An abnormality capturing method of a transient signal in a digital oscilloscope

Abstract

The application discloses an abnormality capturing method for transient signals in a digital oscilloscope. First, abnormal signals and system noise in the transient signals are extracted, then multi-section adaptive signal fitting is performed on the abnormal signals to obtain a reference template matrix, and finally, the Manhattan distance between each sampling point and the corresponding element in the reference template matrix is calculated based on the sampling sliding window comparison mode of the reference template matrix, and the proportion of the distance threshold value in the window is combined with the judgment threshold value. If the proportion of the distance threshold value is met, it is determined to be matched, a capture signal is triggered, and the captured abnormal signal is uploaded.

Description

Technical Field

[0001] This invention belongs to the field of digital oscilloscope technology, and more specifically, relates to a method for abnormal capture of transient signals in a digital oscilloscope. Background Technology

[0002] With the rapid development of electronic technologies such as communication and radar, signal frequencies are constantly increasing, and the characteristics of transient signals are becoming increasingly complex. Their non-stationarity and suddenness pose significant challenges to anomaly detection. Transient signals typically feature short duration, concentrated energy, and rapid frequency changes, making it difficult for traditional signal processing methods to effectively capture and identify anomalies. In practical applications, transient signal anomaly detection requires not only high-precision detection capabilities but also rapid system response and accurate anomaly identification to promptly assess their impact on system performance, quickly locate faults, and improve system reliability. However, the suddenness, diversity, and noise interference of transient signals further increase the difficulty of anomaly detection. Suddenness leads to the randomness of anomaly signal occurrences, and the uncertainty of their characteristic changes increases the complexity of detection. Furthermore, transient anomaly signals in different scenarios may exhibit similar characteristics, which can lead to misjudgments and redundant analysis, increasing the burden of data processing. Therefore, researching an efficient transient signal anomaly detection method is of great significance for improving the system's ability to detect and respond to transient anomalies.

[0003] In recent years, significant progress has been made in the field of transient signal processing with the rapid development of technologies such as compressed sensing, time-frequency analysis, and deep learning. These technologies have not only improved the accuracy of signal processing but also enhanced the robustness of algorithms in complex environments. Anomaly detection methods for transient signals can be mainly categorized into those based on time-domain analysis, frequency-domain analysis, and joint time-frequency analysis. Time-domain analysis methods capture anomalies by directly analyzing the time-domain characteristics of the signal, but they are sensitive to noise. Frequency-domain analysis methods analyze the frequency-domain characteristics of the signal using techniques such as Fourier transform, and are suitable for steady-state signals, but have limited ability to process non-stationary transient signals. Joint time-frequency analysis methods combine the advantages of both the time and frequency domains, enabling better capture of the non-stationary characteristics of transient signals, but their computational complexity is high. Furthermore, deep learning-based methods, through training on large amounts of data, can automatically extract signal features and achieve anomaly detection, but they are highly dependent on sample data and may perform poorly when faced with new types of anomaly signals. Therefore, in the research of transient signal anomaly capture methods, how to design a capture algorithm that combines high accuracy, strong robustness, and real-time performance under high noise, low sample data, and complex and ever-changing scenarios has become the focus of current research. By improving the accuracy and efficiency of transient signal anomaly capture, the system's ability to detect and process transient anomaly signals can be further enhanced, meeting the application requirements in complex and ever-changing environments. Summary of the Invention

[0004] The purpose of this invention is to overcome the shortcomings of the prior art and provide a method for abnormal capture of transient signals in a digital oscilloscope. Based on the real-time capture of transient signals through multi-segment adaptive signal fitting and dynamic threshold determination technology, this method solves the problems of low real-time capture rate of complex sporadic signals and inaccurate classification and poor noise resistance caused by noise interference.

[0005] To achieve the above-mentioned objective, the present invention provides a method for anomaly capture of transient signals in a digital oscilloscope, characterized by comprising the following steps:

[0006] (1) Extract abnormal signals and system noise from transient signals;

[0007] (2) Extract the noise threshold ξ;

[0008] (3) Perform multi-segment adaptive signal fitting on the abnormal signal to obtain the reference template matrix;

[0009] (4) Based on the reference template matrix, complete the anomaly capture of the signal under test;

[0010] The objective of this invention is achieved as follows:

[0011] This invention discloses a method for capturing transient signals in a digital oscilloscope. First, the abnormal signal and system noise in the transient signal are extracted. Then, multi-segment adaptive signal fitting is performed on the abnormal signal to obtain a reference template matrix. Finally, the Manhattan distance between each sampling point and the corresponding element of the reference template matrix is ​​calculated based on the sampling sliding window comparison method of the reference template matrix. The proportion of the distance threshold within the statistical window is combined with the judgment threshold. If the proportion of the distance threshold is met, it is judged as a match, triggering the capture signal and uploading the captured abnormal signal.

[0012] Meanwhile, the method for anomaly capture of transient signals in a digital oscilloscope according to the present invention also has the following beneficial effects:

[0013] (1) This invention employs multi-segment adaptive signal fitting and dynamic threshold determination techniques. By optimizing extreme values ​​and collaboratively eliminating false extreme values ​​through noise thresholding, it significantly improves the robustness of transient signal feature extraction. Compared with traditional fixed threshold or uniform segmentation methods, this invention can effectively distinguish between noise interference and real signal features, maintaining a capture accuracy of over 90% even when the signal-to-noise ratio is below 10dB, thus solving the problem of false triggering caused by noise sensitivity in traditional methods.

[0014] (2) This invention achieves high-precision signal modeling by adaptively optimizing the piecewise polynomial order and dynamically determining the fitting order of each segment based on the criterion of minimizing the residual sum of squares (RSS). Compared with the traditional fixed-order fitting method, this invention reduces the computational complexity by more than 30% while ensuring fitting accuracy, and is especially suitable for the real-time processing requirements of transient signals in high-speed acquisition systems.

[0015] (3) This invention proposes a probability threshold determination mechanism based on Manhattan distance, which, combined with the joint constraint of dynamic noise threshold and probability threshold, significantly reduces the risk of mismatch caused by occasional noise interference. Compared with the traditional single point-to-point matching method, the misjudgment rate is reduced by more than 50%, and it has a stronger tolerance for local distortions in signal morphology.

[0016] (4) This invention designs an extreme value segmentation rule and an adaptive judgment mechanism for signal trends. By analyzing the temporal distribution of extreme points, it dynamically selects the segmentation logic to ensure that the segmentation boundary strictly matches the signal change trend. Compared with manual experience-based segmentation methods, this invention improves the segmentation accuracy of non-stationary transient signals by 40% and significantly reduces the structural error of the fitting model.

[0017] (5) This invention dynamically generates a template matrix and quickly matches it with a sliding window, converting the fitted model into a reference template matrix, and uses parallel window comparison to achieve millisecond-level real-time detection. Compared with traditional full waveform matching technology, memory usage is reduced by 60%, and the processing speed on FPGAs is increased by more than 3 times, meeting the instantaneous response requirements of high-speed oscilloscopes for transient abnormal signals. Attached Figure Description

[0018] Figure 1 This is a flowchart of a transient signal anomaly capture method in a digital oscilloscope according to the present invention;

[0019] Figure 2 It is a diagram of the signal under test and the specific abnormal signals captured. Detailed Implementation

[0020] The specific embodiments of the present invention will now be described with reference to the accompanying drawings to enable those skilled in the art to better understand the invention. It should be particularly noted that in the following description, detailed descriptions of known functions and designs that might obscure the main content of the invention will be omitted here.

[0021] Example

[0022] Figure 1 This is a flowchart of a transient signal anomaly capture method in a digital oscilloscope according to the present invention.

[0023] In this embodiment, as Figure 1As shown, the present invention discloses a method for anomaly capture of transient signals in a digital oscilloscope, comprising the following steps:

[0024] (1) Extract abnormal signals and system noise from transient signals;

[0025] (1.1) Turn on the abnormal detection mode of the digital oscilloscope, and then connect the transient signal for abnormal detection. When an abnormality is detected in the transient signal, extract the abnormal part of the transient signal and store it in matrix form, which is denoted as abnormal signal X.

[0026]

[0027] Where, x ij W represents the value of the i-th sampling point of the abnormal signal collected at the j-th sampling time, W represents the number of sampling points collected at each sampling time, and T represents the number of sampling times.

[0028] In this embodiment, the received transient signal x(n) is a modulated signal with a carrier frequency of 1 GHz, a modulation frequency of 50 MHz, and a modulation depth of 50%, superimposed with a noise signal having a mean of 0 and a standard deviation of 0.03. Then, the sequence signal is discretized at a sampling rate of 20 GSPS, with a total sampling time of 1 second, resulting in a length of 20 × 10⁻⁶. 9 1D sampled data.

[0029] In this embodiment, the abnormal signal X has 80 rows and 250 × 10 columns. 6 This indicates that the signal is processed at a processing clock frequency of 250MHz.

[0030] (1.2) The digital oscilloscope collects system noise when there is no signal input, and stores it in matrix form. Let the stored system noise be...

[0031]

[0032] in, This represents the value of the system noise at the i-th sampling point collected at the j-th sampling time;

[0033] (2) Extract the noise threshold ξ;

[0034] System noise maximum value Subtract the minimum value The difference is used as the noise threshold ξ;

[0035] (3) Perform multi-segment adaptive signal fitting on the abnormal signal X to obtain the reference template matrix;

[0036] (3.1) Determine the segmentation points based on the time-domain extreme value distribution characteristics of the abnormal signal;

[0037] (3.1.1) Extract the amplitude of each sampling point in the abnormal signal X to form the amplitude matrix Y;

[0038]

[0039] Among them, y ij For x ij The corresponding amplitude;

[0040] (3.1.2) The amplitude matrix Y is represented in serial vector form according to the sampling time as follows:

[0041] Y = [y 11 ,…,y W1 ;y 12 ,…,y W2 ;…;y 1T ,…,y WT ]

[0042] (3.1.3) In this embodiment, by finding the local maxima and minima in the magnitude matrix, the segmentation points are the extreme points. The specific process is as follows:

[0043] Traverse each element y in the magnitude matrix Y ij Compare y ij The size of two adjacent elements, if y ij If both are greater than two elements, then record the amplitude y. ij For the maximum value y maxk Its corresponding coordinates are x maxk ;

[0044] After the elements have been traversed, construct the maximum value matrix Y. max ;

[0045]

[0046] Where k is the local maximum number, k = 1, 2, ..., K, and K is the number of local maximums;

[0047] Traversing the maximum matrix Y max For each maximum value, if two adjacent maximum values ​​are equal and there is a minimum value between two adjacent maximum values, then retain both of the two adjacent maximum values; otherwise, retain only one of the two maximum values.

[0048] (3.1.4) Traverse each element y in the magnitude matrix Y ij Compare y ij The size of two adjacent elements, if y ij If both are less than two elements, then record the amplitude y.ij The minimum value y minh Its corresponding coordinates are x minh ;

[0049] After the elements have been traversed, construct the minimum value matrix Y. min ;

[0050]

[0051] Where h is the local minimum number, h = 1, 2, ..., H, and H is the number of local minimums;

[0052] Traversing the minimum matrix Y min For each local minimum, if two adjacent local minimums are equal and there is no local maximum between two adjacent local minimums, then either local minimum is retained; otherwise, both local minimums are retained.

[0053] (3.1.5) Determine the segmentation point based on the coordinates of the first extreme point in the extreme value matrix;

[0054] (3.1.5.1) Using min(K,H) as the benchmark, for the maximum value matrix Y max or the minimum matrix Y min The elements after min(K,H) are deleted, and the remaining elements are numbered l, l=1,2,…,L,L=min(K,H);

[0055] (3.1.5.2) Compare the coordinates x of the first maximum point. max1 The coordinates of the first local minimum point x min1 The size of x max1 >x min1 When x is reached, proceed to step (3.1.5.3); when x is reached... max1 <x min1 When that happens, proceed to step (3.1.5.4);

[0056] (3.1.5.3) Using coordinate x max1 x min1 Starting from l=1, we iterate through each extreme point and determine the maximum and minimum values ​​in the upward trend of the abnormal signal: if y maxl+1 -y maxl >0 and 0 < y maxl -y minl+1 If ξ < ξ, then determine y maxl This is a noise point in the upward trend of the abnormal signal, not a maximum value, and it is derived from the maximum value matrix Y. max Remove from the middle, and at the same time remove y minl+1 From the minimum matrix Y minRemove from the middle; after completing the judgment, continue to judge the maximum and minimum values ​​in the downward trend: if y satisfies minl-1 -y minl >0 and 0 < y maxl -y minl If ξ < ξ, then determine y minl This is a noise point in the downward trend of the abnormal signal, not a minimum value, and it is derived from the minimum value matrix Y. min Remove from the middle, and at the same time remove y maxl From the maximum matrix Y max Remove from the middle, and denote the maximum matrix after removal as . The minimum matrix is

[0057] (3.1.5.4) Using coordinate x max1 x min1 Starting from l=1, we iterate through each extreme point and determine the maximum and minimum values ​​in the upward trend of the abnormal signal: if y maxl+1 -y maxl >0 and 0 < y maxl -y minl If ξ < ξ, then determine y maxl This is a noise point in the upward trend of the abnormal signal, not a maximum value, and it is derived from the maximum value matrix Y. max Remove from the middle, and at the same time remove y minl From the minimum matrix Y min Remove from the middle; after completing the judgment, continue to judge the maximum and minimum values ​​in the downward trend: if y satisfies minl-1 -y minl >0 and 0 < y maxl+1 -y minl If ξ < ξ, then determine y minl This is a noise point in the downward trend of the abnormal signal, not a minimum value, and it is derived from the minimum value matrix Y. min Remove from the middle, and at the same time remove y maxl From the maximum matrix Y max Remove from the middle, and denote the maximum matrix after removal as . The minimum matrix is

[0058] (3.1.5.5) According to the maximum value matrix is The minimum matrix is Obtain the segmentation point;

[0059] In this embodiment, two cases are discussed. The first case is when x max1 <x min1 When l is odd, the segmentation point is a maximum value matrix. The maximum value of x in maxlIf l is even, then the segmentation points form a minimum value matrix. The minimum value of x in minl Therefore, the segmentation point s l Expressed as a formula:

[0060]

[0061] When x max1 >x min1 When l is odd, the segmentation point is the minimum value matrix. The minimum value of x in minl If l is even, then the segmentation points are the maximum value matrix. The maximum value of x in maxl Therefore, the segmentation point s l Expressed as a formula:

[0062]

[0063] Where ρ represents the number of segmentation points.

[0064] (3.2) Perform independent fitting on each segment based on the segmentation points;

[0065] In this embodiment, the start and end intervals of the abnormal signal [x] can be determined based on the abnormal signal matrix X. 11 ,x WT The signal is divided into ρ signal intervals, denoted as follows: [x] 11 ,s1), [s1,s2), ..., [s l ,s l+1 ), …, [s ρ-1 ,x WT ];

[0066] The least squares method is used to fit each signal interval segment, and the fitting formula is as follows:

[0067]

[0068] Where x is the fitted variable, a0, a τ Let n be the coefficients of the polynomial to be fitted. l Let l be the fitting order corresponding to the l signal intervals;

[0069] Take any sampling point x in the l-th signal interval segment ij Substituting into the above fitting formula, we get x ij The corresponding fitted value P l (x ij );

[0070] Calculate sampling point x ij residual r ij :rij =x ij -P l (x ij )

[0071] Calculate the sum of squared differences (RSS) of all sampling points in the l-th signal interval. l ;

[0072]

[0073] For RSS l Find the partial derivative, set it to 0, and thus obtain the polynomial coefficients a. τ Then by increasing the order n l Find the RSS l The smallest corresponding order n l ;

[0074] Traverse each signal interval and determine the minimum order n of the fit for each segment. l and polynomial coefficients a τ Finally, a piecewise polynomial fitting model P(x) for the abnormal signal is constructed.

[0075]

[0076] (3.3) Based on the fitted values ​​of each sampling point in each segment of the piecewise polynomial fitting model P(x), the sampled values ​​of each sampling point in the abnormal signal X are replaced with the fitted values ​​to obtain the reference template matrix X of the abnormal signal. ref ;

[0077]

[0078] in, Indicates sampling point x ij The fitted value;

[0079] (4) Abnormal capture of the signal under test;

[0080] (4.1) Enable the anomaly capture function on the digital oscilloscope, then connect the signal under test for real-time acquisition. Let the matrix of the signal under test acquired in the m-th acquisition be:

[0081]

[0082] In this embodiment, a triangular wave is randomly superimposed on the normal signal, with each abnormality having a length of 256 points and a number of abnormal signals of 25, thereby forming the signal to be tested;

[0083] (4.2) Calculate the matrix C of the signal to be measured. m With reference template matrix X ref Manhattan distance D between corresponding elements ij ;

[0084]

[0085] (4.3) Calculate the distance from Manhattan to D ij The number of elements greater than the distance threshold χ, denoted as N. d ;

[0086] In this embodiment, the distance threshold χ can be obtained from the sum of squared residuals fitted to the abnormal signal and the noise threshold. The specific calculation formula is as follows:

[0087]

[0088] (4.4) Calculate the distance probability P d ;

[0089]

[0090] (4.5) Determine the distance probability P d Is it greater than the preset probability threshold δ? If P is satisfied... d If the value is greater than δ, then data acquisition stops, and the measured signal matrix C is output. m If the detected abnormal signal is positive, proceed to step (4.6); otherwise, proceed to step (4.6).

[0091] In this embodiment, the probability threshold δ satisfies: 0.8δ1;

[0092] (4.6) Let m = m + 1; set C m The first element c m,11 Remove, then in c m,WT Add the next sampling point c at the end. m,1T+1 Thus, the matrix C of the signal to be measured obtained from the (m+1)th acquisition is obtained. m+1 ;

[0093]

[0094] (4.7) Return to step (4.2) for the next iteration.

[0095] In this embodiment, after obtaining the coordinates of the abnormal position in the signal under test, the waveform segment is displayed, such as... Figure 2 As shown, in the test signal with a length of 1 second, at 2.505 × 10 -6 s~2.515×10 -6 An anomaly was detected at position 's', as shown in the left image; the extracted anomaly signal is shown in the right image.

[0096] Although the illustrative specific embodiments of the present invention have been described above to enable those skilled in the art to understand the invention, it should be understood that the invention is not limited to the scope of the specific embodiments. For those skilled in the art, various changes are obvious as long as they are within the spirit and scope of the invention as defined and determined by the appended claims, and all inventions utilizing the concept of the present invention are protected.

Claims

1. A method for anomaly capture of transient signals in a digital oscilloscope, characterized in that, Includes the following steps: (1) Extract abnormal signals and system noise from transient signals; (1.1) Turn on the anomaly detection mode of the digital oscilloscope, and then connect a transient signal for anomaly detection. When an anomaly is detected in the transient signal, extract the abnormal part of the transient signal and store it in matrix form, which is recorded as the abnormal signal. ; ； in, Indicates the abnormal signal at the 1st The first sampling time was collected at the [number] sampling moment. The value of each sampling point, The number of sampling points collected at each sampling time. Indicates the number of sampling times; (1.2) The digital oscilloscope collects system noise when there is no signal input, and stores it in matrix form. Let the stored system noise be... ; ； in, Indicates the system noise at the th The first sampling time was collected at the [number] sampling moment. The values ​​of each sampling point; (2) Extracting the noise threshold ; System noise maximum value Subtract the minimum value The difference is used as the noise threshold. ; (3) Abnormal signals Perform multi-segment adaptive signal fitting to obtain the reference template matrix; (3.1) Determine the segmentation points based on the time-domain extreme value distribution characteristics of the abnormal signal; (3.1.1) Extracting abnormal signals The amplitude of each sampling point constitutes an amplitude matrix. ; ； in, for The corresponding amplitude; (3.1.2) The magnitude matrix According to the sampling time, it can be represented in serial vector form as follows: ； (3.1.3) Traversing the magnitude matrix Each element ,Compare The size of two adjacent elements, if If both values ​​are greater than two elements, then record the magnitude. Maximum value Its corresponding coordinates are ; Construct the maximum value matrix after all elements have been traversed. ; ； in, Number the maximum value. , The number of maxima; Traversing the maximum matrix For each maximum value, if two adjacent maximum values ​​are equal and there is a minimum value between two adjacent maximum values, then retain both of the two adjacent maximum values; otherwise, retain only one of the two maximum values. (3.1.4) Traversing the magnitude matrix Each element ,Compare The size of two adjacent elements, if If both are less than two elements, then record the amplitude. Minimum value Its corresponding coordinates are ; Construct the minimum value matrix after all elements have been traversed. ; ； in, Number the minimum value. , The number of local minima; Traversing the minimum matrix For each local minimum, if two adjacent local minimums are equal and there is no local maximum between two adjacent local minimums, then either local minimum is retained; otherwise, both local minimums are retained. (3.1.5) Determine the segmentation point based on the coordinates of the first extreme point in the extreme value matrix; (3.1.5.1) with Based on the maximum value matrix, or minimum matrix middle The remaining elements are numbered as follows: (The text abruptly ends here, so the translation stops as well.) , , ; (3.1.5.2) Compare the coordinates of the first maximum point. Coordinates of the first minimum point The size, when When, proceed to step (3.1.5.3); when When that happens, proceed to step (3.1.5.4). (3.1.5.3) Using coordinates , As the starting point, i.e., initialization Iterate through each extreme point in turn, and determine the maximum and minimum values ​​in the upward trend of the abnormal signal: if the following conditions are met... and Then determine This represents noise points within the upward trend of abnormal signals, and is derived from the maximum value matrix. Remove from the middle, and at the same time From the minimum matrix Remove from the middle; after completing the judgment, continue to judge the maximum and minimum values ​​in the downward trend: If satisfied and Then determine This represents noise points in the downward trend of abnormal signals, and is derived from the minimum value matrix. Remove from the middle, and at the same time From the maximum matrix Remove from the middle, and denote the maximum matrix after removal as . The minimum value matrix is ; (3.1.5.4) Using coordinates , As the starting point, i.e., initialization Iterate through each extreme point in turn, and determine the maximum and minimum values ​​in the upward trend of the abnormal signal: if the following conditions are met... and Then determine This represents noise points within the upward trend of abnormal signals, and is derived from the maximum value matrix. Remove from the middle, and at the same time From the minimum matrix Remove from the middle; after completing the judgment, continue to judge the maximum and minimum values ​​in the downward trend: If satisfied and Then determine This represents noise points in the downward trend of abnormal signals, and is derived from the minimum value matrix. Remove from the middle, and at the same time From the maximum matrix Remove from the middle, and denote the maximum matrix after removal as . The minimum value matrix is ; (3.1.5.5) Based on the maximum value matrix as follows: The minimum matrix is Obtain the segmentation point; when At that time, if If the number is odd, then the segmentation points are the maximum value matrix. The maximum value in ;if If the number is even, then the segmentation points form a minimum value matrix. Minimum value in Therefore, the segmentation point Expressed as a formula: ； when At that time, if If the number is odd, then the segmentation points are the minimum value matrix. Minimum value in ;if If the number is even, then the segmentation points are the maximum value matrix. The maximum value in Therefore, the segmentation point Expressed as a formula: ； in, Indicates the number of segmentation points; (3.2) Perform independent fitting on each segment based on the segmentation points; Start and end intervals of abnormal signals Divided into The signal intervals are denoted as follows: ; The least squares method is used to fit each signal interval segment, and the fitting formula is as follows: ； in, For the fitted variables, , The coefficients of the polynomial to be fitted are . for The fitting order corresponding to each signal interval segment; Constructing a piecewise polynomial fitting model for abnormal signals ; ； (3.3) Piecewise polynomial fitting model The fitted values ​​of each sampling point within each segment will be used to identify abnormal signals. The sampled values ​​at each sampling point are replaced with fitted values ​​to obtain the reference template matrix of the abnormal signal. ; ； in, Indicates sampling point The fitted value; (4) Abnormal capture of the signal under test; (4.1) Enable the anomaly capture function on the digital oscilloscope, then connect the signal under test for real-time acquisition, and record the result. The matrix of the signals to be measured acquired in this acquisition is: ； (4.2) Calculate the matrix of the signal to be measured With reference template matrix Manhattan distance between corresponding elements ; ； (4.3) Calculate the distance to Manhattan Greater than the distance threshold The number of elements, the total number is denoted as . ; (4.4) Calculate the distance probability ; ； (4.5) Determine the distance probability Is it greater than the preset probability threshold? If satisfied If the acquisition stops, the measured signal matrix is ​​output. If the detected abnormal signal is positive, proceed to step (4.6); otherwise, proceed to step (4.6). (4.6) Order ;Will The first element Remove, then in Add the next sampling point at the end. Thus, the first The measured signal matrix acquired in this second acquisition ; ； (4.7) Return to step (4.2) for the next iteration.

2. The method for anomaly capture of transient signals in a digital oscilloscope according to claim 1, characterized in that, The fitting order The method for obtaining it is as follows: The first Any sampling point in each signal interval segment Substituting into the above fitting formula, we get Corresponding fitted value ; Calculate sampling points residual : ; Calculate the first Sum of squared differences of all sampling points in a signal interval ; ； right Find the partial derivatives, set them to zero, and you will get the polynomial coefficients. Then by increasing the order Find Minimum corresponding order .

3. The method for anomaly capture of transient signals in a digital oscilloscope according to claim 1, characterized in that, The distance threshold The calculation method is as follows: 。

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