Cable defect positioning method, device and equipment and storage medium

Abstract

The invention discloses a cable defect positioning method, device and equipment and a storage medium, and relates to the technical field of computers, and the method comprises the steps: injecting a frequency-increasing pulse signal sequence into a to-be-detected cable, so as to determine a frequency-increasing pulse data collection result; determining a time domain analysis result based on the frequency-increasing pulse data acquisition result, and determining a frequency domain sparse reconstruction result in combination with a preset frequency domain sparse reconstruction strategy; and based on a time domain analysis result and a frequency domain sparse reconstruction result, carrying out time-frequency domain fused cable defect positioning to determine an initial defect positioning result, carrying out cable dispersion characteristic evaluation in combination with the domain sparse reconstruction result, and carrying out frequency domain sparse reconstruction again by using a corresponding characteristic evaluation result until a preset iteration termination condition is met. And determining a target defect positioning result. According to the invention, high-precision cable defect positioning based on time-frequency domain fusion can be realized, the accuracy and noise robustness of cable defect positioning are improved, the positioning error is reduced, and the dispersion effect is solved.

Description

Technical Field

[0001] This invention relates to the field of computer technology, and in particular to a method, apparatus, device, and storage medium for locating cable defects. Background Technology

[0002] Currently, to detect cable defects offline and prevent potential power outages, pure time-domain and pure frequency-domain solutions are widely used. However, these solutions each have their own shortcomings in defect localization.

[0003] 1) Pure time-domain solutions are limited by pulse width, resulting in limited distance resolution. Furthermore, in noisy environments, accurate pulse leading-out is difficult, leading to increased positioning errors. 2) Pure frequency-domain solutions face phase ambiguity issues and are sensitive to cable dispersion, causing systematic errors. The dispersion effect refers to the change in the electromagnetic properties of cable insulation materials (especially the semi-conductive layer) with frequency, causing the signal propagation speed to change with frequency (dispersion effect). Existing solutions assume a constant propagation speed, but this introduces non-negligible positioning errors over a wide frequency range. Summary of the Invention

[0004] In view of this, the purpose of this invention is to provide a cable defect location method, apparatus, device, and storage medium, which can achieve high-precision cable defect location through time-frequency domain fusion, improve the accuracy and noise robustness of cable defect location, reduce location errors, and solve the dispersion effect. The specific solution is as follows:

[0005] Firstly, this application provides a method for locating cable defects, including:

[0006] A frequency-enhanced pulse signal sequence is injected into the cable under test, and the incident and reflected signals at the beginning of the cable under test are collected to determine the frequency-enhanced pulse data acquisition results; wherein, the frequency-enhanced pulse signal sequence includes multiple sinusoidal pulses with frequency steps;

[0007] Based on the frequency-enhanced pulse data acquisition results, time-domain analysis is performed on each of the sinusoidal pulses to determine the time-domain analysis results; the time-domain analysis results include time-domain positioning results and time-domain confidence assessment results.

[0008] Based on the preset frequency domain sparse reconstruction strategy, the frequency-enhanced pulse data acquisition results and the time domain positioning results, a sparse reconstruction optimization problem is constructed and solved to determine the frequency domain sparse reconstruction result.

[0009] Based on the time-domain analysis results and the frequency-domain sparse reconstruction results, time-frequency domain fusion is performed to locate cable defects in order to determine the initial defect location results.

[0010] Based on the frequency domain sparse reconstruction results, the initial defect location results, and the adaptive dispersion compensation strategy, the cable dispersion characteristics are evaluated. Then, using the corresponding characteristic evaluation results, the process jumps back to the steps of constructing and solving the sparse reconstruction optimization problem based on the preset frequency domain sparse reconstruction strategy, the frequency-enhanced pulse data acquisition results, and the time domain location results, until the preset iteration termination condition is met, and the target defect location result is determined.

[0011] Optionally, the step of injecting a frequency-enhanced pulse signal sequence into the cable under test and acquiring the incident and reflected signals at the beginning of the cable under test to determine the frequency-enhanced pulse data acquisition results includes:

[0012] Based on pulse frequency information, pulse frequency step information, pulse generation period, pulse amplitude, and pulse duration, the frequency-enhanced pulse signal sequence is determined.

[0013] Each sinusoidal signal in the frequency-enhanced pulse signal sequence is sequentially input to the cable under test, and the incident and reflected signals at the beginning of the cable under test are collected to determine the frequency-enhanced pulse data acquisition results; the frequency-enhanced pulse data acquisition results include time-domain waveform data and frequency-domain response data.

[0014] Optionally, the step of performing time-domain analysis on each of the sinusoidal pulses based on the frequency-enhanced pulse data acquisition results to determine the time-domain analysis results includes:

[0015] Based on the frequency-enhanced pulse data acquisition results and the preset signal analysis algorithm, the arrival time of the corresponding reflected pulse is identified for the sinusoidal pulses of each frequency to determine the time identification result;

[0016] Based on the frequency-enhanced pulse data acquisition results, the time delay information between each sinusoidal pulse and the corresponding reflected pulse is obtained;

[0017] Based on the time delay information and the average pulse transmission speed, the pulse positioning result of each sinusoidal pulse is determined;

[0018] Based on the time identification result and the pulse positioning result, the time domain positioning result is determined;

[0019] The time-domain localization results are analyzed for signal-to-noise ratio and pulse shape integrity to determine the time-domain confidence assessment results.

[0020] Optionally, the construction and solution of the sparse reconstruction optimization problem based on the preset frequency domain sparse reconstruction strategy, the frequency-enhanced pulse data acquisition results, and the time domain positioning results includes:

[0021] The cable under test is discretized to determine several grid points;

[0022] Based on the aforementioned grid points and a preset propagation constant, a dispersion-aware dictionary matrix is ​​constructed to determine the current dictionary matrix; the preset propagation constant is a propagation constant that takes dispersion into account.

[0023] Based on the frequency-enhanced pulse data acquisition results, the frequency domain reflection coefficient vector is determined;

[0024] Based on the current dictionary matrix and the frequency domain reflection coefficient vector, an initial sparse reconstruction optimization problem is constructed.

[0025] Using the temporal positioning results as prior information, the initial sparse reconstruction optimization problem is improved to determine the improved problem;

[0026] Based on a preset algorithm, the improved problem is solved to determine the sparse reflection coefficient distribution.

[0027] Optionally, the cable defect localization based on the time-domain analysis results and the frequency-domain sparse reconstruction results, to determine the initial defect localization result, includes:

[0028] Based on the frequency-enhanced pulse data acquisition results, frequency domain features are extracted from the sinusoidal pulses at each frequency to determine the frequency domain feature extraction results;

[0029] Based on the instantaneous phase information and the pulse frequency information in the frequency domain feature extraction results, the frequency domain positioning result is determined;

[0030] The frequency domain localization results are subjected to signal-to-noise ratio analysis to determine the frequency domain confidence assessment results;

[0031] Based on the frequency domain confidence assessment results and the time domain confidence assessment results, the current time domain defect location weight and the current frequency domain defect location weight are determined.

[0032] Based on the current time-domain defect location weight, the current frequency-domain defect location weight, the time-domain location result, and the frequency-domain sparse reconstruction result, time-frequency domain fusion is performed to locate cable defects and determine the initial defect location result.

[0033] Optionally, the step of evaluating the cable dispersion characteristics based on the frequency domain sparse reconstruction result, the initial defect location result, and the adaptive dispersion compensation strategy, and then using the corresponding characteristic evaluation results to jump back to the step of constructing and solving the sparse reconstruction optimization problem based on the preset frequency domain sparse reconstruction strategy, the frequency-enhanced pulse data acquisition result, and the time domain location result, until the preset iteration termination condition is met, to determine the target defect location result, includes:

[0034] Obtain a parameterized dispersion model; the parameterized dispersion model includes an initial dispersion intensity coefficient, an initial dispersion characteristic frequency, and an initial propagation speed lower limit;

[0035] Based on the parameterized dispersion model, the frequency domain sparse reconstruction results, and the initial defect localization results, the objective function for dispersion parameter optimization is determined.

[0036] The objective function for optimizing the dispersion parameters is iteratively solved to determine the current characteristic evaluation result of the cable under test; the characteristic evaluation result includes the target dispersion intensity coefficient, the target dispersion characteristic frequency, and the target propagation speed lower limit;

[0037] Based on the aforementioned feature evaluation results, the current dictionary matrix is ​​updated to determine the updated dictionary matrix;

[0038] Based on the updated dictionary matrix, the updated defect location result is determined;

[0039] Determine whether the location difference between the updated defect location result and the initial defect location result is greater than a preset threshold to determine the difference judgment result;

[0040] If the difference judgment result is greater than, then based on the updated defect location result and the adaptive dispersion compensation strategy, the updated characteristic evaluation result is determined, and using the updated characteristic evaluation result, the process jumps back to the step of constructing and solving the sparse reconstruction optimization problem based on the preset frequency domain sparse reconstruction strategy, the frequency-enhanced pulse data acquisition result and the time domain location result.

[0041] If the difference judgment result is not greater than, then the updated defect location result is determined as the target defect location result.

[0042] Optionally, after determining the target defect location result, the method further includes:

[0043] Based on the target defect location results, a Hessian matrix is ​​constructed to determine the Hessian matrix construction result;

[0044] Based on the constructed result of the Hessian matrix, the Cramer-Lao lower bound is solved to determine the lower bound solution result;

[0045] Based on the solution of the lower bound, the positioning uncertainty information is obtained;

[0046] A consistency evaluation is performed on the time-domain positioning result, the frequency-domain positioning result, and the frequency-domain sparse reconstruction result to determine the consistency evaluation result;

[0047] Based on the consistency assessment results and the lower bound solution results, the target confidence assessment results are determined.

[0048] Secondly, this application provides a cable defect location device, comprising:

[0049] The data acquisition module is used to inject a frequency-enhanced pulse signal sequence into the cable under test and acquire the incident and reflected signals at the beginning of the cable under test to determine the frequency-enhanced pulse data acquisition results; wherein, the frequency-enhanced pulse signal sequence includes multiple sinusoidal pulses with frequency steps;

[0050] The time-domain analysis module is used to perform time-domain analysis on each of the sinusoidal pulses based on the frequency-increasing pulse data acquisition results, so as to determine the time-domain analysis results; the time-domain analysis results include time-domain positioning results and time-domain confidence assessment results;

[0051] The frequency domain coefficient reconstruction module is used to construct and solve the sparse reconstruction optimization problem based on the preset frequency domain sparse reconstruction strategy, the frequency-enhanced pulse data acquisition results and the time domain positioning results, so as to determine the frequency domain sparse reconstruction result.

[0052] The fusion positioning module is used to perform time-frequency domain fusion for cable defect positioning based on the time-domain analysis results and the frequency-domain sparse reconstruction results, so as to determine the initial defect positioning results;

[0053] The positioning result determination module is used to evaluate the cable dispersion characteristics based on the frequency domain sparse reconstruction result, the initial defect positioning result, and the adaptive dispersion compensation strategy. Then, using the corresponding characteristic evaluation results, it jumps back to the steps of constructing and solving the sparse reconstruction optimization problem based on the preset frequency domain sparse reconstruction strategy, the frequency-enhanced pulse data acquisition result, and the time domain positioning result, until the preset iteration termination condition is met, and the target defect positioning result is determined.

[0054] Thirdly, this application provides an electronic device, comprising:

[0055] Memory, used to store computer programs;

[0056] A processor is used to execute the computer program to implement the steps of the aforementioned cable defect location method.

[0057] Fourthly, this application provides a computer-readable storage medium for storing a computer program that, when executed by a processor, implements the steps of the aforementioned cable defect location method.

[0058] As can be seen, in this application, a frequency-enhanced pulse signal sequence is injected into the cable under test, and the incident and reflected signals at the beginning of the cable under test are collected to determine the frequency-enhanced pulse data acquisition results; wherein, the frequency-enhanced pulse signal sequence includes multiple sinusoidal pulses with frequency steps; based on the frequency-enhanced pulse data acquisition results, time-domain analysis is performed on each of the sinusoidal pulses to determine the time-domain analysis results; the time-domain analysis results include time-domain positioning results and time-domain confidence evaluation results; based on a preset frequency-domain sparse reconstruction strategy, the frequency-enhanced pulse data acquisition results, and the time-domain positioning results, a sparse reconstruction optimization problem is constructed and solved. The process involves several steps: first, determining the frequency domain sparse reconstruction result; second, using the time domain analysis result and the frequency domain sparse reconstruction result to perform time-frequency domain fusion cable defect localization to determine the initial defect localization result; third, evaluating the cable dispersion characteristics based on the frequency domain sparse reconstruction result, the initial defect localization result, and the adaptive dispersion compensation strategy; and fourth, using the corresponding characteristic evaluation results to return to the steps of constructing and solving the sparse reconstruction optimization problem based on the preset frequency domain sparse reconstruction strategy, the frequency-enhanced pulse data acquisition result, and the time domain localization result, until the preset iteration termination condition is met, thus determining the target defect localization result. In other words, this application first uses a frequency-enhanced pulse signal sequence to detect the cable and collects data after the frequency-enhanced pulse signal sequence is injected. Then, time-domain analysis is performed using the obtained frequency-enhanced pulse data acquisition results to determine the time-domain analysis results. Using the frequency-enhanced pulse data acquisition results and the time-domain positioning results, a sparse reconstruction optimization problem is constructed and solved to determine the frequency-domain sparse reconstruction result. Then, based on the time-domain analysis results and the frequency-domain sparse reconstruction results, time-frequency domain fusion is used to locate the cable defect. Next, using the determined initial defect location result, the characteristic evaluation result of the dispersion characteristics of the cable under test is determined. Based on this evaluation result, the process jumps back to the steps of constructing and solving the sparse reconstruction optimization problem based on the preset frequency-domain sparse reconstruction strategy, the frequency-enhanced pulse data acquisition results, and the time-domain positioning results, until a preset iteration termination condition is met, at which point the target defect location result is determined. This enables high-precision cable defect location through time-frequency domain fusion, improves the accuracy and noise robustness of cable defect location, reduces location errors, and solves the dispersion effect. Attached Figure Description

[0059] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on the provided drawings without creative effort.

[0060] Figure 1A flowchart of a cable defect location method provided in this application;

[0061] Figure 2 This application provides a schematic diagram of the structure of a cable defect location device.

[0062] Figure 3 This application provides a structural diagram of an electronic device. Detailed Implementation

[0063] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0064] Currently, to detect cable defects offline and avoid potential power outages, pure time-domain and pure frequency-domain solutions are widely used. However, these solutions each have their own shortcomings in defect localization: 1) Pure time-domain solutions are limited by pulse width, resulting in limited distance resolution; and in noisy environments, it is difficult to accurately identify the pulse leading edge, leading to increased localization errors. 2) Pure frequency-domain solutions face phase ambiguity issues and are sensitive to cable dispersion effects, leading to systematic errors. The dispersion effect refers to the change in the electromagnetic properties of cable insulation materials (especially the semi-conductive layer) with frequency, causing the signal propagation speed to change with frequency (dispersion effect). Current solutions assume a constant propagation speed, but this introduces non-negligible localization errors over a wide frequency range.

[0065] To address this, this application provides a cable defect location scheme that enables high-precision cable defect location through time-frequency domain fusion, improving the accuracy and noise robustness of cable defect location, reducing location errors, and resolving dispersion effects.

[0066] See Figure 1 As shown in the figure, an embodiment of the present invention discloses a method for locating cable defects, including:

[0067] Step S11: Inject a frequency-enhancing pulse signal sequence into the cable under test, and collect the incident signal and reflected signal at the beginning of the cable under test to determine the frequency-enhancing pulse data acquisition result; wherein, the frequency-enhancing pulse signal sequence includes multiple sinusoidal pulses with frequency steps.

[0068] In this embodiment, firstly, a frequency-enhanced pulse signal sequence is injected into the cable under test. This sequence consists of N sinusoidal pulses with progressively increasing frequencies. Simultaneously, the incident and reflected signals at the cable's head end are acquired, and the i-th incident and reflected signals are denoted as follows: and The process involves acquiring complete time-domain waveforms and frequency-domain responses. Specifically, based on pulse frequency information, pulse frequency step information, pulse generation period, pulse amplitude, and pulse duration, an enhanced pulse signal sequence is determined. Each sinusoidal signal in the enhanced pulse signal sequence is sequentially input to the cable under test, and the incident and reflected signals at the beginning of the cable are collected to determine the enhanced pulse data acquisition results. The enhanced pulse data acquisition results include time-domain waveform data and frequency-domain response data.

[0069] It should be understood that, in this embodiment, the frequency-enhanced pulse signal can be generated according to the following formula:

[0070] .

[0071] In the formula, This represents the frequency of the i-th boost pulse in the sequence. Indicates the starting frequency; It represents the frequency increase step size between two adjacent pulses of the frequency-increasing pulse; T represents the pulse generation period; D represents the duration of the frequency-increasing pulse; A represents the pulse amplitude; t represents the time. It represents pi (π).

[0072] Step S12: Based on the acquisition results of the frequency-increasing pulse data, perform time-domain analysis on each of the sinusoidal pulses to determine the time-domain analysis results; the time-domain analysis results include time-domain positioning results and time-domain confidence assessment results.

[0073] In this embodiment, after determining the frequency-enhanced pulse data acquisition results, time-domain feature extraction and confidence assessment are performed. For each frequency pulse, a time-domain analysis method is used for preliminary localization, and the confidence of the time-domain localization at each frequency point is assessed based on the signal-to-noise ratio and pulse shape integrity. Specifically: based on the frequency-enhanced pulse data acquisition results and a preset signal analysis algorithm, the arrival time of the corresponding reflected pulse is identified for each sinusoidal pulse at each frequency to determine the time identification result; based on the frequency-enhanced pulse data acquisition results, the time delay information between each sinusoidal pulse and the corresponding reflected pulse is obtained; based on the time delay information and the average pulse propagation speed, the pulse localization result of each sinusoidal pulse is determined; based on the time identification result and the pulse localization result, the time-domain localization result is determined; and the time-domain localization result is analyzed for signal-to-noise ratio and pulse shape integrity to determine the time-domain confidence assessment result.

[0074] It is important to understand that, regarding time-domain feature extraction, this embodiment employs the wavelet transform modulus maxima method to accurately identify the arrival time of the reflected pulse. And the preliminary time-domain localization result is calculated:

[0075] .

[0076] In the formula, It is the positioning result of the i-th pulse at time t. This is the average pulse propagation speed, which can generally be taken as 172. This speed will be corrected during the dispersion compensation process later; It is the time delay between the i-th frequency-enhanced pulse and its reflected pulse at time t.

[0077] Furthermore, regarding the assessment of the confidence level for time-domain localization at each frequency point:

[0078] .

[0079] In the formula, Let be the signal power of the i-th pulse at time t. With noise power In comparison, the noise power is determined by noise acquisition conducted before the frequency-enhanced pulse test; It is the maximum value of the ratio of all signal power to noise power; , is the peak value of the cross-correlation between the incident signal and the reflected signal of the i-th pulse; The maximum value of the cross-correlation peak; The standard deviation of the time estimate; This represents the average time delay.

[0080] Step S13: Based on the preset frequency domain sparse reconstruction strategy, the frequency-enhanced pulse data acquisition results and the time domain positioning results, construct and solve the sparse reconstruction optimization problem to determine the frequency domain sparse reconstruction result.

[0081] In this embodiment, not only is time-series analysis performed, but sparse reconstruction theory is also applied to frequency-enhanced pulse data processing to achieve frequency-domain sparse reconstruction and multi-defect identification. Specifically: the cable under test is discretized to determine several grid points; based on the several grid points and a preset propagation constant, a dispersion-sensing dictionary matrix is ​​constructed to determine the current dictionary matrix; the preset propagation constant is a propagation constant that considers dispersion; based on the frequency-enhanced pulse data acquisition results, a frequency-domain reflection coefficient vector is determined; based on the current dictionary matrix and the frequency-domain reflection coefficient vector, an initial sparse reconstruction optimization problem is constructed; the time-domain positioning result is used as prior information to improve the initial sparse reconstruction optimization problem to determine the improved problem; based on a preset algorithm, the improved problem is solved to determine the sparse reflection coefficient distribution.

[0082] It is important to understand that, in this embodiment, the specific process for frequency domain sparse reconstruction is as follows:

[0083] (1) Construct a dispersion sensing dictionary matrix by discretizing the cable into M grid points and constructing the dictionary matrix. ( Let N represent a complex matrix; N represents the total number of rows in the matrix (the number of frequency points); M represents the total number of columns in the matrix (the number of spatial grid points). Each element in the dictionary matrix, i.e., the element in row i and column j... :

[0084] .

[0085] In the formula, i is the frequency index (i=1,2,...N), and j is the spatial location index (j=1,2,...M). It is the propagation constant considering dispersion, where, The attenuation constant varies with frequency. The phase constant varies with frequency. This represents the position of the j-th grid point, i.e., its distance from the beginning of the cable.

[0086] (2) Establishing a sparse reconstruction optimization problem:

[0087] .

[0088] In the formula, It is the measured frequency domain reflection coefficient vector. It is the sparse reflection coefficient distribution to be determined. yes The optimal solution is given by argmin, where argmin represents the parameters that minimize the objective function. It is a regularization parameter. It is a norm symbol, for example, It is an L1 norm. It is an L2 norm.

[0089] (3) Improved reconstruction with embedded temporal priors.

[0090] Using temporal positioning results as prior information improves sparse reconstruction:

[0091] .

[0092] In the formula, It is a prior distribution constructed from the temporal domain positioning results; This is the measured frequency domain reflection coefficient vector; The sparse reflection coefficient distribution to be solved; and This is a regularization parameter that controls the weights of sparsity and prior information.

[0093] (4) Use the FISTA algorithm (Fast Iterative Shrinkage-Thresholding Algorithm) to solve efficiently.

[0094] The Fast Iterative Shrink Threshold Algorithm (FISTA) is used to solve the above optimization problem:

[0095] .

[0096] In the formula, It is a soft threshold operator. It is the Lipschitz constant, usually taken as 1. The largest eigenvalue determines the step size of gradient descent. It is the transpose of the dictionary matrix. Indicates the number of iterations. It is the first The extrapolation point for the next iteration is calculated using the x values ​​from the previous two iterations and is used to accelerate convergence. It is the first The sparse vector estimate obtained in the second iteration. It is the first The momentum coefficient for the next iteration is updated according to the following formula:

[0097] .

[0098] In the formula, This is the initial value.

[0099] Step S14: Based on the time-domain analysis results and the frequency-domain sparse reconstruction results, perform time-frequency domain fusion cable defect localization to determine the initial defect localization results.

[0100] In this embodiment, after completing the time-domain and frequency-domain processing, the time-domain positioning result is fused with the frequency-domain sparse reconstruction result to initially locate defects in the cable. Specifically: based on the frequency-enhanced pulse data acquisition result, frequency-domain features are extracted from the sinusoidal pulses at each frequency to determine the frequency-domain feature extraction result; based on the instantaneous phase information and pulse frequency information in the frequency-domain feature extraction result, the frequency-domain positioning result is determined; signal-to-noise ratio analysis is performed on the frequency-domain positioning result to determine the frequency-domain confidence assessment result; based on the frequency-domain confidence assessment result and the time-domain confidence assessment result, the current time-domain defect positioning weight and the current frequency-domain defect positioning weight are determined; based on the current time-domain defect positioning weight, the current frequency-domain defect positioning weight, the time-domain positioning result, and the frequency-domain sparse reconstruction result, time-frequency domain fusion cable defect positioning is performed to determine the initial defect positioning result.

[0101] It should be understood that, in this embodiment, regarding the traditional frequency domain positioning scheme, for each frequency pulse, its frequency domain features are extracted:

[0102] 1) Extract the instantaneous phase of the incident and reflected signals using Hilbert transform. and .

[0103] 2) Calculate the average phase difference: .

[0104] 3) Based on phase difference and frequency Preliminary frequency domain positioning results :

[0105] .

[0106] 4) Evaluate the confidence level of frequency domain positioning for each frequency point. :

[0107] .

[0108] In the formula, This represents the mean of the average phase difference. For frequency domain signal-to-noise ratio, The maximum value of the frequency domain signal-to-noise ratio is given by the formula shown below:

[0109] .

[0110] In the formula, The power within the signal frequency band is selected as the i-th frequency boosting pulse. Narrow bands on the left and right; The power within the noise band is selected from the frequency region outside the signal band. The number of frequency points within the signal band; This represents the number of frequency points within the noise band. It is the Fourier transform of the i-th reflected pulse.

[0111] Based on this, a fusion model for defect localization is established:

[0112] .

[0113] In the formula, This represents the temporal localization result of the k-th defect; The traditional frequency domain phase difference localization result for the k-th defect; The sparse reconstruction localization result for the k-th defect; The adaptive weight for the k-th defect; This is the fusion localization result for the k-th defect.

[0114] Furthermore, regarding adaptive weights, the weights are dynamically adjusted based on the confidence levels obtained from the above steps:

[0115] .

[0116] .

[0117] In the formula, This refers to confidence information in the time domain; Confidence information in the frequency domain; confidence level Evaluation based on reconstruction residuals and sparsity:

[0118] .

[0119] In the formula, denoted by L0 norm, which is the number of non-zero elements in a sparse vector; This represents the total number of frequency points, i.e., the observation vector. The length of the dictionary matrix is ​​also the length of the dictionary matrix. number of rows.

[0120] Step S15: Based on the frequency domain sparse reconstruction result, the initial defect location result, and the adaptive dispersion compensation strategy, evaluate the cable dispersion characteristics. Using the corresponding characteristic evaluation results, jump back to the step of constructing and solving the sparse reconstruction optimization problem based on the preset frequency domain sparse reconstruction strategy, the frequency-enhanced pulse data acquisition result, and the time domain location result, until the preset iteration termination condition is met, and determine the target defect location result.

[0121] In this embodiment, after initial localization, dispersion compensation is further performed based on the fused localization. Specifically: a parameterized dispersion model is obtained; the parameterized dispersion model includes an initial dispersion intensity coefficient, an initial dispersion characteristic frequency, and an initial propagation velocity lower limit; based on the parameterized dispersion model, the frequency domain sparse reconstruction result, and the initial defect localization result, a dispersion parameter optimization objective function is determined; the dispersion parameter optimization objective function is iteratively solved to determine the current characteristic evaluation result of the cable under test; the characteristic evaluation result includes a target dispersion intensity coefficient, a target dispersion characteristic frequency, and a target propagation velocity lower limit; based on the characteristic evaluation result, the current dictionary matrix is ​​updated to determine the updated dictionary matrix. Based on the updated dictionary matrix, the updated defect location result is determined; the location difference between the updated defect location result and the initial defect location result is determined to be greater than a preset threshold to determine the difference judgment result; if the difference judgment result is greater than the threshold, the updated characteristic evaluation result is determined based on the updated defect location result and the adaptive dispersion compensation strategy, and the updated characteristic evaluation result is used to jump back to the step of constructing and solving the sparse reconstruction optimization problem based on the preset frequency domain sparse reconstruction strategy, the frequency-enhanced pulse data acquisition result, and the time domain location result; if the difference judgment result is not greater than the threshold, the updated defect location result is determined as the target defect location result.

[0122] It should be understood that, in this embodiment, a parameterized dispersion model is established:

[0123] .

[0124] In the formula, This represents the propagation speed (m / s) at the low-frequency limit. This represents the dispersion intensity coefficient (dimensionless). The characteristic frequency (Hz) determines the inflection point of the dispersion curve.

[0125] Then, an objective function for optimizing the dispersion parameters is constructed. Based on the sparse reconstruction results, the dispersion characteristics of the cable are estimated:

[0126] .

[0127] In the formula, In frequency The complex reflection coefficient measured at the location; The estimated reflection coefficient for the k-th defect (obtained by sparse reconstruction).

[0128] Next, the dispersion parameters are optimized. The optimization can be achieved using the Nelder-Mead simplex method, with initial values ​​for the following parameters: =1.68×10⁸ m / s, =0.04, =10×10⁶ Hz. The iteration termination condition is set as follows: the change in the dispersion parameter is less than 1e⁻⁶ or the maximum number of iterations, 1000, is reached.

[0129] In addition, regarding iterative refinement:

[0130] (1) Using the optimized dispersion parameters Update the dictionary matrix, and recalculate each element in the matrix according to the following formula:

[0131] .

[0132] (2) Perform sparse reconstruction again (step S13) to obtain the updated defect location. .

[0133] (3) Using new defect locations Repeat step S15 to optimize the dispersion parameters and update the dispersion parameters.

[0134] (4) Repeat steps (1) to (3) until the change in position estimation is less than a preset threshold, for example, 0.1 meters. , To update the location of defects from the previous round.

[0135] Furthermore, after determining the target defect location result, this embodiment also performs an uncertainty assessment so that the assessment result is output together with the high-precision defect location result. Specifically: based on the target defect location result, a Hessian matrix is ​​constructed to determine the Hessian matrix construction result; based on the Hessian matrix construction result, the Cramer-Lao lower bound is solved to determine the lower bound solution result; based on the lower bound solution result, the location uncertainty information is obtained; the consistency assessment is performed on the time-domain location result, the frequency-domain location result, and the frequency-domain sparse reconstruction result to determine the consistency assessment result; based on the consistency assessment result and the lower bound solution result, the target confidence assessment result is determined. That is:

[0136] (1) Calculate the Cramér-Rao lower bound (i.e., the Cramer-Rao lower bound) based on the Hessian matrix. Calculate the theoretical minimum variance based on the Hessian matrix:

[0137] .

[0138] In the formula, This indicates the location of the k-th defect; the Fisher information matrix is:

[0139] .

[0140] In the formula, is , where is the defect location parameter vector. The predicted reflectance coefficient is based on the dispersion model; This represents the noise variance.

[0141] (2) Consistency assessment. Calculate the consistency measure of the defect localization results of each method. :

[0142] .

[0143] In the formula, The standard deviation of the three positioning results (time domain positioning, traditional frequency domain phase difference positioning, and frequency domain sparse reconstruction positioning) is given by: For the average positioning results, These represent the positioning in the time domain, frequency domain, and sparse reconstruction, respectively.

[0144] (3) Calculation of the final confidence level.

[0145] .

[0146] In the formula, the weights can be set according to actual needs. =0.4, =0.4, =0.2.

[0147] (4) Final Result Output. The final output includes: : Defect location; : Reflection intensity; (meters, 99.7% confidence interval): Location uncertainty; Overall confidence level: (0-1 range).

[0148] In summary, the solution proposed in this embodiment has the following beneficial effects:

[0149] (1) Significantly improved accuracy: The triple information fusion (time domain + frequency domain phase difference + frequency domain sparse reconstruction) fully leverages the advantages of each method, resulting in improved positioning accuracy compared to a single method.

[0150] (2) Extremely strong multi-defect resolution capability: The super-resolution characteristic of sparse reconstruction theory, combined with time-domain prior, can effectively separate multiple defects with extremely close spacing.

[0151] (3) Adaptive dispersion compensation: Through data-driven dispersion parameter estimation, dispersion effects are automatically compensated without prior cable parameters.

[0152] (4) Strong noise robustness: Sparse reconstruction itself has noise suppression capability, and combined with time-domain confidence weighting, it can still maintain good performance under extremely low signal-to-noise ratio.

[0153] (5) Provide uncertainty quantification: For the first time, a systematic uncertainty assessment is introduced in cable defect location, providing a reliable basis for engineering decision-making.

[0154] Therefore, in this application, firstly, the cable is detected using a frequency-enhanced pulse signal sequence, and data after the frequency-enhanced pulse signal sequence is injected is collected. Then, time-domain analysis is performed using the obtained frequency-enhanced pulse data acquisition results to determine the time-domain analysis results. Using the frequency-enhanced pulse data acquisition results and the time-domain positioning results, a sparse reconstruction optimization problem is constructed and solved to determine the frequency-domain sparse reconstruction result. Next, based on the time-domain analysis results and the frequency-domain sparse reconstruction results, time-frequency domain fusion is used to locate the cable defect. Then, using the determined initial defect location result, the characteristic evaluation result of the dispersion characteristics of the cable under test is determined. Based on this evaluation result, the process returns to the steps of constructing and solving the sparse reconstruction optimization problem based on the preset frequency-domain sparse reconstruction strategy, the frequency-enhanced pulse data acquisition results, and the time-domain positioning results, until a preset iteration termination condition is met, at which point the target defect location result is determined. In this way, high-precision cable defect location through time-frequency domain fusion can be achieved, improving the accuracy and noise robustness of cable defect location, reducing location errors, and solving the dispersion effect.

[0155] See Figure 2 As shown in the figure, this application also discloses a cable defect location device, including:

[0156] The data acquisition module 11 is used to inject a frequency-enhanced pulse signal sequence into the cable under test and acquire the incident and reflected signals at the beginning of the cable under test to determine the frequency-enhanced pulse data acquisition results; wherein, the frequency-enhanced pulse signal sequence includes multiple sinusoidal pulses with frequency steps;

[0157] The time-domain analysis module 12 is used to perform time-domain analysis on each of the sinusoidal pulses based on the frequency-increasing pulse data acquisition results, so as to determine the time-domain analysis results; the time-domain analysis results include time-domain positioning results and time-domain confidence assessment results;

[0158] The frequency domain coefficient reconstruction module 13 is used to construct and solve the sparse reconstruction optimization problem based on the preset frequency domain sparse reconstruction strategy, the frequency-enhanced pulse data acquisition results and the time domain positioning results, so as to determine the frequency domain sparse reconstruction result.

[0159] The fusion positioning module 14 is used to perform time-frequency domain fusion for cable defect positioning based on the time-domain analysis results and the frequency-domain sparse reconstruction results, so as to determine the initial defect positioning results.

[0160] The positioning result determination module 15 is used to evaluate the cable dispersion characteristics based on the frequency domain sparse reconstruction result, the initial defect positioning result, and the adaptive dispersion compensation strategy. Then, using the corresponding characteristic evaluation results, it jumps back to the steps of constructing and solving the sparse reconstruction optimization problem based on the preset frequency domain sparse reconstruction strategy, the frequency-enhanced pulse data acquisition result, and the time domain positioning result, until the preset iteration termination condition is met, and the target defect positioning result is determined.

[0161] In some specific embodiments, the data acquisition module 11 can be used to: determine an enhanced pulse signal sequence based on pulse frequency information, pulse frequency step information, pulse generation period, pulse amplitude, and pulse duration; input each sinusoidal signal in the enhanced pulse signal sequence to the cable under test in sequence, and acquire the incident signal and reflected signal at the beginning of the cable under test to determine the enhanced pulse data acquisition result; the enhanced pulse data acquisition result includes time-domain waveform data and frequency-domain response data.

[0162] In some specific embodiments, the time-domain analysis module 12 can be used to: identify the arrival time of the corresponding reflected pulse for each sinusoidal pulse at each frequency based on the frequency-enhanced pulse data acquisition results and a preset signal analysis algorithm, so as to determine the time identification result; obtain the time delay information between each sinusoidal pulse and the corresponding reflected pulse based on the frequency-enhanced pulse data acquisition results; determine the pulse positioning result of each sinusoidal pulse based on the time delay information and the average pulse transmission speed; determine the time-domain positioning result based on the time identification result and the pulse positioning result; and perform signal-to-noise ratio and pulse shape integrity analysis on the time-domain positioning result to determine the time-domain confidence evaluation result.

[0163] In some specific embodiments, the frequency domain coefficient reconstruction module 13 can be used to: discretize the cable under test to determine several grid points; construct a dispersion sensing dictionary matrix based on the several grid points and a preset propagation constant to determine the current dictionary matrix; the preset propagation constant is a propagation constant that considers dispersion; determine the frequency domain reflection coefficient vector based on the frequency-enhanced pulse data acquisition results; construct an initial sparse reconstruction optimization problem based on the current dictionary matrix and the frequency domain reflection coefficient vector; improve the initial sparse reconstruction optimization problem using the time domain positioning result as prior information to determine the improved problem; and solve the improved problem based on a preset algorithm to determine the sparse reflection coefficient distribution.

[0164] In some specific embodiments, the fusion positioning module 14 can be specifically used to: extract frequency domain features from the sinusoidal pulses of each frequency based on the frequency-enhanced pulse data acquisition results to determine the frequency domain feature extraction results; determine the frequency domain positioning results based on the instantaneous phase information and pulse frequency information in the frequency domain feature extraction results; perform signal-to-noise ratio analysis on the frequency domain positioning results to determine the frequency domain confidence evaluation results; determine the current time domain defect positioning weight and the current frequency domain defect positioning weight based on the frequency domain confidence evaluation results and the time domain confidence evaluation results; and perform time-frequency domain fusion cable defect positioning based on the current time domain defect positioning weight, the current frequency domain defect positioning weight, the time domain positioning results, and the frequency domain sparse reconstruction results to determine the initial defect positioning results.

[0165] In some specific embodiments, the positioning result determination module 15 can be used to: acquire a parameterized dispersion model; the parameterized dispersion model includes an initial dispersion intensity coefficient, an initial dispersion characteristic frequency, and an initial propagation speed lower limit; based on the parameterized dispersion model, the frequency domain sparse reconstruction result, and the initial defect positioning result, determine a dispersion parameter optimization objective function; iteratively solve the dispersion parameter optimization objective function to determine the current characteristic evaluation result of the cable under test; the characteristic evaluation result includes a target dispersion intensity coefficient, a target dispersion characteristic frequency, and a target propagation speed lower limit; based on the characteristic evaluation result, update the current dictionary matrix to determine the updated dictionary matrix; based on the... The updated dictionary matrix is ​​used to determine the updated defect location result. The difference between the updated defect location result and the initial defect location result is then determined to be greater than a preset threshold to determine the difference judgment result. If the difference judgment result indicates that it is greater than the threshold, an updated characteristic evaluation result is determined based on the updated defect location result and the adaptive dispersion compensation strategy. Using the updated characteristic evaluation result, the process jumps back to the steps of constructing and solving the sparse reconstruction optimization problem based on the preset frequency domain sparse reconstruction strategy, the frequency-enhanced pulse data acquisition result, and the time domain location result. If the difference judgment result indicates that it is not greater than the threshold, the updated defect location result is determined as the target defect location result.

[0166] In some specific embodiments, the cable defect location device can also be used to: construct a Hessian matrix based on the target defect location result to determine the Hessian matrix construction result; solve for the Cramer-Rao lower bound based on the Hessian matrix construction result to determine the lower bound solution result; obtain location uncertainty information based on the lower bound solution result; perform a consistency evaluation on the time-domain location result, the frequency-domain location result, and the frequency-domain sparse reconstruction result to determine the consistency evaluation result; and determine the target confidence evaluation result based on the consistency evaluation result and the lower bound solution result.

[0167] Furthermore, embodiments of this application also disclose an electronic device, Figure 3 This is a structural diagram of an electronic device 20 according to an exemplary embodiment. The content of the diagram should not be construed as limiting the scope of this application.

[0168] Figure 3 This is a schematic diagram of the structure of an electronic device 20 provided in an embodiment of this application. Specifically, the electronic device 20 may include: at least one processor 21, at least one memory 22, a power supply 23, a communication interface 24, an input / output interface 25, and a communication bus 26. The memory 22 stores a computer program, which is loaded and executed by the processor 21 to implement the relevant steps in the cable defect location method disclosed in any of the foregoing embodiments. Alternatively, the electronic device 20 in this embodiment may specifically be an electronic computer.

[0169] In this embodiment, the power supply 23 is used to provide operating voltage for each hardware device on the electronic device 20; the communication interface 24 can create a data transmission channel between the electronic device 20 and external devices, and the communication protocol it follows can be any communication protocol applicable to the technical solution of this application, and is not specifically limited here; the input / output interface 25 is used to acquire external input data or output data to the outside world, and its specific interface type can be selected according to specific application needs, and is not specifically limited here.

[0170] In addition, the memory 22, as a carrier for resource storage, can be a read-only memory, random access memory, disk or optical disk, etc. The resources stored thereon can include operating system 221, computer program 222, etc., and the storage method can be temporary storage or permanent storage.

[0171] The operating system 221 is used to manage and control the various hardware devices on the electronic device 20 and the computer program 222, which may be Windows Server, Netware, Unix, Linux, etc. In addition to including a computer program capable of performing the cable defect location method executed by the electronic device 20 as disclosed in any of the foregoing embodiments, the computer program 222 may further include computer programs capable of performing other specific tasks.

[0172] Furthermore, this application also discloses a computer-readable storage medium for storing a computer program; wherein, when the computer program is executed by a processor, it implements the aforementioned cable defect location method. Specific steps of this method can be found in the corresponding content disclosed in the foregoing embodiments, and will not be repeated here.

[0173] The various embodiments in this specification are described in a progressive manner, with each embodiment focusing on its differences from other embodiments. Similar or identical parts between embodiments can be referred to interchangeably. For the apparatus disclosed in the embodiments, since it corresponds to the method disclosed in the embodiments, the description is relatively simple; relevant parts can be referred to in the method section.

[0174] Those skilled in the art will further recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, computer software, or a combination of both. To clearly illustrate the interchangeability of hardware and software, the components and steps of the various examples have been generally described in terms of functionality in the foregoing description. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0175] The steps of the methods or algorithms described in conjunction with the embodiments disclosed herein can be implemented directly by hardware, a software module executed by a processor, or a combination of both. The software module can be located in random access memory (RAM), main memory, read-only memory (ROM), electrically programmable ROM, electrically erasable programmable ROM, registers, hard disk, removable disk, CD-ROM, or any other form of storage medium known in the art.

[0176] Finally, it should be noted that in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitations, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element.

[0177] The technical solutions provided in this application have been described in detail above. Specific examples have been used to illustrate the principles and implementation methods of this application. The descriptions of the above embodiments are only for the purpose of helping to understand the methods and core ideas of this application. At the same time, for those skilled in the art, there will be changes in the specific implementation methods and application scope based on the ideas of this application. Therefore, the content of this specification should not be construed as a limitation of this application.

Claims

1. A method of locating a defect in a cable, characterized by, The method comprises the following steps: injecting a frequency-increasing pulse signal sequence into a to-be-tested cable, and collecting incident signals and reflected signals at the head end of the to-be-tested cable to determine frequency-increasing pulse data acquisition results; wherein the frequency-increasing pulse signal sequence comprises a plurality of frequency-stepped sinusoidal pulses; based on the frequency-increasing pulse data acquisition results, performing time domain analysis corresponding to each sinusoidal pulse to determine time domain analysis results; the time domain analysis results comprise time domain positioning results and time domain confidence evaluation results; based on a preset frequency domain sparse reconstruction strategy, the frequency-increasing pulse data acquisition results and the time domain positioning results, constructing and solving a sparse reconstruction optimization problem to determine frequency domain sparse reconstruction results; based on the time domain analysis results and the frequency domain sparse reconstruction results, performing time-frequency domain fusion cable defect positioning to determine initial defect positioning results; based on the frequency domain sparse reconstruction results, the initial defect positioning results and an adaptive dispersion compensation strategy, evaluating the cable dispersion characteristics, and using the corresponding characteristic evaluation results to re-jump to the step of constructing and solving the sparse reconstruction optimization problem based on the preset frequency domain sparse reconstruction strategy, the frequency-increasing pulse data acquisition results and the time domain positioning results, until a preset iteration termination condition is met, to determine target defect positioning results.

2. The method of claim 1, wherein, The method comprises the following steps: based on pulse frequency information, pulse frequency stepping information, pulse generation period, pulse amplitude and pulse duration, determine a frequency-increasing pulse signal sequence; input each sinusoidal signal in the frequency-increasing pulse signal sequence into the to-be-tested cable in turn, and collect incident signals and reflected signals at the head end of the to-be-tested cable to determine frequency-increasing pulse data acquisition results; the frequency-increasing pulse data acquisition results comprise time domain waveform data and frequency domain response data.

3. The method of claim 1, wherein, The method comprises the following steps: based on the frequency-increasing pulse data acquisition results and a preset signal analysis algorithm, identify the arrival time of the reflected pulse corresponding to each frequency sinusoidal pulse to determine time identification results; based on the frequency-increasing pulse data acquisition results, obtain time delay information between each sinusoidal pulse and the corresponding reflected pulse; based on the time delay information and the average pulse transmission speed, determine the pulse positioning results of each sinusoidal pulse; based on the time identification results and the pulse positioning results, determine the time domain positioning results; analyze the signal-to-noise ratio and the pulse shape integrity of the time domain positioning results to determine the time domain confidence evaluation results.

4. A method of locating a defect in a cable as claimed in any one of claims 1 to 3, characterised in that, The method comprises the following steps: discretize the to-be-tested cable to determine a plurality of grid points; Based on the plurality of grid points and a preset propagation constant, a dispersion-aware dictionary matrix is constructed to determine a current dictionary matrix; the preset propagation constant is a propagation constant considering dispersion; Based on the frequency-increased pulse data acquisition result, a frequency domain reflectivity vector is determined; Based on the current dictionary matrix and the frequency domain reflectivity vector, an initial sparse reconstruction optimization problem is constructed; The time domain positioning result is taken as prior information to improve the initial sparse reconstruction optimization problem to determine an improved problem; Based on a preset algorithm, the improved problem is solved to determine a sparse reflectivity distribution.

5. The method of claim 2, wherein, The time-frequency domain fusion cable defect positioning based on the time domain analysis result and the frequency domain sparse reconstruction result is performed to determine an initial defect positioning result, including: Based on the frequency-increased pulse data acquisition result, frequency domain feature extraction is performed on the sinusoidal pulses of each frequency to determine a frequency domain feature extraction result; Based on the instantaneous phase information in the frequency domain feature extraction result and the pulse frequency information, a frequency domain positioning result is determined; The frequency domain positioning result is subjected to signal-to-noise ratio analysis to determine a frequency domain confidence evaluation result; Based on the frequency domain confidence evaluation result and the time domain confidence evaluation result, a current time domain defect positioning weight and a current frequency domain defect positioning weight are determined; Based on the current time domain defect positioning weight, the current frequency domain defect positioning weight, the time domain positioning result and the frequency domain sparse reconstruction result, time-frequency domain fusion cable defect positioning is performed to determine an initial defect positioning result.

6. The method of claim 4, wherein, The cable dispersion characteristic evaluation based on the frequency domain sparse reconstruction result, the initial defect positioning result and an adaptive dispersion compensation strategy is performed, and the corresponding characteristic evaluation result is used to re-jump to the step of constructing and solving the sparse reconstruction optimization problem based on the preset frequency domain sparse reconstruction strategy, the frequency-increased pulse data acquisition result and the time domain positioning result until a preset iteration termination condition is met to determine a target defect positioning result, including: A parameterized dispersion model is obtained; the parameterized dispersion model includes an initial dispersion intensity coefficient, an initial dispersion characteristic frequency and an initial propagation speed lower limit; Based on the parameterized dispersion model, the frequency domain sparse reconstruction result and the initial defect positioning result, a dispersion parameter optimization objective function is determined; The dispersion parameter optimization objective function is iteratively solved to determine a characteristic evaluation result of the cable to be tested; the characteristic evaluation result includes a target dispersion intensity coefficient, a target dispersion characteristic frequency and a target propagation speed lower limit; Based on the characteristic evaluation result, a current dictionary matrix is updated to determine an updated dictionary matrix; Based on the updated dictionary matrix, an updated defect positioning result is determined; It is judged whether a positioning difference between the updated defect positioning result and the initial defect positioning result is greater than a preset threshold to determine a difference judgment result; If the difference value judgment result indicates greater than, based on the updated defect positioning result and the adaptive dispersion compensation strategy, an updated characteristic evaluation result is determined, and the updated characteristic evaluation result is used to jump back to the step of constructing and solving the sparse reconstruction optimization problem based on the preset frequency domain sparse reconstruction strategy, the frequency increasing pulse data acquisition result and the time domain positioning result; If the difference value judgment result indicates not greater than, the updated defect positioning result is determined as the target defect positioning result.

7. The method of claim 5, wherein, After the target defect positioning result is determined, the method further includes: Based on the target defect positioning result, a Hessian matrix is constructed to determine a Hessian matrix construction result; Based on the Hessian matrix construction result, a Kramers-Larmer lower bound is solved to determine a lower bound solution result; Based on the lower bound solution result, positioning uncertainty information is obtained; The time domain positioning result, the frequency domain positioning result and the frequency domain sparse reconstruction result are subjected to consistency evaluation to determine a consistency evaluation result; Based on the consistency evaluation result and the lower bound solution result, a target confidence evaluation result is determined.

8. A cable defect locating apparatus characterized by, The method includes: A data acquisition module is configured to inject a frequency increasing pulse signal sequence into a to-be-tested cable and acquire incident signals and reflected signals of a head end of the to-be-tested cable to determine a frequency increasing pulse data acquisition result; the frequency increasing pulse signal sequence includes a plurality of frequency-stepped sinusoidal pulses; A time domain analysis module is configured to perform time domain analysis corresponding to each of the sinusoidal pulses based on the frequency increasing pulse data acquisition result to determine a time domain analysis result; the time domain analysis result includes a time domain positioning result and a time domain confidence evaluation result; A frequency domain coefficient reconstruction module is configured to construct and solve a sparse reconstruction optimization problem based on a preset frequency domain sparse reconstruction strategy, the frequency increasing pulse data acquisition result and the time domain positioning result to determine a frequency domain sparse reconstruction result; A fusion positioning module is configured to perform time-frequency domain fusion cable defect positioning based on the time domain analysis result and the frequency domain sparse reconstruction result to determine an initial defect positioning result; A positioning result determination module is configured to evaluate a cable dispersion characteristic based on the frequency domain sparse reconstruction result, the initial defect positioning result and an adaptive dispersion compensation strategy, and use a corresponding characteristic evaluation result to jump back to the step of constructing and solving the sparse reconstruction optimization problem based on the preset frequency domain sparse reconstruction strategy, the frequency increasing pulse data acquisition result and the time domain positioning result until a preset iteration termination condition is met, and then determine a target defect positioning result.

9. An electronic device, comprising: The method includes: A memory is configured to save a computer program; A processor is configured to execute the computer program to implement the cable defect positioning method according to any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that, A memory is configured to save a computer program; the computer program is executed by a processor to implement the cable defect positioning method according to any one of claims 1 to 7.

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