Parameter optimization method and system for ultra-wideband antenna of gradually changing structure, and related device

Abstract

The present invention is suitable for the field of ultra-wideband antenna optimization, and particularly relates to a parameter optimization method and system for an ultra-wideband antenna of a gradually changing structure, and a related device. The method comprises: using a structural parameter of an ultra-wideband antenna of a gradually changing structure as a single individual of a genetic algorithm, and initializing a population that contains a plurality of individuals and is used by the genetic algorithm, so as to obtain an initial population; using a preset simulation tool to perform simulation calculation on the structural parameter corresponding to each individual in the initial population, so as to obtain an S parameter corresponding to the structural parameter; calculating the fitness of the initial population on the basis of the S parameters; using the initial population and the fitness as parameters of the genetic algorithm to perform iterative optimization calculation until the fitness meets a preset optimization condition; and outputting, as an optimization result, the structural parameter corresponding to the fitness. The present invention can improve the optimization efficiency of structural parameters of an antenna.

Description

Optimization method, system and related equipment for parameters of gradient structure ultra-wideband antenna Technical Field

[0001] The present invention is applicable to the field of ultra-wideband antenna optimization, and in particular relates to a method, system and related equipment for optimizing parameters of a gradient-structure ultra-wideband antenna. Background Art

[0002] With the rapid development of wireless communication technology, people are becoming increasingly dependent on wireless communication devices. Smart homes and wearable smart mobile devices are becoming increasingly common, and modern society has placed increasingly stringent demands on the communication performance of mobile devices. However, the explosive growth of wireless communication devices is leading to an increasing shortage of spectrum resources. Achieving better communication quality and faster signal transmission rates within limited spectrum resources has become a key research focus in wireless communication technology. To address this issue, ultra-wideband (UBW) technology has been proposed. This new wireless communication technology directly modulates impulses with steep rise and fall times, resulting in signals with bandwidths in the GHz range. Antennas using UWB technology are defined as those with a relative bandwidth greater than 25% or an absolute bandwidth greater than 0.5 GHz.

[0003] Classic ultra-wideband antennas typically include TEM (Transverse Electro Magnetic) horn antennas, frequency-invariant antennas, self-similar spiral antennas, and log-periodic dipole antennas. These antennas all use a tapered structure to achieve broadband effects. The problem is that each ultra-wideband antenna has a different tapered structure and numerous structural parameters. Therefore, optimizing these structural parameters to achieve the desired ultra-wideband antenna parameters is a time-consuming and labor-intensive process.

[0004] Therefore, it is urgent to develop a parameter optimization method for ultra-wideband antennas with gradient structures.

[0005] Summary of the Invention

[0006] The present invention provides a method, system and related equipment for optimizing parameters of an ultra-wideband antenna with a tapered structure, aiming to solve the problem of low efficiency in the prior art in optimizing parameters of an ultra-wideband antenna with a tapered structure.

[0007] To solve the above technical problems, in a first aspect, the present invention provides a method for optimizing parameters of a gradient structure ultra-wideband antenna, the optimization method comprising the following steps:

[0008] S101, using the structural parameters of the gradient structure ultra-wideband antenna as a single individual of the genetic algorithm, and initializing a population including a plurality of the individuals used by the genetic algorithm to obtain an initial population;

[0009] S102, performing simulation calculation on the structural parameters corresponding to each individual in the initial population using a preset simulation tool to obtain S parameters corresponding to the structural parameters;

[0010] S103, calculating the fitness of the initial population according to the S parameter;

[0011] S104, performing iterative optimization calculation using the initial population and the fitness as parameters of the genetic algorithm until the fitness meets a preset optimization condition;

[0012] S105: Output the structural parameters corresponding to the fitness as an optimization result.

[0013] Furthermore, the initial population is defined as x, the fitness is defined as F(x), and the initial population x satisfies the following relationship (1):

[0014] x=[x1,x2,x3,…,xN] (1);

[0015] Wherein, N is the number of individuals, [x1, x2, x3, ..., xN] represents the set of individuals in the initial population;

[0016] The fitness F(x) satisfies the following relationship (2):

[0017] Among them, minfreq and maxfreq represent the optimized minimum frequency and maximum frequency respectively, S 11 (i) represents the S parameter corresponding to the structural parameter when the frequency is i, and Rst. represents the preset optimization target value.

[0018] Furthermore, the preset optimization conditions are:

[0019] The number of iterative optimization calculations of the genetic algorithm reaches the preset number of optimizations;

[0020] or:

[0021] The fitness value after iterative optimization calculation of the genetic algorithm remains unchanged within a preset number of iterations.

[0022] Furthermore, the structural parameters include at least one of the longest structural distance of the tapered structure ultra-wideband antenna, the angle between adjacent rectangular folded sheets, the length of the short side of the rectangular folded sheet, and the length of the wide side of the rectangular folded sheet.

[0023] Furthermore, the preset simulation tool is a CST simulation tool.

[0024] Furthermore, step S104 includes the following sub-steps:

[0025] S1041, using the initial population and the fitness as initial parameters, inputting into a genetic algorithm for calculation;

[0026] S1042: Determine whether the current fitness or the number of iterative optimization calculations of the genetic algorithm meets the preset optimization condition. If so, stop the iterative optimization calculation process of the genetic algorithm and save the structural parameters corresponding to the current fitness. If not:

[0027] S1043. Perform selection, crossover, and mutation processing on the individuals in the initial population to obtain a new generation population including a new generation of individuals;

[0028] S1044. Use the preset simulation tool to calculate the S parameters corresponding to the structural parameters of the new generation individuals, and calculate the fitness of the new generation population, and return to step S1042.

[0029] In a second aspect, the present invention further provides a system for optimizing parameters of a gradient structure ultra-wideband antenna, comprising:

[0030] an initialization module, configured to use the structural parameters of the gradient structure ultra-wideband antenna as a single individual of the genetic algorithm, and initialize a population including a plurality of the individuals used by the genetic algorithm to obtain an initial population;

[0031] A simulation calculation module, configured to perform simulation calculation on the structural parameters corresponding to each individual in the initial population using a preset simulation tool to obtain S parameters corresponding to the structural parameters;

[0032] A parameter calculation module, used to calculate the fitness of the initial population according to the S parameter;

[0033] an iterative optimization module, configured to perform iterative optimization calculations using the initial population and the fitness as parameters of a genetic algorithm until the fitness satisfies a preset optimization condition;

[0034] The output module is used to output the structural parameters corresponding to the fitness as an optimization result.

[0035] Furthermore, the initial population is defined as x, the fitness is defined as F(x), and the initial population x satisfies the following relationship (1):

[0036] x=[x1,x2,x3,…,xN] (1);

[0037] Wherein, N is the number of individuals, [x1, x2, x3, ..., xN] represents the set of individuals in the initial population;

[0038] The fitness F(x) satisfies the following relationship (2):

[0039] Among them, min freq and max freq represent the optimized minimum frequency and maximum frequency respectively, S 11 (i) represents the S parameter corresponding to the structural parameter when the frequency is i, and Rst. represents the preset optimization target value.

[0040] In a third aspect, the present invention further provides a computer device comprising: a memory, a processor, and a computer program stored on the memory and executable on the processor, wherein when the processor executes the computer program, the steps of the method for optimizing the parameters of a gradient structure ultra-wideband antenna as described in any one of the above embodiments are implemented.

[0041] In a fourth aspect, the present invention further provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps in the method for optimizing the parameters of a gradient structure ultra-wideband antenna as described in any one of the above embodiments.

[0042] The beneficial effect achieved by the present invention lies in proposing a parameter optimization method for a gradient structure ultra-wideband antenna combined with a genetic algorithm for optimization. Compared with the traditional parameter scanning optimization method, this method can adaptively select favorable structural parameters through the genetic algorithm, eliminating the process of human intervention and improving the optimization efficiency. In addition, the method provided by the present invention also designs an objective function for constraining the iterative process of the genetic algorithm based on the wideband requirements of the gradient structure ultra-wideband antenna, so that the final optimization result can be closer to the optimization target and meet the wideband performance requirements. BRIEF DESCRIPTION OF THE DRAWINGS

[0043] 1 is a flowchart of the steps of a method for optimizing parameters of a gradient structure ultra-wideband antenna according to an embodiment of the present invention;

[0044] FIG2 is a schematic structural diagram of a gradient structure ultra-wideband antenna according to an embodiment of the present invention;

[0045] FIG3 is another schematic structural diagram of a gradient structure ultra-wideband antenna according to an embodiment of the present invention;

[0046] FIG4 is a schematic diagram of an S parameter curve provided by an embodiment of the present invention;

[0047] 5 is a schematic structural diagram of a system for optimizing parameters of a gradient-structure ultra-wideband antenna according to an embodiment of the present invention;

[0048] FIG6 is a schematic diagram of the structure of a computer device provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0049] In order to make the purpose, technical solutions and advantages of the present invention more clearly understood, the present invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0050] Please refer to FIG1 , which is a flowchart of the steps of a method for optimizing parameters of a gradient structure ultra-wideband antenna provided by an embodiment of the present invention. The optimization method includes the following steps:

[0051] S101 , using structural parameters of a gradient-structure ultra-wideband antenna as a single individual of a genetic algorithm, and initializing a population including a plurality of the individuals used by the genetic algorithm to obtain an initial population.

[0052] Please refer to the schematic diagram of the gradient structure ultra-wideband antenna shown in Figures 2 and 3. Since the structure of the ultra-wideband antenna needs to be determined according to the actual application scenario, some structures can be changed. However, as for the basic shape of the antenna, the embodiment of the present invention mainly targets a representative ultra-wideband antenna containing rectangular folded sheets. The function of the rectangular folded sheets is to adjust the antenna resonant frequency and reduce the antenna size. Specifically, the structural parameters include the maximum structural distance H of the gradient structure ultra-wideband antenna, the angle between adjacent rectangular folded sheets, At least one of the length L of the short side of the rectangular foldable sheet and the length W of the wide side of the rectangular foldable sheet.

[0053] S102 , performing simulation calculation on the structural parameters corresponding to each individual in the initial population using a preset simulation tool to obtain S parameters (scattering coefficients) corresponding to the structural parameters.

[0054] The preset simulation tool is the Computer Simulation Technology (CST) simulation tool. The CST simulation tool is a commonly used electromagnetic simulation software. In the embodiments of the present invention, after importing the corresponding antenna structure parameters into the software, the corresponding electromagnetic response parameters can be automatically calculated and obtained. To clearly compare antenna performance, only S parameters are calculated in the embodiments of the present invention.

[0055] S103. Calculate the fitness of the initial population according to the S parameter.

[0056] Define the initial population as x, the fitness as F(x), and the initial population x satisfies the following relationship (1):

[0057] x=[x1,x2,x3,…,xN] (1);

[0058] Wherein, N is the number of individuals, [x1, x2, x3, ..., xN] represents the set of individuals in the initial population;

[0059] The fitness F(x) satisfies the following relationship (2):

[0060] Among them, min freq and max freq represent the optimized minimum frequency and maximum frequency respectively, S 11 (i) represents the S parameter (in the embodiment of the present invention, specifically the input reflection coefficient) corresponding to the structural parameter when the frequency is i, and Rst. represents the preset optimization target value.

[0061] The expression (2) of the fitness F(x) in the embodiment of the present invention represents the overall optimization goal of the genetic algorithm. Since the embodiment of the present invention needs to take into account the broadband characteristics of the ultra-wideband antenna, the transmission array is optimized within the broadband during the optimization process of the genetic algorithm. This characteristic is reflected in the values ​​of min freq and max freq. In the calculation process, S 11 The value of (i) is constantly approaching the value of Rst. due to the iterative optimization of the genetic algorithm, thus achieving the goal of optimizing the overall parameters of the antenna.

[0062] S104: performing iterative optimization calculations using the initial population and the fitness as parameters of a genetic algorithm until the fitness satisfies a preset optimization condition.

[0063] Step S104 includes the following sub-steps:

[0064] S1041, using the initial population and the fitness as initial parameters, inputting into a genetic algorithm for calculation;

[0065] S1042: Determine whether the current fitness or the number of iterative optimization calculations of the genetic algorithm meets the preset optimization condition. If so, stop the iterative optimization calculation process of the genetic algorithm and save the structural parameters corresponding to the current fitness. If not:

[0066] S1043. Perform selection, crossover, and mutation processing on the individuals in the initial population to obtain a new generation population including a new generation of individuals;

[0067] S1044. Use the preset simulation tool to calculate the S parameters corresponding to the structural parameters of the new generation individuals, and calculate the fitness of the new generation population, and return to step S1042.

[0068] The preset optimization conditions are:

[0069] The number of iterative optimization calculations of the genetic algorithm reaches the preset number of optimizations;

[0070] or:

[0071] The fitness value after iterative optimization calculation of the genetic algorithm remains unchanged within a preset number of iterations.

[0072] S105: Output the structural parameters corresponding to the fitness as an optimization result.

[0073] In the embodiment of the present invention, the optimized minimum frequency min freq and maximum frequency max freq are set to 0.6 GHz and 2 GHz, respectively, and the interval of the preset optimization target value Rst. is set to [-30 dB, -10 dB]. The S parameter curve corresponding to the structural parameters obtained after parameter optimization according to the above parameters is shown in FIG4 . It can be seen that the S parameters of the tapered structure ultra-wideband antenna in the range of 0.65 GHz to 2 GHz are all less than -10 dB, which proves the practicality of the parameter optimization method for the tapered structure ultra-wideband antenna proposed in the embodiment of the present invention.

[0074] The beneficial effect achieved by the present invention lies in proposing a parameter optimization method for a gradient structure ultra-wideband antenna combined with a genetic algorithm for optimization. Compared with the traditional parameter scanning optimization method, this method can adaptively select favorable structural parameters through the genetic algorithm, eliminating the process of human intervention and improving the optimization efficiency. In addition, the method provided by the present invention also designs an objective function for constraining the iterative process of the genetic algorithm based on the wideband requirements of the gradient structure ultra-wideband antenna, so that the final optimization result can be closer to the optimization target and meet the wideband performance requirements.

[0075] The embodiment of the present invention further provides a system 200 for optimizing parameters of a gradient-structured ultra-wideband antenna. Please refer to FIG5 , which is a schematic structural diagram of the system for optimizing parameters of a gradient-structured ultra-wideband antenna provided by an embodiment of the present invention, which includes:

[0076] Initialization module 201 is used to use the structural parameters of the gradient structure ultra-wideband antenna as a single individual of the genetic algorithm, and initialize a population including a plurality of the individuals used by the genetic algorithm to obtain an initial population;

[0077] A simulation calculation module 202 is configured to perform simulation calculation on the structural parameters corresponding to each individual in the initial population using a preset simulation tool to obtain S parameters corresponding to the structural parameters;

[0078] A parameter calculation module 203 is used to calculate the fitness of the initial population according to the S parameter;

[0079] Iterative optimization module 204, configured to perform iterative optimization calculation using the initial population and the fitness as parameters of a genetic algorithm until the fitness satisfies a preset optimization condition;

[0080] The output module 205 is configured to output the structural parameters corresponding to the fitness as an optimization result.

[0081] The initial population is defined as x, the fitness is defined as F(x), and the initial population x satisfies the following relationship (1):

[0082] x=[x1,x2,x3,…,xN] (1);

[0083] Wherein, N is the number of individuals, [x1, x2, x3, ..., xN] represents the set of individuals in the initial population;

[0084] The fitness F(x) satisfies the following relationship (2):

[0085] Among them, min freq and max freq represent the optimized minimum frequency and maximum frequency respectively, S 11 (i) represents the S parameter corresponding to the structural parameter when the frequency is i, and Rst. represents the preset optimization target value.

[0086] The gradient structure ultra-wideband antenna parameter optimization system 200 can implement the steps in the gradient structure ultra-wideband antenna parameter optimization method in the above embodiment and can achieve the same technical effects. Please refer to the description in the above embodiment and will not be repeated here.

[0087] An embodiment of the present invention further provides a computer device. Please refer to Figure 6, which is a structural diagram of the computer device provided by an embodiment of the present invention. The computer device 300 includes: a memory 302, a processor 301, and a computer program stored in the memory 302 and executable on the processor 301.

[0088] The processor 301 calls the computer program stored in the memory 302 to execute the steps of the method for optimizing parameters of a gradient structure ultra-wideband antenna provided by an embodiment of the present invention. Referring to FIG1 , the method specifically includes the following steps:

[0089] S101 , using structural parameters of a gradient-structure ultra-wideband antenna as a single individual of a genetic algorithm, and initializing a population including a plurality of the individuals used by the genetic algorithm to obtain an initial population.

[0090] The structural parameters include at least one of the longest structural distance of the gradient structure ultra-wideband antenna, the angle between adjacent rectangular folded sheets, the length of the short side of the rectangular folded sheet, and the length of the wide side of the rectangular folded sheet.

[0091] S102: Perform simulation calculation on the structural parameters corresponding to each individual in the initial population using a preset simulation tool to obtain S parameters corresponding to the structural parameters.

[0092] The preset simulation tool is the CST simulation tool.

[0093] S103. Calculate the fitness of the initial population according to the S parameter.

[0094] Define the initial population as x, the fitness as F(x), and the initial population x satisfies the following relationship (1):

[0095] x=[x1,x2,x3,…,xN] (1);

[0096] Wherein, N is the number of individuals, [x1, x2, x3, ..., xN] represents the set of individuals in the initial population;

[0097] The fitness F(x) satisfies the following relationship (2):

[0098] Among them, min freq and max freq represent the optimized minimum frequency and maximum frequency respectively, S 11 (i) represents the S parameter corresponding to the structural parameter when the frequency is i, and Rst. represents the preset optimization target value.

[0099] S104: performing iterative optimization calculations using the initial population and the fitness as parameters of a genetic algorithm until the fitness satisfies a preset optimization condition.

[0100] Step S104 includes the following sub-steps:

[0101] S1041, using the initial population and the fitness as initial parameters, inputting into a genetic algorithm for calculation;

[0102] S1042: Determine whether the current fitness or the number of iterative optimization calculations of the genetic algorithm meets the preset optimization condition. If so, stop the iterative optimization calculation process of the genetic algorithm and save the structural parameters corresponding to the current fitness. If not:

[0103] S1043. Perform selection, crossover, and mutation processing on the individuals in the initial population to obtain a new generation population including a new generation of individuals;

[0104] S1044. Use the preset simulation tool to calculate the S parameters corresponding to the structural parameters of the new generation individuals, and calculate the fitness of the new generation population, and return to step S1042.

[0105] The preset optimization conditions are:

[0106] The number of iterative optimization calculations of the genetic algorithm reaches the preset number of optimizations;

[0107] or:

[0108] The fitness value after iterative optimization calculation of the genetic algorithm remains unchanged within a preset number of iterations.

[0109] S105: Output the structural parameters corresponding to the fitness as an optimization result.

[0110] The computer device 300 provided in the embodiment of the present invention can implement the steps in the method for optimizing the parameters of the gradient structure ultra-wideband antenna in the above embodiment and can achieve the same technical effects. Please refer to the description in the above embodiment and will not be repeated here.

[0111] An embodiment of the present invention further provides a computer-readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, the various processes and steps in the method for optimizing the parameters of a gradient structure ultra-wideband antenna provided in an embodiment of the present invention are implemented, and the same technical effects can be achieved. To avoid repetition, they will not be described here.

[0112] Those skilled in the art will appreciate that all or part of the processes in the above-described method embodiments can be implemented by instructing related hardware through a computer program. The program can be stored in a computer-readable storage medium, and when executed, the program can include the processes in the above-described method embodiments. The storage medium can be a magnetic disk, an optical disk, a read-only memory (ROM), or a random access memory (RAM).

[0113] It should be noted that, in this document, the terms "comprises," "includes," or any other variations thereof are intended to encompass non-exclusive inclusion, such that a process, method, article, or apparatus comprising a series of elements includes not only those elements but also other elements not explicitly listed, or elements inherent to such process, method, article, or apparatus. In the absence of further limitations, an element defined by the phrase "comprising a ..." does not exclude the presence of other identical elements in the process, method, article, or apparatus comprising the element.

[0114] Through the description of the above embodiments, those skilled in the art can clearly understand that the above-mentioned embodiment methods can be implemented by means of software plus the necessary general hardware platform, and of course can also be implemented by hardware, but in many cases the former is a better embodiment. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, can be embodied in the form of a software product, which is stored in a storage medium (such as ROM / RAM, magnetic disk, optical disk), and includes a number of instructions for enabling a terminal (which can be a mobile phone, computer, server, air conditioner, or network device, etc.) to execute the methods described in each embodiment of the present invention.

[0115] The embodiments of the present invention are described above in conjunction with the accompanying drawings. What is disclosed is only a preferred embodiment of the present invention. However, the present invention is not limited to the above-mentioned specific implementation methods. The above-mentioned specific implementation methods are merely illustrative and not restrictive. Under the guidance of the present invention, ordinary technicians in this field can also make many forms and equivalent changes without departing from the scope of protection of the purpose of the present invention and the claims, which are all within the protection of the present invention.

Claims

1. A method for optimizing parameters of a gradient structure ultra-wideband antenna, characterized in that: The optimization method comprises the following steps: S101, using the structural parameters of the gradient structure ultra-wideband antenna as a single individual of the genetic algorithm, and initializing a population including a plurality of the individuals used by the genetic algorithm to obtain an initial population; S102, performing simulation calculation on the structural parameter corresponding to each individual in the initial population using a preset simulation tool to obtain an S parameter corresponding to the structural parameter; S103, calculating the fitness of the initial population according to the S parameter; S104, performing iterative optimization calculation using the initial population and the fitness as parameters of the genetic algorithm until the fitness meets a preset optimization condition; S105: Output the structural parameters corresponding to the fitness as optimization results.

2. The method for optimizing parameters of a gradient structure ultra-wideband antenna according to claim 1, characterized in that: Define the initial population as x, the fitness as F(x), and the initial population x satisfies the following relationship (1): x = [x1, x2, x3, ..., xN] (1); Wherein, N is the number of individuals, and [x1, x2, x3, ..., xN] represents the set of individuals in the initial population; The fitness F(x) satisfies the following relation (2): Among them, min freq and max freq represent the optimized minimum frequency and maximum frequency respectively, S 11 (i) represents the S parameter corresponding to the structural parameter when the frequency is i, and Rst. represents the preset optimization target value.

3. The method for optimizing parameters of a gradient structure ultra-wideband antenna according to claim 1, characterized in that: The preset optimization conditions are: The number of iterative optimization calculations of the genetic algorithm reaches the preset number of optimizations; or: The fitness value after iterative optimization calculation of the genetic algorithm remains unchanged within a preset number of iterations.

4. The method for optimizing parameters of a gradient structure ultra-wideband antenna according to claim 1, characterized in that: The structural parameters include at least one of the longest structural distance of the gradient structure ultra-wideband antenna, the angle between adjacent rectangular folded sheets, the length of the short side of the rectangular folded sheet, and the length of the wide side of the rectangular folded sheet.

5. The method for optimizing parameters of a gradient structure ultra-wideband antenna according to claim 1, characterized in that: The preset simulation tool is the CST simulation tool.

6. The method for optimizing parameters of a gradient structure ultra-wideband antenna according to claim 3, characterized in that: Step S104 includes the following sub-steps: S1041, using the initial population and the fitness as initial parameters, inputting into a genetic algorithm for calculation; S1042, determining whether the current fitness or the number of iterative optimization calculations of the genetic algorithm meets the preset optimization condition, and if so, stopping the iterative optimization calculation process of the genetic algorithm, and saving the structural parameters corresponding to the current fitness; If not: S1043, performing selection, crossover, and mutation processing on the individuals in the initial population to obtain a new generation population including a new generation of individuals; S1044. Use the preset simulation tool to calculate the S parameters corresponding to the structural parameters of the new generation individuals, and calculate the fitness of the new generation population, and return to step S1042.

7. A system for optimizing parameters of ultra-wideband antenna with a gradient structure, characterized in that: include: An initialization module is used to use the structural parameters of the gradient structure ultra-wideband antenna as a single individual of the genetic algorithm, and initialize a population including a plurality of the individuals used by the genetic algorithm to obtain an initial population; A simulation calculation module, used to perform simulation calculation on the structural parameters corresponding to each individual in the initial population using a preset simulation tool to obtain S parameters corresponding to the structural parameters; A parameter calculation module, used for calculating the fitness of the initial population according to the S parameter; An iterative optimization module, used to perform iterative optimization calculation using the initial population and the fitness as parameters of the genetic algorithm until the fitness meets a preset optimization condition; The output module is used to output the structural parameters corresponding to the fitness as optimization results.

8. The system for optimizing parameters of a gradient structure ultra-wideband antenna according to claim 7, characterized in that: Define the initial population as x, the fitness as F(x), and the initial population x satisfies the following relationship (1): x = [x1, x2, x3, ..., xN] (1); Wherein, N is the number of individuals, and [x1, x2, x3, ..., xN] represents the set of individuals in the initial population; The fitness F(x) satisfies the following relation (2): Among them, min freq and max freq represent the minimum and maximum frequencies of optimization, respectively. 11 (i) represents the S parameter corresponding to the structural parameter when the frequency is i, and Rst. represents the preset optimization target value.

9. A computer device, characterized in that: include: A memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the computer program, the steps in the method for optimizing parameters of a gradient structure ultra-wideband antenna as claimed in any one of claims 1 to 6 are implemented.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the steps in the method for optimizing parameters of a gradient structure ultra-wideband antenna as claimed in any one of claims 1 to 6 are implemented.

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