Dynamic parameter adaptive adjustment method and system in electromagnetic compatibility test

Abstract

The invention discloses a dynamic parameter adaptive adjustment method and system in an electromagnetic compatibility test, and the method comprises the steps: collecting electromagnetic environment data in an electromagnetic compatibility test environment in real time, and carrying out the adaptive multi-scale curvature smoothing, data cleaning and abnormal value detection preprocessing of the electromagnetic environment data; obtaining preprocessed electromagnetic environment data; performing dynamic analysis and prediction on the preprocessed electromagnetic environment data by using a multi-stage feedback optimization algorithm to obtain preliminary test parameters in the current environment; and after the equipment is adjusted according to the preliminary test parameters, monitoring the electromagnetic compatibility test environment in real time and analyzing to obtain a monitoring result, and performing adaptive adjustment on the preliminary test parameters according to the monitoring result and the external environment change. According to the invention, the accuracy and adaptability of electromagnetic environment data processing and analysis are improved, and dynamic parameter adaptive adjustment can be carried out in a complex and changeable electromagnetic environment.

Description

Technical Field

[0001] The present invention belongs to the technical field of electromagnetic compatibility testing, and relates to a method and system for adaptively adjusting dynamic parameters in electromagnetic compatibility tests. Background Art

[0002] Electromagnetic compatibility (EMC) testing is a crucial part in the design and manufacturing process of modern electronic devices. Its purpose is to ensure that devices can operate stably in an electromagnetic environment and will not generate excessive electromagnetic interference to other devices. With the rapid development of electronic technology and the increasing miniaturization and integration of devices, the sources and propagation paths of electromagnetic interference have become increasingly complex, and electromagnetic compatibility problems have become more severe and difficult to solve. Therefore, how to improve the efficiency and accuracy of electromagnetic compatibility testing, especially in complex and dynamic electromagnetic environments, has become an urgent technical problem in the industry.

[0003] Traditional electromagnetic compatibility testing methods usually rely on standardized testing procedures and set fixed parameters. These methods assume that the electromagnetic environment is static and predictable. However, in reality, the electromagnetic environment often changes dynamically, and external interference sources and environmental conditions may change drastically. Traditional testing methods cannot effectively cope with these complex changes. Therefore, traditional testing schemes often appear inflexible when facing a changing environment, unable to make real-time adjustments and optimizations, thus affecting the accuracy of test results and the electromagnetic compatibility of devices. Summary of the Invention

[0004] To solve the deficiencies in the prior art, the present invention provides a method and system for adaptively adjusting dynamic parameters in electromagnetic compatibility tests, which improves the accuracy and adaptability of electromagnetic environment data processing and analysis, and can perform adaptive adjustment of dynamic parameters in complex and changing electromagnetic environments.

[0005] The present invention adopts the following technical solutions.

[0006] The first aspect of the present invention proposes a method for adaptively adjusting dynamic parameters in electromagnetic compatibility tests, including:

[0007] Real-time collect electromagnetic environment data in the electromagnetic compatibility test environment and perform preprocessing such as adaptive multi-scale curvature smoothing, data cleaning, and outlier detection on the electromagnetic environment data to obtain preprocessed electromagnetic environment data;

[0008] Use a multi-stage feedback optimization algorithm to perform dynamic analysis and prediction on the preprocessed electromagnetic environment data to obtain preliminary test parameters in the current environment;

[0009] After the electromagnetic compatibility test equipment is adjusted according to the preliminary test parameters, it monitors the electromagnetic compatibility test environment in real time and analyzes to obtain the monitoring results, and adaptively adjusts the preliminary test parameters according to the monitoring results and external environmental changes.

[0010] Preferably, the process of the adaptive multi-scale curvature smoothing is as follows:

[0011] Calculate the second derivative of the electromagnetic environment data x(t) at different scales as the corresponding local curvature, and weight and fuse the local curvatures at different scales to obtain the total curvature κ total (t);

[0012] Calculate the adaptive weight according to the total curvature κ total (t), and perform weighted smoothing on the electromagnetic environment data.

[0013] Preferably, the calculation of the adaptive weight according to the total curvature κ total (t) has the following formula:

[0014]

[0015] where w(t) is the adaptive weight at time t; κ avg is the average curvature within the local window; is the standard deviation parameter; λ is the adjustment coefficient of the influence of curvature on smoothing.

[0016] Preferably, the use of the multi-stage feedback optimization algorithm to dynamically analyze and predict the preprocessed electromagnetic environment data to obtain the preliminary test parameters in the current environment includes:

[0017] Extract the short-term and long-term characteristics of the electromagnetic environment from the preprocessed electromagnetic environment data through wavelet transform and Gaussian smoothing;

[0018] Integrate the short-term characteristics and long-term characteristics to obtain the preliminary prediction result Z 1 (t), and perform recursive weighted summation through a non-linear relationship to obtain the prediction results Z n (t);

[0019] Combine the prediction results Z n (t) at different stages with the feedback information of the external environmental factors through a weighted cross-optimization mechanism to form the optimal electromagnetic compatibility test parameter P(t);

[0020] Perform feedback optimization on the optimal electromagnetic compatibility test parameter P(t) through the gradient descent method to obtain the preliminary test parameters in the current environment

[0021] Preferably, the short-term and long-term characteristics of the electromagnetic environment are extracted from the preprocessed electromagnetic environment data through wavelet transform and Gaussian smoothing, and the specific formula is as follows:

[0022]

[0023] Among them, S 1 (t) is the short-term characteristic; S 2 (t) is the long-term characteristic; S(t) is the preprocessed electromagnetic environment data at time t; ψ(t - τ) is the mother wavelet of the wavelet transform at time t - τ; τ is the time shift; μ is the mean value of the preprocessed electromagnetic environment data; is the standard deviation of the preprocessed electromagnetic environment data.

[0024] Preferably, the preliminary prediction result Z 1 (t) is obtained by synthesizing the short-term and long-term characteristics, as follows:

[0025]

[0026] Among them, is the weight coefficient; is the attenuation coefficient; is the time window length; is the short-term characteristic, is the long-term characteristic; is the time integration variable.

[0027] Preferably, the prediction result Z n (t) at different stages is obtained through recursive weighted summation of the non-linear relationship, as follows:

[0028]

[0029] Among them, Z n (t) is the prediction result of the nth stage; Z i (t) is the prediction result of the ith stage; α n is the weight factor of the nth stage; is the weighted coefficient fed back in the ith stage.

[0030] Preferably, the prediction results Z n (t) at different stages and the feedback information of the external environment factors are combined through a weighted cross-optimization mechanism to form the optimal electromagnetic compatibility test parameter P(t), and the formula is as follows:

[0031]

[0032] Among them, is the weighted coefficient of the nth stage; is the adjustment factor for the impact degree of the electromagnetic environment in the nth stage;

[0033] is the influence weight of the kth external environment factor on the prediction result;

[0034] is the feedback information of the kth external environment factor at time t; is the feedback information of the kth external environment factor at time t;

[0035] is the total number of external environment factors; is the total number of stages.

[0036] Preferably, after the electromagnetic compatibility test equipment is adjusted according to the preliminary test parameters, it monitors the electromagnetic compatibility test environment in real time and analyzes to obtain the monitoring results, and adaptively adjusts the preliminary test parameters according to the monitoring results and external environment changes, including:

[0037] After the equipment is adjusted according to the preliminary test parameters, it enters the real-time monitoring stage, continuously monitors the electromagnetic environment during the electromagnetic compatibility test, and obtains monitoring data;

[0038] Denoise, smooth, and clean the monitoring data to obtain the preprocessed monitoring data;

[0039] Use the long short-term memory network to analyze the preprocessed monitoring data to obtain the monitoring results;

[0040] Based on the monitoring results and external environment changes, the equipment optimally adjusts the preliminary test parameters through a multivariable linear regression model combined with the expert experience method.

[0041] A second aspect of the present invention proposes a dynamic parameter adaptive adjustment system in electromagnetic compatibility testing, including:

[0042] A data processing module, configured to collect electromagnetic environment data in the electromagnetic compatibility test environment in real time and perform adaptive multi-scale curvature smoothing, data cleaning, and outlier detection preprocessing on the electromagnetic environment data to obtain preprocessed electromagnetic environment data;

[0043] A data analysis module, configured to perform dynamic analysis and prediction on the preprocessed electromagnetic environment data by using a multi-stage feedback optimization algorithm to obtain preliminary test parameters in the current environment;

[0044] A parameter adjustment module, configured to, after the electromagnetic compatibility test equipment is adjusted according to the preliminary test parameters, monitor the electromagnetic compatibility test environment in real time and analyze to obtain the monitoring results, and adaptively adjust the preliminary test parameters according to the monitoring results and external environment changes.

[0045] A third aspect of the present invention provides a terminal, including a processor and a storage medium; the storage medium is used for storing instructions; the processor is used for operating according to the instructions to execute the steps of the method.

[0046] A fourth aspect of the present invention provides a computer-readable storage medium, on which a computer program is stored, and when the program is executed by a processor, the steps of the method are implemented.

[0047] Compared with the prior art, the beneficial effects of the present invention at least include:

[0048] 1. The present invention performs adaptive multi-scale curvature smoothing on electromagnetic environment data. Through multi-scale curvature fusion and adaptive weight adjustment, it overcomes the limitations of traditional smoothing methods, can dynamically and adaptively optimize the smoothing process, ensures that the key features of the signal are retained while denoising the electromagnetic environment data, effectively smooths the high-frequency fluctuations in the data, improves the reliability of the data, and provides a more accurate basis for subsequent analysis. And preprocessing such as cleaning the original electromagnetic environment data collected can effectively remove abnormal data caused by equipment failures, environmental interferences, etc., thereby reducing the interference of noise and ensuring the accuracy of the data.

[0049] 2. The multi-stage feedback optimization algorithm adopted by the present invention can dynamically analyze and predict electromagnetic environment data from the prediction results of the short-term and long-term features of the electromagnetic environment in multiple dimensions and different time scales; this algorithm extracts the short-term and long-term features of the electromagnetic environment through wavelet transform and Gaussian smoothing, introduces non-linear relationships to recursively obtain the prediction results of different stages, and introduces a weighted cross-optimization mechanism and feedback optimization to obtain the preliminary test parameters in the current environment, can respond in real time to the electromagnetic environment over time and external condition changes, making the test scheme more flexible and efficient. Through the feedback adjustment of different stages, the prediction results are made more accurate, providing more appropriate preliminary test parameters for electromagnetic compatibility testing. BRIEF DESCRIPTION OF THE DRAWINGS

[0050] Figure 1 It is a flowchart of a method for dynamically adapting and adjusting parameters in an electromagnetic compatibility test of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0051] To make the objectives, technical solutions, and advantages of the present invention clearer, the technical solutions of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. The embodiments described in this application are only a part of the embodiments of the present invention, rather than all embodiments. Based on the spirit of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present invention.

[0052] Such as Figure 1As shown in the figure, an embodiment 1 of the present invention provides a method for adaptively adjusting dynamic parameters in electromagnetic compatibility tests, and the method includes the following steps:

[0053] S1. Collect electromagnetic environment data in the electromagnetic compatibility test environment in real time and perform preprocessing on the electromagnetic environment data, including adaptive multi-scale curvature smoothing, data cleaning, and outlier detection, to obtain preprocessed electromagnetic environment data;

[0054] Further preferably, in the test environment, collect electromagnetic environment data in real time through a data acquisition device, and perform preprocessing on the electromagnetic environment data to obtain preprocessed electromagnetic environment data, specifically as follows:

[0055] In the electromagnetic compatibility test environment, first collect electromagnetic environment data in real time through data acquisition devices such as spectrum analyzers, field strength probes, and electric field sensors, including various electromagnetic environment parameters such as electromagnetic wave intensity, spectrum distribution, noise level, and waveform characteristics.

[0056] Further perform preprocessing on the electromagnetic environment data, such as smoothing, outlier detection, and data cleaning, to obtain preprocessed electromagnetic environment data;

[0057] In particular, in the smoothing process during preprocessing, in order to reduce short-term fluctuations in the electromagnetic environment data, an adaptive multi-scale curvature smoothing algorithm is introduced. This algorithm overcomes the limitations of traditional smoothing methods by integrating multi-scale curvature estimation and an adaptive weight adjustment mechanism, and can dynamically and adaptively optimize the smoothing process to ensure that key features of the signal are retained while denoising the electromagnetic environment data. The specific implementation process is as follows:

[0058] (1) Calculate the second derivative of the electromagnetic environment data x(t) at different scales as the corresponding local curvature κ j (t), and weight and fuse the local curvatures at different scales to obtain the total curvature κ total (t);

[0059] First, perform curvature analysis on the electromagnetic environment data x(t) to obtain the local curvature. The local curvature is an important indicator describing the change trend of the data, reflecting the acceleration and direction of the change of the data points. Calculate the second derivative of the data as the local curvature. The calculation formula is:

[0060]

[0061] where κ j (t) is the local curvature at the jth scale, which describes the signal, that is, the electromagnetic environment data;

[0062] is the data at the j-th scale obtained by wavelet transform of the electromagnetic environment data at time t, and is obtained by combining wavelet coefficients;

[0063] t represents the time variable;

[0064] Δt is the time interval, representing the time distance between two adjacent sampling points, which affects the discreteness of the signal. A smaller Δt will result in a higher discreteness of the signal and more accurate captured details;

[0065] represents the limit process as the time interval Δt approaches zero;

[0066] is the difference approximation formula, used to calculate the second derivative of the data at the j-th scale obtained by wavelet transform of the electromagnetic environment data x(t) at time t;

[0067] and respectively represent the data at the j-th scale obtained by wavelet transform of the electromagnetic environment data t(t) at times t + Δt and t - Δt.

[0068] Furthermore, use the above local curvature calculation formula to calculate the local curvature κ at each scale j j , and fuse the local curvatures at different scales with weights. The multi-scale curvature fusion formula is:

[0069]

[0070] where, κ total (t) is the total curvature at time t, which is the curvature value obtained by weighted fusion of the local curvatures at different scales, reflecting the overall change trend of the electromagnetic environment data at this moment, used to measure the change acceleration of the electromagnetic environment data at the current moment, and is the output result of multi-scale curvature fusion;

[0071] w j is the weight of the j-th scale, which determines the contribution degree of each scale to the overall curvature and is determined by the expert experience method;

[0072] κ j (t) is the local curvature at the j-th scale;

[0073] N is the number of decomposition scales.

[0074] The above formula fuses the curvature information at different scales through weighted summation, enabling the comprehensive description of the overall picture of the electromagnetic environment data by integrating multi-scale information.

[0075] (2) According to the total curvature κ total(t) Calculate the adaptive weight and perform weighted smoothing on the electromagnetic environment data in combination with the size of the dynamic sliding window.

[0076] To further enhance the adaptability of the algorithm, an adaptive weight function is introduced, which dynamically adjusts the smoothing weight of each data point according to the difference between the local curvature and the global curvature. This weighting function determines the weight of different points in the signal based on the change of curvature to achieve an adaptive smoothing process. Specifically, the adaptive weight function w(t) is defined as follows:

[0077]

[0078] where w(t) is the adaptive weight function, representing the weight assigned for smoothing at time t, which is used to adjust the importance of the electromagnetic environment data x(t);

[0079] κ avg is the average curvature within a locally selected window, used to compare with the total curvature κ total (t) at the current moment. By calculating the local average curvature, the stationarity of the electromagnetic environment data can be better evaluated;

[0080] is the standard deviation parameter, which controls the influence degree of the curvature difference on the adaptive weight and adjusts the sensitivity of the difference between curvatures. It is determined according to the expert experience method;

[0081] λ is the adjustment coefficient of the influence of curvature on smoothing, which controls the influence of the signal curvature κ total (t) on the smoothing process and is determined by the experimental method.

[0082] Furthermore, weighted smoothing is performed on the electromagnetic environment data based on the adaptive weight w(t). The smoothed data y(t) is calculated by the following formula:

[0083]

[0084] where y(t) is the smoothed signal value, representing the electromagnetic environment data after weighted averaging;

[0085] E(t) is the size of the dynamic sliding window, representing the range of data points considered when performing weighted averaging at the current moment t. By calculating the local curvature, a suitable window size is derived according to the expert experience method to ensure that the smoothing process can adapt to the changing characteristics of the signal. For example, in the region with a large curvature, it indicates that the electromagnetic environment data changes rapidly, and the sliding window is small, thereby reducing the influence of smoothing on the signal trend; while in the region with a small curvature, it indicates that the electromagnetic environment data changes slowly, and the sliding window is large to smooth more obvious noise;

[0086] w(t + i) is an adaptive weight calculated according to local curvature at time t + i;

[0087] x(t + i) is the value of electromagnetic environment data at time t + i. The above weighted average process can effectively reduce the noise in the data while maintaining the trend characteristics of the data.

[0088] S2. Use the multi-stage feedback optimization algorithm to dynamically analyze and predict the preprocessed electromagnetic environment data to obtain preliminary test parameters in the current environment;

[0089] Further preferably, use the multi-stage feedback optimization network (multi-stage feedback optimization algorithm) to dynamically analyze and predict the preprocessed electromagnetic environment data, and evaluate the preliminary test parameters in the current environment, as follows:

[0090] The goal of the multi-stage feedback optimization algorithm is to dynamically adjust the test parameters based on the preprocessed electromagnetic environment data and external environmental factors to ensure the optimal effect of electromagnetic compatibility testing. The specific implementation process is as follows:

[0091] (1) Extract the short-term and long-term characteristics of the electromagnetic environment from the preprocessed electromagnetic environment data through wavelet transform and Gaussian smoothing;

[0092] First, perform preliminary analysis on the preprocessed electromagnetic environment data using wavelet transform and Gaussian smoothing to extract the short-term and long-term characteristics of the electromagnetic environment. The specific formulas are as follows:

[0093]

[0094] Among them, S(t) is the preprocessed electromagnetic environment data;

[0095] S 1 (t) is the short-term characteristic data obtained through wavelet transform;

[0096] S 2 (t) is the long-term characteristic data obtained through Gaussian smoothing;

[0097] ψ(t - τ) is the mother wavelet;

[0098] τ is the time shift;

[0099] μ is the mean value of the preprocessed electromagnetic environment data;

[0100] is the standard deviation of the preprocessed electromagnetic environment data.

[0101] (2) Obtain the preliminary prediction result Z by synthesizing the influences of short-term characteristics and long-term characteristics 1(t), and recursively weighted sum through a non - linear relationship to obtain the prediction results Z at different stages n (t), such as the comprehensive evaluation of the current electromagnetic environment, which can include interference intensity, change trend, etc.;

[0102] Furthermore, after the pre - processed electromagnetic environment data is preliminarily analyzed, it is subjected to non - linear relationship recursive calculation processing. The prediction result Z n (t) at each stage acts jointly with the output result Z n-1 (t) of the previous stage. By means of non - linear weighted summation, the contribution of each stage is dynamically adjusted, and a new prediction result is generated. Its calculation formula is as follows:

[0103]

[0104] Among them, Z n (t) is the prediction result of the nth stage;

[0105] α n is the weight factor of this stage, determined according to the expert experience method;

[0106] is the weighted coefficient fed back from the ith stage, determined through the experimental method;

[0107] In particular, the preliminary prediction result Z 1 (t) is the result obtained by synthesizing the influences of short - term features, long - term features and external environmental factors,

[0108] is the weight coefficient, used to control the contribution degrees of short - term features, long - term features and external environment to the prediction result, determined according to the specific scenario;

[0109] is the attenuation coefficient, indicating the influence degree of historical short - term features and long - term features on the current prediction;

[0110] is the time window length, indicating the time range considered when integrating short - term and long - term features;

[0111] is the short - term feature at time is the long - term feature at time;

[0112] is the time integration variable.

[0113] (3) Through the weighted cross - optimization mechanism, the prediction results Z at different stages n(t) is combined with the feedback information of external environmental factors to form the optimal electromagnetic compatibility test parameters P(t);

[0114] Furthermore, after obtaining the prediction results of each stage, a weighted cross-optimization mechanism is introduced to perform weighted function cross-optimization processing. Through the weighted cross-optimization mechanism, the prediction information of different stages and the external environment feedback information are combined to form the optimal electromagnetic compatibility test parameters. The mathematical model is as follows:

[0115]

[0116] Among them, P(t) is the final electromagnetic compatibility test parameter, such as frequency, power, sampling rate, etc.;

[0117] is the weighting coefficient of stage n, determined by the expert experience method;

[0118] is the adjustment factor of the influence degree of each stage on the electromagnetic environment, determined by the expert experience method;

[0119] is the influence weight of the

[0120] is the feedback information of the

[0121] related external environmental factor to the prediction result;

[0122] is the total number of external environmental factors;

[0123] (4) The optimal electromagnetic compatibility test parameters P(t) are feedback-optimized by the existing gradient descent method to obtain the preliminary test parameters under the current environment

[0124] Finally, based on the weighted optimization results of each stage, adaptive feedback and adjustment are performed. At this time, the parameters of the electromagnetic compatibility test equipment will be dynamically adjusted according to the optimization calculation results. The feedback adjustment is updated by the existing gradient descent method to obtain the updated preliminary test parameters

[0125] S3. After the electromagnetic compatibility test equipment is adjusted according to the preliminary test parameters, it monitors the electromagnetic compatibility test environment in real time and analyzes to obtain the monitoring results. The monitoring results are combined with the external environment changes to perform adaptive adjustment on the preliminary test parameters.

[0126] Further preferably, after the device is adjusted according to the preliminary test parameters, the electromagnetic compatibility test process is monitored in real time, monitoring data is obtained and analyzed to obtain a monitoring result, and the preliminary test parameters are optimized based on the monitoring result in combination with external environmental changes to achieve adaptive adjustment, as follows:

[0127] (1) After the device is adjusted according to the preliminary test parameters, it enters the real-time monitoring stage. The electromagnetic environment during the test is continuously monitored in real time through a variety of sensors and measuring devices (such as spectrum analyzers, electric field strength detectors, temperature and humidity sensors, etc.) to obtain monitoring data, including the frequency, intensity, interference level, noise spectrum of electromagnetic waves, and changes in the external environment, such as temperature, humidity, and other factors that may affect the electromagnetic environment.

[0128] (2) The obtained monitoring data is preprocessed by removing noise, smoothing the data, and performing data cleaning to ensure the effectiveness and accuracy of the monitoring data, and the preprocessed monitoring data is obtained;

[0129] (3) The preprocessed monitoring data is analyzed using the existing long short-term memory network, that is, by comparing the preprocessed monitoring data with the historical detection data and the expected optimal test parameters preset according to the expert experience method in real time, to obtain a monitoring result, including that there is a deviation between the electromagnetic environment and the expected optimal parameters, or it is found that the test conditions change due to changes in external environmental factors (such as temperature increase, humidity change, equipment status fluctuation, etc.);

[0130] (4) Based on the monitoring result and external environmental changes (such as weather changes (e.g., changes in temperature and humidity), the working status of other surrounding devices (such as whether they are turned on, device type, etc.)), the device optimally adjusts the preliminary test parameters through the existing multivariable linear regression model combined with the expert experience method (such as when the environmental temperature is higher than a certain threshold, the radiation value of the test device may increase significantly, or when the noise is greater than a certain value, the influence of the electromagnetic wave intensity on the test result may change nonlinearly. Experts will adjust some parameters in the regression model or add some correction factors on the basis of the regression model to obtain more accurate test parameters). Specifically, the multivariables here are such as electromagnetic wave intensity, frequency, noise in the surrounding environment, and temperature, etc.

[0131] Based on the monitoring results, the device will automatically activate the adaptive adjustment mechanism. The adaptive adjustment mechanism optimizes and adjusts the preliminary test parameters through the existing multivariable linear regression model combined with the expert experience method. This adjustment is not only a fine-tuning of parameters such as electromagnetic signal intensity or frequency, but also involves the comprehensive optimization of other multi-dimensional factors. For example, by adjusting parameters such as the bandwidth and sampling rate of the test frequency to adapt to the new electromagnetic environment; or adjusting the sensitivity of the device or increasing the isolation degree of the interference source according to the change of the interference source. Each adjustment depends on the real-time data and the influence of the external environment to ensure that the test process can maintain a stable and reliable state.

[0132] Embodiment 2 of the present invention provides a dynamic parameter adaptive adjustment system in an electromagnetic compatibility test, including:

[0133] A data processing module, configured to collect electromagnetic environment data in the electromagnetic compatibility test environment in real time and perform preprocessing on the electromagnetic environment data, including adaptive multi-scale curvature smoothing, data cleaning, and outlier detection, to obtain preprocessed electromagnetic environment data;

[0134] A data analysis module, configured to perform dynamic analysis and prediction on the preprocessed electromagnetic environment data by using a multi-stage feedback optimization algorithm to obtain preliminary test parameters in the current environment;

[0135] A parameter adjustment module, configured to, after the electromagnetic compatibility test device adjusts according to the preliminary test parameters, monitor the electromagnetic compatibility test environment in real time and analyze to obtain the monitoring results, and adaptively adjust the preliminary test parameters according to the monitoring results and the change of the external environment.

[0136] Embodiment 3 of the present invention provides a terminal, including a processor and a storage medium;

[0137] The storage medium is used to store instructions; the processor is used to operate according to the instructions to execute the steps of the method.

[0138] Embodiment 4 of the present invention provides a computer-readable storage medium, on which a computer program is stored, and when the program is executed by a processor, the steps of the method are implemented.

[0139] Compared with the prior art, the beneficial effects of the present invention at least include:

[0140] 1. The present invention adaptively performs multi-scale curvature smoothing on electromagnetic environment data. By integrating multi-scale curvature estimation and an adaptive weight adjustment mechanism, it overcomes the limitations of traditional smoothing methods and can dynamically and adaptively optimize the smoothing process, ensuring that while denoising the electromagnetic environment data, the key features of the signal are retained, effectively smoothing the high-frequency fluctuations in the data, improving the reliability of the data, and providing a more accurate basis for subsequent analysis. Additionally, preprocessing such as cleaning, denoising, and outlier detection is performed on the collected raw electromagnetic environment data, which can effectively remove abnormal data caused by equipment failures, environmental interference, etc., thereby reducing noise interference and ensuring the accuracy of the data.

[0141] 2. The multi-stage feedback optimization algorithm adopted by the present invention can dynamically analyze and predict electromagnetic environment data based on the prediction results of the short-term and long-term characteristics of the electromagnetic environment from multiple dimensions and different time scales; this algorithm extracts the short-term and long-term characteristics of the electromagnetic environment through wavelet transform and Gaussian smoothing, introduces non-linear relationships to recursively obtain the prediction results of different stages, and introduces a weighted cross-optimization mechanism and feedback optimization to obtain the preliminary test parameters in the current environment. It can respond in real time to the electromagnetic environment over time and changes in external conditions, making the test scheme more flexible and efficient. Through feedback adjustment at different stages, the prediction results are made more accurate, providing more suitable preliminary test parameters for electromagnetic compatibility testing.

[0142] This disclosure can be a system, method, and / or computer program product. The computer program product can include a computer-readable storage medium having thereon computer-readable program instructions for causing a processor to implement various aspects of this disclosure.

[0143] A computer-readable storage medium can be a tangible device that can retain and store instructions used by an instruction execution device. A computer-readable storage medium can be, for example, but not limited to, an electrical storage device, a magnetic storage device, an optical storage device, an electromagnetic storage device, a semiconductor storage device, or any suitable combination of the foregoing. More specific examples (a non-exhaustive list) of the computer-readable storage medium include: a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), a static random access memory (SRAM), a portable compact disc read-only memory (CD-ROM), a digital versatile disc (DVD), a memory stick, a floppy disk, a mechanically encoded device such as a punched card or raised structures in grooves having instructions stored thereon, and any suitable combination of the foregoing. The computer-readable storage medium used herein is not construed as an instantaneous signal itself, such as a radio wave or other freely propagating electromagnetic wave, an electromagnetic wave propagated through a waveguide or other transmission medium (e.g., an optical pulse through an optical fiber cable), or an electrical signal transmitted through a wire.

[0144] The computer-readable program instructions described herein can be downloaded to various computing / processing devices from a computer-readable storage medium or downloaded to an external computer or external storage device via a network, such as the Internet, a local area network, a wide area network, and / or a wireless network. The network can include copper transmission cables, optical fiber transmission, wireless transmission, routers, firewalls, switches, gateway computers, and / or edge servers. A network adapter card or network interface in each computing / processing device receives the computer-readable program instructions from the network and forwards the computer-readable program instructions for storage in a computer-readable storage medium in each computing / processing device.

[0145] The computer program instructions for performing the operations of the present disclosure may be assembly instructions, instruction set architecture (ISA) instructions, machine instructions, machine-related instructions, microcode, firmware instructions, state-setting data, or source code or object code written in any combination of one or more programming languages, including object-oriented programming languages such as Smalltalk, C++, etc., and conventional procedural programming languages such as the "C" language or similar programming languages. The computer-readable program instructions may be executed entirely on the user's computer, partially on the user's computer, executed as a stand-alone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer through any type of network, including a local area network (LAN) or a wide area network (WAN), or may be connected to an external computer (e.g., via the Internet using an Internet service provider). In some embodiments, by using the state information of the computer-readable program instructions to customize an electronic circuit, such as a programmable logic circuit, a field-programmable gate array (FPGA), or a programmable logic array (PLA), the electronic circuit can execute the computer-readable program instructions to implement various aspects of the present disclosure.

[0146] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit them. Although the present invention has been described in detail with reference to the above embodiments, those of ordinary skill in the art should understand that: modifications or equivalent replacements can still be made to the specific embodiments of the present invention, and any modification or equivalent replacement that does not depart from the spirit and scope of the present invention should be covered by the protection scope of the claims of the present invention.

Claims

1. A method for adaptively adjusting dynamic parameters in electromagnetic compatibility testing, characterized in that: include: Collect electromagnetic environment data in the electromagnetic compatibility test environment in real time and perform adaptive multi-scale curvature smoothing, data cleaning and outlier detection preprocessing on the electromagnetic environment data to obtain preprocessed electromagnetic environment data; Use a multi-stage feedback optimization algorithm to dynamically analyze and predict the pre-processed electromagnetic environment data to obtain preliminary test parameters under the current environment; After the electromagnetic compatibility test equipment is adjusted according to the preliminary test parameters, it monitors the electromagnetic compatibility test environment in real time and analyzes the monitoring results, and adaptively adjusts the preliminary test parameters according to the monitoring results and changes in the external environment.

2. The method for adaptively adjusting dynamic parameters in electromagnetic compatibility testing according to claim 1, characterized in that: The process of adaptive multi-scale curvature smoothing is as follows: The second-order derivative of the electromagnetic environment data X(T) at different scales is calculated as the corresponding local curvature, and the local curvatures of different scales are weighted and fused to obtain the total curvature κ total (T); According to the total curvature κ total (t) Calculate adaptive weights and perform weighted smoothing on the electromagnetic environment data.

3. The method for adaptively adjusting dynamic parameters in electromagnetic compatibility testing according to claim 2 is characterized in that: According to the total curvature κ total (t) Calculate the adaptive weight, the formula is as follows: Where w(t) is the adaptive weight at time t; κ avg is the average curvature within the local window; is the standard deviation parameter; λ is the adjustment coefficient of the curvature on the smoothing effect.

4. The method for adaptively adjusting dynamic parameters in electromagnetic compatibility testing according to claim 1, characterized in that: The multi-stage feedback optimization algorithm is used to dynamically analyze and predict the pre-processed electromagnetic environment data to obtain preliminary test parameters under the current environment, including: The short-term and long-term characteristics of the electromagnetic environment are extracted from the pre-processed electromagnetic environment data by wavelet transform and Gaussian smoothing; The short-term and long-term characteristics are combined to obtain the preliminary prediction result Z1(t), and the prediction results Z at different stages are obtained by recursive weighted summation through nonlinear relationships. n (t); The prediction results Z at different stages are combined through the weighted cross optimization mechanism n (t) is combined with the feedback information of external environmental factors to form the optimal electromagnetic compatibility test parameter P(t); The optimal EMC test parameters P(t) are optimized by feedback through the gradient descent method to obtain the preliminary test parameters under the current environment.

5. The method for adaptively adjusting dynamic parameters in electromagnetic compatibility testing according to claim 4 is characterized in that: The short-term and long-term characteristics of the electromagnetic environment are extracted from the pre-processed electromagnetic environment data by wavelet transform and Gaussian smoothing. The specific formula is as follows: Among them, S1(t) is the short-term feature; S2(t) is the long-term feature; S(t) is the electromagnetic environment data after preprocessing at time t; ψ(t-τ) is the mother wavelet of the wavelet transform at time t-τ; τ is the time shift; μ is the mean value of the electromagnetic environment data after preprocessing; is the standard deviation of the preprocessed electromagnetic environment data.

6. The method for adaptively adjusting dynamic parameters in electromagnetic compatibility testing according to claim 4, characterized in that: The above-mentioned comprehensive short-term characteristics and long-term characteristics are used to obtain the preliminary prediction result Z1(t), which is as follows: in, is the weight coefficient; is the attenuation coefficient; is the time window length; It is a short-term feature. It is a long-term feature; is the time-integrated variable.

7. The method for adaptively adjusting dynamic parameters in electromagnetic compatibility testing according to claim 4, characterized in that: The prediction results Z at different stages are obtained by recursive weighted summation of nonlinear relationships. n (t), as follows: Among them, Z n (t) is the prediction result of the nth stage; Z i (t) is the prediction result of the i-th stage; α n is the weight factor of the nth stage; is the weighted coefficient of the feedback in the i-th stage.

8. The method for adaptively adjusting dynamic parameters in electromagnetic compatibility testing according to claim 4, characterized in that: The prediction results Z of different stages are combined through the weighted cross optimization mechanism. n (t) is combined with the feedback information of external environmental factors to form the optimal electromagnetic compatibility test parameter P(t), the formula is as follows: in, is the weighting coefficient of the nth stage; is the adjustment factor of the impact degree on the electromagnetic environment in the nth stage; For the The weight of the impact of external environmental factors on the prediction results; is the time t Feedback information from external environmental factors; is the total number of external environmental factors; is the total number of stages.

9. The method for adaptively adjusting dynamic parameters in electromagnetic compatibility testing according to claim 1, characterized in that: After the electromagnetic compatibility test equipment is adjusted according to the preliminary test parameters, it monitors the electromagnetic compatibility test environment in real time and analyzes the monitoring results to obtain the monitoring results, and adaptively adjusts the preliminary test parameters according to the monitoring results and changes in the external environment, including: After the equipment is adjusted according to the preliminary test parameters, it enters the real-time monitoring stage, and continuously monitors the electromagnetic environment during the electromagnetic compatibility test in real time to obtain monitoring data; De-noising, smoothing and data cleaning are performed on the monitoring data to obtain the pre-processed monitoring data; The pre-processed monitoring data is analyzed using a long short-term memory network to obtain monitoring results; Based on the monitoring results and changes in the external environment, the equipment optimizes and adjusts the preliminary test parameters through a multivariate linear regression model combined with expert experience.

10. A dynamic parameter adaptive adjustment system in electromagnetic compatibility test, used to run the method according to any one of claims 1 to 9, characterized in that: The system comprises: The data processing module is used to collect electromagnetic environment data in the electromagnetic compatibility test environment in real time and perform adaptive multi-scale curvature smoothing, data cleaning and outlier detection preprocessing on the electromagnetic environment data to obtain preprocessed electromagnetic environment data; The data analysis module is used to dynamically analyze and predict the pre-processed electromagnetic environment data using a multi-stage feedback optimization algorithm to obtain preliminary test parameters under the current environment; The parameter adjustment module is used to monitor the electromagnetic compatibility test environment in real time and analyze the monitoring results after the electromagnetic compatibility test equipment is adjusted according to the preliminary test parameters, and to adaptively adjust the preliminary test parameters according to the monitoring results and changes in the external environment.

11. A terminal comprising a processor and a storage medium; characterized in that: The storage medium is used to store instructions; The processor is configured to operate according to the instructions to execute the steps of the method according to any one of claims 1-9.

12. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the program is executed by a processor, the steps of the method according to any one of claims 1 to 9 are implemented.

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