Radio Frequency Module Electromagnetic Compatibility Analysis Method and System

Abstract

The present invention relates to a method and system for electromagnetic compatibility analysis of a radio frequency module, including obtaining the structural data and electromagnetic parameter information of the radio frequency module, performing electromagnetic simulation on the structural data based on the electromagnetic parameter information to obtain corresponding radio frequency module simulation data; obtaining the near-field scan data of the radio frequency module, performing correlation calculation on the near-field scan data and the radio frequency module simulation data through a preset fully implicit frequency-domain finite volume numerical algorithm to obtain corresponding far-field electromagnetic distribution characteristic data; obtaining the interference emission data of the radio frequency module and performing zero eigenvalue entropy correction to obtain corresponding interference propagation characteristic data; performing optimization analysis on the far-field electromagnetic distribution characteristic data and the interference propagation characteristic data to obtain corresponding initial optimization information; and performing scheme analysis on the initial optimization information and the EMC performance evaluation result to obtain a corresponding EMC optimization scheme. The present invention can more comprehensively evaluate the electromagnetic characteristics of the radio frequency module, thereby improving the accuracy of electromagnetic compatibility analysis.

Description

Technical Field

[0001] The present invention relates to the technical field of radio frequency modules, and particularly to a method and system for analyzing the electromagnetic compatibility of radio frequency modules. Background Art

[0002] As an indispensable key component in modern electronic devices, radio frequency modules have been widely used in fields such as communication and the Internet of Things. With the continuous improvement of the integration level and the working frequency of electronic devices, the problem of electromagnetic compatibility (EMC, i.e., Electromagnetic Compatibility) has become increasingly prominent. How to accurately evaluate and optimize the electromagnetic compatibility ability of radio frequency modules has become one of the key research topics in the industry. Existing electromagnetic compatibility analysis methods are often limited to the analysis of test data in a single dimension. For example, only the near-field or far-field characteristics are concerned, while the propagation characteristics of electromagnetic interference and the correlation between multi-dimensional EMC performance indicators in the actual application environment of radio frequency modules are ignored. Such a one-sided analysis method is difficult to comprehensively reflect the electromagnetic compatibility performance of radio frequency modules and easily leads to the lack of systematicness and pertinence in the formulation of EMC optimization schemes. Summary of the Invention

[0003] The main object of the present invention is to provide a method and system for analyzing the electromagnetic compatibility of radio frequency modules, which can more comprehensively evaluate the electromagnetic characteristics of radio frequency modules, thereby improving the accuracy of electromagnetic compatibility analysis.

[0004] To achieve the above object, the present invention provides a method for analyzing the electromagnetic compatibility of radio frequency modules, including:

[0005] Obtaining the structural data and electromagnetic parameter information of the radio frequency module, and performing electromagnetic simulation on the structural data based on the electromagnetic parameter information to obtain the corresponding radio frequency module simulation data;

[0006] Obtaining the near-field scan data of the radio frequency module, and performing correlation calculation on the near-field scan data and the radio frequency module simulation data through a preset fully implicit frequency-domain finite volume numerical algorithm to obtain the corresponding far-field electromagnetic distribution characteristic data;

[0007] Obtaining the interference emission data of the radio frequency module, and performing zero eigenvalue entropy correction to obtain the corresponding interference propagation characteristic data;

[0008] Performing optimization analysis on the far-field electromagnetic distribution characteristic data and the interference propagation characteristic data to obtain the corresponding initial optimization information;

[0009] Performing an immunity test on the interference emission data, and combining the interference propagation characteristics to perform performance evaluation to obtain the corresponding EMC performance evaluation result;

[0010] Perform a solution analysis on the initial optimization information and the EMC performance evaluation results to obtain the corresponding EMC optimization solution.

[0011] Further, the method for obtaining the structural data and electromagnetic parameter information of the RF module, performing electromagnetic simulation on the structural data based on the electromagnetic parameter information to obtain the corresponding RF module simulation data, includes:

[0012] Perform parametric characterization of the physical structure of the RF module to obtain the structural data including component layout and shielding structure;

[0013] Measure the attribute electromagnetic characteristics of the RF module to obtain the electromagnetic parameter information including dielectric constant, loss tangent, and conductivity;

[0014] Perform mesh division processing on the RF module according to the structural data to obtain the corresponding structural mesh data;

[0015] Assign regional attributes to the structural mesh data according to the electromagnetic parameter information to obtain the computational domain mesh;

[0016] Perform finite element discretization processing on the computational domain mesh to obtain the corresponding electromagnetic field distribution equations;

[0017] Construct unstructured tetrahedral mesh elements according to the electromagnetic field distribution equations to obtain the corresponding computational mesh data;

[0018] Perform time-domain finite difference iterative calculation on the computational mesh data to obtain the corresponding time-domain electromagnetic field distribution data;

[0019] Perform simulation parameter analysis on the time-domain electromagnetic field distribution data to obtain the corresponding RF module simulation data.

[0020] Further, the method for obtaining the near-field scan data of the RF module, and performing correlation calculation on the near-field scan data and the RF module simulation data through a preset fully implicit frequency-domain finite volume numerical algorithm to obtain the corresponding far-field electromagnetic distribution characteristic data, includes:

[0021] Perform time-domain sampling processing on the near-field scan data and the RF module simulation data to obtain the corresponding dual-channel time-domain sampling sequences;

[0022] Perform orthogonal decomposition processing on the dual-channel time-domain sampling sequences to obtain the corresponding real part sequence and imaginary part sequence;

[0023] Perform fast Fourier transform processing on the real part sequence and the imaginary part sequence to obtain the corresponding frequency-domain characteristic data;

[0024] Perform hexahedral mesh division on the frequency-domain feature data to obtain corresponding spatial discrete grid data;

[0025] Construct the fully implicit discrete form of Maxwell's equations for the spatial discrete grid data to obtain the corresponding electromagnetic field component equations, and solve the electromagnetic field component equations by preconditioned conjugate gradient iteration to obtain the frequency-domain field strength distribution data;

[0026] Perform feature mapping on the radio frequency module simulation data according to the frequency-domain field strength distribution data to obtain the corresponding electromagnetic feature vector group;

[0027] Perform spherical wave expansion transformation on the electromagnetic feature vector group to obtain the corresponding far-field distribution matrix;

[0028] Calculate the far-field radiation field strength and radiation pattern according to the far-field distribution matrix to obtain the far-field electromagnetic distribution characteristic data.

[0029] Further, the constructing the fully implicit discrete form of Maxwell's equations for the spatial discrete grid data to obtain the corresponding electromagnetic field component equations, and solving the electromagnetic field component equations by preconditioned conjugate gradient iteration to obtain the frequency-domain field strength distribution data includes:

[0030] Perform spatial grid analysis on the spatial discrete grid data to obtain the corresponding grid node coordinates and grid topology relationships;

[0031] Mark the fixed boundaries of the electric field components for the grid node coordinates to obtain the first type of boundary node set;

[0032] Mark the derivative boundaries of the magnetic field components for the grid topology relationships to obtain the second type of boundary element set;

[0033] Perform multi-layer boundary data integration according to the first type of boundary node set and the second type of boundary element set to obtain the corresponding boundary grid data set;

[0034] Perform discretization processing of Maxwell's equations on the boundary grid data set to obtain the corresponding discrete relationships of electromagnetic field components, where the discretization processing of Maxwell's equations includes:

[0035] Perform difference discretization of the time derivative of the electric field strength for the grid nodes to obtain the electric field time step relationship;

[0036] Perform integral discretization of the spatial derivative of the magnetic field strength for the grid nodes to obtain the magnetic field spatial gradient relationship;

[0037] Construct a coupling relationship according to the electric field time step relationship and the magnetic field spatial gradient relationship to obtain the corresponding field component coupling relationship information;

[0038] Correlate and combine the field component coupling relationship information, the electric field time step relationship, and the magnetic field spatial gradient relationship to obtain the corresponding discrete relationship of electromagnetic field components;

[0039] Construct a diagonal preconditioning matrix based on the field component coupling relationship information to obtain a preconditioned iteration matrix;

[0040] Perform residual calculation on the preconditioned iteration matrix to obtain a convergence criterion parameter;

[0041] Perform electromagnetic field component iterative analysis on the discrete relationship of electromagnetic field components according to the convergence criterion parameter to obtain the frequency domain field strength distribution data.

[0042] Further, the obtaining of the interference emission data of the RF module and performing zero eigenvalue entropy correction to obtain the corresponding interference propagation characteristic data includes:

[0043] Sample the electromagnetic interference source of the RF module to obtain the interference emission data;

[0044] Extract features from the interference emission data to obtain corresponding interference characteristic data;

[0045] Perform interference source spatial vector decomposition on the RF module according to the interference characteristic data to obtain corresponding spatial component data;

[0046] Perform singular value decomposition operation on the spatial component data to obtain a corresponding eigenvalue matrix and eigenvector matrix;

[0047] Extract the main diagonal elements of the eigenvalue matrix to obtain corresponding eigenvalue sequence data;

[0048] Perform zero eigenvalue statistics on the eigenvalue sequence data to obtain the corresponding number of zero eigenvalues;

[0049] Construct an entropy weight function according to the number of zero eigenvalues to obtain a corresponding entropy weight coefficient;

[0050] Perform zero eigenvalue correction processing on the eigenvalue matrix through the entropy weight coefficient to obtain corresponding corrected eigenvalues;

[0051] Perform matrix data reconstruction according to the corrected eigenvalues and the eigenvector matrix to obtain corresponding interference propagation characteristic data.

[0052] Further, the optimization analysis of the far-field electromagnetic distribution characteristic data and the interference propagation characteristic data to obtain the corresponding initial optimization information includes:

[0053] Perform multi-dimensional spectral decomposition processing on the far-field electromagnetic distribution characteristic data to obtain corresponding multi-dimensional spectral characteristic data;

[0054] Perform non-linear correlation mapping on the interference propagation characteristic data according to the multi-dimensional spectral characteristic data to obtain corresponding coupling mapping data;

[0055] Perform fractional-order Hilbert transform processing on the coupling mapping data to obtain corresponding fractional-order time-frequency characteristic data;

[0056] Perform adaptive entropy value calculation according to the fractional-order time-frequency characteristic data to obtain corresponding entropy value distribution data;

[0057] Perform mode decomposition on the entropy value distribution data to obtain corresponding eigenmode data;

[0058] Perform electromagnetic field strength spatial distribution reconstruction according to the eigenmode data to obtain corresponding field strength distribution characteristic data;

[0059] Perform multi-objective information optimization analysis on the field strength distribution characteristic data to obtain corresponding initial optimization information.

[0060] Further, perform immunity testing on the interference emission data, and combine the interference propagation characteristics for performance evaluation to obtain corresponding EMC performance evaluation results, including:

[0061] Perform multi-frequency point injection scanning on the interference emission data to obtain corresponding immunity scanning data;

[0062] Perform burst interference analysis on the immunity scanning data to obtain corresponding burst interference response data;

[0063] Perform transfer characteristic analysis on the immunity scanning data to obtain corresponding immunity transfer characteristics;

[0064] Perform non-linear fitting on the burst interference response data according to the interference propagation characteristics to obtain corresponding EMC response feature vectors;

[0065] Perform matrix mapping construction on the immunity transfer characteristics according to the interference propagation characteristics to obtain an immunity mapping matrix;

[0066] Solve the singular values of the immunity mapping matrix to obtain corresponding immunity eigenvalue sequences;

[0067] Perform immunity response calculation according to the EMC response feature vectors and the immunity eigenvalue sequences to obtain corresponding immunity response data;

[0068] Evaluate the characteristics of the immunity response data according to a preset EMC evaluation matrix to obtain corresponding EMC characteristic score data.

[0069] Further, the step of analyzing the initial optimization information and the EMC performance evaluation result to obtain a corresponding EMC optimization solution includes:

[0070] Associate and match the initial optimization information and the EMC performance evaluation result according to the time series to obtain corresponding associated data pairs;

[0071] Analyze the EMC performance index of the associated data pairs to obtain corresponding EMC performance characteristic indexes;

[0072] Perform adaptive threshold segmentation on the EMC performance characteristic indexes to obtain corresponding electromagnetic field threshold intervals;

[0073] Perform EMC performance grading evaluation according to the electromagnetic field threshold interval to obtain corresponding performance rating data;

[0074] Perform orthogonal transformation on the performance rating data to obtain corresponding EMC characteristic component data;

[0075] Perform non-linear mapping on the EMC characteristic component data to obtain corresponding EMC optimization parameters;

[0076] Solve the EMC performance constraint conditions according to the EMC optimization parameters to obtain corresponding EMC constraint conditions;

[0077] Iteratively optimize the EMC constraint conditions to obtain a corresponding set of EMC optimization target conditions;

[0078] Generate multiple solutions according to the set of EMC optimization target conditions to obtain a corresponding set of EMC solutions;

[0079] Perform solution screening on the set of EMC solutions to obtain the EMC optimization solution.

[0080] The present invention also provides a radio frequency module electromagnetic compatibility analysis system, which is applied to the radio frequency module electromagnetic compatibility analysis method of any one of the above, and includes:

[0081] An acquisition module, which is used to obtain the structure data and electromagnetic parameter information of the radio frequency module, and perform electromagnetic simulation on the structure data based on the electromagnetic parameter information to obtain corresponding radio frequency module simulation data;

[0082] An analysis module, which is used to obtain the near-field scanning data of the RF module, perform correlation calculations on the near-field scanning data and the simulation data of the RF module through a preset fully implicit frequency-domain finite volume numerical algorithm, and obtain corresponding far-field electromagnetic distribution characteristic data;

[0083] A correlation module, which is used to obtain the interference emission data of the RF module, perform zero eigenvalue entropy correction, and obtain corresponding interference propagation characteristic data;

[0084] A processing module, which is used to perform optimization analysis on the far-field electromagnetic distribution characteristic data and the interference propagation characteristic data to obtain corresponding initial optimization information;

[0085] A control module, which is used to perform an immunity test on the interference emission data, and perform performance evaluation in combination with the interference propagation characteristics to obtain corresponding EMC performance evaluation results;

[0086] An execution module, which is used to perform scheme analysis on the initial optimization information and the EMC performance evaluation results to obtain corresponding EMC optimization schemes.

[0087] An electromagnetic compatibility analysis method and system for an RF module provided by the present invention have the following beneficial effects:

[0088] By performing electromagnetic simulation analysis on the structural data and electromagnetic parameter information of the RF module, and combining near-field scanning data, the electromagnetic characteristics of the RF module can be evaluated more comprehensively, thereby improving the accuracy of electromagnetic compatibility analysis and providing reliable data support for EMC optimization design. By performing correlation calculations on the near-field scanning data and the simulation data and analyzing them according to the fully implicit frequency-domain finite volume numerical algorithm, accurate prediction of the far-field electromagnetic distribution characteristics is achieved, which helps to better grasp the electromagnetic radiation characteristics of the RF module. Based on the zero eigenvalue entropy correction algorithm, the propagation analysis of the interference emission data is carried out, which can ensure accurate evaluation of the interference propagation characteristics of the RF module in different application environments and reduce the performance hidden dangers brought by EMC problems. By comprehensively analyzing the far-field electromagnetic distribution characteristic data and the interference propagation characteristic data, a more reasonable optimization scheme is formulated, and the overall EMC performance is improved through immunity test verification and performance evaluation, thereby effectively solving the electromagnetic interference problem. At the same time, by considering the correlation of multi-dimensional EMC performance indicators, the EMC optimization strategy can be flexibly adjusted according to the characteristics and requirements of different application scenarios, making the analysis method more adaptable to complex application environments. Description of the Drawings

[0089] Figure 1 It is a flowchart of an electromagnetic compatibility analysis method for an RF module provided by the present invention;

[0090] Figure 2 It is a structural diagram of an electromagnetic compatibility analysis system for a radio frequency module provided by the present invention.

[0091] The realization of the purpose, functional characteristics and advantages of the present invention will be further described with reference to the embodiments and the accompanying drawings. Specific embodiments

[0092] In order to make the purpose, technical solution and advantages of the present invention clearer, the present invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not used to limit the present invention.

[0093] Next, the present invention will be further described in combination with the accompanying drawings and specific embodiments.

[0094] Referring to Figure 1 as shown, the present invention provides a method for analyzing the electromagnetic compatibility of a radio frequency module, including:

[0095] Step S1: Obtain the structural data and electromagnetic parameter information of the radio frequency module, perform electromagnetic simulation on the structural data based on the electromagnetic parameter information, and obtain the corresponding radio frequency module simulation data;

[0096] Step S2: Obtain the near-field scanning data of the radio frequency module, perform correlation calculation on the near-field scanning data and the radio frequency module simulation data through a preset fully implicit frequency-domain finite volume numerical algorithm, and obtain the corresponding far-field electromagnetic distribution characteristic data;

[0097] Step S3: Obtain the interference emission data of the radio frequency module, and perform zero eigenvalue entropy correction to obtain the corresponding interference propagation characteristic data;

[0098] Step S4: Optimize and analyze the far-field electromagnetic distribution characteristic data and the interference propagation characteristic data to obtain the corresponding initial optimization information;

[0099] Step S5: Perform an immunity test on the interference emission data, and combine the interference propagation characteristics to perform a performance evaluation to obtain the corresponding EMC performance evaluation result;

[0100] Step S6: Analyze the initial optimization information and the EMC performance evaluation result to obtain the corresponding EMC optimization plan.

[0101] Based on the above steps, the detailed step process is as follows:

[0102] Step S1: Obtain the complete structural information of the RF module, including physical structure data such as PCB layout diagrams, component arrangements, trace distributions, and ground plane designs. At the same time, collect key electromagnetic parameter information, such as material property parameters like dielectric constant, loss tangent, conductivity, and permeability. Based on these basic data, construct a 3D model of the RF module. And set appropriate boundary conditions, excitation sources, and mesh divisions to perform full-wave electromagnetic field simulation calculations. Through the simulation, obtain detailed data such as the electric field distribution, magnetic field distribution, and surface current distribution inside the module, which will provide an important theoretical basis for subsequent near-field to far-field conversion.

[0103] Step S2: The electromagnetic field intensity can be measured at a specific height on the surface of the RF module through a near-field scanning device. During the scanning process, control the moving speed and sampling interval of the probe to ensure that the spatial resolution of the data meets the requirements. Calibrate the obtained near-field data including amplitude and phase information and eliminate the influence of the measurement system itself.

[0104] Perform a correlation analysis between the near-field scanning data and the simulation data. Through a preset fully implicit frequency-domain finite volume numerical algorithm, establish a mathematical relationship between the near-field data and the far-field radiation. By solving Maxwell's equations, extrapolate the near-field data to the far-field region.

[0105] During the numerical calculation process, consider the continuity conditions and boundary conditions of the field source to ensure the physical rationality of the calculation results. Obtain the electromagnetic field distribution characteristics in the far-field region, including information such as radiation patterns, polarization characteristics, and field strength distributions.

[0106] Step S3: Obtain the interference emission data of the RF module in a standard test environment. The testing process is carried out in accordance with the requirements of relevant EMC standards, including conducted emission and radiation emission tests. When conducting the conducted emission test, use a line impedance stabilization network (LISN) to measure the conducted interference level on the power line; in the radiation emission test, measure the electromagnetic field intensity at a specific distance in a semi-anechoic chamber or an open test field.

[0107] The original interference data obtained may have measurement uncertainties and need to be processed by zero eigenvalue entropy correction. This correction method is based on information entropy theory. By analyzing the statistical characteristics of the interference signal, identify and correct the uncertainties introduced by the measurement system.

[0108] The data after correction more accurately reflects the actual interference emission characteristics of the RF module, including interference frequency distribution, intensity change laws, and propagation path characteristics. Through comparative analysis with the far-field distribution characteristics, obtain the far-field electromagnetic distribution characteristic data.

[0109] Step S4: Establish an electromagnetic compatibility performance evaluation index system, including parameters such as radiation interference level, conduction interference level, and harmonic distortion. Use a multi-objective optimization algorithm (genetic algorithm or particle swarm algorithm) to analyze the parameters. Based on the mutual influence and weight relationship of each index, establish an objective function. By iteratively calculating the objective function, obtain the optimal solution space of the performance index and determine the optimization direction. The optimization information includes the electromagnetic field strength distribution at key frequency points, the position and intensity of interference sources, and the sensitivity of propagation paths. Combine engineering constraint conditions (cost, volume, process) to form the optimization information. This information serves as the technical basis for the EMC improvement plan.

[0110] Step S5: Perform immunity tests specified by the EMC standard, including electrostatic discharge immunity, electrical fast transient immunity, surge immunity, and radio frequency electromagnetic field radiation immunity. Record the working status and performance parameters of the module under various interference conditions. Perform correlation analysis on the test data and the interference propagation characteristics to quantify the anti-interference ability of the module in the application environment. The performance evaluation covers functional integrity, performance stability, and reliability indicators. By converting the test results into specific performance indicators. The evaluation results reflect the EMC performance level of the RF module and identify improvement items.

[0111] Step S6: Conduct a systematic solution analysis of the initial optimization information and the EMC performance evaluation results. Through comparative analysis, identify the items and areas that need to be improved. When formulating the optimization plan, multiple aspects need to be considered: at the structural design level, including PCB layout optimization, ground plane improvement, adjustment of key signal lines, etc.; at the device selection level, it may involve filter configuration, shielding structure design, etc.;

[0112] At the system integration level, based on the overall electromagnetic compatibility requirements. During the solution formulation process, the cost-benefit ratio needs to be fully considered, and the implementation cost should be minimized while ensuring EMC performance. The optimization plan is divided into three levels: Structural design level: PCB layout optimization, ground plane improvement, adjustment of key signal lines. Device selection level: Filter configuration, shielding structure design. System integration level: Implementation plan for electromagnetic compatibility indicators

[0113] A method for analyzing the electromagnetic compatibility of a radio frequency module can comprehensively evaluate the electromagnetic characteristics of the radio frequency module by performing electromagnetic simulation analysis on the structural data and electromagnetic parameter information of the radio frequency module and combining near-field scanning data, thereby improving the accuracy of electromagnetic compatibility analysis and providing reliable data support for EMC optimization design. By correlating and calculating the near-field scanning data with the simulation data and analyzing based on the fully implicit finite volume numerical algorithm in the frequency domain, the accurate prediction of the far-field electromagnetic distribution characteristics is achieved, which helps to better grasp the electromagnetic radiation characteristics of the radio frequency module. Based on the zero eigenvalue entropy correction algorithm, the propagation analysis of the interference emission data can ensure the accurate evaluation of the interference propagation characteristics of the radio frequency module in different application environments and reduce the performance hidden dangers caused by EMC problems. By comprehensively analyzing the far-field electromagnetic distribution characteristic data and the interference propagation characteristic data, a more reasonable optimization scheme is formulated, and the overall EMC performance is improved through immunity test verification and performance evaluation, thus effectively solving the electromagnetic interference problem. At the same time, by considering the correlation of multi-dimensional EMC performance indicators, the EMC optimization strategy can be flexibly adjusted according to the characteristics and requirements of different application scenarios, making the analysis method more adaptable to complex application environments.

[0114] In one embodiment, the structural data and electromagnetic parameter information of the radio frequency module are obtained, and electromagnetic simulation is performed on the structural data based on the electromagnetic parameter information to obtain the corresponding radio frequency module simulation data, including:

[0115] During the electromagnetic compatibility analysis of the radio frequency module, when performing parametric characterization of the physical structure of the radio frequency module, a three-dimensional reconstruction of the physical structure of the radio frequency module is carried out to establish an accurate geometric model including component layout and shielding structure. The information such as the size parameters, relative position relationship, and material properties of each part of the radio frequency module needs to be considered during the modeling process to form a complete structure database.

[0116] The electromagnetic parameters such as the dielectric constant, loss tangent, and conductivity of the radio frequency module are scanned and measured within a specified frequency range. During the measurement, the environmental temperature needs to be ensured to be 25±2°C and the relative humidity does not exceed 65%. The measured data forms an electromagnetic parameter database after calibration processing.

[0117] The mesh generation process is based on the adaptive mesh refinement algorithm to perform meshing on the structural data of the radio frequency module. The mesh generation density needs to meet the requirement that the minimum mesh edge length is not less than 1 / 10 of the structural feature size, and the mesh quality factor is greater than 0.3. The division result forms a structural mesh data file.

[0118] During the process of assigning regional attributes, the material attributes of each region in the structured grid data are defined based on the electromagnetic parameter information library. The defined content includes parameters such as dielectric constant, loss tangent, and conductivity. The attribute assignment needs to ensure the continuity and consistency of the parameters in each region to generate a complete computational domain grid.

[0119] For the finite element discretization process, the electric field is expanded using basis functions, and the Maxwell's equations are discretized by the weighted residual method. During the discretization process, second-order vector basis functions are selected, and the discretization accuracy is controlled within 1%. The discretization result obtains the electromagnetic field distribution equations.

[0120] The construction of unstructured tetrahedral mesh elements uses the Delaunay triangulation algorithm to reconstruct the mesh of the computational domain. The maximum volume of the mesh elements does not exceed 1 / 8 cubic of the wavelength, and the mesh distortion rate is controlled below 0.1. The construction result forms the computational mesh data.

[0121] The time-domain finite-difference iterative calculation uses an explicit iterative format, and the calculation time step satisfies the CFL stability condition. The iterative convergence criterion is that the relative error between two consecutive iterative results is less than 0.1%. The calculation result outputs the time-domain distribution data of the electromagnetic field.

[0122] Based on the time-domain distribution data of the electromagnetic field, the simulation parameter analysis calculates electromagnetic compatibility indicators such as the radiation emission characteristics and immunity characteristics of the RF module. The analysis results need to meet the requirements of relevant electromagnetic compatibility standards to form complete RF module simulation data.

[0123] In this embodiment, by establishing a complete parametric representation system for physical structure parameters, accurate acquisition of the RF module structure data is achieved, laying a reliable data foundation for subsequent simulation analysis. Adopting a standardized measurement process for attribute electromagnetic characteristics effectively ensures the accuracy and consistency of electromagnetic parameter information. The mesh division process based on the adaptive mesh refinement algorithm improves the mesh quality and enhances the calculation accuracy. Through refined regional attribute assignment and finite element discretization, the solution accuracy of the electromagnetic field distribution equations is ensured. Combining the construction of unstructured tetrahedral mesh elements and time-domain finite-difference iterative calculation, efficient and stable electromagnetic field distribution calculation is realized. This method establishes a complete electromagnetic compatibility analysis system for RF modules, significantly improving the analysis efficiency and accuracy, and providing strong support for the electromagnetic compatibility design optimization of RF modules.

[0124] In one embodiment, the near-field scan data of the RF module is obtained, and the near-field scan data and the RF module simulation data are correlated and calculated through a preset fully implicit frequency-domain finite volume numerical algorithm to obtain the corresponding far-field electromagnetic distribution characteristic data, including:

[0125] The RF module is measured by a high-precision near-field scanner through near-field scanning to collect near-field electromagnetic field distribution data containing amplitude and phase information. The near-field scanning adopts a planar scanning method, where the scanning plane is parallel to the surface of the RF module to be measured. The scanning height is set to 5 mm, and the scanning step is λ / 4 (λ is the operating wavelength). The scanning area covers at least a range of 2 wavelengths around the surface of the RF module to ensure the acquisition of complete near-field information.

[0126] When performing time-domain sampling processing on the obtained near-field scanning data, the sampling frequency is set to 4 times the operating frequency, and the sampling time window is set to 5 cycles, resulting in a two-channel time-domain sampling sequence containing electric field components and magnetic field components. The sampling sequence undergoes orthogonal decomposition processing to decompose the complex field quantities into real part sequences and imaginary part sequences, maintaining the phase relationship of the sampling points.

[0127] The fast Fourier transform is respectively performed on the real part sequence and the imaginary part sequence. The number of transform points is set to 1024 points, and feature analysis is carried out through a window function to obtain frequency-domain feature data. The frequency-domain feature data contains amplitude spectrum and phase spectrum information of the field quantities.

[0128] In the spatial discretization process, the computational domain is meshed with hexahedral grids. The grid size is taken as λ / 10 to ensure the computational accuracy. The grids are encrypted at the boundaries, and the grid size is reduced to λ / 15 to improve the computational accuracy at the boundaries. The spatial discrete grid obtained after meshing contains node coordinate and element topology relationship information.

[0129] Based on the spatial discrete grid, a fully implicit discrete form of the Maxwell equations is constructed. Edge basis functions are used to expand the electric field and magnetic field, and an electromagnetic field component equation set is constructed within each grid cell. The equation is solved using the preconditioned conjugate gradient iteration method. The preconditioner is selected as the incomplete LU decomposition, and the iteration convergence criterion is set to the residual being less than 10 -6 . The obtained frequency-domain field strength distribution data contains three-dimensional component information of the electric field and magnetic field.

[0130] When performing feature mapping on the frequency-domain field strength distribution data, the amplitude and phase features of the field quantities are extracted to construct an electromagnetic feature vector group containing spatial distribution information. The dimension of the feature vector corresponds to the number of grid nodes, and the vector components characterize the distribution features of the field quantities at each point in space.

[0131] The electromagnetic feature vector group is subjected to spherical wave expansion transformation. The number of expansion terms is taken up to 30 orders to ensure the transformation accuracy. The Gaussian quadrature method is used for integration during the transformation process, and the number of integration points is taken as 16×16 points to obtain a far-field distribution matrix composed of spherical harmonic function coefficients.

[0132] Calculate the far-field radiation field strength and pattern in the E-plane and H-plane according to the far-field distribution matrix. The radius for field strength calculation is taken as 10 wavelengths, the angular sampling interval is 1°, covering the range of 0° - 360°, and the complete far-field electromagnetic distribution characteristic data is obtained. The far-field data is used to evaluate the electromagnetic radiation characteristics and electromagnetic compatibility indicators of the RF module.

[0133] In this embodiment, through the combination of high-precision near-field scanning and the fully implicit finite volume numerical algorithm in the frequency domain, an efficient and accurate conversion from the near-field to the far-field is achieved. A scanning step size of λ / 4 and a scanning range of at least 2 wavelengths are adopted to ensure the integrity and accuracy of near-field data acquisition. By sampling at 4 times the operating frequency and setting a time window of 5 cycles, the complete capture of the time-domain signal is guaranteed. During the spatial discretization process, a basic grid size of λ / 10 and a boundary-refined grid of λ / 15 are used, significantly improving the accuracy of electromagnetic field calculation. The use of the preconditioned conjugate gradient iteration method and a convergence criterion of 10 -6 ensures the rapid convergence of the solution process and the reliability of the results. The 30th-order spherical wave expansion and the 16×16-point Gaussian quadrature calculation provide comprehensive and accurate data support for far-field characteristic analysis, effectively improving the reliability and accuracy of the electromagnetic compatibility evaluation of the RF module.

[0134] In one embodiment, a fully implicit discrete form of Maxwell's equations is constructed for the spatial discrete grid data to obtain the corresponding electromagnetic field component equations, and the electromagnetic field component equations are solved by preconditioned conjugate gradient iteration to obtain the frequency-domain field strength distribution data, including:

[0135] Perform spatial grid analysis on the spatial discrete grid data to generate the corresponding grid node coordinates and grid topology relationships. The grid node coordinates are recorded as the specific position information of the nodes in three-dimensional space, and the grid topology relationship is described by the element-node incidence matrix. The elements in the matrix represent the connection relationship between the nodes and the elements, 1 indicating the existence of a connection and 0 indicating the absence of a connection. For tetrahedral grids, each element contains 4 nodes; for hexahedral grids, each element contains 8 nodes.

[0136] The boundary condition marking stage is divided into two parts: fixing the boundary of the electric field components for the grid node coordinates to form the first-type boundary node set. When the node coordinates satisfy the boundary surface equation, the node is marked as a first-type boundary node. Marking the derivative boundary of the magnetic field components for the grid topology relationship to form the second-type boundary element set. When at least one face of the grid element is located on the boundary surface, the element is marked as a second-type boundary element.

[0137] Perform multi-layer boundary data integration based on the first type of boundary node set and the second type of boundary element set to generate the corresponding boundary grid data set. The key to this step lies in effectively integrating different types of boundary conditions into a unified boundary data set to ensure the integrity and accuracy of subsequent calculations.

[0138] The discretization of Maxwell's equations involves several key steps: First, perform a difference discretization of the time derivative of the electric field intensity for grid nodes to determine the relationship of the electric field time step, which describes the difference characteristics of the electric field components changing with time. Perform an integral discretization of the spatial derivative of the magnetic field intensity for grid nodes, and use the finite volume method to form the relationship of the magnetic field spatial gradient, which describes the integral characteristics of the magnetic field components in space.

[0139] Construct a coupling relationship based on the aforementioned electric field time step relationship and magnetic field spatial gradient relationship to obtain the coupling relationship information of field components. Subsequently, correlate and combine the coupling relationship information of field components with the electric field time step relationship and magnetic field spatial gradient relationship to generate the discrete relationship of electromagnetic field components. This step ensures the overall coupling and coordination among the electromagnetic field components.

[0140] Based on the discrete relationship of electromagnetic field components, construct a diagonal preconditioning matrix according to the coupling relationship information of field components to generate a preconditioned iterative matrix. The preconditioning matrix is constructed by extracting the diagonal elements of the field component coupling matrix to ensure the stability and convergence of the iterative process. The residual calculation uses the relative error method to evaluate the error between the current iterative result and the true solution, and the convergence criterion parameter is used to determine whether the iterative process reaches the convergence standard. When the residual satisfies the convergence condition, the frequency-domain field strength distribution data is obtained.

[0141] The preconditioned conjugate gradient iterative method in this embodiment has good convergence and numerical stability. The fully implicit discretization format avoids the time step limit of the explicit format and improves the calculation efficiency. The boundary condition treatment adopts the mixed boundary condition method to accurately simulate the physical behavior of the electromagnetic field at the boundary.

[0142] In this embodiment, there are high requirements for the grid quality. The grid distortion degree should not exceed 0.8, and the grid size should meet the requirement of 1 / 10 of the highest frequency wavelength. The selection of the time step needs to consider the balance between calculation accuracy and efficiency, and usually takes 1 / 10 of the minimum grid size divided by the electromagnetic wave propagation speed. The reasonable selection of these parameters is crucial for ensuring the accuracy of the calculation results.

[0143] In this embodiment, the Maxwell equations are constructed in a fully implicit discrete form, avoiding the time step limit in the explicit format and greatly improving the calculation efficiency. Combining spatial grid analysis and multi-layer boundary data integration technology enables this method to accurately process complex RF module structures and improve the accuracy of the analysis results. By introducing the preconditioned conjugate gradient iteration method, the convergence and numerical stability of the solution process are ensured, effectively solving the numerical divergence problem that easily occurs in the analysis of complex structures by traditional methods. This method adopts a mixed boundary condition treatment method, which can accurately simulate the physical behavior of the electromagnetic field at the boundary, significantly improving the reliability of the simulation results. In addition, by reasonably setting the grid quality parameters and time step, while ensuring the calculation accuracy, the efficient use of computing resources is achieved, making this method highly practical and adaptable in practical engineering applications.

[0144] In one embodiment, the interference emission data of the RF module is obtained and zero eigenvalue entropy correction is performed to obtain the corresponding interference propagation characteristic data, including:

[0145] Magnetic field intensity data is collected, the sampling frequency is set to 1 GHz, and the sampling time is 10 ms. The collected data is the interference emission data.

[0146] When extracting the features of the collected interference emission data, time-domain analysis methods are used to extract the amplitude, phase, and frequency features of the interference signal. By performing Fourier transform on the interference signal, the spectral features of the signal are obtained, and the main frequency components and their corresponding amplitude and phase information are extracted to form an interference feature data set.

[0147] When performing spatial vector decomposition of the interference source, the interference feature data is decomposed in three orthogonal directions of x, y, and z. In each direction, projection operations are performed on the amplitude and phase information of the interference signal to obtain the interference components in each direction, forming a spatial component data matrix.

[0148] When performing singular value decomposition on the spatial component data, the data matrix is decomposed into the product of three sub-matrices, namely the left singular matrix, the singular value matrix, and the right singular matrix. Among them, the main diagonal elements of the singular value matrix are the eigenvalues, and the left and right singular matrices contain the corresponding eigenvector information.

[0149] In the process of eigenvalue sequence processing, by scanning the main diagonal elements of the eigenvalue matrix, the number of eigenvalues whose values are less than the preset threshold is counted, and this value is the number of zero eigenvalues. The preset threshold is set to 0.1% of the maximum eigenvalue value.

[0150] The construction of the entropy weight function is based on the number of zero eigenvalues, and the information entropy theory is used to calculate the weight coefficients of each eigenvalue. The calculation of the weight coefficients takes into account the size distribution of the eigenvalues and the influence of zero eigenvalues, and the weight values range from 0 to 1.

[0151] During the eigenvalue correction process, the entropy weight coefficient is multiplied by the original eigenvalue to correct the zero eigenvalue. The corrected eigenvalue retains the main features of the original data while eliminating the singularity problem caused by the zero eigenvalue.

[0152] During the matrix reconstruction process, the corrected eigenvalue matrix is multiplied by the original eigenvector matrix to reconstruct the interference propagation characteristic data. The reconstructed data reflects the propagation law and spatial distribution characteristics of electromagnetic interference in the RF module.

[0153] During the entire analysis process, the sampling rate and sampling time of data sampling need to satisfy the Nyquist sampling theorem to ensure the integrity of the sampled data. The frequency analysis in the feature extraction process needs to consider the operating frequency range of the RF module and select an appropriate analysis window. When performing spatial vector decomposition, it is necessary to ensure that the basis vectors in three directions are orthogonal to ensure the accuracy of the decomposition result. The singular value decomposition operation uses an iterative algorithm, and the iteration termination condition is that the difference between the calculation results of two adjacent times is less than the preset accuracy. The distribution characteristics of the data are considered in the entropy weight calculation process, and the weight distribution is more reasonable. When reconstructing the data, the consistency between the reconstructed data and the original data is ensured, and the reconstruction error is controlled within the allowable range.

[0154] In this embodiment, by accurately sampling and extracting the features of the electromagnetic interference source of the RF module and combining the spatial vector decomposition technology, the spatial distribution characteristics of electromagnetic interference can be comprehensively obtained, and the position and propagation path of the interference source can be effectively identified. The singular value decomposition and entropy weight correction methods are used to solve the problem of data distortion caused by zero eigenvalues in traditional analysis and improve the accuracy of feature extraction. The weight function constructed based on the information entropy theory makes the eigenvalue correction process more reasonable and ensures the consistency between the reconstructed data and the actual interference characteristics. Through multi-dimensional data analysis and processing, this method not only improves the accuracy of the electromagnetic compatibility analysis of the RF module, but also realizes the accurate characterization of the interference propagation characteristics, providing reliable data support for the electromagnetic compatibility optimization design of the RF module.

[0155] In one embodiment, the far-field electromagnetic distribution characteristic data and the interference propagation characteristic data are optimized and analyzed to obtain corresponding initial optimization information, including:

[0156] When performing multi-dimensional spectral decomposition processing on the far-field electromagnetic distribution characteristic data, the high-order singular value decomposition method is used to perform tensor decomposition on the far-field electromagnetic distribution characteristic data. By establishing a high-order tensor model, the spectral components in different dimensions are separated, and the amplitude, phase and other characteristic parameters of each dimension spectral component are extracted to form multi-dimensional spectral characteristic data. When performing tensor decomposition, it is necessary to satisfy that the decomposition error is less than the preset threshold of 0.01 and the number of iterations does not exceed 100 times.

[0157] After obtaining the multi-dimensional spectral feature data, based on the kernel function mapping principle, non-linear transformation is performed on the interference propagation feature data, mapping it to a high-dimensional feature space. By calculating the cross-correlation coefficient between the mapped data and the multi-dimensional spectral feature data, a non-linear mapping relationship between the two is established to generate coupled mapping data. The Gaussian kernel function is used in the mapping process, and the range of the kernel parameter is [0.1, 10], and the optimal value is determined through cross-validation.

[0158] When performing fractional-order Hilbert transform processing on the coupled mapping data, the fractional-order α ∈ (0, 1) is set, a fractional-order differential operator is constructed, and fractional-order integral transform is performed on the coupled mapping data to obtain the instantaneous amplitude and phase information of the signal, and time-frequency domain characteristic parameters are extracted to form fractional-order time-frequency characteristic data. The fractional-order α is determined by minimizing the reconstruction error during the transformation process.

[0159] Based on the fractional-order time-frequency characteristic data, various entropy value indicators such as sample entropy and approximate entropy are calculated, an entropy value feature vector is constructed, and different entropy value indicators are fused through an adaptive weight allocation method to obtain entropy value distribution data. The entropy value is calculated using a sliding time window, the window length is 1 / 10 of the signal length, and the overlap rate is 50%.

[0160] Perform empirical mode decomposition on the entropy value distribution data, decompose it into several intrinsic mode function (IMF) components, extract the characteristic parameters of each IMF component, including instantaneous frequency, envelope, etc., to form intrinsic mode data. During the decomposition process, the screening times do not exceed 10 times, and the residual energy ratio is less than 0.1.

[0161] Based on the intrinsic mode data, an electromagnetic field distribution reconstruction model is established, spatial interpolation is performed on the field strength distribution, the spatial distribution reconstruction of the electromagnetic field strength is realized, and field strength distribution characteristic data is obtained. The neural network uses a Gaussian radial basis function, and the number of hidden layer nodes is determined through cross-validation.

[0162] Perform multi-objective optimization analysis on the field strength distribution characteristic data, establish a multi-objective optimization model with field strength uniformity and interference suppression as optimization objectives, use the non-dominated sorting genetic algorithm to solve the Pareto optimal solution set, and select the solution with the best comprehensive performance as the initial optimization information. During the optimization process, the population size is 100, the number of evolutionary generations is 200, the crossover probability is 0.8, and the mutation probability is 0.1.

[0163] In this embodiment, the multi-dimensional spectral decomposition of the far-field electromagnetic distribution characteristic data is carried out by using the high-order singular value decomposition method, realizing the accurate characterization of the complex electromagnetic field distribution and effectively improving the accuracy of feature extraction. Based on the kernel function mapping principle, a non-linear correlation mapping relationship is established, overcoming the limitations of traditional linear analysis methods and enhancing the ability to characterize the interference propagation characteristics. The fractional-order Hilbert transform is used to process the coupled mapping data, providing richer time-frequency domain feature information and improving the accuracy of electromagnetic compatibility analysis. Through the combination of adaptive entropy value calculation and modal decomposition, the multi-scale analysis of the electromagnetic field distribution characteristics is realized, enhancing the robustness of feature extraction. Based on the radial basis function neural network, the field strength distribution reconstruction is carried out, providing a high-precision spatial distribution prediction ability. The multi-objective optimization strategy is used for information optimization analysis, effectively suppressing interference while ensuring the field strength uniformity, and significantly improving the electromagnetic compatibility performance of the RF module.

[0164] In one embodiment, the immunity test is carried out on the interference emission data, and the performance evaluation is combined with the interference propagation characteristics to obtain the corresponding EMC performance evaluation results, including:

[0165] During the electromagnetic compatibility analysis of the RF module, when carrying out the immunity test on the interference emission data, the multi-frequency point injection scanning technology is adopted. By applying interference signals of different frequencies at the key nodes of the RF module, the response of the module is recorded. The specific scanning frequency range is from 30 MHz to 6 GHz, the frequency step is 1 MHz, and interference signals from -20 dBm to 20 dBm are injected at each frequency point to form an immunity scanning data set.

[0166] For the obtained immunity scanning data, the characteristics of burst interference are identified by the time-domain analysis method. The scanning data is segmented according to a 100 ms time window, and characteristic parameters such as the amplitude change rate and duration within each time window are extracted to establish a judgment of the burst interference response data. The criterion for burst interference determination is that the amplitude change rate is greater than 6 dB / ms and the duration is less than 10 ms.

[0167] In the transfer characteristic analysis, the transfer function between the input interference signal and the output response is calculated, and the S-parameter characterization method is adopted to establish the frequency-domain transfer characteristic relationship. The amplitude and phase responses of the transfer function reflect the sensitivity of the RF module to interference of different frequencies.

[0168] Based on the obtained burst interference response data, non-linear fitting is carried out in combination with the interference propagation characteristics. The least squares method is used to fit the interference response curve, and the characteristic coefficients are extracted to form the EMC response feature vector. The fitting model selects a cubic polynomial function, and the fitting accuracy requires that the root mean square error is less than 1 dB.

[0169] When performing matrix mapping on the immunity transfer characteristics, the response values of the transfer function at different frequency points are organized into an m×n-dimensional matrix, where m is the number of frequency points and n is the number of test ports. The matrix elements characterize the transfer characteristics corresponding to the frequency points and ports.

[0170] The eigenvalue solution of the immunity mapping matrix is obtained by the singular value decomposition method, and an eigenvalue sequence arranged in descending order is acquired. The eigenvalue sequence reflects the sensitivity of the system to different types of interference, and the larger eigenvalues correspond to the main interference propagation paths.

[0171] In the calculation of the immunity response, the EMC response eigenvector and the immunity eigenvalue sequence are weighted and combined, and the weight coefficients are determined according to the eigenvalue magnitudes. The calculated response data reflects the immunity performance of the RF module under different operating conditions.

[0172] The EMC characteristic scoring data is calculated based on a preset evaluation matrix, and the evaluation matrix includes evaluation indicators such as frequency coverage, immunity margin, and interference suppression ratio. The weight coefficients of each indicator are 0.3, 0.4, and 0.3 respectively, and the scoring result is represented in a hundred-point system. The scoring criteria are: excellent for scores above 90, good for scores between 80 and 90, qualified for scores between 70 and 80, and unqualified for scores below 70.

[0173] In this embodiment, the full-band immunity data is obtained through the multi-frequency point injection scanning technology. By combining the burst interference analysis and the transfer characteristic analysis, a comprehensive evaluation of the EMC performance of the RF module is realized. The non-linear fitting method is used to construct the EMC response eigenvector, and the key features are extracted through matrix mapping and singular value decomposition, making the evaluation result more accurate and reliable. By establishing a systematic scoring system and considering multiple dimensions such as frequency coverage, immunity margin, and interference suppression ratio, the scientificity and comparability of the evaluation result are ensured. The sensitivity of the system to different types of interference is reflected by the eigenvalue sequence, and the main interference propagation paths are effectively identified, providing a clear direction for the EMC performance optimization. This method overcomes the problems of single evaluation index and insufficient result reliability in traditional EMC analysis methods, improves the accuracy and efficiency of the EMC performance evaluation of the RF module, and provides strong support for the design optimization and quality control of the RF module.

[0174] In one embodiment, the initial optimization information and the EMC performance evaluation result are subjected to a solution analysis to obtain the corresponding EMC optimization solution, including:

[0175] During the EMC analysis process, the collected initial optimization information and the EMC performance evaluation result are associated and matched one by one according to the time stamp to form an associated data pair containing optimization parameters and performance indicators. The EMC performance indicators in these associated data pairs are subjected to feature analysis to extract EMC performance characteristic indicators such as electromagnetic interference intensity, frequency response characteristics, and radiation.

[0176] Based on the extracted EMC performance characteristic indicators, the OTSU adaptive threshold segmentation algorithm is used to stratify the electromagnetic field intensity, obtaining the electromagnetic field threshold intervals of different intensity levels. According to the divided threshold intervals and combined with the product EMC standard requirements, the system EMC performance is evaluated and classified into three levels: A, B, and C, generating the corresponding performance rating data matrix.

[0177] To extract the key EMC features, KL orthogonal transformation processing is performed on the performance rating data. During this processing, the performance rating data matrix is preprocessed by centering, calculating the data covariance matrix and solving its eigenvalues and eigenvectors. Through eigenvalue sorting, the eigenvectors corresponding to the main eigenvalues are selected to construct the transformation matrix, realizing data dimensionality reduction and feature extraction. This transformation method can retain the data change information to the greatest extent, effectively removing data redundancy and noise, and providing a reliable feature representation for subsequent optimization.

[0178] After obtaining the EMC feature component data through KL transformation, the RBF neural network is used to perform nonlinear mapping on the feature component data to obtain the key parameter set required for EMC optimization. Based on obtaining the EMC optimization parameters, a constraint optimization model with the minimization of electromagnetic field intensity as the objective is established, and the EMC performance constraint conditions are solved by the Lagrange multiplier method.

[0179] The genetic algorithm is used to iteratively optimize the constraint conditions to obtain the EMC optimization target condition set that meets the performance requirements. According to the optimization target condition set, the multi-objective decision-making method is used to generate multiple alternative EMC optimization schemes. By establishing a scheme evaluation model based on fuzzy comprehensive evaluation, the alternative schemes are comprehensively scored, and the scheme with the highest comprehensive score is selected as the final EMC optimization scheme.

[0180] This embodiment realizes the precise generation of the EMC optimization scheme by establishing a systematic data processing and analysis process. Through the time series correlation matching mechanism, the precise correspondence between the initial optimization information and the performance evaluation results is ensured, improving the accuracy of data analysis. The OTSU adaptive threshold segmentation algorithm is used for electromagnetic field intensity stratification, combined with the standardized performance grading evaluation, realizing the objective quantification of EMC performance.

[0181] Refer to Figure 2 As shown, the present invention also provides a radio frequency module electromagnetic compatibility analysis system, which is characterized in that it is applied to the above radio frequency module electromagnetic compatibility analysis method and includes:

[0182] An acquisition module, which is used to acquire the structural data and electromagnetic parameter information of the radio frequency module, and perform electromagnetic simulation on the structural data based on the electromagnetic parameter information to obtain the corresponding radio frequency module simulation data;

[0183] An analysis module, which is used to obtain the near-field scanning data of the RF module, and perform correlation calculations on the near-field scanning data and the simulation data of the RF module through a preset fully implicit frequency-domain finite volume numerical algorithm to obtain corresponding far-field electromagnetic distribution characteristic data;

[0184] A correlation module, which is used to obtain the interference emission data of the RF module and perform zero eigenvalue entropy correction to obtain corresponding interference propagation characteristic data;

[0185] A processing module, which is used to perform optimization analysis on the far-field electromagnetic distribution characteristic data and the interference propagation characteristic data to obtain corresponding initial optimization information;

[0186] A control module, which is used to perform an immunity test on the interference emission data and conduct performance evaluation in combination with the interference propagation characteristics to obtain corresponding EMC performance evaluation results;

[0187] An execution module, which is used to perform scenario analysis on the initial optimization information and the EMC performance evaluation results to obtain corresponding EMC optimization scenarios.

[0188] An electromagnetic compatibility analysis system for an RF module provided by the present invention can more comprehensively evaluate the electromagnetic characteristics of the RF module by performing electromagnetic simulation analysis on the structural data and electromagnetic parameter information of the RF module and combining the near-field scanning data, thereby improving the accuracy of electromagnetic compatibility analysis and providing reliable data support for EMC optimization design. By performing correlation calculations on the near-field scanning data and the simulation data and analyzing them based on the fully implicit frequency-domain finite volume numerical algorithm, accurate prediction of the far-field electromagnetic distribution characteristics is achieved, which helps to better grasp the electromagnetic radiation characteristics of the RF module. Based on the zero eigenvalue entropy correction algorithm, the propagation analysis of the interference emission data is carried out, which can ensure accurate evaluation of the interference propagation characteristics of the RF module in different application environments and reduce the performance hidden dangers caused by EMC problems. By comprehensively analyzing the far-field electromagnetic distribution characteristic data and the interference propagation characteristic data, a more reasonable optimization scenario is formulated, and the overall EMC performance is improved through immunity test verification and performance evaluation, thereby effectively solving the electromagnetic interference problem. At the same time, by considering the correlation of multi-dimensional EMC performance indicators, the EMC optimization strategy can be flexibly adjusted according to the characteristics and requirements of different application scenarios, making the analysis method more adaptable to complex application environments.

[0189] It should be noted that those skilled in the art can clearly understand that for the convenience and simplicity of description, the specific working processes of the above-described system and each module can refer to the corresponding processes in the foregoing method embodiments and will not be elaborated herein.

[0190] The above are only the preferred embodiments of the present invention, and do not limit the patent scope of the present invention. Any equivalent structure or equivalent process transformation made by using the content of the specification and drawings of the present invention, or directly or indirectly applied in other related technical fields, shall be similarly included in the patent protection scope of the present invention.

Claims

1. A method for analyzing electromagnetic compatibility of a radio frequency module, characterized in that: include: Acquire structural data and electromagnetic parameter information of the radio frequency module, and perform electromagnetic simulation on the structural data based on the electromagnetic parameter information to obtain corresponding radio frequency module simulation data; Acquire near-field scanning data of the radio frequency module, and perform correlation calculation on the near-field scanning data and the radio frequency module simulation data through a preset fully implicit frequency-domain finite volume numerical algorithm to obtain corresponding far-field electromagnetic distribution characteristic data; Obtaining interference transmission data of the radio frequency module, and performing zero eigenvalue entropy correction to obtain corresponding interference propagation characteristic data; Optimizing and analyzing the far-field electromagnetic distribution characteristic data and the interference propagation characteristic data to obtain corresponding initial optimization information; Performing an anti-interference test on the interference emission data, and performing a performance evaluation in combination with the interference propagation characteristics to obtain a corresponding EMC performance evaluation result; Performing solution analysis on the initial optimization information and the EMC performance evaluation result to obtain a corresponding EMC optimization solution; The obtaining of the near-field scanning data of the radio frequency module, and the correlation calculation of the near-field scanning data and the radio frequency module simulation data by a preset fully implicit frequency-domain finite volume numerical algorithm to obtain the corresponding far-field electromagnetic distribution characteristic data include: Performing time domain sampling processing on the near-field scanning data and the RF module simulation data to obtain a corresponding dual-channel time domain sampling sequence; Performing orthogonal decomposition processing on the dual-channel time domain sampling sequence to obtain corresponding real part sequence and imaginary part sequence; Performing fast Fourier transform processing on the real part sequence and the imaginary part sequence to obtain corresponding frequency domain feature data; Performing hexahedral meshing processing on the frequency domain characteristic data to obtain corresponding spatial discrete mesh data; Constructing the fully implicit discretized form of Maxwell equations for the spatial discrete grid data to obtain the corresponding electromagnetic field component equations, and solving the electromagnetic field component equations by preconditioned conjugate gradient iteration to obtain frequency domain field intensity distribution data; Performing feature mapping on the radio frequency module simulation data according to the frequency domain field intensity distribution data to obtain a corresponding electromagnetic feature vector group; Performing a spherical wave expansion transformation on the electromagnetic characteristic vector group to obtain a corresponding far-field distribution matrix; The far-field radiation field intensity and direction diagram are calculated according to the far-field distribution matrix to obtain the far-field electromagnetic distribution characteristic data.

2. The method for analyzing electromagnetic compatibility of a radio frequency module according to claim 1, characterized in that: The acquiring of the structural data and electromagnetic parameter information of the radio frequency module, and performing electromagnetic simulation on the structural data based on the electromagnetic parameter information to obtain corresponding radio frequency module simulation data, includes: Performing parameterized physical structural characterization on the radio frequency module to obtain structural data including component arrangement and shielding structure; Measuring electromagnetic properties of the radio frequency module to obtain electromagnetic parameter information including dielectric constant, loss tangent and conductivity; Performing grid division processing on the radio frequency module according to the structural data to obtain corresponding structural grid data; Assigning regional attributes to the structural grid data according to the electromagnetic parameter information to obtain a computational domain grid; Performing finite element discretization processing on the computational domain grid to obtain a corresponding set of electromagnetic field distribution equations; Constructing unstructured tetrahedral grid units according to the electromagnetic field distribution equations to obtain corresponding computational grid data; Performing time-domain finite-difference iterative calculation on the computational grid data to obtain corresponding electromagnetic field time-domain distribution data; Perform simulation parameter analysis on the electromagnetic field time domain distribution data to obtain corresponding radio frequency module simulation data.

3. The method for analyzing electromagnetic compatibility of a radio frequency module according to claim 1, characterized in that: The fully implicit discrete form of the Maxwell equations is constructed for the spatial discrete grid data to obtain the corresponding electromagnetic field component equations, and the electromagnetic field component equations are solved by preconditioned conjugate gradient iteration to obtain frequency domain field intensity distribution data, including: Performing spatial grid analysis on the spatial discrete grid data to obtain corresponding grid node coordinates and grid topological relationships; Marking the grid node coordinates with fixed boundaries of electric field components to obtain a first type of boundary node set; Performing magnetic field component derivative boundary marking on the grid topological relationship to obtain a second type of boundary unit set; Perform multi-layer boundary data integration according to the first-type boundary node set and the second-type boundary unit set to obtain a corresponding boundary grid data set; The boundary grid data set is subjected to a Maxwell equation discretization process to obtain a corresponding electromagnetic field component discrete relationship, wherein the Maxwell equation discretization process includes: Performing differential discretization of the time derivative of the electric field intensity on the grid nodes to obtain a time step relationship of the electric field; Performing discretization of the spatial derivative integral of the magnetic field intensity on the grid nodes to obtain the spatial gradient relationship of the magnetic field; A coupling relationship is constructed according to the electric field time step relationship and the magnetic field spatial gradient relationship to obtain corresponding field component coupling relationship information; Associating and combining the field component coupling relationship information, the electric field time step relationship and the magnetic field spatial gradient relationship to obtain a corresponding electromagnetic field component discrete relationship; Constructing a diagonal preconditioning matrix according to the field component coupling relationship information to obtain a preconditioning iteration matrix; Performing residual calculation on the preconditioned iterative matrix to obtain convergence criterion parameters; The electromagnetic field component discrete relationship is subjected to iterative analysis of the electromagnetic field component according to the convergence criterion parameter to obtain the frequency domain field intensity distribution data.

4. The method for analyzing electromagnetic compatibility of a radio frequency module according to claim 1, characterized in that: The obtaining of the interference emission data of the radio frequency module and performing zero eigenvalue entropy correction to obtain corresponding interference propagation characteristic data includes: Sampling electromagnetic interference sources on the radio frequency module to obtain the interference emission data; Extracting features from the interference emission data to obtain corresponding interference feature data; Performing interference source space vector decomposition on the radio frequency module according to the interference feature data to obtain corresponding space component data; Performing a singular value decomposition operation on the spatial component data to obtain a corresponding eigenvalue matrix and an eigenvector matrix; Extracting the main diagonal elements of the eigenvalue matrix to obtain corresponding eigenvalue sequence data; Performing zero eigenvalue statistics on the eigenvalue sequence data to obtain the corresponding number of zero eigenvalues; Constructing an entropy weight function according to the number of zero eigenvalues ​​to obtain a corresponding entropy weight coefficient; Performing zero eigenvalue correction processing on the eigenvalue matrix by using the entropy weight coefficient to obtain corresponding corrected eigenvalues; Matrix data is reconstructed according to the modified eigenvalues ​​and the eigenvector matrix to obtain corresponding interference propagation characteristic data.

5. The method for analyzing electromagnetic compatibility of a radio frequency module according to claim 1, characterized in that: The optimizing and analyzing the far-field electromagnetic distribution characteristic data and the interference propagation characteristic data to obtain corresponding initial optimization information includes: Performing multi-dimensional spectrum decomposition processing on the far-field electromagnetic distribution characteristic data to obtain corresponding multi-dimensional spectrum feature data; Performing nonlinear correlation mapping on the interference propagation characteristic data according to the multi-dimensional spectrum characteristic data to obtain corresponding coupling mapping data; Performing fractional-order Hilbert transform processing on the coupling mapping data to obtain corresponding fractional-order time-frequency feature data; Performing adaptive entropy value calculation according to the fractional-order time-frequency feature data to obtain corresponding entropy value distribution data; Performing modal decomposition on the entropy value distribution data to obtain corresponding eigenmode data; Reconstructing the spatial distribution of electromagnetic field intensity according to the eigenmode data to obtain corresponding field intensity distribution characteristic data; A multi-objective information optimization analysis is performed on the field intensity distribution characteristic data to obtain corresponding initial optimization information.

6. The method for analyzing electromagnetic compatibility of a radio frequency module according to claim 1, characterized in that: The anti-interference test is performed on the interference emission data, and the performance evaluation is performed in combination with the interference propagation characteristics to obtain the corresponding EMC performance evaluation result, including: Performing multi-frequency injection scanning on the interference emission data to obtain corresponding anti-interference scanning data; Performing a burst interference analysis on the immunity scanning data to obtain corresponding burst interference response data; Performing transfer characteristic analysis on the anti-interference scanning data, and corresponding anti-interference transfer characteristics; Performing nonlinear fitting on the burst interference response data according to the interference propagation characteristics to obtain a corresponding EMC response feature vector; Performing matrix mapping construction on the anti-interference transfer characteristic according to the interference propagation characteristic to obtain an anti-interference mapping matrix; Solving the singular value of the anti-interference mapping matrix to obtain a corresponding anti-interference eigenvalue sequence; Performing an immunity response calculation according to the EMC response characteristic vector and the immunity characteristic value sequence to obtain corresponding immunity response data; The anti-interference response data is characterized by performing evaluation on the anti-interference response data according to a preset EMC evaluation matrix to obtain corresponding EMC characteristic scoring data.

7. The method for analyzing electromagnetic compatibility of a radio frequency module according to claim 1, characterized in that: The performing solution analysis on the initial optimization information and the EMC performance evaluation result to obtain a corresponding EMC optimization solution includes: Correlation matching is performed between the initial optimization information and the EMC performance evaluation result according to a time series to obtain a corresponding correlation data pair; Performing EMC performance index analysis on the associated data pair to obtain corresponding EMC performance characteristic index; Performing adaptive threshold segmentation on the EMC performance characteristic index to obtain a corresponding electromagnetic field threshold interval; Performing an EMC performance grading assessment according to the electromagnetic field threshold range to obtain corresponding performance rating data; Performing orthogonal transformation processing on the performance rating data to obtain corresponding EMC characteristic component data; Performing nonlinear mapping processing on the EMC characteristic component data to obtain corresponding EMC optimization parameters; Solving EMC performance constraint conditions according to the EMC optimization parameters to obtain corresponding EMC constraint conditions; Iteratively optimizing the EMC constraint conditions to obtain a corresponding EMC optimization target condition set; Generate multiple solutions according to the EMC optimization target condition set to obtain a corresponding EMC solution set; The EMC solution set is subjected to solution screening processing to obtain the EMC optimization solution.

8. A radio frequency module electromagnetic compatibility analysis system, characterized in that: The method for analyzing electromagnetic compatibility of a radio frequency module as described in any one of claims 1 to 7 above comprises: An acquisition module, the acquisition module is used to obtain structural data and electromagnetic parameter information of the radio frequency module, and perform electromagnetic simulation on the structural data based on the electromagnetic parameter information to obtain corresponding radio frequency module simulation data; An analysis module, the analysis module is used to obtain near-field scanning data of the RF module, and to correlate the near-field scanning data with the RF module simulation data through a preset fully implicit frequency-domain finite volume numerical algorithm to obtain corresponding far-field electromagnetic distribution characteristic data; An association module, the association module is used to obtain the interference emission data of the radio frequency module, and perform zero eigenvalue entropy correction to obtain corresponding interference propagation characteristic data; A processing module, the processing module is used to optimize and analyze the far-field electromagnetic distribution characteristic data and the interference propagation characteristic data to obtain corresponding initial optimization information; A control module, the control module is used to perform an anti-interference test on the interference emission data, and perform a performance evaluation in combination with the interference propagation characteristics to obtain a corresponding EMC performance evaluation result; An execution module, the execution module is used to perform a solution analysis on the initial optimization information and the EMC performance evaluation result to obtain a corresponding EMC optimization solution; The obtaining of the near-field scanning data of the radio frequency module, and the correlation calculation of the near-field scanning data and the radio frequency module simulation data by a preset fully implicit frequency-domain finite volume numerical algorithm to obtain the corresponding far-field electromagnetic distribution characteristic data include: Performing time domain sampling processing on the near-field scanning data and the RF module simulation data to obtain a corresponding dual-channel time domain sampling sequence; Performing orthogonal decomposition processing on the dual-channel time domain sampling sequence to obtain corresponding real part sequence and imaginary part sequence; Performing fast Fourier transform processing on the real part sequence and the imaginary part sequence to obtain corresponding frequency domain feature data; Performing hexahedral meshing processing on the frequency domain characteristic data to obtain corresponding spatial discrete mesh data; Constructing the fully implicit discretized form of Maxwell equations for the spatial discrete grid data to obtain the corresponding electromagnetic field component equations, and solving the electromagnetic field component equations by preconditioned conjugate gradient iteration to obtain frequency domain field intensity distribution data; Performing feature mapping on the radio frequency module simulation data according to the frequency domain field intensity distribution data to obtain a corresponding electromagnetic feature vector group; Performing a spherical wave expansion transformation on the electromagnetic characteristic vector group to obtain a corresponding far-field distribution matrix; The far-field radiation field intensity and direction diagram are calculated according to the far-field distribution matrix to obtain the far-field electromagnetic distribution characteristic data.

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