obtain the first population set. Specifically, the velocity matrix obtained by the k-th iteration operation of the j-th individual is updated according to the velocity update algorithm in the particle swarm algorithm to obtain the velocity matrix of the individual at the k+1-th iteration operation, where the velocity update algorithm is:

[0081] v(j k+1 )=w*v(j k )+c1*rand1*(selfbest-pop(j k ))+c2*rand2*(popbest-pop(j k ));

[0082] Among them, v(j k+1 ) represents the velocity matrix of the jth individual in the k+1th iteration operation, v(j k ) represents the velocity matrix obtained by the k-th iteration of the j-th individual, pop(jk ) represents the position matrix of the j-th individual obtained by the k-th iteration operation, rand1 and rand2 represent two random numbers between 0 and 1, selfbest represents the optimal position matrix of the j-th individual in the historical iteration operation, and popbest represents the optimal position matrix of the population in the historical iteration operation.

[0083] According to the position update algorithm of the particle swarm algorithm, the position matrix obtained by the k-th iteration operation of the j-th individual is updated to obtain the position matrix of the k+1-th iteration operation of the j-th individual. The position update algorithm is:

[0084] pop(j k+1 )=pop(j k )+v(j k+1 );

[0085] Among them, pop(j k+1 ) represents the position matrix of the jth individual in the k+1th iteration operation.

[0086] Through the embodiments of the present disclosure, during the k+1th iteration operation, the position matrix of X individuals in the population obtained after the k+1th iteration operation is calculated to obtain the position matrix of X individuals in the population at the k+1th iteration operation, thereby forming the first population set for the k+1th iteration operation.

[0087] In some examples, after obtaining the first population set at the k+1th iteration operation, several individuals that need to be crossed are selected from the population, and for the i-th of the several individuals that need to be crossed, a crossover calculation is performed on the position matrix of the i-th individual that needs to be crossed in the first population set according to the position matrix of the i-th individual that needs to be crossed in the first population set and the position matrix of the i+1-th individual that needs to be crossed in the first population set, where i is 1 to Y, Y is greater than or equal to 2, and Y≤X. Specifically, a crossover calculation is performed on the position matrix of the i-th individual that needs to be crossed in the first population set and the position matrix of the i+1-th individual that needs to be crossed in the first population set according to the crossover algorithm in the genetic algorithm to obtain the position matrix of the i-th individual that needs to be crossed in the second population set, where the crossover algorithm is:

[0088] pop′(i)=rand3*pop(i)+(1-rand3)*pop(i+1), if i+1>popsize, i+1 is replaced by 1;

[0089] Among them, pop′(i) represents the position matrix of the i-th individual that needs to cross in the second population set, pop(i) represents the position matrix of the i-th individual that needs to cross in the first population set, pop(i+1) represents the position matrix of the i+1-th individual that needs to cross in the first population set, rand3 represents a random number between 0 and 1, and popsize represents the size of the population.

[0090] In some examples, the number of individuals in a population that need to be crossovered can be determined based on a preset crossover probability, and a number of individuals that need to be crossovered can be randomly selected from the population based on the number of individuals that need to be crossovered. Specifically, for example, if the preset crossover probability is 0.8 and the population size is 200, then 160 individuals that need to be crossovered are obtained based on 0.8×200, and 160 individuals are randomly selected from the 200 individuals. The position matrix of the 160 individuals in the first population set is crossover-calculated, and the position matrix of the remaining 40 individuals is not crossover-calculated, thereby obtaining a second population set. It should be understood that the examples described herein are only used to illustrate and explain the present invention, and are not used to limit the present invention.

[0091] In some examples, all individuals in the population may be randomly numbered in each iteration, that is, the serial number of the same individual in each iteration may be the same or different. When selecting individuals that need to be crossed, several individuals with different serial numbers are randomly selected from the population to perform crossover calculations, thereby retaining the diversity of the population.

[0092] In some examples, after obtaining the second population set at the k+1th iteration operation according to the first population set at the k+1th iteration operation, several genes that need to be mutated are selected from each individual in the population; for the mth gene that needs to be mutated in the hth individual, the mth gene among the genes that need to be mutated in the hth individual is mutated according to a preset gene threshold, where m is greater than or equal to 1, and h is 1 to X. Specifically, the mth gene that needs to be mutated in the hth individual is mutated according to a mutation algorithm in a genetic algorithm, where the mutation algorithm is:

[0093] pop(h,m)=rand4*popmax;

[0094] Where pop(h,m) represents the mth gene of the hth individual, rand4 represents a random number between 0 and 1, popmax is the preset gene threshold, indicating the maximum value of each gene, and h ranges from 1 to X.

[0095] In some examples, the number of genes that need to be mutated in each individual can be determined based on a preset mutation probability, and a number of genes that need to be mutated can be selected from the individual based on the number of genes that need to be mutated. For example, if the preset mutation probability is 0.2, the population size is 200, and the number of genes in each individual in the population is 100, then based on 0.2×100, 20 genes that need to be mutated in each individual are obtained, 20 genes are randomly selected from each individual, and mutation calculations are performed on the 20 genes of each individual in the second population set, and no calculations are performed on the remaining 80 genes of the individual, thereby obtaining a new position matrix for each individual, and then obtaining a third population set.

[0096] In some examples, when performing mutation calculations on individuals in a population, a certain number of individuals can be randomly selected from the population. For example, the individuals that need to be cross-linked can be directly identified as the individuals that need to be mutated, and cross-calculations can be performed on the genes of the individuals that need to be mutated. The process of cross-calculations is the same as the above example and will not be repeated here.

[0097] In some examples, after obtaining the first population set and before performing a crossover calculation on the first population set, a selection calculation may be performed on the first population set. Specifically, through the selection operation of the genetic algorithm, for example, individuals with fitness values ​​higher than the first fitness value in the first population set are selected to obtain a fourth population set, or individuals with fitness values ​​lower than the second fitness value in the first population set are selected to obtain a fourth population set, wherein the first fitness value is greater than the second fitness value. Afterwards, a crossover calculation and a mutation calculation are performed on the fourth population set. The process of the crossover calculation and the mutation calculation is the same as in the above example and will not be repeated here. Through the examples disclosed herein, the convergence speed of the iterative operation is improved.

[0098] S22. Determine the fitness values ​​of the X individuals in this iterative operation according to the position matrix of the X individuals in the third population set, and determine the optimal individual based on the fitness values ​​of the X individuals.

[0099] This step S22 is performed by the optimizer, the driver, and the collector and probe of the antenna pattern acquisition module. Specifically, the driver loads the corresponding driving voltage to the antenna according to the position matrix of the X individuals in the third group set. After the probe of the antenna pattern acquisition module collects the beam output by the antenna, it is transmitted to the collector. The collector generates the phase pattern of the antenna according to the beam, and obtains the electrical performance of the antenna according to the phase pattern of the antenna, and sends it to the optimizer. The optimizer obtains the fitness values ​​of the X individuals according to the electrical performance of each antenna, selects the largest fitness value, and takes the individual corresponding to the largest fitness value as the optimal individual. The optimizer records the optimal individual and its corresponding fitness value, as well as the historical optimal position of the X individuals in the population. For the historical optimal position of the i-th individual among the X individuals in the population, it is assumed that the population has been iterated 8 times, including this iteration operation. The historical optimal position of the i-th individual in the population is the position matrix in the third population set obtained during the iteration operation in which the fitness value obtained by the i-th individual in the 8 iteration operations is the largest.

[0100] S23, determine whether this iterative operation meets the termination condition; if the termination condition is met, terminate the iteration and output the position matrix corresponding to the optimal individual in the third population set; if the termination condition is not met, return to step S21; the termination condition includes that the number of iterations reaches a preset maximum number of iterations or the fitness value corresponding to the optimal individual reaches a preset maximum fitness value.

[0101] This step S23 is performed by the optimizer. Specifically, the optimizer pre-sets the iteration termination condition before the iterative operation starts, and in each iteration process, determines whether the iteration is terminated according to the iteration termination condition. When the iterative operation reaches the iteration termination condition, the iteration can be terminated, and the position matrix corresponding to the optimal individual is output. In the example disclosed in the present invention, the iteration termination condition may include: the number of iterations reaches a preset maximum number of iterations or the fitness value corresponding to the optimal individual reaches a preset maximum fitness value, thereby obtaining an antenna with the best main lobe electrical performance. In some examples, different termination conditions can also be set according to different uses of the algorithm. For example, a minimum fitness value can be set, and when the fitness value of the optimal individual is less than the minimum fitness value, the iteration is terminated, thereby controlling the side lobe electrical performance; a minimum fitness value can also be set, and the average fitness value of all individuals in the population is calculated. When the average fitness value is less than the minimum fitness value, the iteration is terminated, thereby determining the antenna zero sink.

[0102] In some examples, step S23 specifically includes:

[0103] S231. Determine whether the fitness value corresponding to the optimal individual of this iterative operation reaches the preset maximum fitness value; if the fitness value corresponding to the optimal individual reaches the preset maximum fitness value, execute step S233; if the fitness value corresponding to the optimal individual does not reach the preset maximum fitness value, execute step S232.

[0104] S232. If the fitness value corresponding to the optimal individual does not reach the preset maximum fitness value, determine whether the number of iterations reaches the maximum number of iterations; if the number of iterations reaches the maximum number of iterations, execute step S233; if the number of iterations does not reach the maximum number of iterations, return to the third population set obtained after each iteration of the initial population set, and update the position matrix of X individuals in the third population set based on the particle swarm algorithm.

[0105] S233. Terminate the iteration and output the position matrix corresponding to the optimal individual in the third population set.

[0106] Specifically, in the process of performing each iterative operation on the population set, after obtaining the third population set, it is first determined whether the fitness value of the optimal individual in the population set reaches the preset maximum fitness value. If the fitness value of the optimal individual reaches the preset maximum fitness value, then when the number of iterations has not reached the maximum number of iterations, the iteration is terminated in advance, thereby improving the convergence speed of the iterative operation.

[0107] S30. Adjust the phase of the antenna according to the position matrix of the optimal individual.

[0108] Specifically, according to the value at each position in the position matrix of the determined optimal individual, a power-on voltage is applied to each antenna unit in the antenna to adjust the phase of the antenna, obtain a phase pattern of the antenna under the power-on voltage, and obtain the electrical performance of the antenna under the power-on voltage according to the phase pattern. Since the position matrix of the optimal individual is an optimal solution for antenna performance generated by continuously iterating the individuals of the population, the performance of the antenna obtained in this way is optimal.

[0109] It should be noted that the phase pattern of an antenna refers to a graph showing the relative field strength of the radiation field changing with direction at a certain distance from the antenna. From the phase pattern of the antenna, various parameters of the antenna can be observed to measure the performance of the antenna. In the antenna production process, the antenna phase pattern is generally optimized to maximize the antenna performance.

[0110] The disclosed embodiment simulates the power-on value change of the antenna unit in the antenna by setting a population of a certain size, uses the electrical performance of the antenna in a specified direction as algorithm feedback, and iterates the individuals in the population according to the fitness value with the characteristics of a particle swarm and a genetic algorithm, and finally obtains the optimal solution for the antenna performance, thereby solving the problems of the related technologies regarding antenna calibration, such as the difficulty of antenna calibration, low accuracy, and time-consuming, reducing the time overhead of antenna calibration and improving antenna performance.

[0111] In some embodiments, the method not only includes steps S10, S20, and S30 as described above, but also includes the following step S15 between steps S10 and S20:

[0112] S15, applying voltage to the antenna array based on the position matrix of X individuals in the initial population set to obtain an initial fitness value set; a value in the fitness value set corresponds to an electrical performance of an antenna, and the electrical performance includes a level.

[0113] The difference between the disclosed embodiment of the present invention and the disclosed embodiment described above is that the initial fitness set obtained according to the initial population set during the first iterative operation is also included in the historical fitness set. Thus, in each subsequent iterative operation, when determining the historical optimal position of each individual in the population, the fitness value of the individual in the initial fitness set is also included in the selection range, thereby further improving the antenna performance.

[0114] Based on the same inventive concept, a second aspect of an embodiment of the present disclosure proposes a phase adjustment device for an antenna. Figure 6 The structure diagram of a phase adjustment device for an antenna provided in an embodiment of the present disclosure is shown in FIG. Figure 6 In the embodiment shown, the device specifically includes: a generation module 110, an iterative operation module 120 and a phase adjustment module 130.

[0115] The generation module 110 is configured to generate an initial population set, wherein the position matrix of each individual in the population set corresponds one-to-one to an antenna in the antenna array, the value of a position in the position matrix is ​​the value of a gene in the individual, and the gene corresponds one-to-one to the antenna unit in the antenna; the number of antennas in the antenna array and the number of individuals in the population are both X, and X is greater than or equal to 2.

[0116] The iterative operation module 120 is configured to perform iterative operations on the initial population set to obtain the position matrix of the optimal individual in the population set. Specifically, the iterative operation module 120 may include an operation module 121, a judgment module 122 and a driving module 123. Among them, the operation module 121 is configured to update the position matrix of X individuals in the third population set based on the particle swarm algorithm for the third population set obtained after each iteration of the initial population set to obtain the first population set; perform crossover calculation on the first population set based on the genetic algorithm to obtain the second population set; perform mutation calculation on the second population set based on the genetic algorithm to obtain the third population set. The driving module 123 is configured to determine the fitness values ​​of the X individuals in this iterative operation according to the position matrix of the X individuals in the third population set, and determine the optimal individual based on the fitness values ​​of the X individuals. The judgment module 122 is configured to judge whether the current iterative operation reaches the termination condition; if the termination condition is reached, the iteration is terminated and the position matrix corresponding to the optimal individual in the third population set is output; if the termination condition is not reached, the third population set obtained after each iteration of the initial population set is returned, and the position matrix of X individuals in the third population set is updated based on the particle swarm algorithm; the termination condition includes that the number of iterations reaches a preset maximum number of iterations or the fitness value corresponding to the optimal individual reaches a preset maximum fitness value.

[0117] The phase adjustment module 130 is configured to adjust the phase of the antenna according to the position matrix of the optimal individual.

[0118] The disclosed embodiment simulates the power-on value change of the antenna unit in the antenna by setting a population of a certain size, uses the electrical performance of the antenna in a specified direction as algorithm feedback, and iterates the individuals in the population according to the fitness value with the characteristics of a particle swarm and a genetic algorithm, and finally obtains the optimal solution for the antenna performance, thereby solving the problems of the related technologies regarding antenna calibration, such as the difficulty of antenna calibration, low accuracy, and time-consuming, reducing the time overhead of antenna calibration and improving antenna performance.

[0119] Based on the same inventive concept, according to the third aspect of the present invention, an embodiment of the present invention further provides an electronic device, which includes a processor and a memory, the memory stores a computer program that can be run on the processor, and the processor executes the steps of the above method when executing the program.

[0120] Among them, the memory, as a non-volatile storage medium, can be used to store non-volatile software programs, non-volatile computer executable programs and modules, such as the program instructions / modules corresponding to the phase adjustment method of the antenna in the embodiment of the present application. The processor executes various functional applications and data processing of the system by running the non-volatile software programs, instructions and modules stored in the memory, that is, the phase adjustment method of the antenna in the above method embodiment is implemented.

[0121] The memory may include a program storage area and a data storage area, wherein the program storage area may store an operating system, an application required by at least one function; the data storage area may store data created according to the use of the system, etc. In addition, the memory may include a high-speed random access memory, and may also include a non-volatile memory, such as at least one disk storage device, a flash memory device, or other non-volatile solid-state storage device. In some embodiments, the memory may optionally include a memory remotely arranged relative to the processor, and these remote memories may be connected to the local module via a network. Examples of the above-mentioned network include, but are not limited to, the Internet, an intranet, a local area network, a mobile communication network, and combinations thereof.

[0122] Based on the same inventive concept, according to a fourth aspect of the present invention, an embodiment of the present invention further provides a storage medium, which stores a computer program that performs the above method when executed by a processor.

[0123] The storage medium (e.g., memory) of the disclosed embodiments may be a volatile memory or a nonvolatile memory, or may include both a volatile memory and a nonvolatile memory. As an example and not by way of limitation, a nonvolatile memory may include a read-only memory (ROM), a programmable ROM (PROM), an electrically programmable ROM (EPROM), an electrically erasable programmable ROM (EEPROM), or a flash memory. A volatile memory may include a random access memory (RAM), which may serve as an external cache memory. As an example and not by way of limitation, RAM may be obtained in a variety of forms, such as synchronous RAM (DRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDR SDRAM), enhanced SDRAM (ESDRAM), synchronous link DRAM (SLDRAM), and direct Rambus RAM (DRRAM). The storage device of the disclosed aspects is intended to include, but is not limited to, these and other suitable types of memories.

[0124] It will also be appreciated by those skilled in the art that various exemplary logic blocks, modules, circuits and algorithm steps described in conjunction with the disclosure herein can be implemented as electronic hardware, computer software or a combination of the two. In order to clearly illustrate this interchangeability of hardware and software, a general description has been given to the functions of various schematic components, blocks, modules, circuits and steps. Whether this function is implemented as software or hardware depends on specific applications and the design constraints imposed on the entire system. Those skilled in the art can implement the function in various ways for each specific application, but this implementation decision should not be interpreted as causing a departure from the disclosed scope of the embodiments of the present disclosure.

[0125] The above are exemplary embodiments disclosed in the present invention, but it should be noted that various changes and modifications may be made without departing from the scope of the embodiments disclosed in the present invention as defined in the claims. The functions, steps and / or actions of the method claims according to the disclosed embodiments described herein do not need to be performed in any particular order. The serial numbers of the embodiments disclosed in the above embodiments of the present invention are for description only and do not represent the advantages and disadvantages of the embodiments. In addition, although the elements disclosed in the embodiments of the present invention may be described or required in individual form, they may also be understood as multiple unless explicitly limited to the singular.

Claims

1. A method for adjusting the phase of an antenna, characterized in that: include: Generate an initial population set, wherein a position matrix of each individual in the population set corresponds one-to-one to an antenna in an antenna array, a value of a position in the position matrix is ​​a value of a gene in the individual, and the gene corresponds one-to-one to an antenna unit in the antenna; the number of antennas in the antenna array and the number of individuals in the population are both X, and X is greater than or equal to 2; Iterative operation is performed on the initial population set to obtain a position matrix of the optimal individual in the population set; wherein the step of iterative operation is performed on the initial population set to obtain a position matrix of the optimal individual in the population set comprises: For a third population set obtained after each iteration of the initial population set, the position matrix of X individuals in the third population set is updated based on the particle swarm algorithm to obtain a first population set; a crossover calculation is performed on the first population set based on the genetic algorithm to obtain a second population set; a mutation calculation is performed on the second population set based on the genetic algorithm to obtain a third population set; Determining the fitness values ​​of the X individuals in this iterative operation according to the position matrix of the X individuals in the third population set, and determining the optimal individual based on the fitness values ​​of the X individuals; Determine whether this iterative operation meets the termination condition; if the termination condition is met, terminate the iteration and output the position matrix corresponding to the optimal individual in the third population set; if the termination condition is not met, return to the third population set obtained after each iteration of the initial population set, and update the position matrix of X individuals in the third population set based on the particle swarm algorithm; the termination condition includes that the number of iterations reaches a preset maximum number of iterations or the fitness value corresponding to the optimal individual reaches a preset maximum fitness value; The phase of the antenna is adjusted according to the position matrix of the optimal individual.

2. The method according to claim 1, characterized in that After the step of generating the initial population set, the method further includes: Voltage is applied to the antenna array based on the position matrix of the X individuals in the initial population set to obtain an initial fitness value set; a value in the fitness value set corresponds to an electrical property of the antenna, and the electrical property includes a level.

3. The method according to claim 1 or 2, characterized in that: The step of updating the position matrix of the X individuals in the third population set based on the particle swarm algorithm comprises: For the jth individual in the population, the velocity matrix of the k+1th iterative operation is obtained based on the velocity matrix and position matrix obtained by the kth iterative operation of the jth individual, the optimal position matrix of the jth individual in the position matrices obtained by the 1st to kth iterative operations, and the optimal position matrix of the population in the position matrices obtained by the 1st to kth iterative operations, and the position matrix of the k+1th iterative operation is obtained based on the velocity matrix of the k+1th iterative operation and the position matrix obtained by the kth iterative operation; j is 1~X.

4. The method according to claim 3, characterized in that The step of obtaining a velocity matrix for the k+1th iterative operation based on the velocity matrix and position matrix obtained by the kth iterative operation of the jth individual, the optimal position matrix of the jth individual in the position matrices obtained by the 1st to kth iterative operations, and the optimal position matrix of the population in the position matrices obtained by the 1st to kth iterative operations, and obtaining a position matrix for the k+1th iterative operation based on the velocity matrix for the k+1th iterative operation and the position matrix for the kth iterative operation comprises: The velocity matrix obtained by the k-th iteration operation of the j-th individual is updated according to the velocity update algorithm in the particle swarm algorithm to obtain the velocity matrix of the individual in the k+1-th iteration operation. The velocity update algorithm is: in(j k+1 )=w*v(j k )+c1*rand1*(selfbest-pop(j k ))+c2*rand2*(popbest-pop(j k )); Among them, v(j k+1 ) represents the velocity matrix of the jth individual in the k+1th iteration operation, v(j k ) represents the velocity matrix obtained by the k-th iteration of the j-th individual, pop(j k ) represents the position matrix of the j-th individual obtained by the k-th iteration operation, rand1 and rand2 represent two random numbers between 0 and 1, selfbest represents the optimal position matrix of the j-th individual in the historical iteration operation, and popbest represents the optimal position matrix of the population in the historical iteration operation; The position matrix obtained by the k-th iteration operation of the j-th individual is updated according to the position update algorithm of the particle swarm algorithm to obtain the position matrix of the k+1-th iteration operation of the j-th individual. The position update algorithm is: pop(j k+1 )=pop(j k )+v(j k+1 ); Among them, pop(j k+1 ) represents the position matrix of the jth individual in the k+1th iteration operation.

5. The method according to claim 1, characterized in that The step of performing crossover calculation on the first population set based on a genetic algorithm comprises: Select a number of individuals that need to be crossed from the population; for the i-th individual among the several individuals that need to be crossed, perform a crossover calculation on the position matrix of the i-th individual that needs to be crossed in the first population set according to the position matrix of the i-th individual that needs to be crossed in the first population set and the position matrix of the i+1-th individual that needs to be crossed in the first population set, where i is 1 to Y, Y is greater than or equal to 2, and Y≤X.

6. The method according to claim 5, characterized in that The step of performing crossover calculation on the position matrix of the i-th individual that needs to be crossed in the first population set according to the position matrix of the i-th individual that needs to be crossed in the first population set and the position matrix of the i+1-th individual that needs to be crossed in the first population set comprises: According to the crossover algorithm in the genetic algorithm, a crossover calculation is performed on the position matrix of the i-th individual that needs to be crossed in the first population set and the position matrix of the i+1-th individual that needs to be crossed in the first population set to obtain the position matrix of the i-th individual that needs to be crossed in the second population set. The crossover algorithm is: pop′(i)=rand3*pop(i)+(1-rand3)*pop(i+1), if i+1>popsize, i+1 is replaced by 1; Among them, pop′(i) represents the position matrix of the i-th individual that needs to be crossed in the second population set, pop(i) represents the position matrix of the i-th individual that needs to be crossed in the first population set, pop(i+1) represents the position matrix of the i+1-th individual that needs to be crossed in the first population set, rand3 represents a random number between 0 and 1, and popsize represents the size of the population.

7. The method according to claim 5, characterized in that The step of selecting a number of individuals to be crossed from the population includes: The number of individuals in the population that need to be crossovered is determined according to a preset crossover probability, and a number of individuals that need to be crossovered are randomly selected from the population according to the number of individuals that need to be crossovered.

8. The method according to claim 1, characterized in that The step of performing mutation calculation on the second population set based on the genetic algorithm comprises: A number of genes that need to be mutated are selected from each individual of the population; for the mth gene that needs to be mutated of the hth individual, a mutation calculation is performed on the mth gene among the genes that need to be mutated of the hth individual according to a preset gene threshold, where m is greater than or equal to 1, and h is 1 to X.

9. The method according to claim 8, characterized in that The step of performing mutation calculation on the mth gene among the genes that need to be mutated of the hth individual according to the preset gene threshold comprises: The mutation calculation is performed on the mth gene that needs to be mutated in the hth individual according to the mutation algorithm in the genetic algorithm, and the mutation algorithm is: pop(h,m)=rand4*popmax; Among them, pop(h,m) represents the mth gene of the hth individual, rand4 represents a random number between 0 and 1, and popmax is the preset gene threshold, which represents the maximum value of each gene.

10. The method according to claim 8, characterized in that The step of selecting a plurality of genes to be mutated from each individual in the population comprises: The number of genes that need to be mutated in each individual is determined according to a preset mutation probability, and a number of genes that need to be mutated are selected from the individual according to the number of genes that need to be mutated.

11. The method according to claim 1, characterized in that: The step of determining the fitness values ​​of the X individuals in this iterative operation according to the position matrix of the X individuals in the third population set comprises: Voltage is applied to the individual according to the position matrix of the individual in the third population set to obtain the fitness value of the individual in this iterative operation.

12. The method according to claim 1, characterized in that Determine whether the current iterative operation meets the termination condition; if the termination condition is met, terminate the iteration and output the position matrix corresponding to the optimal individual in the third population set; if the termination condition is not met, return to the third population set obtained after each iteration of the initial population set, and update the position matrix of X individuals in the third population set based on the particle swarm algorithm, including: Determine whether the fitness value corresponding to the optimal individual of this iterative calculation reaches the preset maximum fitness value; If the fitness value corresponding to the optimal individual reaches the preset maximum fitness value, the iteration is terminated, and the position matrix corresponding to the optimal individual in the third population set is output; If the fitness value corresponding to the optimal individual does not reach the preset maximum fitness value, determining whether the number of iterations reaches the preset maximum number of iterations; If the number of iterations reaches a preset maximum number of iterations, the iteration is terminated, and a position matrix corresponding to the optimal individual in the third population set is output; If the number of iterations does not reach the preset maximum number of iterations, returning to the third population set obtained after each iteration of the initial population set, and updating the position matrix of X individuals in the third population set based on the particle swarm algorithm.

13. A phase adjustment device for an antenna, characterized in that: include: A generation module, wherein the generation module is configured to generate an initial population set, wherein a position matrix of each individual in the population set corresponds one-to-one to an antenna in an antenna array, a value of a position in the position matrix is ​​a value of a gene in the individual, and the gene corresponds one-to-one to an antenna unit in the antenna; the number of antennas in the antenna array and the number of individuals in the population are both X, and X is greater than or equal to 2; An iterative operation module, wherein the iterative operation module is configured to perform iterative operation on the initial population set to obtain a position matrix of the optimal individual in the population set; wherein the iterative operation module includes an operation module, a judgment module and a driving module, The operation module is configured to update the position matrix of X individuals in the third population set based on the particle swarm algorithm for the third population set obtained after each iteration of the initial population set to obtain a first population set; perform crossover calculation on the first population set based on the genetic algorithm to obtain a second population set; perform mutation calculation on the second population set based on the genetic algorithm to obtain a third population set; The driving module is configured to determine the fitness values ​​of the X individuals in this iterative operation according to the position matrix of the X individuals in the third population set, and determine the optimal individual based on the fitness values ​​of the X individuals; The judgment module is configured to judge whether the current iteration operation reaches the termination condition; if the termination condition is reached, the iteration is terminated, and the position matrix corresponding to the optimal individual in the third population set is output; if the termination condition is not reached, the third population set obtained after each iteration of the initial population set is returned, and the position matrix of the X individuals in the third population set is updated based on the particle swarm algorithm; the termination condition includes that the number of iterations reaches a preset maximum number of iterations or the fitness value corresponding to the optimal individual reaches a preset maximum fitness value; The phase adjustment module is configured to adjust the phase of the antenna according to the position matrix of the optimal individual.

14. An electronic device comprising: at least one processor; as well as A memory storing a computer program executable on the processor, wherein the processor executes the steps of the method according to any one of claims 1 to 12 when executing the program.

15. A storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 12 are performed.

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