Electromagnetic sensitive threshold estimation method based on sensitive response difference modeling

Abstract

The invention discloses an electromagnetic sensitive threshold estimation method based on sensitive response difference modeling, and the method comprises the steps: determining a first frequency range based on the working frequency range of a device having potential interference with a to-be-tested device and the working frequency range of the to-be-tested device; determining discrete frequency sampling points in a first frequency range based on an ideal electromagnetic sensitive threshold function of the to-be-tested equipment; determining an electromagnetic sensitivity threshold estimation function of the to-be-tested equipment in the first frequency range based on a difference value sequence between an actually measured electromagnetic sensitivity threshold and an ideal electromagnetic sensitivity threshold of the to-be-tested equipment under the discrete frequency sampling points; the electromagnetic sensitive threshold estimation function is used for determining an estimated value of an electromagnetic sensitive threshold of the to-be-tested equipment at a target frequency, and the target frequency is within the first frequency range. The method gives consideration to pertinence, efficiency and reliability of threshold estimation, and can effectively adapt to electromagnetic sensitive threshold evaluation requirements in a complex electromagnetic environment.

Description

Technical Field

[0001] This invention relates to the field of electromagnetic spectrum planning and control technology, and in particular to an electromagnetic sensitivity threshold estimation method based on sensitive response difference modeling. Background Technology

[0002] In electromagnetic compatibility (EMC) testing, the prediction and evaluation of the electromagnetic susceptibility threshold is a key technology for ensuring the reliable operation of electronic equipment. With the increasing complexity of modern electronic systems and the growing complexity of electromagnetic environments, EMC tests are conducted under ideal training environments to understand the changing patterns of equipment sensitivity responses. However, in actual use environments, influenced by factors such as environmental noise and component aging, the sensitivity threshold of equipment to electromagnetic interference exhibits nonlinear characteristics. This nonlinearity is mainly reflected in the random fluctuations of the measured threshold caused by the superposition of various factors, making it difficult to analyze the patterns of threshold variation.

[0003] Traditional methods use fixed-order polynomials or linear regression to fit the threshold variation, which is difficult to adapt to the nonlinear dynamic changes caused by the coupling of multiple factors such as temperature, noise, and material properties in real-world environments. Furthermore, traditional methods often rely on R... 2 Using the coefficient of determination (AIC) and AIC (Akaike Information Criterion) / BIC (Bayesian Information Criterion) as evaluation criteria for goodness of fit can easily lead to overfitting or underfitting. Summary of the Invention

[0004] The main objective of this invention is to provide an electromagnetic susceptibility threshold estimation method based on sensitive response difference modeling, which enables effective estimation of the electromagnetic susceptibility effect threshold of equipment in practical application environments, thereby improving the accuracy and practicality of threshold modeling.

[0005] The technical solution adopted in this invention is: an electromagnetic susceptibility threshold estimation method based on sensitive response difference modeling, comprising: Based on the operating frequency range of the device that may interfere with the device under test and the operating frequency range of the device under test, a first frequency range is determined, wherein the operating frequency range of the device under test includes the first frequency range; Based on the ideal electromagnetic sensitivity threshold function of the device under test, discrete frequency sampling points within the first frequency range are determined; the ideal electromagnetic sensitivity threshold function is used to characterize the correlation between each frequency in the operating frequency range of the device under test and the ideal electromagnetic sensitivity threshold of the device under test at each frequency in an ideal electromagnetic environment. Based on the difference sequence between the measured electromagnetic susceptibility threshold and the ideal electromagnetic susceptibility threshold of the device under test at the discrete frequency sampling points, an electromagnetic susceptibility threshold estimation function for the device under test in a first frequency range is determined; wherein, the electromagnetic susceptibility threshold estimation function is used to determine the estimated value of the electromagnetic susceptibility threshold of the device under test at a target frequency, wherein the target frequency is within the first frequency range.

[0006] According to the above technical solution, the expression for the ideal electromagnetic sensitivity threshold function includes: ; in, For the ideal electromagnetic susceptibility threshold function, For the device under test to be in an ideal electromagnetic environment at a frequency The ideal electromagnetic susceptibility threshold is as follows: For frequency, The preset ideal interference Gaussian variance, It is an ideal Gaussian distribution sequence with interference.

[0007] According to the above technical solution, determining the discrete frequency sampling points within the first frequency range based on the ideal electromagnetic sensitivity threshold function of the device under test includes: The discrete frequency sampling point is determined based on at least one point randomly determined on the curve of the ideal electromagnetic sensitivity threshold function of the device under test; or... The discrete frequency sampling points are determined based on the inflection points on the curve of the ideal electromagnetic sensitivity threshold function of the device under test; or, The discrete frequency sampling points are determined based on the measurable points on the curve of the ideal electromagnetic sensitivity threshold function of the device under test.

[0008] According to the above technical solution, the discrete frequency sampling points are determined based on the inflection points on the curve of the ideal electromagnetic sensitivity threshold function of the device under test, including: The discrete frequency sampling points are determined based on at least one of the following: At least one measurable point among the inflection points; On the curve, at least one measurable point has a frequency difference between the frequency and the frequency of the unmeasurable point in the inflection point that is less than a preset threshold. On the curve, at least one measurable point has a frequency whose difference from the frequency of the measurable point in the inflection point is less than a preset threshold.

[0009] According to the above technical solution, determining the electromagnetic sensitivity threshold estimation function of the device under test in the first frequency range based on the difference sequence between the measured electromagnetic sensitivity threshold and the ideal electromagnetic sensitivity threshold of the device under test at the discrete frequency sampling points includes: Based on the difference sequence between the measured electromagnetic sensitivity threshold and the ideal electromagnetic sensitivity threshold of the device under test at the discrete frequency sampling points, the electromagnetic sensitivity threshold difference function of the device under test in the first frequency range is determined by cubic spline interpolation. Based on the electromagnetic susceptibility threshold difference function of the device under test in the first frequency range and the ideal electromagnetic susceptibility threshold function of the device under test, the electromagnetic susceptibility threshold estimation function of the device under test in the first frequency range is determined.

[0010] According to the above technical solution, the step of constructing the electromagnetic sensitivity threshold difference function of the device under test in the first frequency range based on the difference sequence between the measured electromagnetic sensitivity threshold and the ideal electromagnetic sensitivity threshold of the device under test at the discrete frequency sampling points, using a cubic spline interpolation method, includes: Based on the continuity condition of the cubic spline polynomial curve and the difference sequence between the measured electromagnetic susceptibility threshold and the ideal electromagnetic susceptibility threshold of the device under test at the discrete frequency sampling points, the coefficients are determined. , ; Based on the coefficients The electromagnetic sensitivity threshold difference function of the device under test in the first frequency range is determined.

[0011] According to the above technical solution, the expression of the electromagnetic sensitivity threshold difference function includes: ; in, ; express The th discrete frequency sampling point Discrete frequency sampling points express The th discrete frequency sampling point A discrete frequency sampling point.

[0012] According to the above technical solution, determining the electromagnetic susceptibility threshold estimation function of the device under test in the first frequency range based on the electromagnetic susceptibility threshold difference function of the device under test in the first frequency range and the ideal electromagnetic susceptibility threshold function of the device under test includes: Based on the electromagnetic susceptibility threshold difference function of the device under test in the first frequency range and the ideal electromagnetic susceptibility threshold function of the device under test. Determine the electromagnetic susceptibility threshold estimation function of the device under test in the first frequency range. The expressions include: .

[0013] According to the above technical solution, determining the first frequency range based on the operating frequency range of the device under test that has potential interference with the device under test and the operating frequency range of the device under test includes: A first frequency range is determined based on the overlapping frequency range between the operating frequency range of the device under test and the operating frequency range of the device under test, which may potentially interfere with the device under test.

[0014] Another aspect of the present invention provides an electromagnetic susceptibility threshold estimation system based on sensitive response difference modeling, comprising a first determining module, a second determining module, and a third determining module; wherein, The first determining module is used to: determine a first frequency range based on the operating frequency range of the device under test that has potential interference with the device under test and the operating frequency range of the device under test, wherein the operating frequency range of the device under test includes the first frequency range; The second determining module is used to: determine discrete frequency sampling points in the first frequency range based on the ideal electromagnetic sensitivity threshold function of the device under test, wherein the ideal electromagnetic sensitivity threshold function is used to characterize the correlation between each frequency in the operating frequency range of the device under test and the ideal electromagnetic sensitivity threshold of the device under test in an ideal electromagnetic environment at each frequency; The third determining module is used to: determine an electromagnetic sensitivity threshold estimation function for the device under test in a first frequency range based on the difference sequence between the measured electromagnetic sensitivity threshold and the ideal electromagnetic sensitivity threshold of the device under test at the discrete frequency sampling points; wherein the electromagnetic sensitivity threshold estimation function is used to determine the estimated value of the electromagnetic sensitivity threshold of the device under test at a target frequency, and the target frequency is within the first frequency range.

[0015] The beneficial effects of this invention are as follows: This invention provides an electromagnetic susceptibility threshold estimation method based on sensitive response difference modeling. First, a first frequency range is determined, focusing on the core frequency interval affected by interference sources in the actual electromagnetic environment, enhancing the relevance of interference source frequencies to the threshold estimation problem. Then, discrete frequency sampling points within the first frequency range are determined based on the ideal electromagnetic susceptibility threshold function, completing the preliminary data preparation for threshold estimation. This invention also determines the electromagnetic susceptibility threshold estimation function for the device under test (DUT) within the first frequency range by using the difference sequence between the measured electromagnetic susceptibility threshold and the ideal electromagnetic susceptibility threshold at discrete frequency sampling points. This achieves accurate capture of the nonlinear dynamic changes in the threshold in the actual environment, obtaining the electromagnetic susceptibility threshold estimate of the target probability. Furthermore, only the difference modeling of discrete sampling points is needed to cover the threshold estimation across the entire first frequency range, eliminating the need for a large amount of measured full-frequency data. While ensuring the accuracy and robustness of the threshold estimation, this significantly reduces the workload of actual measurements. Overall, this invention balances the relevance, efficiency, and reliability of threshold estimation, effectively adapting to the electromagnetic susceptibility threshold assessment needs of electronic devices in complex electromagnetic environments.

[0016] Furthermore, this invention uses cubic spline interpolation to fit the difference sequence between the measured electromagnetic sensitivity threshold and the ideal electromagnetic sensitivity threshold at discrete frequency sampling points into an electromagnetic sensitivity threshold difference function. This enhances the adaptability to nonlinear changes in the threshold, avoids the overfitting / underfitting problems of traditional fitting, and significantly improves the threshold estimation accuracy in real-world environments.

[0017] Furthermore, this invention improves the targeting of sampling point selection and reduces redundant measurement workload by using various sampling point rules, such as prioritizing the inflection point of the ideal threshold curve or nearby measurable points as sampling points. At the same time, it ensures that the sampling points reflect key changes in the threshold, thereby improving the modeling quality.

[0018] Furthermore, this invention combines the ideal electromagnetic susceptibility threshold function and the electromagnetic susceptibility threshold difference function of the device under test within the first frequency range to obtain the electromagnetic susceptibility threshold estimation function. By separating the inherent characteristics of the device and the effects of environmental disturbances, it has the advantages of high efficiency, high accuracy and strong robustness.

[0019] Of course, any product implementing this invention does not necessarily need to achieve all of the advantages described above at the same time. Attached Figure Description

[0020] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0021] Figure 1 This is a flowchart of the electromagnetic sensitivity threshold estimation method based on sensitive response difference modeling according to an embodiment of the present invention; Figure 2 This is a schematic diagram comparing the curves of the ideal electromagnetic sensitivity threshold and the measured electromagnetic sensitivity threshold in a specific application of the electromagnetic sensitivity threshold estimation method based on sensitivity response difference modeling in an embodiment of the present invention. Figure 3 This is a schematic diagram comparing the electromagnetic sensitivity threshold estimates obtained by different fitting methods with the measured electromagnetic sensitivity thresholds in a specific application of the electromagnetic sensitivity threshold estimation method based on sensitivity response difference modeling in this embodiment of the invention. Detailed Implementation

[0022] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the invention.

[0023] It should be noted that the illustrations provided in the embodiments of the present invention are only schematic representations of the basic concept of the present invention. Therefore, the drawings only show the components related to the present invention and are not drawn according to the number, shape and size of the components in actual implementation. In actual implementation, the form, quantity and proportion of each component can be arbitrarily changed, and the layout of the components may also be more complex.

[0024] In this invention, it should also be noted that the terms "center," "upper," "lower," "left," "right," "vertical," "horizontal," "inner," and "outer," etc., indicate the orientation or positional relationship based on the orientation or positional relationship shown in the accompanying drawings. They are used only for the convenience of describing this application and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation. Therefore, they should not be construed as limitations on this application. Furthermore, the terms "first" and "second" are used only for descriptive and distinguishing purposes and should not be construed as indicating or implying relative importance.

[0025] Example 1 This embodiment provides an electromagnetic susceptibility threshold estimation method based on sensitive response difference modeling, the process of which is as follows: Figure 1 As shown, the steps include: S1. Based on the operating frequency range of the device under test (DUT) and the operating frequency range of the DUT, a first frequency range is determined, wherein the operating frequency range of the DUT includes the first frequency range. This step focuses on the core frequency range affected by interference sources in the actual electromagnetic environment, enhancing the relevance of interference source frequencies to threshold estimation problems.

[0026] Specifically, the device under test refers to a detection device that receives radio electromagnetic waves.

[0027] Devices that pose potential interference to the device under test refer to devices that can generate electromagnetic signals and whose signals may affect the normal operation of the device under test, including: devices of the same type as the device under test and devices of different types whose spectrum overlaps with that of the device under test.

[0028] The operating frequency range of the device that may interfere with the device under test: refers to the frequency range covered by the above-mentioned interfering device when it is operating normally, which is the frequency distribution range of the interference signal.

[0029] The operating frequency range of the device under test refers to the frequency range used by the device under test to perform its functions and operate normally, or the frequency range set by the user according to their needs.

[0030] The first frequency range can refer to the overlapping portion of the operating frequency range of the device under test (DUT) and the operating frequency range of the DUT, or a portion thereof. Since the first frequency range is the overlapping portion of the two, it must be a subset of the operating frequency range of the DUT, ensuring that the first frequency range is within the actual operating frequency range of the DUT.

[0031] For example, if the operating frequency range of the device under test is The operating frequency range of the device that poses potential interference to the device under test is: ,and In and Between, that is Then the fundamental overlap frequency range of the two is ;like All are in the above-mentioned Within the overlapping range, that is Then the overlapping area can be further divided into , , , , Equal sub-intervals; accordingly, the first frequency range can be either the basic overlapping range or... It can also be a sub-interval within an overlapping range, such as , , , , .

[0032] S2. Based on the ideal electromagnetic susceptibility threshold function of the device under test (DUT), determine the discrete frequency sampling points within the first frequency range. The ideal electromagnetic susceptibility threshold function characterizes the correlation between each frequency within the operating frequency range of the DUT and the ideal electromagnetic susceptibility threshold of the DUT at each frequency in an ideal electromagnetic environment. This step, relying on the ideal threshold function to determine the discrete frequency sampling points, completes the preliminary data preparation for threshold estimation.

[0033] Optionally, determining the discrete frequency sampling points within the first frequency range based on the ideal electromagnetic sensitivity threshold function of the device under test includes: The discrete frequency sampling point is determined based on at least one point randomly determined on the curve of the ideal electromagnetic sensitivity threshold function of the device under test; or... The discrete frequency sampling points are determined based on the inflection points on the curve of the ideal electromagnetic sensitivity threshold function of the device under test; or, The discrete frequency sampling points are determined based on the measurable points on the curve of the ideal electromagnetic sensitivity threshold function of the device under test. The discrete frequency sampling points are determined based on preset sampling rules.

[0034] Optionally, the discrete frequency sampling points are determined based on the inflection points on the curve of the ideal electromagnetic sensitivity threshold function of the device under test, including: The discrete frequency sampling points are determined based on at least one of the following: At least one measurable point among the inflection points; On the curve, at least one measurable point whose frequency is less than the frequency of the unmeasurable point in the inflection point is less than a preset threshold, that is, at least one measurable point near the unmeasurable point in the inflection point. On the curve, at least one measurable point whose frequency differs from the frequency of a measurable point in the inflection point by a preset threshold, i.e., at least one measurable point near a measurable point in the inflection point.

[0035] Optionally, the discrete frequency sampling point is determined based on the inflection point on the curve of the ideal electromagnetic sensitivity threshold function of the device under test, which can be any of the following cases (1)-(8): Case (1): At least one measurable point among the inflection points is determined as the discrete frequency sampling point; Case (2): On the curve, at least one measurable point whose frequency difference with the frequency of the unmeasurable point in the inflection point is less than a preset threshold is determined as the discrete frequency sampling point. Case (3): On the curve, at least one measurable point whose frequency is less than the frequency of the measurable point in the inflection point is determined as the discrete frequency sampling point. Case (4): At least one measurable point in the inflection point, and at least one measurable point on the curve whose frequency is less than the frequency of the unmeasurable point in the inflection point, are determined as the discrete frequency sampling point. Case (5): At least one measurable point in the inflection point, and at least one measurable point on the curve whose frequency is less than the frequency of the measurable point in the inflection point is less than a preset threshold, are determined as the discrete frequency sampling point. Case (6): The discrete frequency sampling point is determined as the point on which the difference between the frequency of the curve and the frequency of the measurable point in the inflection point is less than a preset threshold, and the discrete frequency sampling point is determined as the point on which the difference between the frequency of the curve and the frequency of the non-measurable point in the inflection point is less than a preset threshold. Case (7): At least one measurable point in the inflection point, and at least one measurable point on the curve whose frequency is less than a preset threshold and whose frequency is less than a preset threshold and whose frequency is less than a preset threshold, are determined as the discrete frequency sampling point. Case (8): From any of the measurable points determined in Case (1) to Case (7), at least one point is randomly selected, or a preset number of points are randomly selected, or at least one point is selected based on a preset rule (such as selecting one point every certain number of points after sorting the frequencies from smallest to largest) to determine the discrete frequency sampling point.

[0036] The preset threshold can be predefined or set by staff based on needs; no restrictions are imposed here.

[0037] In this embodiment, the discrete frequency sampling point is determined by the inflection point on the curve of the ideal electromagnetic sensitivity threshold function of the device under test. This can take into account the influence of different frequencies on the fluctuation of the electromagnetic sensitivity threshold and improve the estimation accuracy of the electromagnetic sensitivity threshold.

[0038] Optionally, determining the discrete frequency sampling point based on at least one randomly determined point on the curve of the ideal electromagnetic sensitivity threshold function of the device under test can be: At least one point is randomly determined on the curve of the ideal electromagnetic sensitivity threshold function of the device under test, and then measurable points are determined from it. All measurable points among the at least one randomly determined point are determined as the discrete frequency sampling points; or... The discrete frequency sampling points are determined by randomly selecting at least one measurable point from at least one randomly determined point. For example, the points can be randomly selected, or the first frequency range can be divided into frequency bands of equal or unequal length. Discrete frequency sampling points are selected from at least one measurable point from at least one randomly determined point, so that there is at least one discrete frequency sampling point in each frequency band, or so that the number of discrete frequency sampling points in all frequency bands is equal, or so that the difference in the number of discrete frequency sampling points in any two frequency bands is not greater than a preset difference, such as 1, 2, or 3. Any preset difference that can achieve the embodiments of this application is applicable and is not limited here. This can improve the accuracy of the estimation.

[0039] Optionally, determining the discrete frequency sampling points based on measurable points on the curve of the ideal electromagnetic susceptibility threshold function of the device under test can be: All measurable points on the curve are determined as the discrete frequency sampling points; or... The measurable points on the curve are determined as the discrete frequency sampling points, for example, by random selection, or by dividing the first frequency range into frequency bands of equal or unequal length. Discrete frequency sampling points are selected from the measurable points on the curve so that there is at least one discrete frequency sampling point in each frequency band, or so that the number of discrete frequency sampling points in all frequency bands is equal, or so that the difference in the number of discrete frequency sampling points in any two frequency bands is not greater than a preset difference, such as 1, 2, or 3. Any preset difference that can achieve the embodiments of this application is applicable and is not limited here; this can improve the accuracy of estimation.

[0040] It should be noted that: a measurable point refers to the electromagnetic susceptibility threshold of the device under test at the frequency corresponding to the measurable point, which can be measured. This is information that the staff can directly obtain.

[0041] In an optional embodiment, based on the ideal electromagnetic sensitivity threshold function of the device under test, discrete frequency sampling points within the first frequency range are determined. This can be achieved using a machine learning approach. First, measurable points from the historical estimation process of the device under test in multiple historical scenarios are collected. Then, the following steps are performed: (1) From the measurable points in the historical estimation process of the device under test in multiple historical scenarios, multiple measurable point sets a and at least one measurable point set b are randomly formed based on the following rules: any two measurable point sets a may include the same measurable points or different measurable points, any two measurable point sets b may include the same measurable points or different measurable points, and any measurable point set a and any measurable point set b may not include the same measurable points; (2) For each set of measurable points a, perform the following steps: take the measurable points in the set of measurable points a as discrete frequency sampling points, and then obtain the electromagnetic sensitivity threshold estimation function of the device under test in the first frequency range. Based on the electromagnetic sensitivity threshold estimation function, estimate the electromagnetic sensitivity threshold of at least one set of measurable points b. Based on the measured value and the estimated value of at least one set of measurable points b, determine the root mean square error (RMSE) or mean absolute error (MAE) between the measured value and the estimated value, and determine it as the estimation error corresponding to the set of measurable points a. (3) Determine the set of measurable points a with the smallest estimation error from all measurable point sets a, and perform leave-one-out error analysis on the measurable point set a with the smallest estimation error to obtain the measurable point set a'; (4) Repeat steps (1)-(3) until as many sets of [all measurable point set a, measurable point set a'] as possible are generated as samples; (5) Based on graph neural network (GNN), an initial selection model is established. Based on weighted BCE loss, a loss function is established. Based on the samples, all measurable points in the set a of all measurable points in each sample are used as input, and the set a' of measurable points is used as labels. The selection model is iteratively trained until the loss converges or the number of iterations exceeds the preset number (e.g., 100 times or 90 times, etc., which is not limited in this embodiment of the application), and the trained selection model is obtained.

[0042] Each time step (1) is executed, multiple sets of measurable points a and at least one set of measurable points b are re-formed.

[0043] Then, when determining the discrete frequency sampling points in the first frequency range based on the ideal electromagnetic sensitivity threshold function of the device under test, the measurable points on the curve of the ideal electromagnetic sensitivity threshold function of the device under test in the current real-time estimation scenario can be randomly selected as the input of the selection model, and its output can be used as the discrete frequency sampling points in the first frequency range.

[0044] Optionally, a preset sampling rule is that the discrete frequency sampling points within a first frequency range can be the curve of the ideal electromagnetic susceptibility threshold function of the device under test. Within the first frequency range, measurable inflection points or measurable points near the inflection points are found. Measurable means that the test state is available under experimental conditions. Furthermore, even if the inflection point is measurable, measurable points near the inflection point can also be selected.

[0045] The selectable point selection rules also include: selecting any point on the ideal electromagnetic susceptibility threshold function curve within the first frequency range; or selecting a point on the ideal electromagnetic susceptibility threshold function curve within the first frequency range based on specific needs, conditions, or rules.

[0046] The ideal electromagnetic susceptibility threshold function is related to the ideal electromagnetic susceptibility threshold and ideal interference of the device under test in an ideal electromagnetic environment at each frequency. That is, it is a statistical model composed of the inherent and definite sensitivity threshold reference of the device under test and the random and small interference fluctuations existing in the ideal environment.

[0047] Ideally, the device under test (DUT) should be in a microwave anechoic chamber or in a clean, interference-free external electromagnetic environment.

[0048] S3. Based on the difference sequence between the measured electromagnetic susceptibility threshold and the ideal electromagnetic susceptibility threshold of the device under test (DUT) at the discrete frequency sampling points, determine the electromagnetic susceptibility threshold estimation function of the DUT within a first frequency range. This electromagnetic susceptibility threshold estimation function is used to determine the estimated value of the electromagnetic susceptibility threshold of the DUT at a target frequency within the first frequency range. The electromagnetic susceptibility threshold estimation function obtained in this step only requires modeling the difference between discrete sampling points to cover the threshold estimation across the entire first frequency range, eliminating the need for a large amount of measured full-frequency data. This enhances the accuracy and robustness of the threshold estimation and significantly reduces the workload of actual measurements.

[0049] Specifically, the measured electromagnetic susceptibility threshold is the measured electromagnetic susceptibility threshold obtained through actual measurement. It is related to the amplitude deviation of the measured interference and the electromagnetic susceptibility threshold in the actual application environment. That is, it is a statistical model composed of the inherent sensitivity threshold of the device under test in the ideal state, the strong interference in the actual environment, and the systematic deviation caused by environmental factors.

[0050] The measured electromagnetic susceptibility threshold of the device under test (DUT) at discrete frequency sampling points: This refers to the electromagnetic susceptibility threshold measured at discrete frequency sampling points selected according to preset rules within a first frequency range in a real-world application environment. For example, discrete frequency sampling points include... The device under test is The measured electromagnetic susceptibility threshold below refers to a frequency of When electromagnetic waves are received by the device under test, the electromagnetic sensitivity threshold of the device under test is obtained by actual measurement.

[0051] The ideal electromagnetic susceptibility threshold refers to the ideal electromagnetic susceptibility threshold of the device under test at the aforementioned discrete frequency sampling points in an ideal environment. It is the specific value of the ideal electromagnetic susceptibility threshold function in step S2. For example, the discrete frequency sampling points include... The device under test is The ideal electromagnetic susceptibility threshold under the given frequency refers to... Substituting the ideal electromagnetic susceptibility threshold function, we obtain the specific electromagnetic susceptibility threshold.

[0052] The difference sequence refers to the set of measured electromagnetic susceptibility threshold and ideal electromagnetic susceptibility threshold values ​​corresponding to each discrete frequency sampling point, which is used to quantify the threshold deviation between the actual environment and the ideal environment.

[0053] The electromagnetic susceptibility threshold estimation function of the device under test in the first frequency range is determined by the electromagnetic susceptibility threshold difference function fitted by the difference sequence of the device under test in the first frequency range and the ideal electromagnetic susceptibility threshold function of the device under test, wherein the ideal electromagnetic susceptibility threshold function is selected from the part corresponding to the first frequency range.

[0054] Using the threshold function of the device under test in an ideal electromagnetic environment within a first frequency range as the frequency-related benchmark, and simultaneously fitting the aforementioned difference sequence to derive the electromagnetic susceptibility threshold difference function characterizing actual environmental disturbances, we obtain the electromagnetic susceptibility threshold estimation function. This estimation function is the result of superimposing the benchmark value and the disturbance deviation, enabling high-precision prediction of the threshold across the entire frequency band with only a small number of sampling points.

[0055] This method significantly improves the efficiency and robustness of threshold estimation in complex environments by separating the inherent characteristics of the equipment from the effects of environmental disturbances.

[0056] Example 2 This embodiment, based on the method described in Embodiment 1, provides another electromagnetic susceptibility threshold estimation method based on sensitive response difference modeling. The steps include: T1. Determine the first frequency range of the device under test.

[0057] Based on the operating frequency range of the device that may interfere with the device under test (DUT) and the operating frequency range of the DUT, a first frequency range is determined. The first frequency range is within the operating frequency range of the DUT.

[0058] Specifically, devices that pose potential interference to the device under test include: devices of the same type as the device under test, and devices of different types whose spectra overlap with those of the device under test.

[0059] The method for determining a first frequency range based on the operating frequency range of the device potentially interfering with the device under test (DUT) and the operating frequency range of the DUT includes: taking the overlapping portion or a portion of the overlapping portion of the operating frequency ranges of the device potentially interfering with the DUT and the DUT, to obtain the first frequency range, which is a specific frequency sub-interval directly related to the estimation of the electromagnetic susceptibility threshold of the DUT and requires focused analysis and modeling. Since the first frequency range is the overlapping portion of the two, it must be a subset of the operating frequency range of the DUT, ensuring that the first frequency range is within the actual operating frequency range of the DUT.

[0060] T2. Determine the discrete frequency sampling points of the device under test within the first frequency range.

[0061] Based on the ideal electromagnetic susceptibility threshold function of the device under test (DUT), discrete frequency sampling points within a first frequency range are determined. The ideal electromagnetic susceptibility threshold function characterizes the correlation between each frequency within the DUT's operating frequency range and the ideal electromagnetic susceptibility threshold of the DUT at each frequency in an ideal electromagnetic environment.

[0062] Specifically, the inflection point of the ideal electromagnetic susceptibility threshold function curve of the device under test is found within the first frequency range. An ideal electromagnetic environment refers to a situation where the device under test is in a microwave anechoic chamber, or where the external electromagnetic environment of the scene is clean and free from interference.

[0063] The expression for the ideal electromagnetic susceptibility threshold function includes:

[0064] in, For the ideal electromagnetic susceptibility threshold function, For the device under test to be in an ideal electromagnetic environment at a frequency The ideal electromagnetic susceptibility threshold is as follows: For frequency, The preset ideal interference Gaussian variance, It is an ideal Gaussian distribution sequence with interference.

[0065] The device under test is in an ideal electromagnetic environment at a specific frequency. The inherent basic sensitivity threshold. It is determined by the device's hardware design and reflects the device's own threshold reference for electromagnetic waves of different frequencies without additional interference.

[0066] Ideal interference This refers to the small random disturbances that still exist in an ideal environment, such as thermal noise from the internal circuitry of a device. These disturbances follow a Gaussian distribution. The larger the variance, the wider the range of random fluctuations of the ideal disturbance.

[0067] It depends on the frequency A varying Gaussian distribution sequence of ideal interference. It simulates the random fluctuation characteristics of ideal interference at different frequencies, so that the effect of ideal interference on the threshold reflects frequency correlation, that is, the random fluctuation amplitude of ideal interference is different at different frequencies.

[0068] In this embodiment, for each inflection point found, if the inflection point is a measurable point, the frequency corresponding to the inflection point is used as a discrete frequency sampling point. If the point is not a measurable point, a measurable point is searched near the inflection point, and the frequency corresponding to the measurable point is used as a discrete frequency sampling point. This embodiment improves the targeting of sampling point selection, reduces redundant measurement workload, and ensures that the sampling points reflect key changes in the threshold, supporting high-quality modeling.

[0069] T3. Determine the electromagnetic susceptibility threshold estimation function for the device under test.

[0070] Based on the difference sequence between the measured electromagnetic susceptibility threshold and the ideal electromagnetic susceptibility threshold of the device under test (DUT) at discrete frequency sampling points, an electromagnetic susceptibility threshold estimation function for the DUT in a first frequency range is determined. This electromagnetic susceptibility threshold estimation function is used to determine the estimated value of the electromagnetic susceptibility threshold of the DUT at a target frequency, where the target frequency is within the first frequency range.

[0071] T301. Based on the difference sequence between the measured electromagnetic susceptibility threshold and the ideal electromagnetic susceptibility threshold of the device under test at the discrete frequency sampling points, a cubic spline interpolation method is used to determine the electromagnetic susceptibility threshold difference function of the device under test in the first frequency range. This step uses cubic spline interpolation to determine the difference function and combines it with the estimation function of the ideal threshold, which enhances the adaptability to nonlinear changes in the threshold, avoids the overfitting / underfitting problem of traditional fitting, and significantly improves the threshold estimation accuracy in actual environments.

[0072] Specifically, based on the continuity condition of the cubic spline polynomial curve and the difference sequence between the measured electromagnetic susceptibility threshold and the ideal electromagnetic susceptibility threshold of the device under test at the discrete frequency sampling points, the coefficients are determined. , .

[0073] The continuity conditions for cubic spline polynomial curves include: continuous function values, where the function value at each node of a polynomial segment must be equal to the known difference data value at that point; continuous first derivatives, where the first derivatives of two adjacent polynomial segments at nodes must be equal; and continuous second derivatives, where the second derivatives of two adjacent polynomial segments at nodes must be equal.

[0074] Based on the coefficients The electromagnetic sensitivity threshold difference function of the device under test in the first frequency range is determined.

[0075] The expression for the electromagnetic susceptibility threshold difference function includes:

[0076] in, ; express The th discrete frequency sampling point Discrete frequency sampling points express The th discrete frequency sampling point A discrete frequency sampling point.

[0077] Furthermore, the cubic spline interpolation method can be replaced by the polynomial fitting method.

[0078] T302. Based on the electromagnetic susceptibility threshold difference function of the device under test in the first frequency range and the ideal electromagnetic susceptibility threshold function of the device under test, determine the electromagnetic susceptibility threshold estimation function of the device under test in the first frequency range.

[0079] Based on the electromagnetic susceptibility threshold difference function of the device under test in the first frequency range and the ideal electromagnetic susceptibility threshold function of the device under test. Determine the electromagnetic susceptibility threshold estimation function of the device under test in the first frequency range. The expressions include:

[0080] In this step, the ideal electromagnetic sensitivity threshold function curve segment selected corresponds to the first frequency range.

[0081] In practical applications, the electromagnetic susceptibility threshold of the device under test can be understood as the threshold under ideal conditions plus the deviation of the actual environment from the ideal environment. The ideal electromagnetic susceptibility threshold function... Electromagnetic susceptibility threshold difference function By superimposing these values, we can obtain the electromagnetic susceptibility threshold estimation function of the device under test in the first frequency range under actual application conditions.

[0082] This embodiment also provides an electromagnetic sensitivity threshold estimation system based on sensitivity response difference modeling, characterized in that the system includes a first determining module, a second determining module, and a third determining module; wherein, The first determining module is used to: determine a first frequency range based on the operating frequency range of the device under test that has potential interference with the device under test and the operating frequency range of the device under test, wherein the operating frequency range of the device under test includes the first frequency range.

[0083] Specifically, the inputs to the first determination module include the operating frequency range of the device with potential interference and the operating frequency range of the device under test. This module focuses on the core frequency band of the device under test that is most likely to be interfered with, thus defining the target interval for subsequent analysis.

[0084] The second determining module is used to: determine discrete frequency sampling points within the first frequency range based on the ideal electromagnetic sensitivity threshold function of the device under test, wherein the ideal electromagnetic sensitivity threshold function is used to characterize the correlation between each frequency in the operating frequency range of the device under test and the ideal electromagnetic sensitivity threshold of the device under test at each frequency in an ideal electromagnetic environment.

[0085] Specifically, the input to the second determination module includes the ideal electromagnetic susceptibility threshold function of the device under test. The task performed by this module is to provide discrete reference points for the subsequent comparison between the measured value and the ideal value of the electromagnetic susceptibility threshold, laying the foundation for subsequent difference calculation and function fitting.

[0086] The third determining module is used to: determine an electromagnetic sensitivity threshold estimation function for the device under test in a first frequency range based on the difference sequence between the measured electromagnetic sensitivity threshold and the ideal electromagnetic sensitivity threshold of the device under test at the discrete frequency sampling points; wherein the electromagnetic sensitivity threshold estimation function is used to determine the estimated value of the electromagnetic sensitivity threshold of the device under test at a target frequency, and the target frequency is within the first frequency range.

[0087] Specifically, the input to the third determining module includes a sequence of differences between the measured electromagnetic susceptibility threshold and the ideal electromagnetic susceptibility threshold at discrete frequency sampling points. The electromagnetic susceptibility threshold estimation function obtained by the task executed by this module can output an estimated value of the electromagnetic susceptibility threshold for any target frequency within the first frequency range, thus eliminating the need for actual measurement at each frequency and achieving efficient and accurate estimation from discrete sampling points to a continuous frequency range.

[0088] The way each module performs its tasks has been explained in the above-mentioned electromagnetic sensitivity threshold estimation method based on sensitive response difference modeling, and will not be repeated here.

[0089] This embodiment also provides a computer storage medium storing a computer program that can be executed by a processor. The computer program executes the above-described electromagnetic susceptibility threshold estimation method based on sensitive response difference modeling.

[0090] Example 3 This embodiment provides a third electromagnetic sensitivity threshold estimation method based on sensitivity response difference modeling, building upon the method described in Embodiment 2.

[0091] Let the electromagnetic susceptibility threshold of the device under ideal conditions be... Furthermore, the distribution of random fluctuations in the threshold caused by environmental interference follows a Gaussian distribution, and the ideal electromagnetic susceptibility threshold function curve can be represented as...

[0092] in, For the ideal electromagnetic susceptibility threshold function, For the device under test to be in an ideal electromagnetic environment at a frequency The ideal electromagnetic susceptibility threshold is as follows: For frequency, The preset ideal interference Gaussian variance, It is an ideal Gaussian distribution sequence with interference.

[0093] When the equipment is subjected to actual testing, factors such as the testing site, temperature, and humidity can affect the sensitivity threshold of the equipment compared to the ideal electromagnetic sensitivity threshold. In addition to the different random fluctuations, there is also an amplitude shift. .

[0094] Let the electromagnetic susceptibility threshold of the device under test in the actual test be . Furthermore, the distribution of random fluctuations in the threshold caused by environmental interference still follows a Gaussian distribution. Therefore, the curve of the electromagnetic susceptibility threshold changing with frequency under the influence of environmental interference is... It can be represented as

[0095] in, The preset measured Gaussian variance of the disturbance. The measured Gaussian distribution sequence of interference. This is the amplitude offset, caused by factors such as temperature, humidity, and location.

[0096] Measured interference Gaussian variance Used to quantify the fluctuation intensity of real-world environmental disturbances. The larger the value, the stronger the environmental interference, and the wider the range of random fluctuations in the measured electromagnetic susceptibility threshold.

[0097] Measured Gaussian distribution sequence of interference Used to simulate ubiquitous background electromagnetic noise in real-world environments, such as circuit thermal noise and environmental radiation, its frequency-dependent characteristics indicate that the noise at different discrete frequency sampling points is independent and has the same statistical properties.

[0098] Considering that some data will be measured during the actual test to correct the electromagnetic susceptibility threshold of the equipment under the actual application environment, it is assumed that there is a Q group of frequency points. Corresponding measurement value , .

[0099] The problem to be solved in this embodiment is described as follows: how to measure the electromagnetic susceptibility threshold under ideal training conditions when environmental interference is present. and measured values ​​in actual application environments Realization estimation This involves correcting the electromagnetic susceptibility threshold of the device under test (DUT) and then predicting the DUT at the target frequency in a real-world application environment. This problem can be understood as... arrive The problem of curve mapping, that is, fitting a curve under random disturbance conditions. .

[0100] Typical curve fitting methods include polynomial fitting and spline interpolation fitting.

[0101] (1) Polynomial fitting method Polynomial fitting fits data points by constructing a polynomial function. The expression for the order polynomial is shown below:

[0102] in, These are polynomial coefficients.

[0103] Typical polynomial fitting methods include the least squares method, with the objective function being:

[0104] The optimal solution of the polynomial equation can be obtained by differentiating each coefficient of the above equation.

[0105] Polynomial fitting methods are commonly used for trend analysis of smooth, continuous data. They quickly generate a global approximate model in low-complexity scenarios, featuring low computational complexity, requiring only the solution of coefficients from an nth-order linear equation system, good curve smoothness, and infinite differentiability. However, when the polynomial order is high, overfitting can occur, necessitating the selection of an appropriate order to balance the bias and variance of the fitted data.

[0106] (2) Spline interpolation fitting method Spline interpolation is a method that approximates data points using piecewise polynomial functions, often used to ensure the continuity and smoothness of the function. This method divides the data interval into multiple sub-intervals, uses low-order polynomials for piecewise fitting within each sub-interval, and ensures the smoothness of the overall curve through continuity and smoothness constraints at the nodes.

[0107] Cubic spline interpolation is a typical spline interpolation fitting method. Assume there are data points... ,in In the interval The cubic spline polynomial on is expressed as:

[0108] in ,coefficient The solution is obtained based on the continuity condition of the cubic spline polynomial curve. Coefficients The closed-form solution is

[0109] in, , , yes The second derivative.

[0110] Typical evaluation metrics for fitting results include root mean square error (RMSE) and coefficient of determination (R²). 2 Information guidelines (AIC / BIC).

[0111] (a) Root Mean Square Error (RMSE) The root mean square error (RMSE) characterizes the mean relationship between the squared errors of the predicted and actual values. The smaller the RMSE, the higher the accuracy of the model. The specific mathematical formula is as follows:

[0112] in It is the actual value. This is a predicted value.

[0113] (b) Coefficient of determination R 2 (Coefficient of Determination) Coefficient of determination R 2 This describes the proportion of the model's explainable variance to the total variance, and the specific formula is as follows:

[0114] in It is the mean of the true values, that is .

[0115] R 2 It reflects the model's interpretability and can be used to describe the relative interpretability of the model.

[0116] (c) Information Criteria Information criteria, including the Akaike Information Criterion (AIC) and the Bayesian Information Criterion (BIC), are important indicators used in statistics for model selection. They aim to find a balance between model complexity and goodness of fit to avoid overfitting.

[0117] The specific model expression for AIC is shown below:

[0118] in It refers to the number of model parameters. It is the log-likelihood value.

[0119] The specific model expression for BIC is shown below:

[0120] in It's the sample size. It refers to the number of model parameters. It is the log-likelihood value.

[0121] AIC is suitable for evaluating the fitting results of small samples, while BIC is suitable for evaluating the fitting results of large samples, but overfitting should be avoided.

[0122] Assuming the electromagnetic susceptibility threshold in the training scenario is known to be... Tested and measured in actual application environment , Electromagnetic susceptibility effect threshold data . The ideal electromagnetic susceptibility threshold data corresponding to each frequency are: .

[0123] Amplitude offset With frequency Change with change, The accuracy of the fit determines the ideal electromagnetic susceptibility threshold. Measured threshold in actual application environment Mapping accuracy. Amplitude offset. It can be represented as:

[0124] because The actual physical meaning of is the random fluctuation deviation caused by environmental interference during actual measurement, let

[0125] The above equation can be rewritten as follows

[0126] but This represents the difference between the measured electromagnetic susceptibility threshold and the ideal electromagnetic susceptibility threshold of the device under test at each discrete frequency sampling point.

[0127] With the help of A discrete frequency sampling point is used to obtain the difference sequence. For the difference sequence By fitting the data, a function is obtained that represents the difference between the measured electromagnetic susceptibility threshold and the ideal electromagnetic susceptibility threshold; this is known as the electromagnetic susceptibility threshold difference function. Using the electromagnetic susceptibility threshold difference function and ideal electromagnetic susceptibility threshold Obtain the electromagnetic susceptibility threshold estimation function That is, the threshold function of the device under test in an ideal electromagnetic environment within the first frequency range is used as the frequency-related benchmark value. At the same time, the electromagnetic susceptibility threshold difference function characterizing the actual environmental disturbance is fitted by the above difference sequence, and the electromagnetic susceptibility threshold estimation function is obtained.

[0128] The specific implementation process is as follows: The measured data is , The corresponding ideal electromagnetic susceptibility threshold is ,and The difference corresponding to each measured threshold is

[0129] Based on the cubic spline interpolation method described above, the cubic spline expression is obtained as follows:

[0130] Will Substituting into the above formula, we can obtain... :

[0131] in, , , , , , yes The second derivative.

[0132] Combining this with the above formula, we can obtain the fitted curve for practical application.

[0133] In summary, the steps of the electromagnetic susceptibility threshold estimation method based on sensitive response difference modeling in this embodiment include: P1. Determine the first frequency range and obtain the ideal electromagnetic susceptibility threshold function of the device under test. .

[0134] The method for determining the first frequency range has been described in Example 2 and will not be repeated here.

[0135] P2, obtain Q discrete frequency sampling points ( , The corresponding measured electromagnetic susceptibility threshold .

[0136] P3. Based on the ideal electromagnetic susceptibility threshold function in step P1 Q discrete frequency sampling points are obtained ( , ) corresponding to the ideal electromagnetic susceptibility threshold, calculate The difference between the measured electromagnetic susceptibility threshold and the ideal electromagnetic susceptibility threshold corresponding to each measured discrete frequency sampling point , as the difference sequence.

[0137] P4. Obtain the difference sequence using cubic spline interpolation. cubic spline curve , which serves as the electromagnetic sensitivity threshold difference function.

[0138] P5. Based on the electromagnetic susceptibility threshold difference function and ideal electromagnetic susceptibility threshold function The electromagnetic susceptibility threshold estimation function in practical application environments is obtained. ,pass This allows us to determine an estimated value for the electromagnetic susceptibility threshold of the target frequency within the first frequency range.

[0139] Example 4 This embodiment provides a specific application of the electromagnetic sensitivity threshold estimation method based on sensitivity response difference modeling described in Embodiment 3.

[0140] In this embodiment, an ideal disturbance Gaussian variance is set. Measured interference Gaussian variance Amplitude offset The number of discrete frequency sampling points Q=800. The comparison between the predicted electromagnetic susceptibility threshold and the measured electromagnetic susceptibility threshold obtained by the method in Example 3 is as follows: Figure 2 As shown.

[0141] Figure 2 The curves comparing the ideal electromagnetic sensitivity threshold and the measured electromagnetic sensitivity threshold obtained by the electromagnetic sensitivity threshold estimation method based on sensitivity response difference modeling described in Example 3 are presented intuitively. This verifies the method's ability to accurately estimate the electromagnetic sensitivity threshold under normal environmental interference. The curve trends show high consistency, indicating that the method can effectively capture the threshold variation with frequency.

[0142] Polynomial fitting (R) 2 ), and polynomial fitting (AIC) respectively represent R-based 2 Evaluation criteria and polynomial fitting methods based on the AIC evaluation criteria. Since typical polynomial fitting methods cannot obtain a fitting curve using the difference between the ideal electromagnetic susceptibility threshold and the measured electromagnetic susceptibility threshold, in this embodiment, polynomial fitting (R... 2 The polynomial fitting (AIC) method directly fits the curves using Q measured electromagnetic susceptibility thresholds. The fitting curves of the electromagnetic susceptibility threshold estimation method based on susceptibility response difference modeling described in Example 3 and the polynomial fitting method are shown below. Figure 3 As shown.

[0143] Depend on Figure 3 It can be seen that the estimated value curve of the electromagnetic susceptibility threshold fitted by the method of the present invention has a higher degree of overlap with the measured electromagnetic susceptibility threshold curve, which is significantly better than the traditional polynomial fitting method. The present invention models the "difference between the ideal and the measured threshold" and combines the adaptability of cubic spline interpolation to nonlinear changes, effectively avoiding the overfitting or underfitting problems that are prone to occur in traditional polynomial fitting, and is more adaptable to the nonlinear fluctuations of the electromagnetic susceptibility threshold caused by the coupling of multiple factors in the actual environment.

[0144] Furthermore, based on the RMSE calculation method described in Example 3, the cubic spline interpolation fitting method and polynomial fitting (R²) used in the electromagnetic sensitivity threshold estimation method based on sensitive response difference modeling can be obtained. 2 The RMSE values ​​for the polynomial fitting (AIC) were 0.6031, 1.3132, and 1.3175, respectively. This result indicates that the electromagnetic sensitivity threshold estimation method based on sensitivity response difference modeling described in Example 3 can effectively improve the accuracy of electromagnetic sensitivity threshold estimation.

[0145] Example 5 This embodiment provides a specific application of the electromagnetic sensitivity threshold estimation method based on sensitivity response difference modeling described in Embodiment 3.

[0146] The difference from Example 4 is that this example is used to verify the case where the measured data is subject to significant environmental interference. A Gaussian variance of the measured interference is set. The fitting method in the electromagnetic sensitivity threshold estimation method based on sensitive response difference modeling described in Example 3, based on R... 2 The RMSE of the best polynomial model selected and the best polynomial model selected according to the AIC criterion were calculated and compared in the table below.

[0147]

[0148] Table 1. Comparison of RMSE of various fitting methods under different measured Gaussian variances of interference. Based on the comparison of RMSE values ​​in the table above, it can be seen that the electromagnetic sensitivity threshold estimation method based on sensitive response difference modeling described in Example 3 is less affected by the variation of the Gaussian variance of the measured interference, and has better robustness, which is superior to the method in R... 2 Or the best polynomial model selected under the AIC criterion.

[0149] In summary, the electromagnetic sensitivity threshold estimation method based on sensitive response difference modeling provided by this invention takes into account the relevance, efficiency and reliability of threshold estimation, and can effectively adapt to the electromagnetic sensitivity threshold assessment needs in complex electromagnetic environments.

[0150] It should be noted that, depending on the implementation needs, the various steps / components described in this application can be broken down into more steps / components, or two or more steps / components or parts of the operation of steps / components can be combined into new steps / components to achieve the purpose of this invention.

[0151] The sequence number of each step in the above embodiments does not imply the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of this application.

[0152] It should be understood that those skilled in the art can make improvements or modifications based on the above description, and all such improvements and modifications should fall within the protection scope of the appended claims.

Claims

1. A method for estimating electromagnetic susceptibility threshold based on sensitive response difference modeling, characterized in that, include: Based on the operating frequency range of the device that may interfere with the device under test and the operating frequency range of the device under test, a first frequency range is determined, wherein the operating frequency range of the device under test includes the first frequency range; Based on the ideal electromagnetic sensitivity threshold function of the device under test, discrete frequency sampling points within the first frequency range are determined; the ideal electromagnetic sensitivity threshold function is used to characterize the correlation between each frequency in the operating frequency range of the device under test and the ideal electromagnetic sensitivity threshold of the device under test at each frequency in an ideal electromagnetic environment. Based on the difference sequence between the measured electromagnetic susceptibility threshold and the ideal electromagnetic susceptibility threshold of the device under test at the discrete frequency sampling points, an electromagnetic susceptibility threshold estimation function for the device under test in a first frequency range is determined; wherein, the electromagnetic susceptibility threshold estimation function is used to determine the estimated value of the electromagnetic susceptibility threshold of the device under test at a target frequency, wherein the target frequency is within the first frequency range.

2. The electromagnetic susceptibility threshold estimation method based on sensitive response difference modeling according to claim 1, characterized in that, The expression for the ideal electromagnetic susceptibility threshold function includes: ； in, For the ideal electromagnetic susceptibility threshold function, For the device under test to be in an ideal electromagnetic environment at a frequency The ideal electromagnetic susceptibility threshold is... For frequency, The preset ideal interference Gaussian variance, It is an ideal Gaussian distribution sequence with interference.

3. The electromagnetic susceptibility threshold estimation method based on sensitive response difference modeling according to claim 1, characterized in that, The determination of discrete frequency sampling points within the first frequency range based on the ideal electromagnetic sensitivity threshold function of the device under test includes: The discrete frequency sampling point is determined based on at least one point randomly determined on the curve of the ideal electromagnetic sensitivity threshold function of the device under test; or... The discrete frequency sampling points are determined based on the inflection points on the curve of the ideal electromagnetic sensitivity threshold function of the device under test; or, The discrete frequency sampling points are determined based on the measurable points on the curve of the ideal electromagnetic sensitivity threshold function of the device under test.

4. The electromagnetic susceptibility threshold estimation method based on sensitive response difference modeling according to claim 3, characterized in that, The discrete frequency sampling points are determined based on the inflection point on the curve of the ideal electromagnetic sensitivity threshold function of the device under test, including: The discrete frequency sampling points are determined based on at least one of the following: At least one measurable point among the inflection points; On the curve, at least one measurable point has a frequency difference between the frequency and the frequency of the unmeasurable point in the inflection point that is less than a preset threshold. On the curve, at least one measurable point has a frequency whose difference from the frequency of the measurable point in the inflection point is less than a preset threshold.

5. The electromagnetic susceptibility threshold estimation method based on sensitive response difference modeling according to claim 1, characterized in that, The step of determining the electromagnetic sensitivity threshold estimation function of the device under test in the first frequency range based on the difference sequence between the measured electromagnetic sensitivity threshold and the ideal electromagnetic sensitivity threshold at the discrete frequency sampling points includes: Based on the difference sequence between the measured electromagnetic sensitivity threshold and the ideal electromagnetic sensitivity threshold of the device under test at the discrete frequency sampling points, the electromagnetic sensitivity threshold difference function of the device under test in the first frequency range is determined by cubic spline interpolation. Based on the electromagnetic susceptibility threshold difference function of the device under test in the first frequency range and the ideal electromagnetic susceptibility threshold function of the device under test, the electromagnetic susceptibility threshold estimation function of the device under test in the first frequency range is determined.

6. The electromagnetic susceptibility threshold estimation method based on sensitive response difference modeling according to claim 5, characterized in that, The method of constructing an electromagnetic sensitivity threshold difference function for the device under test in the first frequency range based on the difference sequence between the measured electromagnetic sensitivity threshold and the ideal electromagnetic sensitivity threshold at the discrete frequency sampling points using cubic spline interpolation includes: Based on the continuity condition of the cubic spline polynomial curve and the difference sequence between the measured electromagnetic susceptibility threshold and the ideal electromagnetic susceptibility threshold of the device under test at the discrete frequency sampling points, the coefficients are determined. , ; Based on the coefficients The electromagnetic sensitivity threshold difference function of the device under test in the first frequency range is determined.

7. The electromagnetic susceptibility threshold estimation method based on sensitive response difference modeling according to claim 6, characterized in that, The expression for the electromagnetic susceptibility threshold difference function includes: ； in, ; express The th discrete frequency sampling point Discrete frequency sampling points express The th discrete frequency sampling point A discrete frequency sampling point.

8. The electromagnetic susceptibility threshold estimation method based on sensitive response difference modeling according to claim 5, characterized in that, The step of determining the electromagnetic susceptibility threshold estimation function of the device under test (DUT) in the first frequency range based on the difference function of the electromagnetic susceptibility threshold in the first frequency range and the ideal electromagnetic susceptibility threshold function of the DUT includes: Based on the electromagnetic susceptibility threshold difference function of the device under test in the first frequency range and the ideal electromagnetic susceptibility threshold function of the device under test. Determine the electromagnetic susceptibility threshold estimation function of the device under test in the first frequency range. The expressions include: 。 9. The electromagnetic susceptibility threshold estimation method based on sensitive response difference modeling according to claim 1, characterized in that, The determination of the first frequency range based on the operating frequency range of the device under test that has potential interference with the device under test, and the operating frequency range of the device under test, includes: A first frequency range is determined based on the overlapping frequency range between the operating frequency range of the device under test and the operating frequency range of the device under test that may interfere with the device under test.

10. An electromagnetic susceptibility threshold estimation system based on sensitive response difference modeling, characterized in that, The system includes a first determining module, a second determining module, and a third determining module; wherein, The first determining module is used to: determine a first frequency range based on the operating frequency range of the device under test that has potential interference with the device under test and the operating frequency range of the device under test, wherein the operating frequency range of the device under test includes the first frequency range; The second determining module is used to: determine discrete frequency sampling points in the first frequency range based on the ideal electromagnetic sensitivity threshold function of the device under test, wherein the ideal electromagnetic sensitivity threshold function is used to characterize the correlation between each frequency in the operating frequency range of the device under test and the ideal electromagnetic sensitivity threshold of the device under test in an ideal electromagnetic environment at each frequency; The third determining module is used to: determine an electromagnetic sensitivity threshold estimation function for the device under test in a first frequency range based on the difference sequence between the measured electromagnetic sensitivity threshold and the ideal electromagnetic sensitivity threshold of the device under test at the discrete frequency sampling points; wherein the electromagnetic sensitivity threshold estimation function is used to determine the estimated value of the electromagnetic sensitivity threshold of the device under test at a target frequency, and the target frequency is within the first frequency range.

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