Metal mesh grid design optimization system and method based on electromagnetic shielding effectiveness

Abstract

The application discloses a metal mesh design optimization system and method based on electromagnetic shielding effectiveness, and belongs to the technical field of electromagnetic shielding. The method comprises the following steps: obtaining target region space electromagnetic field distribution data and substrate material parameters; establishing shielding effectiveness and mesh unit geometric parameter correlation data through electromagnetic simulation; identifying key geometric features sensitive to electromagnetic fields; constructing a gradientized non-periodic topological structure model of the metal mesh according to the key geometric features; performing multi-physical field collaborative simulation and iterative optimization on the model in terms of electromagnetic shielding stability and structural strength; and finally generating a digital design package containing a three-dimensional model, engineering drawings and process files. The application realizes the maximization of the shielding effectiveness of the metal mesh, the improvement of stability and the collaborative optimization of electromagnetic and mechanical properties by deeply combining the actual electromagnetic environment and the structural design.

Description

Technical Field

[0001] This invention belongs to the field of electromagnetic shielding technology, specifically relating to a metal mesh grid design optimization system and method based on electromagnetic shielding effectiveness. Background Technology

[0002] Metal mesh grids are an important type of transparent electromagnetic shielding element, widely used in electronic device windows, radomes, and other applications. Their shielding effectiveness mainly depends on the geometry and arrangement of the mesh cells, as well as their characteristics of reflecting, absorbing, and transmitting electromagnetic waves.

[0003] Existing metal mesh designs mostly employ periodic, regular structures, such as square or hexagonal grids. While these structures are stable under uniform electromagnetic fields, their shielding effectiveness may fluctuate or experience localized failures when faced with non-uniform or complex electromagnetic field distributions in real-world environments, making it difficult to achieve optimal broadband, multi-angle shielding. Traditional design methods often rely on empirical formulas or simple simulations, separating electromagnetic performance from structural design. They fail to fully consider the specific distribution of the actual electromagnetic environment and the true electromagnetic properties of the substrate material, and do not incorporate engineering constraints such as mechanical strength into the optimization loop. Furthermore, the shielding performance of traditional uniform or periodic structures is sensitive to the incident wave angle and lacks stability.

[0004] Therefore, there is a need for a metal mesh design method that can closely integrate with the actual electromagnetic environment, achieve synergistic optimization of electromagnetic performance and structural mechanical performance, and automatically generate highly adaptable and shielding-stable designs. Summary of the Invention

[0005] To overcome the shortcomings of existing technologies, such as the inability of metal mesh grid designs to adapt to complex electromagnetic environments, poor shielding stability, and the lack of multi-physics field collaborative optimization in the design process, this invention provides a metal mesh grid design optimization system and method based on electromagnetic shielding effectiveness, aiming to maximize and stabilize the shielding effectiveness of metal mesh grids under specific electromagnetic environments.

[0006] To achieve the above objectives, the present invention adopts the following technical solution:

[0007] In a first aspect, the present invention provides a method for optimizing the design of metal mesh grids based on electromagnetic shielding effectiveness, comprising the following steps:

[0008] S1: Acquire spatial electromagnetic field distribution data of the target area, including electric field strength, magnetic field strength and electromagnetic wave propagation direction, and at the same time acquire the electromagnetic parameters and geometric constraints of the metal mesh substrate material;

[0009] S2: Based on spatial electromagnetic field distribution data, a quantitative analysis model of electromagnetic wave reflection, absorption and transmission by metal mesh grid units is established through electromagnetic simulation to obtain the correlation data between shielding effectiveness and the shape, size and arrangement of the mesh grid units.

[0010] S3: Combining electromagnetic parameters and geometric constraints, nonlinear fitting is performed on the associated data to generate electromagnetic response characteristic curves of metal mesh cells in multiple frequency bands, and key geometric features sensitive to changes in electromagnetic field are identified.

[0011] S4: Based on key geometric features, construct a gradient-based aperiodic topological structure model of the metal mesh.

[0012] S5: Perform electromagnetic structure coupling simulation on the gradient aperiodic topology model to evaluate the shielding stability and mechanical strength under actual electromagnetic environment, and minimize the fluctuation of shielding effectiveness by iteratively adjusting the unit parameters.

[0013] S6: Based on the optimized gradient aperiodic topology model, generate digital design files for the metal mesh grid to obtain the precise definition and fabrication process requirements for each unit.

[0014] According to the above technical solution, the acquisition of spatial electromagnetic field distribution data of the target area, including electric field strength, magnetic field strength, and electromagnetic wave propagation direction, and the acquisition of electromagnetic parameters and geometric constraints of the metal mesh substrate material, include:

[0015] Multiple measurement points are set up in the target area. The electric field strength value of each measurement point is collected using a field strength meter to form the electric field strength. The magnetic field strength value of each measurement point is collected using an electromagnetic field probe to form the magnetic field strength. Based on the electric field strength and the magnetic field strength, the direction of electromagnetic wave propagation is obtained by calculating the direction of the average Poynting vector. The acquired electric field strength, magnetic field strength, and electromagnetic wave propagation direction are used together as spatial electromagnetic field distribution data. The complex relative permittivity and complex relative permeability of the metal mesh substrate material are obtained as electromagnetic parameters. Parameters including the maximum allowable overall thickness, minimum machinable linewidth, and outer contour boundary are obtained as geometric constraints.

[0016] According to the above technical solution, the quantitative analysis model of electromagnetic wave reflection, absorption, and transmission by metal mesh units is established through electromagnetic simulation based on spatial electromagnetic field distribution data, obtaining the correlation data between shielding effectiveness and the shape, size, and arrangement of the mesh units, including:

[0017] Based on the electric field strength, magnetic field strength, and electromagnetic wave propagation direction in the spatial electromagnetic field distribution data, the excitation source parameters required for electromagnetic simulation are determined. A plane wave excitation with corresponding amplitude, polarization, and incident direction is set in the electromagnetic simulation software. According to preset multiple unit shapes, size parameters, and periodic arrangement methods, a parameterized metal mesh unit simulation model library is established in the electromagnetic simulation software. The construction range of the metal mesh unit simulation model library is constrained by the minimum machinable linewidth. For each parameter combination model in the metal mesh unit simulation model library, the reflection coefficient, absorption coefficient, and transmission coefficient of the parameter combination model in the target frequency band are calculated under the plane wave excitation. The shielding effectiveness is calculated based on the transmission coefficient. All parameter combinations and their corresponding shielding effectiveness values ​​are summarized to form associated data.

[0018] According to the above technical solution, by combining electromagnetic parameters and geometric constraints, nonlinear fitting is performed on the associated data to generate electromagnetic response characteristic curves of the metal mesh unit in multiple frequency bands, and key geometric features sensitive to changes in the electromagnetic field are identified, including:

[0019] The electromagnetic parameters, namely the complex relative permittivity and complex relative permeability, are updated as material properties in the simulation settings of the electromagnetic simulation software. The shielding effectiveness of some sample points in the associated data is recalculated using the updated simulation settings. The original associated data is calibrated based on the recalculation results to obtain a calibrated associated dataset. Using the unit shape code, unit feature size, and arrangement period as independent variables, and the shielding effectiveness at each target frequency as the dependent variable, the least squares method is used to perform multivariate nonlinear surface fitting on the calibrated associated dataset to establish a mapping function from geometric parameters to shielding effectiveness. The partial derivatives of the mapping function with respect to each independent variable in the preset frequency band are analyzed. The geometric parameters corresponding to the independent variables that cause the rate of change of shielding effectiveness to exceed a set threshold are identified as key geometric features sensitive to changes in the electromagnetic field.

[0020] According to the above technical solution, the step of constructing a gradient-based aperiodic topological structure model of the metal mesh based on key geometric features includes:

[0021] Based on the key geometric features, the geometric parameter with the highest sensitivity to change among the key geometric features is determined as the target geometric parameter; the electromagnetic wave propagation direction is defined as the spatial gradient direction; a preset functional relationship is established for the target geometric parameter to continuously change along the spatial gradient direction within the outer contour boundary, serving as the gradient change law of the unit parameter; under the premise of satisfying the minimum machinable linewidth and the minimum spacing constraint between units in the geometric constraints, a numerical sequence of the target geometric parameter is generated according to the gradient change law, wherein the values ​​in the numerical sequence change along the spatial gradient direction; each value in the numerical sequence is assigned to an independent metal mesh unit as a unit feature size, and all independent metal mesh units are arranged within the outer contour boundary according to a non-periodic random distribution algorithm to form an initial topology; the maximum thickness dimension of the initial topology in the direction perpendicular to the base plane is calculated, and it is determined whether the maximum thickness dimension is less than or equal to the maximum allowable overall thickness; if the maximum thickness dimension is greater than the maximum allowable overall thickness, the longitudinal dimensions of all units in the initial topology are proportionally compressed until the compressed maximum thickness dimension satisfies the maximum allowable overall thickness constraint, generating the final gradient-based non-periodic topology model.

[0022] According to the above technical solution, the electromagnetic structure coupling simulation of the gradient aperiodic topology model is performed to evaluate the shielding stability and mechanical strength under actual electromagnetic environment, and the shielding effectiveness fluctuation is minimized by iteratively adjusting the unit parameters, including:

[0023] Multi-angle incident electromagnetic simulation is performed on the gradient aperiodic topology model. Within a preset incident angle range, the plane wave incident angle is changed with a fixed step size, and the shielding effectiveness at each incident angle is calculated. The standard deviation of the shielding effectiveness at all incident angles is used to evaluate the shielding stability. Static finite element analysis is performed on the gradient aperiodic topology model to simulate the stress distribution of the gradient aperiodic topology model under a preset load. Whether the maximum stress point exceeds the material yield strength is used as the evaluation criterion for mechanical strength. The optimization objective is to minimize the standard deviation of the shielding effectiveness, while adhering to geometric constraints and passing the mechanical strength evaluation. The parameters of the gradient sequence variation function and the element distribution density are adjusted through an iterative algorithm. The final model parameter set that satisfies the optimization objective and all constraints is output.

[0024] According to the above technical solution, the step of generating a digital design file for the metal mesh based on the optimized gradient aperiodic topology model yields the precise definition and fabrication process requirements for each unit, including:

[0025] Based on the final model parameter set, the optimized gradient aperiodic topological structure 3D model is reconstructed to obtain the final metal mesh 3D model. The center point coordinates, shape control parameters, and feature dimensions of each independent unit are accurately recorded. For the minimum machinable linewidth and substrate material properties, recommended fabrication processes are labeled for each unit in the metal mesh 3D model. The metal mesh 3D model, a list containing all unit parameters, and process labeling information are integrated to output a digital design file containing 3D solid files, 2D engineering drawings, and a manufacturing process manual.

[0026] Secondly, the present invention provides a metal mesh grid design optimization system based on electromagnetic shielding effectiveness, for implementing the above method, the system comprising:

[0027] The data acquisition module is used to acquire spatial electromagnetic field distribution data of the target area, including electric field strength, magnetic field strength and electromagnetic wave propagation direction, and at the same time acquire the electromagnetic parameters and geometric constraints of the metal mesh substrate material;

[0028] The simulation modeling module is used to establish a quantitative analysis model of the reflection, absorption and transmission of electromagnetic waves by metal mesh units based on spatial electromagnetic field distribution data, and to obtain the correlation data between shielding effectiveness and the shape, size and arrangement of the mesh units.

[0029] The feature analysis module is used to combine electromagnetic parameters and geometric constraints to perform nonlinear fitting on the associated data, generate electromagnetic response characteristic curves of metal mesh cells in multiple frequency bands, and identify key geometric features that are sensitive to changes in electromagnetic field.

[0030] The structure building module is used to construct a gradient-based aperiodic topological structure model of the metal mesh based on key geometric features.

[0031] The coupling optimization module is used to perform electromagnetic structure coupling simulation on the gradient aperiodic topology model, evaluate the shielding stability and mechanical strength in the actual electromagnetic environment, and minimize the fluctuation of shielding effectiveness by iteratively adjusting the unit parameters.

[0032] The file generation module is used to generate digital design files for metal mesh grids based on the optimized gradient aperiodic topology model, obtaining the precise definition and fabrication process requirements of each unit.

[0033] Thirdly, the present invention provides an electronic device, including a processor and a memory, wherein the memory stores a computer program, and the processor executes the program to implement the above-mentioned metal mesh grid design optimization method based on electromagnetic shielding effectiveness.

[0034] Fourthly, the present invention provides a computer-readable storage medium having a computer program stored thereon, wherein the program, when executed by a processor, implements the above-described method for optimizing the design of metal mesh grids based on electromagnetic shielding effectiveness.

[0035] Compared with the prior art, the present invention has the following advantages and beneficial effects:

[0036] This invention acquires and integrates actual spatial electromagnetic field distribution data of the target area, enabling the design to closely align with the specific electromagnetic environment and overcoming the limitations of traditional empirical formulas or uniform field assumptions. Through parametric electromagnetic simulation and nonlinear fitting, the geometric features most sensitive to performance changes are quantitatively identified, providing a clear basis for intelligent design. Based on this, an innovative non-periodic topological structure with a gradient variation along the main energy direction of electromagnetic waves is constructed, achieving adaptive matching to complex and non-uniform electromagnetic field distributions and significantly improving shielding stability under wide-bandwidth and multi-angle incidence. Furthermore, through multi-physics co-simulation and iterative optimization of electromagnetic shielding performance and mechanical structure strength, the design ensures that it meets engineering reliability constraints while pursuing high performance. Finally, the entire process can be automated and outputs digital design files directly usable for manufacturing, realizing a closed loop from electromagnetic environment perception to integrated design of high-performance, high-reliability shielding structures, greatly improving the design efficiency, optimization depth, and engineering practicality of metal mesh grids. Attached Figure Description

[0037] Other features, objects, and advantages of this application will become more apparent from the following detailed description of non-limiting embodiments with reference to the accompanying drawings:

[0038] Figure 1 This is a schematic diagram of the overall process of the metal mesh grid design optimization method based on electromagnetic shielding effectiveness provided in the embodiments of this application.

[0039] Figure 2 This is a detailed diagram of the data acquisition process provided in the embodiments of this application.

[0040] Figure 3 This is a detailed diagram of the simulation modeling process provided in the embodiments of this application.

[0041] Figure 4 This is a detailed flowchart of the feature analysis process provided in the embodiments of this application.

[0042] Figure 5 This is a detailed diagram of the structural construction process provided in the embodiments of this application.

[0043] Figure 6 This is a detailed flowchart of the coupling optimization process provided in the embodiments of this application.

[0044] Figure 7This is a detailed diagram of the document generation process provided in the embodiments of this application.

[0045] Figure 8 This is a schematic diagram of the structure of the metal mesh grid design optimization system based on electromagnetic shielding effectiveness provided in the embodiments of this application. Detailed Implementation

[0046] To make the objectives, technical solutions, and advantages of this invention clearer, the technical solutions of this invention will be described in detail and completely below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are for illustrative purposes only and are not intended to limit the scope of this invention.

[0047] Please see Figure 1 , Figure 1 This is a schematic diagram of the overall process of the metal mesh grid design optimization method based on electromagnetic shielding effectiveness provided in the embodiments of this application, which specifically includes the following steps:

[0048] S1: Acquire spatial electromagnetic field distribution data of the target area, including electric field strength, magnetic field strength and electromagnetic wave propagation direction, and at the same time acquire the electromagnetic parameters and geometric constraints of the metal mesh substrate material;

[0049] In this embodiment, step S1 includes the following specific contents, and the process can be found in the attached document. Figure 2 , Figure 2 Here is a detailed diagram of the data acquisition process provided in the embodiments of this application:

[0050] S110: Multiple measurement points are set up in the target area. The electric field strength value of each measurement point is collected using a field strength meter to form the electric field strength. The magnetic field strength value of each measurement point is collected using an electromagnetic field probe to form the magnetic field strength.

[0051] In this embodiment, firstly, 300 measurement points (20 rows and 15 columns) are precisely calibrated on the surface of the radar radome to be shielded, with an interval of 3 centimeters. A total station is used to record the three-dimensional coordinates of each measurement point in the global coordinate system. Next, a broadband isotropic electric field probe covering a frequency range of 100 MHz to 3 GHz is used. This probe is mounted on a high-precision three-axis moving platform. The moving platform is controlled to automatically move the probe to each measurement point sequentially, ensuring that the probe's sensing surface is aligned with the normal direction of the radome surface. At each measurement point, a spectrum analyzer connected to the broadband isotropic electric field probe is controlled to perform fixed-point measurements at three preset frequency points: 1.5 GHz, 2.4 GHz, and 5.8 GHz, recording the effective value of the electric field strength at each frequency point. After completing the measurements at all points, the three-dimensional coordinates of each measurement point and the corresponding electric field strength readings at the three frequency points are input into the central data processing unit, forming a data set containing spatial location information and multi-frequency electric field strength values. This data set is the acquired electric field strength.

[0052] Subsequently, at identical spatial coordinate points, a three-dimensional magnetic field probe capable of simultaneously measuring magnetic field components in three orthogonal directions was used to replace the electric field probe. The moving platform was controlled to move the three-dimensional magnetic field probe to each measurement point in sequence, following the same path and posture. At each measurement point, the measuring instrument was controlled to measure the same three frequency points, and the magnetic field intensity component data of the X-axis, Y-axis, and Z-axis in the spatial rectangular coordinate system at each frequency point were recorded. After all measurements were completed, the coordinates of each measurement point and the corresponding magnetic field component readings of the three frequency points and three axes were entered into the central processing unit to form a complete data set containing spatial position, frequency information, and three-dimensional magnetic field intensity components. This complete data set is the acquired magnetic field intensity.

[0053] S120: Based on the electric field strength and the magnetic field strength, the direction of electromagnetic wave propagation is obtained by calculating the direction of the average Poynting vector;

[0054] For each set of data collected at each measurement point at each frequency, the electric field vector is first reconstructed. Based on the known condition that the axis of the electric field probe is aligned with the Z-axis of the global coordinate system, the measured electric field strength scalar value is assigned a vector along the positive Z-axis. In this embodiment, the specific rule is that the reading is directly used as the magnitude of the electric field vector component in the Z-axis direction, while the magnitudes of the electric field vector components in the X-axis and Y-axis directions are set to zero, thereby generating a spatial electric field vector.

[0055] Simultaneously, the magnetic field vector is synthesized; the magnetic field component readings in the X-axis direction, Y-axis direction, and Z-axis direction obtained at the same location and frequency are directly used as the magnitudes of the magnetic field vector components in the X-axis, Y-axis, and Z-axis directions, respectively, thereby synthesizing a complete spatial magnetic field vector.

[0056] Subsequently, the instantaneous Poynting vector is calculated. According to electromagnetic field theory, the electric field vector and magnetic field vector obtained in the previous step are subjected to a cross product operation to calculate the instantaneous energy flux density vector. The rules for this cross product operation are as follows: First, calculate the X-direction component of the new vector, which is equal to the Y-direction component of the electric field vector multiplied by the Z-direction component of the magnetic field vector, and then subtract the Z-direction component of the electric field vector multiplied by the Y-direction component of the magnetic field vector. Next, calculate the Y-direction component of the new vector, which is equal to the Z-direction component of the electric field vector multiplied by the X-direction component of the magnetic field vector, and then subtract the X-direction component of the electric field vector multiplied by the Z-direction component of the magnetic field vector. Finally, calculate the Z-direction component of the new vector, which is equal to the X-direction component of the electric field vector multiplied by the Y-direction component of the magnetic field vector, and then subtract the Y-direction component of the electric field vector multiplied by the X-direction component of the magnetic field vector. The new vector obtained through this operation is the instantaneous Poynting vector, and its direction represents the direction of electromagnetic energy transmission at that moment.

[0057] To obtain a stable average energy flow direction, it is necessary to average multiple time samples continuously collected at each frequency for each measurement point. First, the vector cross product operation is repeated for each time sample to obtain the instantaneous Poynting vector. All instantaneous Poynting vectors are then projected onto the X, Y, and Z axes of the global coordinate system, and the X, Y, and Z direction components of each vector are extracted. Next, the arithmetic mean of all instantaneous X direction components is calculated as the X-axis component of the average Poynting vector. The arithmetic mean of all instantaneous Y direction components is calculated as the Y-axis component of the average Poynting vector. The arithmetic mean of all instantaneous Z direction components is calculated as the Z-axis component of the average Poynting vector. Thus, the average Poynting vector at that measurement point at that frequency is obtained.

[0058] Finally, the spatial orientation of the average Poynting vector, i.e. the direction determined by its X-axis component, Y-axis component, and Z-axis component, is defined as the electromagnetic wave propagation direction at that frequency at the measurement point. The above calculation is performed on all measurement points and all frequency points to obtain a set of directional data describing the main transmission path of electromagnetic wave energy in the entire target area, i.e., the electromagnetic wave propagation direction.

[0059] S130: The acquired electric field strength, magnetic field strength, and electromagnetic wave propagation direction are used together as spatial electromagnetic field distribution data;

[0060] The electric field intensity value, the three magnetic field components, and the electromagnetic wave propagation direction data of each measurement point at each characteristic frequency are stored together using the three-dimensional coordinates and frequency values ​​of the measurement points as a joint primary key. This results in a complete multidimensional dataset containing information on spatial location, frequency, electric field intensity, magnetic field intensity, and electromagnetic wave propagation direction. This complete multidimensional dataset serves as the spatial electromagnetic field distribution data upon which this method is based.

[0061] S140: Obtain the complex relative permittivity and complex relative permeability of the metal mesh substrate material as electromagnetic parameters; obtain parameters including the maximum allowable overall thickness, minimum machinable linewidth, and outer contour boundary as geometric constraints.

[0062] In this embodiment, the process of obtaining electromagnetic parameters is as follows: A sapphire glass with a thickness of one millimeter is selected as the substrate material for the metal mesh grid; electromagnetic properties are measured using a vector network analyzer and a coaxial air wire clamp; the sapphire sample is precisely machined into a ring and placed between the inner and outer conductors of the coaxial clamp; the vector network analyzer is set to perform frequency sweep measurements in the frequency range of 2 GHz to 8 GHz to accurately obtain the complex reflection coefficient and complex transmission coefficient measured before and after sample insertion; then, the Nicolson-Ross-Weir algorithm is applied to perform inversion calculations on the measurement data; in this embodiment, the specific process is as follows: First, using the measured complex reflection coefficient and transmission coefficient, the equivalent reflection and transmission characteristic parameters of the sample in the transmission line are calculated; then, by solving the complex equations describing the propagation behavior of electromagnetic waves in the medium sample, the normalized coefficient of the material is calculated. The key intermediate parameters are characteristic impedance and complex propagation constant. Finally, based on the inherent physical relationship between the intrinsic parameters of materials and impedance and propagation constant in electromagnetic wave theory, the real and imaginary parts of the complex relative permittivity and the complex relative permeability of the substrate material at each measurement frequency point are calculated from the calculated impedance and propagation constant. After completing the above calculations for all discrete frequency points within the frequency sweep range, the real and imaginary pairs of the complex relative permittivity and the complex relative permeability of the substrate material at each frequency point are obtained. These pairs of complex parameter values ​​at all frequency points are collected and organized in frequency order to form complete spectrum data of the complex relative permittivity and the complex relative permeability of the substrate material in the target frequency band. These two sets of spectrum data are the electromagnetic parameters of the substrate material.

[0063] The process of obtaining geometric constraints is as follows: Based on the overall structural design drawing and assembly relationship of the product, determine the maximum allowable overall thickness of the metal mesh assembly at the installation position as explicitly defined in the overall structural design drawing of the product; based on the technical specifications of the selected micro-machining process, determine the minimum metal linewidth specified in the technical specifications that can stably realize conductive patterns on such substrate materials; extract the precise boundary shape and size of the area that needs to be covered by the metal mesh to achieve the shielding function from the computer-aided design model of the product shell; the above maximum allowable overall thickness, minimum machinable linewidth, and outer contour boundary together constitute the geometric constraints that the metal mesh structure design must follow;

[0064] S2: Based on spatial electromagnetic field distribution data, a quantitative analysis model of electromagnetic wave reflection, absorption and transmission by metal mesh grid units is established through electromagnetic simulation to obtain the correlation data between shielding effectiveness and the shape, size and arrangement of the mesh grid units.

[0065] In this embodiment, step S2 includes the following specific details, which can be found in the flowchart below. Figure 3 , Figure 3 Here is a detailed diagram of the simulation modeling process provided in the embodiments of this application:

[0066] S210: Based on the electric field strength, magnetic field strength and electromagnetic wave propagation direction in the spatial electromagnetic field distribution data, determine the excitation source parameters required for electromagnetic simulation, and set up a plane wave excitation with corresponding amplitude, polarization and incident direction in the electromagnetic simulation software.

[0067] First, the acquired spatial electromagnetic field distribution data is analyzed, and the electric field polarization direction that appears most frequently from all measurement point data is taken as the main polarization direction. Then, the electromagnetic wave propagation direction vector of each measurement point is averaged to calculate the average electromagnetic wave propagation direction of the entire target area. Finally, the arithmetic mean of the electric field strength readings of all measurement points is calculated as the typical field strength amplitude.

[0068] Then, create a new simulation project in the three-dimensional full-wave electromagnetic simulation software; set a plane wave as the excitation source in the simulation project; set the electric field polarization direction of the plane wave excitation to the main polarization direction obtained by statistical analysis; set the incident direction of the plane wave excitation to the opposite direction of the calculated average electromagnetic wave propagation direction; and set the electric field amplitude of the plane wave excitation to the typical field strength amplitude.

[0069] S220: Based on a variety of preset unit shapes, size parameters, and periodic arrangement methods, a parameterized metal mesh unit simulation model library is established in the electromagnetic simulation software, wherein the construction range of the metal mesh unit simulation model library is constrained by the minimum machinable linewidth.

[0070] In the parametric modeling module of the electromagnetic simulation software, a basic variable geometry model is defined. In this embodiment, the selectable unit shapes of the variable geometry model are first preset to include square rings, circular rings, and hexagonal rings. Then, the variable size parameters of the variable geometry model are defined, including the linewidth of the metal wire, the outer contour side length of the unit graphic, and the diameter. The lower limit of the linewidth parameter is set to the minimum processable linewidth. Then, the periodic arrangement of the variable geometry model is defined as two types: square grid arrangement and hexagonal grid arrangement, and the periodic parameters of the arrangement are defined accordingly. Next, a control script is written to automatically traverse all preset shapes, all size parameter combinations that satisfy the linewidth constraint, and the two arrangement methods to batch generate metal mesh periodic structure simulation models covering all parameter combinations. A unique parameter combination identifier is recorded for each generated metal mesh periodic structure simulation model. All generated metal mesh unit simulation models and parameter identifiers together constitute a parametric metal mesh unit simulation model library.

[0071] S230: For each parameter combination model in the metal mesh unit simulation model library, calculate the reflection coefficient, absorption coefficient and transmission coefficient of the parameter combination model in the target frequency band under the plane wave excitation, and calculate the shielding effectiveness based on the transmission coefficient. Summarize all parameter combinations and the shielding effectiveness values ​​corresponding to the parameter combinations to form associated data.

[0072] First, electromagnetic simulation calculations are performed on each metal mesh element simulation model in the metal mesh element simulation model library. A plane wave excitation is applied to the surface of the metal mesh element simulation model. Next, a frequency scanning range is set in the simulation software. In this embodiment, the frequency scanning range starts from the lowest measured characteristic frequency minus 200 MHz and ends at the highest measured characteristic frequency plus 200 MHz. Then, the electromagnetic field solver of the metal mesh element simulation model is started for calculation. The electromagnetic field solver first discretizes the simulation space into fine mesh elements. For each scanning frequency point, the electromagnetic field solver, based on the finite element method, establishes and solves a large complex linear equation system formed by discretizing Maxwell's equations at all mesh nodes, thereby calculating the complex electric field and complex magnetic field values ​​at each node, and obtaining the complete electromagnetic field distribution around the metal mesh element simulation model at that frequency.

[0073] Based on the calculated complete electromagnetic field distribution, the power of each component is calculated through numerical integration. Three rectangular surfaces are set as power monitoring surfaces in the simulation software. The incident power monitoring surface is positioned between the plane wave excitation source and the metal mesh structure. This surface is parallel to and coplanar with the outer contour plane of the metal mesh structure, with a length equal to the outer contour length plus 40 mm and a width equal to the outer contour width plus 40 mm. The Poynting vector at all points on this incident power monitoring surface is integrally applied to obtain the total incident power. The reflected power monitoring surface is also positioned between the metal mesh structure and the plane wave excitation source. This surface is parallel to the outer contour plane of the structure, located on the front surface of the structure facing the excitation source, 50 mm from the front surface, with a length equal to the outer contour length plus 100 mm and a width equal to the outer contour width plus 100 mm. The total reflected power is obtained by integrating the Poynting vectors of all points on the reflected power monitoring surface for 100 mm. The transmitted power monitoring surface is located on the side of the metal mesh structure facing away from the excitation source. This transmitted power monitoring surface is parallel to the outer contour plane of the structure, located on the rear surface of the structure facing away from the excitation source, 50 mm away from the rear surface of the structure. Its length is equal to the length of the outer contour of the structure plus 100 mm, and its width is equal to the width of the outer contour of the structure plus 100 mm. The total transmitted power is obtained by integrating the Poynting vectors of all points on this transmitted power monitoring surface. Then, the modulus of the reflection coefficient is calculated by dividing the total reflected power by the total incident power and then taking the square root. The modulus of the transmission coefficient is calculated by dividing the total transmitted power by the total incident power and then taking the square root. The absorptivity is calculated by subtracting the square of the modulus of the reflection coefficient from the value and then subtracting the square of the modulus of the transmission coefficient.

[0074] Then, the shielding effectiveness is calculated based on the transmission coefficient. The specific process is to first take the modulus of the complex transmission coefficient, then calculate the logarithm of the modulus of the complex transmission coefficient to the base 10, and finally multiply the logarithm by -20. The result is the shielding effectiveness at that frequency point.

[0075] The above simulation, calculation, and data processing process is repeated for all metal mesh unit simulation models in the metal mesh unit simulation model library. Finally, the unique parameter combination identifier recorded for each metal mesh unit simulation model, which includes the specific shape code, all size values, and arrangement period, is associated one-to-one with the calculated shielding effectiveness values ​​at each point across the entire frequency band. The parameter identifiers and corresponding shielding effectiveness data of all metal mesh unit simulation models are summarized and stored in a structured database table. This database table contains the association data between shielding effectiveness and the shape, size, and arrangement of the mesh units.

[0076] S3: Combining electromagnetic parameters and geometric constraints, nonlinear fitting is performed on the associated data to generate electromagnetic response characteristic curves of metal mesh cells in multiple frequency bands, and key geometric features sensitive to changes in electromagnetic field are identified.

[0077] In this embodiment, step S3 includes the following specific details, which can be found in the flowchart below. Figure 4 , Figure 4 Here is a detailed flowchart of the feature analysis process provided in this application embodiment:

[0078] S310: The electromagnetic parameters, namely the complex relative permittivity and the complex relative permeability, are updated as material properties in the simulation settings of the electromagnetic simulation software. The shielding effectiveness of some sample points in the associated data is recalculated using the updated simulation settings. The original associated data is calibrated based on the recalculation results to obtain the calibrated associated dataset.

[0079] From the acquired electromagnetic parameter dataset of the substrate material, the specific values ​​of the complex relative permittivity and complex relative permeability at various frequency points are read. The real and imaginary values ​​of the complex relative permittivity and the complex relative permeability are used as the precise electromagnetic properties of the substrate material, and are input and updated into the material property definition library of the three-dimensional full-wave electromagnetic simulation software, replacing the original default material parameters. Then, from the established complete correlation data table of shielding effectiveness and geometric parameters, a systematic sampling method is used to sample, that is, starting from the first record, one record is selected every six records, for a total of 15% of the total number of records in the original data table.

[0080] Next, using simulation software settings updated with real material properties, a complete full-wave electromagnetic simulation was re-executed for each selected sample model. This simulation involved reapplying the same plane wave excitation conditions, setting the same frequency scan range, starting the solver to calculate the electromagnetic field distribution around the model, and using the same power monitoring surface numerical integration method as when establishing the correlation data to calculate a new set of shielding effectiveness data for each frequency point within the target frequency band. The newly calculated full set of shielding effectiveness data was then compared with the original effectiveness data of the corresponding sample model in the original correlation data table, frequency by frequency, to calculate the relative deviation percentage for each sample at each frequency point. The average of the relative deviation percentages of all samples at the same frequency point was then taken as the value for that frequency. The representative systematic deviation of each frequency point is determined; a scatter plot of the deviation versus frequency is drawn with frequency as the x-axis and the representative systematic deviation of each frequency point as the y-axis; then, a least-squares fit is performed on the above scatter data using a cubic polynomial to obtain the coefficients of each term of the polynomial, thus obtaining a cubic polynomial correction function with frequency as the variable; then, this cubic polynomial correction function is applied to calibrate the original associated data table. In this embodiment, the specific calibration method is as follows: for any shielding effectiveness value at a specific frequency point in the original data table, firstly, the frequency value is substituted into the correction function to calculate the correction amount, and then the original effectiveness value is added to the correction amount to obtain the calibrated new effectiveness value; this operation is performed on all data points, and finally a set of calibrated associated datasets that eliminates the systematic deviation caused by inaccurate material parameters is obtained;

[0081] S320: Using the unit shape code, unit characteristic size and arrangement period as independent variables and the shielding effectiveness value at each target frequency as dependent variable, the least squares method is used to perform multivariate nonlinear surface fitting on the calibrated associated dataset to establish a mapping function from geometric parameters to shielding effectiveness.

[0082] Data for training the machine learning model is extracted from the calibrated associated dataset. For each record in the data table, its independent variable parameters are extracted. First, the unit shape is processed. In this embodiment, the three preset shape categories of square ring, circular ring, and hexagonal ring are converted into three-dimensional one-hot encoded vectors, that is, the square ring corresponds to vector 100, the circular ring corresponds to vector 010, and the hexagonal ring corresponds to vector 001. Then, continuous numerical independent variables are extracted, including the width value of the metal wire, the outer contour size value of the unit graphic, and the period value of the periodic arrangement. At the same time, the shielding effectiveness value of the record at three key target frequency points selected in advance and used for subsequent evaluation is extracted as the dependent variable. In this embodiment, the three key frequency points are 1.8 GHz, 5.2 GHz, and 10 GHz.

[0083] Next, a machine learning algorithm based on Gaussian process regression is used to perform multivariate nonlinear surface fitting on the processed dataset. The specific training process is as follows: all recorded independent variable data are combined to form a feature matrix, and the shielding effectiveness values ​​of the three corresponding key frequency points are respectively used to form three target vectors. First, a Gaussian process regression model is initialized, where the covariance function is selected as the radial basis function. By maximizing the marginal likelihood function of the model, the length scale parameter and signal variance parameter of the covariance function are automatically adjusted using the conjugate gradient optimization algorithm to minimize the sum of squared errors between the predicted values ​​of the Gaussian process regression model and the true observed values ​​in the training data. After the training process is completed, the trained Gaussian process regression model is obtained. This model constitutes a mapping function from geometric parameters to shielding effectiveness, and can predict the shielding effectiveness values ​​at the three key target frequency points based on a set of geometric parameter vectors containing the shape encoding line width, outline size, and period.

[0084] S330: Analyze the partial derivatives of the mapping function with respect to each independent variable within the preset frequency band, and identify the geometric parameters corresponding to the independent variables that cause the shielding effectiveness to change rate to exceed the set threshold as key geometric features sensitive to electromagnetic field changes.

[0085] Next, a global sensitivity analysis is performed on the trained Gaussian process regression mapping function. In this embodiment, within the preset target operating frequency band of 1.8 GHz to 10 GHz, ten frequency points are uniformly selected as the analysis objects at intervals of 0.9 GHz, namely 1.8 GHz, 2.7 GHz, 3.6 GHz, 4.5 GHz, 5.4 GHz, 6.3 GHz, 7.2 GHz, 8.1 GHz, 9 GHz, and 10 GHz. For each continuous numerical independent variable, in this embodiment, taking the metal linewidth as an example, within the design value range of 1 micrometer to 50 micrometers, fifty different numerical points are uniformly selected in increments of 1 micrometer, referred to as linewidth sample points. At each selected frequency point, the partial derivatives of the shielding effectiveness prediction value with respect to the target independent variable are calculated at all fifty linewidth sample points. In this embodiment, the specific calculation process is as follows: For each linewidth sample point, firstly, all other geometric... The parameters are preset reference values. Then, using the independent variable value of the linewidth sample point as a benchmark, an input parameter vector is generated and input into the trained Gaussian process regression model to obtain the benchmark shielding effectiveness prediction value. Next, an extremely small perturbation is added to the benchmark value of the independent variable. In this embodiment, it is increased by 10 to the power of negative 6 micrometers, while keeping other parameters unchanged, forming a new input parameter vector, which is then input into the trained Gaussian process regression model again to obtain the perturbed shielding effectiveness prediction value. Finally, the ratio of the change in the predicted shielding effectiveness value to the perturbation of the independent variable is calculated. This ratio is the value of the partial derivative of the shielding effectiveness at the linewidth sample point with respect to the independent variable. Its physical meaning is to characterize the change in shielding effectiveness caused by a one-micrometer change in the independent variable when all other geometric parameters are fixed. The above calculation is repeated for fifty linewidth sample points to obtain fifty sensitivity values ​​of the shielding effectiveness at that frequency point to the change of the independent variable.

[0086] Next, a clear sensitivity threshold is set. In this embodiment, it is set to a change in shielding effectiveness exceeding two decibels per micrometer. All continuous numerical independent variables, including metal linewidth, unit outer contour size, and arrangement period, are traversed, and their partial derivative calculation results at all frequency points are checked. Then, the number of frequency points where the absolute value of the partial derivative of the independent variable is greater than the sensitivity threshold is counted. If the number of such frequency points exceeds half of the total number of frequency points analyzed, the independent variable is determined to be highly sensitive to changes in the electromagnetic field. The specific geometric parameters corresponding to these independent variables that meet the conditions are formally identified as key geometric features that are highly sensitive to changes in the electromagnetic field, and the specific numerical ranges that exhibit high sensitivity are recorded.

[0087] S4: Based on key geometric features, construct a gradient-based aperiodic topological structure model of the metal mesh.

[0088] In this embodiment, step S4 includes the following specific details, which can be found in the flowchart below. Figure 5 , Figure 5 Here is a detailed diagram of the structural construction process provided in the embodiments of this application:

[0089] S410: Based on the key geometric features, the geometric parameter with the highest change sensitivity among the key geometric features is determined as the target geometric parameter; the electromagnetic wave propagation direction is defined as the spatial gradient direction; a preset functional relationship is established for the target geometric parameter to continuously change along the spatial gradient direction within the outer contour boundary, as the gradient change law of the unit parameter;

[0090] First, based on the identified key geometric features, including metal linewidth, unit outer contour size, and arrangement period, the absolute values ​​of the partial derivatives of the three geometric parameters, metal linewidth, unit outer contour size, and arrangement period, are calculated at ten analysis frequency points. The three average values ​​are compared, and the geometric parameter corresponding to the largest average value is selected as the target geometric parameter for spatial gradient control in this round of design.

[0091] Next, the calculated average electromagnetic wave propagation direction is obtained, and the projection direction of the average electromagnetic wave propagation direction onto the metal mesh substrate plane is formally defined as the main direction of spatial gradient change.

[0092] Then, a preset functional relationship is established to show that the target geometric parameters continuously change along the spatial gradient direction within the outer contour boundary. This serves as the gradient change law for all subsequent unit parameter allocations. In this embodiment, the preset functional relationship is specifically determined to be a linear change relationship. The determination method is as follows: First, on the rectangular outer contour boundary of the metal mesh, the coordinates of the midpoint of the boundary located on the side of the reverse extension line of the spatial gradient direction are defined as the gradient start point. The value of the target geometric parameters at the gradient start point is set as the base value of the preset function, and this base value is set as the minimum machinable linewidth obtained. Next, the coordinates of the midpoint of the boundary located on the side of the positive extension line of the spatial gradient direction are defined as the gradient end point. The target... The geometric parameters at the gradient endpoint are set to three times the minimum machinable linewidth. Then, the coordinate distance between the gradient start and end points along the spatial gradient direction is calculated. Next, the gradient slope coefficient is calculated, which is equal to the difference between the parameter values ​​at the gradient endpoint and the gradient start point, divided by the coordinate distance between the gradient start and end points. Finally, a function describing the linear change of the target geometric parameters along the gradient direction is obtained: the target geometric parameter value equals the minimum machinable linewidth plus the gradient slope coefficient multiplied by the gradient direction coordinate value of the cell center point relative to the gradient start point. This function serves as the gradient change rule guiding the allocation of all subsequent cell parameters.

[0093] S420: Under the premise of satisfying the minimum machinable linewidth and the minimum spacing constraint between units in the geometric constraints, a numerical sequence of the target geometric parameters is generated according to the gradient change law, wherein the values ​​in the numerical sequence change along the spatial gradient direction; each value in the numerical sequence is assigned to an independent metal mesh unit as a unit feature size, and all independent metal mesh units are arranged in the outer contour boundary according to a non-periodic random distribution algorithm to form an initial topology;

[0094] First, based on the projected length of the acquired outer contour boundary in the defined gradient direction, and combined with the determined gradient change law—that is, the target geometric parameter value is equal to the minimum machinable linewidth plus the gradient slope coefficient multiplied by the gradient direction coordinate value of the unit center point relative to the gradient start point—the theoretical value of the target geometric parameter corresponding to each coordinate position is calculated sequentially along the gradient direction with a set fixed step size, starting from the gradient start point. In this embodiment, the fixed step size is set to one micrometer, thereby generating a parameter sequence containing a series of specific values. Next, each value in this parameter sequence is checked. If the value is less than the acquired minimum machinable linewidth, the value is corrected to be equal to the minimum machinable linewidth to ensure that all values ​​in the sequence meet the machinability constraints, forming the final gradient parameter sequence for allocation.

[0095] Then, the Poisson disk sampling algorithm is used to generate non-periodic distribution of cell center point coordinates within the outer contour boundary plane. In this embodiment, the specific process is as follows: First, an initial point is randomly selected within the outer contour boundary region as the center position of the first metal mesh cell. Then, a circular region is defined as a restricted area with the initial point as the center and a set minimum point spacing of fifty micrometers as the radius. Next, a new candidate point is randomly generated within the region outside the defined restricted area. It is checked whether the candidate point is located within the outer contour boundary, and the shortest distance between the candidate point and the initial point is calculated. If the shortest distance is greater than or equal to fifty micrometers, the candidate point is accepted as a new cell center position, and a new restricted area is defined with the candidate point as the center. The process of generating candidate points, checking distances and boundary conditions, and accepting new points is repeated until no new candidate point that satisfies the minimum distance constraint can be found within the boundary. Finally, a set of points randomly and uniformly distributed in space is generated as the center point coordinates of each independent metal mesh cell.

[0096] Next, the values ​​in the final generated gradient parameter sequence are assigned to each metal mesh cell as feature sizes based on the specific coordinates of the center point of each metal mesh cell in the spatial gradient direction using a linear interpolation method. The specific calculation method for linear interpolation is as follows: First, obtain the coordinates of all the center points of the metal mesh cells generated by the Poisson disk sampling algorithm; for each metal mesh cell center point, calculate the projected coordinate values ​​in the defined spatial gradient direction; then, in the coordinate sequence corresponding to the final generated gradient parameter sequence and arranged at equal intervals along the gradient direction, find two reference coordinate points adjacent to the projected coordinate values ​​of the metal mesh cell; these two reference coordinate points must satisfy the condition that the coordinate value of one of the points is not significantly different from the other. Given the projected coordinates of this element, the coordinates of another point are not less than the projected coordinates of this element. Obtain the target geometric parameter values ​​corresponding to these two reference coordinate points, denoted as the smaller parameter value and the larger parameter value, respectively. Next, calculate the interpolation scaling factor, which is equal to the difference between the projected coordinates of the metal mesh element and the coordinates of the smaller reference point, divided by the difference between the coordinates of the two reference points. Finally, calculate the feature size to be assigned to the metal mesh element, which is equal to the smaller parameter value plus the interpolation scaling factor multiplied by the difference between the larger and smaller parameter values. Repeat this calculation process for all element center points to obtain the continuously varying feature size value along the gradient direction for each independent metal mesh element.

[0097] Subsequently, a uniform planar shape is assigned to all metal mesh grid units. This assignment process is not random but based on an established mapping function that reflects the quantitative relationship between geometric parameters and shielding effectiveness. In this embodiment, the specific implementation process is as follows: From a preset shape library, square rings, circular rings, and hexagonal rings are selected as candidate shapes. For each candidate shape, while maintaining the same unit feature size, arrangement period, and other geometric conditions, the mapping function from geometric parameters to shielding effectiveness is called to calculate the predicted shielding effectiveness at all key target frequencies. For each shape, the average shielding effectiveness at all key frequencies is calculated. The average shielding effectiveness of all candidate shapes is compared, and the shape with the highest average shielding effectiveness is selected as the uniform configuration for all metal mesh grid units. In this embodiment, based on this calculation and analysis process, the square ring is selected as the uniform unit shape.

[0098] Finally, an initial gradient aperiodic topological structure model is constructed in 3D computer-aided design software. First, based on the acquired substrate material parameters, a 3D solid model of a sapphire glass substrate with a thickness of one millimeter and a shape and size conforming to the outer contour boundary requirements is established. Then, the metal layer thickness is set for each metal mesh unit. Considering the dependence of electromagnetic shielding effectiveness on conductor conductivity, to balance performance and manufacturability, in this embodiment, a thickness generation rule is set: the metal layer thickness of each unit is set to 30% of the metal linewidth value allocated to that unit; for example, for a unit with a linewidth of five micrometers, its metal layer thickness... The thickness is 1.5 micrometers; a unit with a linewidth of 20 micrometers has a metal layer thickness of 6 micrometers; next, a script program is written to accurately generate a 3D solid model of each square ring unit at the corresponding position on the upper surface of the substrate model, based on the center coordinates, uniform shape, allocated linewidth, and thickness calculated according to these rules; this process is repeated until all units are modeled; these metal unit models with different linewidths and thicknesses are attached to a 1-millimeter-thick substrate, spatially aperiodic and randomly distributed with feature dimensions varying along the gradient direction, together forming the initial gradient aperiodic topological structure model;

[0099] S430: Calculate the maximum thickness dimension of the initial topology in the direction perpendicular to the base plane, and determine whether the maximum thickness dimension is less than or equal to the maximum allowable overall thickness; if the maximum thickness dimension is greater than the maximum allowable overall thickness, proportionally compress and adjust the longitudinal dimensions of all units in the initial topology until the compressed maximum thickness dimension satisfies the maximum allowable overall thickness constraint, and generate the final gradient aperiodic topology model.

[0100] First, the metal layer thickness of all metal mesh cells in the initial topology model is measured, and the maximum value is taken as the maximum thickness dimension of the current model in the direction perpendicular to the base plane. Then, this maximum thickness dimension is compared with the obtained maximum allowable overall thickness. If the maximum thickness dimension is less than or equal to the maximum allowable overall thickness, the current initial model is determined to meet the thickness constraints and can be directly used as the final gradient aperiodic topology model. If the maximum thickness dimension is greater than the maximum allowable overall thickness, a scaling factor is calculated, which is equal to the maximum allowable overall thickness divided by the currently measured maximum thickness dimension. Next, the longitudinal dimension parameter of all metal mesh cells in the model, i.e., the thickness value of the metal layer, is uniformly multiplied by this scaling factor for global proportional compression. This compression operation may slightly change the original set ratio of cell thickness to linewidth, but it ensures the feasibility of the design in the overall physical space. After the compression operation is completed, the maximum thickness dimension of the model is remeasured and calculated to confirm that it is less than or equal to the maximum allowable overall thickness, thereby generating the final gradient aperiodic topology model that meets all thickness constraints.

[0101] S5: Perform electromagnetic structure coupling simulation on the gradient aperiodic topology model to evaluate the shielding stability and mechanical strength under actual electromagnetic environment, and minimize the fluctuation of shielding effectiveness by iteratively adjusting the unit parameters.

[0102] In this embodiment, step S5 includes the following specific details, which can be found in the flowchart below. Figure 6 , Figure 6 Here is a detailed flowchart of the coupling optimization process provided in the embodiments of this application:

[0103] S510: Perform multi-angle incident electromagnetic simulation on the gradient aperiodic topology model. Within the preset incident angle range, change the plane wave incident angle with a fixed step size, calculate the shielding effectiveness at each incident angle, and evaluate the shielding stability by the standard deviation of the shielding effectiveness at all incident angles.

[0104] The generated final gradient-based aperiodic topological structure model is imported into a 3D electromagnetic simulation software. First, a plane wave excitation is set, with its frequency set as the key target frequency. In this embodiment, it is set to 5.2 GHz, and the electric field polarization direction is consistent with the previously determined main polarization direction. The range of the incident angle is defined as 0 to 60 degrees. A fixed step size of 10 degrees is set for the incident angle variation. Within this angle range, the incident direction vector of the plane wave is gradually changed according to this step size to simulate the scenario of electromagnetic waves incident from different directions. Seven cases are simulated sequentially, with incident angles of 0, 10, 20, 30, 40, 50, and 60 degrees. For each set incident angle... To determine the shielding effectiveness, the simulation software is used to adjust the plane wave propagation direction vector to the corresponding direction, and then a full-wave electromagnetic simulation is performed. After the full-wave electromagnetic simulation is completed, the transmission coefficient of the gradient-based aperiodic topology model at the target frequency is extracted, and the shielding effectiveness value is calculated based on the transmission coefficient. The above simulation and calculation process is repeated for all seven incident angles to obtain seven shielding effectiveness values ​​corresponding to different incident angles. Finally, the standard deviation of these seven shielding effectiveness values ​​is calculated. This standard deviation quantifies the degree of dispersion of shielding effectiveness as the incident angle of electromagnetic waves changes. The smaller the standard deviation value, the more stable the shielding performance is under different incoming wave directions, that is, the higher the shielding stability.

[0105] S520: Perform static finite element analysis on the gradient aperiodic topology model to simulate the stress distribution of the gradient aperiodic topology model under a preset load, and use whether the maximum stress point exceeds the material yield strength as the evaluation standard for mechanical strength.

[0106] The same gradient aperiodic topology model was imported into the finite element analysis software; a fixed constraint was applied to the bottom surface of the sapphire glass substrate to restrict all translational and rotational degrees of freedom of all nodes on the plane; then, according to the mechanical load that the metal mesh may bear in the application scenario, a uniformly distributed vertical pressure load of one kPa was applied to the entire upper surface of the gradient aperiodic topology model to simulate uniform wind pressure.

[0107] Next, set the material properties and assign them to the geometry. In the software's material library management interface, create a new custom material. Based on the known physical properties of the substrate material, name this material sapphire glass. In the material property table, enter 350 GPa for the elastic modulus, 0.22 for the Poisson's ratio, and 300 MPa for the yield strength. Then, create a new custom material and name it electroplated copper. In the material property table, enter 110 GPa for the elastic modulus, 0.34 for the Poisson's ratio, and 70 MPa for the yield strength. In the software's view area, select the sapphire glass substrate geometry in the gradient aperiodic topology model and specify its material as sapphire glass in the property panel. Select the geometry of all metal mesh elements in the model and uniformly specify their material as electroplated copper in the property panel.

[0108] Subsequently, mesh generation is performed; mesh generation parameters are set in the analysis project; tetrahedral elements are selected as the element type, and curvature-adaptive mesh generation method is selected; the global element size is set to half of the smallest feature size in the target model. In this embodiment, the smallest feature size is a 5-micron linewidth, so the initial element size is set to 2.5 microns; automatic mesh generation is started. In this embodiment, the specific process is as follows: nodes are arranged according to the initial size; the curvature of the gradient aperiodic topology model surface is calculated, and nodes are automatically densified in areas with high curvature, such as the edges of metal wires; finally, the model is discretized into a computational mesh composed of dense tetrahedral elements.

[0109] Next, the solution calculation is performed. The solver first reads the coordinates of the four nodes of each tetrahedral element. Using these coordinates, the solver calculates the geometric matrix of the element. The specific calculation method of the geometric matrix is ​​as follows: the solver first performs a linear operation of subtracting and multiplying the coordinate values ​​of the four nodes pairwise to obtain a set of constant values ​​that reflect the shape of the element. Then, this set of constant values ​​is divided by six times the element volume to construct a matrix of six rows and twelve columns. This matrix establishes the numerical conversion relationship between the nodal displacement of the element and the strain inside the element.

[0110] Simultaneously, the solver calculates the material elasticity matrix for each element. The calculation process is as follows: The material properties of the element are read, namely the elastic modulus and Poisson's ratio. First, two Lamé constants are calculated using the elastic modulus and Poisson's ratio. The first Lamé constant equals the elastic modulus multiplied by Poisson's ratio, then divided by the product of one plus Poisson's ratio and one minus two times Poisson's ratio. The second Lamé constant equals the elastic modulus divided by twice the one plus Poisson's ratio. Then, the solver constructs a six-row, six-column matrix; the material elasticity matrix has the first row and first column, the second row and second column... The element values ​​in the first row and third column of the material elasticity matrix are all equal to the first Lamé constant plus twice the second Lamé constant; the element values ​​in the first row and second column, first row and third column, second row and first column, second row and third column, third row and first column, and third row and second column of the material elasticity matrix are all equal to the first Lamé constant; the element values ​​in the fourth row and fourth column, fifth row and fifth column, and sixth row and sixth column of the material elasticity matrix are all equal to the second Lamé constant; all element values ​​in the material elasticity matrix not mentioned in the above rules are set to zero.

[0111] Next, the solver transposes the aforementioned geometric matrix, multiplies it by the material elasticity matrix and the geometric matrix itself, and then multiplies the result by the element volume to obtain the element's stiffness matrix. This process is repeated for all elements. Then, based on the global node number, the solver assembles the stiffness matrix values ​​of all elements into a large global stiffness matrix. Simultaneously, the solver converts the applied 1 kPa surface pressure into equivalent nodal forces based on the area and direction of each stressed surface, summing them into a nodal load vector. Subsequently, the solver modifies the global stiffness matrix and load vector according to fixed constraints and solves a large system of linear equations using numerical methods, with the global stiffness matrix as coefficients, nodal displacements as unknowns, and nodal loads as results. The displacements of all nodes in the model are calculated. After obtaining the node displacements, the solver performs back-substitution calculations: for each element, the node displacement values ​​belonging to that element are extracted and multiplied by the previously calculated geometric matrix of that element to obtain the strain component at the center of the element; then, this strain component is multiplied by the material elasticity matrix of that element to obtain the stress component at the center of the element; finally, according to the von Mises yield criterion, the solver substitutes the calculated six stress components into the criterion formula for synthesis calculation. The formula includes the sum of squares of the differences between the normal stress components and the sum of squares of the shear stress components, and finally calculates a scalar value, that is, the equivalent stress of the element; this process is performed on all elements to obtain the equivalent stress distribution of the entire gradient aperiodic topology model.

[0112] Finally, the results are evaluated. After the solution is completed, an equivalent stress distribution cloud map of the gradient aperiodic topological structure model is generated. By observing the cloud map, the location with the darkest color, representing the highest stress, can be clearly seen in the model. The data probe tool in the software is used to click on this location to read the specific value of the equivalent stress. This equivalent stress value is directly compared with the yield strength of the electroplated copper material, which is 70 MPa. If the read value is less than 70 MPa, the mechanical strength of the gradient aperiodic topological structure model is determined to meet the design requirements. If the value is greater than or equal to 70 MPa, the mechanical strength of the gradient aperiodic topological structure model is determined to not meet the requirements.

[0113] S530: The optimization objective is to minimize the standard deviation of shielding effectiveness, while adhering to geometric constraints and passing mechanical strength evaluation. The gradient sequence variation function parameters and cell distribution density are adjusted through an iterative algorithm.

[0114] First, establish an automated optimization process; set the standard deviation of shielding effectiveness calculated in multi-angle incident electromagnetic simulation as the primary objective to be minimized; the optimization process must meet two constraints: first, strictly adhere to all initially given geometric constraints, including minimum machinable linewidth, maximum allowable overall thickness, and outer contour boundary; second, any new design must pass the aforementioned static analysis, and its maximum equivalent stress must be lower than the yield strength of the electroplated copper material.

[0115] Next, two core design variables were identified as optimization targets: the first was the slope parameter of the linear function controlling the rate of change of the metal linewidth along the spatial gradient direction, and the second was the minimum point spacing parameter of the Poisson disk sampling, which controls the density of the unit distribution on the plane. A genetic algorithm was then used as the optimization engine, with a population size of fifty, a maximum number of iterations of one hundred, and crossover and mutation probabilities set. After optimization began, an initial population containing fifty sets of random parameter combinations was generated. Subsequently, for each set of parameters in the initial population, an automated script executed a complete design evaluation loop: recalculating the linewidth gradient sequence based on the current slope parameter, regenerating the coordinates of the unit center points based on the current minimum point spacing parameter, and reconstructing a completely new three-dimensional gradient aperiodic topological structure model. The total thickness of the new gradient aperiodic topological structure model was automatically checked and proportionally compressed as required. The new gradient aperiodic topological structure model was then submitted to electromagnetic simulation software and finite element analysis software, respectively, automatically completing the same multi-angle shielding effectiveness simulation and static strength analysis as before, obtaining the shielding effectiveness standard deviation and maximum equivalent stress value, respectively.

[0116] Then, the fitness of each set of parameters is calculated based on the standard deviation of shielding effectiveness and the maximum equivalent stress value. The specific method for fitness calculation is as follows: First, constraint judgment is performed; it is determined whether the maximum equivalent stress value is less than 70 MPa, and it is checked whether the dimensions of the new gradient aperiodic topology model meet all geometric constraints; if any of the above conditions are not met, a fitness score of zero is directly assigned to the set of parameters, so that it is eliminated in subsequent selections; if all constraints are met, a formal fitness calculation is performed, dividing the constant 1000 by the standard deviation of shielding effectiveness plus one, and the quotient obtained is the fitness score of the set of parameters; through this calculation method, the smaller the standard deviation of shielding effectiveness, the closer the denominator is to one, and the higher the fitness score, with an upper limit of 1000;

[0117] Next, based on the calculated fitness scores, a roulette wheel selection method is used to select superior individuals from the current population as parents. The specific operation of the roulette wheel selection method is as follows: First, calculate the sum of the fitness scores of all individuals in the current population; then calculate the proportion of each individual's fitness score in the total score, which represents the probability of that individual being selected; then generate a random number between zero and one, and select the individual whose probability cumulative interval the random number falls into; repeat this selection process until a sufficient number of individuals are selected as parents; then, these parent individuals are... The process involves performing a simulated binary crossover operation, where a partial parameter value is swapped between any two paired parent individuals with a probability of 0.8, thus generating offspring individuals. Next, a polynomial mutation operation is performed on the offspring individuals, where a small random perturbation is applied to the parameter values ​​of the offspring individuals with a probability of 0.1, introducing new genetic changes. The new individuals generated through crossover and mutation, along with the five most fit elite individuals in the current population, form the next generation of the population. This process is iterated until the fitness score of the best individual in the population no longer increases after twenty consecutive generations. At this point, the optimization process is considered converged, and the iteration is terminated.

[0118] S540: Outputs the final set of model parameters that satisfy the optimization objective and all constraints;

[0119] After the genetic algorithm completes its optimization cycle, it selects all individuals that meet the constraints from the final generation population. These individuals correspond to gradient-based aperiodic topological structures that satisfy geometric constraints and whose maximum equivalent stress is lower than the material yield strength. Then, from these qualified individuals, it selects the individual with the smallest standard deviation of shielding effectiveness. The set of design parameters corresponding to this individual is the global optimal solution. This set of parameters explicitly includes the optimal gradient slope value and the minimum spacing value of the optimal cell distribution determined through optimization. The output of these two key parameter values ​​defines the final metal mesh grid topology configuration with the most stable shielding performance and reliable structure under given constraints.

[0120] S6: Based on the optimized gradient aperiodic topology model, generate a digital design file for the metal mesh grid to obtain the precise definition and fabrication process requirements of each unit;

[0121] In this embodiment, step S6 includes the following specific details, which can be found in the flowchart below. Figure 7 , Figure 7 Here is a detailed diagram of the document generation process provided in this application embodiment:

[0122] S610: Based on the final model parameter set, reconstruct the optimized gradient aperiodic topological structure 3D model to obtain the final metal mesh 3D model, and accurately record the center point coordinates, shape control parameters and feature dimensions of each independent unit;

[0123] Based on the final model parameter set from the optimized output, a control script program is written to drive the application programming interface of the computer-aided design software. Through program control, the optimized gradient-based aperiodic topological structure 3D model is accurately reconstructed in the computer-aided design software to obtain the final metal mesh 3D model. During the automatic modeling process, a globally unique digital identifier is generated for each independent metal mesh unit. At the same time, the complete attributes of each unit are accurately recorded in a structured data format, including the X, Y, and Z coordinates of the unit's center point in the global coordinate system, the unit's shape type control code, and the specific values ​​of all characteristic dimensions such as the metal wire width, inner diameter, and outer diameter. All these attribute data of all units are summarized to generate a complete unit parameter list file that can be read by a computer.

[0124] S620: For the minimum machinable linewidth and substrate material properties, annotate the recommended fabrication process for each unit in the three-dimensional model of the metal mesh.

[0125] Read the linewidth feature dimension value of each cell from the cell parameter list file; call the pre-established process rule knowledge base for matching. The rules of this process rule knowledge base are defined as follows: if the cell linewidth is greater than or equal to 10 micrometers, screen printing is recommended; if the cell linewidth is less than 10 micrometers but greater than or equal to 2 micrometers, ultraviolet lithography combined with electroplating is recommended; if the cell linewidth is less than 2 micrometers, electron beam lithography combined with sputtering is recommended; traverse the linewidth values ​​of all cells, perform logical judgment and matching according to the above rule base, and add the corresponding recommended fabrication process annotation information to the attribute data of each cell;

[0126] S630: Integrates the metal mesh 3D model, a list of all unit parameters, and process annotation information, and outputs a digital design file containing 3D solid files, 2D engineering drawings, and a manufacturing process manual;

[0127] The reconstructed 3D solid model of the metal mesh is exported from the computer-aided design software as a common 3D geometry exchange format file. Based on this 3D solid model, the software automatically generates orthographic 2D engineering drawings with key contour dimensions, positioning dimensions, and tolerance annotations, and exports them as common 2D drawing format files. The list file containing the precise parameters of all units is integrated with the added process annotation information to generate a structured manufacturing process specification document, which details the required base material and metal material requirements, the overall process flow, and the specific processing method corresponding to each unit identification code. Finally, the 3D model file, 2D engineering drawing file, and manufacturing process specification document are packaged to form a complete digital design file package that can be directly used to guide production, manufacturing, and quality inspection.

[0128] Please see Figure 8 , Figure 8 This is a schematic diagram of the structure of a metal mesh grid design optimization system based on electromagnetic shielding effectiveness provided in an embodiment of this application;

[0129] This embodiment demonstrates the overall system architecture for implementing the above method; the system constructs a complete technology chain from actual electromagnetic environment perception, electromagnetic characteristic modeling, intelligent structure generation, multi-physics field collaborative optimization to digital manufacturing output through the collaborative work of six core functional modules;

[0130] The data acquisition module serves as the system's environment and constraint input interface, responsible for acquiring spatial electromagnetic field distribution data of the target area, including electric field strength, magnetic field strength, and electromagnetic wave propagation direction; it is also responsible for acquiring the electromagnetic parameters of the metal mesh substrate material and the product's predetermined geometric constraints.

[0131] The simulation modeling module, as the basic electromagnetic property quantification unit, is responsible for establishing a quantitative analysis model of the reflection, absorption and transmission of electromagnetic waves by the metal mesh unit based on the acquired spatial electromagnetic field distribution data and through parametric electromagnetic simulation, thereby obtaining the correlation data between the shielding effectiveness and the shape, size and arrangement of the mesh unit.

[0132] The feature analysis module, as the design rule mining unit, is responsible for combining the electromagnetic parameters and geometric constraints of the substrate material to perform nonlinear fitting on the correlation data obtained from the simulation, generating electromagnetic response characteristic curves of the metal mesh unit in multiple frequency bands, and identifying the key geometric features that are most sensitive to changes in the electromagnetic field.

[0133] The structure building module, as an adaptive topology generation unit, is responsible for determining the target parameters and spatial gradient direction that need to be gradient-controlled based on the identified key geometric features, and constructing a three-dimensional model of the gradient-based aperiodic topology of the metal mesh grid according to the preset gradient change law.

[0134] The coupling optimization module, as a multi-objective collaborative decision-making unit, is responsible for performing electromagnetic shielding stability simulation and mechanical structure strength analysis on the constructed gradient aperiodic topology model. With the goal of minimizing shielding effectiveness fluctuations and geometric and strength constraints as conditions, it automatically adjusts the design parameters through iterative algorithms and outputs the final model parameter set after multi-physics collaborative optimization.

[0135] The document generation module, as a digital manufacturing output unit, is responsible for reconstructing an accurate 3D model in a computer-aided design environment based on the optimized final model parameter set, recording all attributes of each unit, and labeling the manufacturing process for each unit according to the process rules. Finally, it integrates and outputs a complete digital design file package including a 3D model, 2D engineering drawings, and manufacturing process manual.

[0136] Embodiments of the present invention also provide an electronic device, including a memory, a processor, and a communication bus; the memory and the processor are connected via the communication bus. The memory stores a metal mesh grid design optimization method based on electromagnetic shielding effectiveness, which can be loaded and executed by the processor as provided in the above embodiments.

[0137] The memory can be used to store instructions, programs, code, code sets, or instruction sets. The memory may include a program storage area and a data storage area. The program storage area may store instructions for implementing an operating system, instructions for at least one function, and instructions for implementing the metal mesh grid design optimization method based on electromagnetic shielding effectiveness provided in the above embodiments. The data storage area may store data involved in the metal mesh grid design optimization method based on electromagnetic shielding effectiveness provided in the above embodiments.

[0138] The processor may include one or more processing cores. The processor executes instructions, programs, code sets, or instruction sets stored in memory, and calls data stored in memory to perform various functions and process data as described in this application. The processor may be at least one of the following: Application-Specific Integrated Circuit (ASIC), Digital Signal Processor (DSP), Digital Signal Processing Device (DSPD), Programmable Logic Device (PLD), Field Programmable Gate Array (FPGA), Central Processing Unit (CPU), controller, microcontroller, and microprocessor. It is understood that, for different devices, the electronic devices used to implement the above-described processor functions may also be other types, and the embodiments of this application do not specifically limit this.

[0139] A communication bus can include a pathway for transmitting information between the aforementioned components. The communication bus can be a PCI (Peripheral Component Interconnect) bus or an EISA (Extended Industry Standard Architecture) bus, etc. Communication buses can be categorized as address buses, data buses, control buses, etc.

[0140] This application provides a computer-readable storage medium storing a computer program that can be loaded by a processor and executed as described in the above embodiments, which is a method for optimizing the design of a metal mesh grid based on electromagnetic shielding effectiveness.

[0141] In this embodiment, a computer-readable storage medium can be a tangible device that holds and stores instructions used by an instruction execution device. A computer-readable storage medium can be, but is not limited to, an electrical storage device, a magnetic storage device, an optical storage device, an electromagnetic storage device, a semiconductor storage device, or any combination thereof. Specifically, a computer-readable storage medium can be a portable computer disk, a hard disk, a USB flash drive, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), spoofing random access memory (SRAM), portable compact disc read-only memory (CD-ROM), digital multifunction disc (DVD), memory stick, floppy disk, optical disk, magnetic disk, mechanical encoding device, or any combination thereof.

[0142] The terms “comprising,” “including,” or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, article, or apparatus.

[0143] The above description is merely a preferred embodiment of this application and an explanation of the technical principles employed. Those skilled in the art should understand that the scope of this application is not limited to technical solutions formed by specific combinations of the above-described technical features, but should also cover other technical solutions formed by arbitrary combinations of the above-described technical features or their equivalents without departing from the foregoing application concept. For example, technical solutions formed by substituting the above-described features with (but not limited to) technical features with similar functions claimed in this application.

Claims

1. A method for designing and optimizing metal mesh grids based on electromagnetic shielding effectiveness, characterized in that, Includes the following steps: S1: Acquire spatial electromagnetic field distribution data of the target area, including electric field strength, magnetic field strength and electromagnetic wave propagation direction, and at the same time acquire the electromagnetic parameters and geometric constraints of the metal mesh substrate material; S2: Based on spatial electromagnetic field distribution data, a quantitative analysis model of the reflection, absorption, and transmission of electromagnetic waves by metal mesh grid units is established through electromagnetic simulation, obtaining the correlation data between shielding effectiveness and the shape, size, and arrangement of the mesh grid units; including: S210: Based on the electric field strength, magnetic field strength and electromagnetic wave propagation direction in the spatial electromagnetic field distribution data, determine the excitation source parameters required for electromagnetic simulation, and set up a plane wave excitation with corresponding amplitude, polarization and incident direction in the electromagnetic simulation software. S220: Based on a variety of preset unit shapes, size parameters, and periodic arrangement methods, a parameterized metal mesh unit simulation model library is established in the electromagnetic simulation software. The construction range of the metal mesh unit simulation model library is constrained by the minimum machinable linewidth. S230: For each parameter combination model in the metal mesh unit simulation model library, calculate the reflection coefficient, absorption coefficient and transmission coefficient of the parameter combination model in the target frequency band under the plane wave excitation, and calculate the shielding effectiveness based on the transmission coefficient. Summarize all parameter combinations and the shielding effectiveness values ​​corresponding to the parameter combinations to form associated data. S3: Combining electromagnetic parameters and geometric constraints, nonlinear fitting is performed on the correlated data to generate electromagnetic response characteristic curves of the metal mesh element in multiple frequency bands, and key geometric features sensitive to changes in the electromagnetic field are identified; including: S310: The electromagnetic parameters, namely the complex relative permittivity and the complex relative permeability, are updated as material properties in the simulation settings of the electromagnetic simulation software. The shielding effectiveness of some sample points in the associated data is recalculated using the updated simulation settings. The original associated data is calibrated based on the recalculation results to obtain the calibrated associated dataset. S320: Using the unit shape code, unit characteristic size and arrangement period as independent variables and the shielding effectiveness value at each target frequency as dependent variable, the least squares method is used to perform multivariate nonlinear surface fitting on the calibrated associated dataset to establish a mapping function from geometric parameters to shielding effectiveness. S330: Analyze the partial derivatives of the mapping function with respect to each independent variable within the preset frequency band, and identify the geometric parameters corresponding to the independent variables that cause the shielding effectiveness to change rate to exceed the set threshold as key geometric features sensitive to electromagnetic field changes. S4: Based on key geometric features, construct a gradient-based aperiodic topological structure model of the metal mesh; including: S410: Based on the key geometric features, the geometric parameter with the highest change sensitivity among the key geometric features is determined as the target geometric parameter; the electromagnetic wave propagation direction is defined as the spatial gradient direction; a preset functional relationship is established for the target geometric parameter to continuously change along the spatial gradient direction within the outer contour boundary, as the gradient change law of the unit parameter; S420: Under the premise of satisfying the minimum machinable linewidth and the minimum spacing constraint between units in the geometric constraints, a numerical sequence of the target geometric parameters is generated according to the gradient change law, wherein the values ​​in the numerical sequence change along the spatial gradient direction; each value in the numerical sequence is assigned to an independent metal mesh unit as a unit feature size, and all independent metal mesh units are arranged in the outer contour boundary according to a non-periodic random distribution algorithm to form an initial topology; S430: Calculate the maximum thickness dimension of the initial topology in the direction perpendicular to the base plane, and determine whether the maximum thickness dimension is less than or equal to the maximum allowable overall thickness; if the maximum thickness dimension is greater than the maximum allowable overall thickness, proportionally compress and adjust the longitudinal dimensions of all units in the initial topology until the compressed maximum thickness dimension satisfies the maximum allowable overall thickness constraint, and generate the final gradient aperiodic topology model. S5: Perform electromagnetic structure coupling simulation on the gradient aperiodic topology model to evaluate the shielding stability and mechanical strength under actual electromagnetic environment, and minimize the fluctuation of shielding effectiveness by iteratively adjusting the unit parameters. S6: Based on the optimized gradient aperiodic topology model, generate digital design files for the metal mesh grid to obtain the precise definition and fabrication process requirements for each unit.

2. The method for designing and optimizing metal mesh grids based on electromagnetic shielding effectiveness according to claim 1, characterized in that, The acquisition of spatial electromagnetic field distribution data of the target area includes electric field strength, magnetic field strength, and electromagnetic wave propagation direction. Simultaneously, the electromagnetic parameters and geometric constraints of the metal mesh substrate material are acquired, including: S110: Multiple measurement points are set up in the target area. The electric field strength value of each measurement point is collected using a field strength meter to form the electric field strength. The magnetic field strength value of each measurement point is collected using an electromagnetic field probe to form the magnetic field strength. S120: Based on the electric field strength and the magnetic field strength, the direction of electromagnetic wave propagation is obtained by calculating the direction of the average Poynting vector; S130: The acquired electric field strength, magnetic field strength, and electromagnetic wave propagation direction are used together as spatial electromagnetic field distribution data; S140: Obtain the complex relative permittivity and complex relative permeability of the metal mesh substrate material as electromagnetic parameters; obtain parameters including the maximum allowable overall thickness, minimum machinable linewidth, and outer contour boundary as geometric constraints.

3. The method for designing and optimizing metal mesh grids based on electromagnetic shielding effectiveness according to claim 2, characterized in that, The electromagnetic structure coupling simulation of the gradient aperiodic topology model is performed to evaluate the shielding stability and mechanical strength under actual electromagnetic conditions, and the shielding effectiveness fluctuation is minimized by iteratively adjusting the unit parameters, including: S510: Perform multi-angle incident electromagnetic simulation on the gradient aperiodic topology model. Within the preset incident angle range, change the plane wave incident angle with a fixed step size, calculate the shielding effectiveness at each incident angle, and evaluate the shielding stability by the standard deviation of the shielding effectiveness at all incident angles. S520: Perform static finite element analysis on the gradient aperiodic topology model to simulate the stress distribution of the gradient aperiodic topology model under a preset load, and use whether the maximum stress point exceeds the material yield strength as the evaluation standard for mechanical strength. S530: The optimization objective is to minimize the standard deviation of shielding effectiveness, while adhering to geometric constraints and passing mechanical strength evaluation. The variation function parameters and element distribution density of the numerical sequence are adjusted through an iterative algorithm. S540: Outputs the final set of model parameters that satisfy the optimization objective and all constraints.

4. The method for designing and optimizing metal mesh grids based on electromagnetic shielding effectiveness according to claim 3, characterized in that, The process involves generating a digital design file for the metal mesh based on the optimized gradient aperiodic topology model, obtaining the precise definition and fabrication process requirements for each unit, including: S610: Based on the final model parameter set, reconstruct the optimized gradient aperiodic topological structure 3D model to obtain the final metal mesh 3D model, and accurately record the center point coordinates, shape control parameters and feature dimensions of each independent unit; S620: For the minimum machinable linewidth and substrate material properties, annotate the recommended fabrication process for each unit in the three-dimensional model of the metal mesh. S630: Integrates the three-dimensional model of the metal mesh, a list of all unit parameters, and process annotation information, and outputs a digital design file containing a three-dimensional solid file, two-dimensional engineering drawings, and manufacturing process specifications.

5. A metal mesh grid design optimization system based on electromagnetic shielding effectiveness, used to implement the metal mesh grid design optimization method based on electromagnetic shielding effectiveness as described in any one of claims 1 to 4, characterized in that, include: The data acquisition module is used to acquire spatial electromagnetic field distribution data of the target area, including electric field strength, magnetic field strength and electromagnetic wave propagation direction, and at the same time acquire the electromagnetic parameters and geometric constraints of the metal mesh substrate material; The simulation modeling module is used to establish a quantitative analysis model of the reflection, absorption and transmission of electromagnetic waves by metal mesh units based on spatial electromagnetic field distribution data, and to obtain the correlation data between shielding effectiveness and the shape, size and arrangement of the mesh units. The feature analysis module is used to combine electromagnetic parameters and geometric constraints to perform nonlinear fitting on the associated data, generate electromagnetic response characteristic curves of metal mesh cells in multiple frequency bands, and identify key geometric features that are sensitive to changes in electromagnetic field. The structure building module is used to construct a gradient-based aperiodic topological structure model of the metal mesh based on key geometric features. The coupling optimization module is used to perform electromagnetic structure coupling simulation on the gradient aperiodic topology model, evaluate the shielding stability and mechanical strength in the actual electromagnetic environment, and minimize the fluctuation of shielding effectiveness by iteratively adjusting the unit parameters. The file generation module is used to generate digital design files for metal mesh grids based on the optimized gradient aperiodic topology model, obtaining the precise definition and fabrication process requirements of each unit.

6. An electronic device comprising a processor and a memory, wherein, The memory stores a computer program that can be called by the processor; the processor executes the metal mesh grid design optimization method based on electromagnetic shielding effectiveness as described in any one of claims 1 to 4 by calling the computer program stored in the memory.

7. A computer-readable storage medium having a computer program stored thereon, wherein the computer program, when executed by a processor, implements the metal mesh grid design optimization method based on electromagnetic shielding effectiveness as described in any one of claims 1 to 4.

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