Screening material evaluation method and system based on big data and transient electromagnetic method

Abstract

The invention discloses a shielding material evaluation method and system based on big data and a transient electromagnetic method, and relates to the technical field of shielding material evaluation, and the method comprises the steps: exciting a shielding material through a high-frequency pulse, obtaining electromagnetic parameters such as magnetic conductivity, dielectric constant, conductivity and shielding effectiveness, and carrying out the evaluation of the shielding material; and the transient electromagnetic response of the material is recorded by using a sensor array in different incident angles, polarization directions and frequency ranges, and an excitation signal is dynamically adjusted. An electromagnetic signal is decomposed by adopting an empirical mode, different frequency components are extracted, signal complexity is calculated through multi-scale entropy analysis, and an electromagnetic absorption mode is quantified. And optimizing a shielding effectiveness calculation model on the basis of electromagnetic wave transmittance in combination with time attenuation, multiple scattering and dynamic absorption characteristics, and finally optimizing shielding material parameters. The method has better effects in the aspects of calculation efficiency, adaptability and accuracy.

Description

Technical Field

[0001] The present invention relates to the technical field of shielding material evaluation, and in particular to a method and system for evaluating shielding materials based on big data and transient electromagnetic method. Background Art

[0002] With the wide application of electronic devices, the electromagnetic environment has become increasingly complex, and electromagnetic compatibility (EMC) problems have become an important factor affecting the stability of electronic systems. In order to reduce the impact of electromagnetic interference (EMI) on electronic devices and improve the shielding effectiveness of shielding materials, researchers have been continuously exploring high-efficiency 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 measure the transmission coefficient or reflection coefficient to calculate the shielding effectiveness. However, with the popularization of high-frequency electronic devices, electromagnetic signals have gradually developed towards broadband, high-power, and complex modulation modes. The existing shielding effectiveness evaluation methods based on frequency-domain measurements have limitations in dealing with non-linear 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 because it can capture the dynamic propagation characteristics of electromagnetic waves. The shielding material evaluation technology combined with TDEM can effectively analyze the shielding effectiveness of shielding materials in transient environments and optimize material design by combining big data analysis methods, thereby improving the adaptability of shielding materials in complex electromagnetic environments.

[0003] Currently, the methods for evaluating shielding materials mainly have the following deficiencies: (1) Traditional shielding effectiveness measurement methods rely on steady-state frequency-domain analysis, making it difficult to accurately evaluate the response characteristics of shielding materials in pulsed electromagnetic interference or complex transient electromagnetic environments, resulting in evaluation results being limited to linear systems and unable to cover dynamic shielding characteristics. (2) Existing signal processing methods, such as Fourier transform and wavelet transform, have resolution losses when analyzing non-linear 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 a long design cycle, low calculation efficiency, and limited optimization effect for shielding materials. Since the existing technology cannot achieve high-precision and efficient evaluation of shielding materials in complex electromagnetic environments, a method for evaluating shielding materials based on big data and transient electromagnetic method is proposed, which uses TDEM to measure the transient electromagnetic characteristics of shielding materials and combines data-driven signal processing methods to establish an optimization model to improve the evaluation accuracy and design efficiency of shielding materials. Summary of the Invention

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

[0005] Therefore, the technical problem to be solved by the present invention is that the existing evaluation methods for shielding materials cannot accurately characterize the transient electromagnetic response characteristics of shielding materials, lack efficient non-linear signal processing methods, resulting in insufficient calculation accuracy of shielding effectiveness, and the optimization methods are limited to empirical design, unable to achieve efficient parameter optimization, and the problem of how to establish an accurate and efficient shielding material evaluation system by combining transient electromagnetic method with big data analysis.

[0006] To solve the above technical problems, the present invention provides the following technical solution: An evaluation method for shielding materials based on big data and transient electromagnetic method, including obtaining the electromagnetic parameters of the shielding material, including permeability, permittivity, conductivity and shielding effectiveness, using a high-frequency pulse excitation signal to make the shielding material generate a transient response, and using a sensor array to record the electromagnetic response data of the material at different incident angles, polarization directions and frequency ranges, and dynamically adjusting the form of the excitation signal according to the transient response characteristics of the material; using the empirical mode decomposition method to decompose the obtained 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; based on the electromagnetic wave transmittance, combining time decay, multiple scattering effects and dynamic absorption characteristics, optimizing the shielding effectiveness calculation model, calculating the target shielding effectiveness, and optimizing the parameters of the shielding material.

[0007] As a preferred scheme of the evaluation method for shielding materials based on big data and transient electromagnetic method of the present invention, wherein: the electromagnetic parameters of the shielding material include shielding effectiveness SE, and the calculation method of SE adopts integral calculation of the time decay effect and combines Bessel functions to evaluate the multiple scattering effect of electromagnetic waves inside the material.

[0008] As a preferred scheme of the evaluation method for shielding materials based on big data and transient electromagnetic method of the present invention, wherein: the step of using a high-frequency pulse excitation signal to make the shielding material generate a transient response includes using a high-frequency pulse excitation signal and recording the electromagnetic response data of the shielding material at different polarization directions and frequency ranges through a sensor array.

[0009] As a preferred scheme of the evaluation method for shielding materials based on big data and transient electromagnetic method of the present invention, wherein: the step of dynamically adjusting the form of the excitation signal includes calculating the permeability, permittivity and conductivity to reflect the propagation 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 of the present invention, the method includes: using the empirical mode decomposition method to decompose the acquired electromagnetic signal into multiple intrinsic mode functions, including decomposing the transient electromagnetic response signal of the shielding material into multiple intrinsic mode functions, extracting different frequency components, and based on the multi-scale entropy analysis method, calculating the probability density distribution of the electromagnetic response signal of the shielding material at different time scales 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 of the present invention, the method includes: calculating the target shielding effectiveness, including calculating the basic transmission ratio of the shielding effectiveness based on the transmission coefficient of the shielding material, constructing a time integral model based on the time decay characteristic 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 of the present invention, the method includes: optimizing the parameters of the shielding material, including constructing a material optimization objective function based on the calculation result of the shielding effectiveness of the shielding material, and taking the maximization of the shielding effectiveness, the minimization of the material thickness, and the optimization of the absorption loss as the objectives to obtain the optimal combination of shielding material parameters.

[0013] Another object of the present invention is to provide a shielding material evaluation system based on big data and transient electromagnetic method, which can use the transient electromagnetic method to excite the material with a rectangular wave pulse, capture its transient electromagnetic response, and combine the time integral model and fractional differential calculation to construct a complete shielding effectiveness evaluation system, so as to quantify and optimize the dynamic adaptability of the shielding material, and solve the problem that the current traditional shielding material evaluation method depends on the steady-state electromagnetic signal and is difficult to accurately describe the performance of the shielding material in dynamic electromagnetic environments such as pulsed electromagnetic interference, high-speed signal transmission, and electromagnetic mutation.

[0014] As a preferred solution of the shielding material evaluation system based on big data and transient electromagnetic method of the present invention, 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 obtain the electromagnetic parameters of the shielding material, including permeability, permittivity, conductivity, and shielding effectiveness, use a high-frequency pulse excitation signal to make the shielding material generate a transient response, and use a sensor array to record the electromagnetic response data of the material at different incident angles, polarization directions, and frequency ranges, and dynamically adjust the form of the excitation signal according to the transient response characteristics of the material; the signal feature extraction module is used to decompose the acquired electromagnetic signal into multiple intrinsic mode functions by 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 the electromagnetic wave transmission ratio, combine time attenuation, multiple scattering effects, and dynamic absorption characteristics, 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 stores a computer program, and when the processor executes the computer program, the steps of the shielding material evaluation method based on big data and transient electromagnetic method are implemented.

[0016] A computer-readable storage medium stores a computer program thereon, and when the computer program is executed by a processor, the steps of the shielding material evaluation method based on big data and transient electromagnetic method are implemented.

[0017] Advantages of the present invention: The shielding material evaluation method based on big data and transient electromagnetic method provided by the present invention uses 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. Construct a non-convex optimization model, perform constraint optimization by combining the Lagrange multiplier method, and realize the global optimization of the shielding material through the Bayesian optimization method combined with Gaussian process regression, so as to maximize the shielding effectiveness, reduce the material usage at the same time, and improve the design efficiency and application adaptability. The present invention has better effects in terms of calculation efficiency, adaptability, and accuracy. Description of the Drawings

[0018] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings required for the description of the embodiments will be briefly introduced below. Obviously, the drawings in the following description are only some embodiments of the present invention, and those of ordinary skill in the art can also obtain other drawings based on these drawings without creative efforts.

[0019] Figure 1The overall flowchart of an evaluation method for shielding materials based on big data and transient electromagnetic method provided for the first embodiment of the present invention. Detailed implementation manners

[0020] To make the above objects, features and advantages of the present invention more obvious and understandable, the following will describe the detailed implementation manners of the present invention with reference to the accompanying drawings of the specification. Obviously, the described embodiments are part of the embodiments of the present invention, rather than all of them. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the scope of protection of the present invention.

[0021] Example 1, referring to Figure 1 , an embodiment of the present invention, provides an evaluation method for shielding materials based on big data and transient electromagnetic method, including: S1: Obtain the electromagnetic parameters of the shielding material, including permeability, permittivity, conductivity and shielding effectiveness. Use a high-frequency pulse excitation signal to make the shielding material generate a transient response, and use a sensor array to record the electromagnetic response data of the material at different incident angles, polarization directions and frequency ranges. According to the transient response characteristics of the material, dynamically adjust the form of the excitation signal.

[0022] Furthermore, the electromagnetic parameters of the shielding material include shielding effectiveness SE, and the calculation method of SE adopts integral calculation of the time decay effect and combines Bessel functions to evaluate the multiple scattering effect of electromagnetic waves inside the material.

[0023] It should be noted that the shielding effectiveness (SE) includes: reflection loss (SR), which measures the degree to which electromagnetic waves are reflected by the surface of the material and is usually related to the conductivity of the material.

[0024] Absorption loss (SA), which measures the ability of electromagnetic waves to be absorbed by the material and is related to the permeability and permittivity of the material.

[0025] Multiple reflection loss (SMR), the energy loss of electromagnetic waves due to multiple scattering inside the shielding layer.

[0026] It should also be noted that a high-frequency pulse excitation signal is used, and the electromagnetic response data of the shielding material at different polarization directions and frequency ranges are recorded through a sensor array. Select appropriate pulse signal frequencies and waveforms to enable the excitation of the electromagnetic response of the shielding material in a wide frequency band and avoid the limitations brought by single-frequency measurement. Arrange multiple electromagnetic sensors at different positions of the shielding material to obtain electromagnetic response data at different incident angles and polarization directions, improving the comprehensiveness and accuracy of the measurement.

[0027] The Transient Electromagnetic Method (TDEM) is a high-precision measurement technique widely used for the evaluation of electromagnetic shielding materials. This method uses a rectangular wave pulse to excite the shielding material and records its transient electromagnetic response. In a traditional TDEM acquisition system, the excitation signal is usually fixed, which may lead to inaccurate measurement of the shielding effectiveness at certain specific frequencies. The present invention adopts adaptive pulse modulation, dynamically adjusts the waveform of the excitation signal according to the transient response of the material, improves the signal quality, reduces measurement noise, and ensures consistent measurement accuracy across different frequency ranges. By adjusting the polarization angles of the transmitting antenna and the receiving sensor, the shielding performance of the material is measured under the conditions of transverse electromagnetic wave (TEM), transverse electric wave (TE), and transverse magnetic wave (TM), and the anisotropic characteristics of the shielding material are analyzed.

[0028] The signal excitation method uses a high-frequency pulse excitation signal to generate a transient response of electromagnetic waves inside the material and records its decay over time through an electromagnetic sensor.

[0029] The data acquisition method uses an ultra-high-sensitivity sensor array to record the response data of the shielding material at different incident angles, polarization directions, and frequency ranges. 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. Based on Maxwell's equations, the dielectric constant, magnetic permeability, and conductivity of the material are calculated. During the calculation process, according to the propagation characteristics of electromagnetic waves in the shielding material, combined with the electric field distribution, magnetic field distribution, and current density, the characteristic impedance, wave number, and relaxation time are calculated to determine the inherent electromagnetic parameters of the shielding material.

[0030] Dynamically adjusting the waveform of the excitation signal includes calculating the magnetic permeability, dielectric constant, and conductivity by measuring the propagation speed and attenuation of electromagnetic waves in the material 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 as follows: where, represents the Laplacian operator of the electric field , represents the magnetic permeability of the material, represents the dielectric constant of the material, represents the conductivity of the material, represents the conduction loss, represents the dielectric response. By measuring the propagation speed and attenuation of electromagnetic waves in the material, the magnetic permeability, dielectric constant, and conductivity can be calculated, providing basic data for subsequent shielding effectiveness analysis.

[0031] where: where, represents the characteristic impedance, represents the angular frequency, represents the wave number, represents the propagation speed of electromagnetic waves in free space, represents the relaxation time of the material.

[0032] S2: Using the empirical mode decomposition method, 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.

[0033] Furthermore, using the empirical mode decomposition method, decomposing 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 the empirical mode decomposition method, and extracting different frequency components. Based on the multi-scale entropy analysis method, calculate the probability density distribution of the electromagnetic response signal of the shielding material at different time scales to quantify the shielding effectiveness characteristics of the material.

[0034] Using the empirical mode decomposition (EMD) method, decompose the signal into multiple intrinsic mode functions (IMFs) for the analysis of different frequency components, expressed as: where, is the original signal, is the th decomposed signal component, is the remaining low-frequency trend term, is the scale resolution.

[0035] It should be noted that based on the multi-scale entropy analysis method, calculate the probability density distribution of the electromagnetic response signal of the shielding material at different time scales to quantify the shielding effectiveness characteristics of the material, expressed as: where, represents the signal probability density at different scales.

[0036] S3: Based on the electromagnetic wave transmittance ratio, combined with the time decay, multiple scattering effects, and dynamic absorption characteristics, optimize the shielding effectiveness calculation model, calculate the target shielding effectiveness, and optimize the parameters of the shielding material.

[0037] Furthermore, calculating the target shielding effectiveness includes calculating the basic transmittance ratio 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 in the shielding effectiveness of the shielding material under different time conditions, expressed as: where, represents the shielding effectiveness, represents the transmission coefficient, wei1 is the attenuation rate of electromagnetic waves in the shielding material, represents the Bessel function, is the time upper limit, represents time, represents the thickness of the shielding material, represents the relative conductivity, represents the relative permeability, represents the absorbed power.

[0038] It should be noted that optimizing the parameters of the shielding material includes constructing a material optimization objective function based on the shielding effectiveness calculation results of the shielding material, aiming at maximizing the shielding effectiveness, minimizing the material thickness, and optimizing the absorption loss, so as to obtain the optimal combination of shielding material parameters.

[0039] Specifically, in the implementation process of the non-convex optimization algorithm, based on the irregularity of the shielding material parameter space, the adaptive gradient descent method is used for iterative calculation, and the learning rate is dynamically adjusted to avoid local optimal solutions. In the implementation process of the Lagrange multiplier method, the objective function of the shielding effectiveness and the constraint conditions of the material thickness are defined, the Lagrangian function is constructed, and the optimal solution under the constraints is calculated based on the multiplier update strategy. In the implementation process of the Bayesian optimization method, a performance prediction model of the shielding material is constructed based on Gaussian process regression, the acquisition function selection strategy is adopted, the permeability and conductivity parameter spaces are sampled, and the optimization search direction is dynamically adjusted according to the predicted distribution of the shielding effectiveness to obtain the global optimal combination of shielding material parameters, which is expressed as: Among them, the goal is to find the optimal combination of permeability , conductivity , and thickness , are the minimum and maximum frequencies, is the wavelength of the shielding material at the operating frequency, is the Lagrange multiplier, is the conductivity of the nth layer of material. is the thickness of the nth layer of material.

[0040] Example 2, which is the second example of the present invention, is different from the previous example in that: When the above-mentioned functions are implemented in the form of 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 the present invention, in essence, or the part that contributes to the prior art, or a part of this 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 for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the methods described in various embodiments of the present invention. The aforementioned storage medium includes: various media such as USB flash drives, mobile hard disks, read-only memories (ROM, Read-Only Memory), random access memories (RAM, Random Access Memory), magnetic disks, or optical discs that can store program codes.

[0041] The logic and / or steps represented in the flowchart or described in other ways herein, for example, can be considered as a predefined sequence list of executable instructions for implementing logical functions, and can be specifically implemented in any computer-readable medium for use by an instruction execution system, apparatus, or device (such as a computer-based system, a system including a processor, or other systems that can fetch instructions from the instruction execution system, apparatus, or device and execute the instructions), or in combination with these instruction execution systems, apparatus, or devices. For the purposes of this specification, a "computer-readable medium" can be any device that can contain, store, communicate, propagate, or transmit a program for use by or in combination with an instruction execution system, apparatus, or device.

[0042] More specific examples (non-exhaustive list) of computer-readable media include the following: an electrical connection portion with one or more wirings (electronic device), a portable computer disk cartridge (magnetic device), random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), fiber optic device, and portable compact disc read-only memory (CDROM). Additionally, the computer-readable medium 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 media, then editing, interpreting, or otherwise processing it as appropriate, and then storing it in a computer memory.

[0043] It should be understood that the various parts of the present invention can be implemented by hardware, software, firmware or a combination thereof. In the above embodiments, multiple steps or methods can be implemented by software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented by hardware, as in another embodiment, any one or a combination of the following techniques well known in the art can be used: discrete logic circuits having logic gate circuits for implementing logic functions on data signals, application specific integrated circuits having appropriate combinational logic gate circuits, programmable gate arrays (PGAs), field programmable gate arrays (FPGAs), etc.

[0044] Embodiment 3 is the third embodiment of the present invention. This embodiment provides a system for a load balancing method of a computing platform based on a particle swarm genetic algorithm, including an electromagnetic parameter acquisition module, a signal feature extraction module, and a shielding effectiveness calculation module.

[0045] Among them, the electromagnetic parameter acquisition module is used to obtain the electromagnetic parameters of the shielding material, including permeability, permittivity, conductivity, and shielding effectiveness. A high-frequency pulse excitation signal is used to make the shielding material generate a transient response, and a sensor array is used to record the electromagnetic response data of the material at different incident angles, polarization directions, and frequency ranges. According to the transient response characteristics of the material, the form of the excitation signal is dynamically adjusted. The signal feature extraction module is used to decompose the acquired electromagnetic signal into multiple intrinsic mode functions by 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 the electromagnetic wave transmission ratio, combined with time attenuation, multiple scattering effects, and dynamic absorption characteristics, calculate the target shielding effectiveness, and optimize the parameters of the shielding material.

[0046] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit them. Although the present invention has been described in detail with reference to the preferred embodiments, those of ordinary skill in the art should understand that the technical solutions of the present invention can be modified or equivalently replaced without departing from the spirit and scope of the technical solutions of the present invention, and they should all be covered within the scope of the claims of the present invention.

Claims

1. An evaluation method for shielding materials based on big data and transient electromagnetic method, characterized in that, Including: Obtain the electromagnetic parameters of the shielding material, including permeability, permittivity, conductivity and shielding effectiveness. Use a high-frequency pulse excitation signal to make the shielding material generate a transient response, and use a sensor array to record the electromagnetic response data of the material at different incident angles, polarization directions and frequency ranges. According to the transient response characteristics of the material, dynamically adjust the form of the excitation signal; Adopt the empirical mode decomposition method to decompose the obtained 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; Based on the electromagnetic wave transmission ratio, combine the time decay, multiple scattering effects and dynamic absorption characteristics to optimize the shielding effectiveness calculation model, calculate the target shielding effectiveness, and optimize the parameters of the shielding material.

2. The method for evaluating shielding materials based on big data and transient electromagnetic method according to claim 1, characterized in that: The electromagnetic parameters of the shielding material include the shielding effectiveness SE, and the calculation method of SE uses integral calculation of the time decay effect and combines the Bessel function to evaluate the multiple scattering effect of electromagnetic waves inside the material.

3. The method for evaluating shielding materials based on big data and transient electromagnetic method according to claim 2, wherein: The step of using a high-frequency pulse excitation signal to make the shielding material generate a transient response includes using a high-frequency pulse excitation signal and recording the electromagnetic response data of the shielding material at different polarization directions and frequency ranges through a sensor array.

4. The method for evaluating shielding materials based on big data and transient electromagnetic method according to claim 3, characterized in that: The step of dynamically adjusting the form of the excitation signal includes calculating the permeability, permittivity and conductivity by measuring the propagation speed and attenuation of electromagnetic waves in the material to reflect the propagation of electromagnetic waves in the shielding material.

5. The method for evaluating shielding materials based on big data and transient electromagnetic method according to claim 4, wherein: The step of adopting the empirical mode decomposition method to decompose the obtained 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 the empirical mode decomposition method, 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 the multi-scale entropy analysis method to quantify the shielding effectiveness characteristics of the material.

6. The method for evaluating shielding materials based on big data and transient electromagnetic method according to claim 5, wherein: The step of calculating the target shielding effectiveness includes calculating the basic transmission ratio of the shielding effectiveness based on the transmission 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.

7. The method for evaluating shielding materials based on big data and transient electromagnetic method according to claim 6, wherein: The step of optimizing the parameters of the shielding material includes constructing a material optimization objective function based on the calculation result of the shielding effectiveness of the shielding material, and taking the maximization of the shielding effectiveness, the minimization of the material thickness and the optimization of the absorption loss as the goals to obtain the optimal combination of shielding material parameters.

8. A system adopting the evaluation method of shielding materials based on big data and transient electromagnetic method as described in any one of claims 1 to 7, characterized in that: Including an electromagnetic parameter acquisition module, a signal feature extraction module, and a shielding effectiveness calculation module; The electromagnetic parameter acquisition module is used to obtain the electromagnetic parameters of the shielding material, including permeability, permittivity, conductivity and shielding effectiveness. Use a high-frequency pulse excitation signal to make the shielding material generate a transient response, and use a sensor array to record the electromagnetic response data of the material at different incident angles, polarization directions and frequency ranges. According to the transient response characteristics of the material, dynamically adjust the form of the excitation signal; The signal feature extraction module is used to adopt the empirical mode decomposition method to decompose the obtained 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; The shielding effectiveness calculation module is used to optimize the shielding effectiveness calculation model based on the electromagnetic wave transmittance, combined with time attenuation, multiple scattering effects, and dynamic absorption characteristics, calculate the target shielding effectiveness, and optimize the parameters of the shielding material.

9. A computer device, comprising a memory and a processor, the memory storing 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 according to any one of claims 1 to 7.

10. 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 according to any one of claims 1 to 7.

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