FDR defect location method based on k-means clustering and fractional Fourier transform

Abstract

The present invention provides an FDR defect location method based on k-means clustering and fractional Fourier transform, which can be applied to the field of high voltage technology. The method includes: according to the same sampling method, different sweep frequency signals are passed through the cable to be detected to obtain an initial reflection coefficient spectrum set; the initial reflection coefficient spectrum set is clustered to obtain multiple reflection coefficient spectrum clustering clusters; multiple reflection coefficient spectra obtained based on multiple reflection coefficient spectrum clustering clusters are pre-processed to reduce spectrum leakage to obtain multiple target reflection coefficient spectra; multiple target reflection coefficient spectra are respectively subjected to fractional Fourier transform to obtain multiple two-dimensional time-frequency spectrum graphs; multiple two-dimensional time-frequency spectrum graphs are respectively subjected to spectrum conversion to obtain multiple positioning curve graphs for defect detection; and based on multiple positioning curve graphs, defect detection results of the cable to be detected are obtained.

Description

Technical Field

[0001] The present invention relates to the technical field of high voltage, and in particular to an FDR defect location method based on k-means clustering and fractional Fourier transform. Background Art

[0002] As an important carrier of power transmission, the safety and stability of cables are directly related to the normal operation of the power system. However, since cables operate in complex environments for a long time, they are affected by various factors, such as external force damage, insulation aging, moisture intrusion, etc., and are prone to various defects. If these defects are not discovered and handled in time, they may lead to cable failures and even cause serious consequences such as fire. Therefore, accurately and quickly locating cable defects is of great significance to ensure the safe operation of the power system. Frequency domain reflectometry (FDR) is a widely used cable defect detection method that can locate cable defects by analyzing frequency domain signals.

[0003] In the process of implementing the present invention, it is found that there are at least the following problems in the prior art: due to the influence of the dispersion effect, the frequency of the reflected wave changes with the acquisition time, which further leads to the generation of simple harmonic waves. Therefore, there are false peaks in the defect positioning map, the detection accuracy is low, and it is easy to misjudge the defects. Summary of the invention

[0004] In view of the above problems, the present invention provides an FDR defect location method based on k-means clustering and fractional Fourier transform.

[0005] According to a first aspect of the present invention, a FDR defect location method based on k-means clustering and fractional Fourier transform is provided, comprising: according to the same sampling method, different sweep frequency signals are passed through the cable to be detected to obtain an initial reflection coefficient spectrum set; the initial reflection coefficient spectrum set is clustered to obtain a plurality of reflection coefficient spectrum clustering clusters; a plurality of reflection coefficient spectra obtained based on the plurality of reflection coefficient spectrum clustering clusters are respectively subjected to spectrum leakage reduction preprocessing to obtain a plurality of target reflection coefficient spectra; a plurality of target reflection coefficient spectra are respectively subjected to fractional Fourier transform to obtain a plurality of two-dimensional time-frequency spectrum graphs; a plurality of two-dimensional time-frequency spectrum graphs are respectively subjected to spectrum conversion to obtain a plurality of positioning curve graphs for defect detection; and a plurality of positioning curve graphs based on the plurality of positioning curve graphs are respectively subjected to spectrum conversion to obtain a plurality of positioning curve graphs for defect detection. A positioning curve diagram is provided to obtain the defect detection result of the cable to be detected; wherein, a plurality of reflection coefficient spectra obtained based on a plurality of reflection coefficient spectrum clustering clusters are respectively preprocessed to reduce spectrum leakage to obtain a plurality of target reflection coefficient spectra, including: taking the reflection coefficient spectra corresponding to the centroids of each reflection coefficient spectrum clustering cluster as the reflection coefficient spectrum; adding window functions to the reflection coefficient spectra respectively; and performing a bilinear transformation on the reflection coefficient spectra with the window function added thereto to obtain the target reflection coefficient spectrum; performing a bilinear transformation on the reflection coefficient spectrum with the window function added thereto to obtain the target reflection coefficient spectrum, including: performing a convolution process on the reflection coefficient spectrum with the window function added thereto and the conjugate reflection coefficient spectrum with the window function added thereto to obtain the target reflection coefficient spectrum.

[0006] According to an embodiment of the present invention, different sweep frequency signals are passed through the cable to be detected according to the same sampling method to obtain an initial reflection coefficient spectrum set, including: according to the same sampling frequency and number of sampling points, signal acquisition is performed on the cable to be detected through which different sweep frequency signals are passed to obtain an initial reflection coefficient spectrum set, wherein the sweep frequency signal includes an angular frequency of 0.15MHz-100MHz.

[0007] According to an embodiment of the present invention, the initial reflection coefficient spectrum set is clustered to obtain multiple reflection coefficient spectrum clusters, including: clustering the initial reflection coefficient spectrum set based on a predetermined number of clusters and signal information of the swept frequency signal to obtain multiple reflection coefficient spectrum clusters.

[0008] According to an embodiment of the present invention, fractional Fourier transform is performed on multiple target reflection coefficient spectra respectively to obtain multiple two-dimensional time-frequency spectrum diagrams, including: performing fractional Fourier transform on the target reflection coefficient spectrum by adding a rotation angle to obtain a two-dimensional time-frequency spectrum diagram.

[0009] According to an embodiment of the present invention, multiple two-dimensional time-frequency spectrum graphs are respectively converted into spectra to obtain multiple positioning curve graphs for defect detection, including: performing pseudo-peak suppression processing on the rotation angle in the two-dimensional time-frequency spectrum graph to obtain a pseudo-peak suppressed spectrum graph; performing time-domain integration on the pseudo-peak suppressed spectrum graph to obtain a frequency response function; and performing spectrogram conversion on the frequency response function to obtain a positioning curve graph.

[0010] According to an embodiment of the present invention, a defect detection result of a cable to be detected is obtained based on multiple positioning curve graphs, including: updating the reflection intensity of each positioning curve graph to obtain multiple target positioning curve graphs; and obtaining the defect detection result based on the amplitude of each of the multiple target positioning curve graphs.

[0011] The second aspect of the present invention provides an FDR defect location device based on k-means clustering and fractional Fourier transform, comprising:

[0012] A signal sampling module is used to pass different frequency sweep signals into the cable to be detected in the same sampling mode to obtain an initial reflection coefficient spectrum set;

[0013] A set clustering module, used for clustering the initial reflection coefficient spectrum set to obtain multiple reflection coefficient spectrum clustering clusters;

[0014] A digital spectrum preprocessing module is used to perform spectrum leakage reduction preprocessing on multiple reflection coefficient spectra obtained based on multiple reflection coefficient spectrum clustering clusters to obtain multiple target reflection coefficient spectra;

[0015] A digital spectrum transformation module is used to perform fractional Fourier transform on multiple target reflection coefficient spectra to obtain multiple two-dimensional time-frequency spectrum diagrams;

[0016] A spectrum conversion module is used to perform spectrum conversion on multiple two-dimensional time-frequency spectrum graphs to obtain multiple positioning curve graphs for defect detection;

[0017] The result determination module is used to obtain the defect detection result of the cable to be detected based on multiple positioning curve graphs.

[0018] A third aspect of the present invention provides an electronic device, comprising: one or more processors; a memory for storing one or more computer programs, wherein the one or more processors execute the one or more computer programs to implement the steps of the above method.

[0019] The fourth aspect of the present invention further provides a computer-readable storage medium on which a computer program or instruction is stored, and the steps of the above method are implemented when the above computer program or instruction is executed by a processor.

[0020] The fifth aspect of the present invention also provides a computer program product, including a computer program or instructions, which implement the steps of the above method when executed by a processor.

[0021] According to an embodiment of the present invention, different sweep frequency signals are passed through the cable to be detected, and sampling is performed using the same sampling method. The initial reflection coefficient spectrum set obtained by sampling is clustered to obtain multiple reflection coefficient spectrum clustering clusters, thereby achieving the effect of reducing noise and reducing errors. The reflection coefficient spectrum clustering cluster is preprocessed to reduce spectrum leakage to obtain a target reflection coefficient spectrum, thereby further reducing the error caused by spectrum leakage. After performing fractional Fourier transform and spectrum transformation on the target reflection coefficient spectrum, the two-dimensional spectrum is converted into a one-dimensional spectrum to obtain multiple positioning curves for defect detection, thereby improving the accuracy of defect detection. By analyzing the positioning curve, the defect detection result of the cable to be detected is determined. In summary, the present invention achieves the effect of reducing errors and improving the accuracy of cable defect detection through a variety of processing methods. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] The above contents and other objects, features and advantages of the present invention will become more apparent through the following description of the embodiments of the present invention with reference to the accompanying drawings, in which:

[0023] Figure 1 A flow chart of an FDR defect location method based on k-means clustering and fractional Fourier transform according to an embodiment of the present invention is shown;

[0024] Figure 2 A schematic diagram showing a rotation angle according to an embodiment of the present invention;

[0025] Figure 3 A schematic diagram of a two-dimensional time-frequency spectrum diagram according to an embodiment of the present invention is shown;

[0026] Figure 4 A diagram showing positioning results of a simulation model according to an embodiment of the present invention and the frequency domain reflection method is shown;

[0027] Figure 5 A structural block diagram of an FDR defect location device based on k-means clustering and fractional Fourier transform according to an embodiment of the present invention is shown;

[0028] Figure 6 A block diagram of an electronic device suitable for implementing an FDR defect location method based on k-means clustering and fractional Fourier transform according to an embodiment of the present invention is shown. DETAILED DESCRIPTION

[0029] Below, embodiments of the present invention will be described with reference to the accompanying drawings. However, it should be understood that these descriptions are exemplary only and are not intended to limit the scope of the present invention. In the following detailed description, for ease of explanation, many specific details are set forth to provide a comprehensive understanding of embodiments of the present invention. However, it is apparent that one or more embodiments may also be implemented without these specific details. In addition, in the following description, descriptions of known structures and technologies are omitted to avoid unnecessary confusion of concepts of the present invention.

[0030] The terms used herein are only for describing specific embodiments and are not intended to limit the present invention. The terms "comprise", "include", etc. used herein indicate the existence of the features, steps, operations and / or components, but do not exclude the existence or addition of one or more other features, steps, operations or components.

[0031] All terms (including technical and scientific terms) used herein have the meanings commonly understood by those skilled in the art unless otherwise defined. It should be noted that the terms used herein should be interpreted as having a meaning consistent with the context of this specification and should not be interpreted in an idealized or overly rigid manner.

[0032] When using expressions such as "at least one of A, B, and C, etc.", they should generally be interpreted according to the meaning of the expression commonly understood by those skilled in the art (for example, "a system having at least one of A, B, and C" should include but is not limited to a system having A alone, B alone, C alone, A and B, A and C, B and C, and / or A, B, C, etc.).

[0033] In the technical solution of the present invention, the user information (including but not limited to user personal information, user image information, user device information, such as location information, etc.) and data (including but not limited to data used for analysis, stored data, displayed data, etc.) involved are all information and data authorized by the user or fully authorized by all parties, and the collection, storage, use, processing, transmission, provision, disclosure and application of the relevant data comply with relevant laws, regulations and standards, take necessary confidentiality measures, do not violate public order and good morals, and provide corresponding operation entrances for users to choose to authorize or refuse.

[0034] In the scenario of using personal information for automated decision-making, the methods, devices, and systems provided by the embodiments of the present invention provide users with corresponding operation portals for users to choose to agree or reject the automated decision-making results; if the user chooses to reject, the expert decision-making process will be entered. The expression "automated decision-making" here refers to the activity of automatically analyzing and evaluating an individual's behavioral habits, interests and hobbies, or economic, health, credit status, etc. through computer programs, and making decisions. The expression "expert decision-making" here refers to the activity of making decisions by people who specialize in a certain field, have specialized experience, knowledge and skills, and have reached a certain level of professionalism.

[0035] Due to the presence of noise in the measurement environment, the test signal is attenuated and distorted, which will also generate noise. The above noise will affect the accuracy of defect detection.

[0036] An embodiment of the present invention provides an FDR defect location method based on k-means clustering and fractional Fourier transform, comprising: according to the same sampling method, passing different sweep frequency signals into a cable to be detected to obtain an initial reflection coefficient spectrum set; clustering the initial reflection coefficient spectrum set to obtain a plurality of reflection coefficient spectrum clustering clusters; performing spectrum leakage reduction preprocessing on a plurality of reflection coefficient spectra obtained based on the plurality of reflection coefficient spectrum clustering clusters to obtain a plurality of target reflection coefficient spectra; performing fractional Fourier transform on the plurality of target reflection coefficient spectra to obtain a plurality of two-dimensional time-frequency spectrum graphs; performing spectrum conversion on the plurality of two-dimensional time-frequency spectrum graphs to obtain a plurality of positioning curve graphs for defect detection; and obtaining a defect detection result of the cable to be detected based on the plurality of positioning curve graphs.

[0037] Figure 1 A flow chart of an FDR defect location method based on k-means clustering and fractional Fourier transform according to an embodiment of the present invention is shown.

[0038] like Figure 1 As shown, the FDR defect location method based on k-means clustering and fractional Fourier transform of this embodiment includes operations S110 to S160.

[0039] In operation S110, different frequency sweep signals are passed through the cable to be detected in the same sampling manner to obtain an initial reflection coefficient spectrum set.

[0040] According to an embodiment of the present invention, the cable to be detected includes a cable that needs to be tested for cable defects to determine whether there are defects. The sampling method includes parameters such as sampling frequency and number of sampling points, wherein the sampling frequency is the frequency of signal acquisition for the cable to be detected after the sweep frequency signal is passed, and the number of sampling points is the number of locations where sampling is performed when the signal is acquired for the cable to be detected after the sweep frequency signal is passed.

[0041] According to an embodiment of the present invention, a swept frequency signal is a signal whose frequency changes linearly or nonlinearly with time. The swept frequency signal can be generated by modulation technology, such as direct digital synthesis technology or frequency modulation technology in analog circuits, or by software algorithms.

[0042] According to an embodiment of the present invention, after the sampling mode is determined, different sweep frequency signals are passed through the cable to be detected for multiple times to obtain multiple initial reflection coefficient spectra, thereby determining an initial reflection coefficient spectrum set.

[0043] In operation S120, the initial reflection coefficient spectrum set is clustered to obtain a plurality of reflection coefficient spectrum clusters.

[0044] According to an embodiment of the present invention, after mapping the initial reflection coefficient spectrum set into the feature space and determining the multiple nodes corresponding to the initial reflection coefficient spectrum set, a center-based clustering algorithm can be used to cluster the multiple nodes corresponding to the initial reflection coefficient spectrum set to obtain multiple reflection coefficient spectrum clustering clusters, wherein the center-based clustering algorithm can minimize the sum of the distances from each node to its nearest center of mass by iteratively moving the center of mass, and each reflection coefficient spectrum clustering cluster includes a center of mass and multiple nodes centered on the clustering center.

[0045] In operation S130, spectrum leakage reduction preprocessing is performed on a plurality of reflection coefficient spectra obtained based on the clustering of the plurality of reflection coefficient spectra to obtain a plurality of target reflection coefficient spectra.

[0046] According to an embodiment of the present invention, in frequency domain analysis, especially when the frequency domain signal is not periodic or the length of the frequency domain signal is not an integer multiple of the sampling frequency, spectrum leakage may occur. Spectrum leakage will cause the signal energy that should have been concentrated on a specific frequency to be dispersed to other frequencies, thereby resulting in a decrease in the accuracy of the spectrum analysis results.

[0047] According to an embodiment of the present invention, in order to reduce spectrum leakage during frequency domain analysis, the signal can be preprocessed by adding a window function, padding the signal with 0 to increase the signal length, signal overlap, periodic extension of the signal, etc. Adding a window function is to apply a window function to the frequency domain signal before Fourier transforming the frequency domain signal to reduce the mutation at both ends of the signal, thereby reducing spectrum leakage. Padding the signal with 0 to increase the signal length can improve the spectrum resolution to reduce spectrum leakage. Signal overlap can be achieved by making an overlapping part between two adjacent segments of the signal and applying a window function in the overlapping area to reduce the spectrum leakage caused by the discontinuity between segments. Periodic extension of the signal can convert the signal into a periodic signal, thereby avoiding the situation of spectrum leakage during Fourier transform.

[0048] According to an embodiment of the present invention, a reflection coefficient spectrum is determined based on each reflection coefficient spectrum cluster, and multiple reflection coefficient spectra are processed by a preprocessing method for reducing spectrum leakage to determine multiple target reflection coefficient spectra.

[0049] In operation S140, fractional-order Fourier transform is performed on the multiple target reflection coefficient spectra to obtain multiple two-dimensional time-frequency spectrum graphs.

[0050] According to an embodiment of the present invention, the fractional Fourier transform (FrFT) is a variant of the traditional Fourier transform. The traditional Fourier transform converts the signal from the time domain to the frequency domain, while the FrFT allows non-integer-order frequency domain analysis of the signal, that is, partial conversion can be performed between the time domain and the frequency domain. Therefore, the FrFT can be used to analyze and process non-stationary signals and improve the flexibility and efficiency of signal processing.

[0051] According to an embodiment of the present invention, since the signal to be detected is a swept frequency signal, the obtained initial reflection coefficient spectrum set, reflection coefficient spectrum cluster, reflection coefficient spectrum, and target reflection coefficient spectrum are all non-stationary signals, and fractional-order Fourier transform is performed on multiple target reflection coefficient spectra respectively to obtain multiple two-dimensional time-frequency spectrum diagrams.

[0052] In operation S150, spectrum conversion is performed on the plurality of two-dimensional time-frequency spectrum graphs to obtain a plurality of positioning curve graphs for defect detection.

[0053] According to an embodiment of the present invention, spectrogram conversion is performed on a plurality of two-dimensional time-frequency spectrum graphs respectively, so as to convert the plurality of two-dimensional time-frequency spectrum graphs into corresponding one-dimensional signals, namely, positioning curve graphs for defect detection.

[0054] In operation S160, a defect detection result of the cable to be detected is obtained based on the plurality of positioning curve graphs.

[0055] According to an embodiment of the present invention, analysis is performed based on multiple positioning curve graphs, and based on the analysis results, a defect detection result of the cable to be detected is determined, wherein the defect detection result can be used to characterize whether there is a defect in the cable to be detected, and if there is a defect, determine the position where the defect occurs in the cable to be detected.

[0056] According to an embodiment of the present invention, different sweep frequency signals are passed through the cable to be detected, and the same sampling method is used for sampling. The initial reflection coefficient spectrum set obtained by sampling is clustered to obtain multiple reflection coefficient spectrum clustering clusters, thereby achieving the effect of reducing noise and reducing errors. The reflection coefficient spectrum clustering cluster is preprocessed to reduce spectrum leakage to obtain the target reflection coefficient spectrum, thereby further reducing the error caused by spectrum leakage. After the target reflection coefficient spectrum is subjected to fractional Fourier transform and spectrum transformation, the two-dimensional spectrum is converted into a one-dimensional spectrum to obtain multiple positioning curves for defect detection, thereby improving the accuracy of defect detection. By analyzing the positioning curve, the defect detection result of the cable to be detected is determined. Through a variety of processing means, the effect of reducing errors and improving the accuracy of cable defect detection is achieved.

[0057] According to an embodiment of the present invention, signal acquisition is performed on the cables to be detected through which different sweep frequency signals are passed according to the same sampling method to obtain an initial reflection coefficient spectrum set, including: signal acquisition is performed on the cables to be detected through which different sweep frequency signals are passed according to the same sampling frequency and number of sampling points to obtain an initial reflection coefficient spectrum set, wherein the sweep frequency signal includes an angular frequency of 0.15MHz - 100MHz.

[0058] According to an embodiment of the present invention, different sweep frequency signals are successively passed through the cable to be detected, and after each sweep frequency signal is passed through, signal acquisition is performed on the cable to be detected according to the same sampling frequency and number of sampling points to obtain an initial reflection coefficient spectrum, wherein the range of the value of the angular frequency of the sweep frequency signal that changes with time is 0.15MHz-100MHz, and the different sweep frequency signals can be sweep frequency signals with the same law of frequency change with time but different initial angular frequencies, or can be sweep frequency signals with different laws of frequency change with time.

[0059] According to an embodiment of the present invention, an initial reflection coefficient spectrum set x(ω) is determined based on a plurality of initial reflection coefficient spectra corresponding to a plurality of different swept frequency signals, as shown in formula (1):

[0060] (1)

[0061] Where q is the number of sweep signals, A i is the amplitude of the i-th frequency sweep signal, △f is the time interval of the frequency sweep signal passing into the cable to be tested, n is an arbitrary integer, ω i is the angular frequency of the i-th sweep signal, , is the equivalent frequency of the i-th swept frequency signal.

[0062] According to an embodiment of the present invention, signal acquisition is performed on the cable to be detected that is fed with sweep frequency signals of multiple different angular frequencies, so that by analyzing the output of the cable to be detected in response to different sweep frequency signals, the detection result of the cable to be detected can be comprehensively and accurately determined, thereby improving the accuracy of cable defect detection.

[0063] According to an embodiment of the present invention, the initial reflection coefficient spectrum set is clustered to obtain multiple reflection coefficient spectrum clusters, including: clustering the initial reflection coefficient spectrum set based on a predetermined number of clusters and signal information of the swept frequency signal to obtain multiple reflection coefficient spectrum clusters.

[0064] According to an embodiment of the present invention, a center-based clustering algorithm, such as a K-means algorithm, is used to cluster the initial reflection coefficient spectrum set.

[0065] According to an embodiment of the present invention, the clustering process is as follows: based on a predetermined number of clustering clusters, a plurality of initial centroids are determined in the feature space, wherein the predetermined number of clustering clusters may include noise and non-noise, and therefore the predetermined number of clustering clusters may be determined according to the number of noise and the number of non-noise, and the positions of the plurality of initial centroids are determined randomly. Determine the distances of the plurality of nodes corresponding to the initial reflection coefficient spectrum set in the feature space from the plurality of initial centroids, and divide each node into the cluster corresponding to the centroid with the smallest distance thereto. After determining the plurality of clustering clusters, re-determine the respective intermediate centroids based on each clustering cluster, and repeat the above operation until the centroid position no longer changes, and complete the clustering of the initial reflection coefficient spectrum set, and obtain the reflection coefficient spectrum clustering clusters corresponding to the plurality of centroids.

[0066] According to an embodiment of the present invention, the initial reflection coefficient spectrum is clustered, so that similar data points in the reflection coefficient spectrum are classified into one category by utilizing the intrinsic structure and statistical characteristics of the initial reflection coefficient spectrum. Since the matrix composed of similar data points has low rank, while the matrix composed of noise data points does not have this characteristic, the clustering cluster can be judged as noise or non-noise through the clustering results, thereby reducing noise.

[0067] According to an embodiment of the present invention, multiple reflection coefficient spectra obtained based on multiple reflection coefficient spectrum clustering clusters are respectively subjected to spectrum leakage reduction preprocessing to obtain multiple target reflection coefficient spectra, including: taking the reflection coefficient spectra corresponding to the centroids of each reflection coefficient spectrum clustering cluster as the reflection coefficient spectrum; adding window functions to the reflection coefficient spectra respectively; and performing bilinear transformation on the reflection coefficient spectra with the window functions added to obtain the target reflection coefficient spectrum.

[0068] According to an embodiment of the present invention, after taking the reflection coefficient spectra corresponding to the multiple centroids in the reflection coefficient spectrum cluster as the reflection coefficient spectrum, a window function is added to each reflection coefficient spectrum to obtain a reflection coefficient spectrum x'(ω) with the window function added, as shown in formula (2):

[0069] (2)

[0070] Among them, Kaiser (ω) is a window function, which is a locally optimized window function with strong capabilities and can be used to adjust the relationship between the window function spectrum parameters, thereby reducing the spectrum leakage problem.

[0071] According to an embodiment of the present invention, the reflection coefficient spectrum with the window function added thereto may be further processed by means of bilinear transformation to reduce the influence of the non-periodic characteristics of the reflection coefficient spectrum with the window function added thereto.

[0072] According to an embodiment of the present invention, the multiple clusters obtained by clustering are respectively used as reflection coefficient spectra, and a window function is added and then bilinear transformation is performed, thereby reducing the problem of spectrum leakage and reducing the error of defect detection.

[0073] According to an embodiment of the present invention, a bilinear transformation is performed on the reflection coefficient spectrum with a window function added thereto to obtain a target reflection coefficient spectrum, including: convolution processing is performed on the reflection coefficient spectrum with a conjugate of the reflection coefficient spectrum with a window function added thereto to obtain a target reflection coefficient spectrum.

[0074] According to an embodiment of the present invention, the reflection coefficient spectrum x' (ω) with the window function added is conjugated to obtain x' * (ω), and convolve x' (ω) with the conjugate reflection coefficient spectrum with the window function added to obtain the target reflection coefficient spectrum x' '(ω), as shown in formula (3):

[0075] (3)

[0076] Among them, conv[·] is the convolution function, (·) * To conjugate the contents in brackets.

[0077] According to the embodiment of the present invention, by convolving the reflection coefficient spectrum of the added window function with its conjugate, the information of the signal itself can be enhanced according to the self-similarity of the signal, and the noise can be suppressed, thereby further reducing the error.

[0078] According to an embodiment of the present invention, fractional Fourier transform is performed on multiple target reflection coefficient spectra respectively to obtain multiple two-dimensional time-frequency spectrum diagrams, including: performing fractional Fourier transform on the target reflection coefficient spectrum by adding a rotation angle to obtain a two-dimensional time-frequency spectrum diagram.

[0079] According to an embodiment of the present invention, a rotation angle α is added to the traditional Fourier transform to change the traditional Fourier transform into a fractional Fourier transform, thereby converting the time domain characteristics of the reflection coefficient spectrum into frequency domain characteristics, so that the converted signal better reflects the frequency distribution of the signal. By changing the rotation angle, the order of the fractional Fourier transform can be changed.

[0080] According to an embodiment of the present invention, the rotation angle α can be determined using formula (4):

[0081] (4)

[0082] Where p is the order of the transformation.

[0083] Figure 2 A schematic diagram showing the rotation angle according to an embodiment of the present invention is shown.

[0084] like Figure 2 As shown, Figure 2 In the case where p is 1, ω is a time domain variable, u is a time domain variable or a frequency domain variable, calculate p·(π / 2), determine the rotation angle α, and rotate the time domain ω axis counterclockwise by α to obtain the new domain axis u. The rotation angle γ in ω remains unchanged after being converted to u.

[0085] By using different values ​​of p, the time domain signal can be converted to different phase spaces, thereby realizing the rotation of the signal in the time-frequency domain space, and thus discovering the different characteristics of the signal. For example, when p is 1, the rotation angle α=π / 2, so the ω axis is rotated counterclockwise by π / 2 to obtain the u axis, and the time domain signal can be converted to the frequency domain. When p is 2, the rotation angle α=π, so the ω axis is rotated counterclockwise by π to reverse the phase of the time domain signal. When the value range of p is [0, 1], the value range of the rotation angle α is [0, π / 2], and the time domain signal can be converted to a state between the time domain and the frequency domain.

[0086] According to an embodiment of the present invention, by changing the rotation angle α of s(ω), F can be obtained. α {s(ω)}, as shown in formula (5):

[0087] (5)

[0088] Among them, K α ( , u) is the kernel function of the fractional Fourier transform, and the form of the kernel function is shown in formula (6):

[0089] (6)

[0090] Among them, j is a hyperparameter, and u is the time domain obtained by rotating the ω axis of the time domain counterclockwise by an angle α.

[0091] According to an embodiment of the present invention, s(ω) is replaced by the target reflection coefficient spectrum x' '(ω) to obtain a two-dimensional time-frequency spectrum F α {x' '(ω)}, as shown in formula (7):

[0092] (7)

[0093] According to an embodiment of the present invention, since the integral value in the two-dimensional time-spectrogram has a negative value, the amplitude of each position in the two-dimensional time-spectrogram may be calculated by taking the absolute value, and the two-dimensional time-spectrogram is updated, as shown in formula (8):

[0094] (8)

[0095] Where A is the amplitude at each position and abs(·) is the absolute value.

[0096] According to an embodiment of the present invention, a fractional-order Fourier transform is performed on the target reflection coefficient by adding a rotation angle, thereby providing localized information of the signal at different time frequencies, making full use of the characteristics of non-stationary signals, and improving the accuracy of cable defect detection.

[0097] Figure 3 A schematic diagram of a two-dimensional time-frequency spectrum diagram according to an embodiment of the present invention is shown.

[0098] like Figure 3 As shown, the two-dimensional time-frequency spectrum diagram includes the position in the cable to be detected, the selected rotation angle and the amplitude. By taking the absolute value of each point in the two-dimensional time-frequency spectrum diagram, the amplitude of each position of the cable to be detected when different rotation angles are selected can be obtained. By expressing multiple amplitudes in the coordinate system, an updated two-dimensional time-frequency spectrum diagram can be obtained.

[0099] According to an embodiment of the present invention, multiple two-dimensional time-frequency spectrum graphs are respectively converted into spectra to obtain multiple positioning curve graphs for defect detection, including: performing pseudo-peak suppression processing on the rotation angle in the two-dimensional time-frequency spectrum graph to obtain a pseudo-peak suppressed spectrum graph; performing time-domain integration on the pseudo-peak suppressed spectrum graph to obtain a frequency response function; and performing spectrogram conversion on the frequency response function to obtain a positioning curve graph.

[0100] According to an embodiment of the present invention, the matrix expression of the two-dimensional time-frequency spectrum is shown in formula (9):

[0101] (9)

[0102] Among them, N and M are the dimensions of the matrix expression of the two-dimensional time-frequency spectrum diagram.

[0103] According to an embodiment of the present invention, the pseudo peak suppression process includes setting the values ​​at positions where the rotation angle in the matrix is ​​not equal to 1 to 0, thereby obtaining a pseudo peak suppression spectrum graph F α {x' '(ω)}'.

[0104] According to an embodiment of the present invention, F α The rotation angle α of {x' '(ω)}' is considered as a time variable and has an influence on F α {x''(ω)}' is integrated in the time domain to obtain the frequency response function L(ω), as shown in formula (10):

[0105] (10)

[0106] According to an embodiment of the present invention, the frequency response function is converted into a spectrum according to the propagation speed of the sweep frequency signal in the cable to be detected, and a positioning curve graph L is obtained. location , as shown in formula (11):

[0107] (11)

[0108] Wherein, v is the propagation speed of the sweep frequency signal in the cable to be detected, and preferably, v can be 168 m / μs.

[0109] According to an embodiment of the present invention, by performing pseudo-peak suppression processing on the rotation angle of the two-dimensional time-frequency spectrum diagram, performing time-domain integration on the obtained pseudo-peak suppressed spectrum diagram, and further performing spectrum conversion to obtain a positioning curve diagram, the influence of pseudo-peaks on the positioning curve diagram can be reduced, thereby reducing the influence on the accuracy of the defect detection results.

[0110] According to an embodiment of the present invention, a defect detection result of a cable to be detected is obtained based on multiple positioning curve graphs, including: updating the reflection intensity of each positioning curve graph to obtain multiple target positioning curve graphs; and obtaining the defect detection result based on the amplitude of each of the multiple target positioning curve graphs.

[0111] According to an embodiment of the present invention, when there is a defect in the cable to be detected, the reflection intensity at the position where the defect exists is small, so it is difficult to visually observe the position of the defect. Therefore, the reflection intensity of each positioning curve graph can be updated in the form of a logarithm, as shown in formula (12):

[0112] (12)

[0113] Wherein, log(·) is a logarithmic function with the natural number e as the base.

[0114] According to an embodiment of the present invention, based on the respective amplitudes of a plurality of target positioning curve graphs, it is determined whether there are defects at various positions of the cable to be detected, thereby obtaining a defect detection result, wherein in the target positioning curve graph, the closer the amplitude of a certain position is to 1, the more likely it is that the cable to be detected has a defect at that position.

[0115] According to an embodiment of the present invention, the amplitude difference between the position where the defect exists and the position where the defect does not exist in the target positioning curve diagram is increased in logarithmic form, so that cable defect detection can be performed more accurately and easier according to the target positioning curve diagram, thereby improving the accuracy of cable defect detection.

[0116] According to an embodiment of the present invention, when using the target positioning curve graph and the curve graph obtained by other defect detection methods to perform result analysis, the obtained amplitude can be normalized to avoid errors in result analysis caused by different data ranges. For example, after updating the reflection intensity of each positioning curve graph using formula (12), since the multiplier of the logarithmic function in formula (12) is 20, before performing result analysis, the value of each point in the target positioning curve graph can be divided by 20 to control the amplitude range of the target positioning curve graph to [0, 1] to complete normalization.

[0117] Figure 4 A positioning curve diagram of a simulation model according to an embodiment of the present invention and the frequency domain reflection method is shown.

[0118] like Figure 4 As shown, curve 1 represents the positioning curve diagram obtained by using the frequency domain reflection method to detect cable defects in the simulation model, and curve 2 represents the target positioning curve diagram obtained by using the FDR defect positioning method based on k-means clustering and fractional Fourier transform to detect cable defects in the simulation model, wherein the abscissa represents the detection results at different positions of the cable to be detected, and the ordinate represents the result after normalizing the amplitude. In the cable to be detected in the simulation model, defects exist at 0 meters and 500 meters.

[0119] According to the positioning curve graph and the target positioning curve graph, it can be determined that when the FDR defect positioning method based on k-means clustering and fractional Fourier transform is used for detection, the defect detection result is that the cable to be detected has defects at 0 meters and 500 meters. When the frequency domain reflection method is used for detection, the defect detection result is that the cable to be detected has defects at 0 meters, 200 meters and 500 meters. Therefore, the accuracy of the defect detection result is higher than that of the frequency domain reflection method.

[0120] In addition, the target positioning curve has values ​​close to 0 at positions other than 0 m, 200 m, and 500 m, while the positioning curve of the frequency domain reflection method has values ​​at other positions between 0.3 and 0.6. When analyzing, the positioning curve of the frequency domain reflection method is prone to misjudgment, while the target positioning curve is not prone to misjudgment because the values ​​at positions without defects are close to 0.

[0121] Based on the above-mentioned FDR defect location method based on k-means clustering and fractional Fourier transform, the present invention also provides an FDR defect location device based on k-means clustering and fractional Fourier transform. Figure 5 The device is described in detail.

[0122] Figure 5 A structural block diagram of an FDR defect location device based on k-means clustering and fractional Fourier transform according to an embodiment of the present invention is shown.

[0123] like Figure 5 As shown, the FDR defect location device 500 based on k-means clustering and fractional Fourier transform of this embodiment includes a signal sampling module 510, a set clustering module 520, a digital spectrum preprocessing module 530, a digital spectrum transformation module 540, a spectrum conversion module 550 and a result determination module 560.

[0124] The signal sampling module 510 is used to pass different frequency sweep signals to the cable to be detected in the same sampling manner to obtain an initial reflection coefficient spectrum set. In one embodiment, the signal sampling module 510 can be used to perform the operation S110 described above, which will not be described in detail here.

[0125] The set clustering module 520 is used to cluster the initial reflection coefficient spectrum set to obtain a plurality of reflection coefficient spectrum clusters. In one embodiment, the set clustering module 520 can be used to perform the operation S120 described above, which will not be described in detail here.

[0126] The digital spectrum preprocessing module 530 is used to perform spectrum leakage reduction preprocessing on multiple reflection coefficient spectra obtained based on multiple reflection coefficient spectrum clustering clusters to obtain multiple target reflection coefficient spectra. In one embodiment, the digital spectrum preprocessing module 530 can be used to perform the operation S130 described above, which will not be repeated here.

[0127] The digital spectrum conversion module 540 is used to perform fractional Fourier transform on multiple target reflection coefficient spectra to obtain multiple two-dimensional time-frequency spectra. In one embodiment, the digital spectrum conversion module 540 can be used to perform the operation S140 described above, which will not be repeated here.

[0128] The spectrogram conversion module 550 is used to perform spectrogram conversion on the multiple two-dimensional time-frequency spectrograms to obtain multiple positioning curves for defect detection. In one embodiment, the spectrogram conversion module 550 can be used to perform the operation S150 described above, which will not be repeated here.

[0129] The result determination module 560 is used to obtain the defect detection result of the cable to be detected based on the multiple positioning curves. In one embodiment, the result determination module 560 can be used to perform the operation S160 described above, which will not be repeated here.

[0130] According to an embodiment of the present invention, the signal sampling module 510 includes a signal sampling submodule.

[0131] The signal sampling submodule is used to collect signals from the cables to be tested with different sweep frequency signals according to the same sampling frequency and number of sampling points to obtain an initial reflection coefficient spectrum set, wherein the sweep frequency signal includes an angular frequency of 0.15 MHz to 100 MHz.

[0132] According to an embodiment of the present invention, the set clustering module 520 includes a set clustering submodule.

[0133] The set clustering submodule is used to cluster the initial reflection coefficient spectrum set based on a predetermined number of clusters and signal information of the swept frequency signal to obtain a plurality of reflection coefficient spectrum clusters.

[0134] According to an embodiment of the present invention, the digital spectrum preprocessing module 530 includes a digital spectrum determination submodule, a function addition submodule and a digital spectrum transformation submodule.

[0135] The digital spectrum determination submodule is used to use the reflection coefficient spectrum corresponding to the centroid of each reflection coefficient spectrum cluster as the reflection coefficient spectrum.

[0136] The function adds a submodule, which is used to add window functions to the reflection coefficient spectrum.

[0137] The digital spectrum transformation submodule is used to perform bilinear transformation on the reflection coefficient spectrum with the window function added to obtain the target reflection coefficient spectrum.

[0138] According to an embodiment of the present invention, the digital spectrum conversion submodule includes a digital spectrum convolution unit.

[0139] The digital spectrum convolution unit is used to perform convolution processing on the reflection coefficient spectrum with the window function added and the conjugate reflection coefficient spectrum with the window function added to obtain the target reflection coefficient spectrum.

[0140] According to an embodiment of the present invention, the digital-spectrum transformation module 540 includes a digital-spectrum transformation submodule.

[0141] The digital spectrum transformation submodule is used to perform fractional Fourier transform on the target reflection coefficient spectrum by adding a rotation angle to obtain a two-dimensional time-frequency spectrum diagram.

[0142] According to an embodiment of the present invention, the spectrum conversion module 550 includes a rotation angle suppression submodule, a spectrum integration submodule and a spectrum conversion submodule.

[0143] The rotation angle suppression submodule is used to perform pseudo peak suppression processing on the rotation angle in the two-dimensional time-frequency spectrum to obtain a pseudo peak suppression spectrum.

[0144] The spectrum integration submodule is used to perform time domain integration on the pseudo-peak suppression spectrum to obtain a frequency response function.

[0145] The spectrum conversion submodule is used to perform spectrum conversion on the frequency response function to obtain a positioning curve diagram.

[0146] According to an embodiment of the present invention, the result determination module 560 includes a strength updating submodule and a result determination submodule.

[0147] The intensity update submodule is used to update the reflection intensity of each positioning curve graph to obtain multiple target positioning curve graphs.

[0148] The result determination submodule is used to obtain defect detection results based on the amplitudes of the multiple target positioning curve graphs.

[0149] According to an embodiment of the present invention, any multiple modules among the signal sampling module 510, the set clustering module 520, the digital spectrum preprocessing module 530, the digital spectrum conversion module 540, the spectrogram conversion module 550 and the result determination module 560 can be combined in one module for implementation, or any one of the modules can be split into multiple modules. Alternatively, at least part of the functions of one or more of these modules can be combined with at least part of the functions of other modules and implemented in one module. According to an embodiment of the present invention, at least one of the signal sampling module 510, the set clustering module 520, the digital spectrum preprocessing module 530, the digital spectrum conversion module 540, the spectrogram conversion module 550 and the result determination module 560 can be at least partially implemented as a hardware circuit, such as a field programmable gate array (FPGA), a programmable logic array (PLA), a system on a chip, a system on a substrate, a system on a package, an application specific integrated circuit (ASIC), or can be implemented by hardware or firmware such as any other reasonable way of integrating or packaging the circuit, or implemented in any one of the three implementation methods of software, hardware and firmware or in a suitable combination of any of them. Alternatively, at least one of the signal sampling module 510, the set clustering module 520, the digital spectrum preprocessing module 530, the digital spectrum transformation module 540, the spectrogram conversion module 550 and the result determination module 560 can be at least partially implemented as a computer program module, which can perform corresponding functions when the computer program module is executed.

[0150] Figure 6 A block diagram of an electronic device suitable for implementing an FDR defect location method based on k-means clustering and fractional Fourier transform according to an embodiment of the present invention is shown.

[0151] like Figure 6 As shown, the electronic device 600 according to an embodiment of the present invention includes a processor 601, which can perform various appropriate actions and processes according to the program stored in the read-only memory ROM 602 or the program loaded from the storage part 608 to the random access memory RAM 603. The processor 601 may include, for example, a general-purpose microprocessor (such as a CPU), an instruction set processor and / or a related chipset and / or a special-purpose microprocessor (for example, an application-specific integrated circuit (ASIC)), etc. The processor 601 may also include an onboard memory for caching purposes. The processor 601 may include a single processing unit or multiple processing units for performing different actions of the method flow according to an embodiment of the present invention.

[0152] In RAM 603, various programs and data required for the operation of electronic device 600 are stored. Processor 601, ROM 602 and RAM 603 are connected to each other via bus 604. Processor 601 performs various operations of the method flow according to the embodiment of the present invention by executing the program in ROM 602 and / or RAM 603. It should be noted that the program can also be stored in one or more memories other than ROM 602 and RAM 603. Processor 601 can also perform various operations of the method flow according to the embodiment of the present invention by executing the program stored in the one or more memories.

[0153] According to an embodiment of the present invention, the electronic device 600 may further include an input / output (I / O) interface 605, which is also connected to the bus 604. The electronic device 600 may further include one or more of the following components connected to the input / output (I / O) interface 605: an input portion 606 including a keyboard, a mouse, etc.; an output portion 607 including a cathode ray tube (CRT), a liquid crystal display (LCD), etc., and a speaker, etc.; a storage portion 608 including a hard disk, etc.; and a communication portion 609 including a network interface card such as a LAN card, a modem, etc. The communication portion 609 performs communication processing via a network such as the Internet. A drive 610 is also connected to the input / output (I / O) interface 605 as needed. A removable medium 611, such as a magnetic disk, an optical disk, a magneto-optical disk, a semiconductor memory, etc., is installed on the drive 610 as needed, so that a computer program read therefrom is installed into the storage portion 608 as needed.

[0154] The present invention also provides a computer-readable storage medium, which may be included in the device / apparatus / system described in the above embodiment; or may exist independently without being assembled into the device / apparatus / system. The above computer-readable storage medium carries one or more programs, and when the above one or more programs are executed, the method according to the embodiment of the present invention is implemented.

[0155] According to an embodiment of the present invention, the computer-readable storage medium may be a non-volatile computer-readable storage medium, for example, it may include but is not limited to: a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination thereof. In the present invention, the computer-readable storage medium may be any tangible medium containing or storing a program, which may be used by or in combination with an instruction execution system, an apparatus or a device. For example, according to an embodiment of the present invention, the computer-readable storage medium may include the ROM 602 and / or RAM 603 described above and / or one or more memories other than ROM 602 and RAM 603.

[0156] The embodiment of the present invention also includes a computer program product, which includes a computer program, and the computer program includes a program code for executing the method shown in the flowchart. When the computer program product is run in a computer system, the program code is used to enable the computer system to implement the FDR defect location method based on k-means clustering and fractional Fourier transform provided by the embodiment of the present invention.

[0157] The computer program executes the above functions defined in the system / device of the embodiment of the present invention when it is executed by the processor 601. According to the embodiment of the present invention, the system, device, module, unit, etc. described above can be implemented by a computer program module.

[0158] In one embodiment, the computer program may rely on tangible storage media such as optical storage devices, magnetic storage devices, etc. In another embodiment, the computer program may also be transmitted and distributed in the form of signals on a network medium, and downloaded and installed through the communication part 609, and / or installed from a removable medium 611. The program code contained in the computer program may be transmitted using any appropriate network medium, including but not limited to: wireless, wired, etc., or any suitable combination of the above.

[0159] In such an embodiment, the computer program can be downloaded and installed from the network through the communication part 609, and / or installed from the removable medium 611. When the computer program is executed by the processor 601, the above functions defined in the system of the embodiment of the present invention are performed. According to the embodiment of the present invention, the system, device, means, module, unit, etc. described above can be implemented by a computer program module.

[0160] According to an embodiment of the present invention, the program code for executing the computer program provided by the embodiment of the present invention can be written in any combination of one or more programming languages. Specifically, these computing programs can be implemented using high-level process and / or object-oriented programming languages, and / or assembly / machine languages. Programming languages ​​include, but are not limited to, Java, C++, python, "C" language or similar programming languages. The program code can be executed entirely on the user computing device, partially on the user device, partially on the remote computing device, or entirely on the remote computing device or server. In the case of a remote computing device, the remote computing device can be connected to the user computing device through any type of network, including a local area network (LAN) or a wide area network (WAN), or can be connected to an external computing device (e.g., using an Internet service provider to connect through the Internet).

[0161] The flow chart and block diagram in the accompanying drawings illustrate the possible architecture, function and operation of the system, method and computer program product according to various embodiments of the present invention. In this regard, each box in the flow chart or block diagram can represent a module, a program segment, or a part of a code, and the above-mentioned module, program segment, or a part of a code contains one or more executable instructions for realizing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the box can also occur in a different order from the order marked in the accompanying drawings. For example, two boxes represented in succession can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each box in the block diagram or flow chart, and the combination of the boxes in the block diagram or flow chart can be implemented with a dedicated hardware-based system that performs a specified function or operation, or can be implemented with a combination of dedicated hardware and computer instructions.

[0162] It will be appreciated by those skilled in the art that the features described in the various embodiments and / or claims of the present invention may be combined and / or combined in various ways, even if such combinations and / or combinations are not explicitly described in the present invention. In particular, the features described in the various embodiments and / or claims of the present invention may be combined and / or combined in various ways without departing from the spirit and teachings of the present invention. All of these combinations and / or combinations fall within the scope of the present invention.

[0163] The embodiments of the present invention are described above. However, these embodiments are only for the purpose of illustration, and are not intended to limit the scope of the present invention. Although the embodiments are described above, this does not mean that the measures in the various embodiments cannot be used in combination. The scope of the present invention is defined by the appended claims and their equivalents. Without departing from the scope of the present invention, those skilled in the art may make various substitutions and modifications, which should all fall within the scope of the present invention.

Claims

1. A FDR defect location method based on k-means clustering and fractional Fourier transform, characterized in that: The method comprises: According to the same sampling method, different frequency sweep signals are passed through the cable to be tested to obtain an initial reflection coefficient spectrum set; Clustering the initial reflection coefficient spectrum set to obtain a plurality of reflection coefficient spectrum clustering clusters; Performing spectrum leakage reduction preprocessing on a plurality of reflection coefficient spectra obtained based on a plurality of reflection coefficient spectrum clustering clusters to obtain a plurality of target reflection coefficient spectra; Performing fractional Fourier transform on the multiple target reflection coefficient spectra respectively to obtain multiple two-dimensional time-frequency spectrum graphs; Performing spectrum conversion on the plurality of two-dimensional time-frequency spectrum graphs respectively to obtain a plurality of positioning curve graphs for defect detection; and Based on the plurality of positioning curve graphs, a defect detection result of the cable to be detected is obtained; The step of performing spectrum leakage reduction preprocessing on the multiple reflection coefficient spectra obtained based on the multiple reflection coefficient spectrum clustering clusters to obtain multiple target reflection coefficient spectra includes: Taking the reflection coefficient spectrum corresponding to the centroid of each of the reflection coefficient spectrum clusters as the reflection coefficient spectrum; adding window functions to the reflection coefficient spectra respectively; and Performing a bilinear transformation on the reflection coefficient spectrum with the window function added thereto to obtain the target reflection coefficient spectrum; The bilinear transformation of the reflection coefficient spectrum with the window function added thereto to obtain the target reflection coefficient spectrum comprises: Performing convolution processing on the reflection coefficient spectrum with the window function added and the conjugate reflection coefficient spectrum with the window function added to obtain the target reflection coefficient spectrum; The step of converting the two-dimensional time-frequency spectrum graphs into spectra to obtain a plurality of positioning curve graphs for defect detection includes: Performing pseudo-peak suppression processing on the rotation angle in the two-dimensional time-frequency spectrum to obtain a pseudo-peak suppressed frequency spectrum; Performing time domain integration on the pseudo peak suppression spectrum to obtain a frequency response function; and Performing spectrum conversion on the frequency response function to obtain the positioning curve graph; The step of converting the frequency response function into a spectrum to obtain the positioning curve graph includes: determining a propagation speed of the frequency sweep signal in the cable to be detected; and The positioning curve diagram is determined based on the propagation speed and the frequency response function.

2. The method according to claim 1, characterized in that: According to the same sampling method, different frequency sweep signals are passed through the cable to be detected to obtain an initial reflection coefficient spectrum set, including: According to the same sampling frequency and number of sampling points, the signal of the cable to be detected is collected by passing different sweep frequency signals to obtain an initial reflection coefficient spectrum set. Wherein, the frequency sweep signal includes an angular frequency of 0.15 MHz - 100 MHz.

3. The method according to claim 1, characterized in that The clustering of the initial reflection coefficient spectrum set to obtain a plurality of reflection coefficient spectrum clusters includes: Based on a predetermined number of clusters and signal information of the frequency sweep signal, the initial reflection coefficient spectrum set is clustered to obtain a plurality of reflection coefficient spectrum clusters.

4. The method according to claim 1, characterized in that The multiple target reflection coefficient spectra are subjected to fractional Fourier transform respectively to obtain multiple two-dimensional time-frequency spectrum diagrams, including: The target reflection coefficient spectrum is subjected to fractional Fourier transform by adding a rotation angle to obtain the two-dimensional time-frequency spectrum diagram.

5. The method according to claim 4, characterized in that The method of adding a rotation angle to perform a fractional Fourier transform on the target reflection coefficient spectrum to obtain the two-dimensional time-frequency spectrum diagram includes: determining the rotation angle based on the order of the fractional Fourier transform; and Based on the Fourier transform method and the rotation angle, a fractional-order Fourier transform is performed on the target reflection coefficient spectrum to obtain the two-dimensional time-frequency spectrum diagram.

6. The method according to claim 1, characterized in that The step of obtaining the defect detection result of the cable to be detected based on the plurality of positioning curve graphs includes: updating the reflection intensity of each positioning curve graph to obtain a plurality of target positioning curve graphs; and Based on the amplitudes of the plurality of target positioning curve graphs, a defect detection result is obtained.

7. An FDR defect location device based on k-means clustering and fractional Fourier transform, characterized in that: The device comprises: A signal sampling module is used to pass different frequency sweep signals into the cable to be detected in the same sampling mode to obtain an initial reflection coefficient spectrum set; A set clustering module, used for clustering the initial reflection coefficient spectrum set to obtain a plurality of reflection coefficient spectrum clusters; A spectrum preprocessing module, used for performing spectrum leakage reduction preprocessing on a plurality of reflection coefficient spectra obtained based on a plurality of reflection coefficient spectrum clustering clusters, to obtain a plurality of target reflection coefficient spectra; A digital spectrum conversion module, used for performing fractional Fourier transform on the multiple target reflection coefficient spectra respectively to obtain multiple two-dimensional time-frequency spectrum graphs; a spectrum conversion module, used to perform spectrum conversion on the plurality of two-dimensional time-frequency spectrum graphs respectively to obtain a plurality of positioning curve graphs for defect detection; and A result determination module, used for obtaining a defect detection result of the cable to be detected based on the plurality of positioning curve graphs; Wherein, the digital spectrum preprocessing module includes: A digital spectrum determination submodule, used for taking the reflection coefficient spectrum corresponding to the centroid of each of the reflection coefficient spectrum clusters as the reflection coefficient spectrum; a function adding submodule, used for adding window functions to the reflection coefficient spectra respectively; and A digital spectrum transformation submodule, used for performing a bilinear transformation on the reflection coefficient spectrum with the window function added thereto to obtain the target reflection coefficient spectrum; The digital spectrum conversion submodule includes: A digital spectrum convolution unit, used for performing convolution processing on the reflection coefficient spectrum with the window function added and the conjugate reflection coefficient spectrum with the window function added, to obtain the target reflection coefficient spectrum; The spectrum conversion module comprises: A rotation angle suppression submodule, used for performing pseudo peak suppression processing on the rotation angle in the two-dimensional time-frequency spectrum to obtain a pseudo peak suppression spectrum; a spectrum integration submodule, used for performing time domain integration on the pseudo-peak suppression spectrum to obtain a frequency response function; and A spectrum conversion submodule, used for performing spectrum conversion on the frequency response function to obtain the positioning curve graph; The step of converting the frequency response function into a spectrum to obtain the positioning curve graph includes: determining a propagation speed of the frequency sweep signal in the cable to be detected; and The positioning curve diagram is determined based on the propagation speed and the frequency response function.

8. An electronic device comprising: one or more processors; a memory for storing one or more computer programs, It is characterized in that the one or more processors execute the one or more computer programs to implement the steps of the method according to any one of claims 1 to 6.

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