An antenna size optimization method, device and terminal equipment
By combining a multilayer perceptron model and a genetic algorithm, and utilizing polynomial curve fitting and a penalty function, the problem of low efficiency in the joint optimization of multiple parameters of metasurface circularly polarized antennas was solved, and efficient and accurate antenna size optimization was achieved.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Patents(China)
- Current Assignee / Owner
- HEFEI NORMAL UNIV
- Filing Date
- 2025-09-16
- Publication Date
- 2026-06-09
AI Technical Summary
In the existing technology, the multi-parameter joint optimization of metasurface circularly polarized antennas is inefficient, consumes a lot of computing resources and time, and is difficult to quickly process the complex nonlinear relationships between multiple parameters to obtain the optimal size combination.
A method combining a multilayer perceptron (MLP) model and a genetic algorithm is adopted. The initial antenna size parameters are screened and optimized through a pre-set antenna size optimization model. Polynomial curve fitting is used to capture the nonlinear relationship between electromagnetic performance and size parameters. The parameter thresholds are forced by a penalty function to improve optimization efficiency.
While ensuring optimization accuracy, it significantly reduces the overall optimization time, substantially improves the efficiency of multi-parameter optimization of metasurface circularly polarized antennas, and enhances the effectiveness and accuracy of antenna performance optimization.
Smart Images
Figure CN121189159B_ABST
Abstract
Description
Technical Field
[0001] This application belongs to the field of wireless communication technology, and in particular relates to antenna size optimization methods, apparatus and terminal equipment. Background Technology
[0002] Metasurfaces, as an emerging technology, are being applied in the field of antennas. By utilizing the artificial structural arrangement of electromagnetic metasurfaces, the characteristics of radiated electromagnetic waves can be flexibly adjusted. For multi-layered metasurface antennas, the polarization and radiation pattern characteristics of the antenna are often reconstructed by rotating the relative position of the metasurface layers with respect to the antenna below.
[0003] In existing technologies, full-wave simulation software, such as HFSS, is commonly used for the design and optimization of multi-layer metasurface antennas. This involves manually constructing a three-dimensional model of the antenna, setting dimensional parameters, and then using simulation software to simulate the antenna's electromagnetic performance, obtaining performance indicators such as impedance, axial ratio, and gain. For multi-parameter joint optimization, parameter scanning is typically performed in the simulation software, continuously adjusting dimensional parameters to find the optimal combination.
[0004] However, when using full-wave simulation software to perform multi-parameter joint optimization of metasurface antennas, the parameter scanning process consumes a lot of computational resources and time, and the joint simulation often takes several hours, making it difficult to complete efficiently. For multi-layer metasurface antennas, there are many dimensional parameters involved, and there are complex nonlinear relationships between the parameters. Traditional optimization methods are inefficient in handling multi-parameter joint optimization and cannot quickly obtain the optimal combination of dimensions that meets the performance requirements. Summary of the Invention
[0005] In view of this, embodiments of this application provide an antenna size optimization method, apparatus, and terminal device, aiming to solve the problems of low efficiency, high computational resource and time consumption, and difficulty in quickly processing complex nonlinear relationships between multiple parameters to obtain the optimal size combination when performing multi-parameter joint optimization of metasurface circularly polarized antennas in the prior art.
[0006] The first aspect of this application provides an antenna size optimization method, including:
[0007] Obtain multiple initial antenna size parameter information;
[0008] Based on the multiple initial antenna size parameters and the preset antenna performance characterization parameter calculation model, multiple antenna performance characterization parameters are calculated.
[0009] Based on a preset antenna size optimization model, the initial antenna size parameters are filtered and optimized according to the multiple antenna performance characterization parameters to obtain multiple target antenna size parameters.
[0010] A second aspect of this application provides an antenna size optimization apparatus, comprising:
[0011] The initial antenna size parameter information acquisition module is used to acquire multiple initial antenna size parameter information.
[0012] The antenna performance characterization parameter information calculation module is used to calculate multiple antenna performance characterization parameter information based on the multiple initial antenna size parameter information and the preset antenna performance characterization parameter calculation model.
[0013] The target antenna size parameter information generation module is used to filter and optimize the multiple initial antenna size parameter information based on a preset antenna size optimization model and the multiple antenna performance characterization parameter information to obtain multiple target antenna size parameter information.
[0014] A third aspect of this application provides a terminal device, the terminal device including a memory and a processor, the memory storing a computer program executable on the processor, the processor executing the computer program to implement the steps of the antenna size optimization method described in the first aspect above.
[0015] A fourth aspect of this application provides a computer-readable storage medium, comprising: storing a computer program, which, when executed by a processor, implements the steps of the antenna size optimization method described in the first aspect above.
[0016] The beneficial effects of this application embodiment compared with the prior art are as follows: This application is used to solve the problem of low efficiency of multi-parameter joint optimization of metasurface circularly polarized antennas. Compared with the traditional optimization method using commercial electromagnetic simulation software in the prior art, it significantly reduces the overall optimization time while ensuring optimization accuracy, and significantly improves the efficiency of multi-parameter optimization of metasurface circularly polarized antennas. By exploring and identifying potential solutions for various antenna sizes through a preset antenna size optimization model, the effectiveness of antenna performance optimization is improved. Attached Figure Description
[0017] To more clearly illustrate the technical solutions in the embodiments of this application, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.
[0018] Figure 1 This is a schematic diagram illustrating the implementation process of the antenna size optimization method provided in Embodiment 1 of this application;
[0019] Figure 2This is a schematic diagram illustrating the implementation process of the antenna size optimization method provided in Embodiment 2 of this application;
[0020] Figure 3 This is a schematic diagram illustrating the implementation process of the antenna size optimization method provided in Embodiment 3 of this application;
[0021] Figure 4 This is a schematic diagram illustrating the implementation process of the antenna size optimization method provided in Embodiment 4 of this application;
[0022] Figure 5 This is a schematic diagram illustrating the implementation process of the antenna size optimization method provided in Embodiment 5 of this application;
[0023] Figure 6 This is a schematic diagram illustrating the implementation process of the antenna size optimization method provided in Embodiment Six of this application;
[0024] Figure 7 This is a schematic diagram of the antenna size optimization device provided in the embodiments of this application;
[0025] Figure 8 This is a schematic diagram of the terminal device provided in the embodiments of this application.
[0026] Figure 9 This is a schematic diagram of the slot parameters of the antenna provided in the embodiments of this application. Detailed Implementation
[0027] In the following description, specific details such as particular system architectures and techniques are set forth for illustrative purposes and not for limitation, in order to provide a thorough understanding of the embodiments of this application. However, those skilled in the art will understand that this application may also be implemented in other embodiments without these specific details. In other instances, detailed descriptions of well-known systems, apparatuses, circuits, and methods have been omitted so as not to obscure the description of this application with unnecessary detail.
[0028] To illustrate the technical solution described in this application, specific embodiments are provided below.
[0029] Figure 1 A flowchart illustrating the implementation of the antenna size optimization method provided in Embodiment 1 of this application is shown, and is described in detail below:
[0030] Step S101: Obtain multiple initial antenna size parameter information.
[0031] In this embodiment, the initial antenna size parameter information can refer to the specific values of nine key size parameters that have a significant impact on the antenna's electromagnetic performance (such as impedance, axial ratio, and gain) in the four-layer structure of the metasurface circularly polarized antenna (metasurface layer, radiating patch layer, feed network layer, and reflector layer). These parameters include: the side length of the metasurface layer (Wc), the chamfer size of the metasurface layer (a), the side length of the radiating patch layer (W2), the length of the feed wire extension gap of the feed network layer (Ta), the gap parameters between the four layers (Da, La, Wa, Sa), and the distance from the radiating patch layer to the feed network layer (H2). Initial antenna size parameters can be obtained by manually building a 3D model of the antenna in the CST simulator. The simulation history tree records the VBA scripts for each modeling step. Based on the VBA scripts in CST, code is written in Matlab. Through Matlab-CST-API co-simulation, the antenna model can be automatically built, parameters modified, and electromagnetic performance parameters automatically exported. Orthogonal experimental tables are designed for nine key size parameters to explore the impact of multiple parameters and their interactions on antenna performance with the fewest number of experiments, thereby determining the value range of each parameter. Finally, multiple sets of initial size parameter combinations covering the parameter range are obtained to form a dataset. The value ranges for each parameter are as follows: Ta ranges from 2.6 to 4 mm, W2 from 29.55 to 30.95 mm, H2 from 7.4 to 8.45 mm, Wc from 36.4 to 37.45 mm, a from 21.4 to 22.45 mm, Da from 0.25 to 1.3 mm, La from 13.75 to 16.375 mm, Wa from 0.55 to 1.6 mm, and Sa from 5.3 to 6.7 mm. By setting specific step sizes, parameters within specific value ranges can be truncated to obtain multiple values within those ranges to construct a dataset.
[0032] Step S102: Based on the multiple initial antenna size parameter information and the preset antenna performance characterization parameter calculation model, multiple antenna performance characterization parameter information are calculated.
[0033] In this embodiment, the preset antenna performance characterization parameter calculation model can be a trained MLP model. The trained MLP model can adopt a three-layer network structure, with the number of input layer neurons being consistent with the number of initial antenna size parameters, and the number of hidden layer neurons being determined according to the Hecht-Nelson method as 2n+1 (n is the number of input layer neurons). The activation function is the Sigmoid function, and it has been trained on a dataset obtained through Matlab-CST-API co-simulation, which can learn the nonlinear relationship between the nine size parameters and electromagnetic performance parameters. The initial antenna size parameters can be processed first: the side length Wc of the metasurface layer, the chamfer size a, the side length W2 of the radiating patch layer, the feed wire extension gap length Ta of the feed network layer, the gap parameters Da, La, Wa, Sa, and the distance H2 from the radiating patch layer to the feed network layer. These parameters are then converted to data with a mean of 0 and a standard deviation of 1 through Z-Score standardization to eliminate dimensional differences between the features. The processed initial antenna size parameters are then input into a preset antenna performance characterization parameter calculation model, namely the multilayer perceptron (MLP) model. The MLP model outputs multiple antenna performance characterization parameters such as reflection coefficient, axial ratio, and gain. The amplitude of the S-parameters derived from CST, in dB, is converted into the reflection coefficient using a formula and mapped to the 0 to 1 interval. The axial ratio and gain are directly output by the MLP model.
[0034] In this embodiment, the antenna performance characterization parameters include antenna impedance bandwidth, antenna axial ratio bandwidth, and antenna gain. Antenna impedance bandwidth refers to the frequency range within which the antenna's impedance characteristics meet design requirements, typically defined as the frequency range where the S-parameter (reflection coefficient) is less than -10dB. Antenna axial ratio bandwidth refers to the frequency range where the axial ratio parameter is less than 3dB when the antenna radiates a circularly polarized wave, represented by the frequency difference between the intersection of the axial ratio curve and the 3dB line, used to measure the purity of circular polarization. Antenna gain refers to the antenna's ability to concentrate and radiate input power, measured in dBi, and can reflect the strength of the radiated signal. In this application, the optimization target is a gain of not less than 13dBi. Antenna impedance bandwidth, axial ratio bandwidth, and antenna gain information can all be calculated by constructing a 3D antenna model using the CST simulator. Alternatively, the MLP model can be trained using a dataset obtained from Matlab-CST-API co-simulation. After the model learns the nonlinear relationship between nine size parameters and electromagnetic performance parameters, the S-parameters, axial ratio, and gain data can be predicted by inputting the initial antenna size parameters. Alternatively, polynomial curve fitting can be performed on the S-parameters and axial ratio data predicted by the MLP to find the intersection points with -10dB (impedance) and 3dB (axial ratio), respectively, and the impedance bandwidth and axial ratio bandwidth can be calculated from the intersection frequency values. Gain information can be directly output from the MLP model or extracted from the simulation data.
[0035] Step S103: Based on the preset antenna size optimization model, the multiple initial antenna size parameter information is filtered and optimized according to the multiple antenna performance characterization parameter information to obtain multiple target antenna size parameter information.
[0036] In this embodiment, the preset antenna size optimization model can be designed based on a genetic algorithm. It can begin by initializing a population, selecting 200 individuals from the population each generation, and then selecting 10 parent individuals from these. The selection strategy can be a roulette wheel selection, where the selection probability is proportional to the individual's fitness. Crossover and mutation operations are then performed. The crossover probability can be set to 0.8. The mutation operation uses the mutation Gaussian function, randomly selecting a number from a Gaussian distribution with a mean of 0 and a standard deviation of 1 and adding it to each gene of the parent individuals. The mutation amplitude gradually decreases to 0 as evolution progresses. In each round of optimization, fitness is calculated based on antenna performance characterization parameters. The fitness function is fitness = a1*w1 + b1*w2 + c1*w3 - penalty (where a1, b1, and c1 are impedance bandwidth, axial ratio bandwidth, and gain, respectively; w1, w2, and w3 are weighting coefficients, which can be manually set; and penalty is the penalty function, i.e., penalty = λ1*max(0, a−a1). 2+λ2*max(0, b−b1) 2 +λ3*max(0, c-c1) 2 λ1, λ2, and λ3 are penalty coefficients (which can be set manually). Individuals that do not meet the set conditions (impedance bandwidth ≥ 11%, axial ratio bandwidth ≥ 0.55, gain ≥ 13dBi) have their fitness reduced. The algorithm iterates for 1000 generations until the conditions are met, and then stops. The final output of the individual's size parameters is the size parameter information of multiple target antennas.
[0037] The antenna size optimization method provided in this application is used to solve the problem of low efficiency in the joint optimization of multiple parameters of metasurface circularly polarized antennas. Compared with the traditional optimization method using commercial electromagnetic simulation software in the prior art, it significantly reduces the overall optimization time while ensuring optimization accuracy, and significantly improves the efficiency of multi-parameter optimization of metasurface circularly polarized antennas. By exploring and identifying potential solutions for various antenna sizes through a preset antenna size optimization model, the effectiveness of antenna performance optimization is improved.
[0038] Figure 2 The flowchart illustrating the antenna size optimization method provided in Embodiment 2 of this application is shown. The difference between this method and Embodiment 1 is that step S103 specifically includes:
[0039] Step S201: Based on a preset polynomial fitting coordinate system, and according to the antenna impedance bandwidth information, the preset antenna impedance bandwidth reference line, the preset antenna center frequency, the preset antenna impedance bandwidth optimization calibration threshold, and the preset antenna impedance bandwidth optimization weight information, the antenna impedance bandwidth optimization characterization variables are calculated.
[0040] In this embodiment, the preset polynomial fitting coordinate system can be manually set, with frequency as the horizontal axis and S-parameters (reflection coefficient) as the vertical axis. The preset antenna impedance bandwidth reference line is y=-10dB. The preset antenna center frequency can be manually set, determined by the antenna hardware, and can be 3.5GHz. The preset antenna impedance bandwidth optimization calibration threshold is 11%, and the preset antenna impedance bandwidth optimization weight information can be manually set, such as w1=3. By performing polynomial curve fitting on the S-parameter data corresponding to the antenna impedance bandwidth information, the two intersection points x1 and x2 of the fitted curve and the antenna impedance bandwidth reference line are found. The impedance bandwidth is calculated as (x2-x1) / 3.5×100%. This impedance bandwidth is then compared with the antenna impedance bandwidth optimization calibration threshold to obtain the antenna impedance bandwidth optimization characterization variable, which is the product of the impedance bandwidth and the weight w1. It is understood that if the impedance bandwidth is lower than the threshold, a penalty term needs to be calculated in conjunction with a penalty coefficient before participating in the variable calculation.
[0041] Step S202: Based on a preset polynomial fitting coordinate system, and according to the multiple antenna axial ratio bandwidth information, the preset antenna axial ratio bandwidth reference line, the preset antenna axial ratio bandwidth optimization calibration threshold, and the preset antenna axial ratio bandwidth optimization weight information, the antenna axial ratio bandwidth optimization characterization variable is calculated.
[0042] In this embodiment, a preset polynomial fitting coordinate system is used with frequency as the horizontal axis and axial ratio as the vertical axis. The preset antenna axial ratio bandwidth reference line is y=-3dB, the preset antenna axial ratio bandwidth optimization calibration threshold is 0.55, and the preset antenna axial ratio bandwidth optimization weight information is w2=2. By performing polynomial curve fitting on the axial ratio data corresponding to multiple antenna axial ratio bandwidth information, the two intersection points x1 and x2 of the fitted curve and the antenna axial ratio bandwidth reference line are found. The axial ratio bandwidth is calculated as x2-x1. This axial ratio bandwidth is compared with the antenna axial ratio bandwidth optimization calibration threshold to obtain the antenna axial ratio bandwidth optimization characterization variable, which is the product of the axial ratio bandwidth and the weight w2. If the axial ratio bandwidth is lower than the threshold, a penalty term needs to be calculated in conjunction with a penalty coefficient before participating in the variable calculation.
[0043] Step S203: Calculate the antenna gain optimization characterization variables based on the antenna gain information, the preset antenna gain threshold, and the preset antenna gain optimization weight information.
[0044] In this embodiment, the preset antenna gain threshold can be 13 dBi, and the preset antenna gain optimization weight information is w3=1. By comparing the antenna gain information with the antenna gain threshold, the antenna gain optimization characterization variable is obtained, which is the product of the gain value and the weight w3.
[0045] Step S204: Calculate the antenna performance optimization characterization information based on the antenna impedance bandwidth optimization characterization variable, the antenna axial ratio bandwidth optimization characterization variable, and the antenna gain optimization characterization variable.
[0046] In this embodiment, the antenna performance optimization characterization information is obtained by adding the antenna impedance bandwidth optimization characterization variable, the antenna axial ratio bandwidth optimization characterization variable, and the antenna gain optimization characterization variable, and then subtracting the penalty term calculated according to the penalty function. That is, antenna performance optimization characterization information = antenna impedance bandwidth optimization characterization variable + antenna axial ratio bandwidth optimization characterization variable + antenna gain optimization characterization variable - penalty, where penalty = λ1⋅max(0, a−a1)² + λ2⋅max(0, b−b1)² + λ3⋅max(0, c−c1)², λ1=2, λ2=2, λ3=1, a, b, and c are the thresholds of antenna impedance bandwidth, axial ratio bandwidth, and gain, respectively, and a1, b1, and c1 are the actual calculated impedance bandwidth, axial ratio bandwidth, and gain, respectively.
[0047] Step S205: Based on the preset antenna size optimization model, and according to the antenna performance optimization characterization information, the multiple initial antenna size parameter information is filtered and optimized to obtain multiple target antenna size parameter information.
[0048] In this embodiment, the preset antenna size optimization model is a genetic algorithm. Based on antenna performance optimization characterization information, multiple initial antenna size parameters are screened and optimized. This can be achieved by initializing a population (200 individuals per generation), selecting 10 parent individuals using a roulette wheel selection method (the selection probability is proportional to the antenna performance optimization characterization information), performing crossover with a crossover probability of 0.8, and using the mutation Gaussian function for mutation (the mutation amplitude gradually decreases to 0 with evolution). After 1000 iterations, until the antenna performance optimization characterization information meets the preset conditions, the corresponding size parameters are finally output as multiple target antenna size parameters.
[0049] The antenna size optimization method provided in this application accurately captures the nonlinear relationship between electromagnetic performance and size parameters by fitting impedance and axial ratio data using polynomial curves. By assigning weights to different performance indicators and combining them with penalty functions to forcefully constrain parameter thresholds, it ensures that key indicators are prioritized for achievement. This enables the preset antenna size optimization model to more accurately screen and optimize initial antenna size parameter information, thereby improving the efficiency and accuracy of multi-parameter joint optimization of metasurface circularly polarized antennas while ensuring accuracy.
[0050] Figure 3 The flowchart illustrating the antenna size optimization method provided in Embodiment 3 of this application is shown. The difference between this method and Embodiment 2 is that step S201 specifically includes:
[0051] Step S301: Based on a preset polynomial fitting coordinate system, fit the multiple antenna impedance bandwidth information to generate an antenna impedance bandwidth curve.
[0052] In this embodiment, a preset polynomial fitting coordinate system is used with frequency as the horizontal axis and S-parameters (reflection coefficient) as the vertical axis. Polynomial curve fitting is performed on the S-parameter data corresponding to multiple antenna impedance bandwidth information to generate an antenna impedance bandwidth curve that can reflect the trend of S-parameters changing with frequency.
[0053] Step S302: Based on the antenna impedance bandwidth curve and the preset antenna impedance bandwidth reference line, obtain multiple antenna impedance bandwidth curve reference intersection point information.
[0054] In this embodiment, the preset antenna impedance bandwidth reference line is a straight line with a reflection coefficient of -10dB. The frequency information corresponding to the intersection point of the antenna impedance bandwidth curve and the reference line is the reference intersection point information of multiple antenna impedance bandwidth curves.
[0055] Step S303: Calculate the difference in the reference intersection information of the multiple antenna impedance bandwidth curves to obtain the antenna impedance bandwidth reference variable.
[0056] In this embodiment, the frequency values corresponding to the multiple antenna impedance bandwidth curve reference intersection points are calculated by subtracting the smaller frequency value from the larger frequency value of the two intersection points. The result is the antenna impedance bandwidth reference variable.
[0057] Step S304: Calculate the antenna impedance bandwidth optimization characterization variable based on the antenna impedance bandwidth reference variable, the preset antenna center frequency, the preset antenna impedance bandwidth optimization calibration threshold, and the preset antenna impedance bandwidth optimization weight information.
[0058] In this embodiment, the preset antenna center frequency is 3.5 GHz, the preset antenna impedance bandwidth optimization calibration threshold is 11%, and the preset antenna impedance bandwidth optimization weight information is 3. The antenna impedance bandwidth reference variable is divided by the antenna center frequency and then multiplied by 100% to obtain the actual antenna impedance bandwidth. This impedance bandwidth is compared with the optimization calibration threshold. If the threshold requirement is met, the impedance bandwidth is multiplied by the weight information to obtain the antenna impedance bandwidth optimization characterization variable. If the requirement is not met, a penalty term is calculated by combining the penalty coefficient and then multiplied by the weight information to obtain the antenna impedance bandwidth optimization characterization variable.
[0059] The antenna size optimization method provided in this application accurately captures the characteristics of antenna impedance bandwidth, thereby improving the accuracy of antenna size optimization model selection and initial antenna size parameter information optimization. While ensuring the optimization effect, it significantly improves the accuracy and efficiency of multi-parameter joint optimization of metasurface circularly polarized antennas.
[0060] Figure 4 The flowchart illustrating the antenna size optimization method provided in Embodiment 4 of this application is shown. The difference between this method and Embodiment 3 above is that step S304 specifically includes:
[0061] Step S401: Obtain the antenna impedance bandwidth optimization variable based on the antenna impedance bandwidth reference variable and the preset antenna center frequency.
[0062] In this embodiment, the preset antenna center frequency is 3.5GHz. The antenna impedance bandwidth reference variable is divided by the antenna center frequency and then multiplied by 100% to obtain the antenna impedance bandwidth optimization variable expressed as a percentage.
[0063] Step S402: Determine whether the antenna impedance bandwidth optimization variable is greater than or equal to the preset antenna impedance bandwidth optimization calibration threshold. If yes, proceed to step S403; otherwise, proceed to step S404.
[0064] In this embodiment, the preset antenna impedance bandwidth optimization calibration threshold is 11%. The calculated antenna impedance bandwidth optimization variable is compared with this threshold to determine whether it is greater than or equal to the threshold, so as to determine whether the antenna impedance bandwidth meets the optimization requirements.
[0065] Step S403: Based on the preset antenna impedance bandwidth optimization weight information, the antenna impedance bandwidth information is weighted to calculate the antenna impedance bandwidth optimization characterization variable.
[0066] In this embodiment, the preset antenna impedance bandwidth optimization weight information is 3. When the antenna impedance bandwidth optimization variable meets the preset antenna impedance bandwidth optimization calibration threshold, the antenna impedance bandwidth optimization variable is directly multiplied by the weight information to obtain the antenna impedance bandwidth optimization characterization variable, so as to highlight the importance of the antenna impedance bandwidth that meets the requirements in the overall optimization.
[0067] Step S404: Calculate the antenna impedance bandwidth penalty value based on the antenna impedance bandwidth information, the preset antenna impedance bandwidth threshold, and the preset antenna impedance bandwidth optimization penalty coefficient.
[0068] In this embodiment, the preset antenna impedance bandwidth threshold is 11%, and the preset antenna impedance bandwidth optimization penalty coefficient is 2. When the antenna impedance bandwidth optimization variable does not meet the preset antenna impedance bandwidth optimization calibration threshold, the difference between the antenna impedance bandwidth information and the antenna impedance bandwidth threshold is calculated, and the square of the difference is multiplied by the penalty coefficient to obtain the antenna impedance bandwidth penalty value, so as to penalize the antenna impedance bandwidth that does not meet the requirements.
[0069] Step S405: Calculate the antenna impedance bandwidth optimization characterization variable based on the antenna impedance bandwidth information, the preset antenna impedance bandwidth optimization weight information, and the antenna impedance bandwidth penalty value.
[0070] In this embodiment, the antenna impedance bandwidth optimization variable is multiplied by the preset antenna impedance bandwidth optimization weight information, and then the calculated antenna impedance bandwidth penalty value is subtracted to obtain the antenna impedance bandwidth optimization characterization variable, thereby comprehensively considering the optimization effect and penalty factors of the antenna impedance bandwidth.
[0071] The antenna size optimization method provided in this application enhances the targeting and accuracy of antenna impedance bandwidth optimization, and can more effectively screen out antenna size parameters that meet the requirements. At the same time, it reasonably penalizes parameters that do not meet the requirements, thereby improving the efficiency and accuracy of multi-parameter joint optimization of metasurface circularly polarized antennas while ensuring optimization accuracy.
[0072] Figure 5 The flowchart illustrating the antenna size optimization method provided in Embodiment 5 of this application is shown. The difference between this method and Embodiment 2 described above is that step S202 specifically includes:
[0073] Step S501: Based on a preset polynomial fitting coordinate system, the axial ratio bandwidth information of the multiple antennas is fitted to generate an antenna axial ratio bandwidth curve.
[0074] In this embodiment, a preset polynomial fitting coordinate system is used with frequency as the horizontal axis and axial ratio as the vertical axis. Polynomial curve fitting is performed on the axial ratio data corresponding to multiple antenna axial ratio bandwidth information to generate an antenna axial ratio bandwidth curve that can reflect the trend of axial ratio change with frequency.
[0075] Step S502: Based on the antenna axial ratio bandwidth curve and the preset antenna axial ratio bandwidth reference line, obtain multiple antenna axial ratio bandwidth curve reference intersection point information.
[0076] In this embodiment, the preset antenna axial ratio bandwidth reference line is a straight line with an axial ratio of 3dB. The frequency information corresponding to the intersection point of the antenna axial ratio bandwidth curve and the reference line is the reference intersection point information of multiple antenna axial ratio bandwidth curves.
[0077] Step S503: Calculate the difference in the reference intersection information of the multiple antenna axial ratio bandwidth curves to obtain the antenna axial ratio bandwidth reference variable.
[0078] In this embodiment, the frequency values corresponding to the multiple antenna axial ratio bandwidth curve reference intersection information are calculated by subtracting the smaller frequency value from the larger frequency value of the two intersection points. The result is the antenna axial ratio bandwidth reference variable.
[0079] Step S504: Calculate the antenna axial ratio bandwidth optimization characterization variable based on the antenna axial ratio bandwidth reference variable, the preset antenna axial ratio bandwidth optimization calibration threshold, and the preset antenna axial ratio bandwidth optimization weight information.
[0080] In this embodiment, the preset antenna axial ratio bandwidth optimization calibration threshold is 0.55, and the preset antenna axial ratio bandwidth optimization weight information is 2. The antenna axial ratio bandwidth reference variable is compared with the optimization calibration threshold. If the threshold requirement is met, the antenna axial ratio bandwidth reference variable is multiplied by the weight information to obtain the antenna axial ratio bandwidth optimization characterization variable. If the requirement is not met, a penalty term is calculated by combining the penalty coefficient and then multiplied by the weight information to obtain the antenna axial ratio bandwidth optimization characterization variable.
[0081] The antenna size optimization method provided in this application accurately captures the characteristics of axial ratio bandwidth, making the calculation of the antenna axial ratio bandwidth optimization characterization variables more reliable. This improves the accuracy of the antenna size optimization model in selecting and optimizing initial antenna size parameter information, and enhances the accuracy and efficiency of multi-parameter joint optimization of metasurface circularly polarized antennas while ensuring the optimization effect.
[0082] Figure 6 The flowchart illustrating the antenna size optimization method provided in Embodiment Six of this application is shown. The difference between this method and Embodiment Five is that step S504 specifically includes:
[0083] Step S601: Determine whether the antenna axis ratio bandwidth reference variable is greater than or equal to the preset antenna axis ratio bandwidth optimization calibration threshold. If yes, proceed to step S602; otherwise, proceed to step S603.
[0084] In this embodiment, the preset antenna axial ratio bandwidth optimization calibration threshold is 0.55. The antenna axial ratio bandwidth reference variable is compared with the antenna axial ratio bandwidth optimization calibration threshold to determine whether the antenna axial ratio bandwidth reference variable is greater than or equal to the antenna axial ratio bandwidth optimization calibration threshold, so as to determine whether the antenna axial ratio bandwidth meets the optimization requirements.
[0085] Step S602: Based on the preset antenna axial ratio bandwidth optimization weight information, the antenna axial ratio bandwidth information is weighted to calculate the antenna axial ratio bandwidth optimization characterization variable.
[0086] In this embodiment, the preset antenna axial ratio bandwidth optimization weight information is 2. When the antenna axial ratio bandwidth reference variable meets the preset antenna axial ratio bandwidth optimization calibration threshold, the antenna axial ratio bandwidth reference variable is directly multiplied by the weight information to obtain the antenna axial ratio bandwidth optimization characterization variable, so as to highlight the importance of the antenna axial ratio bandwidth that meets the requirements in the overall optimization.
[0087] Step S603: Calculate the antenna axial ratio bandwidth penalty value based on the antenna axial ratio bandwidth information, the preset antenna axial ratio bandwidth threshold, and the preset antenna axial ratio bandwidth optimization penalty coefficient.
[0088] In this embodiment, the preset antenna axial ratio bandwidth threshold is 0.55, and the preset antenna axial ratio bandwidth optimization penalty coefficient is 2. When the antenna axial ratio bandwidth reference variable does not meet the preset antenna axial ratio bandwidth optimization calibration threshold, the difference between the antenna axial ratio bandwidth information and the antenna axial ratio bandwidth threshold is calculated, and the square of the difference is multiplied by the penalty coefficient to obtain the antenna axial ratio bandwidth penalty value, so as to penalize the antenna axial ratio bandwidth that does not meet the requirements.
[0089] Step S604: Calculate the antenna axial ratio bandwidth optimization characterization variable based on the antenna axial ratio bandwidth information, the preset antenna axial ratio bandwidth optimization weight information, and the antenna axial ratio bandwidth penalty value.
[0090] In this embodiment, the antenna axial ratio bandwidth reference variable can be multiplied by the preset antenna axial ratio bandwidth optimization weight information, and then the calculated antenna axial ratio bandwidth penalty value can be subtracted to obtain the antenna axial ratio bandwidth optimization characterization variable, thereby comprehensively considering the optimization effect and penalty factors of the antenna axial ratio bandwidth.
[0091] The antenna size optimization method provided in this application enhances the targeting and accuracy of antenna axial ratio bandwidth optimization, effectively filters out antenna size parameters that meet the requirements, and reasonably penalizes parameters that do not meet the requirements. Thus, while ensuring optimization accuracy, it improves the efficiency and accuracy of multi-parameter joint optimization of metasurface circularly polarized antennas.
[0092] Corresponding to the method in the above embodiments, Figure 7 A structural block diagram of the antenna size optimization device provided in the embodiments of this application is shown. For ease of explanation, only the parts related to the embodiments of this application are shown. Figure 7 The example antenna size optimization device can be the execution subject of the antenna size optimization method provided in the aforementioned embodiment 1.
[0093] Reference Figure 7 The antenna size optimization device includes:
[0094] The initial antenna size parameter information acquisition module 710 is used to acquire multiple initial antenna size parameter information.
[0095] The antenna performance characterization parameter information calculation module 720 is used to calculate multiple antenna performance characterization parameter information based on the multiple initial antenna size parameter information and the preset antenna performance characterization parameter calculation model.
[0096] The target antenna size parameter information generation module 730 is used to filter and optimize the multiple initial antenna size parameter information based on a preset antenna size optimization model and the multiple antenna performance characterization parameter information to obtain multiple target antenna size parameter information.
[0097] The process by which each module in the antenna size optimization device provided in this application implements its respective function can be found in the foregoing. Figure 1 The description of Embodiment 1 shown will not be repeated here.
[0098] It should be understood that the sequence number of each step in the above embodiments does not imply the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of this application.
[0099] It should be understood that, when used in this application specification and the appended claims, the term "comprising" indicates the presence of the described features, integrals, steps, operations, elements and / or components, but does not exclude the presence or addition of one or more other features, integrals, steps, operations, elements, components and / or a collection thereof.
[0100] It should also be understood that the term “and / or” as used in this application specification and the appended claims means any combination of one or more of the associated listed items and all possible combinations, and includes such combinations.
[0101] As used in this application specification and the appended claims, the term "if" may be interpreted, depending on the context, as "when," "once," "in response to determination," or "in response to detection." Similarly, the phrase "if determined" or "if detected [the described condition or event]" may be interpreted, depending on the context, as meaning "once determined," "in response to determination," "once detected [the described condition or event]," or "in response to detection [the described condition or event]."
[0102] Furthermore, in the description of this application and the appended claims, the terms "first," "second," "third," etc., are used only for distinguishing descriptions and should not be construed as indicating or implying relative importance. It should also be understood that although the terms "first," "second," etc., are used in the text to describe various elements in some embodiments of this application, these elements should not be limited by these terms. These terms are merely used to distinguish one element from another. For example, a first table may be named a second table, and similarly, a second table may be named a first table, without departing from the scope of the various described embodiments. Both the first table and the second table are tables, but they are not the same table.
[0103] References to "one embodiment" or "some embodiments" as described in this specification mean that one or more embodiments of this application include a specific feature, structure, or characteristic described in connection with that embodiment. Therefore, the phrases "in one embodiment," "in some embodiments," "in other embodiments," "in still other embodiments," etc., appearing in different parts of this specification do not necessarily refer to the same embodiment, but rather mean "one or more, but not all, embodiments," unless otherwise specifically emphasized. The terms "comprising," "including," "having," and variations thereof mean "including but not limited to," unless otherwise specifically emphasized.
[0104] The antenna size optimization method provided in this application can be applied to terminal devices such as mobile phones, tablets, wearable devices, vehicle-mounted devices, augmented reality (AR) / virtual reality (VR) devices, laptops, ultra-mobile personal computers (UMPCs), netbooks, and personal digital assistants (PDAs). This application does not impose any restrictions on the specific type of terminal device.
[0105] For example, the terminal device may be a station (STAION, ST) in a WLAN, a cellular phone, a cordless phone, a Session Initiation Protocol (SIP) phone, a Wireless Local Loop (WLL) station, a Personal Digital Assistant (PDA) device, a handheld device with wireless communication capabilities, a computing device or other processing device connected to a wireless modem, an in-vehicle device, a vehicle networking terminal, a computer, a laptop computer, a handheld communication device, a handheld computing device, a satellite wireless device, a wireless modem card, a set-top box (STB), customer premises equipment (CPE), and / or other devices used for communication over a wireless system, as well as next-generation communication systems, such as mobile terminals in 5G networks or mobile terminals in future evolved Public Land Mobile Network (PLMN) networks.
[0106] As an example and not a limitation, when the terminal device is a wearable device, the term "wearable device" can also refer to any device that utilizes wearable technology to intelligently design and develop everyday wearables, such as glasses, gloves, watches, clothing, and shoes. Wearable devices are portable devices worn directly on the body or integrated into a user's clothing or accessories. Wearable devices are not merely hardware devices; they achieve powerful functions through software support, data interaction, and cloud interaction. Broadly defined, wearable smart devices include those with comprehensive functions, large sizes, and the ability to perform complete or partial functions without relying on a smartphone, such as smartwatches or smart glasses, as well as those focused on a specific application function that require interaction with other devices such as smartphones, such as various smart bracelets and smart jewelry for vital sign monitoring.
[0107] Figure 8 This is a schematic diagram of the structure of a terminal device provided in an embodiment of this application. For example... Figure 8 As shown, the terminal device 8 of this embodiment includes: at least one processor 80 ( Figure 8 Only one is shown in the diagram), and a memory 81 is stored in which a computer program 82 can be run on the processor 80. When the processor 80 executes the computer program 82, it implements the steps in the various antenna size optimization method embodiments described above, for example... Figure 1 Steps S101 to S103 are shown. Alternatively, when the processor 80 executes the computer program 82, it implements the functions of each module / unit in the above-described device embodiments, for example... Figure 7 The functions of modules 710 to 730 are shown.
[0108] The terminal device 8 can be a desktop computer, laptop, handheld computer, or cloud server, etc. The terminal device may include, but is not limited to, a processor 80 and a memory 81. Those skilled in the art will understand that... Figure 8 This is merely an example of terminal device 8 and does not constitute a limitation on terminal device 8. It may include more or fewer components than shown, or combine certain components, or different components. For example, the terminal device may also include input transmission devices, network access devices, buses, etc.
[0109] The processor 80 may be a Central Processing Unit (CPU), or other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor may be a microprocessor or any conventional processor.
[0110] In some embodiments, the memory 81 may be an internal storage unit of the terminal device 8, such as a hard disk or memory of the terminal device 8. The memory 81 may also be an external storage device of the terminal device 8, such as a plug-in hard disk, smart media card (SMC), secure digital card (SD), flash card, etc., equipped on the terminal device 8. Furthermore, the memory 81 may include both internal and external storage units of the terminal device 8. The memory 81 is used to store the operating system, applications, bootloader, data, and other programs, such as the program code of the computer program. The memory 81 can also be used to temporarily store data that has been sent or will be sent.
[0111] Furthermore, the functional units in the various embodiments of this application can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.
[0112] This application also provides a terminal device, which includes at least one memory, at least one processor, and a computer program stored in the at least one memory and executable on the at least one processor. When the processor executes the computer program, it causes the terminal device to implement the steps in any of the above method embodiments.
[0113] This application also provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the steps described in the various method embodiments above.
[0114] This application provides a computer program product that, when run on a terminal device, enables the terminal device to implement the steps described in the various method embodiments above.
[0115] If the integrated module / unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, all or part of the processes in the methods of the above embodiments can also be implemented by a computer program instructing related hardware. The computer program can be stored in a computer-readable storage medium, and when executed by a processor, it can implement the steps of the various method embodiments described above. The computer program includes computer program code, which can be in the form of source code, object code, executable files, or certain intermediate forms. The computer-readable medium can include: any entity or device capable of carrying the computer program code, a recording medium, a USB flash drive, a portable hard drive, a magnetic disk, an optical disk, a computer memory, a read-only memory (ROM), a random access memory (RAM), an electrical carrier signal, a telecommunication signal, and a software distribution medium, etc.
[0116] In the above embodiments, the descriptions of each embodiment have different focuses. For parts that are not described in detail or recorded in a certain embodiment, please refer to the relevant descriptions of other embodiments.
[0117] Those skilled in the art will recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.
[0118] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.
[0119] The above-described embodiments are only used to illustrate the technical solutions of this application, and are not intended to limit them. Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of this application, and should all be included within the protection scope of this application.
Claims
1. A method for optimizing antenna size, characterized in that, include: Obtain multiple initial antenna size parameter information; Based on the multiple initial antenna size parameters and the preset antenna performance characterization parameter calculation model, multiple antenna performance characterization parameters are calculated. Based on a preset antenna size optimization model, the initial antenna size parameters are filtered and optimized according to the multiple antenna performance characterization parameters to obtain multiple target antenna size parameters. The antenna performance characterization parameters include antenna impedance bandwidth information, antenna axial ratio bandwidth information, and antenna gain information. The step of filtering and optimizing multiple initial antenna size parameters based on a preset antenna size optimization model to obtain multiple target antenna size parameters, specifically includes: Based on a preset polynomial fitting coordinate system, and according to the antenna impedance bandwidth information, the preset antenna impedance bandwidth reference line, the preset antenna center frequency, the preset antenna impedance bandwidth optimization calibration threshold, and the preset antenna impedance bandwidth optimization weight information, the antenna impedance bandwidth optimization characterization variables are calculated. Based on a preset polynomial fitting coordinate system, and according to the multiple antenna axial ratio bandwidth information, the preset antenna axial ratio bandwidth reference line, the preset antenna axial ratio bandwidth optimization calibration threshold, and the preset antenna axial ratio bandwidth optimization weight information, the antenna axial ratio bandwidth optimization characterization variables are calculated. Based on the antenna gain information, the preset antenna gain threshold, and the preset antenna gain optimization weight information, the antenna gain optimization characterization variables are calculated. Antenna performance optimization characterization information is calculated based on the antenna impedance bandwidth optimization characterization variables, antenna axial ratio bandwidth optimization characterization variables, and antenna gain optimization characterization variables. Based on the preset antenna size optimization model, and according to the antenna performance optimization characterization information, the multiple initial antenna size parameter information is filtered and optimized to obtain multiple target antenna size parameter information.
2. The antenna size optimization method as described in claim 1, characterized in that, The step of calculating the antenna impedance bandwidth optimization characterization variables based on a preset polynomial fitting coordinate system, according to the antenna impedance bandwidth information, a preset antenna impedance bandwidth reference line, a preset antenna center frequency, a preset antenna impedance bandwidth optimization calibration threshold, and preset antenna impedance bandwidth optimization weight information, specifically includes: Based on a preset polynomial fitting coordinate system, the multiple antenna impedance bandwidth information are fitted to generate an antenna impedance bandwidth curve. Based on the antenna impedance bandwidth curve and the preset antenna impedance bandwidth reference line, multiple antenna impedance bandwidth curve reference intersection point information are obtained. Calculate the difference in the reference intersection information of the multiple antenna impedance bandwidth curves to obtain the antenna impedance bandwidth reference variable; The antenna impedance bandwidth optimization characteristic variable is calculated based on the antenna impedance bandwidth reference variable, the preset antenna center frequency, the preset antenna impedance bandwidth optimization calibration threshold, and the preset antenna impedance bandwidth optimization weight information.
3. The antenna size optimization method as described in claim 2, characterized in that, The step of calculating the antenna impedance bandwidth optimization characteristic variable based on the antenna impedance bandwidth reference variable, the preset antenna center frequency, the preset antenna impedance bandwidth optimization calibration threshold, and the preset antenna impedance bandwidth optimization weight information specifically includes: Based on the antenna impedance bandwidth reference variable and the preset antenna center frequency, the antenna impedance bandwidth optimization variable is obtained; Determine whether the antenna impedance bandwidth optimization variable is greater than or equal to a preset antenna impedance bandwidth optimization calibration threshold; If so, the antenna impedance bandwidth information is weighted according to the preset antenna impedance bandwidth optimization weight information to calculate the antenna impedance bandwidth optimization characterization variable. If not, the antenna impedance bandwidth penalty value is calculated based on the antenna impedance bandwidth information, the preset antenna impedance bandwidth threshold, and the preset antenna impedance bandwidth optimization penalty coefficient. Based on the antenna impedance bandwidth information, the preset antenna impedance bandwidth optimization weight information, and the antenna impedance bandwidth penalty value, the antenna impedance bandwidth optimization characterization variables are calculated.
4. The antenna size optimization method as described in claim 1, characterized in that, The step of calculating the antenna axial ratio bandwidth optimization characterization variables based on a preset polynomial fitting coordinate system, according to the multiple antenna axial ratio bandwidth information, a preset antenna axial ratio bandwidth reference line, a preset antenna axial ratio bandwidth optimization calibration threshold, and preset antenna axial ratio bandwidth optimization weight information, specifically includes: Based on a preset polynomial fitting coordinate system, the axial ratio bandwidth information of the multiple antennas is fitted to generate an antenna axial ratio bandwidth curve. Based on the antenna axial ratio bandwidth curve and the preset antenna axial ratio bandwidth reference line, multiple antenna axial ratio bandwidth curve reference intersection point information are obtained; Calculate the difference in the reference intersection information of the multiple antenna axial ratio bandwidth curves to obtain the antenna axial ratio bandwidth reference variable; The antenna axial ratio bandwidth optimization characterization variable is calculated based on the antenna axial ratio bandwidth reference variable, the preset antenna axial ratio bandwidth optimization calibration threshold, and the preset antenna axial ratio bandwidth optimization weight information.
5. The antenna size optimization method as described in claim 4, characterized in that, The step of calculating the antenna axial ratio bandwidth optimization characterization variable based on the antenna axial ratio bandwidth reference variable, the preset antenna axial ratio bandwidth optimization calibration threshold, and the preset antenna axial ratio bandwidth optimization weight information specifically includes: Determine whether the antenna axial ratio bandwidth reference variable is greater than or equal to a preset antenna axial ratio bandwidth optimization calibration threshold; If so, the antenna axial ratio bandwidth information is weighted according to the preset antenna axial ratio bandwidth optimization weight information to calculate the antenna axial ratio bandwidth optimization characterization variable; If not, the antenna axial ratio bandwidth penalty value is calculated based on the antenna axial ratio bandwidth information, the preset antenna axial ratio bandwidth threshold, and the preset antenna axial ratio bandwidth optimization penalty coefficient. The antenna axial ratio bandwidth optimization characteristic variable is calculated based on the antenna axial ratio bandwidth information, the preset antenna axial ratio bandwidth optimization weight information, and the antenna axial ratio bandwidth penalty value.
6. An antenna size optimization device, characterized in that, include: The initial antenna size parameter information acquisition module is used to acquire multiple initial antenna size parameter information. The antenna performance characterization parameter information calculation module is used to calculate multiple antenna performance characterization parameter information based on the multiple initial antenna size parameter information and the preset antenna performance characterization parameter calculation model. The target antenna size parameter information generation module is used to filter and optimize the multiple initial antenna size parameter information based on the preset antenna size optimization model and the multiple antenna performance characterization parameter information to obtain multiple target antenna size parameter information. The antenna performance characterization parameters include antenna impedance bandwidth information, antenna axial ratio bandwidth information, and antenna gain information. The step of filtering and optimizing multiple initial antenna size parameters based on a preset antenna size optimization model to obtain multiple target antenna size parameters, specifically includes: Based on a preset polynomial fitting coordinate system, and according to the antenna impedance bandwidth information, the preset antenna impedance bandwidth reference line, the preset antenna center frequency, the preset antenna impedance bandwidth optimization calibration threshold, and the preset antenna impedance bandwidth optimization weight information, the antenna impedance bandwidth optimization characterization variables are calculated. Based on a preset polynomial fitting coordinate system, and according to the multiple antenna axial ratio bandwidth information, the preset antenna axial ratio bandwidth reference line, the preset antenna axial ratio bandwidth optimization calibration threshold, and the preset antenna axial ratio bandwidth optimization weight information, the antenna axial ratio bandwidth optimization characterization variables are calculated. Based on the antenna gain information, the preset antenna gain threshold, and the preset antenna gain optimization weight information, the antenna gain optimization characterization variables are calculated. Antenna performance optimization characterization information is calculated based on the antenna impedance bandwidth optimization characterization variables, antenna axial ratio bandwidth optimization characterization variables, and antenna gain optimization characterization variables. Based on the preset antenna size optimization model, and according to the antenna performance optimization characterization information, the multiple initial antenna size parameter information is filtered and optimized to obtain multiple target antenna size parameter information.
7. A terminal device, characterized in that, The terminal device includes a memory and a processor. The memory stores a computer program that can run on the processor. When the processor executes the computer program, it implements the steps of the method as described in any one of claims 1 to 5.
8. A computer-readable storage medium storing a computer program, characterized in that, When the computer program is executed by a processor, it implements the steps of the method as described in any one of claims 1 to 5.