A near-field characterization method of equivalent electromagnetic parameters of an electromagnetic metamaterial

Abstract

The application relates to the technical field of electromagnetic super-material characterization, in particular to a near-field characterization method for equivalent electromagnetic parameters of electromagnetic super-materials, which comprises the steps of multi-probe array sampling, data calibration, signal decomposition and phase compensation, boundary effect correction, non-uniformity compensation, parameter inversion, adaptive filtering and real-time monitoring, etc. By comprehensively using the above technical means, the accuracy and reliability of electromagnetic field distribution sampling under the near-field condition are remarkably improved, the influence of boundary effect and non-uniformity is effectively reduced, and the accuracy and stability of equivalent electromagnetic parameter extraction are ensured. Meanwhile, the real-time monitoring and feedback control mechanism enhances the dynamic tracking capability and system optimization efficiency. The application can meet the needs of modern electromagnetic super-material design and optimization, and solves the deficiencies of the prior art in characterization accuracy, calculation efficiency and adaptability to complex structures.

Description

Technical Field

[0001] This invention relates to the field of electromagnetic metamaterial characterization technology, and more specifically, to a near-field characterization method for the equivalent electromagnetic parameters of electromagnetic metamaterials. Background Technology

[0002] Characterization of electromagnetic metamaterials is a crucial step in studying their performance and optimizing their design, playing a key role particularly in applications such as communications, radar, and stealth. This field involves various techniques and methods aimed at extracting the equivalent electromagnetic parameters of metamaterials, such as dielectric constant and permeability, through measurement and analysis under near-field conditions. Achieving high-precision and efficient characterization requires comprehensive consideration of the configuration of the testing system, the design of the data analysis model, and the handling of boundary effects and inhomogeneities.

[0003] A method for simulating near-field electromagnetic scattering characteristics is disclosed in patent publication number CN108445303B. This method generates the matrix equations of target surface elements under near-field conditions based on a multi-layer fast multi-level sub-algorithm, and combines the current distribution and polarized receiving electric field on the surface elements to characterize the near-field electromagnetic scattering characteristics of the target. However, this technical solution focuses primarily on electromagnetic scattering characteristics and does not address methods for extracting the equivalent electromagnetic parameters of metamaterials. Furthermore, its use of complex numerical calculation methods may reduce computational efficiency and affect the fulfillment of real-time requirements. Simultaneously, this method has shortcomings in handling boundary effects and non-uniformity under near-field conditions, which may have a certain impact on the accuracy and universality of the characterization results.

[0004] The aforementioned problems indicate that existing technologies still have limitations in the near-field characterization of the equivalent electromagnetic parameters of electromagnetic metamaterials. These limitations mainly manifest in the limited coverage of characterization methods, the need for improved computational efficiency, insufficient consideration of boundary effects and inhomogeneities, and inadequate adaptability to complex structures. Therefore, this invention proposes an improved near-field characterization method for the equivalent electromagnetic parameters of electromagnetic metamaterials. By optimizing the testing system and data analysis model, this method improves characterization accuracy and efficiency, simplifies the operational process, and enhances adaptability to complex metamaterials, thereby better meeting the needs of modern electromagnetic metamaterial design and optimization. Summary of the Invention

[0005] The purpose of this invention is to provide a near-field characterization method for the equivalent electromagnetic parameters of electromagnetic metamaterials, so as to solve the problems mentioned in the background art.

[0006] To achieve the above objectives, the present invention provides the following technical solution: a near-field characterization method for equivalent electromagnetic parameters of electromagnetic metamaterials, comprising the following steps:

[0007] Based on the near-field testing system, a multi-probe array is used to sample the electromagnetic field distribution of the target area, and the initial measurement dataset is obtained by eliminating measurement errors through a data calibration module.

[0008] The initial measurement dataset is decomposed to extract the spatial spectrum information of the target area, and the complete electromagnetic field distribution is reconstructed by combining phase compensation technology to obtain the processed field distribution data.

[0009] The processed field distribution data is corrected for boundary effects, the boundary influence under near-field conditions is quantified, and the field distribution data is optimized by combining a non-uniformity compensation algorithm to obtain the corrected field distribution data.

[0010] Based on the corrected field distribution data, a parameter inversion algorithm is used to extract the equivalent electromagnetic parameters of the metamaterial through iterative calculation, and preliminary parameter estimation results are generated.

[0011] Based on the preliminary parameter estimation results, adaptive filtering technology is used to smooth the parameter estimates, and confidence interval evaluation is combined to generate the final parameter estimation results.

[0012] During the characterization process, a real-time monitoring module is used to continuously track the dynamic changes in the characterization process, and an anomaly detection algorithm is used to identify potential problems and generate monitoring records.

[0013] Based on the monitoring records and the final parameter estimation results, a feedback control mechanism is used to adjust the configuration parameters of the test system and generate an optimized test execution.

[0014] As a further improvement to this technical solution, the initial measurement dataset specifically includes electric field strength, magnetic field strength, and their phase information collected by multiple probes; the processed field distribution data includes the electromagnetic field distribution after signal decomposition and phase compensation; the corrected field distribution data includes the field distribution after boundary effect correction and non-uniformity compensation; the preliminary parameter estimation results are used to reflect the dielectric constant and permeability characteristics of the metamaterial; the final parameter estimation results are parameter values ​​after smoothing and confidence interval evaluation; and the monitoring record specifically refers to the record of dynamic changes and the marking of anomalies during the test.

[0015] As a further improvement to this technical solution, based on the near-field testing system, a multi-probe array is used to sample the electromagnetic field distribution of the target area, and a data calibration module is used to eliminate measurement errors and generate an initial measurement dataset. The specific steps are as follows:

[0016] Based on the near-field testing system, a multi-probe array is used to sample the electromagnetic field distribution of the target area and generate raw measurement data.

[0017] The probe response characteristics and environmental noise in the original measurement data are corrected using a data calibration module to generate calibrated measurement data;

[0018] The calibrated measurement data is denoised using wavelet transform, and then normalized to generate preprocessed measurement data.

[0019] The preprocessed measurement data are integrated into the electromagnetic field distribution of the target area to generate an initial measurement dataset.

[0020] The multi-probe array is specifically a group of uniformly distributed probe units used to cover the target area and improve sampling accuracy. The data calibration module is specifically a correction algorithm based on the probe calibration curve and the environmental noise model.

[0021] As a further improvement to this technical solution, the steps of decomposing the initial measurement dataset to extract the spatial spectrum information within the target area, and reconstructing the complete electromagnetic field distribution using phase compensation technology to obtain the processed field distribution data are as follows:

[0022] Based on the initial measurement dataset, a signal decomposition algorithm is used to perform frequency domain decomposition on the electromagnetic field data to generate spatial spectrum information.

[0023] Based on the spatial spectrum information, phase compensation technology is applied to correct the phase of the frequency domain components to generate phase-compensated spectrum data.

[0024] Based on the phase-compensated spectral data, the electromagnetic field distribution of the target region is reconstructed using inverse Fourier transform to generate initial field distribution data;

[0025] Based on the initial field distribution data, consistency verification is performed to generate processed field distribution data.

[0026] As a further improvement to this technical solution, based on the processed field distribution data, a boundary effect correction model is adopted to quantify the boundary influence under near-field conditions, and a non-uniformity compensation algorithm is combined to optimize the field distribution data to generate corrected field distribution data. The specific steps are as follows:

[0027] Based on the processed field distribution data, a boundary effect correction model is used to quantify and analyze the electromagnetic field distribution at the edge of the target region, generating boundary effect correction data.

[0028] Based on the boundary effect correction data, a non-uniformity compensation algorithm is applied to compensate for the non-uniformity in the field distribution data, generating non-uniformity compensation data.

[0029] Based on the non-uniformity compensation data, distribution consistency verification is performed to generate corrected field distribution data;

[0030] The boundary effect correction module uses a finite element model based on the electromagnetic field tangential continuity condition, and the specific formula is as follows: The boundary region is discretized into a triangular mesh with an element size ≤ λ / 10. Quantitative analysis is achieved by solving the matrix equation [K]{E} = {F}, where [K] is the stiffness matrix and {F} is the load vector. The non-uniformity compensation algorithm employs locally weighted linear regression using a Gaussian kernel function, defined as: ;in This represents the distance between the i-th data point and the current regression point; It is the width of the kernel function, used to control the rate at which weights decay with distance. The larger its value, the higher the weights of neighboring points, and the smoother the regression. To assign a weight to the i-th data point.

[0031] As a further improvement to this technical solution, based on the corrected field distribution data, the steps of using a parameter inversion algorithm to iteratively calculate and extract the equivalent electromagnetic parameters of the metamaterial to generate preliminary parameter estimation results are as follows:

[0032] Based on the corrected field distribution data, an initial parameter estimation model is constructed using a parameter inversion algorithm to generate initial parameter estimates.

[0033] Based on the initial parameter estimates, iterative calculation techniques are applied to optimize the parameter values, generating optimized parameter estimates.

[0034] Based on the optimized parameter estimates, convergence verification is performed to generate preliminary parameter estimation results.

[0035] The parameter inversion algorithm is specifically a mathematical inversion method based on electromagnetic field distribution and material properties, used to extract equivalent electromagnetic parameters, and the iterative calculation technique is specifically an optimization algorithm based on gradient descent.

[0036] As a further improvement to this technical solution, based on the preliminary parameter estimation results, the steps of using adaptive filtering technology to smooth the parameter estimates and combining confidence interval evaluation to generate the final parameter estimation results are as follows:

[0037] Based on the preliminary parameter estimation results, adaptive filtering technology is used to smooth the parameter estimates to generate smoothed parameter estimates.

[0038] Based on the smoothed parameter estimates, the reliability of the parameter estimates is evaluated using confidence interval assessment techniques, and confidence interval assessment results are generated.

[0039] Based on the confidence interval assessment results, parameter consistency verification is performed to generate the final parameter estimation results;

[0040] The adaptive filtering technique is specifically a filtering method based on local statistical characteristics, used to reduce the fluctuation of parameter estimates. The confidence interval evaluation technique is specifically an evaluation method based on statistical inference, used to measure the reliability of parameter estimates.

[0041] As a further improvement to this technical solution, the following steps are taken to continuously track dynamic changes during the characterization process using a real-time monitoring module, and combine this with an anomaly detection algorithm to identify potential problems and generate monitoring records:

[0042] During the characterization process, a real-time monitoring module is used to continuously track dynamic changes and generate dynamic change records.

[0043] Based on the dynamic change records, an anomaly detection algorithm is applied to identify and mark potential problems, and anomaly detection results are generated.

[0044] Based on the anomaly detection results, the problems are classified and prioritized, and monitoring records are generated.

[0045] The real-time monitoring module is specifically a monitoring system based on sensor networks and data acquisition technology, used to track dynamic changes in the characterization process in real time. The anomaly detection algorithm adopts the isolated forest model, and the input features include the Z-score standardized values ​​of temperature, humidity, and probe position deviation. The training data comes from 100 sets of time series collected under normal operating conditions, with the hyperparameter settings as follows: number of trees = 100, sample size = 256.

[0046] As a further aspect of the present invention, the steps for adjusting the configuration parameters of the test system and generating optimized test execution based on the monitoring records and final parameter estimation results using a feedback control mechanism are as follows:

[0047] Based on the monitoring records and the final parameter estimation results, a feedback control mechanism is used to adjust the configuration parameters of the test system and generate the adjusted configuration parameters.

[0048] Based on the adjusted configuration parameters, parameter optimization techniques are applied to optimize the test system and generate an optimized test configuration.

[0049] Based on the optimized test configuration, perform test operations and generate optimized test execution.

[0050] The feedback control mechanism is specifically an adjustment method based on the closed-loop control principle, used to optimize the test system based on monitoring records and parameter estimation results. The parameter optimization technique is specifically an optimization method based on genetic algorithms, used to find the optimal test configuration.

[0051] Compared with the prior art, the beneficial effects of the present invention are:

[0052] This invention improves the accuracy and reliability of electromagnetic field distribution sampling under near-field conditions by utilizing a multi-probe array and a data calibration module. High-precision reconstruction of the complete electromagnetic field distribution of the target region is achieved through signal decomposition algorithms and phase compensation techniques. The application of boundary effect correction models and non-uniformity compensation algorithms significantly reduces the impact of boundary effects and non-uniformity on the characterization results under near-field conditions. The combination of parameter inversion algorithms and adaptive filtering techniques ensures the accuracy and stability of equivalent electromagnetic parameter extraction. The introduction of real-time monitoring modules and anomaly detection algorithms enhances the dynamic tracking and problem identification capabilities of the characterization process. The use of feedback control mechanisms and parameter optimization techniques further improves the adaptability and efficiency of the testing system. This invention effectively addresses the shortcomings of existing technologies in characterization accuracy, computational efficiency, and adaptability to complex structures, meeting the needs of modern electromagnetic metamaterial design and optimization. Attached Figure Description

[0053] Figure 1 This is a flowchart of the near-field characterization method for equivalent electromagnetic parameters proposed in this invention.

[0054] Figure 2 This is a flowchart of the multi-probe array sampling and data calibration steps in this invention;

[0055] Figure 3 This is a flowchart of the signal decomposition and phase compensation steps in this invention;

[0056] Figure 4 This is a flowchart illustrating the steps of boundary effect correction and non-uniformity compensation in this invention.

[0057] Figure 5 This is a flowchart of the parameter inversion steps in this invention;

[0058] Figure 6 This is a flowchart illustrating the steps of adaptive filtering and confidence interval evaluation in this invention.

[0059] Figure 7 This is a flowchart of the real-time monitoring and anomaly detection steps in this invention;

[0060] Figure 8 This is a flowchart of the feedback control and optimization steps in this invention. Detailed Implementation

[0061] The technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without creative effort are within the scope of protection of the present invention.

[0062] Those skilled in the art will understand that, unless specifically stated otherwise, the singular forms “a,” “an,” and “described” used herein may also include the plural forms. It should be further understood that the term “comprising” as used in this specification means the presence of the described features, integers, steps, operations, elements, and / or components, but does not exclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and / or groups thereof.

[0063] This invention provides a near-field characterization method for the equivalent electromagnetic parameters of electromagnetic metamaterials, comprising the following steps:

[0064] Multi-probe array sampling and data calibration

[0065] Based on the near-field testing system, a multi-probe array is used to sample the electromagnetic field distribution of the target area. The probe units employ miniature electric field dipole probes or small-loop magnetic field probes, uniformly arranged within the target area at preset intervals. The probe spacing should satisfy the Nyquist sampling theorem and should not exceed half the wavelength of the highest spatial frequency of the measured electromagnetic field. In this embodiment, the spacing is λ / 4 to ensure the capture of fine field distribution and avoid spatial aliasing. Each probe unit is connected to a multi-channel vector network analyzer or spectrum analyzer via a coaxial cable to simultaneously acquire the electric field strength, magnetic field strength, and their corresponding amplitude and phase information at multiple discrete points within the target area, forming the raw measurement data.

[0066] To eliminate measurement errors, the raw measurement data is transmitted to the data calibration module. The data calibration module corrects the probe's response characteristics using a pre-established probe calibration curve. The probe calibration curve is obtained by measuring the mapping relationship between the probe's output at different frequencies and the actual field strength in a known standard electromagnetic field environment. Simultaneously, the data calibration module incorporates an environmental noise model to correct for environmental noise, generating calibrated measurement data. In practical applications, the calibration algorithm can employ linear correction or a deconvolution-based method.

[0067] Subsequently, signal preprocessing techniques were applied to denoise and normalize the calibrated measurement data. Denoising employed wavelet transform, effectively filtering out noise through wavelet decomposition, thresholding, and reconstruction. Normalization used Z-score normalization, converting the data into a distribution with a mean of 0 and a standard deviation of 1, eliminating dimensional differences and amplitude biases. These preprocessed data were then integrated to represent the electromagnetic field distribution of the target region, generating the initial measurement dataset.

[0068] Signal decomposition and phase compensation

[0069] Based on the initial measurement dataset, the signal decomposition module employs a frequency domain decomposition algorithm to extract spatial spectral information within the target region. This process is achieved by performing a two-dimensional fast Fourier transform on the electromagnetic field data, converting the field distribution in the spatial domain into spectral information in the spatial frequency domain, revealing the energy distribution of the electromagnetic field at different spatial periods.

[0070] The obtained spatial spectrum information is transmitted to the phase compensation module, which uses a preset reference signal (the known phase of the excitation source or the phase of a specified reference probe in the array) to correct the phase error in the frequency domain components and generate phase-compensated spectrum data to ensure the phase consistency of data collected by different probes or at different time points.

[0071] The phase-compensated spectral data is converted back to the spatial domain distribution using inverse Fourier transform, reconstructing a complete and high-precision electromagnetic field distribution within the target region, generating initial field distribution data. To verify the accuracy of the reconstruction, the initial field distribution data undergoes consistency verification, including energy conservation checks, smoothness checks, and cross-validation with some known points, ultimately generating the processed field distribution data.

[0072] Boundary effect correction and non-uniformity compensation

[0073] Based on the processed field distribution data, the boundary effect correction module performs quantitative analysis on the electromagnetic field distribution at the edge of the target region. For example... Figure 4 As shown, the boundary effect correction module adopts a finite element model based on the electromagnetic field tangential continuity condition, and the specific formula is as follows: The boundary region is discretized into a triangular mesh with an element size ≤ λ / 10. Quantitative analysis is achieved by solving the matrix equation [K]{E} = {F}, where [K] is the stiffness matrix and {F} is the load vector.

[0074] Subsequently, the non-uniformity compensation module applies local weighted regression technology to compensate for the non-uniformity in the field distribution data. The boundary effect correction module adopts a finite element model based on the tangential continuity condition of the electromagnetic field, with the specific formula as follows: The boundary region is discretized into a triangular mesh with an element size ≤ λ / 10. Quantitative analysis is achieved by solving the matrix equation [K]{E} = {F}, where [K] is the stiffness matrix and {F} is the load vector. The non-uniformity compensation algorithm employs locally weighted linear regression using a Gaussian kernel function, defined as: ;in This represents the distance between the i-th data point and the current regression point; It is the width of the kernel function, used to control the rate at which weights decay with distance. The larger its value, the higher the weights of neighboring points, and the smoother the regression. To assign a weight to the i-th data point.

[0075] After distribution consistency verification (including statistical analysis such as variance and standard deviation checks, as well as visualization checks), the corrected field distribution data is finally generated, which significantly reduces the impact of boundary effects and non-uniformity on the characterization results under near-field conditions.

[0076] Parameter inversion

[0077] The corrected field distribution data is input into the parameter inversion algorithm module. The module first constructs an initial parameter estimation model, which generates initial parameter estimates based on preliminary numerical simulation results of the metamaterial. The core of the parameter inversion algorithm is to establish a forward model that can predict the electromagnetic field distribution of the metamaterial under near-field conditions based on given equivalent electromagnetic parameters. These equivalent electromagnetic parameters are the permittivity ε and permeability μ, and the forward model employs an analytical solution based on Maxwell's equations or an effective medium theory model.

[0078] Based on the initial parameter estimates, iterative calculation techniques are applied to optimize the parameter values. Gradient descent is used as the optimization algorithm, with the aim of finding a set of parameters. This minimizes the difference between the field distribution predicted by the forward model and the corrected actual measured field distribution. This is achieved by defining an objective function (or cost function). To achieve this, its expression is as follows:

[0079] in This represents the objective function, whose value measures the degree of matching between simulation results and measurement results. The goal of the inversion algorithm is to minimize this function. This represents the equivalent dielectric constant of the metamaterial to be inverted. This represents the equivalent magnetic permeability of the metamaterial to be inverted. This represents the corrected electric field distribution data obtained after data calibration, signal decomposition and phase compensation, boundary effect correction and non-uniformity compensation, which includes the electric field intensity (amplitude and phase) of all sampling points in the target area. This represents the corrected magnetic field distribution data obtained after data calibration, signal decomposition and phase compensation, boundary effect correction and non-uniformity compensation, which includes the magnetic field strength of all sampling points in the target area. This indicates that the equivalent dielectric constant is determined by the forward model based on the current iteration. and equivalent permeability The electric field distribution predicted by simulation.

[0080] This represents the magnetic field distribution predicted by the forward model based on the equivalent permittivity and equivalent permeability of the current iteration.

[0081] This represents the L2 norm (Euclidean norm), which is the square root of the sum of the squares of all elements in a vector or matrix. Here, This represents the sum of squares of the differences between vectors A and B, used to quantify the deviation between the measured field and the simulated field.

[0082] In each iteration, the algorithm follows the objective function J. and Update parameters in the negative direction of the partial derivative (gradient) (i.e., the direction of fastest descent): , where α is the learning rate, which controls the step size of parameter updates in each iteration; This represents the updated equivalent dielectric constant after this iteration; This represents the updated equivalent dielectric constant since the last iteration; This represents the updated equivalent permeability after this iteration; This represents the updated equivalent permeability since the last iteration; The objective function J represents the pair of The partial derivatives; The objective function J represents the pair of The partial derivatives are calculated; the gradient is calculated using the finite difference method or automatic differentiation. The iterative process continues until the convergence verification condition is met.

[0083] Adaptive Filtering and Confidence Interval Evaluation

[0084] To reduce random noise and fluctuations in the initial parameter estimation results, adaptive filtering technology is used to smooth the initial parameter estimation results, generating smoothed parameter estimates. This invention employs the Kalman filter algorithm. The state equation of the Kalman filter is defined as: x_k = A x_{k-1} + w_k, where A is the state transition matrix. The observation equation is: z_k = H x_k + v_k, where H is the observation matrix. The covariances Q and R of the process noise w_k and the observation noise v_k are calibrated using historical data. In this embodiment, Q = 0.01 and R = 0.05.

[0085] Confidence interval assessment techniques use statistical inference methods to evaluate the reliability of smoothed parameter estimates and generate confidence interval assessment results. The covariance matrix of the parameters is estimated by calculating the Hessian matrix of the objective function during parameter inversion, thereby constructing a 95% confidence interval; alternatively, the Bootstrap resampling method or Monte Carlo simulation is used to obtain the empirical distribution of the parameter estimates.

[0086] After parameter consistency verification (including physical boundary checks, comparison with theoretical / simulation values, and cross-validation), the final parameter estimation results are generated, ensuring the accuracy and stability of the extracted equivalent electromagnetic parameters.

[0087] Real-time monitoring and anomaly detection

[0088] During the characterization process, the real-time monitoring module continuously tracks dynamic changes through a sensor network (including temperature, humidity, probe position, and excitation power sensors), generating dynamic change records. These records include time-series data for all key parameters.

[0089] Based on the dynamically changing records, anomaly detection algorithms are applied to identify and label potential problems, generating anomaly detection results. This invention employs machine learning-based anomaly identification methods (such as SVM, Isolation Forest, or neural networks) to learn patterns from normal data and identify anomalous points deviating from normal behavior. For time series data, ARIMA models or LSTM networks can also be used for prediction, identifying points with significant deviations from the predictions.

[0090] Based on the anomaly detection results, problems are categorized (such as "excessive data noise", "probe position deviation", "unstable excitation source") and prioritized, and finally monitoring records are generated to support subsequent optimization.

[0091] Feedback control and optimization

[0092] Based on monitoring records and final parameter estimation results, the feedback control module uses closed-loop control principles to adjust the configuration parameters of the test system, generating adjusted configuration parameters. The feedback control mechanism dynamically adjusts parameters such as probe scanning speed, step size, excitation source power, measurement frequency, or data acquisition time based on anomaly information in the monitoring records and the accuracy of the parameter estimation results.

[0093] Based on the adjusted configuration parameters, parameter optimization techniques are applied to optimize the test system, generating an optimized test configuration. This invention employs a genetic algorithm as the parameter optimization method. The genetic algorithm iteratively searches for the optimal combination of test configuration parameters by simulating the biological evolution process, including steps such as population initialization, fitness evaluation, selection, crossover, and mutation.

[0094] The test operation is carried out by loading the optimal test configuration parameters found by the genetic algorithm into the control software of the near-field test system, automatically executing the measurement task, generating the optimized test execution, and further improving the adaptability and efficiency of the test system.

[0095] As can be seen from the above specific implementation methods, this invention achieves high-precision characterization of the equivalent electromagnetic parameters of electromagnetic metamaterials through the comprehensive application of multi-probe arrays, signal decomposition and phase compensation, boundary effect correction and non-uniformity compensation, parameter inversion algorithms, adaptive filtering technology, real-time monitoring modules, and feedback control mechanisms. The clear connection relationships and collaborative mechanisms between the modules ensure the efficiency and reliability of the entire characterization process.

[0096] The foregoing has shown and described the basic principles, main features, and advantages of the present invention. Those skilled in the art should understand that the present invention is not limited to the above embodiments. The embodiments and descriptions in the specification are merely preferred examples and are not intended to limit the invention. Various changes and modifications can be made to the invention without departing from its spirit and scope, and all such changes and modifications fall within the scope of the present invention as claimed. The scope of protection of the present invention is defined by the appended claims and their equivalents.

Claims

1. A near-field characterization method for the equivalent electromagnetic parameters of electromagnetic metamaterials, characterized in that, Includes the following steps: A multi-probe array is used to sample the electromagnetic field distribution in the target area, and a data calibration module is used to eliminate measurement errors to generate an initial measurement dataset. Based on the initial measurement dataset, a signal decomposition module is used to extract the spatial spectrum information within the target area, and a phase compensation module is used to reconstruct the complete electromagnetic field distribution to generate processed field distribution data. Based on the processed field distribution data, a boundary effect correction module is used to quantify the boundary effects under near-field conditions, and a non-uniformity compensation module is used to optimize the field distribution data to generate corrected field distribution data. Based on the corrected field distribution data, a parameter inversion algorithm is used to extract the equivalent electromagnetic parameters of the metamaterial through iterative calculation to generate preliminary parameter estimation results. Based on the preliminary parameter estimation results, adaptive filtering technology is used to smooth the parameter estimates, and confidence interval evaluation is combined to generate the final parameter estimation results. During the characterization process, a real-time monitoring module is used to continuously track the dynamic changes during the characterization process, and an anomaly detection algorithm is used to identify potential problems and generate monitoring records. Based on the monitoring records and the final parameter estimation results, the feedback control module is used to adjust the configuration parameters of the test system and generate an optimized test execution.

2. The near-field characterization method for equivalent electromagnetic parameters of electromagnetic metamaterials according to claim 1, characterized in that: The initial measurement dataset includes electric field strength, magnetic field strength and their phase information. The processed field distribution data includes the electromagnetic field distribution after signal decomposition and phase compensation. The corrected field distribution data includes the field distribution after boundary effect correction and non-uniformity compensation. The preliminary parameter estimation results are used to reflect the dielectric constant and magnetic permeability characteristics of the metamaterial. The final parameter estimation results are parameter values ​​after smoothing and confidence interval evaluation. The monitoring record specifically refers to the record of dynamic changes and the marking of anomalies during the test.

3. The near-field characterization method for equivalent electromagnetic parameters of electromagnetic metamaterials according to claim 1, characterized in that: The steps for generating an initial measurement dataset based on a near-field testing system using a multi-probe array to sample the electromagnetic field distribution of the target area and eliminating measurement errors through a data calibration module are as follows: Based on the near-field testing system, a multi-probe array is used to sample the electromagnetic field distribution of the target area to generate raw measurement data; based on the raw measurement data, a data calibration module is used to correct the probe response characteristics and environmental noise to generate calibrated measurement data. Based on the calibrated measurement data, signal preprocessing techniques are applied to denoise and normalize the sampled data to generate preprocessed measurement data. The preprocessed measurement data are integrated into the electromagnetic field distribution of the target area to generate an initial measurement dataset.

4. The near-field characterization method for equivalent electromagnetic parameters of electromagnetic metamaterials according to claim 3, characterized in that: The multi-probe array is specifically a group of uniformly distributed probe units used to cover the target area and improve sampling accuracy. The data calibration module is specifically a correction algorithm based on the probe calibration curve and the environmental noise model. The signal preprocessing technology specifically includes wavelet transform denoising and normalization processing.

5. The near-field characterization method for equivalent electromagnetic parameters of electromagnetic metamaterials according to claim 1, characterized in that: Based on the initial measurement dataset, the steps of extracting spatial spectrum information within the target area using a signal decomposition module and reconstructing the complete electromagnetic field distribution using a phase compensation module to generate processed field distribution data are as follows: Based on the initial measurement dataset, the electromagnetic field data is decomposed in the frequency domain using a signal decomposition module to generate spatial spectrum information; based on the spatial spectrum information, the frequency domain components are phase-corrected using a phase compensation module to generate phase-compensated spectrum data. Based on the phase-compensated spectral data, the electromagnetic field distribution of the target region is reconstructed using inverse Fourier transform technology to generate initial field distribution data; Based on the initial field distribution data, consistency verification is performed to generate processed field distribution data.

6. The near-field characterization method for equivalent electromagnetic parameters of electromagnetic metamaterials according to claim 5, characterized in that: The signal decomposition module is specifically a technique for extracting spatial spectrum information by performing frequency domain decomposition on electromagnetic field data; the phase compensation module is specifically a method for correcting phase errors based on a reference signal; and the inverse Fourier transform technique is specifically a mathematical tool for converting frequency domain data into spatial domain distribution.

7. The near-field characterization method for equivalent electromagnetic parameters of electromagnetic metamaterials according to claim 1, characterized in that: The steps for generating corrected field distribution data based on the processed field distribution data are as follows: First, based on the processed field distribution data, the boundary effect correction module is used to quantify the boundary influence under near-field conditions, and the non-uniformity compensation module is combined to optimize the field distribution data. Second, based on the boundary effect correction data, the non-uniformity compensation module is applied to compensate for the non-uniformity in the field distribution data, generating non-uniformity compensated data. Third, based on the non-uniformity compensated data, distribution consistency verification is performed to generate corrected field distribution data.

8. The near-field characterization method for equivalent electromagnetic parameters of electromagnetic metamaterials according to claim 7, characterized in that: The boundary effect correction module is specifically a mathematical model based on boundary conditions and electromagnetic field theory, used to quantify the influence of the boundary on the field distribution. The non-uniformity compensation module is specifically a compensation technique based on local weighted regression, used to optimize the consistency of field distribution data.

9. The near-field characterization method for equivalent electromagnetic parameters of electromagnetic metamaterials according to claim 1, characterized in that: Based on the corrected field distribution data, the steps of extracting the equivalent electromagnetic parameters of the metamaterial through iterative calculation using a parameter inversion algorithm to generate preliminary parameter estimation results are as follows: Based on the corrected field distribution data, an initial parameter estimation model is constructed using a parameter inversion algorithm to generate initial parameter estimates; based on the initial parameter estimates, iterative calculation techniques are applied to optimize the parameter values ​​to generate optimized parameter estimates; based on the optimized parameter estimates, convergence verification is performed to generate preliminary parameter estimation results.

10. The near-field characterization method for equivalent electromagnetic parameters of electromagnetic metamaterials according to claim 9, characterized in that: The parameter inversion algorithm is specifically a mathematical inversion method based on electromagnetic field distribution and material properties, used to extract equivalent electromagnetic parameters, and the iterative calculation technique is specifically an optimization algorithm based on gradient descent.

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