Optimization method and device for microstrip converter and interface, and storage medium

Abstract

The invention provides an optimization method and system for a micro-strip converter and an interface and related equipment. The method comprises the following steps: acquiring structural parameters of the micro-strip converter and the interface connected with the micro-strip converter; setting an optimization target according to the structure parameters; according to the structure parameters, an optimization method based on an evolutionary strategy algorithm is used for iterative optimization, the structure parameters meeting the optimization target are output to serve as optimization parameters, a roulette selection method is used for selecting population individuals used for iteration, and the optimization parameters meet the optimization target. Random disturbance is carried out on the structure parameters corresponding to the individuals through a Gaussian variation processing method; and optimizing the microstrip converter and the interface connected with the microstrip converter according to the optimization parameters to obtain an optimized microstrip converter and an optimized interface. According to the invention, the design period is accelerated, the efficiency of the design process is improved, and the optimized microstrip converter and interface have better radio frequency performance.

Description

Technical Field

[0001] The present invention is applicable to the field of radio frequency optimization technology, and in particular relates to an optimization method, device and storage medium for microstrip converters and interfaces. Background Art

[0002] Coaxial-microstrip converters are extremely important passive devices in radar system equipment, guidance systems and microwave test systems. They are used to realize signal conversion between coaxial cables and microstrips to meet the needs of different microwave systems. SMA (Sub Miniature version A) connector is a commonly used RF coaxial converter with a small size and 50Ω impedance characteristics. The inner diameter of the outer conductor of the standard SMA connector is 4.13mm, and the outer diameter of the inner conductor is 1.27mm. SMA connectors have been widely used in the microwave frequency range due to their small size and good electrical properties. In the frequency band exceeding the operating frequency of the SMA connector, high-order modes may be generated inside the coaxial connector, resulting in resonance spikes and affecting the performance of the adapter. In order to overcome these problems, it is necessary to improve the converter structure, such as adding a transition stage between the SMA connector and the microstrip to suppress the resonance spikes generated by the high-order modes. Using SMA connectors instead of other connectors with superior performance but high prices can effectively reduce production costs.

[0003] In the process of designing SMA connector and microstrip interface converter, the existing design methods usually rely on experience and known physical principles. In some cases, the existing methods may not require complex computing resources and may be more cost-effective. For parameters and designs that have been fully understood and tested, traditional methods can provide stable performance. However, the defects of this design method are also very prominent. The accumulation of experience comes from multiple iterations and experiments to optimize the design, which may lead to a long design cycle, making it difficult for existing methods to find the global optimal solution. In complex multi-parameter optimization problems, the existing methods rely heavily on the designer's experience and intuition, which may lead to poor consistency and repeatability of the design. Summary of the invention

[0004] The present invention provides an optimization method, device and storage medium for a microstrip converter and an interface, aiming to solve the problems of difficulty in optimization and consistency caused by the existing design method relying on experience.

[0005] In order to solve the above technical problems, in a first aspect, the present invention provides an optimization method for a microstrip converter and an interface, comprising the following steps: S101, obtaining structural parameters of a microstrip converter and an interface connected thereto; S102, setting an optimization target according to the structural parameters; S103, performing iterative optimization according to the structural parameters using an optimization method based on an evolutionary strategy algorithm, and outputting the structural parameters that meet the optimization objectives as optimization parameters, wherein the optimization method based on the evolutionary strategy algorithm uses a roulette wheel selection method to select individuals of the population for iteration, and performs random perturbations on the structural parameters corresponding to the individuals through a Gaussian mutation processing method; S104, optimizing the microstrip converter and the interface connected thereto according to the optimization parameters to obtain an optimized microstrip converter and an optimized interface.

[0006] Furthermore, the structural parameters include the access point position of the microstrip converter, the radius of curvature of the arc gap, the stripline length, the stripline width, the straight gap width, and at least one of the connector inner core diameter and the connector inner core outer diameter of the interface.

[0007] Furthermore, the step of setting the optimization target according to the structural parameters specifically includes: obtaining the overall RF parameters of the microstrip converter and the interface according to the structural parameters, the overall RF parameters including at least one of a port reflection coefficient and a port transmission coefficient, and determining the data interval of the optimization target according to the overall RF parameters.

[0008] Furthermore, the optimization method based on the evolution strategy algorithm is specifically as follows: S1031, performing iterative optimization using the structural parameters as the initial population; S1032, performing electromagnetic simulation calculation according to the structural parameters of the population in the current iteration to obtain an adaptation function value; S1033, and determine whether the fitness function value meets the optimization goal: if so, end the iteration and output the structural parameters corresponding to the current population as the optimization parameters; if not, execute S1034; S1034. Randomly select the structural parameters as the parent from the current population, perform crossover, mutation and offspring selection according to the evolution strategy algorithm to obtain a new population, and return to step S1032.

[0009] Furthermore, step S1034 includes the following sub-steps: S10341. Based on the evolution strategy algorithm, randomly select μ individuals as parents from the current population to generate λ individuals as offspring; S10342. Use the roulette wheel selection method to select n individuals that meet the fitness requirements from the set consisting of μ parents and λ children to form a new population; S10343. Perform crossover mutation on individuals in the new population, wherein the structural parameters corresponding to the individuals are randomly perturbed using a Gaussian mutation processing method.

[0010] Furthermore, the Gaussian variation processing method in step S10343 adds a random perturbation that obeys the Gaussian distribution to each of the structural parameters, defining is the i-th structural parameter, is the standard deviation, is a random number sampled from a standard normal distribution that satisfies the following relationship: ; The standard deviation changes according to a preset update parameter as the number of iterations of the optimization method based on the evolution strategy algorithm increases. The preset update parameter is defined as , which satisfies the following relationship: ; in, is the standard deviation at the previous iteration, is the rate of change of the fitness function value between the current iteration and the previous iteration, is the preset standard deviation coefficient.

[0011] Furthermore, the adaptation function value is defined as Fitness, the port reflection coefficient is S11, the port transmission coefficient is S21, and the adaptation function value satisfies the following relationship: ; in, The preset weights.

[0012] In a second aspect, the present invention further provides an optimization device for a microstrip converter and an interface, comprising: A parameterization module, used to obtain the structural parameters of the microstrip converter and the interface connected thereto; A setting module, used for setting an optimization target according to the structural parameters; An iterative module, used for iteratively optimizing the structural parameters using an optimization method based on an evolutionary strategy algorithm, and outputting the structural parameters that meet the optimization objectives as optimization parameters, wherein the optimization method based on the evolutionary strategy algorithm uses a roulette wheel selection method to select individuals of the population for iteration, and randomly perturbing the structural parameters corresponding to the individuals by a Gaussian mutation processing method; The optimization module is used to optimize the microstrip converter and the interface connected thereto according to the optimization parameters to obtain an optimized microstrip converter and an optimized interface.

[0013] In a third aspect, the present invention also provides an optimization device for microstrip converters and interfaces, comprising: 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 processor implements the steps in the optimization method for microstrip converters and interfaces as described in any one of the above embodiments.

[0014] In a fourth aspect, the present invention further provides a storage medium having a computer program stored thereon, wherein the computer program, when executed by a processor, implements the steps in the optimization method for microstrip converters and interfaces as described in any one of the above embodiments.

[0015] The beneficial effect achieved by the present invention is that an optimization method for microstrip converters and interfaces is proposed. The method applies an evolutionary strategy algorithm to the design process of microstrip converters and interfaces to reduce dependence on manual experience, speed up the design cycle, and thus improve the efficiency of the design process; further, based on this method, the present invention can solve the optimal structural parameters of the microstrip converter and interface as a whole, so that the optimized microstrip converter and interface have better radio frequency performance. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] Figure 1 is a flowchart of the steps of an optimization method for a microstrip converter and an interface provided by an embodiment of the present invention; Figure 2 is a schematic structural diagram of a microstrip converter and an interface connected thereto provided by an embodiment of the present invention; Figure 3 is a schematic diagram of top view structural parameters of a microstrip converter and an interface connected thereto provided by an embodiment of the present invention; Figure 4 is a schematic diagram of side structural parameters of a microstrip converter and an interface connected thereto provided by an embodiment of the present invention; Figure 5 It is a schematic diagram comparing the port reflection coefficient results of the method provided by the embodiment of the present invention and the existing method; Figure 6 is a schematic diagram comparing the port transmission coefficient results of the method provided by the embodiment of the present invention and the existing method; Figure 7 is a schematic structural diagram of an optimization device for a microstrip converter and an interface provided by an embodiment of the present invention; Figure 8 It is a structural schematic diagram of another microstrip converter and interface optimization device provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0017] In order to make the purpose, technical solution and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with 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.

[0018] Please refer to Figure 1 , Figure 1 1 is a flowchart of a method for optimizing a microstrip converter and an interface provided by an embodiment of the present invention. The method for optimizing a microstrip converter and an interface comprises the following steps: S101. Obtain structural parameters of a microstrip converter and an interface connected thereto.

[0019] Specifically, the structure of the microstrip converter and the interface connected thereto according to the embodiment of the present invention is as follows: Figure 2 As shown, at this time, the microstrip converter and the interface are taken as a whole, and the embodiment of the present invention analyzes and optimizes the RF performance of the whole. In particular, the embodiment of the present invention performs parametric analysis on the parameters that have the greatest impact on the RF performance in the structure, and uses them as basic parameters for subsequent optimization processes. The access point position Lt of the microstrip converter, the radius of curvature of the arc gap Do / 2, the stripline length Lg, the stripline width Wgi, the straight gap width Wgo, and at least one of the connector inner core diameter Di and the connector inner core outer diameter Do of the interface. As shown Figure 3 and Figure 4 As shown, Figure 3 and Figure 4 The physical positions represented by the above structural parameters are shown from two perspectives: top view and side view.

[0020] S102: Setting optimization targets according to the structural parameters.

[0021] The step of setting the optimization target according to the structural parameters specifically includes: obtaining the overall RF parameters (S parameters) of the microstrip converter and the interface according to the structural parameters, the overall RF parameters including at least one of a port reflection coefficient (S11) and a port transmission coefficient (S21), and determining the data interval of the optimization target according to the overall RF parameters.

[0022] During the implementation process, the optimization interval is set according to the actual use requirements of the microstrip converter and the interface. It should be noted that, according to the optimization of the above structural parameters, the performance change reflected in the microstrip converter is mainly in the capacity transmission efficiency between the input and output interfaces. When in the data interval of the above optimization target, the microstrip converter can show good matching characteristics, that is, lower reflection performance, and at the same time, it also meets lower transmission loss (including dielectric loss, etc.).

[0023] S103. Perform iterative optimization according to the structural parameters using an optimization method based on an evolutionary strategy algorithm (ES), and output the structural parameters that meet the optimization objectives as optimization parameters, wherein the optimization method based on the evolutionary strategy algorithm uses a roulette wheel selection method to select individuals from the population for iteration, and randomly perturbs the structural parameters corresponding to the individuals through a Gaussian mutation processing method.

[0024] The evolution strategy algorithm is a random search optimization algorithm. The embodiment of the present invention constructs an optimization method for optimizing the structural parameters based on the evolution strategy algorithm. The optimization method based on the evolution strategy algorithm is specifically as follows: S1031, performing iterative optimization using the structural parameters as the initial population; S1032, performing electromagnetic simulation calculation according to the structural parameters of the population in the current iteration to obtain an adaptation function value; S1033, and determine whether the fitness function value meets the optimization goal: if so, end the iteration and output the structural parameters corresponding to the current population as the optimization parameters; if not, execute S1034; S1034. Randomly select the structural parameters as the parent from the current population to perform crossover, mutation and offspring selection to obtain a new population, and return to step S1032.

[0025] In the above process, the fitness function value of step S1032 is used as the characteristics of the population individuals to be compared and analyzed with the optimization target in turn.

[0026] According to the characteristics of the evolution strategy algorithm, if the fitness of the population does not improve significantly during the iteration process, but does not reach the numerical range that meets the optimization goal, the initial values ​​and structural parameters can be adjusted and then iterative optimization can be performed according to step S103.

[0027] In the embodiment of the present invention, multiple measures are also provided to optimize the evolution strategy algorithm. Specifically, step S1034 includes the following sub-steps: S10341. Based on the evolution strategy algorithm, randomly select μ individuals as parents from the current population to generate λ individuals as offspring; S10342. Use the roulette wheel selection method to select n individuals that meet the fitness requirements from the set consisting of μ parents and λ children to form a new population; S10343. Perform crossover mutation on individuals in the new population, wherein the structural parameters corresponding to the individuals are randomly perturbed using a Gaussian mutation processing method.

[0028] The offspring selection strategy of the roulette wheel selection method accelerates the convergence speed of the evolution strategy algorithm and maintains sufficient individual diversity.

[0029] Specifically, the Gaussian variation processing method in step S10343 adds a random perturbation that obeys the Gaussian distribution to each of the structural parameters, and defines is the i-th structural parameter, is the standard deviation, is a random number sampled from a standard normal distribution that satisfies the following relationship: ; The standard deviation changes according to a preset update parameter as the number of iterations of the optimization method based on the evolution strategy algorithm increases. The preset update parameter is defined as , which satisfies the following relationship: ; in, is the standard deviation at the previous iteration, is the rate of change of the fitness function value between the current iteration and the previous iteration, is the preset standard deviation coefficient.

[0030] Gaussian mutation processing helps the evolution strategy algorithm to perform local optimization in a smaller range, while adaptively controlling the degree of mutation with the help of the step size adjustment mechanism of the standard deviation.

[0031] During the iterative optimization process, the fitness function value is defined as Fitness, the port reflection coefficient is S11, the port transmission coefficient is S21, and the fitness function value satisfies the following relationship: ; in, It is a preset weight used to control the balance between reflection loss and transmission loss.

[0032] S104, optimizing the microstrip converter and the interface connected thereto according to the optimization parameters to obtain an optimized microstrip converter and an optimized interface.

[0033] For ease of understanding, the embodiment of the present invention performs a performance analysis on the microstrip converter structure before and after optimization based on electromagnetic simulation, wherein the optimization method proposed in the embodiment of the present invention is compared with the existing method based on experience mentioned in the background technology, and the port reflection coefficient (S 11 , S 22 ) and port transmission coefficient (S 21 ) The comparison results are as follows Figure 5 and Figure 6As shown, it can be seen that the optimization method proposed in the embodiment of the present invention achieves better matching of the microstrip converter with port impedances of various impedance values ​​compared to the existing method, improves the performance of the microstrip converter, and ensures its stability and adaptability under different working conditions.

[0034] The beneficial effect achieved by the present invention is that an optimization method for microstrip converters and interfaces is proposed. The method applies an evolutionary strategy algorithm to the design process of microstrip converters and interfaces to reduce dependence on manual experience, speed up the design cycle, and thus improve the efficiency of the design process; further, based on this method, the present invention can solve the optimal structural parameters of the microstrip converter and interface as a whole, so that the optimized microstrip converter and interface have better radio frequency performance.

[0035] The embodiment of the present invention also provides an optimization device 200 for a microstrip converter and an interface, please refer to Figure 7 , Figure 7 1 is a schematic diagram of a structure of an optimization device for a microstrip converter and an interface provided by an embodiment of the present invention. The optimization device 200 for a microstrip converter and an interface includes: Parameterization module 201, used to obtain structural parameters of the microstrip converter and the interface connected thereto; A setting module 202, used to set an optimization target according to the structural parameters; An iterative module 203 is used to perform iterative optimization according to the structural parameters using an optimization method based on an evolutionary strategy algorithm, and output the structural parameters that meet the optimization objectives as optimization parameters, wherein the optimization method based on the evolutionary strategy algorithm uses a roulette wheel selection method to select individuals of the population for iteration, and randomly perturbs the structural parameters corresponding to the individuals through a Gaussian mutation processing method; The optimization module 204 is used to optimize the microstrip converter and the interface connected thereto according to the optimization parameters to obtain an optimized microstrip converter and an optimized interface.

[0036] The microstrip converter and interface optimization device 200 can implement the steps in the microstrip converter and interface 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.

[0037] The embodiment of the present invention also provides another microstrip converter and interface optimization device 300, please refer to Figure 8 , Figure 8 It is a structural schematic diagram of another microstrip converter and interface optimization device provided in an embodiment of the present invention. The microstrip converter and interface optimization device 300 includes: a memory 302, a processor 301, and a computer program stored in the memory 302 and executable on the processor 301.

[0038] The processor 301 calls the computer program stored in the memory 302 to execute the steps of the optimization method for microstrip converters and interfaces provided in the embodiment of the present invention. Figure 1 , specifically including the following steps: S101. Obtain structural parameters of a microstrip converter and an interface connected thereto.

[0039] The structural parameters include the access point position of the microstrip converter, the radius of curvature of the arc gap, the stripline length, the stripline width, the straight gap width, and at least one of the connector inner core diameter and the connector inner core outer diameter of the interface.

[0040] S102: Setting optimization targets according to the structural parameters.

[0041] The step of setting the optimization target according to the structural parameters specifically includes: obtaining the overall RF parameters of the microstrip converter and the interface according to the structural parameters, the overall RF parameters including at least one of a port reflection coefficient and a port transmission coefficient, and determining the data interval of the optimization target according to the overall RF parameters.

[0042] S103. Perform iterative optimization according to the structural parameters using an optimization method based on an evolutionary strategy algorithm, and output the structural parameters that meet the optimization objectives as optimization parameters, wherein the optimization method based on the evolutionary strategy algorithm uses a roulette wheel selection method to select individuals from the population for iteration, and randomly perturbs the structural parameters corresponding to the individuals through a Gaussian mutation processing method.

[0043] The optimization method based on the evolution strategy algorithm is specifically as follows: S1031, performing iterative optimization using the structural parameters as the initial population; S1032, performing electromagnetic simulation calculation according to the structural parameters of the population in the current iteration to obtain an adaptation function value; S1033, and determine whether the fitness function value meets the optimization goal, if so: end the iteration, and output the structural parameters corresponding to the current population as the optimization parameters; if not, execute S1034; S1034. Randomly select the structural parameters as the parent from the current population to perform crossover, mutation and offspring selection to obtain a new population, and return to step S1032.

[0044] Furthermore, step S1034 includes the following sub-steps: S10341. Based on the evolution strategy algorithm, randomly select μ individuals as parents from the current population to generate λ individuals as offspring; S10342. Use the roulette wheel selection method to select n individuals that meet the fitness requirements from the set consisting of μ parents and λ children to form a new population; S10343. Perform crossover mutation on individuals in the new population, wherein the structural parameters corresponding to the individuals are randomly perturbed using a Gaussian mutation processing method.

[0045] Furthermore, the Gaussian variation processing method in step S10343 adds a random perturbation that obeys the Gaussian distribution to each of the structural parameters, defining is the i-th structural parameter, is the standard deviation, is a random number sampled from a standard normal distribution that satisfies the following relationship: ; The standard deviation changes according to a preset update parameter as the number of iterations of the optimization method based on the evolution strategy algorithm increases. The preset update parameter is defined as , which satisfies the following relationship: ; in, is the standard deviation at the previous iteration, is the rate of change of the fitness function value between the current iteration and the previous iteration, is the preset standard deviation coefficient.

[0046] Furthermore, the adaptation function value is defined as Fitness, the port reflection coefficient is S11, the port transmission coefficient is S21, and the adaptation function value satisfies the following relationship: ; in, The preset weights.

[0047] S104, optimizing the microstrip converter and the interface connected thereto according to the optimization parameters to obtain an optimized microstrip converter and an optimized interface.

[0048] The microstrip converter and interface optimization device 300 provided in the embodiment of the present invention can implement the steps in the optimization method for microstrip converters and interfaces 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.

[0049] An embodiment of the present invention further provides a storage medium having a computer program stored thereon. When the computer program is executed by a processor, the various processes and steps in the optimization method for microstrip converters and interfaces provided in the embodiment of the present invention are implemented, and the same technical effect can be achieved. To avoid repetition, it will not be repeated here.

[0050] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiments can be implemented by instructing related hardware (which can be a mobile phone, a computer, a server, an air conditioner, or a network device, etc.) through a computer program, and the program can be stored in a computer-readable storage medium, and when the program is executed, it can include the processes of the embodiments of the above-mentioned methods. The storage medium can be a disk, an optical disk, a read-only memory (ROM) or a random access memory (RAM), etc.

[0051] It should be noted that, in this article, the terms "include", "comprises" or any other variations thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device. In the absence of further restrictions, an element defined by the sentence "comprises a ..." does not exclude the existence of other identical elements in the process, method, article or device including the element.

[0052] The embodiments of the present invention are described above in conjunction with the accompanying drawings. What is disclosed is only the preferred embodiment of the present invention. However, the present invention is not limited to the above-mentioned specific implementation manner. The above-mentioned specific implementation manner is only illustrative rather than restrictive. Under the enlightenment 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, all of which are within the protection of the present invention.

Claims

1. A method for optimizing microstrip converters and interfaces, characterized in that: The following steps are involved: S101, obtaining structural parameters of a microstrip converter and an interface connected thereto; S102, setting an optimization target according to the structural parameters; S103, performing iterative optimization according to the structural parameters using an optimization method based on an evolutionary strategy algorithm, and outputting the structural parameters that meet the optimization objectives as optimization parameters, wherein the optimization method based on the evolutionary strategy algorithm uses a roulette wheel selection method to select individuals of the population for iteration, and performs random perturbations on the structural parameters corresponding to the individuals through a Gaussian mutation processing method; S104, optimizing the microstrip converter and the interface connected thereto according to the optimization parameters to obtain an optimized microstrip converter and an optimized interface.

2. The optimization method for microstrip converters and interfaces according to claim 1, characterized in that: The structural parameters include the access point position of the microstrip converter, the radius of curvature of the arc gap, the stripline length, the stripline width, the straight gap width, and at least one of the connector inner core diameter and the connector inner core outer diameter of the interface.

3. The optimization method for microstrip converters and interfaces according to claim 1, characterized in that: The step of setting the optimization target according to the structural parameters specifically includes: obtaining the overall RF parameters of the microstrip converter and the interface according to the structural parameters, the overall RF parameters including at least one of a port reflection coefficient and a port transmission coefficient, and determining the data interval of the optimization target according to the overall RF parameters.

4. The optimization method for microstrip converters and interfaces according to claim 3, characterized in that: The optimization method based on the evolution strategy algorithm is specifically as follows: S1031, performing iterative optimization using the structural parameters as the initial population; S1032, performing electromagnetic simulation calculation according to the structural parameters of the population in the current iteration to obtain an adaptation function value; S1033, and determine whether the fitness function value meets the optimization goal: if so, end the iteration and output the structural parameters corresponding to the current population as the optimization parameters; if not, execute S1034; S1034. Randomly select the structural parameters as the parent from the current population, perform crossover, mutation and offspring selection according to the evolution strategy algorithm to obtain a new population, and return to step S1032.

5. The optimization method for microstrip converters and interfaces according to claim 4, characterized in that: Step S1034 includes the following sub-steps: S10341. Based on the evolution strategy algorithm, randomly select μ individuals as parents from the current population to generate λ individuals as offspring; S10342. Use the roulette wheel selection method to select n individuals that meet the fitness requirements from the set consisting of μ parents and λ children to form a new population; S10343. Perform crossover mutation on individuals in the new population, wherein the structural parameters corresponding to the individuals are randomly perturbed using a Gaussian mutation processing method.

6. The optimization method for microstrip converters and interfaces according to claim 5, characterized in that: The Gaussian variation processing method in step S10343 adds a random perturbation that obeys the Gaussian distribution to each of the structural parameters, and defines is the i-th structural parameter, is the standard deviation, is a random number sampled from a standard normal distribution that satisfies the following relationship: ； The standard deviation changes according to a preset update parameter as the number of iterations of the optimization method based on the evolution strategy algorithm increases. The preset update parameter is defined as , which satisfies the following relationship: ； in, is the standard deviation at the previous iteration, is the rate of change of the fitness function value between the current iteration and the previous iteration, is the preset standard deviation coefficient.

7. The optimization method for microstrip converters and interfaces according to claim 4, characterized in that: Define the fitness function value as Fitness, the port reflection coefficient as S11, the port transmission coefficient as S21, and the fitness function value satisfies the following relationship: ； in, The preset weights.

8. An optimization device for microstrip converters and interfaces, characterized in that: include: A parameterization module, used to obtain the structural parameters of the microstrip converter and the interface connected thereto; A setting module, used for setting an optimization target according to the structural parameters; An iterative module, used for iteratively optimizing the structural parameters using an optimization method based on an evolutionary strategy algorithm, and outputting the structural parameters that meet the optimization objectives as optimization parameters, wherein the optimization method based on the evolutionary strategy algorithm uses a roulette wheel selection method to select individuals of the population for iteration, and randomly perturbing the structural parameters corresponding to the individuals by a Gaussian mutation processing method; The optimization module is used to optimize the microstrip converter and the interface connected thereto according to the optimization parameters to obtain an optimized microstrip converter and an optimized interface.

9. An optimization device for microstrip converters and interfaces, 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 optimization method for microstrip converters and interfaces as claimed in any one of claims 1 to 7 are implemented.

10. A storage medium, characterized in that: The storage medium stores a computer program, and when the computer program is executed by the processor, the steps in the optimization method for microstrip converters and interfaces as described in any one of claims 1 to 7 are implemented.

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