Defect signal identification and extraction method, device, electronic equipment and storage medium

Abstract

The present invention relates to a defect signal identification and extraction method, device, electronic device and storage medium, the method comprising: reading in a signal to be inspected, decomposing the signal using a wavelet basis similar to a typical defect waveform, identifying a signal segment whose wavelet decomposition high-frequency signal distribution pattern is the same as the typical defect high-frequency signal distribution pattern as a signal segment containing a defect, extracting the boundary coordinates of the first dimension of the defect; judging the connectivity of the defect coordinates of adjacent channels, integrating the defects that meet the connectivity into one defect, and obtaining the boundary coordinates of the second dimension of the defect. The present invention realizes signal extraction within the two-dimensional boundary of the defect, effectively decomposes and identifies the defect signal from a complex signal, has no redundant information, and improves the accuracy of defect signal identification and the precision of defect signal extraction.

Description

Technical Field

[0001] The present invention relates to the technical field of nondestructive testing, and in particular to a defect signal recognition and extraction method, device, electronic equipment and computer storage medium of a magnetic flux leakage detection signal based on similar wavelet decomposition. Background Art

[0002] Defect detection of ferromagnetic components (such as oil and gas pipelines and railway tracks) is an important basis for ensuring structural integrity. During the service of ferromagnetic components such as oil and gas pipelines, defects will be generated in the pipe wall under the multiple effects of operating loads and environmental corrosion. If they are not discovered in time through detection and protective measures are not taken, the defects may continue to expand, eventually leading to pipeline ruptures, oil and gas leaks, and major safety accidents. Magnetic leakage detection has the advantages of simple operation and high detection efficiency. It can effectively detect metal missing defects in ferromagnetic components and is currently widely used in the detection of pipeline defects.

[0003] The methods for identifying defect signals in leakage magnetic detection that are currently widely used at home and abroad mainly include the amplitude difference threshold method, that is, the corresponding threshold is calculated based on the amplitude information of the detection data and the differential information between adjacent data, and then the threshold is used to determine whether the data is an abnormal point. Although this method can roughly identify defect information, in actual engineering, it cannot effectively identify multiple complex defect signals at close range, and the accuracy of defect signal identification is low. Summary of the invention

[0004] This summary is provided to introduce concepts in a brief form that will be described in detail in the detailed description below. This summary is not intended to identify key features or essential features of the claimed technical solution, nor is it intended to limit the scope of the claimed technical solution.

[0005] In a first aspect, the present invention discloses a defect signal recognition and extraction method, comprising the following steps:

[0006] Read in the signal to be detected of each channel in the magnetic flux leakage detection signal;

[0007] The signal to be inspected of each channel is decomposed by wavelet using a wavelet basis similar to the waveform of a typical defect signal, so as to obtain a high-frequency signal of the signal to be inspected of each channel after wavelet decomposition;

[0008] Traversing and searching for high-frequency signals of the signal to be inspected in each channel after wavelet decomposition, and identifying a signal segment in which the distribution pattern of the high-frequency signal in the signal to be inspected is the same as the distribution pattern of the high-frequency signal of a typical defect signal as a signal segment containing a defect;

[0009] According to the corresponding relationship between the coordinates of the defect center and the two boundaries and the distribution characteristics of the high-frequency signal, the coordinates of the defect center and the two boundaries of all defect signals contained in the signal to be inspected of each channel are extracted, and the extracted coordinates of the defect center and the two boundaries are stored to obtain the boundary coordinates of the first dimension of all defects;

[0010] Traversing the coordinates of the defect centers and the two boundaries of all the extracted channels, determining the connectivity of the coordinates of the defect centers and the two boundaries in the signals to be inspected of adjacent channels, integrating the defects whose coordinates of the defect centers and the two boundaries in the signals to be inspected of adjacent channels satisfy the connectivity into one defect, and obtaining the boundary coordinates of the second dimension of each defect;

[0011] Obtaining coordinates of a two-dimensional boundary of the defect according to the boundary coordinates of the first dimension and the boundary coordinates of the second dimension of the defect;

[0012] The signal within the two-dimensional boundary coordinate range of the defect is intercepted from the magnetic flux leakage detection signal to obtain the defect signal in the signal to be detected.

[0013] In a second aspect, the present invention discloses a defect signal identification and extraction device, comprising:

[0014] A reading unit, used for reading the signals to be detected of each channel in the magnetic flux leakage detection signal;

[0015] A decomposition unit, used to perform wavelet decomposition on the signal to be detected of each channel using a wavelet basis similar to the waveform of the typical defect signal, to obtain a high-frequency signal of the signal to be detected of each channel after wavelet decomposition;

[0016] A search and identification unit, configured to traverse and search the high-frequency signals of the signal to be detected in each channel after wavelet decomposition, and identify the signal segment whose distribution pattern of the high-frequency signal in the signal to be detected is the same as the distribution pattern of the high-frequency signal of the typical defect signal as a signal segment containing a defect;

[0017] A first coordinate extraction unit is used to extract the coordinates of the defect center and the two boundaries of all defects contained in the signal to be inspected of each channel according to the corresponding relationship between the coordinates of the defect center and the two boundaries and the distribution characteristics of the high-frequency signal, and store the extracted coordinates of the defect center and the two boundaries to obtain the boundary coordinates of the first dimension of all defects;

[0018] A second coordinate extraction unit is used to traverse the coordinates of the defect center and the two boundaries of all the extracted channels, determine the connectivity of the coordinates of the defect center and the two boundaries in the to-be-tested signal of the adjacent channels, integrate the defects whose coordinates of the defect center and the two boundaries in the to-be-tested signal of the adjacent channels satisfy the connectivity into one defect, and obtain the boundary coordinates of the second dimension of each defect;

[0019] A third coordinate extraction unit, used to obtain the coordinates of the two-dimensional boundary of the defect according to the boundary coordinates of the first dimension and the boundary coordinates of the second dimension of the defect;

[0020] The interception unit is used to intercept the signal within the two-dimensional boundary coordinate range of the defect from the magnetic leakage detection signal to obtain the defect signal in the signal to be detected.

[0021] In a third aspect, the present invention discloses an electronic device, comprising:

[0022] Processor and memory;

[0023] The memory is used to store computer operation instructions and input and output data;

[0024] The processor is used to execute the defect signal identification and extraction method by calling the computer operation instruction.

[0025] In a fourth aspect, the present invention discloses a computer storage medium having computer instructions stored thereon, characterized in that the computer storage medium stores at least one instruction, at least one program, a code set or an instruction set, and the at least one instruction, the at least one program, the code set or the instruction set are loaded and executed by the processor to implement the defect signal identification and extraction method.

[0026] The beneficial effects of the present invention are as follows: the present invention determines a wavelet basis similar to the waveform of the defect signal of a typical defect according to the waveform of the defect signal of a typical defect, thereby improving the accuracy of wavelet basis selection; the signal to be inspected of each channel is subjected to wavelet decomposition using a wavelet basis similar to the waveform of the typical defect signal to obtain a high-frequency signal, thereby accurately determining the distribution pattern and distribution characteristics of the defect signal; according to the distribution pattern of the defect signal, the defect signal in the signal to be inspected of each channel can be accurately identified; according to the coordinates of the typical defect signal and the connectivity of the coordinates of the defect center and the two boundaries in the signal to be inspected of adjacent channels, the boundary coordinates of the defect signal in two dimensions in the signal to be inspected of each channel in the magnetic flux leakage detection signal can be determined, thereby realizing the extraction of the signal within the two-dimensional boundary coordinate range of the defect; the present invention can effectively identify the defect signal from a complex signal without redundant information, thereby improving the accuracy of defect signal identification.

[0027] Based on the above technical solution, the present invention can also be improved as follows.

[0028] Furthermore, the method for obtaining a wavelet basis similar to the waveform of a typical defect signal comprises the following steps:

[0029] Pre-acquire a magnetic flux leakage detection signal of a channel containing a typical defect signal in the magnetic flux leakage detection signal;

[0030] According to the magnetic flux leakage detection signal of one channel containing the typical defect signal, the waveform of the typical defect signal, the coordinates of the defect center and two boundaries are obtained;

[0031] According to the waveform of the typical defect signal, a waveform similarity determination method is adopted to determine a wavelet basis similar to the waveform of the typical defect signal.

[0032] The beneficial effect of adopting the above further scheme is: according to the waveform of the defect signal of the typical defect, a waveform similarity determination method is adopted to determine the wavelet basis similar to the waveform of the defect signal of the typical defect, which can improve the accuracy of wavelet basis selection.

[0033] Furthermore, the method for acquiring the distribution pattern of the high-frequency signal of the typical defect signal comprises the following steps:

[0034] Performing wavelet decomposition on the typical defect signal using a wavelet basis similar to the waveform of the typical defect signal to obtain a high-frequency signal after wavelet decomposition;

[0035] The high-frequency signal after wavelet decomposition is subjected to feature analysis to determine the distribution features of the high-frequency signal after wavelet decomposition of the typical defect signal and the distribution pattern of the distribution features.

[0036] The beneficial effects of adopting the above further scheme are: using a wavelet basis similar to the waveform of a typical defect signal to perform wavelet decomposition on the signal to be inspected in each channel to obtain a high-frequency signal, which is conducive to accurately determining the distribution pattern and distribution characteristics of the defect signal; and is conducive to accurately identifying the signal segments containing defects in the signal to be inspected in each channel based on the distribution pattern of the defect signal.

[0037] Furthermore, the method for obtaining the corresponding relationship between the coordinates of the defect center and the two boundaries and the distribution characteristics of the high-frequency signal comprises the following steps:

[0038] According to the coordinates of the typical defect center and boundary corresponding to the distribution characteristics of the high-frequency signal after wavelet decomposition, a corresponding relationship between the coordinates of the defect center and two boundaries and the distribution characteristics of the high-frequency signal is established;

[0039] The distribution characteristic of the high-frequency signal is the local extreme value of the high-frequency signal; after performing wavelet decomposition on the signal to be detected of each channel using a wavelet basis similar to the waveform of the typical defect signal to obtain the high-frequency signal of the signal to be detected of each channel after wavelet decomposition, the method further includes:

[0040] Obtaining the local extreme value of the high-frequency signal of the signal to be inspected in each channel after wavelet decomposition, traversing and searching the obtained local extreme value of the high-frequency signal, and identifying the signal segment whose distribution pattern of the local extreme value of the high-frequency signal in the signal to be inspected is the same as the distribution pattern of the local extreme value of the high-frequency signal of the typical defect signal as the signal segment containing the defect;

[0041] According to the corresponding relationship between the coordinates of the defect center and the two boundaries of the typical defect signal and the distribution characteristics of the local extreme value of the high-frequency signal, the coordinates of the defect center and the two boundaries of all defect signals contained in the signal to be inspected of each channel are extracted, and the extracted coordinates of the defect center and the two boundaries are stored to obtain the boundary coordinates of the first dimension of all defects;

[0042] The local extreme value is an extreme value within a set signal length range in the high-frequency signal; the distribution characteristics of the local extreme value of the high-frequency signal include the position and size of the local extreme value in the high-frequency signal.

[0043] The beneficial effect of adopting the above further scheme is: determining the distribution characteristics of the local extreme values ​​of the high-frequency signal after wavelet decomposition by the position and size of the local extreme values ​​of the high-frequency signal, and accurately identifying the signal segment containing defects by identifying the signal segment whose distribution pattern of the local extreme values ​​of the high-frequency signal in the signal to be inspected is the same as the distribution pattern of the local extreme values ​​of the high-frequency signal of the typical defect signal as the signal segment containing defects; based on the distribution characteristics of the local extreme values ​​of the high-frequency signal after wavelet decomposition, the coordinates of the defect center and the two boundaries can be determined.

[0044] Further, the determining of the connectivity of the coordinates of the defect center and the two boundaries in the signal to be detected of the adjacent channel includes: if the coordinates of the defect center and the two boundaries in the signal to be detected of the adjacent channel meet the connectivity determination condition, then the signal segments containing defects in the signal to be detected of the adjacent channel belong to the same defect; if the coordinates of the defect center and the two boundaries in the signal to be detected of the adjacent channel do not meet the connectivity determination condition, then the defect signal segments in the signal to be detected of the adjacent channel do not belong to the same defect;

[0045] The connectivity determination conditions of the coordinates of the defect center and the two boundaries in the signal to be detected of the adjacent channels include:

[0046] If the difference between the coordinates of the defect centers in the signals to be inspected of adjacent channels and the difference between the coordinates of the two corresponding boundaries of the defect are both less than the set value, the defects in the signals to be inspected of adjacent channels are connected;

[0047] If any of the difference between the coordinates of the defect centers in the signals to be inspected in adjacent channels or the difference between the coordinates corresponding to the two boundaries of the defects in the signals to be inspected in adjacent channels is greater than a set value, the defects in the signals to be inspected in adjacent channels are not connected.

[0048] The beneficial effect of adopting the above further scheme is: by judging the connectivity of the coordinates of the defect center and the two boundaries in the signals to be inspected in adjacent channels, it is possible to judge whether the defect signal segments in the signals to be inspected in adjacent channels belong to the same defect, which is conducive to obtaining a complete defect.

[0049] The connectivity between the defect signals in the signals to be inspected in adjacent channels can be more accurately determined by the difference between the defect center coordinates in the signals to be inspected in adjacent channels and the difference between the boundaries corresponding to the coordinates of the two boundaries of the defect.

[0050] Further, the method for obtaining the boundary coordinates of the second dimension of each defect includes: determining the boundary of the second dimension of each defect according to the center coordinates and boundary coordinates of each defect signal in the signal to be detected, and the connectivity between the defects in the signal to be detected of adjacent channels;

[0051] The two-dimensional boundary of the defect is used to define the boundary position of the defect; the two-dimensional boundary of the defect includes the sequence number of the channel of the defect in the signal to be detected and the coordinates of the two boundaries of the first dimension of the defect signal;

[0052] For the defect that does not satisfy connectivity: the two-dimensional boundary of the defect includes the sequence number of the defect signal in the channel of the signal to be detected and the coordinates of the two boundaries of the first dimension of the defect;

[0053] For the defect that satisfies connectivity: the two-dimensional boundary includes the maximum channel sequence number and the minimum channel sequence number of all channels contained in the defect, and the maximum coordinate value and the minimum coordinate value of the boundary of the defect containing all the defect signals;

[0054] The coordinates of the two-dimensional boundary of the defect include boundary coordinates of the first dimension and boundary coordinates of the second dimension; the boundary coordinates of the first dimension are the maximum and minimum values ​​of the boundary coordinates of the defect in all channels containing the defect; the boundary coordinates of the second dimension of the defect are the maximum and minimum values ​​of the sequence of all channels containing the defect.

[0055] The beneficial effect of adopting the above further solution is that the second dimension boundary of each defect can be determined through the connectivity between the defects in the signals to be detected of adjacent channels, which is conducive to obtaining the two-dimensional boundary of a defect.

[0056] Furthermore, the defect signal identification and extraction method also includes: storing the defect signal in the signal to be inspected; when storing, densely storing the signal segments containing defects in the signal to be inspected, and sparsely storing the signal segments without defects in the signal to be inspected.

[0057] The beneficial effects of adopting the above further scheme are: by intensively storing the signal segments containing defects in the signal to be detected, high-resolution storage of defective signals is achieved; by sparsely storing the signal segments containing no defects in the signal to be detected, the storage amount of non-defective useless signals is greatly reduced. BRIEF DESCRIPTION OF THE DRAWINGS

[0058] Figure 1 It is a flow chart of the defect signal identification and extraction method in the present invention;

[0059] Figure 2 It is a schematic diagram of the defect signal identification and extraction device in the present invention;

[0060] Figure 3 A schematic diagram of an electronic device of the present invention;

[0061] Figure 4 This is a simulation diagram of the original signal of the magnetic flux leakage detection in Experiment 1 of the present invention;

[0062] Figure 5 This is a simulation diagram of the first defect magnetic leakage detection signal extracted in Experiment 1 of the present invention;

[0063] Figure 6 This is a simulation diagram of the second defect magnetic leakage detection signal extracted in Experiment 1 of the present invention;

[0064] Figure 7 This is a simulation diagram of the third defect magnetic leakage detection signal extracted in Experiment 1 of the present invention;

[0065] Figure 8 This is a simulation diagram of a two-dimensional magnetic flux leakage detection signal of Experiment 2 of the present invention;

[0066] Fig. 9 This is a simulation diagram of defect area identification and magnetic flux leakage detection signal extraction results of Experiment 2 of the present invention;

[0067] Fig.10 This is a simulation diagram of the two-dimensional signal of the magnetic flux leakage detection in Experiment 3 of the present invention;

[0068] Fig.11 This is a simulation diagram of the defect area identification and magnetic flux leakage detection signal extraction results of Experiment 3 of the present invention.

[0069] In the accompanying drawings, the components represented by the reference numerals are listed as follows:

[0070] Figure 4-11 Middle: x-length; y-width; V-amplitude. DETAILED DESCRIPTION

[0071] The principles and features of the present invention are described below. The examples given are only used to explain the present invention and are not used to limit the scope of the present invention.

[0072] Embodiment 1:

[0073] like Figure 1 As shown, the defect signal recognition and extraction method includes the following steps:

[0074] Acquire a magnetic flux leakage detection signal of a channel containing typical defects in the magnetic flux leakage detection signal;

[0075] According to the magnetic flux leakage detection signal of a channel containing typical defects, the waveform of the typical defect signal, the coordinates of the defect center and two boundaries are obtained;

[0076] According to the waveform of the typical defect signal, a waveform similarity determination method is adopted to determine the wavelet basis similar to the waveform of the typical defect signal;

[0077] Use wavelet basis to perform wavelet decomposition on typical defect signals to obtain high-frequency signals after wavelet decomposition;

[0078] Perform feature analysis on the high-frequency signal after wavelet decomposition to determine the distribution characteristics and distribution pattern of the high-frequency signal after wavelet decomposition of the typical defect signal;

[0079] According to the coordinates of the typical defect center and boundary corresponding to the distribution characteristics of the high-frequency signal after wavelet decomposition, the corresponding relationship between the coordinates of the defect center and the two boundaries and the distribution characteristics of the high-frequency signal is established;

[0080] Among them, optionally, the distribution characteristics of the high-frequency signal after wavelet decomposition of the typical defect signal are the distribution characteristics of local extreme values ​​in the high-frequency signal; the local extreme value is the extreme value within a set signal length range in the high-frequency signal; the distribution characteristics of the high-frequency signal include the position and size of the local extreme value in the high-frequency signal.

[0081] The distribution pattern of the high-frequency signal after wavelet decomposition of the typical defect signal is used to distinguish the distribution characteristics of the high-frequency signal according to the distribution characteristics of the high-frequency signal, that is, the position and size of the local extreme value in the high-frequency signal.

[0082] Among them, according to the distribution characteristics of the high-frequency signal, that is, the position and size of the local extreme value in the high-frequency signal, the coordinates of the defect center and the two boundaries are extracted. The corresponding relationship between the coordinates of the defect center and the two boundaries and the distribution characteristics of the high-frequency signal is the corresponding relationship between the position and size of the local extreme value in the high-frequency signal and the coordinates of the defect center and the two boundaries.

[0083] The above processing process for typical defect signals only needs to be performed once in advance, and can be built into the processing software of the electronic device. In actual application, the above processing process for typical defect signals does not need to be performed again.

[0084] Read in the signal to be detected of each channel in the magnetic flux leakage detection signal;

[0085] Among them, the leakage magnetic detection signal includes leakage magnetic detection signals of multiple channels; the leakage magnetic detection signal is a signal obtained by leakage magnetic detection. Leakage magnetic detection has the advantages of simple operation and high detection efficiency, and can effectively detect metal missing defects in ferromagnetic components.

[0086] The signal to be inspected of each channel is decomposed by wavelet using a wavelet basis similar to the waveform of a typical defect signal, so as to obtain a high-frequency signal of the signal to be inspected of each channel after wavelet decomposition;

[0087] Among them, the high-frequency signal obtained by performing wavelet decomposition on the signal to be detected of each channel using a wavelet basis similar to the waveform of a typical defect signal can accurately reflect the characteristics of the signal to be detected.

[0088] The high-frequency signals of the signals to be inspected in each channel after wavelet decomposition are searched traversally, and the signal segments whose high-frequency signal distribution pattern in the signals to be inspected is the same as the high-frequency signal distribution pattern of the typical defect are identified as signal segments containing defects;

[0089] According to the established correspondence between the coordinates of the defect center and the two boundaries and the distribution characteristics of the high-frequency signal, the coordinates of the defect center and the two boundaries of all defects contained in the signal to be inspected of each channel are extracted, and the extracted coordinates of the defect center and the two boundaries are stored to obtain the boundary coordinates of the first dimension of all defects;

[0090] Traverse the coordinates of the defect centers and two boundaries of all extracted channels, determine the connectivity of the coordinates of the defect centers and two boundaries in the signals to be inspected of adjacent channels, integrate the defects whose coordinates of the defect centers and two boundaries in the signals to be inspected of adjacent channels satisfy the connectivity into one defect, and obtain the boundary coordinates of the second dimension of each defect;

[0091] According to the boundary coordinates of the first dimension of the defect and the boundary coordinates of the second dimension, the coordinates of the two-dimensional boundary of the defect are obtained;

[0092] The signal within the two-dimensional boundary coordinate range of the defect is intercepted from the magnetic flux leakage detection signal to obtain the defect signal in the signal to be detected.

[0093] The present invention determines a wavelet basis similar to the waveform of the defect signal of a typical defect based on the waveform of the defect signal of the typical defect, thereby improving the accuracy of wavelet basis selection; performs wavelet decomposition on the signal to be inspected of each channel using the wavelet basis similar to the waveform of the typical defect signal to obtain a high-frequency signal, thereby accurately determining the distribution pattern and distribution characteristics of the defect signal; according to the distribution pattern of the defect signal, the defect signal in the signal to be inspected of each channel can be accurately identified; according to the coordinates of the defect center and the two boundaries of the typical defect, and the connectivity of the coordinates of the defect center and the two boundaries in the signal to be inspected of adjacent channels, the boundary coordinates of the two dimensions of the defect signal in the signal to be inspected of each channel in the magnetic leakage detection signal can be determined, thereby realizing the extraction of the signal within the two-dimensional boundary coordinate range of the defect; the present invention can effectively identify the defect signal from the complex signal without redundant information, thereby improving the accuracy of defect signal identification.

[0094] Optionally, a method for obtaining a wavelet basis similar to the waveform of a typical defect signal comprises the following steps:

[0095] Pre-acquire a magnetic flux leakage detection signal of a channel containing a typical defect signal in the magnetic flux leakage detection signal;

[0096] According to the magnetic flux leakage detection signal of a channel containing a typical defect signal, the waveform of the typical defect signal, the coordinates of the defect center and two boundaries are obtained;

[0097] According to the waveform of the typical defect signal, a waveform similarity determination method is adopted to determine the wavelet basis similar to the waveform of the typical defect signal.

[0098] Among them, there are many ways to implement the method of waveform similarity determination based on the waveform of the typical defect signal to determine the wavelet basis similar to the waveform of the typical defect signal. In an optional embodiment of the present invention, the leakage magnetic detection signal containing defects in the channel of the leakage magnetic detection signal is pre-extracted; a waveform similarity determination algorithm (such as the Pearson coefficient waveform similarity calculation method) is used to select the wavelet basis most similar to the waveform of the leakage magnetic detection signal containing defects, such as the Rbio3.1 wavelet basis.

[0099] In actual application, based on the waveform of the defect signal of a typical defect, a waveform similarity determination method is adopted to determine a wavelet basis similar to the waveform of the defect signal of the typical defect, which can improve the accuracy of wavelet basis selection.

[0100] Optionally, a method for acquiring a distribution pattern of a high-frequency signal of a typical defect signal comprises the following steps:

[0101] The typical defect signal is decomposed by wavelet using a wavelet basis similar to the waveform of the typical defect signal to obtain a high-frequency signal after wavelet decomposition;

[0102] The characteristic analysis of the high-frequency signal after wavelet decomposition is performed to determine the distribution characteristics and distribution pattern of the high-frequency signal after wavelet decomposition of the typical defect signal.

[0103] In actual application, the signal to be inspected in each channel is decomposed by wavelet using a wavelet basis similar to the waveform of a typical defect signal to obtain a high-frequency signal, which is beneficial to accurately determine the distribution pattern and distribution characteristics of the defect signal; it is beneficial to accurately identify the signal segments of the defects contained in the signal to be inspected in each channel according to the distribution pattern of the defect signal.

[0104] Optionally, a method for acquiring the corresponding relationship between the coordinates of the defect center and the two boundaries and the distribution characteristics of the high-frequency signal comprises the following steps:

[0105] According to the coordinates of the typical defect center and boundary corresponding to the distribution characteristics of the high-frequency signal after wavelet decomposition, the corresponding relationship between the coordinates of the defect center and the two boundaries and the distribution characteristics of the high-frequency signal is established;

[0106] The distribution characteristic of the high-frequency signal is the local extreme value of the high-frequency signal; after the wavelet decomposition of the signal to be inspected of each channel is performed using a wavelet basis similar to the waveform of the typical defect signal to obtain the high-frequency signal of the signal to be inspected of each channel after the wavelet decomposition, it also includes:

[0107] Obtain the local extreme value of the high-frequency signal of the signal to be inspected in each channel after wavelet decomposition, traverse and search the local extreme value of the high-frequency signal obtained, and identify the signal segment whose distribution pattern of the local extreme value of the high-frequency signal in the signal to be inspected is the same as the distribution pattern of the local extreme value of the high-frequency signal of the typical defect signal as the signal segment containing the defect;

[0108] According to the corresponding relationship between the coordinates of the defect center and the two boundaries of the typical defect signal and the distribution characteristics of the local extreme value of the high-frequency signal, the coordinates of the defect center and the two boundaries of all defect signals contained in the signal to be inspected of each channel are extracted, and the extracted coordinates of the defect center and the two boundaries are stored to obtain the boundary coordinates of the first dimension of all defects;

[0109] The local extreme value is an extreme value within a set signal length range in the high-frequency signal; the distribution characteristics of the local extreme value of the high-frequency signal include the position and size of the local extreme value in the high-frequency signal.

[0110] In actual application, the distribution characteristics of the local extreme values ​​of the high-frequency signal after wavelet decomposition are determined by the position and size of the local extreme values ​​of the high-frequency signal. By identifying the signal segment with the same distribution pattern of the local extreme values ​​of the high-frequency signal in the signal to be inspected as the signal segment containing defects, the signal segment containing defects can be accurately identified; according to the distribution characteristics of the local extreme values ​​of the high-frequency signal after wavelet decomposition, the coordinates of the defect center and the two boundaries can be determined.

[0111] Optionally, determining the connectivity of the coordinates of the defect center and two boundaries in the signal to be inspected of the adjacent channel, including: if the coordinates of the defect center and two boundaries in the signal to be inspected of the adjacent channel meet the connectivity determination condition, then the signal segments containing defects in the signal to be inspected of the adjacent channel belong to the same defect; if the coordinates of the defect center and two boundaries in the signal to be inspected of the adjacent channel do not meet the connectivity determination condition, then the signal segments containing defects in the signal to be inspected of the adjacent channel do not belong to the same defect;

[0112] The connectivity determination conditions of the coordinates of the defect center and the two boundaries in the adjacent channel's to-be-detected signals include:

[0113] If the difference between the coordinates of the defect centers in the signals to be inspected of adjacent channels and the difference between the coordinates of the two corresponding boundaries of the defect are both less than the set value, the defects in the signals to be inspected of adjacent channels are connected;

[0114] If any of the difference between the coordinates of the defect centers in the signals to be inspected in adjacent channels or the difference between the coordinates corresponding to the two boundaries of the defects in the signals to be inspected in adjacent channels is greater than a set value, the defects in the signals to be inspected in adjacent channels are not connected.

[0115] The setting value is set according to the needs in the actual application process. Generally, the setting value ranges from 3 to 5, which can distinguish whether there is connectivity between the defect signals in the signals to be detected of adjacent channels.

[0116] As an example: let the setting value be 3, assuming that the magnetic flux leakage detection signal includes 10 channels, each channel includes 1000 signals, and the channel sequence numbers of two adjacent channels are 2 and 3 respectively. Assume that there is a defect in the channel with a channel sequence number of 2, the coordinates of the defect center are 103, and the coordinates of the two boundaries are 100 and 106 respectively; there is a defect in the channel with a channel sequence number of 3, the coordinates of the defect center are 102, and the coordinates of the two boundaries are 97 and 105 respectively; the difference between the coordinates of the defect center in the adjacent channels to be detected and the difference between the boundaries corresponding to the coordinates of the two boundaries of the defect are both less than the setting value 3, then the defect signals in the adjacent channels to be detected are connected.

[0117] If any of the difference between the defect center coordinates in the signals to be inspected in adjacent channels or the difference between the two boundary coordinates of the defects in the signals to be inspected in adjacent channels is greater than the set value, there is no connectivity between the defect signals in the signals to be inspected in adjacent channels, and no further details will be given.

[0118] In actual application, by judging the connectivity of the coordinates of the defect center and the two boundaries in the signals to be inspected of adjacent channels, it is possible to judge whether the signal segments containing defects in the signals to be inspected of adjacent channels belong to the same defect, which is conducive to obtaining a complete defect; by judging the difference between the coordinates of the defect centers in the signals to be inspected of adjacent channels and the difference between the boundaries corresponding to the coordinates of the two boundaries of the defect, it is possible to more accurately judge the connectivity between the signal segments containing defects in the signals to be inspected of adjacent channels.

[0119] Optionally, the method for obtaining the boundary coordinates of the second dimension of each defect includes: determining the boundary of the second dimension of each defect according to the center coordinates and boundary coordinates of each defect signal in the to-be-detected signal, and the connectivity between the defects in the to-be-detected signals of adjacent channels;

[0120] The two-dimensional boundary of a defect is used to define the boundary position of a defect; the two-dimensional boundary of a defect includes the sequence number of the channel of the defect signal in the signal to be detected and the coordinates of the two boundaries of the first dimension of the defect signal;

[0121] For defect signals that do not meet connectivity requirements: the two-dimensional boundary of a defect includes the sequence number of the defect signal in the channel of the signal to be tested and the coordinates of the two boundaries of the first dimension of the defect signal;

[0122] For defect signals that meet connectivity requirements: the two-dimensional boundary includes the maximum channel sequence number and the minimum channel sequence number of all channels in a defect, as well as the maximum coordinate value and the minimum coordinate value of the boundary of the defect containing all defect signals;

[0123] The coordinates of the two-dimensional boundary of the defect include the boundary coordinates of the first dimension and the boundary coordinates of the second dimension; the boundary coordinates of the first dimension are the maximum and minimum values ​​of the boundary coordinates of the defect in all channels containing the defect; the boundary coordinates of the second dimension of the defect are the maximum and minimum values ​​of the sequence of all channels containing the defect.

[0124] In actual application, the connectivity between defect signals in the signals to be inspected in adjacent channels can be used to determine the boundary of the second dimension of each defect, which is helpful for obtaining the two-dimensional boundary of a defect.

[0125] As an example: let the setting value be 3, assume that the magnetic flux leakage detection signal includes 10 channels, each channel includes 1000 signals, and assume that the channel sequence numbers are 2, 3, and 4. Assume that there is a defect in the channel with the channel sequence number 2, the coordinate of the defect center is 103, the coordinate of the first boundary is 100, and the coordinate of the second boundary is 106; there is a defect in the channel with the channel sequence number 3, the coordinate of the defect center is 102, the coordinate of the first boundary is 97, and the coordinate of the second boundary is 105; there is a defect in the channel with the channel sequence number 4, the coordinate of the defect center is 101, the coordinate of the first boundary is 96, and the coordinate of the second boundary is 105; then the first boundary of the boundary coordinate of the first dimension of the defect is the smallest value of the coordinate of the first boundary among the three defects, that is, 96; the second boundary of the boundary coordinate of the first dimension of the defect is the largest value of the coordinate of the second boundary among the three defects, that is, 106; the boundary coordinate of the first dimension of the defect is (96,106), and the boundary coordinate of the second dimension of the defect is (2,4).

[0126] Optionally, the defect signal identification and extraction method also includes: storing the defect signal in the signal to be inspected, and when storing, densely storing the signal segments containing defects in the signal to be inspected, and sparsely storing the signal segments containing no defects in the signal to be inspected, so as to achieve compression of the stored data.

[0127] In actual application, the signal segments containing defects in the signal to be inspected are densely stored to achieve high-resolution storage of the defective signals; the signal segments without defects in the signal to be inspected are sparsely stored to significantly reduce the storage amount of non-defective useless signals.

[0128] Example 2

[0129] like Figure 2 As shown, based on the above-mentioned embodiment 1, this embodiment provides a defect signal recognition and extraction device, including:

[0130] A reading unit, used for reading the signals to be detected of each channel in the magnetic flux leakage detection signal;

[0131] A decomposition unit, used to perform wavelet decomposition on the signal to be detected of each channel using a wavelet basis similar to the waveform of the typical defect signal, to obtain a high-frequency signal of the signal to be detected of each channel after wavelet decomposition;

[0132] A search and identification unit, configured to traverse and search the high-frequency signals of the signal to be detected in each channel after wavelet decomposition, and identify the signal segment whose distribution pattern of the high-frequency signal in the signal to be detected is the same as the distribution pattern of the high-frequency signal of the typical defect signal as a signal segment containing a defect;

[0133] A first coordinate extraction unit is used to extract the coordinates of the defect center and the two boundaries of all defects contained in the signal to be inspected of each channel according to the corresponding relationship between the coordinates of the defect center and the two boundaries and the distribution characteristics of the high-frequency signal, and store the extracted coordinates of the defect center and the two boundaries to obtain the boundary coordinates of the first dimension of all defects;

[0134] The second coordinate extraction unit is used to traverse the coordinates of the defect center and the two boundaries of all the extracted channels, determine the connectivity of the coordinates of the defect center and the two boundaries in the signal to be inspected of adjacent channels, integrate the defects whose coordinates of the defect center and the two boundaries in the signal to be inspected of adjacent channels satisfy the connectivity into one defect, and obtain the boundary coordinates of the second dimension of each defect; optionally, the method for determining the connectivity of the coordinates of the defect center and the two boundaries in the signal to be inspected of adjacent channels is as described in Example 1.

[0135] The third coordinate extraction unit is used to obtain the coordinates of the two-dimensional boundary of the defect based on the boundary coordinates of the first dimension and the boundary coordinates of the second dimension of the defect; optionally, a method for obtaining the boundary coordinates of the second dimension of each defect.

[0136] The interception unit is used to intercept the signal within the two-dimensional boundary coordinate range of the defect from the magnetic flux leakage detection signal to obtain the defect signal in the signal to be detected.

[0137] Optionally, the defect signal identification and extraction device further includes:

[0138] A first acquisition unit is used to pre-acquire a magnetic flux leakage detection signal of a channel containing a typical defect signal in the magnetic flux leakage detection signal;

[0139] A second acquisition unit is used to acquire the waveform of the typical defect signal, the coordinates of the defect center and two boundaries according to the magnetic flux leakage detection signal of a channel containing the typical defect signal;

[0140] A determination unit, for determining a wavelet basis similar to the waveform of the typical defect signal by using a waveform similarity determination method according to the waveform of the typical defect signal;

[0141] Optionally, the defect signal identification and extraction device further includes:

[0142] The third acquisition unit is used to acquire the distribution pattern of the high-frequency signal of the typical defect signal; the third acquisition unit includes:

[0143] The second decomposition unit is used to perform wavelet decomposition on the typical defect signal using a wavelet basis similar to the waveform of the typical defect signal to obtain a high-frequency signal after wavelet decomposition;

[0144] The feature analysis unit is used to perform feature analysis on the high-frequency signal after wavelet decomposition, and determine the distribution characteristics and distribution mode of the high-frequency signal after wavelet decomposition of the typical defect signal.

[0145] Optionally, the defect signal identification and extraction device further includes:

[0146] A storage unit, used for storing defect signals in the signal to be inspected;

[0147] The storage unit includes: a first storage subunit, which is used to perform intensive storage on signal segments containing defects in the to-be-detected signal during storage; and a second storage subunit, which is used to perform sparse storage on signal segments containing no defects in the to-be-detected signal during storage.

[0148] In actual application, the signal segments containing defects in the signal to be inspected are densely stored by the first storage subunit to achieve high-resolution storage of the defective signal; the signal segments without defects in the signal to be inspected are sparsely stored by the second storage subunit to greatly reduce the storage amount of non-defective useless signals.

[0149] Example 3

[0150] Based on the same principle as the method shown in the embodiment of the present invention, an electronic device is also provided in the embodiment of the present invention, as shown in the attached Figure 3 As shown, the electronic device may include but is not limited to: a processor and a memory; the memory is used to store computer operation instructions and input and output data; the processor is used to execute the defect signal identification and extraction method shown in any embodiment of the present invention by calling a computer program.

[0151] In an optional embodiment, an electronic device is provided. Figure 3 The electronic device 30 shown includes: a processor 310 and a memory 330. The processor 310 and the memory 330 are connected, for example, via a bus 320.

[0152] Optionally, the electronic device 30 may further include a transceiver 340, which may be used for data interaction between the electronic device and other electronic devices, such as data transmission and / or data reception. It should be noted that in actual applications, the transceiver 340 is not limited to one, and the structure of the electronic device 30 does not constitute a limitation on the embodiments of the present invention.

[0153] The processor 310 may be a CPU (Central Processing Unit), a general-purpose processor, a DSP (Digital Signal Processor), an ASIC (Application Specific Integrated Circuit), an FPGA (Field Programmable Gate Array) or other programmable logic devices, transistor logic devices, hardware components or any combination thereof. It may implement or execute various exemplary logic blocks, modules and circuits described in conjunction with the disclosure of the present invention. The processor 310 may also be a combination that implements computing functions, such as a combination of one or more microprocessors, a combination of a DSP and a microprocessor, etc.

[0154] The bus 320 may include a path to transmit information between the above components. The bus 320 may be a PCI (Peripheral Component Interconnect) bus or an EISA (Extended Industry Standard Architecture) bus. The bus 320 may be divided into an address bus, a data bus, a control bus, etc. For ease of representation, Figure 8 Only one thick line is used in the diagram, but this does not mean that there is only one bus or only one type of bus.

[0155] The memory 330 can be a ROM (Read Only Memory) or other types of static storage devices that can store static information and instructions, a RAM (Random Access Memory) or other types of dynamic storage devices that can store information and instructions, or an EEPROM (Electrically Erasable Programmable Read Only Memory), a CD-ROM (Compact Disc Read Only Memory) or other optical disk storage, optical disk storage (including compressed optical disk, laser disk, optical disk, digital versatile disk, Blu-ray disk, etc.), a magnetic disk storage medium or other magnetic storage device, or any other medium that can be used to carry or store the desired program code in the form of instructions or data structures and can be accessed by a computer, but is not limited to this.

[0156] The memory 330 is used to store application code (computer program) for executing the solution of the present invention, and is controlled to be executed by the processor 310. The processor 310 is used to execute the application code stored in the memory 330 to implement the contents shown in the above method embodiment.

[0157] Example 4

[0158] This embodiment provides a computer storage medium on which computer instructions are stored, characterized in that the computer storage medium stores at least one instruction, at least one program, a code set or an instruction set, and the at least one instruction, the at least one program, the code set or the instruction set are loaded and executed by the processor to implement an embodiment of the aforementioned method.

[0159] The beneficial effects of the present invention are verified by experimental simulation. Figure 4-11 In the figure, x is length (unit: mm), y is width (unit: mm), and V is amplitude (unit: mV).

[0160] Experiment 1: Figure 4 As shown in the simulation diagram, the magnetic flux leakage detection signal contains 3 different defect signal extraction effects as shown in Figure 5 , 6 As shown in ,7, the stripping and separate extraction of single defect signals in defect-intensive areas are achieved.

[0161] Experiment 2: Figure 8 It is a two-dimensional magnetic flux leakage detection signal of actual engineering, including one defect (convex part). Fig. 9 is the defect extraction effect. Fig. 9 It can be seen that the defect leakage magnetic signal is extracted completely and there is no redundant information.

[0162] Experiment 3: Fig.10 It is the two-dimensional magnetic flux leakage detection signal of actual engineering. Fig.11 is the extraction result of defect leakage magnetic signal. Fig.11 It can be seen that the defect signal extraction is complete and there is no redundant information.

[0163] In the description of the present invention, it is necessary to understand that the orientation or positional relationship indicated by the terms is based on the orientation or positional relationship shown in the drawings, and is only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore cannot be understood as a limitation on the present invention.

[0164] In the present invention, unless otherwise clearly specified and limited, the terms "installed", "connected", "connected", "fixed" and the like should be understood in a broad sense, for example, it can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection or an electrical connection; it can be a direct connection or an indirect connection through an intermediate medium, it can be the internal connection of two elements or the interaction relationship between two elements, unless otherwise clearly defined. For ordinary technicians in this field, the specific meanings of the above terms in the present invention can be understood according to specific circumstances.

[0165] In this specification, the schematic representations of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described can be combined in any one or more embodiments or examples in a suitable manner. In addition, those skilled in the art can combine and combine different embodiments or examples described in this specification and the features of different embodiments or examples without contradiction.

[0166] Although the embodiments of the present invention have been shown and described above, it is to be understood that the above embodiments are exemplary and are not to be construed as limitations of the present invention. A person skilled in the art may change, modify, replace and vary the above embodiments within the scope of the present invention.

Claims

1. A defect signal recognition and extraction method, characterized in that: The steps include: Read in the signal to be detected of each channel in the magnetic flux leakage detection signal; The signal to be inspected of each channel is decomposed by wavelet using a wavelet basis similar to the waveform of a typical defect signal, so as to obtain a high-frequency signal of the signal to be inspected of each channel after wavelet decomposition; Traversing and searching for high-frequency signals of the signal to be inspected in each channel after wavelet decomposition, and identifying a signal segment in which the distribution pattern of the high-frequency signal in the signal to be inspected is the same as the distribution pattern of the high-frequency signal of a typical defect signal as a signal segment containing a defect; According to the corresponding relationship between the coordinates of the defect center and the two boundaries and the distribution characteristics of the high-frequency signal, the coordinates of the defect center and the two boundaries of all defects contained in the signal to be inspected of each channel are extracted, and the extracted coordinates of the defect center and the two boundaries are stored to obtain the boundary coordinates of the first dimension of all defects; Traversing the coordinates of the defect centers and the two boundaries of all the extracted channels, determining the connectivity of the coordinates of the defect centers and the two boundaries in the signals to be inspected of adjacent channels, integrating the defects whose coordinates of the defect centers and the two boundaries in the signals to be inspected of adjacent channels satisfy the connectivity into one defect, and obtaining the boundary coordinates of the second dimension of each defect; Obtaining coordinates of a two-dimensional boundary of the defect according to the boundary coordinates of the first dimension and the boundary coordinates of the second dimension of the defect; The signal within the two-dimensional boundary coordinate range of the defect is intercepted from the magnetic flux leakage detection signal to obtain the defect signal in the signal to be detected.

2. The defect signal identification and extraction method according to claim 1, characterized in that: The method for obtaining a wavelet basis similar to the waveform of a typical defect signal comprises the following steps: Pre-acquire a magnetic flux leakage detection signal of a channel containing a typical defect signal in the magnetic flux leakage detection signal; According to the magnetic flux leakage detection signal of one channel containing the typical defect signal, the waveform of the typical defect signal, the coordinates of the defect center and two boundaries are obtained; According to the waveform of the typical defect signal, a waveform similarity determination method is adopted to determine a wavelet basis similar to the waveform of the typical defect signal.

3. The defect signal identification and extraction method according to claim 1, characterized in that: The method for acquiring the distribution pattern of the high-frequency signal of the typical defect signal comprises the following steps: Performing wavelet decomposition on the typical defect signal using a wavelet basis similar to the waveform of the typical defect signal to obtain a high-frequency signal after wavelet decomposition; The high-frequency signal after wavelet decomposition is subjected to feature analysis to determine the distribution features of the high-frequency signal after wavelet decomposition of the typical defect signal and the distribution pattern of the distribution features.

4. The defect signal identification and extraction method according to claim 1, characterized in that: The method for obtaining the corresponding relationship between the coordinates of the defect center and the two boundaries and the distribution characteristics of the high-frequency signal comprises the following steps: According to the coordinates of the typical defect center and boundary corresponding to the distribution characteristics of the high-frequency signal after wavelet decomposition, a corresponding relationship between the coordinates of the defect center and two boundaries and the distribution characteristics of the high-frequency signal is established; The distribution characteristic of the high-frequency signal is the local extreme value of the high-frequency signal; after performing wavelet decomposition on the signal to be detected of each channel using a wavelet basis similar to the waveform of the typical defect signal to obtain the high-frequency signal of the signal to be detected of each channel after wavelet decomposition, the method further includes: Obtaining the local extreme value of the high-frequency signal of the signal to be inspected in each channel after wavelet decomposition, traversing and searching the obtained local extreme value of the high-frequency signal, and identifying the signal segment whose distribution pattern of the local extreme value of the high-frequency signal in the signal to be inspected is the same as the distribution pattern of the local extreme value of the high-frequency signal of the typical defect signal as the signal segment containing the defect; According to the correspondence between the coordinates of the defect center and the two boundaries of the typical defect signal and the distribution characteristics of the local extreme value of the high-frequency signal, the coordinates of the defect center and the two boundaries of all defects contained in the signal to be inspected of each channel are extracted, and the extracted coordinates of the defect center and the two boundaries are stored to obtain the boundary coordinates of the first dimension of all defects; The local extreme value is an extreme value within a set signal length range in the high-frequency signal; the distribution characteristics of the local extreme value of the high-frequency signal include the position and size of the local extreme value in the high-frequency signal.

5. The defect signal identification and extraction method according to claim 1, characterized in that: The determining of the connectivity of the coordinates of the defect center and the two boundaries in the signal to be inspected of the adjacent channel comprises: if the coordinates of the defect center and the two boundaries in the signal to be inspected of the adjacent channel meet the connectivity determination condition, then the signal segments containing defects in the signal to be inspected of the adjacent channel belong to the same defect; if the coordinates of the defect center and the two boundaries in the signal to be inspected of the adjacent channel do not meet the connectivity determination condition, then the defect signal segments in the signal to be inspected of the adjacent channel do not belong to the same defect; The connectivity determination conditions of the coordinates of the defect center and the two boundaries in the signal to be detected of the adjacent channels include: If the difference between the coordinates of the defect centers in the signals to be inspected of adjacent channels and the difference between the coordinates of the two corresponding boundaries of the defect are both less than the set value, the defects in the signals to be inspected of adjacent channels are connected; If any of the difference between the coordinates of the defect centers in the signals to be inspected in adjacent channels or the difference between the coordinates corresponding to the two boundaries of the defects in the signals to be inspected in adjacent channels is greater than a set value, the defects in the signals to be inspected in adjacent channels are not connected.

6. The defect signal identification and extraction method according to claim 1, characterized in that: The method for obtaining the boundary coordinates of the second dimension of each defect includes: determining the boundary of the second dimension of each defect according to the center coordinates and boundary coordinates of each defect in the signal to be detected, and the connectivity between the defects in the signal to be detected of adjacent channels; The two-dimensional boundary of the defect is used to define the boundary position of the defect; the two-dimensional boundary of the defect includes the sequence number of the channel of the defect in the signal to be detected and the coordinates of the two boundaries of the first dimension of the defect signal; For the defect signal that does not satisfy connectivity: the two-dimensional boundary of the defect includes the sequence number of the defect signal in the channel of the signal to be detected and the coordinates of the two boundaries of the first dimension of the defect; For the defect signal that satisfies connectivity: the two-dimensional boundary includes the maximum channel sequence number and the minimum channel sequence number of all channels contained in the one defect, and the maximum coordinate value and the minimum coordinate value of the boundary of the one defect containing all the defect signals; The coordinates of the two-dimensional boundary of the defect include boundary coordinates of the first dimension and boundary coordinates of the second dimension; the boundary coordinates of the first dimension are the maximum and minimum values ​​of the boundary coordinates of the defect in all channels containing the defect; the boundary coordinates of the second dimension of the defect are the maximum and minimum values ​​of the sequence of all channels containing the defect.

7. The defect signal identification and extraction method according to claim 1, characterized in that: The method further includes: storing defective signals in the signal to be detected; when storing, densely storing signal segments containing defects in the signal to be detected, and sparsely storing signal segments without defects in the signal to be detected.

8. A defect signal recognition and extraction device, characterized in that: include: A reading unit, used for reading the signals to be detected of each channel in the magnetic flux leakage detection signal; A decomposition unit is used to perform wavelet decomposition on the signal to be detected of each channel using a wavelet basis similar to the waveform of a typical defect signal, so as to obtain a high-frequency signal of the signal to be detected of each channel after wavelet decomposition; A search and identification unit, configured to traverse and search the high-frequency signals of the signal to be detected in each channel after wavelet decomposition, and identify the signal segment whose distribution pattern of the high-frequency signal in the signal to be detected is the same as the distribution pattern of the high-frequency signal of the typical defect signal as a signal segment containing a defect; A first coordinate extraction unit is used to extract the coordinates of the defect center and the two boundaries of all defects contained in the signal to be inspected of each channel according to the corresponding relationship between the coordinates of the defect center and the two boundaries and the distribution characteristics of the high-frequency signal, and store the extracted coordinates of the defect center and the two boundaries to obtain the boundary coordinates of the first dimension of all defects; A second coordinate extraction unit is used to traverse the coordinates of the defect center and the two boundaries of all the extracted channels, determine the connectivity of the coordinates of the defect center and the two boundaries in the to-be-tested signal of the adjacent channels, integrate the defects whose coordinates of the defect center and the two boundaries in the to-be-tested signal of the adjacent channels satisfy the connectivity into one defect, and obtain the boundary coordinates of the second dimension of each defect; A third coordinate extraction unit, used to obtain the coordinates of the two-dimensional boundary of the defect according to the boundary coordinates of the first dimension and the boundary coordinates of the second dimension of the defect; The interception unit is used to intercept the signal within the two-dimensional boundary coordinate range of the defect from the magnetic leakage detection signal to obtain the defect signal in the signal to be detected.

9. An electronic device, characterized in that: include: Processor and memory; The memory is used to store computer operation instructions and input and output data; The processor is used to execute the method according to any one of claims 1 to 7 by calling the computer operation instructions.

10. A computer storage medium having computer instructions stored thereon, characterized in that: The computer storage medium stores at least one instruction, at least one program, a code set or an instruction set, and the at least one instruction, the at least one program, the code set or the instruction set is loaded and executed by a processor to implement the method described in any one of claims 1 to 7.

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