Radio frequency module electromagnetic compatibility analysis method and system

Abstract

The invention relates to a radio frequency module electromagnetic compatibility analysis method and system, and the method comprises the steps: obtaining the structure data and electromagnetic parameter information of a radio frequency module, carrying out the electromagnetic simulation of the structure data based on the electromagnetic parameter information, and obtaining the corresponding simulation data of the radio frequency module; acquiring near-field scanning data of the radio frequency module, and performing 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 emission data of the radio frequency module, and performing zero eigenvalue entropy correction to obtain corresponding interference propagation feature 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 an EMC performance evaluation result to obtain a corresponding EMC optimization scheme. According to the invention, the electromagnetic characteristics of the radio frequency module can be evaluated more comprehensively, so that the accuracy of electromagnetic compatibility analysis is improved.

Description

Technical Field

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

[0002] As an indispensable key component in modern electronic devices, RF modules have been widely used in the fields of communications and the Internet of Things. With the continuous improvement of the integration of electronic equipment and the continuous increase in operating frequency, the problem of electromagnetic compatibility (EMC) has become increasingly prominent. How to accurately evaluate and optimize the electromagnetic compatibility capabilities of RF modules has become one of the key research topics in the industry. Existing electromagnetic compatibility analysis methods are often limited to single-dimensional test data analysis, such as focusing only on near-field or far-field characteristics, while ignoring the propagation characteristics of electromagnetic interference in the actual application environment of the RF module and the correlation between multi-dimensional EMC performance indicators. This one-sided analysis method is difficult to fully reflect the electromagnetic compatibility performance of the RF module, which easily leads to the lack of systematicity and pertinence in the formulation of EMC optimization solutions. Summary of the invention

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

[0004] To achieve the above object, the present invention provides a method for analyzing electromagnetic compatibility of a radio frequency module, comprising: 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; The initial optimization information and the EMC performance evaluation result are analyzed to obtain a corresponding EMC optimization solution.

[0005] Further, the acquiring of 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.

[0006] Furthermore, the near-field scanning data of the RF module is obtained, and the near-field scanning data is correlated 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, including: 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.

[0007] Furthermore, the spatial discrete grid data is constructed in a fully implicit discrete form of the Maxwell equations 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.

[0008] Further, the obtaining of 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.

[0009] Furthermore, 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.

[0010] Furthermore, the interference emission data is subjected to an anti-interference test, and a performance evaluation is performed in combination with the interference propagation characteristics to obtain a 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 a characteristic evaluation on the anti-interference response data according to a preset EMC evaluation matrix to obtain corresponding EMC characteristic scoring data.

[0011] Furthermore, the initial optimization information and the EMC performance evaluation result are subjected to solution analysis to obtain a corresponding EMC optimization solution, including: 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.

[0012] The present invention also provides a radio frequency module electromagnetic compatibility analysis system, which is applied to any of the above radio frequency module electromagnetic compatibility analysis methods, including: 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 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.

[0013] The present invention provides a method and system for analyzing electromagnetic compatibility of a radio frequency module, which has the following beneficial effects: By performing electromagnetic simulation analysis on the structural data and electromagnetic parameter information of the RF module and combining it with the near-field scanning data, the electromagnetic characteristics of the RF module can be evaluated more comprehensively, thereby improving the accuracy of the electromagnetic compatibility analysis and providing reliable data support for EMC optimization design. By correlating the near-field scanning data with the simulation data and analyzing it based on the fully implicit frequency-domain finite volume numerical algorithm, the accurate prediction of the far-field electromagnetic distribution characteristics is achieved, which helps to better grasp the electromagnetic radiation characteristics of the RF module. Propagation analysis of interference emission data based on the zero eigenvalue entropy correction algorithm can ensure accurate evaluation of the interference propagation characteristics of the RF module under different application environments, reducing the performance risks caused by EMC problems. By comprehensively analyzing the far-field electromagnetic distribution characteristic data and the interference propagation characteristic data, a more reasonable optimization plan is formulated, and the overall EMC performance is improved through anti-interference 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. BRIEF DESCRIPTION OF THE DRAWINGS

[0014] Figure 1 This is a flow chart of a method for analyzing electromagnetic compatibility of a radio frequency module provided by the present invention; Figure 2 This is a structural diagram of the electromagnetic compatibility analysis system of a radio frequency module provided by the present invention.

[0015] The realization of the purpose, functional features and advantages of the present invention will be further explained in conjunction with embodiments and with reference to the accompanying drawings. DETAILED DESCRIPTION

[0016] In order to make the purpose, technical solution and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with 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 intended to limit the present invention.

[0017] The present invention is further described below in conjunction with the accompanying drawings and specific implementation methods.

[0018] Reference Figure 1 As shown, the present invention provides a method for analyzing electromagnetic compatibility of a radio frequency module, comprising: Step S1: acquiring structural data and electromagnetic parameter information of the radio frequency module, performing electromagnetic simulation on the structural data based on the electromagnetic parameter information, and obtaining corresponding radio frequency module simulation data; Step S2: Acquire near-field scanning data of the RF module, and perform correlation calculation on the near-field scanning data and the RF module simulation data by using a preset fully implicit frequency-domain finite volume numerical algorithm to obtain corresponding far-field electromagnetic distribution characteristic data; Step S3: Obtain interference emission data of the radio frequency module, and perform zero eigenvalue entropy correction to obtain corresponding interference propagation characteristic data; Step S4: Optimizing and analyzing the far-field electromagnetic distribution characteristic data and the interference propagation characteristic data to obtain corresponding initial optimization information; Step S5: 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; Step S6: Perform solution analysis on the initial optimization information and the EMC performance evaluation results to obtain a corresponding EMC optimization solution.

[0019] Based on the above steps, the detailed process is as follows: Step S1: Obtain the complete structural information of the RF module, including physical structure data such as PCB layout diagram, component arrangement, routing distribution, ground plane design, etc. At the same time, collect key electromagnetic parameter information, such as dielectric constant, loss tangent, conductivity, magnetic permeability and other material characteristic parameters. Based on these basic data, build a three-dimensional model of the RF module. And set appropriate boundary conditions, excitation sources and grid divisions to perform full-wave electromagnetic field simulation calculations. Through simulation, detailed data such as the electric field distribution, magnetic field distribution, surface current distribution, etc. inside the module are obtained, which will provide an important theoretical basis for subsequent near-field and far-field conversion.

[0020] Step S2: The electromagnetic field strength can be measured at a specific height on the surface of the RF module through a near-field scanning device. During the scanning process, the moving speed and sampling interval of the probe are controlled to ensure that the spatial resolution of the data meets the requirements. The acquired near-field data, including amplitude and phase information, is calibrated and the influence of the measurement system itself is eliminated.

[0021] The near-field scan data is correlated with the simulation data. The mathematical relationship between the near-field data and the far-field radiation is established through the preset fully implicit frequency-domain finite volume numerical algorithm. The near-field data is extrapolated to the far-field region by solving the Maxwell equations.

[0022] In the numerical calculation process, the continuity conditions and boundary conditions of the field source are considered to ensure the physical rationality of the calculation results. The electromagnetic field distribution characteristics in the far field area are obtained, including the directivity diagram, polarization characteristics, field intensity distribution and other information.

[0023] Step S3: Obtain the interference emission data of the RF module under the standard test environment. The test process is carried out in accordance with the requirements of relevant EMC standards, including conducted emission and radiated emission tests. When conducting conducted emission tests, a line impedance stabilization network (LISN) is used to measure the conducted interference level on the power line; in radiated emission tests, the electromagnetic field strength at a specific distance is measured in a semi-anechoic chamber or an open test field.

[0024] The original interference data may contain measurement uncertainty and need to be corrected by zero eigenvalue entropy. This correction method is based on information entropy theory and identifies and corrects the uncertainty introduced by the measurement system by analyzing the statistical characteristics of the interference signal.

[0025] The corrected data more accurately reflects the actual interference emission characteristics of the RF module, including the interference frequency distribution, intensity variation law and propagation path characteristics. By comparing and analyzing with the far-field distribution characteristics, the far-field electromagnetic distribution characteristic data is obtained.

[0026] Step S4: Establish an electromagnetic compatibility performance evaluation index system, including parameters such as radiation interference level, conducted interference level, and harmonic distortion. Use a multi-objective optimization algorithm (genetic algorithm or particle swarm algorithm) to analyze the parameters. The optimization process establishes an objective function based on the mutual influence and weight relationship of various indicators. By iteratively calculating the objective function, the optimal solution space of the performance indicator is obtained and the optimization direction is determined. The optimization information includes the electromagnetic field intensity distribution at key frequencies, the location and intensity of the interference source, and the sensitivity of the propagation path. Combined with engineering constraints (cost, volume, process), optimization information is formed. This information serves as the technical basis for the EMC improvement plan.

[0027] Step S5: Perform the immunity test specified in 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 module's anti-interference ability 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.

[0028] Step S6: Conduct a systematic solution analysis on the initial optimization information and the EMC performance evaluation results. Through comparative analysis, identify the projects and areas that need to be improved. The formulation of the optimization plan needs to consider multiple levels: at the structural design level, including PCB layout optimization, ground plane improvement, key signal line adjustment, etc.; at the device selection level, it may involve filter configuration, shielding structure design, etc. At the system integration level, based on the overall electromagnetic compatibility requirements. The cost-benefit ratio needs to be fully considered during the plan formulation process, 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, and key signal line adjustment. Component selection level: filter configuration, shielding structure design. System integration level: electromagnetic compatibility indicator implementation plan The electromagnetic compatibility analysis method of a radio frequency module provided by the present invention can more 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, combined with near-field scanning data, thereby improving the accuracy of electromagnetic compatibility analysis and providing reliable data support for EMC optimization design. By correlating the near-field scanning data with the simulation data and analyzing it according to the fully implicit frequency domain finite volume numerical algorithm, an 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. Propagation analysis of interference emission data based on the zero eigenvalue entropy correction algorithm can ensure accurate evaluation of the interference propagation characteristics of the radio frequency module under different application environments, reducing the performance risks 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 anti-interference 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, so that the analysis method is more adaptable to complex application environments.

[0029] In one embodiment, 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 corresponding radio frequency module simulation data, including: During the electromagnetic compatibility analysis of the RF module, when the physical structure of the RF module is parametrically characterized, the physical structure of the RF module is reconstructed in three dimensions to establish an accurate geometric model including component layout and shielding structure. The modeling process needs to consider the dimensional parameters, relative position relationship, material properties and other information of each part of the RF module to form a complete structural database.

[0030] Scan and measure the electromagnetic parameters of the RF module, such as dielectric constant, loss tangent and conductivity, within the specified frequency range. The ambient temperature must be 25±2℃ and the relative humidity must not exceed 65% during measurement. The measurement data is calibrated to form an electrochemical parameter database.

[0031] The meshing process is based on the adaptive meshing algorithm to mesh the structural data of the RF module. The meshing density must meet the minimum mesh side length of not less than 1 / 10 of the structural feature size, and the mesh quality factor must be greater than 0.3. The meshing results form a structural mesh data file.

[0032] During the process of assigning regional attributes, material attributes of each region in the structural mesh data are defined based on the electromagnetic parameter information library. The definition includes parameters such as dielectric constant, loss tangent, and conductivity. Attribute assignment must ensure the continuity and consistency of parameters in each region to generate a complete computational domain mesh.

[0033] The finite element discretization process uses basis functions to expand the electric field and discretizes the Maxwell equations by the weighted residual method. The second-order vector basis function is selected in the discretization process, and the discretization accuracy is controlled within 1%. The discretization result is the electromagnetic field distribution equation group.

[0034] The unstructured tetrahedral grid unit is constructed using the Delaunay triangulation algorithm to reconstruct the grid of the computational domain. The maximum volume of the grid unit does not exceed 1 / 8 of the wavelength cubed, and the grid distortion rate is controlled below 0.1. The construction results form the computational grid data.

[0035] The time-domain finite difference iterative calculation adopts an explicit iterative format, and the calculation time step meets the CFL stability condition. The iterative convergence criterion is that the relative error of two consecutive iteration results is less than 0.1%. The calculation results output the electromagnetic field time domain distribution data.

[0036] The simulation parameter analysis is based on the electromagnetic field time domain distribution data to calculate the electromagnetic compatibility indicators such as the RF module's radiation emission characteristics and anti-interference characteristics. The analysis results must meet the requirements of relevant electromagnetic compatibility standards to form complete RF module simulation data.

[0037] This embodiment achieves accurate acquisition of RF module structural data by establishing a complete physical structure parameterization characterization system, laying a reliable data foundation for subsequent simulation analysis. The standardized attribute electromagnetic property measurement process is adopted to effectively ensure the accuracy and consistency of electromagnetic parameter information. The meshing process based on the adaptive meshing algorithm improves the mesh quality and enhances the calculation accuracy. The solution accuracy of the electromagnetic field distribution equation group is ensured by refined regional attribute assignment and finite element discretization. Combined with the construction of unstructured tetrahedral mesh units and time-domain finite difference iterative calculations, efficient and stable electromagnetic field distribution calculations are achieved. This method establishes a complete set of RF module electromagnetic compatibility analysis system, significantly improves the analysis efficiency and accuracy, and provides strong support for the electromagnetic compatibility design optimization of RF modules.

[0038] In one embodiment, near-field scanning data of the radio frequency module is obtained, and the near-field scanning data and the radio frequency module simulation data are correlated and calculated by a preset fully implicit frequency-domain finite volume numerical algorithm to obtain corresponding far-field electromagnetic distribution characteristic data, including: The RF module is scanned and measured in the near field by a high-precision near-field scanner to collect near-field electromagnetic field distribution data containing amplitude and phase information. The near-field scan adopts a planar scanning method. The scanning plane is parallel to the surface of the RF module to be tested. The scanning height is set to 5mm and the scanning step is λ / 4 (λ is the working wavelength). The scanning area covers a range of at least 2 wavelengths around the surface of the RF module to ensure that complete near-field information is collected.

[0039] When the acquired near-field scanning data is sampled in the time domain, the sampling frequency is set to 4 times the operating frequency, and the sampling time window is set to 5 cycles, so as to obtain a dual-channel time domain sampling sequence containing electric field components and magnetic field components. The sampling sequence is processed by orthogonal decomposition, and the field quantity in complex form is decomposed into a real part sequence and an imaginary part sequence, maintaining the phase relationship of the sampling points.

[0040] The real part sequence and the imaginary part sequence are respectively subjected to fast Fourier transform, the number of transformation points is set to 1024 points, and the characteristic analysis is performed through the windowing function to obtain the frequency domain characteristic data. The frequency domain characteristic data contains the amplitude spectrum and phase spectrum information of the field quantity.

[0041] In the spatial discretization process, the computational domain is divided into hexahedral meshes with a mesh size of λ / 10 to ensure the computational accuracy. The mesh is encrypted at the boundary and the mesh size is reduced to λ / 15 to improve the computational accuracy at the boundary. The spatial discrete mesh obtained after division contains the node coordinates and the unit topological relationship information.

[0042] The fully implicit discretized form of Maxwell's equations is constructed based on the spatial discrete grid. The electric and magnetic fields are expanded using edge basis functions, and the electromagnetic field component equations are constructed in each grid unit. The equations are solved using the preconditioned conjugate gradient iteration method. The preconditioner uses incomplete LU decomposition, and the iterative convergence criterion is set to a residual error less than 10 -6 The frequency domain field intensity distribution data obtained by the solution contains the three-dimensional component information of the electric field and the magnetic field.

[0043] When feature mapping is performed on frequency domain field intensity distribution data, the amplitude and phase characteristics of the field quantity 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 component represents the distribution characteristics of the field quantity at each point in space.

[0044] The electromagnetic characteristic vector group is transformed into a spherical wave expansion, and the number of expansion items is taken to 30 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 to 16×16 points to obtain the far-field distribution matrix composed of spherical harmonic function coefficients.

[0045] The far-field radiation intensity and directivity of the E and H planes are calculated based on the far-field distribution matrix. The field intensity calculation radius is 10 wavelengths, the angle sampling interval is 1°, and the range of 0°-360° is covered to obtain complete far-field electromagnetic distribution characteristic data. The far-field data is used to evaluate the electromagnetic radiation characteristics and electromagnetic compatibility indicators of the RF module.

[0046] This embodiment achieves efficient and accurate conversion from near field to far field by combining high-precision near-field scanning and fully implicit frequency-domain finite volume numerical algorithm. The scanning step size of λ / 4 and the scanning range of at least 2 wavelengths are adopted to ensure the integrity and accuracy of near-field data acquisition. The complete capture of time domain signals is guaranteed by sampling at 4 times the operating frequency and setting a time window of 5 cycles. In the process of spatial discretization, the basic grid size of λ / 10 and the boundary encryption grid of λ / 15 are adopted to significantly improve the accuracy of electromagnetic field calculation. The preconditioned conjugate gradient iteration method and 10 -6 The use of convergence criteria ensures the rapid convergence of the solution process and the reliability of the results. The 30th-order spherical wave expansion and 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 assessment of the RF module.

[0047] In one embodiment, the fully implicit discretization form of Maxwell equations is constructed for 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: Spatial mesh analysis is performed on spatial discrete mesh data to generate corresponding mesh node coordinates and mesh topological relationships. The mesh node coordinates are recorded as the specific location information of the node in three-dimensional space, while the mesh topological relationship is described by the unit-node association matrix. The elements in the matrix represent the connection relationship between the node and the unit, 1 indicates that there is a connection, and 0 indicates that there is no connection. For tetrahedral meshes, each unit contains 4 nodes; for hexahedral meshes, each unit contains 8 nodes.

[0048] The boundary condition marking stage is divided into two parts: the grid node coordinates are marked with fixed boundaries of the electric field component to form a set of first-class boundary nodes. When the node coordinates satisfy the boundary surface equation, the node is marked as a first-class boundary node. The grid topology relationship is marked with the magnetic field component derivative boundary to form a set of second-class boundary cells. When at least one face of a grid cell is located on the boundary surface, the cell is marked as a second-class boundary cell.

[0049] Multi-layer boundary data integration is performed based on the first type boundary node set and the second type boundary unit set to generate the corresponding boundary grid data set. The key to this step is to effectively integrate different types of boundary conditions into a unified boundary data set to ensure the integrity and accuracy of subsequent calculations.

[0050] The discretization of Maxwell equations includes several key steps: First, the time derivative of the electric field intensity is differentially discretized for the grid nodes to determine the time step relationship of the electric field, which describes the differential characteristics of the electric field component over time. The spatial derivative of the magnetic field intensity is integrally discretized for the grid nodes, and the finite volume method is used to form the spatial gradient relationship of the magnetic field, which describes the integral characteristics of the magnetic field component in space.

[0051] The coupling relationship is constructed based on the aforementioned electric field time step relationship and magnetic field spatial gradient relationship to obtain the field component coupling relationship information. Subsequently, the field component coupling relationship information is associated and combined with the electric field time step relationship and magnetic field spatial gradient relationship to generate the electromagnetic field component discrete relationship. This step ensures the overall coupling and coordination between the electromagnetic field components.

[0052] Based on the discrete relationship of electromagnetic field components, a diagonal preconditioning matrix is ​​constructed according to the field component coupling relationship information to generate a preconditioning iteration 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 adopts 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 meets the convergence standard. When the residual meets the convergence condition, the frequency domain field strength distribution data is obtained.

[0053] The preconditioned conjugate gradient iteration 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 computational efficiency. The boundary condition processing adopts a hybrid boundary condition method to accurately simulate the physical behavior of the electromagnetic field at the boundary.

[0054] In this embodiment, the mesh quality is highly required, the mesh distortion should not exceed 0.8, and the mesh size should meet the requirement of 1 / 10 of the highest frequency wavelength. The selection of the time step should consider the balance between calculation accuracy and efficiency, and usually the minimum mesh size is divided by 1 / 10 of the electromagnetic wave propagation speed. The reasonable selection of these parameters is crucial to ensure the accuracy of the calculation results.

[0055] This embodiment constructs the Maxwell equations by adopting a fully implicit discrete form, avoiding the time step limitation in the explicit format and greatly improving the computational efficiency. Combined with spatial grid analysis and multi-layer boundary data integration technology, this method can accurately handle 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 guaranteed, and the numerical divergence problem that is prone to occur in the analysis of complex structures of traditional methods is effectively solved. This method adopts a mixed boundary condition processing method, which can accurately simulate the physical behavior of the electromagnetic field at the boundary and significantly improve the reliability of the simulation results. In addition, by reasonably setting the grid quality parameters and time step, the efficient use of computing resources is achieved while ensuring the calculation accuracy, making this method highly practical and adaptable in actual engineering applications.

[0056] In one embodiment, interference emission data of a radio frequency module is obtained, and zero eigenvalue entropy correction is performed to obtain corresponding interference propagation characteristic data, including: The magnetic field strength data is collected, the sampling frequency is set to 1 GHz, the sampling time is 10 ms, and the collected data is the interference emission data.

[0057] When extracting features from the collected interference emission data, the time domain analysis method is used to extract the amplitude, phase and frequency characteristics of the interference signal. By performing Fourier transform on the interference signal, the spectrum characteristics 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.

[0058] When performing spatial vector decomposition of the interference source, the interference feature data is decomposed according to three orthogonal directions: x, y, and z. In each direction, the amplitude and phase information of the interference signal are projected to obtain the interference components in each direction and form a spatial component data matrix.

[0059] When performing singular value decomposition on 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.

[0060] 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 number is the number of zero eigenvalues. The preset threshold is set to 0.1% of the maximum eigenvalue value.

[0061] The entropy weight function is constructed based on the number of zero eigenvalues, and the information entropy theory is used to calculate the weight coefficient of each eigenvalue. The calculation of the weight coefficient takes into account the size distribution of the eigenvalue and the influence of the zero eigenvalue, and the weight value ranges from 0 to 1.

[0062] When correcting the eigenvalue, the entropy weight coefficient is multiplied by the original eigenvalue to correct the zero eigenvalue. The corrected eigenvalue maintains the main characteristics of the original data and eliminates the singularity problem caused by the zero eigenvalue.

[0063] In 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.

[0064] During the entire analysis process, the sampling rate and sampling time of data sampling need to meet the Nyquist sampling theorem to ensure the integrity of the sampled data. Frequency analysis during feature extraction needs to consider the operating frequency range of the RF module and select a suitable analysis window. When decomposing the space vector, it is necessary to ensure that the basis vectors in the three directions are orthogonal to ensure the accuracy of the decomposition results. The singular value decomposition operation uses an iterative algorithm, and the iteration termination condition is that the difference between the results of two adjacent calculations is less than the preset accuracy. The distribution characteristics of the data are taken into account during the entropy weight calculation process, and the weight distribution is more reasonable. When reconstructing the data, the consistency of the reconstructed data and the original data is guaranteed, and the reconstruction error is controlled within the allowable range.

[0065] This embodiment can comprehensively obtain the spatial distribution characteristics of electromagnetic interference and effectively identify the location and propagation path of the interference source by accurately sampling and feature extracting the electromagnetic interference source of the RF module and combining it with the space vector decomposition technology. The singular value decomposition and entropy weight correction method are adopted to solve the data distortion problem caused by zero eigenvalues ​​in traditional analysis and improve the accuracy of feature extraction. The weight function constructed based on 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 electromagnetic compatibility analysis of RF modules, but also realizes the accurate characterization of interference propagation characteristics, providing reliable data support for the electromagnetic compatibility optimization design of RF modules.

[0066] 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: When performing multidimensional spectrum decomposition processing on far-field electromagnetic distribution characteristic data, a 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, spectral components of different dimensions are separated, and characteristic parameters such as amplitude and phase of spectral components of each dimension are extracted to form multidimensional spectrum characteristic data. When performing tensor decomposition, the decomposition error must be less than the preset threshold of 0.01, and the number of iterations must not exceed 100 times.

[0067] After obtaining the multi-dimensional spectrum feature data, the interference propagation feature data is nonlinearly transformed based on the kernel function mapping principle and mapped to the high-dimensional feature space. By calculating the mutual correlation coefficient between the mapped data and the multi-dimensional spectrum feature data, a nonlinear mapping relationship between the two is established to generate coupled mapping data. The Gaussian kernel function is used in the mapping process, and the kernel parameter range is [0.1, 10]. The optimal value is determined by cross-validation.

[0068] When the coupled mapping data is processed by fractional Hilbert transform, the fractional order α∈(0,1) is set, the fractional differential operator is constructed, the coupled mapping data is transformed by fractional integral, the instantaneous amplitude and phase information of the signal is obtained, and the time-frequency domain characteristic parameters are extracted to form fractional time-frequency characteristic data. The fractional order α is determined by minimizing the reconstruction error during the transformation process.

[0069] Based on the fractional time-frequency feature data, we calculate various entropy indicators such as sample entropy and approximate entropy, construct entropy feature vectors, and fuse different entropy indicators through adaptive weight allocation method to obtain entropy distribution data. The entropy calculation uses a sliding time window with a window length of 1 / 10 of the signal length and an overlap rate of 50%.

[0070] The entropy distribution data is subjected to empirical mode decomposition, which is decomposed into several intrinsic mode function (IMF) components, and the characteristic parameters of each IMF component, including instantaneous frequency, envelope and other information, are extracted to form the intrinsic mode data. During the decomposition process, the number of screenings does not exceed 10 times, and the residual energy ratio is less than 0.1.

[0071] The electromagnetic field distribution reconstruction model is established based on the intrinsic mode data, and the field intensity distribution is spatially interpolated to achieve the spatial distribution reconstruction of the electromagnetic field intensity and obtain the field intensity distribution characteristic data. The neural network uses the Gaussian radial basis function, and the number of hidden layer nodes is determined by cross-validation.

[0072] A multi-objective optimization analysis was performed on the characteristic data of field intensity distribution, and a multi-objective optimization model with field intensity uniformity and interference suppression as optimization targets was established. The non-dominated sorting genetic algorithm was used to solve the Pareto optimal solution set, and the solution with the best comprehensive performance was selected as the initial optimization information. During the optimization process, the population size was 100, the evolutionary generation was 200, the crossover probability was 0.8, and the mutation probability was 0.1.

[0073] This embodiment uses a high-order singular value decomposition method to perform multidimensional spectral decomposition on the far-field electromagnetic distribution characteristic data, thereby achieving accurate characterization of complex electromagnetic field distribution and effectively improving the accuracy of feature extraction. Based on the kernel function mapping principle, a nonlinear correlation mapping relationship is established to overcome the limitations of traditional linear analysis methods and enhance the ability to characterize interference propagation characteristics. Fractional-order Hilbert transform is used to process coupling mapping data, providing richer time-frequency domain feature information and improving the accuracy of electromagnetic compatibility analysis. By combining adaptive entropy value calculation and modal decomposition, multi-scale analysis of electromagnetic field distribution characteristics is achieved, enhancing the robustness of feature extraction. Field strength distribution reconstruction based on radial basis function neural network provides high-precision spatial distribution prediction capabilities. A multi-objective optimization strategy is used for information optimization analysis, which effectively suppresses interference while ensuring field strength uniformity, significantly improving the electromagnetic compatibility performance of the RF module.

[0074] In one embodiment, an anti-interference test is performed on the interference emission data, and a performance evaluation is performed in combination with the interference propagation characteristics to obtain a corresponding EMC performance evaluation result, including: During the electromagnetic compatibility analysis of the RF module, when performing the anti-interference test on the interference emission data, the multi-frequency injection scanning technology is used to apply interference signals of different frequencies at the key nodes of the RF module and record the response of the module. The specific scanning frequency range is 30MHz to 6GHz, the frequency step is 1MHz, and the interference signal of -20dBm to 20dBm is injected at each frequency point to form an anti-interference scanning data set.

[0075] For the acquired immunity scan data, the burst interference characteristics are identified by time domain analysis method. The scan data is segmented according to 100ms time windows, and characteristic parameters such as amplitude change rate and duration in each time window are extracted to establish burst interference response data judgment. The burst interference judgment standard is that the amplitude change rate is greater than 6dB / ms and the duration is less than 10ms.

[0076] In the transfer characteristic analysis, the transfer function between the input interference signal and the output response is calculated, and the frequency domain transfer characteristic relationship is established using the S parameter characterization method. The amplitude and phase response of the transfer function reflect the sensitivity of the RF module to interference of different frequencies.

[0077] Based on the obtained burst interference response data, nonlinear fitting is performed in combination with the interference propagation characteristics. The least square method is used to fit the interference response curve, and the characteristic coefficients are extracted to form the EMC response characteristic vector. The fitting model uses a cubic polynomial function, and the fitting accuracy requires the root mean square error to be less than 1dB.

[0078] When performing matrix mapping on the immunity transfer characteristics, the response values ​​of the transfer function at different frequencies are organized into an m×n dimensional matrix, where m is the number of frequencies and n is the number of test ports. The matrix elements represent the transfer characteristics of the corresponding frequencies and ports.

[0079] The eigenvalues ​​of the interference immunity mapping matrix are solved by the singular value decomposition method to obtain the eigenvalue sequence arranged in descending order. The eigenvalue sequence reflects the sensitivity of the system to different types of interference, and the larger eigenvalue corresponds to the main interference propagation path.

[0080] In the calculation of the anti-interference response, the EMC response eigenvector and the anti-interference eigenvalue sequence are weighted and combined, and the weight coefficient is determined according to the eigenvalue size. The calculated response data reflects the anti-interference performance of the RF module under different working conditions.

[0081] The EMC characteristic scoring data is calculated based on the preset evaluation matrix, which includes evaluation indicators such as frequency coverage, immunity margin, and interference suppression. The weight coefficients of each indicator are 0.3, 0.4, and 0.3 respectively, and the scoring results are expressed in percentage. The scoring standard is: 90 points or more are excellent, 80-90 points are good, 70-80 points are qualified, and below 70 points are unqualified.

[0082] This embodiment obtains full-band anti-interference data through multi-frequency injection scanning technology, combined with burst interference analysis and transfer characteristic analysis, to achieve a comprehensive evaluation of the EMC performance of the RF module. The EMC response eigenvector is constructed by nonlinear fitting method, and key features are extracted by matrix mapping and singular value decomposition, making the evaluation results more accurate and reliable. By establishing a systematic scoring system, multiple dimensions such as frequency coverage, anti-interference margin, and interference suppression are taken into consideration to ensure the scientificity and comparability of the evaluation results. The system's sensitivity to different types of interference is reflected through the eigenvalue sequence, and the main interference propagation path is effectively identified, providing a clear direction for EMC performance optimization. This method overcomes the problems of single evaluation indicators and insufficient reliability of results in traditional EMC analysis methods, improves the accuracy and efficiency of EMC performance evaluation of RF modules, and provides strong support for design optimization and quality control of RF modules.

[0083] In one embodiment, the initial optimization information and the EMC performance evaluation results are analyzed to obtain a corresponding EMC optimization solution, including: During the EMC analysis process, the collected initial optimization information and the EMC performance evaluation results are matched one by one according to the timestamp to form a correlation data pair containing optimization parameters and performance indicators. The EMC performance indicators in these correlation data pairs are analyzed to extract EMC performance characteristic indicators such as electromagnetic interference intensity, frequency response characteristics, and radiation.

[0084] Based on the extracted EMC performance characteristic indicators, the OTSU adaptive threshold segmentation algorithm is used to stratify the electromagnetic field intensity and obtain 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 graded into three levels: A, B, and C, and the corresponding performance rating data matrix is ​​generated.

[0085] In order to extract key EMC features, the performance rating data is processed by KL orthogonal transformation. In this process, the performance rating data matrix is ​​preprocessed by centralization, the data covariance matrix is ​​calculated, and its eigenvalues ​​and eigenvectors are solved. By sorting the eigenvalues, the eigenvectors corresponding to the main eigenvalues ​​are selected to construct the transformation matrix to achieve data dimensionality reduction and feature extraction. This transformation method can retain data change information to the greatest extent, effectively remove data redundancy and noise, and provide reliable feature representation for subsequent optimization.

[0086] After obtaining the EMC characteristic component data through KL transformation, the characteristic component data is nonlinearly mapped through RBF neural network to obtain the key parameter set required for EMC optimization. Based on the obtained EMC optimization parameters, a constrained optimization model with the goal of minimizing the electromagnetic field intensity is established, and the EMC performance constraints are solved through the Lagrange multiplier method.

[0087] The constraints are iteratively optimized using genetic algorithms to obtain an EMC optimization target condition set that meets performance requirements. Based on the optimization target condition set, multiple alternative EMC optimization schemes are generated using a multi-objective decision-making method. 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.

[0088] This embodiment achieves accurate generation of EMC optimization solutions by establishing a systematic data processing and analysis process. Through the time series association matching mechanism, the accurate correspondence between the initial optimization information and the performance evaluation results is ensured, and the accuracy of data analysis is improved. The adaptive threshold segmentation algorithm is used for electromagnetic field intensity stratification, combined with standardized performance grading evaluation, to achieve objective quantification of EMC performance.

[0089] Reference Figure 2 As shown, the present invention also provides a radio frequency module electromagnetic compatibility analysis system, characterized in that it is applied to the above radio frequency module electromagnetic compatibility analysis method, including: An acquisition module is used to obtain structural data and electromagnetic parameter information of the RF module, and perform electromagnetic simulation on the structural data based on the electromagnetic parameter information to obtain corresponding RF module simulation data; An analysis module is used to obtain the 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 the corresponding far-field electromagnetic distribution characteristic data; The correlation module is used to obtain the interference transmission data of the radio frequency module and perform zero eigenvalue entropy correction to obtain the 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, which is used to perform an anti-interference test on the interference emission data, and to perform a performance evaluation based on the interference propagation characteristics to obtain a corresponding EMC performance evaluation result; The execution module is used to perform solution analysis on the initial optimization information and the EMC performance evaluation results to obtain the corresponding EMC optimization solution.

[0090] The electromagnetic compatibility analysis system of a radio frequency module provided by the present invention can more 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, combined with near-field scanning data, thereby improving the accuracy of electromagnetic compatibility analysis and providing reliable data support for EMC optimization design. By correlating the near-field scanning data with the simulation data and analyzing it according to the fully implicit frequency domain finite volume numerical algorithm, an 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 interference transmission data is propagated and analyzed, which can ensure that the interference propagation characteristics of the radio frequency module are accurately evaluated under different application environments, reducing the performance risks 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 anti-interference 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, so that the analysis method is more adaptable to complex application environments.

[0091] It should be noted that technicians in the relevant technical field can clearly understand that for the convenience and conciseness of description, the specific working process of the system and each module described above can refer to the corresponding process in the aforementioned method embodiment, and will not be repeated here.

[0092] The above description is only a preferred embodiment of the present invention, and does not limit the patent scope of the present invention. Any equivalent structure or equivalent process transformation made by using the contents of the present invention specification and drawings, or directly or indirectly applied in other related technical fields, are also 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; The initial optimization information and the EMC performance evaluation result are analyzed to obtain a corresponding EMC optimization solution.

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 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.

4. The method for analyzing electromagnetic compatibility of a radio frequency module according to claim 3, characterized in that: The fully implicit discretization 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.

5. 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.

6. 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.

7. 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.

8. 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.

9. 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 8 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 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.

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