Intelligent Electromagnetic Compatibility Optimization Design Method and System

Abstract

This invention relates to the field of electromagnetic compatibility (EMC) optimization design methods, and more specifically, to an intelligent EMC optimization design method and system thereof, comprising the following steps: establishing an electromagnetic simulation unit model of components and constructing a component model library; performing interference path analysis on the component models using a path optimization algorithm, identifying key nodes and interference source nodes, and constructing an EMC path model; adaptively adjusting the electromagnetic radiation and immunity parameters of the system components according to the EMC path model to optimize EMC; dynamically predicting and optimizing the EMC parameters of the system using a deep learning model; and employing a progressive feedback adjustment mechanism to perform overall optimization of the EMC of the system and its sub-modules. By introducing innovative mechanisms such as path weight calculation, nonlinear adaptive adjustment, and multi-level non-uniform optimization, the technical contradictions in traditional methods are effectively resolved.

Description

Technical Field

[0001] This invention relates to the field of electromagnetic compatibility optimization design methods, and more specifically, to a system-wide intelligent electromagnetic compatibility optimization design method and system. Background Technology

[0002] With the increasing complexity of electronic systems, electromagnetic compatibility (EMC) issues have become increasingly important in modern electronic devices. Especially in high-frequency circuits and densely packed component structures, various electromagnetic radiations and interferences can easily cause the superposition of internal interference, affecting the normal operation of the equipment. Therefore, how to effectively reduce electromagnetic radiation levels and improve system immunity during the design phase has become a core technical challenge in the electronics field.

[0003] Most existing electromagnetic compatibility (EMC) optimization methods are based on static path analysis and fixed adjustments. They typically use pre-defined models to allocate paths to interference sources and sensitive nodes, and employ fixed parameter adjustment models to set the electromagnetic parameters of components. However, these traditional methods have significant drawbacks. First, path analysis methods often lack recursive optimization algorithms, resulting in inaccurate identification of interference paths and an inability to dynamically respond to complex EMC environments. For example, in high-frequency circuits with densely distributed components, the weights of interference paths vary greatly. Static path analysis easily overlooks in-depth analysis of key nodes, generating numerous redundant paths, which not only increases computational load but also makes it difficult to improve the system's immunity.

[0004] Secondly, existing methods mostly employ a single fixed adjustment factor, which cannot achieve nonlinear adaptive adjustment and lacks the ability to sensitively adjust to interference of different frequency bands and intensities. For example, traditional fixed-factor adjustment cannot adapt to the frequently changing interference frequencies in complex environments, often resulting in electromagnetic radiation and immunity failing to reach the ideal optimized state. Such methods often have limited effect on adjusting interference of different frequency bands, and the overall electromagnetic compatibility improvement of the system is not significant. Furthermore, traditional adjustment methods ignore the synergistic relationship between parameters, resulting in low adjustment efficiency and accuracy, and failing to effectively suppress electromagnetic radiation while maintaining immunity.

[0005] In recent years, deep learning has been widely applied in various optimization scenarios. However, most traditional electromagnetic compatibility (EMC) optimization methods have failed to incorporate deep learning models to achieve dynamic adaptive adjustment. Even if some methods use simple machine learning models for prediction, they fail to adjust component parameters in real time in rapidly changing environments, making it difficult for EMC to achieve dynamic response in variable environments. Furthermore, existing methods lack optimization for feedback adjustment, typically employing fixed-frequency feedback, which is ill-suited to the frequent adjustment requirements of interference changes in complex environments. The lack of adaptive feedback limits the overall dynamic adaptability of the system, especially in multi-level EMC environments, where traditional feedback mechanisms struggle to achieve coordinated optimization of various sub-modules.

[0006] The necessity of this invention lies in solving the aforementioned problems in electromagnetic compatibility optimization in the prior art, especially in providing an intelligent adaptive optimization scheme that can effectively identify and analyze electromagnetic interference paths, and optimize the electromagnetic characteristics of the system in real time through nonlinear adaptive adjustment and deep learning prediction, so that the system can achieve excellent compatibility in interference environments of different frequency bands and different intensities. Summary of the Invention

[0007] This invention addresses the shortcomings of existing technologies by proposing an intelligent electromagnetic compatibility (EMC) optimization design method and system to achieve dynamic adaptability to complex electromagnetic environments. Based on a path optimization recursive algorithm, this invention can identify and dynamically adjust key interference paths and nodes. Through path weight optimization, it achieves accurate identification and effective adjustment of path nodes in multi-level interference environments. The recursive optimization path analysis method helps to screen out the main interference paths in complex electromagnetic environments while avoiding the waste of resources on redundant paths, greatly improving system immunity and reducing the impact of electromagnetic interference on key nodes.

[0008] This invention provides a system intelligent electromagnetic compatibility optimization design method, comprising the following steps:

[0009] Obtain the components of the system and their design parameters, composition structure and electromagnetic characteristics, establish electromagnetic simulation unit models of the components and build a component model library;

[0010] The interference path analysis of the component model is performed by the path optimization algorithm to identify key nodes and interference source nodes and construct an electromagnetic interference path model.

[0011] Based on the electromagnetic interference path model, the electromagnetic radiation and immunity parameters of the system components are adaptively adjusted to optimize electromagnetic compatibility.

[0012] The electromagnetic compatibility parameters of the system are dynamically predicted and optimized using a deep learning model.

[0013] A progressive feedback adjustment mechanism is adopted to optimize the electromagnetic compatibility of the system and its sub-modules as a whole, so that the system meets the electromagnetic compatibility requirements.

[0014] Preferably, the path optimization algorithm uses a recursive formula for calculating path weights:

[0015]

[0016] Where, ω(N) i N j ) is node N i and N j Path weights between, d ij Let λ be the distance between the two nodes, λ be the path decay factor, and φ be the path decay factor. p κ represents the weight of the interference source node. p δ is the weight factor for sensitive nodes. ij The path weight is used to determine the key nodes in the electromagnetic interference path and generate the interference path model.

[0017] Preferably, the adaptive adjustment of the electromagnetic radiation and immunity parameters adopts a nonlinear adjustment formula:

[0018]

[0019] Wherein, ΔE is the optimization quantity, α, β, and γ are adjustment factors, f(x), g(x), and h(x) are the electromagnetic characteristic functions of the component, and ψ(x) is the dynamic attenuation function, used to nonlinearly adjust the electromagnetic radiation and disturbance rejection of the component.

[0020] Preferably, the system-level non-uniform electromagnetic compatibility optimization adopts a multi-level non-uniform optimization model, and the system-level optimal adjustment factor is calculated using the following formula:

[0021]

[0022] Where θ is the set of adjustment parameters used to control the electromagnetic radiation and disturbance rejection of the system, representing the specific adjustment state of each component during the optimization process. opt This represents the optimal set of adjustment factors for the system, aiming to derive the adjustment parameters that best achieve the overall electromagnetic compatibility effect through an optimization process. 'n' represents the number of components in the system, each with independent electromagnetic characteristic parameters, and γ... i β is the electromagnetic radiation adjustment factor for each component, used to weight the electromagnetic radiation intensity of different components in the system. i This is the immunity adjustment factor, used to control the immunity adjustment range of each component, σ. i τ is the radiation parameter adjustment coefficient, used to determine the sensitivity of electromagnetic radiation parameters to changes during adjustment.i E is the immunity parameter adjustment coefficient, used to adjust the adjustment range of the immunity parameter. i Let E be the current electromagnetic radiation level of the i-th component in the system. 标准 S represents the standard electromagnetic radiation value required by the system, and S represents the pre-set target radiation level. i Let S be the current immunity value of the i-th component, representing its immunity to interference in an electromagnetic environment. 标准 Let η be the standard immunity of the system, and η be the desired immunity target value of the system. i Let ξ be the electromagnetic radiation characteristic parameter of the i-th component, used to describe the desired state of the component's electromagnetic characteristics. i Let be the immunity characteristic parameter of the i-th component, used to describe the expected state of the component's immunity to interference in an electromagnetic environment.

[0023] Preferably, the deep learning model achieves real-time optimization of the electromagnetic compatibility parameters of components and systems through dynamic adaptive parameter prediction and adjustment, and the calculation formula is as follows:

[0024]

[0025] Where Δλ is the parameter adjustment amount of the deep learning model, representing the adjustment value of the electromagnetic compatibility parameters of the components in the current environment; σ is the learning rate adjustment factor of the deep learning model, used to control the model's response speed to changes in electromagnetic compatibility; f ML (P i Let P be the prediction function output by the deep learning model. i Let ξ represent the current electromagnetic characteristic input of the i-th component. The model generates corresponding predicted values ​​based on the input electromagnetic characteristics. ξ is a time-dependent adjustment factor used in adaptive prediction to balance the responsiveness of the adjustment result to changes in real-time data. This is the time derivative of the optimal electromagnetic compatibility parameter of the j-th component in the system. It is used to describe the rate of change of the component parameter over time and helps the deep learning model to achieve dynamic adaptive adjustment.

[0026] Preferably, the progressive feedback adjustment mechanism is used to collaboratively optimize the electromagnetic compatibility of the system and its sub-modules. It employs a feedback adjustment coefficient matrix to achieve overall system adjustment, and the feedback calculation formula is as follows:

[0027]

[0028] Where ψ is the set of feedback adjustment coefficients, used to adjust the electromagnetic compatibility parameters of various components in the system during the progressive feedback process. final The final set of feedback adjustment coefficients represents the overall electromagnetic compatibility adjustment scheme obtained after feedback optimization. 'o' represents the total number of system submodules, and δ...kk The diagonal elements of the feedback regulation coefficient matrix represent the self-feedback regulation strength of each component or submodule within the system, used to control the feedback strength of individual parameters. M k Let be the target interference level of the k-th submodule, be the set desired interference suppression value, and let a and b be the upper and lower limits of integration, representing the start and end points of the time period during the feedback adjustment process. δ ij The off-diagonal elements in the feedback adjustment coefficient matrix represent the mutual adjustment coefficients between different components, used to coordinate the electromagnetic compatibility between various components or sub-modules within the system. ω is the feedback adjustment frequency, used to adjust the response rate to electromagnetic changes during the feedback process; a higher frequency value indicates more frequent feedback adjustments. t is a time variable used for integral calculation of the impact of the adjustment frequency on the feedback process. M ij The target interference suppression level of the feedback adjustment coefficient represents the electromagnetic interference impact on the control target between different components or sub-modules.

[0029] An intelligent electromagnetic compatibility optimization design system for performing the method includes:

[0030] The data processing module is used for data collection, preprocessing, feature extraction, and deep learning model training of the electromagnetic characteristics of components. It includes a data collection unit, a preprocessing unit, a feature extraction unit, and a model training unit.

[0031] The simulation module is used to establish the electromagnetic simulation model of the electrical and electronic equipment, perform parameter simulation analysis, and extract simulation result data. It includes a modeling submodule, a parameter simulation submodule, and a simulation result submodule.

[0032] The optimization module is used to determine the simulation result parameters, predict the parameters using a deep learning model, and adjust the parameters using a progressive optimization algorithm. It includes an electromagnetic interference optimization unit, an electromagnetic radiation optimization unit, and an electromagnetic immunity optimization unit.

[0033] The visualization module is used to display the data processing results and simulation optimization results, and includes a data visualization submodule and a result visualization submodule.

[0034] Preferably, the model training unit in the data processing module trains the deep learning model through the component model library to evaluate the electromagnetic compatibility performance of the components, which is then called by the optimization module.

[0035] Preferably, the electromagnetic interference optimization unit in the optimization module dynamically adjusts the key nodes in the electromagnetic interference path based on the simulation results and the path weight recursive algorithm, and optimizes the configuration of the interference source and sensitive nodes.

[0036] Preferably, the electromagnetic radiation optimization unit in the optimization module uses the nonlinear adaptive adjustment formula to optimize the electromagnetic radiation parameters of the component, so that the electromagnetic radiation of the component meets the electromagnetic compatibility standards of the system.

[0037] The beneficial effects of this invention are mainly reflected in the following aspects:

[0038] This invention employs a systems engineering approach combining bottom-up and top-down methods, innovatively dividing the optimization process into four levels: device level, circuit level, system level, and propagation path analysis. In algorithm design, it effectively resolves the technical contradictions inherent in traditional methods by introducing innovative mechanisms such as path weight calculation, nonlinear adaptive adjustment, and multi-level non-uniform optimization. For example, the path weight calculation method cleverly balances the conflict between computational accuracy and efficiency, while the nonlinear adaptive adjustment mechanism effectively addresses the conflict between system stability and dynamic performance.

[0039] The method of this invention has achieved significant technical results in practical applications. By applying device-level deep learning models, accurate evaluation and prediction of electromagnetic characteristics are achieved, improving optimization efficiency by over 50%. In circuit-level optimization, automated optimization based on an electromagnetic simulation engine shortens the design cycle by approximately 40%. The system-level multi-level collaborative optimization mechanism improves overall electromagnetic compatibility indicators by over 30%. Particularly in electromagnetic interference propagation path analysis, the innovative iterative narrowing strategy achieves a critical path identification accuracy of over 95%. These technical achievements are attributed to the synergistic effect between the various innovations of this invention. For example, the accurate predictions of the deep learning model provide reliable data support for nonlinear adaptive adjustment, while the dynamic feedback mechanism ensures the convergence and stability of the multi-level optimization process.

[0040] In summary, the intelligent electromagnetic compatibility optimization design method and system provided by this invention not only solves various technical problems existing in the prior art, but also achieves a qualitative leap in optimization effect through algorithm innovation and mechanism synergy, providing a more scientific and efficient technical solution for the electromagnetic compatibility design of electronic devices. Attached Figure Description

[0041] Figure 1 This is the overall logical structure of the system in this invention.

[0042] Figure 2 This is a logic block diagram of the core algorithm of the present invention. Detailed Implementation

[0043] To further illustrate the technical means and effects adopted by the present invention to achieve its intended purpose, the specific implementation methods, structures, features, and effects are described in detail below with reference to the accompanying drawings and preferred embodiments. In the following description, different "one embodiment" or "another embodiment" do not necessarily refer to the same embodiment. Furthermore, specific features, structures, or characteristics in one or more embodiments can be combined in any suitable form.

[0044] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains.

[0045] The technical solution of the present invention will be clearly and completely described below with reference to specific embodiments. Please refer to... Figure 1-2 The method proposed in this invention includes an intelligent electromagnetic compatibility optimization design method and system, which aims to improve the electromagnetic compatibility of the system and reduce electromagnetic interference.

[0046] The intelligent electromagnetic compatibility optimization design method of the present invention includes several key steps. First, the method establishes an electromagnetic simulation unit model of the components by acquiring the design parameters, structural composition, and electromagnetic characteristics of each component in the system. For example, in a complex system containing multiple components, the electrical and electromagnetic characteristics of its basic components such as transistors, capacitors, and resistors, such as voltage withstand values, electromagnetic immunity, and radiation values, can be acquired and stored in the component model library in the data processing module 1. Preferably, in one embodiment of the present invention, the initial parameters of each component in the system are modeled within a frequency range of 11000MHz to ensure the comprehensiveness of the data. These simulation unit models can be established parametrically, for example, using numerical models of electrical properties and structural parameters to convert the specific electromagnetic behavior of the components into adjustable parameters suitable for simulation, so as to facilitate subsequent electromagnetic compatibility optimization. Preferably, in one embodiment of the present invention, the established simulation unit model can be stored in the data processing module 1, which normalizes the electrical characteristics of different components to unify the data format for subsequent use.

[0047] Next, this invention employs a path optimization recursive algorithm to perform interference path analysis on the component model, identifying key nodes and interference source nodes and constructing an electromagnetic interference path model. For example, the path optimization recursive algorithm calculates the path weight ω for each component and recursively optimizes based on the path weight, where the path weight ω(N) i N j The formula for calculating ) is:

[0048]

[0049] Where, ω(N) iN j ) is node N i and N j Path weights between, d ij Let λ be the distance between the two nodes, λ be the path decay factor, and φ be the path decay factor. p κ represents the weight of the interference source node. p δ is the weight factor for sensitive nodes. ij The path weights represent the differences in electromagnetic interference; they are used to identify key nodes in the electromagnetic interference path and generate the interference path model. This path weight calculation identifies paths that contribute significantly to system interference, facilitating their focused adjustment in subsequent optimization steps.

[0050] In a practical application scenario, if λ = 0.01, φ p =0.7, κ p A weighting of 1.5 allows for more effective allocation of lower weights to components far from interference sources, thus highlighting paths with higher interference levels. This model facilitates subsequent optimization of key paths and reduces the computational resource consumption of ineffective paths. By controlling the λ parameter to adjust path weights, the system can focus more on path nodes with significant interference impact. This algorithm effectively reduces the weight calculation burden on low-interference nodes, optimizes system resource utilization, and exhibits stronger anti-interference capabilities in environments with a wider frequency band.

[0051] In the adaptive adjustment process of electromagnetic radiation and immunity parameters, the electromagnetic radiation and immunity parameters of components are adjusted according to the electromagnetic interference path model to optimize electromagnetic compatibility. Preferably, in one embodiment of the present invention, the nonlinear adjustment formula is:

[0052]

[0053] Wherein, ΔE is the optimization quantity, α, β, and γ are adjustment factors, f(x), g(x), and h(x) are the electromagnetic characteristic functions of the component, and ψ(x) is the dynamic attenuation function, used to nonlinearly adjust the electromagnetic radiation and immunity of the component. The adjusted parameters will be used to feed back to the overall electromagnetic compatibility analysis of the system, achieving interference suppression without changing the component design.

[0054] In one embodiment, adjustment factors α = 0.1, β = 0.3, and γ = 0.6 are set, corresponding to the structure, material, and sensitivity characteristics of the components, respectively. This allows for priority adjustment of the main interference sources, improving the system's adaptability to interference sources in different frequency bands. It enables the system to maintain its adaptability to different radiation sources under multi-frequency interference conditions, reducing overall electromagnetic radiation. This optimization method, by preferably setting the α, β, and γ parameters, enables the system to exhibit good stability in both high-interference and low-interference environments.

[0055] In system-level non-uniform electromagnetic compatibility optimization, this invention uses a multi-level non-uniform optimization model to adjust the system's electromagnetic radiation and immunity in a hierarchical manner. The calculation formula is as follows:

[0056]

[0057] Where θ is the set of adjustment parameters used to control the electromagnetic radiation and disturbance rejection of the system, representing the specific adjustment state of each component during the optimization process. opt This represents the optimal set of adjustment factors for the system, aiming to derive the adjustment parameters that best achieve the overall electromagnetic compatibility effect through an optimization process. 'n' represents the number of components in the system, each with independent electromagnetic characteristic parameters, and γ... i β is the electromagnetic radiation adjustment factor for each component, used to weight the electromagnetic radiation intensity of different components in the system. i This is the immunity adjustment factor, used to control the immunity adjustment range of each component, σ. i τ is the radiation parameter adjustment coefficient, used to determine the sensitivity of electromagnetic radiation parameters to changes during adjustment. i E is the immunity parameter adjustment coefficient, used to adjust the adjustment range of the immunity parameter. i Let E be the current electromagnetic radiation level of the i-th component in the system. 标准 S represents the standard electromagnetic radiation value required by the system, and S represents the pre-set target radiation level. i Let S be the current immunity value of the i-th component, representing its immunity to interference in an electromagnetic environment. 标准 Let η be the standard immunity of the system, and η be the desired immunity target value of the system. i Let ξ be the electromagnetic radiation characteristic parameter of the i-th component, used to describe the desired state of the component's electromagnetic characteristics. i Let be the immunity characteristic parameter of the i-th component, used to describe the expected state of the component's immunity to interference in an electromagnetic environment. This enables the system to have better compatibility in different electromagnetic environments, especially exhibiting excellent electromagnetic immunity in scenarios with unstable frequencies.

[0058] Preferably, in one embodiment of the present invention, the multi-level optimization model adjusts the overall electromagnetic radiation and immunity parameters of the system layer by layer based on non-uniform distribution to achieve the global optimal effect.

[0059] Preferably, in one embodiment of the present invention, η is set i and ξ i The value range is 4060 to ensure a wide range of electromagnetic compatibility adjustment, thereby adapting to the electromagnetic compatibility requirements of different systems.

[0060] In addition, deep learning adaptive models are used to optimize the electromagnetic compatibility parameters of components and systems in real time, achieving dynamic prediction and parameter adjustment through the following formula:

[0061]

[0062] Where Δλ is the parameter adjustment amount of the deep learning model, representing the adjustment value of the electromagnetic compatibility parameters of the components in the current environment; σ is the learning rate adjustment factor of the deep learning model, used to control the model's response speed to changes in electromagnetic compatibility; f ML (P i Let P be the prediction function output by the deep learning model. i Let ξ represent the current electromagnetic characteristic input of the i-th component. The model generates corresponding predicted values ​​based on the input electromagnetic characteristics. ξ is a time-dependent adjustment factor used in adaptive prediction to balance the responsiveness of the adjustment result to changes in real-time data. This is the time derivative of the optimal electromagnetic compatibility parameter of the j-th component in the system. It is used to describe the rate of change of the component parameter over time and helps the deep learning model to achieve dynamic adaptive adjustment.

[0063] σ = 0.05 and ξ = 0.1 are set to accommodate the electromagnetic variation characteristics of most components. For example, when the electromagnetic parameters of a component in the system undergo a sudden change, the deep learning model can promptly adjust the corresponding radiation value and immunity parameters, effectively improving the dynamic response speed of the system. By achieving adaptive parameter adjustment through a deep learning model, this invention can react quickly to changes in the electromagnetic parameters within the system and effectively adjust immunity and radiation values ​​in a short time.

[0064] Preferably, in one embodiment of the present invention, the deep learning model can continuously receive electromagnetic characteristic data at the system level and device level to achieve adaptive parameter adjustment.

[0065] In the progressive feedback adjustment mechanism, this invention optimizes the electromagnetic compatibility of the system and its sub-modules as a whole through a progressive feedback adjustment algorithm to meet the overall system compatibility requirements. The progressive feedback adjustment mechanism is based on the following feedback adjustment formula:

[0066]

[0067] Where ψ is the set of feedback adjustment coefficients, used to adjust the electromagnetic compatibility parameters of various components in the system during the progressive feedback process. final The final set of feedback adjustment coefficients represents the overall electromagnetic compatibility adjustment scheme obtained after feedback optimization. 'o' represents the total number of system submodules, and δ... kkThe diagonal elements of the feedback regulation coefficient matrix represent the self-feedback regulation strength of each component or submodule within the system, used to control the feedback strength of individual parameters. M k Let be the target interference level of the k-th submodule, be the set desired interference suppression value, and let a and b be the upper and lower limits of integration, representing the start and end points of the time period during the feedback adjustment process. δ ij The off-diagonal elements in the feedback adjustment coefficient matrix represent the mutual adjustment coefficients between different components, used to coordinate the electromagnetic compatibility between various components or sub-modules within the system. ω is the feedback adjustment frequency, used to adjust the response rate to electromagnetic changes during the feedback process; a higher frequency value indicates more frequent feedback adjustments. t is a time variable used for integral calculation of the impact of the adjustment frequency on the feedback process. M ij The target interference suppression level of the feedback adjustment coefficient represents the electromagnetic interference impact on the control target between different components or sub-modules.

[0068] Preferably, ω = 0.2, δ ij =0.8 to balance the feedback frequency of the submodule, making the adjustment effect of electromagnetic compatibility more stable, thereby achieving the compatibility optimization of the whole system and maintaining a high level of electromagnetic compatibility, which is especially suitable for complex electromagnetic environments.

[0069] Preferably, in one embodiment of the present invention, the feedback adjustment mechanism adjusts key parameters at a certain frequency when optimizing the system's electromagnetic radiation and immunity, ensuring that the system maintains stable electromagnetic compatibility when the external environment changes.

[0070] Corresponding to the above method, this invention also discloses an intelligent electromagnetic compatibility optimization design system, including multiple functional modules. First, the data processing module 1 is used to collect, process, and store the electrical and electromagnetic characteristic parameters of each component. Through preprocessing and feature extraction of the raw data, the consistency of the input data and the model training effect are ensured. Preferably, in one embodiment of this invention, the data processing module 1 may include normalization and noise reduction steps to reduce data redundancy and improve system processing efficiency.

[0071] Secondly, simulation module 2 is used to create electromagnetic simulation models of electrical and electronic equipment, supporting parametric simulation analysis and extracting simulation result data. Specifically, simulation module 2 may include a modeling submodule, a parametric simulation submodule, and a simulation result submodule, ensuring the system's compatibility testing and evaluation in different scenarios. Preferably, in one embodiment of the present invention, simulation result data can be directly input into optimization module 3 to achieve rapid parameter adjustment.

[0072] Then, optimization module 3 is responsible for using a deep learning model to predict and adjust the electromagnetic interference path, radiation, and immunity parameters. Optimization module 3 includes an electromagnetic interference parameter optimization unit, an electromagnetic radiation optimization unit, and an electromagnetic immunity optimization unit. Specifically, these units adjust the interference sources and sensitive nodes based on the analysis results of the electromagnetic interference path to ensure that the system's electromagnetic compatibility reaches the optimal state.

[0073] Finally, the system also includes a visualization module 4 for graphically displaying the optimization design and simulation results. The data visualization submodule displays the process results of data processing and simulation analysis, while the results visualization submodule displays the final effect of system optimization. Preferably, in one embodiment of the invention, the visualization module 4 can also monitor data during the optimization process, allowing users to understand the progress of electromagnetic compatibility optimization in real time.

[0074] Preferably, in one embodiment of the present invention, the model training unit of the data processing module 1 can train a deep learning model using training data from the component model library to evaluate the electromagnetic compatibility parameters of the components. The real-time updated model enables the optimization module to be more intelligent and flexible in handling interference in different electrical environments.

[0075] Secondly, the electromagnetic interference parameter optimization unit in optimization module 3 dynamically adjusts key nodes based on simulation results and a path optimization recursive algorithm. Preferably, in one embodiment of the present invention, the optimized electromagnetic interference path will be used to further optimize electromagnetic radiation and immunity parameters to meet the overall system compatibility standards.

[0076] Furthermore, the electromagnetic radiation optimization unit uses a nonlinear adaptive adjustment formula to optimize the electromagnetic radiation parameters of the components. Preferably, in one embodiment of the present invention, the optimized electromagnetic radiation level will be limited to a specific range, so that the system meets the electromagnetic compatibility requirements under different environments.

[0077] To verify the superiority of the intelligent electromagnetic compatibility optimization design method of this invention, a specific embodiment is provided, and a performance comparison is conducted through comparative examples to demonstrate its significant improvement in electromagnetic compatibility. The test indicators include electromagnetic radiation level, immunity, path weight distribution uniformity, and deep learning prediction response time. These indicators are all set around the innovative points of this invention, aiming to reflect its stability and adaptability in different environments. The specific configurations and test results of Embodiment 1 and Comparative Example 1 are described in detail below.

[0078] In Example 1, the electromagnetic compatibility optimization system is implemented based on the method of the present invention, specifically using the following configuration:

[0079] Electromagnetic interference path analysis: The path weight of each key node and interference source node is calculated through a path optimization recursive algorithm to better identify and adjust paths with significant electromagnetic interference effects. The path weight recursion coefficient λ is set to 0.05 to ensure accuracy.

[0080] Nonlinear adaptive adjustment: The electromagnetic radiation and immunity parameters are dynamically adjusted using a nonlinear adjustment formula. The adjustment factors α, β, and γ are set to 0.1, 0.3, and 0.6, respectively, to balance the various electromagnetic characteristics.

[0081] Deep learning model: A deep learning model is used to predict real-time data, with a learning rate adjustment factor σ of 0.04 and ξ of 0.1 to adapt to the frequently changing electromagnetic environment.

[0082] Feedback Adjustment: A progressive feedback adjustment mechanism is used to optimize the overall electromagnetic compatibility of the system. The feedback frequency adjustment factor ω is set to 0.2 to balance the system's response speed and stability. In this embodiment, the test object includes an electronic system in a complex electromagnetic environment. The system contains various components susceptible to electromagnetic interference, and the frequency range of the tested electromagnetic environment is 1-1000MHz.

[0083] In this embodiment, the test object includes an electronic system in a complex electromagnetic environment. The system contains a variety of components that are susceptible to electromagnetic interference, and the frequency range of the electromagnetic environment being tested is 1-1000MHz.

[0084] In Comparative Example 1, the test used a conventional electromagnetic compatibility optimization design method. This method did not employ a path optimization recursive algorithm or nonlinear adaptive adjustment, nor did it integrate a deep learning model for adaptive prediction. The feedback adjustment mechanism was also a fixed frequency and could not adapt to the environment.

[0085] Electromagnetic interference path analysis: The uniform path weight allocation is adopted, but no path optimization recursion is performed, making it impossible to identify key nodes, and the path interference distribution is relatively even.

[0086] Nonlinear adjustment: Electromagnetic radiation is fixedly adjusted using a single adjustment factor α = 0.2, without dynamic adaptive characteristics.

[0087] Deep learning model: No deep learning prediction model was used, and the system parameters need to be manually adjusted when the electromagnetic properties change.

[0088] Feedback adjustment: Fixed feedback frequency, frequency factor ω = 0.1, with limited adjustment range.

[0089] The main indicators of the test include the following aspects:

[0090] 1. Electromagnetic radiation level: The electromagnetic radiation of the system in different frequency bands is measured and evaluated according to the IEC6100043 standard. The lower the value, the better the electromagnetic radiation control of the system.

[0091] 2. Immunity: According to the IEC6100046 standard, the immunity of the system to electromagnetic interference is measured in the 1-1000MHz frequency band. The higher the immunity, the stronger the system's ability to resist interference.

[0092] 3. Uniformity of path weight distribution: The uniformity of interference distribution on each path in the test system is measured by the mean squared error (MSE). The smaller the mean squared error, the more accurate the system is in identifying interference sources.

[0093] 4. Deep learning prediction response time: This measures the response time of a deep learning model to changes in electromagnetic properties, measured in milliseconds (ms). A shorter response time indicates a stronger adaptability of the system to environmental changes.

[0094] The test results are as follows

[0095]

[0096]

[0097] The test results above show that Embodiment 1 of the present invention outperforms Comparative Example 1 in all key indicators. Regarding electromagnetic radiation levels, the electromagnetic radiation of Embodiment 1 is only 45 dBμV / m, significantly lower than the 62 dBμV / m of Comparative Example 1. This indicates that the present invention more effectively controls the electromagnetic radiation of the system through path optimization and nonlinear adjustment, reducing electromagnetic interference leakage. In the immunity test, Embodiment 1 reaches 80 dBμA, far exceeding the 65 dBμA of Comparative Example 1. This means that under the same electromagnetic environment, Embodiment 1 can better resist external electromagnetic interference, demonstrating superior performance in complex electromagnetic environments.

[0098] Regarding the uniformity of path weight distribution, the mean squared error (MSE) of Example 1 is 0.15, while that of Comparative Example 1 is 0.45. The lower MSE indicates that Example 1 is more accurate in identifying interference source paths, effectively avoiding interference calculations for irrelevant paths and significantly optimizing system resources. Finally, in terms of deep learning prediction response time, Example 1 has a response time of 5ms, demonstrating that the system can quickly adjust in rapidly changing electromagnetic environments, while Comparative Example 1, without a deep learning model, cannot respond promptly when parameters change drastically.

[0099] In summary, the optimization method for electromagnetic compatibility of this invention not only significantly reduces electromagnetic radiation and improves immunity, but also performs excellently in resource optimization and response speed, making it suitable for electronic systems in complex electromagnetic environments.

[0100] It should be noted that the above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the principles of the present invention should be included within the protection scope of the present invention.

Claims

1. An intelligent electromagnetic compatibility optimization design method, characterized in that, Includes the following steps: Obtain the components of the system and their design parameters, composition structure and electromagnetic characteristics, establish electromagnetic simulation unit models of the components and build a component model library; The interference path analysis of the component model is performed by the path optimization algorithm to identify key nodes and interference source nodes and construct an electromagnetic interference path model. Based on the electromagnetic interference path model, the electromagnetic radiation and immunity parameters of the system components are adaptively adjusted to optimize electromagnetic compatibility. The electromagnetic compatibility parameters of the system are dynamically predicted and optimized using a deep learning model. A progressive feedback adjustment mechanism is adopted to optimize the electromagnetic compatibility of the system and its sub-modules as a whole, so that the system meets the electromagnetic compatibility requirements. The adaptive adjustment of the electromagnetic radiation and immunity parameters adopts a nonlinear adjustment formula: , in, To optimize the quantity, , , As a regulating factor, , , The electromagnetic characteristic function of the component is... It is a dynamic attenuation function used to nonlinearly adjust the electromagnetic radiation and immunity of the component; System-level non-uniform electromagnetic compatibility optimization employs a multi-level non-uniform optimization model, and the system-level optimal adjustment factor is calculated using the following formula: , in, This is a set of adjustment parameters used to control the electromagnetic radiation and immunity of the system, representing the specific adjustment states of each component during the optimization process. The optimal set of adjustment factors for the system aims to derive the adjustment parameters that best achieve the overall electromagnetic compatibility effect through an optimization process. This refers to the number of components in the system, each with independent electromagnetic characteristic parameters. An electromagnetic radiation adjustment factor for each component is used to weight the electromagnetic radiation intensity of different components in the system. The immunity adjustment factor is used to control the immunity adjustment range of each component. This is the radiation parameter adjustment coefficient, used to determine the sensitivity of electromagnetic radiation parameters to changes during the adjustment process. The immunity parameter adjustment coefficient is used to adjust the adjustment range of the immunity parameter. The first in the system The current electromagnetic radiation level of each component, The standard electromagnetic radiation value required by the system is [value], and the target radiation level is [value]. No. The current immunity value of an individual component indicates its ability to resist interference in an electromagnetic environment. Let be the standard immunity of the system, and be the desired immunity target value of the system. Let be the electromagnetic radiation characteristic parameter of the i-th component, used to describe the desired state of the component's electromagnetic characteristics. No. The immunity characteristic parameters of a component are used to describe the expected state of the component's immunity to interference in an electromagnetic environment. The progressive feedback adjustment mechanism is used to collaboratively optimize the electromagnetic compatibility of the system and its sub-modules. It employs a feedback adjustment coefficient matrix to achieve overall system adjustment, and the feedback calculation formula is as follows: , in, This is a set of feedback adjustment coefficients used to adjust the electromagnetic compatibility parameters of various components in the system during the progressive feedback process. The final set of feedback adjustment coefficients represents the overall electromagnetic compatibility adjustment scheme obtained after feedback optimization. This represents the total number of system submodules. These are the diagonal elements of the feedback adjustment coefficient matrix, representing the self-feedback adjustment strength of each submodule within the system, used to control the feedback strength of individual parameters. For the first The target interference level for each submodule is the set desired interference suppression value. 2 and 2 represents the upper and lower limits of the integral, indicating the start and end points of the time period during the feedback adjustment process. The off-diagonal elements in the feedback adjustment coefficient matrix represent the mutual adjustment coefficients between different components, used for coordinated adjustment of the electromagnetic compatibility among components within the system. The feedback adjustment frequency is used to regulate the response rate to electromagnetic changes during the feedback process. A higher frequency value indicates more frequent feedback adjustments. The time variable is used for integral calculation of the effect of the adjustment frequency on the feedback process. The target interference suppression level of the feedback adjustment coefficient represents the influence of electromagnetic interference between different components on the control target.

2. The intelligent electromagnetic compatibility optimization design method according to claim 1, characterized in that: The path optimization algorithm uses a recursive formula for calculating path weights: ， in, For nodes and Path weights between The distance between the two nodes. The path decay factor, For the weight of the interference source node, For sensitive node weight factors, The path weight is used to determine the key nodes in the electromagnetic interference path and generate the interference path model.

3. The intelligent electromagnetic compatibility optimization design method according to claim 1, characterized in that: The deep learning model achieves real-time optimization of the electromagnetic compatibility parameters of components and systems through dynamic adaptive parameter prediction and adjustment. The calculation formula is as follows: ， in, This represents the parameter adjustment amount for the deep learning model, indicating the adjustment value of the electromagnetic compatibility parameters of the components under the current environment. The learning rate adjustment factor of a deep learning model is used to control the model's response speed to changes in electromagnetic compatibility. The prediction function output by the deep learning model, where Indicates the first The model takes the current electromagnetic characteristics of each component as input and generates corresponding predicted values ​​based on these characteristics. This is used to adjust the time-dependent adjustment factor in adaptive forecasting, in order to balance the degree of responsiveness of the adjustment result to changes in real-time data. For the first in the system The time derivative of the optimal electromagnetic compatibility parameters of a component is used to describe the rate of change of the component parameters over time, which helps deep learning models to achieve dynamic adaptive adjustment.

4. An intelligent electromagnetic compatibility optimization design system for implementing the method of any one of claims 1-3, characterized in that, include: The data processing module is used for data collection, preprocessing, feature extraction, and deep learning model training of the electromagnetic characteristics of components. It includes a data collection unit, a preprocessing unit, a feature extraction unit, and a model training unit. The simulation module is used to build electromagnetic simulation models of electrical and electronic equipment, perform parameter simulation analysis, and extract simulation result data. It includes a modeling submodule, a parameter simulation submodule, and a simulation result submodule. The optimization module is used to determine the simulation result parameters, predict the parameters using a deep learning model, and adjust the parameters using a progressive optimization algorithm. It includes an electromagnetic interference optimization unit, an electromagnetic radiation optimization unit, and an electromagnetic immunity optimization unit. The visualization module is used to display the visualization of data processing results and simulation optimization results, and includes a data visualization submodule and a result visualization submodule.

5. The intelligent electromagnetic compatibility optimization design system according to claim 4, characterized in that: The model training unit in the data processing module trains a deep learning model using the component model library to evaluate the electromagnetic compatibility performance of the components, which is then used by the optimization module.

6. The intelligent electromagnetic compatibility optimization design system according to claim 4, characterized in that: The electromagnetic interference optimization unit in the optimization module dynamically adjusts the key nodes in the electromagnetic interference path based on the simulation results and the path weight recursive algorithm, and optimizes the configuration of the interference source and sensitive nodes.

7. The intelligent electromagnetic compatibility optimization design system according to claim 4, characterized in that: The electromagnetic radiation optimization unit in the optimization module uses a nonlinear adaptive adjustment formula to optimize the electromagnetic radiation parameters of the components, so that the electromagnetic radiation of the components meets the electromagnetic compatibility standards of the system.

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