Electromagnetic compatibility test data automatic acquisition and analysis method and system

Abstract

The invention discloses an electromagnetic compatibility test data automatic acquisition and analysis method and system. The method comprises the following steps: automatically acquiring electromagnetic compatibility test data as an input signal and extracting a target signal; respectively extracting an amplitude component and a change rate component of the target signal, combining to obtain a complex eigenvalue, and performing time integration on the complex eigenvalue to obtain an eigenvector component of the target signal; dividing a frequency space into a low-frequency band and a high-frequency band, and calculating the power density of each frequency point of the target signal in a segmented manner; and carrying out quantitative analysis on the electromagnetic compatibility to obtain an electromagnetic compatibility score. According to the method, the target signal can be accurately extracted from the mixed input signal in real time, the multi-dimensional features are extracted from the target signal, the time domain and frequency domain features of the target signal are comprehensively described, the frequency spectrum power density is accurately calculated in a segmented and adaptive manner, the accurate quantitative analysis of the electromagnetic compatibility level is realized, and the accuracy of the electromagnetic compatibility is improved. And the accurate evaluation requirements of different test scenes on the compatibility level can be met.

Description

Technical Field

[0001] The invention belongs to the technical field of electromagnetic compatibility test, and relates to an automatic acquisition and analysis method and system for electromagnetic compatibility test data. Background Art

[0002] Electromagnetic compatibility is an important technical requirement in the design and production of electronic and electrical equipment, which aims to ensure that the equipment can operate normally in an electromagnetic environment and does not cause unacceptable electromagnetic interference to other equipment. With the widespread application of electronic equipment and the increasing requirements for equipment stability and reliability in the fields of communications, industrial control, automobiles, and medical treatment, the importance of electromagnetic compatibility testing has become increasingly prominent.

[0003] Traditional electromagnetic compatibility tests mainly rely on manual operations for data collection and analysis, which is not only time-consuming and laborious, but also easily affected by human factors, resulting in data accuracy and analysis efficiency that are difficult to meet modern needs. Therefore, the automatic collection and analysis method of electromagnetic compatibility test data is an inevitable trend in the development of modern electromagnetic compatibility technology. By integrating advanced hardware acquisition equipment, automated control systems and intelligent data analysis tools, the efficiency and accuracy of electromagnetic compatibility tests can be significantly improved, the burden of manual operations can be reduced, and strong technical support can be provided for equipment design and production. With the continuous advancement of technology, it will play an increasingly important role in future electromagnetic compatibility tests.

[0004] However, there are many technical problems in the existing automatic acquisition and analysis technology of electromagnetic compatibility test data, such as the accuracy of electromagnetic compatibility test signal acquisition in complex environments; the destructiveness of high-frequency interference signals to target signals; traditional signal feature extraction methods are limited to a single dimension (such as time domain or frequency domain), and cannot fully reflect the multi-dimensional characteristics of target signals in complex scenarios, resulting in insufficient reliability of subsequent evaluation results; the signal frequency components in electromagnetic compatibility tests are complex, and the distribution of spectral power density may show the characteristics of large dynamic range in the high-frequency band and complex details in the low-frequency band. It is difficult for traditional modeling methods to simultaneously take into account the description accuracy and adaptability of high-frequency and low-frequency signals; quantitative analysis of electromagnetic compatibility assessment requires the extraction of comprehensive information from the spectral power density, however, the unevenness of spectral distribution and the existence of extreme points can easily cause distortion or large volatility in the scoring results, and lack of robustness. Summary of the invention

[0005] In order to solve the deficiencies in the prior art, the present invention provides a method and system for automatically collecting and analyzing electromagnetic compatibility test data.

[0006] The present invention adopts the following technical solution.

[0007] The first aspect of the present invention provides a method for automatically collecting and analyzing electromagnetic compatibility test data, comprising:

[0008] Automatically collect electromagnetic compatibility test data as input signal and extract target signal through signal enhancement based on frequency weighting factor, interference signal suppression and time domain weighting;

[0009] The amplitude component and the change rate component of the target signal are extracted based on the time weight function and the change rate weight function respectively, the amplitude component and the change rate component are combined to obtain a complex eigenvalue and the complex eigenvalue is time-integrated to obtain the eigenvector component of the target signal;

[0010] The frequency space is divided into a low frequency band and a high frequency band according to the frequency characteristics of the target signal, and the power density of each frequency point of the target signal is calculated segmentally based on the characteristic vector components of the target signal;

[0011] The electromagnetic compatibility score is obtained by quantitatively analyzing the electromagnetic compatibility based on the power density of each frequency point of the target signal.

[0012] Preferably, the target signal is extracted by signal enhancement based on frequency weighting factors, interference signal suppression and time domain weighting, and the specific formula is as follows:

[0013]

[0014] Where S′(t) represents the target signal at time t;

[0015] represents the signal enhancement term; S(t) represents the input signal at time t, t∈[0,T], T is the total duration of the input signal; represents the frequency weighting factor; f represents the frequency of the signal; f0 represents the reference frequency;

[0016] represents the interference signal suppression term; n represents the number of interference signals; tanh() represents the use of the hyperbolic tangent function; D i represents the amplitude of the i-th interference signal;

[0017] cos 2 (ω·t) represents the time domain weighting term; cos 2 () represents the cosine square function; ω represents the main frequency angular velocity of the target signal.

[0018] Preferably, the amplitude component and the change rate component of the target signal are extracted based on the time weight function and the change rate weight function respectively, the amplitude component and the change rate component are combined to obtain a complex eigenvalue and the complex eigenvalue is time-integrated to obtain the eigenvector component of the target signal, as follows:

[0019]

[0020] Among them, Fx The x-th dimension component of the feature vector representing the target signal;

[0021] represents a complex eigenvalue;

[0022] Represents the amplitude component of the target signal S′(t) at time t; represents the time weight function; p represents the attenuation control parameter of the time weight function;

[0023] represents the change rate component of the target signal S′(t) at time t; ln(1+t q ) represents the derivative weight function; ln() represents the natural logarithm function; q represents the nonlinear adjustment parameter of the derivative weight function;

[0024] j represents the imaginary unit; T represents the length of the integration time window.

[0025] Preferably, dividing the frequency space into a low frequency band and a high frequency band according to the frequency characteristics of the target signal comprises:

[0026] The part of the target signal whose frequency is less than the frequency segmentation threshold is classified into the low frequency band, and the part of the target signal whose frequency is greater than or equal to the frequency segmentation threshold is classified into the high frequency band.

[0027] Preferably, the segmented calculation of the power density of each frequency point of the target signal includes: for the low frequency band, the power density of each frequency point of the target signal is calculated by a polynomial-based weighted and nonlinear correction method, specifically as follows:

[0028]

[0029] Among them, P(f k ) represents the power density of the target signal at the kth frequency point;

[0030] f k Indicates the frequency value of the kth frequency point of the target signal;

[0031] F x The x-th dimension component of the feature vector representing the target signal;

[0032] X represents the number of dimensions of the feature vector of the target signal;

[0033] ∣·∣ means taking the absolute value;

[0034] Represents the nonlinear correction factor.

[0035] Preferably, the segmented calculation of the power density of each frequency point of the target signal includes: for the high frequency band, using exponential decay and logarithmic compression to calculate the power density of each frequency point of the target signal, specifically as follows:

[0036]

[0037] Among them, P(f k ) represents the power density of the target signal at the kth frequency point;

[0038] ln() represents the natural logarithm function, which is used for logarithmic compression;

[0039] f k Indicates the frequency value of the kth frequency point of the target signal;

[0040] F x The x-th dimension component of the feature vector representing the target signal;

[0041] X represents the number of dimensions of the feature vector of the target signal;

[0042] ∣·∣ means taking the absolute value;

[0043] represents the exponential decay term;

[0044] a represents the high frequency adjustment parameter.

[0045] Preferably, the quantitative analysis of electromagnetic compatibility based on the power density of each frequency point of the target signal is performed as follows:

[0046]

[0047] Where E represents the electromagnetic compatibility score;

[0048] N f Indicates the total number of frequency points;

[0049] P(f k ) represents the power density at the kth frequency point;

[0050] f k Indicates the frequency value of the kth frequency point of the target signal;

[0051] δ represents the frequency weighting index;

[0052] ln() means natural logarithm;

[0053] η represents the parameter regulating the strength of index inhibition;

[0054] b represents the power exponential smoothing coefficient.

[0055] The second aspect of the present invention provides an automatic acquisition and analysis system for electromagnetic compatibility test data, comprising:

[0056] A target signal calculation module, which is used to automatically collect electromagnetic compatibility test data as input signals and extract target signals through signal enhancement based on frequency weighting factors, interference signal suppression and time domain weighting;

[0057] The eigenvector component calculation module is used to extract the amplitude component and the change rate component of the target signal based on the time weight function and the change rate weight function respectively, combine the amplitude component and the change rate component to obtain a complex eigenvalue and perform time integration on the complex eigenvalue to obtain the eigenvector component of the target signal;

[0058] A frequency point power density calculation module is used to divide the frequency space into a low frequency band and a high frequency band according to the frequency characteristics of the target signal, and to calculate the power density of each frequency point of the target signal in sections based on the characteristic vector components of the target signal;

[0059] The quantitative analysis module is used to perform quantitative analysis of electromagnetic compatibility based on the power density of each frequency point of the target signal to obtain an electromagnetic compatibility score.

[0060] A third aspect of the present invention provides a terminal, comprising a processor and a storage medium; 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.

[0061] A fourth aspect of the present invention provides a computer-readable storage medium having a computer program stored thereon, which implements the steps of the method when executed by a processor.

[0062] Compared with the prior art, the beneficial effects of the present invention include at least:

[0063] 1. In order to automatically collect high-quality target signals in complex electromagnetic compatibility test scenarios and provide accurate and stable data input for subsequent links of electromagnetic compatibility tests, the present invention eliminates multi-source interference through multiple optimization means, and implements multi-dimensional optimization of nonlinear enhancement, frequency weighting, interference separation and time domain correction based on signal enhancement of frequency weighting factors, interference signal suppression and time domain weighting to obtain target signals, thereby reducing dependence on manual intervention, and being able to extract target signals from mixed input signals in real time and accurately, significantly improving the efficiency and accuracy of the test. Specifically:

[0064] The input signal collected by the electromagnetic compatibility test equipment includes the target signal and the interference signal. In order to highlight the significance of the target signal, the present invention performs nonlinear enhancement on the amplitude of the input signal. By calculating the square of the amplitude of the input signal and performing a logarithmic transformation, weak signals are amplified while suppressing abnormally strong signals, thereby ensuring the priority of the target signal in subsequent processing and effectively avoiding the oversaturation problem that may be caused by excessive amplitude.

[0065] The present invention further introduces a frequency weighting factor for adjustment, and calculates the frequency weight according to the frequency component of the input signal in combination with a preset reference frequency, so as to control the response strength of the target signal in the frequency domain;

[0066] The present invention analyzes the amplitude characteristics of interference signals one by one and uses nonlinear compression for dynamic adjustment. Each interference signal is assigned a weight according to the amplitude size and time evolution characteristics, and the amplitude is compressed using a hyperbolic tangent function to ensure that the contribution of strong interference signals is significantly suppressed. The influence of interference signals will gradually decrease with the adjustment of time weights, further reducing the interference effect on target signals.

[0067] In order to fully suppress interference signals in the high frequency band, the present invention applies additional weight attenuation to the high frequency interference in an inverse square proportional manner, thereby ensuring that the influence of the interference signal in the high frequency region is significantly weakened while maintaining the integrity and clarity of the low frequency band signal;

[0068] After nonlinear enhancement, frequency weighting and interference suppression, the present invention further utilizes the main frequency characteristics of the target signal to perform time domain correction. By performing cosine function weighted processing on the time variable, the characteristics can be enhanced within the main frequency range of the target signal, while effectively suppressing high-frequency noise and instantaneous interference in the time series. The time dimension of the signal is optimized according to the main frequency characteristics of the target signal to ensure that the processed target signal finally extracted has time stability and frequency consistency.

[0069] The present invention separates the target signal from the interference signal in the input signal in a complex electromagnetic compatibility test environment through the above method, significantly improves the acquisition quality and signal-to-noise ratio of the target signal, reduces the need for manual intervention, and ensures the accuracy and stability of subsequent test data.

[0070] 2. The present invention extracts the amplitude component and the change rate component of the target signal based on the time weight function and the change rate weight function respectively, combines the amplitude component and the change rate component to obtain the complex eigenvalue and performs time integration on the complex eigenvalue to obtain the eigenvector component of the target signal, thereby improving the accuracy of target signal feature extraction and multi-dimensional expression capability in electromagnetic compatibility testing, thereby providing high-quality input data for electromagnetic compatibility assessment in complex environments, and being able to extract multi-dimensional features from the target signal to fully describe the time domain and frequency domain characteristics of the target signal. Specifically,

[0071] In order to weaken the noise influence at the tail of the target signal, the present invention introduces a time weight function, dynamically adjusts the weight value according to the time position, assigns a higher weight to the front section of the target signal time series, and gradually decays it in the back section, traverses the target signal time series, and multiplies the time weight by the target signal point by point, thereby forming a weighted time domain signal, highlighting the feature changes at the initial stage of the target signal, and reducing the interference of the tail noise on the overall feature extraction result;

[0072] Since the drastically changing part of the target signal contains important feature information, the first-order derivative of the target signal is calculated to extract the change rate. In order to highlight the significant change part in the derivative, a derivative weight function is further designed to enhance the expression ability of the derivative characteristics in multi-dimensional feature extraction.

[0073] In order to capture the amplitude and phase characteristics of the target signal at the same time, the complex form is introduced into the target signal feature representation. The real part and imaginary part of the complex number represent the amplitude component and the change rate component of the signal respectively. The complex form feature combination of the target signal is performed at each time point to generate the complex eigenvalues ​​in the time series, which can fully reflect the multi-dimensional information of the target signal in the time domain.

[0074] In order to obtain the global features of the target signal in the entire time window, the complex eigenvalues ​​are time-integrated. The integration operation traverses the entire time series and accumulates the complex eigenvalues ​​of all time points to form a global feature description of the signal, ensuring the comprehensiveness and stability of the features.

[0075] The multi-dimensional characteristics of the time domain and frequency domain are extracted through the above method, which improves the accuracy and expression ability of target signal feature extraction and provides more comprehensive basic data for the analysis of complex signals in electromagnetic compatibility tests.

[0076] 3. The present invention divides the frequency space into low-frequency band and high-frequency band according to the frequency characteristics of the target signal, and calculates the power density of each frequency point of the target signal in segments based on the characteristic vector components of the target signal, which solves the accuracy and adaptability problems of spectrum modeling in electromagnetic compatibility tests, and accurately calculates the spectrum power density in segments and adaptively, which can provide reliable data support for electromagnetic compatibility assessment. Specifically,

[0077] The present invention divides the frequency space into low-frequency band and high-frequency band according to the frequency characteristics of the collected signal, and accurately models the power density distribution through different calculation strategies, including low-frequency modeling and high-frequency modeling. Each part adopts different calculation paths according to the range to which the frequency point belongs, thereby realizing a comprehensive description of the spectrum power density in the electromagnetic compatibility test and improving the accuracy and adaptability of power density modeling.

[0078] The present invention introduces a frequency segmentation threshold as a basis for dividing the frequency range. The part with a frequency less than the frequency segmentation threshold is classified into a low frequency band. A weighted sum calculation strategy based on a polynomial is adopted. The part with a frequency greater than or equal to the frequency segmentation threshold is classified into a high frequency band. The exponential decay and logarithmic compression method is used for modeling, which takes into account both the detailed description of the low-frequency signal and the dynamic range control of the high-frequency signal.

[0079] In the calculation process of the low-frequency band, the characteristic vector components of the target signal are combined with the polynomial of the frequency point to reflect the contribution of each characteristic dimension in the low-frequency signal to the power density, while maintaining a fine description of the power distribution of the low-frequency part. In order to avoid the imbalance of the model caused by the concentration of energy in the low-frequency signal, a frequency-related nonlinear correction factor is introduced to adjust the calculation result to ensure the smoothness of the power density distribution in the low-frequency band.

[0080] For the calculation of high-frequency bands, an exponential attenuation term is introduced to make the amplitude of the high-frequency signal decay with the increase of frequency and eigenvector dimension, suppressing the peak value of the high-frequency frequency points and improving the robustness. In order to further control the dynamic range of the high-frequency band, logarithmic compression is applied to make the high-frequency power density evenly distributed, while reducing the influence of extreme points on the overall results.

[0081] The above method uses frequency segmentation thresholds to divide low-frequency bands and high-frequency bands, and uses polynomial fitting and exponential decay for modeling, respectively, to ensure a fine description of the power density in the low-frequency band and enhance the robustness of the high-frequency band modeling, providing a reliable basis for spectrum analysis in complex environments. It balances the differences between high and low frequencies, so that the calculation results can accurately reflect the power density characteristics of the low-frequency band and provide an effective description of the complexity of the high-frequency band signal. Through frequency segmentation, the fine fitting ability of the low-frequency band and the smoothness of the high-frequency band are optimized, and it has adaptability and robustness, especially in complex electromagnetic compatibility test environments.

[0082] 4. The present invention performs a quantitative analysis of electromagnetic compatibility based on the power density of each frequency point of the target signal to obtain an electromagnetic compatibility score, thereby achieving an accurate quantitative analysis of the electromagnetic compatibility level. The quantitative electromagnetic compatibility score result is both stable and adaptable, and can meet the needs of accurate evaluation of the compatibility level in different test scenarios. Specifically,

[0083] The present invention adjusts the power density of each frequency point, introduces a frequency weighting index, and controls the weight of the frequency point to reflect the degree of influence in the compatibility test; after the frequency point weighting processing, in order to further smooth the spectrum data, a mapping method based on the combination of logarithms and exponents is introduced, the logarithm is used to enhance the resolution of high-frequency signals and smooth the transition of low-frequency signals, and the exponential suppression term is used to perform nonlinear compression on the frequency points; the mapping method effectively suppresses the excessive influence of the high frequency band and enhances the continuity of signal changes between frequency points, especially in the case of uneven signal distribution, and can significantly improve the robustness and accuracy of the compatibility evaluation results; the smoothed weighted power density values ​​of all frequency points are accumulated one by one, and the power operation is applied to the accumulated result, so that the scoring result has a nonlinear smoothing effect, and the contribution of all frequency points is normalized to obtain a compatibility score.

[0084] The electromagnetic compatibility score obtained by the above method covers the electromagnetic compatibility performance in the entire frequency band, supports flexible adjustment in different test scenarios, and adapts to various electromagnetic environments. BRIEF DESCRIPTION OF THE DRAWINGS

[0085] Figure 1 The present invention is a flow chart of an automatic acquisition and analysis method of electromagnetic compatibility test data. DETAILED DESCRIPTION

[0086] In order to make the purpose, technical scheme and advantages of the present invention clearer, the technical scheme 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 part of the embodiments of the present invention, not all of them. Based on the spirit of the present invention, other embodiments obtained by ordinary technicians in this field without creative work are all within the scope of protection of the present invention.

[0087] like Figure 1 As shown, an embodiment 1 of the present invention provides a method for automatically collecting and analyzing electromagnetic compatibility test data, the method comprising the following steps:

[0088] S1: Automatically collect electromagnetic compatibility test data as input signals such as electromagnetic radiation signals, and extract target signals through signal enhancement based on frequency weighting factors, interference signal suppression and time domain weighting, which is the optimized input signal;

[0089] Further preferably, an input signal collected by an electromagnetic compatibility test device is received, and a target signal is calculated using an adaptive signal enhancement and interference separation algorithm. The specific analysis is as follows:

[0090] In order to automatically collect high-quality target signals in complex electromagnetic compatibility test scenarios and provide accurate and stable data input for subsequent links of electromagnetic compatibility tests, the present invention eliminates multi-source interference through multiple optimization means, and uses an adaptive signal enhancement and interference separation algorithm to achieve multi-dimensional optimization of the target signal by combining nonlinear enhancement, frequency weighting, interference separation and time domain correction to obtain the target signal;

[0091] The adaptive signal enhancement and interference separation algorithm automatically collects electromagnetic compatibility test data as input signals. The input signals include target signals and interference signals. In order to highlight the significance of the target signal, the amplitude of the input signal is nonlinearly enhanced. The square of the amplitude of the input signal is calculated and logarithmically transformed to amplify weak signals and suppress abnormally strong signals, thereby ensuring the priority of the target signal in subsequent processing and effectively avoiding the oversaturation problem that may be caused by excessive amplitude.

[0092] A frequency weighting factor is further introduced for adjustment. The frequency weight is calculated according to the frequency component of the input signal in combination with a preset reference frequency to control the response strength of the target signal in the frequency domain.

[0093] Since the input signal is mixed with interference signals, it is necessary to analyze the amplitude characteristics of the interference signals one by one and make dynamic adjustments using nonlinear compression. Each interference signal is assigned a weight according to its amplitude and time evolution characteristics. The amplitude is compressed using the hyperbolic tangent function to ensure that the contribution of strong interference signals is significantly suppressed. The impact of the interference signal will gradually decrease with the adjustment of the time weight, further weakening the interference effect on the target signal.

[0094] In order to fully suppress interference signals in the high frequency band, additional weight attenuation is applied to high frequency interference in an inverse square ratio of the frequency, ensuring that the impact of interference signals in the high frequency region is significantly weakened while maintaining the integrity and clarity of low frequency signals.

[0095] After nonlinear enhancement, frequency weighting and interference suppression, the main frequency characteristics of the target signal are further used for time domain correction. By weighting the time variable with the cosine function, the characteristics can be enhanced within the main frequency range of the target signal, while effectively suppressing high-frequency noise and instantaneous interference in the time series. The time dimension of the signal is optimized according to the main frequency characteristics of the target signal to ensure that the final extracted processed target signal has time stability and frequency consistency.

[0096] The target signal is calculated as:

[0097]

[0098] Where S′(t) represents the target signal at time t;

[0099] Indicates signal enhancement item;

[0100] S(t) represents the input signal collected at time t, t∈[0,T], T is the total duration of the input signal;

[0101] ln(1+S(t) 2 ) represents taking the natural logarithm of the square of the input signal collected at time t, introducing nonlinear characteristics to amplify weak signals while suppressing abnormally strong signals;

[0102] Represents the frequency weighting factor, which is used to control the response strength of the target signal in the frequency domain;

[0103] f represents the frequency of the signal, characterizing the frequency distribution of the original input signal, and is used for interference separation and weight adjustment. It is obtained by performing frequency domain analysis on the collected original input signal through fast Fourier transform. The fast Fourier transform is a technical means well known to those skilled in the art and will not be described in detail here.

[0104] f0 represents the reference frequency, which is used to normalize the frequency range to avoid the interference of high-frequency components on the overall signal;

[0105] represents the interference signal suppression item;

[0106] represents the accumulation of all interference signals, and n represents the number of interference signals;

[0107] tanh(D i ·t) indicates that the i-th interference signal is smoothed using the hyperbolic tangent function to limit the interference effect on the signal. The hyperbolic tangent function has a good amplitude saturation characteristic and can significantly reduce the amplitude without completely losing the interference information;

[0108] D i Represents the amplitude of the i-th interference signal, quantifies the strength of the interference signal, and is used to suppress interference in the target signal;

[0109] 1+f 2 Represents the square weighting term of frequency, which is used to apply additional attenuation to high-frequency interference signals;

[0110] cos 2 (ω·t) represents the time domain weighting term, which highlights the main frequency components of the target signal through the cosine square function, enhances the time stability of the target signal, and suppresses instantaneous interference mutations; ω represents the main frequency angular velocity of the target signal;

[0111] Through the automated collection and processing of electromagnetic compatibility test data, the reliance of traditional methods on manual intervention is reduced, and the target signal can be accurately extracted from the mixed signal in real time, which significantly improves the efficiency and accuracy of the test.

[0112] S2: extracting the amplitude component and the change rate component of the target signal based on the time weight function and the change rate weight function respectively, combining the amplitude component and the change rate component to obtain a complex eigenvalue and performing time integration on the complex eigenvalue to obtain a eigenvector component of the target signal;

[0113] Further preferably, based on the target signal, a multidimensional feature is extracted from the target signal by a multidimensional signal feature extraction algorithm, and the specific analysis is as follows:

[0114] In order to improve the accuracy and multi-dimensional expression ability of target signal feature extraction in electromagnetic compatibility tests, and thus provide high-quality input data for electromagnetic compatibility assessment in complex environments, multi-dimensional features are extracted from target signals through multi-dimensional signal feature extraction algorithms to comprehensively describe the time domain and frequency domain characteristics of target signals.

[0115] In order to weaken the influence of noise at the tail of the target signal, a time weight function is introduced. The weight value is dynamically adjusted according to the time position. A higher weight is given to the front part of the target signal time series, and gradually decays in the back part. The time series of the target signal is traversed, and the time weight is multiplied by the target signal point by point to form a weighted time domain signal, highlighting the feature changes in the early stage of the target signal, while reducing the interference of the tail noise on the overall feature extraction results.

[0116] Since the drastically changing part of the target signal contains important feature information, the first-order derivative of the target signal is calculated to extract the change rate. In order to highlight the significant change part in the derivative, a derivative weight function is further designed to enhance the expression ability of the derivative characteristics in multi-dimensional feature extraction.

[0117] In order to capture the amplitude and phase characteristics of the target signal at the same time, the complex form is introduced into the target signal feature representation. The real part and imaginary part of the complex number represent the amplitude component and the change rate component of the signal respectively. The complex form feature combination of the target signal is performed at each time point to generate the complex eigenvalues ​​in the time series, which can fully reflect the multi-dimensional information of the target signal in the time domain.

[0118] In order to obtain the global features of the target signal in the entire time window, the complex eigenvalues ​​are time-integrated. The integration operation traverses the entire time series, and the complex eigenvalues ​​of all time points are accumulated to form a global feature description of the signal, ensuring the comprehensiveness and stability of the features.

[0119] The calculation formula of the eigenvector component of the target signal is:

[0120]

[0121] Among them, F x The x-dimensional component of the feature vector representing the target signal, such as the temporal local features (by combining the time weight function and the signal amplitude, the temporal local features of the signal, especially the early changes of the signal), the dynamic features of the signal (by calculating the first-order derivative of the target signal, the temporal rate of change of the signal is captured, thereby reflecting the change trend of the signal), and the global features (by time integration, the global features of the signal in the entire time window are calculated);

[0122] S′(t) represents the target signal at time t;

[0123] It represents the time weight function, which assigns different weights to the time series of the signal and dynamically adjusts the weights to weaken the noise at the tail of the signal;

[0124] p represents the attenuation control parameter of the time weight function, which adjusts the attenuation speed of the time weight function and affects the degree of noise attenuation;

[0125] j represents the imaginary unit, which is used for complex number representation to unify the amplitude and phase characteristics of the signal;

[0126] It represents the time first derivative of the target signal, reflecting the rate at which the target signal changes over time;

[0127] ln(1+t q ) represents the derivative weight function, which enhances the expressiveness of derivative characteristics in multidimensional feature extraction;

[0128] q represents the nonlinear adjustment parameter of the derivative weight function, which adjusts the amplification range of the derivative characteristics in the time dimension;

[0129] T represents the length of the integration time window, which limits the integration range and ensures that the feature extraction is stable and representative;

[0130] S3: Divide the frequency space into a low frequency band and a high frequency band according to the frequency characteristics of the target signal, and calculate the power density of each frequency point of the target signal based on the characteristic vector components of the target signal;

[0131] Further preferably, based on the characteristic vector of the target signal, a segmented adaptive spectrum modeling algorithm is used to accurately model the spectrum power density, and the specific analysis is as follows:

[0132] In order to solve the accuracy and adaptability problems of spectrum modeling in electromagnetic compatibility tests, the segmented adaptive spectrum modeling algorithm is used to accurately model the spectrum power density, which can provide reliable data support for electromagnetic compatibility assessment.

[0133] The segmented adaptive spectrum modeling algorithm aims to comprehensively describe the spectrum power density in the electromagnetic compatibility test. According to the frequency characteristics of the collected signal, the frequency space is divided into low-frequency band and high-frequency band, and the power density distribution is accurately modeled through different calculation strategies, including low-frequency modeling and high-frequency modeling. Each part adopts different calculation paths according to the range of the frequency point to improve the accuracy and adaptability of power density modeling.

[0134] The segmented adaptive spectrum modeling algorithm introduces a frequency threshold as the basis for dividing the frequency range. The part with a frequency less than the frequency threshold is classified into a low frequency band, and a weighted sum calculation strategy based on a polynomial is adopted. The part with a frequency greater than or equal to the frequency threshold is classified into a high frequency band. The modeling is performed by exponential decay and logarithmic compression, which takes into account both the detailed description of the low-frequency signal and the dynamic range control of the high-frequency signal.

[0135] In the calculation process of the low-frequency band, the characteristic vector components of the target signal are combined with the polynomial of the frequency point to reflect the contribution of each characteristic dimension in the low-frequency signal to the power density, while maintaining a detailed description of the power distribution of the low-frequency part. In order to avoid the imbalance of the model caused by the concentration of energy in the low-frequency signal, a frequency-related nonlinear correction factor is introduced to adjust the calculation results to ensure the smoothness of the power density distribution in the low-frequency band.

[0136] For the calculation of high frequency band, an exponential attenuation term is introduced to make the amplitude of high frequency signal decay with the increase of frequency and eigenvector dimension, suppress the peak value of high frequency band frequency points and improve the robustness. In order to further control the dynamic range of high frequency band, logarithmic compression is applied to make the high frequency power density evenly distributed, while reducing the influence of extreme value points on the overall results.

[0137] The power density at a frequency point is calculated as:

[0138]

[0139] Among them, P(f k ) represents the power density at the kth frequency point;

[0140] f k Represents the frequency value of the kth frequency point;

[0141] f c Indicates the frequency segmentation threshold, which distinguishes high-frequency and low-frequency signals. It can be set according to the specific implementation scenario and is not limited here;

[0142] F x The x-th dimension component of the feature vector representing the target signal;

[0143] It means that the power density contribution of each dimension in the feature vector to the kth frequency point is accumulated;

[0144] X represents the number of dimensions of the feature vector of the target signal;

[0145] ∣F x ∣ represents the absolute value of the x-th dimension component of the eigenvector of the target signal, ensuring that the eigenvalue is non-negative and removing the influence of the positive and negative signs of the eigenvalue on the power density calculation;

[0146] represents the nonlinear correction factor;

[0147] The polynomial term representing the kth frequency point, with an exponent of x-1, is used for polynomial fitting of low-frequency band modeling to improve the accuracy of low-frequency band power density description;

[0148] Represents the negative power of the kth frequency point, which is used to correct the denominator of the low-frequency band, and to make nonlinear adjustments to the low-frequency power density, further enhancing the description accuracy of the low-frequency band;

[0149] ln represents the natural logarithmic function, which is used to compress the power density in the high-frequency band, reduce the dynamic range of the high-frequency band signal change, and enhance the stability of the high-frequency band power description;

[0150] Represents an exponential decay term, which decays as the frequency and eigenvector dimension increase, suppressing the peak value of high-frequency frequency points and improving robustness;

[0151] a represents the high-frequency adjustment parameter, which controls the smoothness of the high-frequency band;

[0152] The core of the calculation process of the segmented adaptive spectrum modeling algorithm is to balance the differences between high frequency and low frequency, so that the modeling results can accurately reflect the power density characteristics of the low frequency band and provide an effective description of the complexity of the high frequency band signal. By means of frequency segmentation, the fine fitting ability of the low frequency band and the smoothness of the high frequency band are optimized respectively, and the algorithm has adaptability and robustness, especially in complex electromagnetic compatibility test environments.

[0153] S4: Perform a quantitative analysis of electromagnetic compatibility based on the power density of each frequency point of the target signal to obtain an electromagnetic compatibility score.

[0154] Further preferably, based on the power density of the frequency point, an electromagnetic compatibility evaluation algorithm is used to automatically process the spectrum power density to calculate the electromagnetic compatibility score, and the specific analysis is as follows:

[0155] In order to quantify the electromagnetic compatibility level, the electromagnetic compatibility evaluation algorithm is used to automatically process the spectrum power density to calculate the electromagnetic compatibility score, thus realizing the accurate quantitative analysis of electromagnetic compatibility.

[0156] The electromagnetic compatibility assessment algorithm adjusts the power density of each frequency point, introduces a frequency weighting index, and controls the weight of the frequency point to reflect the degree of influence in the compatibility test; after the frequency point weighting processing, in order to further smooth the spectrum data, a mapping method based on a combination of logarithms and exponents is introduced, the logarithm is used to enhance the resolution of high-frequency signals and smooth the transition of low-frequency signals, and the exponential suppression term is used to perform nonlinear compression on the frequency points; the mapping method effectively suppresses the excessive influence of the high frequency band and enhances the continuity of signal changes between frequency points, especially in the case of uneven signal distribution, which can significantly improve the robustness and accuracy of the compatibility assessment results; the smoothed weighted power density values ​​of all frequency points are accumulated one by one, and the power operation is applied to the accumulated results to make the scoring results have a nonlinear smoothing effect, and the contributions of all frequency points are normalized to obtain a compatibility score;

[0157] The electromagnetic compatibility score is calculated using the formula:

[0158]

[0159] Among them, E stands for electromagnetic compatibility score, which quantitatively represents electromagnetic compatibility. In engineering practice, the electromagnetic compatibility score can be compared with the score threshold of international or domestic electromagnetic compatibility standards (such as IEC 61000, FCC Part 15) to determine whether the equipment meets the requirements. The electromagnetic compatibility score can also help equipment designers evaluate and optimize electromagnetic compatibility.

[0160] It represents the full-band summation operation, which accumulates all frequency points in the spectrum to synthesize the contribution of all frequency bands to electromagnetic compatibility;

[0161] N f Indicates the total number of frequency points;

[0162] P(f k ) represents the power density at the kth frequency point;

[0163] f k Represents the frequency value of the kth frequency point;

[0164] δ represents the frequency weighting index, which controls the weight of the frequency point;

[0165] ln represents the natural logarithm, which enhances the resolution of high-frequency signals and smoothes the transition of low-frequency signals;

[0166] e -ηfkrepresents the exponential suppression term, which performs nonlinear compression on the kth frequency point;

[0167] η represents the parameter regulating the strength of index inhibition;

[0168] b represents the power exponential smoothing coefficient, It represents the power smoothing root part, which smoothes the electromagnetic compatibility score as a whole to prevent significant deviations caused by individual extreme values;

[0169] The electromagnetic compatibility score covers the electromagnetic compatibility performance of the entire frequency band, supports flexible adjustment in different test scenarios, and adapts to various electromagnetic environments.

[0170] Embodiment 2 of the present invention provides an automatic collection and analysis system for electromagnetic compatibility test data, comprising:

[0171] A target signal calculation module, which is used to automatically collect electromagnetic compatibility test data as input signals and extract target signals through signal enhancement based on frequency weighting factors, interference signal suppression and time domain weighting;

[0172] The eigenvector component calculation module is used to extract the amplitude component and the change rate component of the target signal based on the time weight function and the change rate weight function respectively, combine the amplitude component and the change rate component to obtain a complex eigenvalue and perform time integration on the complex eigenvalue to obtain the eigenvector component of the target signal;

[0173] A frequency point power density calculation module is used to divide the frequency space into a low frequency band and a high frequency band according to the frequency characteristics of the target signal, and to calculate the power density of each frequency point of the target signal in sections based on the characteristic vector components of the target signal;

[0174] The quantitative analysis module is used to perform quantitative analysis of electromagnetic compatibility based on the power density of each frequency point of the target signal to obtain an electromagnetic compatibility score.

[0175] Embodiment 3 of the present invention provides a terminal, including a processor and a storage medium; 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.

[0176] Embodiment 4 of the present invention provides a computer-readable storage medium having a computer program stored thereon, which implements the steps of the method when executed by a processor.

[0177] Compared with the prior art, the beneficial effects of the present invention include at least:

[0178] 1. In order to automatically collect high-quality target signals in complex electromagnetic compatibility test scenarios and provide accurate and stable data input for subsequent links of electromagnetic compatibility tests, the present invention eliminates multi-source interference through multiple optimization means, and implements multi-dimensional optimization of nonlinear enhancement, frequency weighting, interference separation and time domain correction based on signal enhancement of frequency weighting factors, interference signal suppression and time domain weighting to obtain target signals, thereby reducing dependence on manual intervention, and being able to extract target signals from mixed input signals in real time and accurately, significantly improving the efficiency and accuracy of the test. Specifically:

[0179] The input signal collected by the electromagnetic compatibility test equipment includes the target signal and the interference signal. In order to highlight the significance of the target signal, the present invention performs nonlinear enhancement on the amplitude of the input signal. By calculating the square of the amplitude of the input signal and performing a logarithmic transformation, weak signals are amplified while suppressing abnormally strong signals, thereby ensuring the priority of the target signal in subsequent processing and effectively avoiding the oversaturation problem that may be caused by excessive amplitude.

[0180] The present invention further introduces a frequency weighting factor for adjustment, and calculates the frequency weight according to the frequency component of the input signal in combination with a preset reference frequency, so as to control the response strength of the target signal in the frequency domain;

[0181] The present invention analyzes the amplitude characteristics of interference signals one by one and uses nonlinear compression for dynamic adjustment. Each interference signal is assigned a weight according to the amplitude size and time evolution characteristics, and the amplitude is compressed using a hyperbolic tangent function to ensure that the contribution of strong interference signals is significantly suppressed. The influence of interference signals will gradually decrease with the adjustment of time weights, further reducing the interference effect on target signals.

[0182] In order to fully suppress interference signals in the high frequency band, the present invention applies additional weight attenuation to the high frequency interference in an inverse square proportional manner, thereby ensuring that the influence of the interference signal in the high frequency region is significantly weakened while maintaining the integrity and clarity of the low frequency band signal;

[0183] After nonlinear enhancement, frequency weighting and interference suppression, the present invention further utilizes the main frequency characteristics of the target signal to perform time domain correction. By performing cosine function weighted processing on the time variable, the characteristics can be enhanced within the main frequency range of the target signal, while effectively suppressing high-frequency noise and instantaneous interference in the time series. The time dimension of the signal is optimized according to the main frequency characteristics of the target signal to ensure that the processed target signal finally extracted has time stability and frequency consistency.

[0184] The present invention separates the target signal from the interference signal in the input signal in a complex electromagnetic compatibility test environment through the above method, significantly improves the acquisition quality and signal-to-noise ratio of the target signal, reduces the need for manual intervention, and ensures the accuracy and stability of subsequent test data.

[0185] 2. The present invention extracts the amplitude component and the change rate component of the target signal based on the time weight function and the change rate weight function respectively, combines the amplitude component and the change rate component to obtain the complex eigenvalue and performs time integration on the complex eigenvalue to obtain the eigenvector component of the target signal, thereby improving the accuracy of target signal feature extraction and multi-dimensional expression capability in electromagnetic compatibility testing, thereby providing high-quality input data for electromagnetic compatibility assessment in complex environments, and being able to extract multi-dimensional features from the target signal to fully describe the time domain and frequency domain characteristics of the target signal. Specifically,

[0186] In order to weaken the noise influence at the tail of the target signal, the present invention introduces a time weight function, dynamically adjusts the weight value according to the time position, assigns a higher weight to the front section of the target signal time series, and gradually decays it in the back section, traverses the target signal time series, and multiplies the time weight by the target signal point by point, thereby forming a weighted time domain signal, highlighting the feature changes at the initial stage of the target signal, and reducing the interference of the tail noise on the overall feature extraction result;

[0187] Since the drastically changing part of the target signal contains important feature information, the first-order derivative of the target signal is calculated to extract the change rate. In order to highlight the significant change part in the derivative, a derivative weight function is further designed to enhance the expression ability of the derivative characteristics in multi-dimensional feature extraction.

[0188] In order to capture the amplitude and phase characteristics of the target signal at the same time, the complex form is introduced into the target signal feature representation. The real part and imaginary part of the complex number represent the amplitude component and the change rate component of the signal respectively. The complex form feature combination of the target signal is performed at each time point to generate the complex eigenvalues ​​in the time series, which can fully reflect the multi-dimensional information of the target signal in the time domain.

[0189] In order to obtain the global features of the target signal in the entire time window, the complex eigenvalues ​​are time-integrated. The integration operation traverses the entire time series and accumulates the complex eigenvalues ​​of all time points to form a global feature description of the signal, ensuring the comprehensiveness and stability of the features.

[0190] The multi-dimensional characteristics of the time domain and frequency domain are extracted through the above method, which improves the accuracy and expression ability of target signal feature extraction and provides more comprehensive basic data for the analysis of complex signals in electromagnetic compatibility tests.

[0191] 3. The present invention divides the frequency space into low-frequency band and high-frequency band according to the frequency characteristics of the target signal, and calculates the power density of each frequency point of the target signal in segments based on the characteristic vector components of the target signal, which solves the accuracy and adaptability problems of spectrum modeling in electromagnetic compatibility tests, and accurately calculates the spectrum power density in segments and adaptively, which can provide reliable data support for electromagnetic compatibility assessment. Specifically,

[0192] The present invention divides the frequency space into low-frequency band and high-frequency band according to the frequency characteristics of the collected signal, and accurately models the power density distribution through different calculation strategies, including low-frequency modeling and high-frequency modeling. Each part adopts different calculation paths according to the range to which the frequency point belongs, thereby realizing a comprehensive description of the spectrum power density in the electromagnetic compatibility test and improving the accuracy and adaptability of power density modeling.

[0193] The present invention introduces a frequency segmentation threshold as a basis for dividing the frequency range. The part with a frequency less than the frequency segmentation threshold is classified into a low frequency band. A weighted sum calculation strategy based on a polynomial is adopted. The part with a frequency greater than or equal to the frequency segmentation threshold is classified into a high frequency band. The exponential decay and logarithmic compression method is used for modeling, which takes into account both the detailed description of the low-frequency signal and the dynamic range control of the high-frequency signal.

[0194] In the calculation process of the low-frequency band, the characteristic vector components of the target signal are combined with the polynomial of the frequency point to reflect the contribution of each characteristic dimension in the low-frequency signal to the power density, while maintaining a fine description of the power distribution of the low-frequency part. In order to avoid the imbalance of the model caused by the concentration of energy in the low-frequency signal, a frequency-related nonlinear correction factor is introduced to adjust the calculation result to ensure the smoothness of the power density distribution in the low-frequency band.

[0195] For the calculation of high-frequency bands, an exponential attenuation term is introduced to make the amplitude of the high-frequency signal decay with the increase of frequency and eigenvector dimension, suppressing the peak value of the high-frequency frequency points and improving the robustness. In order to further control the dynamic range of the high-frequency band, logarithmic compression is applied to make the high-frequency power density evenly distributed, while reducing the influence of extreme points on the overall results.

[0196] The above method uses frequency segmentation thresholds to divide low-frequency bands and high-frequency bands, and uses polynomial fitting and exponential decay for modeling, respectively, to ensure a fine description of the power density in the low-frequency band and enhance the robustness of the high-frequency band modeling, providing a reliable basis for spectrum analysis in complex environments. It balances the differences between high and low frequencies, so that the calculation results can accurately reflect the power density characteristics of the low-frequency band and provide an effective description of the complexity of the high-frequency band signal. Through frequency segmentation, the fine fitting ability of the low-frequency band and the smoothness of the high-frequency band are optimized, and it has adaptability and robustness, especially in complex electromagnetic compatibility test environments.

[0197] 4. The present invention performs a quantitative analysis of electromagnetic compatibility based on the power density of each frequency point of the target signal to obtain an electromagnetic compatibility score, thereby achieving an accurate quantitative analysis of the electromagnetic compatibility level. The quantitative electromagnetic compatibility score result is both stable and adaptable, and can meet the needs of accurate evaluation of the compatibility level in different test scenarios. Specifically,

[0198] The present invention adjusts the power density of each frequency point, introduces a frequency weighting index, and controls the weight of the frequency point to reflect the degree of influence in the compatibility test; after the frequency point weighting processing, in order to further smooth the spectrum data, a mapping method based on the combination of logarithms and exponents is introduced, the logarithm is used to enhance the resolution of high-frequency signals and smooth the transition of low-frequency signals, and the exponential suppression term is used to perform nonlinear compression on the frequency points; the mapping method effectively suppresses the excessive influence of the high frequency band and enhances the continuity of signal changes between frequency points, especially in the case of uneven signal distribution, and can significantly improve the robustness and accuracy of the compatibility evaluation results; the smoothed weighted power density values ​​of all frequency points are accumulated one by one, and the power operation is applied to the accumulated result, so that the scoring result has a nonlinear smoothing effect, and the contribution of all frequency points is normalized to obtain a compatibility score.

[0199] The electromagnetic compatibility score obtained by the above method covers the electromagnetic compatibility performance in the entire frequency band, supports flexible adjustment in different test scenarios, and adapts to various electromagnetic environments.

[0200] The present disclosure may be a system, a method and / or a computer program product. The computer program product may include a computer-readable storage medium carrying computer-readable program instructions for causing a processor to implement various aspects of the present disclosure.

[0201] A computer-readable storage medium may be a tangible device that can hold and store instructions used by an instruction execution device. A computer-readable storage medium may 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 of computer-readable storage media (a non-exhaustive list) 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 disk read-only memory (CD-ROM), a digital versatile disk (DVD), a memory stick, a floppy disk, a mechanical encoding device, such as a punch card or a raised structure in a groove on which instructions are stored, and any suitable combination of the foregoing. As used herein, a computer-readable storage medium is not to be interpreted as a transient signal per se, such as a radio wave or other freely propagating electromagnetic wave, an electromagnetic wave propagating through a waveguide or other transmission medium (e.g., a light pulse through a fiber optic cable), or an electrical signal transmitted through a wire.

[0202] The computer-readable program instructions described herein can be downloaded from a computer-readable storage medium to each computing / processing device, 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 transmissions, wireless transmissions, routers, firewalls, switches, gateway computers, and / or edge servers. The 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 the computer-readable storage medium in each computing / processing device.

[0203] The computer program instructions for performing the operation 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 "C" language or similar programming languages. Computer-readable program instructions may be executed completely on a user's computer, partially on a user's computer, as an independent software package, partially on a user's computer, partially on a remote computer, or completely on a remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer via 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., using an Internet service provider to connect via the Internet). In some embodiments, an electronic circuit, such as a programmable logic circuit, a field programmable gate array (FPGA), or a programmable logic array (PLA), may be customized by utilizing the state information of the computer-readable program instructions, and the electronic circuit may execute the computer-readable program instructions, thereby realizing various aspects of the present disclosure.

[0204] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the above embodiments, ordinary technicians in the relevant field should understand that the specific implementation methods of the present invention can still be modified or replaced by equivalents, and any modifications or equivalent replacements that do not depart from the spirit and scope of the present invention should be covered within the scope of protection of the claims of the present invention.

Claims

1. A method for automatically collecting and analyzing electromagnetic compatibility test data, characterized in that: include: Automatically collect electromagnetic compatibility test data as input signal and extract target signal through signal enhancement based on frequency weighting factor, interference signal suppression and time domain weighting; The amplitude component and the change rate component of the target signal are extracted based on the time weight function and the change rate weight function respectively, the amplitude component and the change rate component are combined to obtain a complex eigenvalue and the complex eigenvalue is time-integrated to obtain the eigenvector component of the target signal; The frequency space is divided into a low frequency band and a high frequency band according to the frequency characteristics of the target signal, and the power density of each frequency point of the target signal is calculated segmentally based on the characteristic vector components of the target signal; The electromagnetic compatibility score is obtained by quantitatively analyzing the electromagnetic compatibility based on the power density of each frequency point of the target signal.

2. The method for automatically collecting and analyzing electromagnetic compatibility test data according to claim 1, characterized in that: The target signal is extracted by signal enhancement based on frequency weighting factor, interference signal suppression and time domain weighting. The specific formula is as follows: Among them, S ′ (t) represents the target signal at time t; represents the signal enhancement term; S(t) represents the input signal at time t, t∈[0,T], T is the total duration of the input signal; represents the frequency weighting factor; f represents the frequency of the signal; f0 represents the reference frequency; represents the interference signal suppression term; n represents the number of interference signals; tanh() represents the use of the hyperbolic tangent function; D i represents the amplitude of the i-th interference signal; cos 2 (ω·t) represents the time domain weighting term; cos 2 () represents the cosine square function; ω represents the main frequency angular velocity of the target signal.

3. The method for automatically collecting and analyzing electromagnetic compatibility test data according to claim 1, characterized in that: The amplitude component and the change rate component of the target signal are extracted based on the time weight function and the change rate weight function respectively, the amplitude component and the change rate component are combined to obtain a complex eigenvalue and the complex eigenvalue is time-integrated to obtain the eigenvector component of the target signal, as follows: Among them, F x The x-th dimension component of the feature vector representing the target signal; represents a complex eigenvalue; Represents the target signal S at time t ＇ The magnitude component of (t); represents the time weight function; p represents the attenuation control parameter of the time weight function; Represents the target signal S at time t ＇ (t) is the rate of change component; ln(1+t q ) represents the derivative weight function; ln() represents the natural logarithm function; q represents the nonlinear adjustment parameter of the derivative weight function; j represents the imaginary unit; T represents the length of the integration time window.

4. The method for automatically collecting and analyzing electromagnetic compatibility test data according to claim 1, characterized in that: The step of dividing the frequency space into a low frequency band and a high frequency band according to the frequency characteristics of the target signal comprises: The part of the target signal whose frequency is less than the frequency segmentation threshold is classified into the low frequency band, and the part of the target signal whose frequency is greater than or equal to the frequency segmentation threshold is classified into the high frequency band.

5. The method for automatically collecting and analyzing electromagnetic compatibility test data according to claim 1, characterized in that: The segmented calculation of the power density of each frequency point of the target signal includes: for the low frequency band, the power density of each frequency point of the target signal is calculated by using a polynomial-based weighting and nonlinear correction method, specifically as follows: Among them, P(f k ) represents the power density of the target signal at the kth frequency point; f k Indicates the frequency value of the kth frequency point of the target signal; F x The x-th dimension component of the feature vector representing the target signal; X represents the number of dimensions of the feature vector of the target signal; ∣·∣ means taking the absolute value; Represents the nonlinear correction factor.

6. The method for automatically collecting and analyzing electromagnetic compatibility test data according to claim 1, characterized in that: The segmented calculation of the power density of each frequency point of the target signal includes: for the high frequency band, using exponential decay and logarithmic compression to calculate the power density of each frequency point of the target signal, as follows: Among them, p(f k ) represents the power density of the target signal at the kth frequency point; ln() represents the natural logarithm function, which is used for logarithmic compression; f k Indicates the frequency value of the kth frequency point of the target signal; F x The x-th dimension component of the feature vector representing the target signal; X represents the number of dimensions of the feature vector of the target signal; ∣·∣ means taking the absolute value; represents the exponential decay term; a represents the high frequency adjustment parameter.

7. The method for automatically collecting and analyzing electromagnetic compatibility test data according to claim 1, characterized in that: The quantitative analysis of electromagnetic compatibility based on the power density of each frequency point of the target signal is as follows: Where E represents the electromagnetic compatibility score; N f Indicates the total number of frequency points; P(f k ) represents the power density at the kth frequency point; f k Indicates the frequency value of the kth frequency point of the target signal; δ represents the frequency weighting index; ln() means natural logarithm; η represents the parameter regulating the strength of index inhibition; b represents the power exponential smoothing coefficient.

8. An automatic acquisition and analysis system for electromagnetic compatibility test data, using the method according to any one of claims 1 to 7, characterized in that: The system comprises: A target signal calculation module, which is used to automatically collect electromagnetic compatibility test data as input signals and extract target signals through signal enhancement based on frequency weighting factors, interference signal suppression and time domain weighting; The eigenvector component calculation module is used to extract the amplitude component and the change rate component of the target signal based on the time weight function and the change rate weight function respectively, combine the amplitude component and the change rate component to obtain a complex eigenvalue and perform time integration on the complex eigenvalue to obtain the eigenvector component of the target signal; A frequency point power density calculation module is used to divide the frequency space into a low frequency band and a high frequency band according to the frequency characteristics of the target signal, and to calculate the power density of each frequency point of the target signal in sections based on the characteristic vector components of the target signal; The quantitative analysis module is used to perform quantitative analysis of electromagnetic compatibility based on the power density of each frequency point of the target signal to obtain an electromagnetic compatibility score.

9. 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 1-7.

10. 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 7 are implemented.

Images