Simulation analysis method and system based on electromagnetic compatibility test

Abstract

The present invention discloses a simulation analysis method and system based on electromagnetic compatibility testing, which relates to the field of electromagnetic compatibility testing. The method comprises: constructing a three-dimensional hardware simulation model for the hardware environment of the electromagnetic compatibility to be tested, and acquiring hardware environment data; establishing a multi-device thermal balance model, and dynamically adjusting the temperature values ​​and spacings between multiple components and between component groups; analyzing the electromagnetic compatibility of the hardware environment data based on FDTD and MoM algorithms; establishing an electromagnetic compatibility dynamic adjustment model, and dynamically optimizing the stability of the electromagnetic compatibility hardware configuration and software analysis to be tested; and optimizing and adjusting the electromagnetic compatibility dynamic adjustment model based on actual design feedback. The present invention has the advantages of achieving collaborative design of hardware layout, thermal balance, and electromagnetic compatibility through multi-physics field coupling simulation and dynamic feedback optimization, significantly improving the compliance rate of electromagnetic compatibility testing and reducing R&D costs.

Description

Technical Field

[0001] The present invention relates to the field of electromagnetic compatibility testing, and in particular to a simulation analysis method and system based on electromagnetic compatibility testing. Background Art

[0002] As electronic equipment develops towards high frequency, high integration and multi-function, electromagnetic compatibility (EMC) issues are becoming increasingly prominent and have become a key factor affecting product reliability and market access. Traditional EMC design mainly relies on post-testing and trial-and-error rectification. This method is not only long-term and costly, but also difficult to fundamentally solve the problem. The complexity of modern electronic systems has increased significantly, the density of components in the hardware environment has increased, and the coupling phenomenon of thermal effects and electromagnetic interference has become more obvious, making EMC design face unprecedented challenges. Against this background, simulation-based EMC analysis methods have gradually become a hot topic in industry research. Through numerical calculation and virtual testing, they can predict and optimize electromagnetic compatibility performance in the design stage, thereby reducing the number of physical tests and shortening the R&D cycle.

[0003] The core of electromagnetic compatibility simulation analysis lies in the precise modeling and collaborative calculation of multiple physical fields. The electromagnetic field distribution and thermal conductivity characteristics in the hardware environment influence each other, requiring coupled analysis through multidisciplinary simulation tools. For example, high temperature can cause changes in the dielectric properties of the material, which in turn affects the propagation characteristics of electromagnetic waves. At the same time, adjustments to component layout and spacing will change the heat flow path and electromagnetic coupling strength. Therefore, constructing a three-dimensional simulation model that can reflect the actual hardware environment and conducting thermal-electric-magnetic multi-physics field coupling analysis on this basis is a necessary condition for achieving accurate EMC predictions. In recent years, with the development of computational electromagnetics algorithms (such as the finite-difference time-domain method (FDTD) and the method of moments (MoM)) and high-performance computing technologies, electromagnetic simulation of complex electronic systems has become possible, providing a new technical approach for EMC design optimization.

[0004] Existing electromagnetic compatibility analysis technologies ignore the coupling effects of heat conduction, radiation, and convection between multiple devices, and are unable to dynamically adjust the temperature and spacing of components, making it difficult to solve the problem of electromagnetic performance degradation caused by heat accumulation. Secondly, existing technologies mostly use FDTD or MoM algorithms alone, lacking deep fusion analysis of time-frequency domain information, making it difficult to fully capture the synergistic effects of transient interference and steady-state disturbances, and unable to accurately evaluate electromagnetic compatibility in complex hardware environments. Furthermore, existing technologies lack a dynamic adjustment mechanism that combines thermal effects with electromagnetic compatibility analysis, and are unable to optimize hardware layout and software analysis parameters in real time based on thermal equilibrium status and electromagnetic compatibility assessment results. As a result, hardware configuration and software simulation are difficult to adapt to complex and changing working scenarios, and cannot ensure the long-term stable operation of the system. Summary of the Invention

[0005] In order to solve the above technical problems, a simulation analysis method and system based on electromagnetic compatibility testing are provided. This technical solution solves the problem proposed in the above background technology that the coupling effect of heat conduction, radiation and convection between multiple devices is ignored, and the temperature and spacing of components cannot be dynamically adjusted, resulting in the difficulty in solving the problem of electromagnetic performance degradation caused by heat accumulation. Secondly, the use of FDTD or MoM algorithm alone lacks deep fusion analysis of time-frequency domain information, making it difficult to fully capture the synergistic effects of transient interference and steady-state disturbances, and unable to accurately evaluate electromagnetic compatibility in complex hardware environments. Furthermore, there is a lack of a dynamic adjustment mechanism that combines thermal effects with electromagnetic compatibility analysis, and it is impossible to optimize hardware layout and software analysis parameters in real time according to the thermal equilibrium state and electromagnetic compatibility evaluation results, resulting in hardware configuration and software simulation being difficult to adapt to complex and changeable working scenarios, and unable to ensure the long-term stable operation of the system.

[0006] In order to achieve the above objects, the technical solution adopted by the present invention is:

[0007] A simulation analysis method based on electromagnetic compatibility testing, comprising:

[0008] Construct a hardware 3D simulation model for the hardware environment of the electromagnetic compatibility to be tested, and obtain hardware environment data based on the hardware 3D simulation scenario;

[0009] Based on the thermal effects between hardware, a multi-device thermal balance model is established to dynamically adjust the temperature values ​​and spacing between multiple components and between component groups;

[0010] Based on FDTD and MoM algorithms, the time domain information and frequency domain information in the hardware environment data are extracted to analyze the electromagnetic compatibility of the hardware environment data;

[0011] Based on the multi-device thermal balance model and the hardware environment data electromagnetic compatibility evaluation model, an electromagnetic compatibility dynamic adjustment model is established to dynamically optimize the electromagnetic compatibility hardware configuration to be tested and the stability of the software analysis;

[0012] According to the adjustment results of the electromagnetic compatibility dynamic adjustment model and combined with actual design feedback, the electromagnetic compatibility dynamic adjustment model is optimized and adjusted.

[0013] Preferably, the step of establishing a multi-device thermal balance model based on the thermal effects between hardware and dynamically adjusting the temperature values ​​and spacings between multiple components and between component groups specifically includes:

[0014] According to the hardware three-dimensional simulation model, calibration data of each component and each component group in the hardware three-dimensional simulation model is obtained;

[0015] Use normalization formula to normalize the calibration data of each component and each component group to eliminate the influence of data dimension;

[0016] Based on Fourier's law of heat conduction, the heat conduction power between two components on the same PCB is obtained through temperature, material thermal conductivity and contact area.

[0017] Based on the Stefan-Boltzmann law, combined with the surface temperature, emissivity and surface area of ​​the two component groups, the mutual radiation heat power when they are radiated through the air is calculated;

[0018] A multi-device thermal balance model is established based on the heat conduction power between two components on the same PCB board and the mutual radiation heat power between two component groups;

[0019] Based on the multi-device thermal balance model, through simultaneous equations and combined with device product requirements, the temperature values ​​and spacing between multiple components and between component groups are dynamically adjusted;

[0020] The multi-device thermal balance model expression is:

[0021]

[0022] Where, For the The heat conduction power of each component, For the Components and The heat conduction power between components, For the The radiant heat power of each component group, For the Component group and The radiant heat power between the components.

[0023] Preferably, extracting time domain information and frequency domain information from the hardware environment data based on the FDTD and MoM algorithms and analyzing the electromagnetic compatibility of the hardware environment data specifically includes:

[0024] According to the hardware environment data in the hardware 3D simulation model, the grids of FDTD and MoM algorithms are preliminarily divided;

[0025] Adjust the grid size and time step in the FDTD algorithm according to the Courant stability condition;

[0026] Establish an adaptive grid density formula to dynamically adjust the grid density according to the field intensity gradient to reduce the impact of the number of grids in non-critical areas;

[0027] According to the FDTD time domain analysis method, by injecting the excitation source signal, the time domain data and waveform diagram of the electric field and magnetic field under the excitation source signal are obtained;

[0028] Through Fourier transform, the hardware environment data in the hardware 3D simulation model is converted into frequency domain data;

[0029] Based on the hardware environment data converted into the frequency domain and the MoM algorithm, the near-field and far-field radiation patterns under the same excitation source signal are obtained;

[0030] Based on the CNN neural network, a hardware environment data electromagnetic compatibility assessment model is constructed to analyze the electromagnetic compatibility of hardware environment data;

[0031] The adaptive grid density formula is:

[0032]

[0033] Where, In three-dimensional space The mesh size after orientation adjustment, In three-dimensional space The grid size in the direction, is the adjustment coefficient, which is a positive real number. is the distance balance constant term, which is used to control the value and eliminate the influence of data dimension. is the gradient amplitude of the electric field intensity, is the magnitude of the electric field strength.

[0034] Preferably, establishing an electromagnetic compatibility dynamic adjustment model based on a multi-device thermal balance model and a hardware environment data electromagnetic compatibility evaluation model to dynamically optimize the stability of the electromagnetic compatibility hardware configuration to be tested and the software analysis specifically includes:

[0035] According to the design requirements, set up the initial hardware environment for the electromagnetic compatibility to be tested;

[0036] Obtaining initial calculation results of the multi-device thermal balance model and the hardware environment data electromagnetic compatibility evaluation model based on the multi-device thermal balance model and the hardware environment data electromagnetic compatibility evaluation model;

[0037] Based on the temperature and distance deviation and the electromagnetic compatibility evaluation value, the distance and temperature of the components are limited, and the electromagnetic compatibility evaluation value is optimized by adjusting the step size feedback;

[0038] Establish an electromagnetic compatibility dynamic adjustment model to dynamically optimize the electromagnetic compatibility hardware configuration and software analysis stability to be tested;

[0039] The establishment of the electromagnetic compatibility dynamic adjustment model specifically includes:

[0040] Based on the temperature and distance deviation and the electromagnetic compatibility evaluation value, the objective function of the dynamic adjustment of electromagnetic compatibility is constructed;

[0041] According to the mean square error formula, the loss function of the electromagnetic compatibility dynamic adjustment objective function is constructed;

[0042] Through the gradient descent algorithm, the parameter values ​​in the loss function of the electromagnetic compatibility objective function are dynamically adjusted to achieve the optimal solution.

[0043] Furthermore, this solution proposes a simulation analysis system based on electromagnetic compatibility testing, which is used to implement the above-mentioned simulation analysis method based on electromagnetic compatibility testing, including:

[0044] A three-dimensional modeling module, the three-dimensional modeling module is used to build a hardware three-dimensional simulation model for the hardware environment of the electromagnetic compatibility to be tested, and obtain hardware environment data according to the hardware three-dimensional simulation scenario;

[0045] A thermal balance module, which is used to establish a multi-device thermal balance model based on the thermal effects between hardware and dynamically adjust the temperature values ​​and spacing between multiple components and between component groups;

[0046] A compatibility analysis module, which is used to extract time domain information and frequency domain information from hardware environment data based on FDTD and MoM algorithms, and analyze the electromagnetic compatibility of the hardware environment data;

[0047] A dynamic optimization module is used to establish an electromagnetic compatibility dynamic adjustment model based on a multi-device thermal balance model and an electromagnetic compatibility evaluation model of hardware environment data, and dynamically optimize the stability of the electromagnetic compatibility hardware configuration and software analysis to be tested;

[0048] The model feedback module is used to optimize and adjust the electromagnetic compatibility dynamic adjustment model according to the adjustment result of the electromagnetic compatibility dynamic adjustment model in combination with actual design feedback.

[0049] Preferably, the compatibility analysis module includes:

[0050] A data analysis unit, configured to extract time domain information and frequency domain information from the hardware environment data based on FDTD and MoM algorithms, and analyze the electromagnetic compatibility of the hardware environment data;

[0051] The model building unit is used to build an electromagnetic compatibility assessment model for hardware environment data based on a CNN neural network and analyze the electromagnetic compatibility of the hardware environment data.

[0052] Preferably, the dynamic optimization module includes:

[0053] A dynamic optimization unit, configured to establish an electromagnetic compatibility dynamic adjustment model based on a multi-device thermal balance model and an electromagnetic compatibility evaluation model of hardware environment data, and dynamically optimize the stability of the electromagnetic compatibility hardware configuration and software analysis to be tested;

[0054] A dynamic model building unit is used to establish an electromagnetic compatibility dynamic adjustment model to dynamically optimize the stability of the electromagnetic compatibility hardware configuration and software analysis to be tested.

[0055] Compared with the prior art, the present invention has the following beneficial effects:

[0056] The present invention provides a simulation analysis method based on electromagnetic compatibility testing. By establishing a multi-device thermal balance model, the dynamic impact of heat conduction on the temperature and spacing of components is accurately quantified, and the degradation of electromagnetic performance caused by heat accumulation is avoided. Based on the fusion of FDTD and MoM algorithms, time-frequency domain information is extracted, and the synergistic effect of transient interference and steady-state disturbance is deeply analyzed to improve the accuracy of electromagnetic compatibility evaluation. A dynamic adjustment model of electromagnetic compatibility is further constructed to achieve cross-feedback optimization of thermal effects and electromagnetic performance, and automatically adjust hardware layout parameters and software analysis strategies. Finally, the iterative optimization model is optimized in combination with actual design feedback to ultimately form an adaptive dynamic optimization mechanism, which significantly improves the electromagnetic compatibility and operational stability of the hardware system under complex working conditions, effectively reduces product R&D costs and cycles, and thus breaks through the limitations of independent evaluation of thermal effects and electromagnetic performance, separate processing of time-frequency domain information, and separation of hardware and software optimization in traditional electromagnetic compatibility analysis, and innovatively constructs a closed-loop system from three-dimensional modeling, data acquisition to dynamic optimization. BRIEF DESCRIPTION OF THE DRAWINGS

[0057] Figure 1 This is a flow chart of the simulation analysis method based on electromagnetic compatibility testing of the present invention;

[0058] Figure 2 The present invention establishes a multi-device thermal balance model based on the thermal effects between hardware, and dynamically adjusts the temperature values ​​and spacing between multiple components and between component groups;

[0059] Figure 3 This is a flow chart of the invention's method of extracting time domain information and frequency domain information from hardware environment data and analyzing the electromagnetic compatibility of the hardware environment data based on FDTD and MoM algorithms;

[0060] Figure 4 The invention establishes an electromagnetic compatibility dynamic adjustment model based on a multi-device thermal balance model and an electromagnetic compatibility evaluation model of hardware environment data, and dynamically optimizes the stability flow chart of the electromagnetic compatibility hardware configuration and software analysis to be tested. DETAILED DESCRIPTION

[0061] The following description is intended to disclose the present invention so that those skilled in the art can implement the present invention. The preferred embodiments described below are merely examples, and those skilled in the art may conceive of other obvious variations.

[0062] Reference Figure 1 As shown, a simulation analysis method based on electromagnetic compatibility testing includes:

[0063] Construct a hardware 3D simulation model for the hardware environment of the electromagnetic compatibility to be tested, and obtain hardware environment data based on the hardware 3D simulation scenario;

[0064] Based on the thermal effects between hardware, a multi-device thermal balance model is established to dynamically adjust the temperature values ​​and spacing between multiple components and between component groups;

[0065] Based on FDTD and MoM algorithms, the time domain information and frequency domain information in the hardware environment data are extracted to analyze the electromagnetic compatibility of the hardware environment data;

[0066] Based on the multi-device thermal balance model and the hardware environment data electromagnetic compatibility evaluation model, an electromagnetic compatibility dynamic adjustment model is established to dynamically optimize the electromagnetic compatibility hardware configuration to be tested and the stability of the software analysis;

[0067] According to the adjustment results of the electromagnetic compatibility dynamic adjustment model and combined with actual design feedback, the electromagnetic compatibility dynamic adjustment model is optimized and adjusted.

[0068] It can be explained that traditional electromagnetic compatibility analysis has problems such as independent evaluation of thermal effects and electromagnetic performance, separation of time-frequency domain information processing, and separation of hardware and software optimization, which easily lead to inaccuracy and incompleteness of electromagnetic compatibility analysis. Therefore, this solution establishes a multi-device thermal balance model to accurately quantify the dynamic impact of heat conduction on component temperature and spacing, avoid the degradation of electromagnetic performance caused by heat accumulation, and extracts time-frequency domain information based on the fusion of FDTD and MoM algorithms. It deeply analyzes the synergistic effect of transient interference and steady-state disturbance, improves the accuracy of electromagnetic compatibility evaluation, and further constructs an electromagnetic compatibility dynamic adjustment model to achieve cross-feedback optimization of thermal effects and electromagnetic performance, automatically adjust hardware layout parameters and software analysis strategies, and finally, combines the actual design feedback to iterate the optimization model to eventually form an adaptive dynamic optimization mechanism, which significantly improves the electromagnetic compatibility and operational stability of the hardware system under complex working conditions, and effectively reduces product R&D costs and cycles.

[0069] The step of constructing a hardware three-dimensional simulation model for the hardware environment of the electromagnetic compatibility to be tested and obtaining hardware environment data according to the hardware three-dimensional simulation scenario specifically includes:

[0070] Based on the hardware environment of the electromagnetic compatibility to be tested, obtain the circuit diagram, component distribution, component attributes, component spacing, and spacing data of the component group of the hardware environment, where a component group refers to a collection of components that implement a specific function on the same PCB;

[0071] According to the hardware environment layout of the electromagnetic compatibility to be tested, a hardware 3D simulation model is constructed based on 3D simulation software;

[0072] Based on the hardware three-dimensional simulation model, the data of component distribution, component properties, component spacing and component group spacing in the hardware environment to be tested for electromagnetic compatibility are calibrated, and modular design is performed according to the types of components and component groups.

[0073] What can be explained is that by obtaining the circuit diagram, component distribution, component properties, component spacing and spacing data of the hardware environment, where the component group is a collection of devices that implement specific functions on the same PCB, based on 3D simulation software, a 3D simulation model is constructed according to the hardware environment layout, and the distribution, properties and spacing data of components and groups are accurately calibrated. Modular design is performed according to component and group types to achieve structured modeling and data mapping of the hardware environment, providing accurate 3D scenes and data support for subsequent thermal effect analysis and electromagnetic compatibility assessment.

[0074] Reference Figure 2 As shown, the method of establishing a multi-device thermal balance model based on the thermal effects between hardware and dynamically adjusting the temperature values ​​and spacing between multiple components and between component groups specifically includes:

[0075] According to the hardware three-dimensional simulation model, calibration data of each component and each component group in the hardware three-dimensional simulation model is obtained;

[0076] Use normalization formula to normalize the calibration data of each component and each component group to eliminate the influence of data dimension;

[0077] Based on Fourier's law of heat conduction, the heat conduction power between two components on the same PCB is obtained through temperature, material thermal conductivity and contact area.

[0078] Based on the Stefan-Boltzmann law, combined with the surface temperature, emissivity and surface area of ​​the two component groups, the mutual radiation heat power when they are radiated through the air is calculated;

[0079] A multi-device thermal balance model is established based on the heat conduction power between two components on the same PCB board and the mutual radiation heat power between two component groups;

[0080] Based on the multi-device thermal balance model, through simultaneous equations and combined with device product requirements, the temperature values ​​and spacing between multiple components and between component groups are dynamically adjusted;

[0081] The multi-device thermal balance model expression is:

[0082]

[0083] Where, For the The heat conduction power of each component, For the Components and The heat conduction power between components, For the The radiant heat power of each component group, For the Component group and The radiant heat power between the components.

[0084] It can be explained that the thermal effect of multiple devices working in the hardware will lead to temperature field coupling. If the temperature is too high or the spacing is unreasonable, it is easy to cause component parameter drift, electromagnetic interference aggravation and other problems. Therefore, this solution can dynamically calculate the temperature distribution of each component and group by establishing a multi-device thermal balance model, optimize the spacing to balance heat dissipation and layout, avoid the negative impact of heat accumulation on electromagnetic compatibility, ensure that the hardware operates stably within a reasonable temperature range, and improve the system's electromagnetic compatibility performance from the perspective of thermal management.

[0085] The heat conduction power expression between two components on the same PCB is:

[0086]

[0087] Where, is the heat conduction power between two components, is the thermal conductivity of the medium, is the contact area between the two components and the PCB, is the temperature difference between the two components, is the distance between two components;

[0088] The expression for calculating the mutual radiation heat power of two component groups when radiating heat through air is:

[0089]

[0090] Where, is the mutual radiation heat power of the two component groups when they transfer heat through air radiation, is the surface emissivity of the component, is the Stefan-Boltzmann constant, 、 are the radiation surface areas of the two component groups, 、 are the temperature values ​​of the two component groups, is the distance between two component groups.

[0091] Reference Figure 3 As shown, the extraction of time domain information and frequency domain information from hardware environment data based on FDTD and MoM algorithms and the analysis of electromagnetic compatibility of the hardware environment data specifically include:

[0092] According to the hardware environment data in the hardware 3D simulation model, the grids of FDTD and MoM algorithms are preliminarily divided;

[0093] Adjust the grid size and time step in the FDTD algorithm according to the Courant stability condition;

[0094] Establish an adaptive grid density formula to dynamically adjust the grid density according to the field intensity gradient to reduce the impact of the number of grids in non-critical areas;

[0095] According to the FDTD time domain analysis method, by injecting the excitation source signal, the time domain data and waveform diagram of the electric field and magnetic field under the excitation source signal are obtained;

[0096] Through Fourier transform, the hardware environment data in the hardware 3D simulation model is converted into frequency domain data;

[0097] Based on the hardware environment data converted into the frequency domain and the MoM algorithm, the near-field and far-field radiation patterns under the same excitation source signal are obtained;

[0098] Based on the CNN neural network, a hardware environment data electromagnetic compatibility assessment model is constructed to analyze the electromagnetic compatibility of hardware environment data;

[0099] The adaptive grid density formula is:

[0100]

[0101] Where, In three-dimensional space The mesh size after orientation adjustment, In three-dimensional space The grid size in the direction, is the adjustment coefficient, which is a positive real number. is the distance balance constant term, which is used to control the value and eliminate the influence of data dimension. is the gradient amplitude of the electric field intensity, is the magnitude of the electric field strength.

[0102] It can be explained that, since electromagnetic compatibility needs to comprehensively consider the synergistic effects of time-domain transient interference (such as ESD pulses and switching noise) and frequency-domain steady-state disturbances (such as harmonic radiation and continuous wave interference), the FDTD algorithm can capture the transient evolution characteristics of the time-domain electromagnetic field, and the MoM algorithm can accurately solve the frequency-domain radiation and scattering characteristics. The combination of the two can extract electromagnetic characteristics containing three-dimensional information of time-frequency-space, which can not only analyze the time-domain coupling path of pulse interference, but also identify the interference amplification effect of the frequency-domain resonance point, thereby comprehensively evaluating the compatibility of hardware in complex electromagnetic environments and providing multi-dimensional data support for the location and suppression of interference sources. Therefore, this paper The scheme uses FDTD and MoM algorithms to extract time domain information and frequency domain information from hardware environment data, and generates time domain data, waveform diagrams, and near-field and far-field radiation patterns respectively. It also uses CNN neural network to build an electromagnetic compatibility evaluation model for hardware environment data, and analyzes the electromagnetic compatibility of hardware environment data. It also establishes an adaptive grid density formula to dynamically adjust the grid density according to the field intensity gradient, automatically encrypts the grid in key areas where the electromagnetic field changes drastically to ensure calculation accuracy, and reduces the impact of the number of grids in non-critical areas, thereby improving the accuracy and efficiency of FDTD and MoM algorithms.

[0103] The Courant stability condition expression is:

[0104]

[0105] Where, is the time step calculated for each iteration, is the propagation speed of electromagnetic waves in the medium, 、 、 In three-dimensional space 、 、 Grid spacing in the direction.

[0106] The electromagnetic compatibility evaluation model for hardware environment data is constructed based on the CNN neural network, and the electromagnetic compatibility analysis of the hardware environment data specifically includes:

[0107] Extract input feature items based on the time domain data and waveform generated by the FDTD algorithm and the near-field and far-field radiation patterns generated by the MoM algorithm;

[0108] Through the normalization formula, the data in the input feature item is normalized and mapped to the interval [0, 1] to eliminate the influence of the data dimension;

[0109] Based on historical data or conducting test experiments, set up training sets, test sets, and validation sets for CNN neural network training respectively;

[0110] The cross entropy loss function is used to quantify the information entropy difference between the CNN neural network predicted probability distribution and the true label, and the model parameters are optimized based on the gradient descent algorithm;

[0111] Based on the CNN neural network, an electromagnetic compatibility evaluation model for hardware environment data is constructed. The electromagnetic compatibility of the hardware environment data is analyzed and evaluated through the collected hardware environment data.

[0112] It can be explained that since the electromagnetic compatibility analysis of the hardware environment needs to be analyzed through multi-source heterogeneous data such as FDTD time domain waveforms and MoM radiation patterns, the transient interference characteristics and frequency domain resonance modes contained in data such as time domain waveforms and radiation patterns are highly nonlinear and complex. Therefore, this scheme uses CNN neural networks to automatically extract deep features such as the pulse characteristics of time domain waveforms and the energy distribution of frequency domain patterns through convolutional layers, combines normalization processing to eliminate dimensional effects, and uses cross-entropy loss function and gradient descent algorithm to optimize model parameters. It can realize nonlinear mapping from multi-dimensional electromagnetic data to compatibility assessment results, effectively identify hidden interference that is difficult to capture by traditional methods (such as cross-band coupling and nonlinear device harmonics), and improve the automation and accuracy of electromagnetic compatibility assessment in complex hardware environments.

[0113] Reference Figure 4 As shown, the establishment of an electromagnetic compatibility dynamic adjustment model based on a multi-device thermal balance model and an electromagnetic compatibility evaluation model based on hardware environment data, and the dynamic optimization of the electromagnetic compatibility hardware configuration to be tested and the stability of the software analysis specifically include:

[0114] According to the design requirements, set up the initial hardware environment for the electromagnetic compatibility to be tested;

[0115] Obtaining initial calculation results of the multi-device thermal balance model and the hardware environment data electromagnetic compatibility evaluation model based on the multi-device thermal balance model and the hardware environment data electromagnetic compatibility evaluation model;

[0116] Based on the temperature and distance deviation and the electromagnetic compatibility evaluation value, the distance and temperature of the components are limited, and the electromagnetic compatibility evaluation value is optimized by adjusting the step size feedback;

[0117] Establish an electromagnetic compatibility dynamic adjustment model to dynamically optimize the electromagnetic compatibility hardware configuration and software analysis stability to be tested;

[0118] The establishment of the electromagnetic compatibility dynamic adjustment model specifically includes:

[0119] Based on the temperature and distance deviation and the electromagnetic compatibility evaluation value, the objective function of the dynamic adjustment of electromagnetic compatibility is constructed;

[0120] According to the mean square error formula, the loss function of the electromagnetic compatibility dynamic adjustment objective function is constructed;

[0121] Through the gradient descent algorithm, the parameter values ​​in the loss function of the electromagnetic compatibility objective function are dynamically adjusted to achieve the optimal solution.

[0122] It can be explained that since the electromagnetic compatibility of the hardware environment is affected by the cross-influence of thermal effects and electromagnetic interference of multiple devices, single thermal management or electromagnetic analysis cannot solve the temperature-electromagnetic coupling problem. Therefore, this solution limits the distance and temperature of components according to the temperature and distance deviation and the electromagnetic compatibility evaluation value, and optimizes the electromagnetic compatibility evaluation value by adjusting the feedback of the step size. Based on this, the objective function and loss function are constructed by taking the temperature deviation, distance deviation and electromagnetic compatibility evaluation value as constraints, and the gradient descent algorithm is used to optimize the hardware configuration and software analysis results, thereby establishing an electromagnetic compatibility dynamic adjustment model, dynamically optimizing the stability of the electromagnetic compatibility hardware configuration and software analysis to be tested, and balancing the contradiction between parameter drift caused by heat accumulation and increased electromagnetic interference in real time, thereby improving the accuracy and reliability of electromagnetic compatibility analysis. Secondly,

[0123] The objective function expression of the dynamic adjustment of electromagnetic compatibility is:

[0124]

[0125] Where, Dynamically adjust the objective function value for electromagnetic compatibility, 、 、 are the parameters of temperature, distance and electromagnetic compatibility evaluation value of components and component groups, For the The temperature value of a component or component group, For the The temperature limit of a component or component group, For the A component or group of components and The spacing between components or groups of components, For the A component or group of components and The minimum spacing between components or groups of components, It is the electromagnetic compatibility evaluation value.

[0126] The optimization and adjustment of the electromagnetic compatibility dynamic adjustment model based on the adjustment result of the electromagnetic compatibility dynamic adjustment model and in combination with actual design feedback specifically includes:

[0127] Obtaining a dynamic adjustment result of the electromagnetic compatibility dynamic adjustment model through the electromagnetic compatibility dynamic adjustment model;

[0128] Optimize and adjust the electromagnetic compatibility dynamic adjustment model based on test experiments or actual design feedback results;

[0129] Based on the historical data of test experiments or actual design feedback results, the stability of the electromagnetic compatibility dynamic adjustment model is evaluated based on the F1-Score algorithm.

[0130] It can be explained that due to the difference between the theoretical optimization results of the dynamic adjustment model of electromagnetic compatibility and the actual engineering practice, it is necessary to improve the adaptability of the model through a measured feedback loop. This solution collects the hardware test data after dynamic adjustment or the iterative feedback of the test experiment, and uses the F1-Score algorithm to quantify the balance between the model's accuracy and recall rate in compatibility assessment. It can identify the deviations of the model in thermal-electromagnetic coupling prediction, parameter optimization strategy, etc., and continuously iterate the model parameters based on historical feedback data. It can correct the errors between the theoretical model and the actual working conditions, avoid the "simulation-measurement" disconnection problem, and enable the dynamic adjustment model to gradually approach the actual engineering needs from a data-driven perspective, and finally form a self-evolving electromagnetic compatibility optimization system to ensure that the model continues to maintain high stability and reliability in a complex hardware environment.

[0131] Furthermore, based on the same inventive concept as the above-mentioned simulation analysis method based on electromagnetic compatibility testing, this solution proposes a simulation analysis system based on electromagnetic compatibility testing, including:

[0132] A three-dimensional modeling module, the three-dimensional modeling module is used to build a hardware three-dimensional simulation model for the hardware environment of the electromagnetic compatibility to be tested, and obtain hardware environment data according to the hardware three-dimensional simulation scenario;

[0133] A thermal balance module, which is used to establish a multi-device thermal balance model based on the thermal effects between hardware and dynamically adjust the temperature values ​​and spacing between multiple components and between component groups;

[0134] A compatibility analysis module, which is used to extract time domain information and frequency domain information from hardware environment data based on FDTD and MoM algorithms, and analyze the electromagnetic compatibility of the hardware environment data;

[0135] A dynamic optimization module is used to establish an electromagnetic compatibility dynamic adjustment model based on a multi-device thermal balance model and an electromagnetic compatibility evaluation model of hardware environment data, and dynamically optimize the stability of the electromagnetic compatibility hardware configuration and software analysis to be tested;

[0136] A model feedback module, the model feedback module is used to optimize and adjust the electromagnetic compatibility dynamic adjustment model according to the adjustment results of the electromagnetic compatibility dynamic adjustment model in combination with actual design feedback;

[0137] The compatibility analysis module includes:

[0138] A data analysis unit, configured to extract time domain information and frequency domain information from the hardware environment data based on FDTD and MoM algorithms, and analyze the electromagnetic compatibility of the hardware environment data;

[0139] A model building unit, the model building unit is used to build an electromagnetic compatibility assessment model for hardware environment data based on a CNN neural network, and analyze the electromagnetic compatibility of the hardware environment data;

[0140] The dynamic optimization module includes:

[0141] A dynamic optimization unit, configured to establish an electromagnetic compatibility dynamic adjustment model based on a multi-device thermal balance model and an electromagnetic compatibility evaluation model of hardware environment data, and dynamically optimize the stability of the electromagnetic compatibility hardware configuration and software analysis to be tested;

[0142] A dynamic model building unit is used to establish an electromagnetic compatibility dynamic adjustment model to dynamically optimize the stability of the electromagnetic compatibility hardware configuration and software analysis to be tested.

[0143] In summary, the advantages of the present invention are: through multi-physics field coupling simulation and dynamic feedback optimization, the collaborative design of hardware layout, thermal balance and electromagnetic compatibility is achieved, which significantly improves the compliance rate of electromagnetic compatibility testing and reduces R&D costs.

[0144] The above shows and describes the basic principles, main features, and advantages of the present invention. Those skilled in the art should understand that the present invention is not limited to the above embodiments. The above embodiments and descriptions merely illustrate the principles of the present invention. Various changes and modifications may be made to the present invention without departing from the spirit and scope of the present invention. Such changes and modifications are intended to fall within the scope of the present invention. The scope of protection claimed by the present invention is defined by the appended claims and their equivalents.

Claims

1. A simulation analysis method based on electromagnetic compatibility testing, characterized in that: include: Construct a hardware 3D simulation model for the hardware environment of the electromagnetic compatibility to be tested, and obtain hardware environment data based on the hardware 3D simulation scenario; Based on the thermal effects between hardware, a multi-device thermal balance model is established to dynamically adjust the temperature values ​​and spacing between multiple components and between component groups; Based on FDTD and MoM algorithms, the time domain information and frequency domain information in the hardware environment data are extracted to analyze the electromagnetic compatibility of the hardware environment data; Based on the multi-device thermal balance model and the hardware environment data electromagnetic compatibility assessment model, an electromagnetic compatibility dynamic adjustment model is established to dynamically optimize the stability of the electromagnetic compatibility hardware configuration and software analysis to be tested. Specifically, it includes: According to the design requirements, set up the initial hardware environment for the electromagnetic compatibility to be tested; Obtaining initial calculation results of the multi-device thermal balance model and the hardware environment data electromagnetic compatibility evaluation model based on the multi-device thermal balance model and the hardware environment data electromagnetic compatibility evaluation model; Based on the temperature and distance deviation and the electromagnetic compatibility evaluation value, the distance and temperature of the components are limited, and the electromagnetic compatibility evaluation value is optimized by adjusting the step size feedback; Establish an electromagnetic compatibility dynamic adjustment model to dynamically optimize the electromagnetic compatibility hardware configuration and software analysis stability to be tested; The establishment of the electromagnetic compatibility dynamic adjustment model specifically includes: Based on the temperature and distance deviation and the electromagnetic compatibility evaluation value, the objective function of the dynamic adjustment of electromagnetic compatibility is constructed; According to the mean square error formula, the loss function of the electromagnetic compatibility dynamic adjustment objective function is constructed; Dynamically adjust the parameter values ​​in the loss function of the electromagnetic compatibility objective function through the gradient descent algorithm to achieve the optimal solution; The objective function expression of the dynamic adjustment of electromagnetic compatibility is: Where f is the objective function value of dynamic adjustment of electromagnetic compatibility, λ1, λ2, and λ3 are the parameters of temperature, distance, and electromagnetic compatibility evaluation value of components and component groups, respectively. i is the temperature value of the i-th component or component group, is the temperature limit value of the i-th component or component group, d ij is the distance between the i-th component or component group and the j-th component or component group, d min is the minimum distance between the i-th component or component group and the j-th component or component group, and h is the electromagnetic compatibility assessment value; According to the adjustment results of the electromagnetic compatibility dynamic adjustment model and combined with actual design feedback, the electromagnetic compatibility dynamic adjustment model is optimized and adjusted.

2. The simulation analysis method based on electromagnetic compatibility testing according to claim 1, characterized in that: The step of constructing a hardware three-dimensional simulation model for the hardware environment of the electromagnetic compatibility to be tested and obtaining hardware environment data according to the hardware three-dimensional simulation scenario specifically includes: Based on the hardware environment of the electromagnetic compatibility to be tested, obtain the circuit diagram, component distribution, component attributes, component spacing, and spacing data of the component group of the hardware environment, where a component group refers to a collection of components that implement a specific function on the same PCB; According to the hardware environment layout of the electromagnetic compatibility to be tested, a hardware 3D simulation model is constructed based on 3D simulation software; Based on the hardware three-dimensional simulation model, the data of component distribution, component properties, component spacing and component group spacing in the hardware environment to be tested for electromagnetic compatibility are calibrated, and modular design is performed according to the types of components and component groups.

3. The simulation analysis method based on electromagnetic compatibility testing according to claim 2, characterized in that: The method of establishing a multi-device thermal balance model based on the thermal effects between hardware and dynamically adjusting the temperature values ​​and spacing between multiple components and between component groups specifically includes: According to the hardware three-dimensional simulation model, calibration data of each component and each component group in the hardware three-dimensional simulation model is obtained; Use normalization formula to normalize the calibration data of each component and each component group to eliminate the influence of data dimension; Based on Fourier's law of heat conduction, the heat conduction power between two components on the same PCB is obtained through temperature, material thermal conductivity and contact area. Based on the Stefan-Boltzmann law, combined with the surface temperature, emissivity and surface area of ​​the two component groups, the mutual radiation heat power when they are radiated through the air is calculated; A multi-device thermal balance model is established based on the heat conduction power between two components on the same PCB board and the mutual radiation heat power between two component groups; Based on the multi-device thermal balance model, through simultaneous equations and combined with device product requirements, the temperature values ​​and spacing between multiple components and between component groups are dynamically adjusted; The multi-device thermal balance model expression is: Where q i is the heat conduction power of the i-th component, Q ij is the heat conduction power between the i-th component and the j-th component, r i is the radiant heat power of the i-th component group, R ij is the radiant heat power between the i-th component group and the j-th component group.

4. The simulation analysis method based on electromagnetic compatibility testing according to claim 3 is characterized in that: The extraction of time domain information and frequency domain information from the hardware environment data based on the FDTD and MoM algorithms and the analysis of the electromagnetic compatibility of the hardware environment data specifically include: According to the hardware environment data in the hardware 3D simulation model, the grids of FDTD and MoM algorithms are preliminarily divided; Adjust the grid size and time step in the FDTD algorithm according to the Courant stability condition; Establish an adaptive grid density formula to dynamically adjust the grid density according to the field intensity gradient to reduce the impact of the number of grids in non-critical areas; According to the FDTD time domain analysis method, by injecting the excitation source signal, the time domain data and waveform diagram of the electric field and magnetic field under the excitation source signal are obtained; Through Fourier transform, the hardware environment data in the hardware 3D simulation model is converted into frequency domain data; Based on the hardware environment data converted into the frequency domain and the MoM algorithm, the near-field and far-field radiation patterns under the same excitation source signal are obtained; Based on the CNN neural network, a hardware environment data electromagnetic compatibility assessment model is constructed to analyze the electromagnetic compatibility of hardware environment data; The adaptive grid density formula is: Where, is the grid size adjusted along the i direction in three-dimensional space, Δs i is the grid size along the i direction in three-dimensional space, α is the adjustment coefficient, which is a positive real number, L is the distance balance constant term, which is used to control the value and eliminate the influence of the data dimension, ▽E is the gradient amplitude of the electric field intensity, and E is the amplitude of the electric field intensity.

5. The simulation analysis method based on electromagnetic compatibility testing according to claim 4, characterized in that: The electromagnetic compatibility evaluation model for hardware environment data is constructed based on the CNN neural network, and the electromagnetic compatibility analysis of the hardware environment data specifically includes: Extract input feature items based on the time domain data and waveform generated by the FDTD algorithm and the near-field and far-field radiation patterns generated by the MoM algorithm; Through the normalization formula, the data in the input feature item is normalized and mapped to the interval [0, 1] to eliminate the influence of the data dimension; Based on historical data or conducting test experiments, set up training sets, test sets, and validation sets for CNN neural network training respectively; The cross entropy loss function is used to quantify the information entropy difference between the CNN neural network predicted probability distribution and the true label, and the model parameters are optimized based on the gradient descent algorithm; Based on the CNN neural network, an electromagnetic compatibility evaluation model for hardware environment data is constructed. The electromagnetic compatibility of the hardware environment data is analyzed and evaluated through the collected hardware environment data.

6. The simulation analysis method based on electromagnetic compatibility testing according to claim 5, characterized in that: The establishment of an electromagnetic compatibility dynamic adjustment model based on the multi-device thermal balance model and the hardware environment data electromagnetic compatibility evaluation model, and the dynamic optimization of the electromagnetic compatibility hardware configuration to be tested and the stability of the software analysis specifically include: According to the design requirements, set up the initial hardware environment for the electromagnetic compatibility to be tested; Based on the multi-device thermal balance model and the hardware environment data electromagnetic compatibility evaluation model, obtain initial calculation results of the multi-device thermal balance model and the hardware environment data electromagnetic compatibility evaluation model; Based on the temperature and distance deviation and the electromagnetic compatibility evaluation value, the distance and temperature of the components are limited, and the electromagnetic compatibility evaluation value is optimized by adjusting the step size feedback; Establish an electromagnetic compatibility dynamic adjustment model to dynamically optimize the electromagnetic compatibility hardware configuration and software analysis stability to be tested; The establishment of the electromagnetic compatibility dynamic adjustment model specifically includes: Based on the temperature and distance deviation and the electromagnetic compatibility evaluation value, the objective function of the dynamic adjustment of electromagnetic compatibility is constructed; According to the mean square error formula, the loss function of the electromagnetic compatibility dynamic adjustment objective function is constructed; Through the gradient descent algorithm, the parameter values ​​in the loss function of the electromagnetic compatibility objective function are dynamically adjusted to achieve the optimal solution.

7. The simulation analysis method based on electromagnetic compatibility testing according to claim 6, characterized in that: The optimization and adjustment of the electromagnetic compatibility dynamic adjustment model based on the adjustment result of the electromagnetic compatibility dynamic adjustment model and in combination with actual design feedback specifically includes: Obtaining a dynamic adjustment result of the electromagnetic compatibility dynamic adjustment model through the electromagnetic compatibility dynamic adjustment model; Optimize and adjust the electromagnetic compatibility dynamic adjustment model based on test experiments or actual design feedback results; Based on the historical data of test experiments or actual design feedback results, the stability of the electromagnetic compatibility dynamic adjustment model is evaluated based on the F1-Score algorithm.

8. A simulation analysis system based on electromagnetic compatibility testing, characterized in that: A simulation analysis method for implementing electromagnetic compatibility testing according to any one of claims 1 to 7, comprising: A three-dimensional modeling module, the three-dimensional modeling module is used to build a hardware three-dimensional simulation model for the hardware environment of the electromagnetic compatibility to be tested, and obtain hardware environment data according to the hardware three-dimensional simulation scenario; A thermal balance module, which is used to establish a multi-device thermal balance model based on the thermal effects between hardware and dynamically adjust the temperature values ​​and spacing between multiple components and between component groups; A compatibility analysis module, which is used to extract time domain information and frequency domain information from hardware environment data based on FDTD and MoM algorithms, and analyze the electromagnetic compatibility of the hardware environment data; A dynamic optimization module is used to establish an electromagnetic compatibility dynamic adjustment model based on a multi-device thermal balance model and an electromagnetic compatibility evaluation model of hardware environment data, and dynamically optimize the stability of the electromagnetic compatibility hardware configuration and software analysis to be tested; The model feedback module is used to optimize and adjust the electromagnetic compatibility dynamic adjustment model according to the adjustment result of the electromagnetic compatibility dynamic adjustment model in combination with actual design feedback.

9. The simulation analysis system based on electromagnetic compatibility testing according to claim 8, characterized in that: The compatibility analysis module includes: A data analysis unit, configured to extract time domain information and frequency domain information from the hardware environment data based on FDTD and MoM algorithms, and analyze the electromagnetic compatibility of the hardware environment data; The model building unit is used to build an electromagnetic compatibility assessment model for hardware environment data based on a CNN neural network and analyze the electromagnetic compatibility of the hardware environment data.

10. The simulation analysis system based on electromagnetic compatibility testing according to claim 9, characterized in that: The dynamic optimization module includes: A dynamic optimization unit, configured to establish an electromagnetic compatibility dynamic adjustment model based on a multi-device thermal balance model and an electromagnetic compatibility evaluation model of hardware environment data, and dynamically optimize the stability of the electromagnetic compatibility hardware configuration and software analysis to be tested; A dynamic model building unit is used to establish an electromagnetic compatibility dynamic adjustment model to dynamically optimize the stability of the electromagnetic compatibility hardware configuration and software analysis to be tested.

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