A screen material evaluation method and system based on big data and transient electromagnetic method

Abstract

This invention discloses a method and system for evaluating shielding materials based on big data and transient electromagnetic methods, belonging to the field of shielding material evaluation technology. It includes exciting the shielding material with high-frequency pulses to obtain electromagnetic parameters such as permeability, dielectric constant, conductivity, and shielding effectiveness. A sensor array is used to record the transient electromagnetic response of the material within different incident angles, polarization directions, and frequency ranges, dynamically adjusting the excitation signal. Empirical mode decomposition of the electromagnetic signal is employed to extract different frequency components, and multi-scale entropy analysis is used to calculate signal complexity and quantify electromagnetic absorption modes. Based on electromagnetic wave transmittance, combined with time decay, multiple scattering, and dynamic absorption characteristics, the shielding effectiveness calculation model is optimized, ultimately optimizing the shielding material parameters. This invention achieves better results in terms of computational efficiency, adaptability, and accuracy.

Description

Technical Field

[0001] This invention relates to the field of shielding material evaluation technology, specifically to a shielding material evaluation method and system based on big data and transient electromagnetic methods. Background Technology

[0002] With the widespread application of electronic devices, the electromagnetic environment is becoming increasingly complex, and electromagnetic compatibility (EMC) issues have become a significant factor affecting the stability of electronic systems. To reduce the impact of electromagnetic interference (EMI) on electronic devices and improve the shielding effectiveness of shielding materials, researchers are constantly exploring efficient electromagnetic shielding materials and their performance evaluation methods. Traditional shielding material evaluation methods mainly rely on frequency domain measurements, such as using a vector network analyzer (VNA) to test transmission or reflection coefficients to calculate shielding effectiveness. However, with the popularization of high-frequency electronic devices, electromagnetic signals are gradually developing towards broadband, high-power, and complex modulation modes. Existing frequency domain measurement-based shielding effectiveness evaluation methods have limitations in dealing with nonlinear and transient electromagnetic interference. In recent years, the transient electromagnetic method (TDEM) has been widely used in geophysical exploration, radar signal processing, and other fields due to its ability to capture the dynamic propagation characteristics of electromagnetic waves. Combining TDEM with shielding material evaluation technology can effectively analyze the shielding effectiveness of shielding materials in transient environments, and by combining big data analysis methods, optimize material design, thereby improving the adaptability of shielding materials in complex electromagnetic environments.

[0003] Currently, the main shortcomings of the methods for evaluating shielding materials are as follows: (1) Traditional shielding effectiveness measurement methods rely on steady-state frequency domain analysis, which makes it difficult to accurately evaluate the response characteristics of shielding materials in pulse electromagnetic interference or complex transient electromagnetic environments, resulting in evaluation results limited to linear systems and unable to cover dynamic shielding characteristics. (2) Existing signal processing methods, such as Fourier transform and wavelet transform, suffer from resolution loss when analyzing nonlinear and non-stationary electromagnetic signals, making it difficult to accurately decompose the transient response signals of shielding materials and affecting the accuracy of shielding effectiveness calculation. (3) In terms of optimizing the performance of shielding materials, traditional methods usually rely on experimental data and empirical formulas, without combining non-convex optimization and machine learning methods for parameter optimization, resulting in long shielding material design cycles, low computational efficiency, and limited optimization effects. Since existing technologies cannot achieve high-precision and efficient evaluation of shielding materials in complex electromagnetic environments, a shielding material evaluation method based on big data and transient electromagnetic methods is proposed. The transient electromagnetic characteristics of shielding materials are measured using TDEM, and an optimization model is established by combining data-driven signal processing methods to improve the evaluation accuracy and design efficiency of shielding materials. Summary of the Invention

[0004] In view of the above-mentioned problems, the present invention is proposed.

[0005] Therefore, the technical problem solved by this invention is that existing shielding material evaluation methods cannot accurately characterize the transient electromagnetic response characteristics of shielding materials, lack efficient nonlinear signal processing methods, resulting in insufficient accuracy in shielding effectiveness calculations, and optimization methods are limited to empirical design, making it impossible to achieve efficient parameter optimization. The invention also addresses the problem of how to use transient electromagnetic methods combined with big data analysis to establish an accurate and efficient shielding material evaluation system.

[0006] To address the aforementioned technical problems, this invention provides the following technical solution: a shielding material evaluation method based on big data and transient electromagnetic methods, comprising acquiring the electromagnetic parameters of the shielding material, including permeability, dielectric constant, conductivity, and shielding effectiveness; employing a high-frequency pulse excitation signal to induce a transient response in the shielding material; recording the electromagnetic response data of the material using a sensor array within different incident angles, polarization directions, and frequency ranges; dynamically adjusting the excitation signal morphology based on the transient response characteristics of the material; employing an empirical mode decomposition method to decompose the acquired electromagnetic signal into multiple intrinsic mode functions, extracting different frequency components, and calculating the signal complexity through multi-scale entropy analysis to quantify the electromagnetic absorption mode of the material; optimizing the shielding effectiveness calculation model based on electromagnetic wave transmittance, combined with time decay, multiple scattering effects, and dynamic absorption characteristics, calculating the target shielding effectiveness, and optimizing the parameters of the shielding material.

[0007] As a preferred embodiment of the shielding material evaluation method based on big data and transient electromagnetic method described in this invention, the electromagnetic parameters of the shielding material include shielding effectiveness SE, wherein the calculation method of SE adopts integral calculation of time decay effect, and combines Bessel function to evaluate the multiple scattering effect of electromagnetic waves inside the material.

[0008] As a preferred embodiment of the shielding material evaluation method based on big data and transient electromagnetic method described in this invention, the step of using a high-frequency pulse excitation signal to induce a transient response in the shielding material includes using a high-frequency pulse excitation signal and recording the electromagnetic response data of the shielding material in different polarization directions and frequency ranges through a sensor array.

[0009] As a preferred embodiment of the shielding material evaluation method based on big data and transient electromagnetic method described in this invention, the dynamic adjustment of the excitation signal morphology includes calculating the permeability, dielectric constant, and conductivity of electromagnetic waves in the shielding material by measuring the propagation speed and attenuation of electromagnetic waves in the material.

[0010] As a preferred embodiment of the shielding material evaluation method based on big data and transient electromagnetic method described in this invention, the step of using empirical mode decomposition (EMD) to decompose the acquired electromagnetic signal into multiple intrinsic mode functions includes decomposing the transient electromagnetic response signal of the shielding material into multiple intrinsic mode functions based on EMD, extracting different frequency components, and calculating the probability density distribution of the electromagnetic response signal of the shielding material at different time scales based on multi-scale entropy analysis to quantify the shielding effectiveness characteristics of the material.

[0011] As a preferred embodiment of the shielding material evaluation method based on big data and transient electromagnetic method described in this invention, the calculation of the target shielding effectiveness includes: calculating the basic transmittance of the shielding effectiveness based on the transmittance coefficient of the shielding material; constructing a time integral model based on the time decay characteristics of the shielding material; and calculating the change of the shielding effectiveness of the shielding material under different time conditions.

[0012] As a preferred embodiment of the shielding material evaluation method based on big data and transient electromagnetic method described in this invention, the optimized shielding material parameters include constructing a material optimization objective function based on the shielding effectiveness calculation results of the shielding material, with the objectives of maximizing shielding effectiveness, minimizing material thickness, and optimizing absorption loss, to obtain the optimal combination of shielding material parameters.

[0013] Another objective of this invention is to provide a shielding material evaluation system based on big data and transient electromagnetic methods. This system uses transient electromagnetic methods to excite materials with rectangular wave pulses, capture their transient electromagnetic responses, and combine time integral models and fractional differential calculations to construct a complete shielding effectiveness evaluation system. This allows for the quantification and optimization of the dynamic adaptability of shielding materials, solving the problem that current traditional shielding material evaluation methods rely on steady-state electromagnetic signals and are difficult to accurately describe the performance of shielding materials in dynamic electromagnetic environments such as pulse electromagnetic interference, high-speed signal transmission, and electromagnetic mutations.

[0014] As a preferred embodiment of the shielding material evaluation system based on big data and transient electromagnetic methods described in this invention, the system includes: an electromagnetic parameter acquisition module, a signal feature extraction module, and a shielding effectiveness calculation module. The electromagnetic parameter acquisition module acquires the electromagnetic parameters of the shielding material, including permeability, dielectric constant, conductivity, and shielding effectiveness. It uses a high-frequency pulse excitation signal to induce a transient response in the shielding material and records the electromagnetic response data of the material using a sensor array within different incident angles, polarization directions, and frequency ranges. Based on the transient response characteristics of the material, the excitation signal shape is dynamically adjusted. The signal feature extraction module uses an empirical mode decomposition method to decompose the acquired electromagnetic signal into multiple intrinsic mode functions, extracts different frequency components, and calculates the signal complexity through multi-scale entropy analysis to quantify the electromagnetic absorption mode of the material. The shielding effectiveness calculation module optimizes the shielding effectiveness calculation model based on the electromagnetic wave transmittance, combined with time decay, multiple scattering effects, and dynamic absorption characteristics, to calculate the target shielding effectiveness and optimize the parameters of the shielding material.

[0015] A computer device includes a memory and a processor, the memory storing a computer program, the processor executing the computer program to implement steps of a method for evaluating shielding materials based on big data and transient electromagnetic methods.

[0016] A computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of a method for evaluating shielding materials based on big data and transient electromagnetic methods.

[0017] The beneficial effects of this invention are as follows: The shielding material evaluation method based on big data and transient electromagnetic methods provided by this invention employs empirical mode decomposition, which can accurately decompose the transient response signal of the shielding material, remove noise interference, improve the accuracy of shielding effectiveness calculation, and provide more reliable data support for subsequent material optimization. A non-convex optimization model is constructed, combined with the Lagrange multiplier method for constraint optimization, and a Bayesian optimization method combined with Gaussian process regression is used to achieve global optimization of the shielding material, maximizing shielding effectiveness while reducing material usage and improving design efficiency and application adaptability. This invention achieves better results in terms of computational efficiency, adaptability, and accuracy. Attached Figure Description

[0018] To more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings used in the following description of the embodiments will be briefly introduced. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0019] Figure 1The first embodiment of the present invention provides an overall flowchart of a shielding material evaluation method based on big data and transient electromagnetic method. Detailed Implementation

[0020] To make the above-mentioned objects, features, and advantages of the present invention more apparent and understandable, specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of the present invention, and not all of them. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the protection scope of the present invention.

[0021] Example 1, referring to Figure 1 As an embodiment of the present invention, a method for evaluating shielding materials based on big data and transient electromagnetic methods is provided, comprising:

[0022] S1: Obtain the electromagnetic parameters of the shielding material, including permeability, dielectric constant, conductivity and shielding effectiveness. Use a high-frequency pulse excitation signal to induce a transient response in the shielding material. Use a sensor array to record the electromagnetic response data of the material in different incident angles, polarization directions and frequency ranges. Dynamically adjust the form of the excitation signal according to the transient response characteristics of the material.

[0023] Furthermore, the electromagnetic parameters of the shielding material include the shielding effectiveness SE. The SE is calculated by integrating the time decay effect and combining it with the Bessel function to evaluate the multiple scattering effect of electromagnetic waves inside the material.

[0024] It should be noted that shielding effectiveness (SE) includes reflection loss (SR), which measures the degree to which electromagnetic waves are reflected by the surface of a material and is usually related to the conductivity of the material.

[0025] Absorption loss (SA) measures the ability of a material to absorb electromagnetic waves, and is related to the material's permeability and dielectric constant.

[0026] Multiple reflection loss (SMR) is the energy loss caused by electromagnetic waves being scattered multiple times inside the shielding layer.

[0027] It should also be noted that a high-frequency pulse excitation signal is used, and the electromagnetic response data of the shielding material in different polarization directions and frequency ranges are recorded through a sensor array. Appropriate pulse signal frequencies and waveforms are selected to excite the electromagnetic response of the shielding material across a wide frequency band, avoiding the limitations of single-frequency measurements. Multiple electromagnetic sensors are arranged at different locations on the shielding material to obtain electromagnetic response data under different incident angles and polarization directions, improving the comprehensiveness and accuracy of the measurement.

[0028] Transient electromagnetic method (TDEM) is a high-precision measurement technique widely used for evaluating electromagnetic shielding materials. This method excites the shielding material with a rectangular wave pulse and records its transient electromagnetic response. In traditional TDEM acquisition systems, the excitation signal is usually fixed, which may lead to inaccurate measurement of shielding effectiveness at certain frequencies. This invention employs adaptive pulse modulation to dynamically adjust the shape of the excitation signal according to the transient response of the material, improving signal quality, reducing measurement noise, and ensuring consistent measurement accuracy across different frequency ranges. By adjusting the polarization angles of the transmitting antenna and receiving sensor, the shielding performance of the material under transverse electromagnetic wave (TEM), transverse electric field wave (TE), and transverse magnetic field wave (TM) conditions is measured, and the anisotropic characteristics of the shielding material are analyzed.

[0029] The signal excitation method uses a high-frequency pulse excitation signal to make electromagnetic waves generate transient responses inside the material, and records the attenuation over time by an electromagnetic sensor.

[0030] Data acquisition utilizes an ultra-high sensitivity sensor array to record the shielding material's response data across different incident angles, polarization directions, and frequency ranges. The transient electromagnetic method is employed, applying pulsed electromagnetic excitation signals to the shielding material and recording its electromagnetic response data at different time points. Based on Maxwell's equations, the material's dielectric constant, permeability, and conductivity are calculated. During the calculation process, the characteristic impedance, wavenumber, and relaxation time are calculated based on the shielding material's electromagnetic wave propagation characteristics, combined with electric field distribution, magnetic field distribution, and current density, to determine the inherent electromagnetic parameters of the shielding material.

[0031] Dynamically adjusting the excitation signal shape involves measuring the propagation speed and attenuation of electromagnetic waves in the material, and calculating the permeability, dielectric constant, and conductivity to reflect the propagation of electromagnetic waves in the shielding material. In the transient electromagnetic method (TDEM), the propagation of electromagnetic waves in the shielding material can be described by the wave equation:

[0032]

[0033] in, Represents electric field The Laplace operator, Indicates the magnetic permeability of a material. Indicates the dielectric constant of the material. Indicates the electrical conductivity of a material. Represents electrical conductance loss. This represents the dielectric response. By measuring the propagation speed and attenuation of electromagnetic waves in a material, the permeability, dielectric constant, and conductivity can be calculated, providing fundamental data for subsequent shielding effectiveness analysis.

[0034] in:

[0035]

[0036] in, Indicates characteristic impedance, Represents angular frequency. Indicates wave number, This represents the speed at which electromagnetic waves propagate in free space. Indicates the relaxation time of the material.

[0037] S2: The empirical mode decomposition method is used to decompose the acquired electromagnetic signal into multiple intrinsic mode functions, extract different frequency components, and calculate the signal complexity through multi-scale entropy analysis to quantify the electromagnetic absorption mode of the material.

[0038] Furthermore, an empirical mode decomposition (EMD) method is employed to decompose the acquired electromagnetic signal into multiple intrinsic mode functions (EMFs). This includes using EMD to decompose the transient electromagnetic response signal of the shielding material into multiple EEMs and extracting different frequency components. Based on multi-scale entropy analysis, the probability density distribution of the shielding material's electromagnetic response signal at different time scales is calculated, quantifying the material's shielding effectiveness characteristics.

[0039] The Empirical Mode Decomposition (EMD) method is used to decompose the signal into multiple intrinsic mode functions (IMFs) for the analysis of different frequency components, as shown below:

[0040]

[0041] in, It is the original signal. It is the first Each decomposed signal component These are the remaining low-frequency trend terms. For scale resolution.

[0042] It should be noted that, based on the multi-scale entropy analysis method, the probability density distribution of the electromagnetic response signal of the shielding material at different time scales is calculated, and the shielding effectiveness characteristics of the material are quantified, expressed as:

[0043]

[0044] in, This represents the signal probability density at different scales.

[0045] S3: Based on electromagnetic wave transmittance, combined with time decay, multiple scattering effect and dynamic absorption characteristics, optimize the shielding effectiveness calculation model, calculate the target shielding effectiveness, and optimize the parameters of the shielding material.

[0046] Furthermore, calculating the target shielding effectiveness includes calculating the basic transmittance of the shielding effectiveness based on the transmittance coefficient of the shielding material, and constructing a time integral model based on the time decay characteristics of the shielding material to calculate the change in shielding effectiveness of the shielding material under different time conditions, expressed as:

[0047]

[0048] in, Indicates shielding effectiveness. Indicates the transmission coefficient. The attenuation rate of electromagnetic waves in shielding materials. Represents the Bessel function. The upper limit of time, Indicates time, Indicates the thickness of the shielding material. Represents relative conductivity. Represents relative permeability. This indicates the absorbed power.

[0049] It should be noted that optimizing the parameters of the shielding material involves constructing a material optimization objective function based on the shielding effectiveness calculation results of the shielding material, with the goals of maximizing shielding effectiveness, minimizing material thickness, and optimizing absorption loss, to obtain the optimal combination of shielding material parameters.

[0050] Specifically, in the implementation of the non-convex optimization algorithm, based on the irregularity of the shielding material parameter space, an adaptive gradient descent method is used for iterative calculation, and the learning rate is dynamically adjusted to avoid local optima. In the implementation of the Lagrange multiplier method, the objective function of shielding effectiveness and the constraint condition of material thickness are defined, a Lagrange function is constructed, and the optimal solution under the constraints is calculated based on the multiplier update strategy. In the implementation of the Bayesian optimization method, a performance prediction model of the shielding material is constructed based on Gaussian process regression. A sampling function selection strategy is adopted to sample the permeability and conductivity parameter spaces, and the optimization search direction is dynamically adjusted according to the predicted distribution of shielding effectiveness to obtain the globally optimal combination of shielding material parameters, expressed as:

[0051]

[0052] in, The goal is to find the permeability electrical conductivity ,thickness The optimal combination, For minimum and maximum frequencies, To shield the wavelength of the material at the operating frequency, For Lagrange multipliers, For the first Electrical conductivity of the layer material. For the first The thickness of the layer material.

[0053] Example 2 is the second embodiment of the present invention, which differs from the previous embodiment in that:

[0054] If the aforementioned functions are implemented as software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this invention, essentially, or the part that contributes to the prior art, or a portion of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0055] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as a sequenced list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by, or in conjunction with, an instruction execution system, apparatus, or device (such as a computer-based system, a processor-including system, or other system that can fetch and execute instructions from, an instruction execution system, apparatus, or device). For the purposes of this specification, "computer-readable medium" can be any means that can contain, store, communicate, propagate, or transmit programs for use by, or in conjunction with, an instruction execution system, apparatus, or device.

[0056] More specific examples of computer-readable media (a non-exhaustive list) include: electrical connections (electronic devices) having one or more wires, portable computer disk drives (magnetic devices), random access memory (RAM), read-only memory (ROM), erasable and editable read-only memory (EPROM or flash memory), fiber optic devices, and portable optical disc read-only memory (CDROM). Furthermore, computer-readable media can even be paper or other suitable media on which the program can be printed, because the program can be obtained electronically, for example, by optically scanning the paper or other medium, followed by editing, interpreting, or otherwise processing as necessary, and then stored in computer memory.

[0057] It should be understood that various parts of the present invention can be implemented in hardware, software, firmware, or a combination thereof. In the above embodiments, multiple steps or methods can be implemented in software or firmware stored in memory and executed by a suitable instruction execution system. For example, if implemented in hardware, as in another embodiment, it can be implemented using any one or a combination of the following techniques known in the art: discrete logic circuits having logic gates for implementing logical functions on data signals, application-specific integrated circuits (ASICs) having suitable combinational logic gates, programmable gate arrays (PGAs), field-programmable gate arrays (FPGAs), etc.

[0058] Example 3 is the third embodiment of the present invention. This embodiment provides a system for a computational platform load balancing method based on particle swarm genetic algorithm, including an electromagnetic parameter acquisition module, a signal feature extraction module, and a shielding effectiveness calculation module.

[0059] The electromagnetic parameter acquisition module is used to acquire the electromagnetic parameters of the shielding material, including permeability, dielectric constant, conductivity, and shielding effectiveness. It uses a high-frequency pulse excitation signal to induce a transient response in the shielding material and records the electromagnetic response data of the material within different incident angles, polarization directions, and frequency ranges using a sensor array. Based on the transient response characteristics of the material, the excitation signal shape is dynamically adjusted. The signal feature extraction module uses empirical mode decomposition (EMD) to decompose the acquired electromagnetic signal into multiple intrinsic mode functions, extracting different frequency components and calculating signal complexity through multi-scale entropy analysis to quantify the electromagnetic absorption modes of the material. The shielding effectiveness calculation module optimizes the shielding effectiveness calculation model based on electromagnetic wave transmittance, combined with time decay, multiple scattering effects, and dynamic absorption characteristics, to calculate the target shielding effectiveness and optimize the parameters of the shielding material.

[0060] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention, and all such modifications or substitutions should be covered within the scope of the claims of the present invention.

Claims

1. A method for evaluating shielding materials based on big data and transient electromagnetic methods, characterized in that, include: The electromagnetic parameters of the shielding material are obtained, including magnetic permeability, dielectric constant, conductivity and shielding effectiveness. A high-frequency pulse excitation signal is used to induce a transient response in the shielding material. The electromagnetic response data of the material is recorded by a sensor array in different incident angles, polarization directions and frequency ranges. The form of the excitation signal is dynamically adjusted according to the transient response characteristics of the material. The empirical mode decomposition method is used to decompose the obtained electromagnetic response into multiple intrinsic mode functions, extract different frequency components, and calculate the signal complexity through multi-scale entropy analysis to quantify the electromagnetic absorption mode of the material. Based on electromagnetic wave transmittance, combined with time decay, multiple scattering effect and dynamic absorption characteristics, the shielding effectiveness calculation model is optimized to calculate the target shielding effectiveness and optimize the parameters of the shielding material. Using the transient electromagnetic method, a pulsed electromagnetic excitation signal is applied to the shielding material, and the electromagnetic response data of the shielding material at different time points are recorded. Dynamically adjusting the excitation signal shape involves measuring the propagation speed and attenuation of electromagnetic waves in materials, and calculating the permeability, dielectric constant, and conductivity to reflect the propagation of electromagnetic waves in shielding materials.

2. The shielding material evaluation method based on big data and transient electromagnetic method as described in claim 1, characterized in that: The electromagnetic parameters of the shielding material include the shielding effectiveness SE, wherein the SE is calculated by integrating the time decay effect and combining the Bessel function to evaluate the multiple scattering effect of electromagnetic waves inside the material.

3. The shielding material evaluation method based on big data and transient electromagnetic method as described in claim 2, characterized in that: The dynamic adjustment of the excitation signal shape includes calculating the permeability, dielectric constant, and conductivity of electromagnetic waves in the shielding material by measuring the propagation speed and attenuation of electromagnetic waves in the material.

4. The shielding material evaluation method based on big data and transient electromagnetic method as described in claim 3, characterized in that: The method of employing empirical mode decomposition (EMD) to decompose the acquired electromagnetic signal into multiple intrinsic mode functions includes decomposing the transient electromagnetic response signal of the shielding material into multiple intrinsic mode functions based on EMD, extracting different frequency components, and calculating the probability density distribution of the electromagnetic response signal of the shielding material at different time scales based on multi-scale entropy analysis to quantify the shielding effectiveness characteristics of the material.

5. The shielding material evaluation method based on big data and transient electromagnetic method as described in claim 4, characterized in that: The calculation of the target shielding effectiveness includes calculating the basic transmittance of the shielding effectiveness based on the transmittance coefficient of the shielding material, and constructing a time integral model based on the time decay characteristics of the shielding material to calculate the change of the shielding effectiveness of the shielding material under different time conditions.

6. The shielding material evaluation method based on big data and transient electromagnetic method as described in claim 5, characterized in that: The parameters for optimizing the shielding material include constructing a material optimization objective function based on the shielding effectiveness calculation results of the shielding material, with the objectives of maximizing shielding effectiveness, minimizing material thickness, and optimizing absorption loss, to obtain the optimal combination of shielding material parameters.

7. A system employing the shielding material evaluation method based on big data and transient electromagnetic method as described in any one of claims 1 to 6, characterized in that: It includes an electromagnetic parameter acquisition module, a signal feature extraction module, and a shielding effectiveness calculation module; The electromagnetic parameter acquisition module is used to acquire the electromagnetic parameters of the shielding material, including magnetic permeability, dielectric constant, conductivity and shielding effectiveness. It uses a high-frequency pulse excitation signal to induce a transient response in the shielding material, and uses a sensor array to record the electromagnetic response data of the material in different incident angles, polarization directions and frequency ranges. The excitation signal shape is dynamically adjusted according to the transient response characteristics of the material. The signal feature extraction module is used to decompose the acquired electromagnetic response into multiple intrinsic mode functions using the empirical mode decomposition method, extract different frequency components, and calculate the signal complexity through multi-scale entropy analysis to quantify the electromagnetic absorption mode of the material. The shielding effectiveness calculation module is used to optimize the shielding effectiveness calculation model based on electromagnetic wave transmittance, combined with time decay, multiple scattering effect and dynamic absorption characteristics, to calculate the target shielding effectiveness and optimize the parameters of the shielding material.

8. A computer device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that, When the processor executes the computer program, it implements the steps of the shielding material evaluation method based on big data and transient electromagnetic method as described in any one of claims 1 to 6.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the steps of the shielding material evaluation method based on big data and transient electromagnetic method as described in any one of claims 1 to 6.

Images