Cable fault diagnosis method, system and terminal

Abstract

The invention relates to the technical field of fault diagnosis, and discloses a cable fault diagnosis method, system and terminal, and the method comprises the steps: applying a broadband excitation signal to a target cable, so as to excite the target cable to generate a first response signal; extracting multi-dimensional time-frequency features of the first response signal after adaptive wavelet threshold denoising, and matching a fault template of a preset cable reference model according to energy distribution features in the multi-dimensional time-frequency features; determining a fault area in a fault template corresponding to the target cable according to the propagation time and the propagation speed of the first response signal; applying the narrowband excitation signal to a fault area of the target cable to obtain an impedance spectrum; the resonant frequency point based on the impedance frequency spectrum and the corresponding impedance amplitude value are compared with a typical fault mode in a fault feature database, and the fault type and the fault position of the target cable are obtained; generating a diagnosis report of the target cable according to the fault type and the fault position; the cable fault diagnosis accuracy can be improved.

Description

Technical Field

[0001] This invention relates to the field of fault diagnosis technology, and in particular to a cable fault diagnosis method, system and terminal. Background Technology

[0002] In critical infrastructure sectors such as power transmission and communication networks, cables serve as core signal and energy transmission carriers. Their operational status directly impacts the overall stability of the system. Faults can easily lead to severe consequences such as large-scale outages and signal interruptions. Therefore, accurate fault diagnosis is a core requirement for ensuring the reliable operation of infrastructure. However, existing cable fault diagnosis technologies have significant shortcomings in the signal excitation and feature extraction stages. Most technologies use excitation signals with fixed parameters, failing to dynamically adjust them based on the material, length, and other characteristics of the target cable, as well as the quality of the initial response signal. This results in low signal-to-noise ratios and insufficient effective information in the excited response signals. Furthermore, traditional denoising methods have limited effectiveness in filtering out complex interference, and their time-frequency feature extraction dimensions are singular, failing to comprehensively capture the energy distribution patterns related to the fault. This makes the matching process with preset fault templates prone to deviations, hindering accurate initial fault identification.

[0003] Furthermore, existing technologies fall short of meeting practical requirements in terms of accuracy during fault location and final fault determination. When determining fault areas based on the propagation characteristics of response signals, traditional methods struggle to accurately identify weak signal distortion points caused by fault point reflections. Moreover, the propagation speed parameter is not dynamically adapted to cable type and operating environment, leading to significant errors in the approximate distance calculation. In the impedance spectrum comparison stage, traditional matching degree calculations do not assign differentiated weights to key resonant frequency points, and the baseline features of typical fault modes in the fault feature database are incomplete. This results in low reliability of the resonant frequency and impedance amplitude comparison results, failing to accurately distinguish between different types of faults and causing fault location deviations exceeding engineering allowable limits, severely impacting fault repair efficiency and system recovery speed. Summary of the Invention

[0004] This disclosure provides a cable fault diagnosis method, system, and terminal.

[0005] In a first aspect, this disclosure provides a cable fault diagnosis method, including:

[0006] S1: A broadband excitation signal is applied to the target cable to excite the target cable to generate a first response signal;

[0007] S2: Extract the multi-dimensional time-frequency features of the first response signal after adaptive wavelet threshold denoising, and match the fault template of the preset cable reference model according to the energy distribution features in the multi-dimensional time-frequency features;

[0008] S3: Determine the fault area in the fault template corresponding to the target cable based on the propagation time and propagation speed of the first response signal;

[0009] S4: Apply a narrowband excitation signal to the fault region of the target cable to obtain the impedance spectrum of the target cable;

[0010] S5: Based on the resonant frequency point and corresponding impedance amplitude value of the impedance spectrum, compare them with the typical fault modes in the fault feature database to obtain the fault type and fault location of the target cable.

[0011] S6: Generate a diagnostic report for the target cable based on the fault type and the fault location.

[0012] In a preferred embodiment, the broadband excitation signal is applied to the target cable to induce the target cable to generate a first response signal, including:

[0013] Select the initial frequency band range and signal form corresponding to the target cable from the preset excitation parameter library;

[0014] Based on the initial frequency band range and signal form, an initial broadband excitation signal for the target cable is generated;

[0015] The initial broadband excitation signal is applied to the beginning of the target cable, and the initial response of the target cable is acquired simultaneously.

[0016] Based on the signal quality of the initial response, the amplitude of the initial broadband excitation signal is adaptively adjusted to obtain the broadband excitation signal of the target cable.

[0017] In a preferred embodiment, the step of matching a fault template of a preset cable reference model based on the energy distribution characteristics in the multi-dimensional time-frequency characteristics includes:

[0018] The first response signal after adaptive wavelet threshold denoising is decomposed into eigenmode components that characterize different oscillation modes.

[0019] By combining the skewness value, impulse factor, and energy percentage of the intrinsic mode components, the multi-dimensional time-frequency characteristics of the target cable are obtained.

[0020] In a preferred embodiment, the step of matching a fault template of a preset cable reference model based on the energy distribution characteristics in the multi-dimensional time-frequency characteristics includes:

[0021] Extract the energy distribution features from the multi-dimensional time-frequency features;

[0022] The extracted energy distribution features are compared one by one with the reference energy distributions under various typical fault states stored in the preset cable reference model to calculate the morphological similarity of the target cable.

[0023] The fault state corresponding to the reference energy distribution with the highest morphological similarity is selected as the fault template of the target cable.

[0024] In a preferred embodiment, the formula for calculating the morphological similarity is as follows:

[0025] ;

[0026] in, The morphological similarity is... For the first Adaptive weights for frequency band allocation The vectorized energy distribution characteristics in the first... Energy value in each frequency band For vectorized baseline features in the first Energy value in each frequency band The total number of preset frequency bands, This is the ordinal number of the preset frequency band.

[0027] In a preferred embodiment, determining the fault region in the fault template corresponding to the target cable based on the propagation time and propagation speed of the first response signal includes:

[0028] Identify significant distortion points caused by reflections from fault points in the first response signal, and use the offset of the distortion points relative to the time when the broadband excitation signal was emitted as the propagation time of the first response signal.

[0029] Based on the cable type indicated by the fault template, a preset propagation speed parameter table is queried to obtain the signal propagation speed corresponding to the cable type, which is used as the propagation time of the first response signal;

[0030] The product of the propagation time and the propagation speed is used as the approximate distance between the fault point and the measurement point in the target cable;

[0031] The approximate distance is compared with the predefined area distance boundary in the fault template to determine the specific segment of the fault point, and the specific segment is output as the fault area of ​​the target cable.

[0032] In a preferred embodiment, the resonant frequency point based on the impedance spectrum and the corresponding impedance amplitude value are compared with typical fault modes in the fault feature database to obtain the fault type and fault location of the target cable, including:

[0033] Identify resonance peaks whose amplitudes exceed a preset threshold from the impedance spectrum, and record the frequency value and corresponding impedance amplitude value of the resonance peaks to obtain the feature set of the resonance peaks to be analyzed.

[0034] The matching degree of the feature set to be analyzed is calculated between the benchmark feature set corresponding to the typical fault modes in the fault feature database.

[0035] Based on the matching degree calculation results, the candidate fault modes with the highest matching degree are selected, wherein the matching degree is calculated as follows:

[0036] ;

[0037] In the formula, The degree of matching between the feature set to be analyzed and the typical fault patterns in the fault feature database is given. An adaptive weighting system is determined by the saliency index of the benchmark feature set at key resonant frequency points. The initial matching degree between the feature set to be analyzed and the typical fault modes in the fault feature database is given. This is a normalized value of the relative error of impedance amplitude between the feature set to be analyzed and the reference feature set at a preset key resonant frequency point;

[0038] Based on the fault location calculation rules corresponding to the candidate fault modes and the resonant frequency points in the feature set to be analyzed, the fault location of the target cable is determined, and the candidate fault mode with the highest matching degree is identified as the fault type.

[0039] In a preferred embodiment, determining the final fault location of the target cable based on the fault location calculation rules corresponding to the candidate fault modes and the resonant frequency points in the feature set to be analyzed includes:

[0040] The fault location calculation rule corresponding to the candidate fault mode is called from the pre-stored rule base;

[0041] The resonant frequency points in the feature set to be analyzed are sorted according to their frequency values ​​from high to low to obtain the resonant frequency point sequence of the feature set to be analyzed.

[0042] The fault locations of the resonant frequency point sequence are derived based on the mapping relationship of the fault location calculation rules.

[0043] Compared with the prior art, the present invention has the following beneficial effects:

[0044] 1. The cable fault diagnosis method of the present invention has significant advantages in the signal excitation and feature extraction stages, effectively improving the accuracy and reliability of the early diagnosis stage. It generates an initial broadband excitation signal by selecting an initial frequency band range and signal form from a preset excitation parameter library, and adaptively adjusts the amplitude according to the signal quality of the initial response, ensuring that the broadband excitation signal applied to the target cable is highly compatible with the cable characteristics, thereby exciting a high-quality first response signal. Simultaneously, after adaptive wavelet threshold denoising, the first response signal is decomposed into eigenmode components representing different oscillation modes. Skewness values, impulse factors, and energy proportions are extracted to form multi-dimensional time-frequency features. Then, based on adaptively weighted morphological similarity calculation, a fault template matching a preset cable reference model is obtained, comprehensively and accurately capturing fault-related information and laying a solid foundation for subsequent fault location.

[0045] 2. The design of the fault area determination and final fault judgment stages in this invention further improves the accuracy and specificity of the overall diagnosis. It determines the propagation time by identifying significant distortion points caused by the reflection of the fault point in the first response signal, and calculates the approximate distance to the fault point by combining this with the preset propagation speed for the corresponding cable type. This distance is then compared with the regional distance boundary in the fault template to determine the fault area, achieving precise fault area division. Subsequently, a narrowband excitation is applied to the fault area to obtain the impedance spectrum. Based on the matching degree calculation including adaptive weights for key resonant frequency points, the resonant frequency points and impedance amplitude values ​​of the impedance spectrum are compared with typical fault modes in the fault feature database. This enables accurate identification of the fault type and determination of the fault location. The final diagnostic report can directly provide accurate basis for fault repair, significantly improving the diagnostic effect of cable faults. Attached Figure Description

[0046] The present disclosure will be described in more detail below based on embodiments and with reference to the accompanying drawings:

[0047] Figure 1 The flowchart of a cable fault diagnosis method according to Embodiment 1 of the present invention is shown;

[0048] Figure 2 This diagram shows a functional block diagram of a cable fault diagnosis system according to Embodiment 2 of the present invention;

[0049] Figure 3 The diagram shows the structural composition of a terminal implementing the cable fault diagnosis method according to Embodiment 3 of the present invention. Detailed Implementation

[0050] To enable those skilled in the art to better understand the technical solutions of this disclosure, and to fully understand and implement the process of how this disclosure applies technical means to solve technical problems and achieve corresponding technical effects, the technical solutions in the embodiments of this disclosure will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this disclosure, not all embodiments. The embodiments of this disclosure and the various features within them can be combined with each other without conflict, and the resulting technical solutions are all within the protection scope of this disclosure. All other embodiments obtained by those skilled in the art based on the embodiments of this disclosure without creative effort should fall within the protection scope of this disclosure.

[0051] It should be noted that the steps shown in the flowchart in the accompanying drawings can be executed in a computer system such as a set of computer-executable instructions, and although a logical order is shown in the flowchart, in some cases the steps shown or described may be executed in a different order than that shown here.

[0052] Example 1

[0053] Figure 1 This is a schematic flowchart illustrating a cable fault diagnosis method provided in an embodiment of this disclosure. Figure 1 As shown, a smart device control method includes:

[0054] S1: A broadband excitation signal is applied to the target cable to excite the target cable to generate a first response signal;

[0055] In this embodiment of the invention, the broadband excitation signal is applied to the target cable to excite the target cable to generate a first response signal, including:

[0056] Select the initial frequency band range and signal form corresponding to the target cable from the preset excitation parameter library;

[0057] Based on the initial frequency band range and signal form, an initial broadband excitation signal for the target cable is generated;

[0058] The initial broadband excitation signal is applied to the beginning of the target cable, and the initial response of the target cable is acquired simultaneously.

[0059] Based on the signal quality of the initial response, the amplitude of the initial broadband excitation signal is adaptively adjusted to obtain the broadband excitation signal of the target cable.

[0060] Specifically, the model and specifications of the target cable are confirmed from the preset excitation parameter library, matching entries are found and corresponding content is extracted to obtain the initial frequency band range and signal form of the target cable.

[0061] Furthermore, the initial frequency band range and signal type are input into the signal generating device. After the frequency band and signal type settings are completed, the device is started. The signal output by the device is the initial broadband excitation signal of the target cable.

[0062] Furthermore, the signal generating device is connected to the beginning of the target cable using a shielded cable and started to output an initial broadband excitation signal; at the same time, the data acquisition device is connected to the end of the cable and started to acquire the signal at the end. The acquired signal is the initial response of the target cable.

[0063] Furthermore, determine whether the waveform of the initial response is complete and whether the amplitude is within the effective range of the data acquisition device. If it does not meet the standard, adjust the amplitude knob of the signal generator, then reapply the signal, acquire the response and judge it until the signal quality meets the requirements. At this time, the output of the signal generator is the broadband excitation signal of the target cable.

[0064] In summary, the initial frequency band range and signal form corresponding to the target cable are selected from the preset excitation parameter library to generate an initial broadband excitation signal. Then, the signal amplitude is adaptively adjusted according to the signal quality of the initial response, so that the final applied broadband excitation signal can accurately match the material, length and other characteristics of the target cable. This avoids the problem of poor compatibility between fixed parameter excitation signals and cables, effectively improves the signal-to-noise ratio and effective information ratio of the first response signal, and ensures that fault-related features in the signal are clearly identifiable.

[0065] In summary, a high-quality first response signal provides a reliable data foundation for subsequent adaptive wavelet threshold denoising, intrinsic mode component decomposition, multi-dimensional time-frequency feature extraction, and fault template matching. It reduces feature extraction deviations caused by insufficient signal quality, ensures the accuracy of subsequent fault template matching, and lays a key foundation for initially identifying the fault status and advancing the overall diagnostic process.

[0066] S2: Extract the multi-dimensional time-frequency features of the first response signal after adaptive wavelet threshold denoising, and match the fault template of the preset cable reference model according to the energy distribution features in the multi-dimensional time-frequency features;

[0067] In this embodiment of the invention, the step of matching a fault template of a preset cable reference model based on the energy distribution characteristics in the multi-dimensional time-frequency characteristics includes:

[0068] The first response signal after adaptive wavelet threshold denoising is decomposed into eigenmode components that characterize different oscillation modes.

[0069] By combining the skewness value, impulse factor, and energy percentage of the intrinsic mode components, the multi-dimensional time-frequency characteristics of the target cable are obtained.

[0070] In this embodiment of the invention, the step of matching a fault template of a preset cable reference model based on the energy distribution characteristics in the multi-dimensional time-frequency characteristics includes:

[0071] Extract the energy distribution features from the multi-dimensional time-frequency features;

[0072] The extracted energy distribution features are compared one by one with the reference energy distributions under various typical fault states stored in the preset cable reference model to calculate the morphological similarity of the target cable.

[0073] The fault state corresponding to the reference energy distribution with the highest morphological similarity is selected as the fault template of the target cable.

[0074] In this embodiment of the invention, the formula for calculating the morphological similarity is as follows:

[0075] ;

[0076] in, The morphological similarity is... For the first Adaptive weights for frequency band allocation The vectorized energy distribution characteristics in the first... Energy value in each frequency band For vectorized baseline features in the first Energy value in each frequency band The total number of preset frequency bands, This is the ordinal number of the preset frequency band.

[0077] Specifically, the first response signal after adaptive wavelet threshold denoising is obtained and processed using the empirical mode decomposition method. First, all local extrema in the signal are identified. Cubic spline curves are used to connect all maximum points to form an upper envelope and connect all minimum points to form a lower envelope. The average value of the upper and lower envelopes is calculated. The average value is subtracted from the original signal to obtain a component. It is checked whether the component meets the conditions of an intrinsic mode component, that is, the number of extrema points is equal to or differs from the number of zero-crossing points by at most one, and the average value of the upper and lower envelopes at any point is zero. If it meets the conditions, it is taken as the first intrinsic mode component. The component is subtracted from the original signal to obtain the remaining signal. The above process is repeated for the remaining signal until the remaining signal is a monotonic function or a constant. At this time, all components that meet the conditions are the intrinsic mode components that characterize different oscillation modes.

[0078] Furthermore, for each intrinsic mode component, the skewness value is determined by analyzing the difference between its data distribution and the symmetrical distribution. A positive value indicates a right-skewed distribution, a negative value indicates a left-skewed distribution, and zero indicates a symmetrical distribution. When calculating the impulse factor, the peak value in the component is first identified, and then the average of the absolute values ​​of all data points is calculated. The peak value is divided by the average value to obtain the result. When calculating the energy percentage, the squares of each data point of the component are first squared and then added together to obtain the component energy. Then, the total energy is obtained by adding all the component energies together and dividing the component energy by the total energy to obtain the result. The skewness value, impulse factor, and energy percentage corresponding to each intrinsic mode component are arranged in sequence. These data of all components together constitute the multi-dimensional time-frequency characteristics of the target cable.

[0079] Specifically, when extracting energy distribution features from multi-dimensional time-frequency features, the energy proportion data corresponding to each intrinsic mode component is first located in the multi-dimensional time-frequency features. Then, according to the oscillation mode order of each intrinsic mode component, these energy proportion data are sequentially organized into an ordered set, which is the extracted energy distribution feature of the target cable.

[0080] Furthermore, when calculating the morphological similarity by comparing the extracted energy distribution features with the baseline energy distributions under various typical fault states pre-stored in the preset cable reference model, it is first clarified that each typical fault state in the preset cable reference model corresponds to a set of baseline energy distributions arranged in the same oscillation mode order. During the comparison, a set of baseline energy distributions is selected, and the energy proportion of each component in the energy distribution feature is compared with the energy proportion of the corresponding component of the baseline energy distribution in the oscillation mode order. The difference between the corresponding values ​​of each set is recorded, and the differences of all components are comprehensively summarized and calculated. The result is the morphological similarity of the target cable corresponding to the baseline energy distribution. This process is repeated until the energy distribution features are compared with all baseline energy distributions and the corresponding morphological similarity is obtained.

[0081] Furthermore, when selecting the fault state corresponding to the reference energy distribution with the highest morphological similarity as the fault template of the target cable, all obtained morphological similarities are first compared in terms of numerical value. The morphological similarity with the largest value is then identified, and the reference energy distribution corresponding to the largest morphological similarity is determined. The typical fault state associated with the reference energy distribution is then searched in the preset cable reference model. This typical fault state is the fault template of the target cable.

[0082] Specifically, no. Adaptive weighting for frequency band allocation The source is determined based on the importance of each frequency band in distinguishing cable fault states. Specifically, by analyzing historical cable fault data, the contribution of energy changes in different frequency bands in distinguishing different fault types is statistically analyzed. Frequency bands with higher contribution are assigned higher weights, and frequency bands with lower contribution are assigned lower weights. Each frequency band corresponds to a specific adaptive weight.

[0083] Furthermore, the vectorized energy distribution characteristics in the first... Energy value in each frequency band The source is obtained by vectorizing the energy distribution features extracted from multi-dimensional time-frequency features. During vectorization, the energy values ​​corresponding to each preset frequency band in the energy distribution features are arranged sequentially according to the order of preset frequency bands, and the sequence number of each preset frequency band is... The corresponding energy value after arrangement is... .

[0084] Furthermore, the vectorized baseline features in the first... Energy value in each frequency band The source is obtained by vectorizing the reference energy distribution under typical fault conditions pre-stored in the preset cable reference model. During the vectorization process, the energy values ​​corresponding to each preset frequency band in the reference energy distribution are arranged sequentially according to the preset frequency band order, which is the same as that of the energy distribution feature vectorization. The sequence number of each preset frequency band is... The corresponding energy value after arrangement is... Total number of preset frequency bands The source is the number of frequency bands determined based on the oscillation mode corresponding to the intrinsic mode component when decomposing the first response signal after adaptive wavelet threshold denoising of the target cable. This number is preset before processing and remains consistent with the energy distribution characteristics and reference energy distribution in subsequent vectorization processing.

[0085] Furthermore, the ordinal number of the preset frequency band The source is the numbering of each frequency band according to the preset frequency band arrangement order, starting from 1 and increasing sequentially to the total number of preset frequency bands. Each number corresponds to a unique preset frequency band, which is used to locate the energy value and adaptive weight of each frequency band during calculation.

[0086] Furthermore, the significance of this formula lies in obtaining the morphological similarity, which characterizes the degree of morphological similarity between the target cable and the vectorized reference energy distribution in the preset cable reference model, by quantifying the difference between their vectorized energy distribution characteristics and the vectorized reference energy distribution. The specific calculation process is as follows: First, for each preset frequency band, calculate the difference between the energy value of the vectorized energy distribution feature and the energy value of the vectorized baseline feature corresponding to that frequency band. Then, square this difference, multiply the result by the adaptive weight of that frequency band, and sum the multiplication results for all preset frequency bands. Finally, take the square root of the sum, and subtract 1 from the square root result to obtain the morphological similarity. This value can directly reflect the similarity between the two in terms of energy distribution patterns, providing a basis for subsequent selection of fault templates.

[0087] In summary, adaptive wavelet thresholding denoising effectively filters out interference components in the first response signal, ensuring signal purity and providing a high-quality data foundation for subsequent feature extraction. Based on this, the denoised signal is decomposed into eigenmode components representing different oscillation modes, and their skewness values, impulse factors, and energy proportions are combined to form multi-dimensional time-frequency features. This comprehensively captures fault-related signal characteristics, avoiding the omission of fault information due to the limitations of a single feature dimension, and ensuring the completeness and representativeness of the features.

[0088] In summary, the matching stage focuses on the energy distribution characteristics in the multi-dimensional time-frequency features, comparing them one by one with the benchmark energy distribution of typical fault states in the preset cable reference model. By calculating the morphological similarity formula with adaptive weights, the influence of key frequency bands on the matching results can be highlighted, interference from non-key frequency bands can be reduced, and the matching accuracy can be significantly improved. It can accurately screen out the fault template that best matches the fault state of the target cable, providing a reliable preliminary judgment basis for subsequent determination of the fault area and accurate diagnosis.

[0089] S3: Determine the fault area in the fault template corresponding to the target cable based on the propagation time and propagation speed of the first response signal;

[0090] In this embodiment of the invention, determining the fault region in the fault template corresponding to the target cable based on the propagation time and propagation speed of the first response signal includes:

[0091] Identify significant distortion points caused by reflections from fault points in the first response signal, and use the offset of the distortion points relative to the time when the broadband excitation signal was emitted as the propagation time of the first response signal.

[0092] Based on the cable type indicated by the fault template, a preset propagation speed parameter table is queried to obtain the signal propagation speed corresponding to the cable type, which is used as the propagation time of the first response signal;

[0093] The product of the propagation time and the propagation speed is used as the approximate distance between the fault point and the measurement point in the target cable;

[0094] The approximate distance is compared with the predefined area distance boundary in the fault template to determine the specific segment of the fault point, and the specific segment is output as the fault area of ​​the target cable.

[0095] Specifically, when identifying significant distortion points from the first response signal, the response signal of the same type of cable under normal fault-free conditions is first obtained as a reference waveform. The first response signal and the reference waveform are compared moment by moment. Points in the first response signal that show sudden bulges, depressions, or waveform breaks compared to the reference waveform are found. These points are significant distortion points caused by reflection from the fault point. The start time of the broadband excitation signal is read using a time recording device. At the same time, the time when each significant distortion point appears in the first response signal is read. The time offset obtained by subtracting the start time of the broadband excitation signal from the time when the significant distortion point appears is the propagation time of the first response signal.

[0096] Furthermore, the specific type of the indicated target cable is extracted from the fault template. The preset propagation speed parameter table stores the correlation data between different cable types and corresponding signal propagation speeds. The table is searched for entries that perfectly match the cable type extracted from the fault template. The corresponding signal propagation speed value is extracted from the matching entries. The extracted signal propagation speed value is the propagation speed of the first response signal.

[0097] Furthermore, the propagation time value of the first response signal obtained in the first step is taken out, and the propagation speed value of the first response signal obtained in the second step is taken out. The two values ​​are multiplied, and the result is the approximate distance between the fault point and the measurement point in the target cable.

[0098] Furthermore, predefined regional distance boundaries are retrieved from the fault template. These regional distance boundaries clearly define the distance ranges corresponding to different sections of the target cable. The approximate distance values ​​obtained in the third step are compared with these regional distance boundaries one by one to determine which regional distance boundary the approximate distance value falls within. The cable section corresponding to this distance range is the specific section of the fault point. This specific section is output as the result, and the output specific section is the fault area of ​​the target cable.

[0099] In summary, by accurately identifying significant distortion points caused by the reflection of fault points from the first response signal, and using the offset of these distortion points relative to the time of the broadband excitation signal as the propagation time, the influence of non-fault interference signals can be effectively eliminated, ensuring the accuracy of the propagation time acquisition and providing a reliable basis for subsequent distance calculations.

[0100] In summary, by querying the preset propagation speed parameter table based on the cable type indicated by the fault template, the acquired signal propagation speed is highly compatible with the characteristics of the target cable, avoiding calculation deviations caused by fixed propagation speeds and significantly improving the calculation accuracy of the approximate distance between the fault point and the measurement point.

[0101] In summary, by comparing the approximate distance with the predefined regional distance boundaries in the fault template, the specific segment where the fault point is located can be accurately located, the fault area range can be clearly defined, and a clear target can be provided for the subsequent narrowband excitation signal to be accurately applied to the fault area. This avoids the waste of resources and efficiency loss caused by large-scale detection, effectively ensures the pertinence and effectiveness of subsequent diagnostic steps, and further improves the overall accuracy of fault diagnosis.

[0102] S4: Apply a narrowband excitation signal to the fault region of the target cable to obtain the impedance spectrum of the target cable;

[0103] In this embodiment of the invention, a narrowband excitation signal is first generated using a signal generator. The frequency range of the signal generator is set to a fixed narrow frequency range to meet the narrowband requirements. Then, the output terminal of the signal generator is connected to the starting interface of the target cable fault area through a shielded cable. At the same time, a current acquisition device is connected in series in the connection line between the signal generator and the starting point of the fault area, and a voltage acquisition device is connected in parallel between the starting and ending points of the fault area to ensure that all connections are secure and the shielding layer is intact to avoid interference.

[0104] Furthermore, the signal generator is activated to stably input the narrowband excitation signal into the target cable fault area. At the same time, the current acquisition device and the voltage acquisition device are activated. The current acquisition device collects the current signal in the line in the fault area in real time, and the voltage acquisition device collects the voltage signal between the beginning and end of the fault area in real time. The acquisition process is synchronized with the frequency of the narrowband excitation signal to ensure that each frequency point corresponds to a set of current and voltage data.

[0105] Furthermore, the voltage and current signal values ​​corresponding to each frequency point are calculated. The voltage value at that frequency point is divided by the corresponding current value to obtain the impedance value of the fault area at that frequency point. According to the frequency order of the narrowband excitation signal, all frequency points and their corresponding impedance values ​​are arranged in sequence to form a dataset. The dataset is organized with frequency as the horizontal axis and impedance value as the vertical axis. The resulting graphical data set is the impedance spectrum of the target cable.

[0106] In summary, the narrowband excitation signal is applied specifically to the identified fault area rather than the entire target cable. This allows the excitation energy to be concentrated on the fault-related section, effectively avoiding electrical characteristic interference from non-fault areas and preventing impedance spectrum distortion caused by irrelevant signals. The acquired impedance spectrum accurately focuses on the electrical state of the fault area, significantly improving the correlation between spectrum information and fault characteristics, and ensuring that the spectrum contains clear fault-related resonant frequency points and impedance amplitude values.

[0107] In summary, this impedance spectrum serves as the core data foundation for subsequent comparisons with typical fault modes in the fault feature database. High-quality, highly correlated impedance spectra provide a reliable basis for identifying resonance peaks and calculating matching degrees, directly ensuring the accuracy of fault type determination and fault location identification. Furthermore, targeted excitation eliminates the need for full-range cable excitation, reducing signal loss and detection redundancy, further improving the efficiency and accuracy of fault diagnosis.

[0108] S5: Based on the resonant frequency point and corresponding impedance amplitude value of the impedance spectrum, compare them with the typical fault modes in the fault feature database to obtain the fault type and fault location of the target cable.

[0109] In this embodiment of the invention, the resonant frequency point based on the impedance spectrum and the corresponding impedance amplitude value are compared with typical fault modes in the fault feature database to obtain the fault type and fault location of the target cable, including:

[0110] Identify resonance peaks whose amplitudes exceed a preset threshold from the impedance spectrum, and record the frequency value and corresponding impedance amplitude value of the resonance peaks to obtain the feature set of the resonance peaks to be analyzed.

[0111] The matching degree of the feature set to be analyzed is calculated between the benchmark feature set corresponding to the typical fault modes in the fault feature database.

[0112] Based on the matching degree calculation results, the candidate fault modes with the highest matching degree are selected, wherein the matching degree is calculated as follows:

[0113] ;

[0114] In the formula, The degree of matching between the feature set to be analyzed and the typical fault patterns in the fault feature database is given. An adaptive weighting system is determined by the saliency index of the benchmark feature set at key resonant frequency points. The initial matching degree between the feature set to be analyzed and the typical fault modes in the fault feature database is given. This is a normalized value of the relative error of impedance amplitude between the feature set to be analyzed and the reference feature set at a preset key resonant frequency point;

[0115] Based on the fault location calculation rules corresponding to the candidate fault modes and the resonant frequency points in the feature set to be analyzed, the fault location of the target cable is determined, and the candidate fault mode with the highest matching degree is identified as the fault type.

[0116] In this embodiment of the invention, determining the final fault location of the target cable based on the fault location calculation rules corresponding to the candidate fault modes and the resonant frequency points in the feature set to be analyzed includes:

[0117] The fault location calculation rule corresponding to the candidate fault mode is called from the pre-stored rule base;

[0118] The resonant frequency points in the feature set to be analyzed are sorted according to their frequency values ​​from high to low to obtain the resonant frequency point sequence of the feature set to be analyzed.

[0119] The fault locations of the resonant frequency point sequence are derived based on the mapping relationship of the fault location calculation rules.

[0120] Specifically, the impedance spectrum is first defined as containing impedance amplitude values ​​corresponding to each frequency point. The preset threshold is a pre-set impedance amplitude standard used to determine the resonance peak. The impedance amplitude values ​​in the impedance spectrum are checked point by point, and each impedance amplitude value is compared with the preset threshold. When the impedance amplitude value at a certain frequency point exceeds the preset threshold, that frequency point is the frequency value corresponding to the resonance peak. At the same time, the impedance amplitude value corresponding to that frequency point is recorded. All the recorded resonance peak frequency values ​​and corresponding impedance amplitude values ​​are organized into an ordered data set, which is the feature set of the resonance peak to be analyzed.

[0121] Furthermore, the fault feature database pre-stores multiple typical fault modes, each corresponding to a unique benchmark feature set. The benchmark feature set contains the standard frequency value and corresponding standard impedance amplitude value of the resonant peak under that fault mode. The feature set to be analyzed is retrieved, and the frequency value and impedance amplitude value of each resonant peak in the feature set to be analyzed are compared with the standard frequency value and standard impedance amplitude value of the corresponding position in the benchmark feature set of a typical fault mode, according to the order of the resonant peaks. The degree of difference of each set of comparison data is recorded, and the matching degree between the typical fault mode and the feature set to be analyzed is obtained by summing all the degree of difference. This process is repeated until the matching degree calculation of the feature set to be analyzed and the benchmark feature sets of all typical fault modes in the fault feature database is completed.

[0122] Furthermore, all the calculated matching scores are compared to find the matching score with the largest value. The typical fault mode corresponding to the largest matching score is then determined, and this typical fault mode is the candidate fault mode with the highest matching score selected.

[0123] Furthermore, the corresponding fault location calculation rule is retrieved from the candidate fault mode with the highest matching degree. This rule clarifies the association between the resonant frequency point and the fault location. The resonant frequency points recorded in the feature set to be analyzed are substituted into the calculation rule, and the fault location of the target cable is determined according to the association method required by the rule. At the same time, the candidate fault mode with the highest matching degree is directly determined as the fault type of the target cable.

[0124] Specifically, matching degree The value is derived from the matching degree between the feature set to be analyzed and the typical fault modes in the fault feature database, calculated by the formula, and is used to characterize the overall matching situation between the two.

[0125] Furthermore, adaptive weighting coefficients The source is determined based on the eigensignificance index of the baseline feature set at key resonant frequency points. This eigensignificance index is derived by analyzing the importance of key resonant frequency points in distinguishing different failure modes; the higher the importance of a key resonant frequency point, the higher its eigensignificance index. The larger the value, the smaller the value. The value ranges from 0 to 1.

[0126] Furthermore, the initial matching degree The source is the matching value obtained by comparing the feature set to be analyzed with the benchmark feature set of typical fault modes. During the comparison, the overall similarity of the frequency values ​​and impedance amplitude values ​​at all resonant frequency points is taken into account. The higher the similarity, the better. The larger the value, the better.

[0127] Furthermore, normalized value The method involves first calculating the relative error of the impedance amplitude between the feature set to be analyzed and the reference feature set at preset key resonant frequency points. The relative error is the difference between the impedance amplitude of the feature set to be analyzed and the impedance amplitude of the reference feature set, divided by the impedance amplitude of the reference feature set. Then, the relative errors at all key resonant frequency points are normalized to a value between 0 and 1. The resulting value is the [value / value]. The smaller the relative error, The larger the value, the better.

[0128] Furthermore, the significance of this formula lies in obtaining the final matching degree by combining the initial matching degree and the normalized value of the relative error of the impedance amplitude at the key resonant frequency point. The specific calculation process involves using adaptive weighting coefficients. Multiply by the initial matching degree Then subtract 1 The difference multiplied by the normalized value Add the two products together to get the sum. This value reflects both the overall feature matching and the feature matching of key frequency points, making the matching degree calculation more comprehensive.

[0129] Specifically, the pre-stored rule base stores the association data of various candidate fault modes and corresponding fault location calculation rules. Each candidate fault mode entry is clearly marked with its own fault location calculation rule. The rule base is searched for entries that completely match the identified candidate fault modes. The marked calculation rule content is extracted from the matching entries. The extracted content is the fault location calculation rule corresponding to the candidate fault mode.

[0130] Furthermore, the specific values ​​of multiple resonant frequency points in the feature set to be analyzed are compared one by one. The resonant frequency point with the smallest value is first selected as the first element of the sequence, and then the second smallest value is selected as the second element from the remaining values. This process is repeated until all resonant frequency points are arranged in ascending order of value. The resulting ordered set of frequency points is the resonant frequency point sequence of the feature set to be analyzed.

[0131] Furthermore, the fault location calculation rules clearly define the mapping relationship between resonant frequency points and fault locations. This relationship specifies the specific fault location range or specific location value corresponding to different frequency values. Each frequency point in the resonant frequency point sequence is substituted into this mapping relationship in turn. Based on the value of each frequency point, the corresponding fault location information is found in the mapping relationship. The fault location information corresponding to all frequency points is comprehensively sorted out, and the location result obtained after sorting out is the fault location corresponding to the resonant frequency point sequence.

[0132] In summary, identifying resonance peaks with amplitudes exceeding a preset threshold from the impedance spectrum and recording their frequency and impedance amplitude values ​​can accurately screen out effective fault-related features, eliminate irrelevant spectral interference, and ensure that the feature set to be analyzed can truly reflect the electrical characteristics of the fault area, laying a high-quality data foundation for subsequent comparisons.

[0133] In summary, the matching degree formula with adaptive weights is used during the comparison. The adaptive weights are determined by the significance of the key resonant frequency points of the benchmark feature set. This can highlight the influence of key features on the matching results, reduce the interference of non-key factors, and significantly improve the matching accuracy between the feature set to be analyzed and typical fault modes, ensuring that the selected candidate fault modes are highly consistent with the actual fault state.

[0134] In summary, by using the location calculation rules corresponding to candidate fault modes and combining them with the sorted resonant frequency points to deduce the fault location, the location calculation is precisely matched with the characteristics of the fault type, avoiding deviations from general calculation rules. Ultimately, the fault type and location are obtained synchronously and accurately, providing core evidence for generating diagnostic reports, directly ensuring the targeted nature of fault repair, and significantly improving the reliability and practicality of diagnostic results.

[0135] S6: Generate a diagnostic report for the target cable based on the fault type and the fault location.

[0136] In this embodiment of the invention, basic information about the target cable is first collected, including its model, specifications, and installation time. Simultaneously, previously determined fault types and locations are retrieved, and this information is compiled into an information list to ensure that each piece of information accurately corresponds to the target cable. A fixed structure for the diagnostic report is established, including a cable basic information module, a fault type description module, a fault location description module, and a brief description of the testing process module. Each module clearly defines its content direction; for example, the cable basic information module specifies the model and specifications, while the fault type description module describes the fault type and typical manifestations.

[0137] Furthermore, fill in the content according to the report structure. In the cable basic information module, enter the compiled target cable model, specifications, and installation time; in the fault type description module, enter the fault type name and typical characteristics of the fault; in the fault location description module, enter a detailed description of the fault location, including the distance from the measurement point and the cable section in which it is located; in the detection process summary module, describe the key detection steps from applying the excitation signal to determining the fault type and location, ensuring that the content is consistent with the actual detection results of the target cable. Verify the completed report content, checking whether the cable basic information matches the actual situation of the target cable, whether the fault type and fault location description are consistent with the previous judgment results, and whether the detection process summary covers the key steps and is error-free. If any inconsistencies or errors are found, correct them immediately until all content is accurate.

[0138] Furthermore, the verified report is formatted according to a preset format, with uniform font, paragraph spacing, and title style. After formatting, it is output by printing or exporting an electronic document, which is the diagnostic report for the target cable.

[0139] In summary, by generating diagnostic reports based on accurately determined fault types and locations, the system can systematically and logically integrate key results from multiple stages of diagnosis (such as fault types derived from impedance spectrum comparison and fault locations deduced from resonant frequencies). This avoids comprehension barriers caused by fragmented fault information, allowing relevant personnel to intuitively and quickly obtain core fault information of the target cable without the need for additional complex diagnostic processes and data, thus significantly reducing information extraction costs.

[0140] In summary, the report's content is based on accurate diagnostic results verified throughout the entire process, ensuring the information's authenticity and reliability. It can directly serve as the core guidance for fault repair, clarifying specific directions and objectives for repair work, avoiding repair deviations or blind troubleshooting caused by ambiguous information, and improving fault repair efficiency. Furthermore, the complete diagnostic report can be archived as a cable fault record, providing data support for subsequent analysis of similar faults and the formulation of long-term cable operation and maintenance strategies, further extending the practical value of the diagnostic results and contributing to the standardization of cable operation and maintenance management.

[0141] Example 2

[0142] like Figure 2 As shown in the figure, this embodiment also provides a functional block diagram of a cable fault diagnosis system.

[0143] The cable fault diagnosis system 100 described in this embodiment can be installed in a terminal. Depending on the functions implemented, the cable fault diagnosis system 100 may include a broadband excitation module 101, a fault template matching module 102, a fault area determination module 103, a narrowband excitation module 104, an impedance spectrum comparison module 105, and a diagnostic report generation module 106. The modules described in this invention can also be referred to as units, which are a series of computer program segments that can be executed by the terminal processor and perform a fixed function, stored in the terminal's memory.

[0144] In this embodiment, the functions of each module / unit are as follows:

[0145] The broadband excitation module 101 is used to apply a broadband excitation signal to the target cable to excite the target cable to generate a first response signal;

[0146] The fault template matching module 102 is used to extract multi-dimensional time-frequency features of the first response signal after adaptive wavelet threshold denoising, and match the fault template of the preset cable reference model according to the energy distribution features in the multi-dimensional time-frequency features.

[0147] The fault area determination module 103 is used to determine the fault area in the fault template corresponding to the target cable based on the propagation time and propagation speed of the first response signal.

[0148] The narrowband excitation module 104 is used to apply a narrowband excitation signal to the fault region of the target cable to obtain the impedance spectrum of the target cable.

[0149] The impedance spectrum comparison module 105 is used to compare the resonant frequency point and the corresponding impedance amplitude value of the impedance spectrum with the typical fault modes in the fault feature database to obtain the fault type and fault location of the target cable.

[0150] The diagnostic report generation module 106 is used to generate a diagnostic report for the target cable based on the fault type and the fault location.

[0151] In detail, each module in the cable fault diagnosis system 100 described in this embodiment of the invention uses the same technical means as the cable fault diagnosis method described in Embodiment 1 and Embodiment 2, and can produce the same technical effect, which will not be repeated here.

[0152] Example 3

[0153] like Figure 3 As shown, this embodiment also provides a computer terminal, which may include a processor 10, a memory 11, a communication bus 12 and a communication interface 13, and may also include a computer program stored in the memory 11 and executable on the processor 10, such as a cable fault diagnosis program.

[0154] In some embodiments, the processor 10 may be composed of integrated circuits, such as a single packaged integrated circuit or multiple integrated circuits with the same or different functions, including combinations of one or more central processing units (CPUs), microprocessors, digital processing chips, graphics processors, and various control chips. The processor 10 is the control unit of the terminal, connecting various components of the terminal via various interfaces and lines. It executes programs or modules stored in the memory 11 (e.g., executing a cable fault diagnosis program) and calls data stored in the memory 11 to perform various functions of the terminal and process data.

[0155] The memory 11 includes at least one type of medium, including flash memory, portable hard drive, multimedia card, card-type memory (e.g., SD or DX memory), magnetic memory, magnetic disk, optical disk, etc. In some embodiments, the memory 11 can be an internal storage unit of the terminal, such as the portable hard drive of the terminal. In other embodiments, the memory 11 can also be an external storage device of the terminal, such as a plug-in portable hard drive, smart media card (SMC), secure digital (SD) card, flash card, etc. Furthermore, the memory 11 can include both internal storage units and external storage devices of the terminal. The memory 11 can be used not only to store application software and various types of data installed on the terminal, such as the code of a cable fault diagnosis program, but also to temporarily store data that has been output or will be output.

[0156] The communication bus 12 can be a peripheral component interconnect (PCI) bus or an extended industry standard architecture (EISA) bus, etc. This bus can be divided into an address bus, a data bus, a control bus, etc. The bus is configured to enable communication between the memory 11 and at least one processor 10, etc.

[0157] The communication interface 13 is used for communication between the aforementioned terminal and other terminals, including a network interface and a user interface. Optionally, the network interface may include a wired interface and / or a wireless interface (such as a Wi-Fi interface, Bluetooth interface, etc.), typically used to establish communication connections between the terminal and other terminals. The user interface may be a display, an input unit (such as a keyboard), or optionally, a standard wired or wireless interface. Optionally, in some embodiments, the display may be an LED display, a liquid crystal display, a touch-sensitive liquid crystal display, or an OLED (Organic Light-Emitting Diode) touchscreen, etc. The display may also be appropriately referred to as a screen or display unit, used to display information processed in the terminal and to display a visual user interface.

[0158] The figure only shows a terminal with components. Those skilled in the art will understand that the structure shown in the figure does not constitute a limitation on the terminal and may include fewer or more components than shown, or combine certain components, or have different component arrangements.

[0159] For example, although not shown, the terminal may also include a power supply (such as a battery) to power the various components. Preferably, the power supply can be logically connected to the at least one processor 10 through a power management system, thereby enabling functions such as charging management, discharging management, and power consumption management through the power management system. The power supply may also include one or more DC or AC power supplies, a recharging system, a power fault detection circuit, a power converter or inverter, a power status indicator, or any other components. The terminal may also include various sensors, Bluetooth modules, Wi-Fi modules, etc., which will not be described in detail here.

[0160] It should be understood that the embodiments described are for illustrative purposes only and are not limited to this structure in the scope of the patent application.

[0161] The cable fault diagnosis program stored in the memory 11 of the terminal is a combination of multiple instructions, which, when run in the processor 10, can achieve the following:

[0162] S1: A broadband excitation signal is applied to the target cable to excite the target cable to generate a first response signal;

[0163] S2: Extract the multi-dimensional time-frequency features of the first response signal after adaptive wavelet threshold denoising, and match the fault template of the preset cable reference model according to the energy distribution features in the multi-dimensional time-frequency features;

[0164] S3: Determine the fault area in the fault template corresponding to the target cable based on the propagation time and propagation speed of the first response signal;

[0165] S4: Apply a narrowband excitation signal to the fault region of the target cable to obtain the impedance spectrum of the target cable;

[0166] S5: Based on the resonant frequency point and corresponding impedance amplitude value of the impedance spectrum, compare them with the typical fault modes in the fault feature database to obtain the fault type and fault location of the target cable.

[0167] S6: Generate a diagnostic report for the target cable based on the fault type and the fault location.

[0168] Specifically, the specific implementation method of the processor 10 for the above instructions can be referred to the description of the relevant steps in the corresponding embodiment of the accompanying drawings, and will not be repeated here.

[0169] Furthermore, if the modules / units integrated into the terminal are implemented as software functional units and sold or used as independent products, they can be stored in a medium. The medium can be volatile or non-volatile. For example, the medium may include: any entity or system capable of carrying the computer program code, a recording medium, a USB flash drive, a portable hard drive, a magnetic disk, an optical disk, a computer memory, or a read-only memory (ROM).

[0170] In the several embodiments provided by this invention, it should be understood that the disclosed terminals, systems, and methods can be implemented in other ways. For example, the system embodiments described above are merely illustrative; for instance, the division of modules is only a logical functional division, and other division methods may be used in actual implementation.

[0171] The modules described as separate components may or may not be physically separate. The components shown as modules may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs.

[0172] Furthermore, the functional modules in the various embodiments of the present invention can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or in the form of hardware plus software functional modules.

[0173] It will be apparent to those skilled in the art that the present invention is not limited to the details of the exemplary embodiments described above, and that the present invention can be implemented in other specific forms without departing from the spirit or essential characteristics of the present invention.

[0174] The embodiments of this application can acquire and process relevant data based on artificial intelligence technology. Artificial intelligence (AI) refers to the theories, methods, technologies, and application systems that use digital computers or machines controlled by digital computers to simulate, extend, and expand human intelligence, perceive the environment, acquire knowledge, and use that knowledge to obtain optimal results.

[0175] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention.

Claims

1. A cable fault diagnosis method, characterized in that, The method includes: S1: A broadband excitation signal is applied to the target cable to excite the target cable to generate a first response signal; S2: Extract the multi-dimensional time-frequency features of the first response signal after adaptive wavelet threshold denoising, and match the fault template of the preset cable reference model according to the energy distribution features in the multi-dimensional time-frequency features; S3: Determine the fault area in the fault template corresponding to the target cable based on the propagation time and propagation speed of the first response signal; S4: Apply a narrowband excitation signal to the fault region of the target cable to obtain the impedance spectrum of the target cable; S5: Based on the resonant frequency point and corresponding impedance amplitude value of the impedance spectrum, compare them with the typical fault modes in the fault feature database to obtain the fault type and fault location of the target cable. S6: Generate a diagnostic report for the target cable based on the fault type and the fault location.

2. The cable fault diagnosis method as described in claim 1, characterized in that, The broadband excitation signal is applied to the target cable to induce the target cable to generate a first response signal, including: Select the initial frequency band range and signal form corresponding to the target cable from the preset excitation parameter library; Based on the initial frequency band range and signal form, an initial broadband excitation signal for the target cable is generated; The initial broadband excitation signal is applied to the beginning of the target cable, and the initial response of the target cable is acquired simultaneously. Based on the signal quality of the initial response, the amplitude of the initial broadband excitation signal is adaptively adjusted to obtain the broadband excitation signal of the target cable.

3. The cable fault diagnosis method as described in claim 1, characterized in that, The fault template for matching a preset cable reference model based on the energy distribution characteristics in the multi-dimensional time-frequency characteristics includes: The first response signal after adaptive wavelet threshold denoising is decomposed into eigenmode components that characterize different oscillation modes. By combining the skewness value, impulse factor, and energy percentage of the intrinsic mode components, the multi-dimensional time-frequency characteristics of the target cable are obtained.

4. The cable fault diagnosis method as described in claim 3, characterized in that, The fault template for matching a preset cable reference model based on the energy distribution characteristics in the multi-dimensional time-frequency characteristics includes: Extract the energy distribution features from the multi-dimensional time-frequency features; The extracted energy distribution features are compared one by one with the baseline energy distributions under various typical fault conditions stored in the preset cable reference model to calculate the morphological similarity of the target cable. The fault state corresponding to the reference energy distribution with the highest morphological similarity is selected as the fault template of the target cable.

5. The cable fault diagnosis method as described in claim 4, characterized in that, The formula for calculating the morphological similarity is as follows: ； in, The morphological similarity is... For the first Adaptive weights for frequency band allocation The vectorized energy distribution characteristics in the first... Energy value in each frequency band For vectorized baseline features in the first Energy value in each frequency band The total number of preset frequency bands, This is the ordinal number of the preset frequency band.

6. The cable fault diagnosis method as described in claim 5, characterized in that, The step of determining the fault region in the fault template corresponding to the target cable based on the propagation time and propagation speed of the first response signal includes: Identify significant distortion points caused by reflections from fault points in the first response signal, and use the offset of the distortion points relative to the time when the broadband excitation signal was emitted as the propagation time of the first response signal. Based on the cable type indicated by the fault template, a preset propagation speed parameter table is queried to obtain the signal propagation speed corresponding to the cable type, which is used as the propagation time of the first response signal; The product of the propagation time and the propagation speed is used as the approximate distance between the fault point and the measurement point in the target cable; The approximate distance is compared with the predefined area distance boundary in the fault template to determine the specific segment of the fault point, and the specific segment is output as the fault area of ​​the target cable.

7. A cable fault diagnosis method as described in claim 6, characterized in that, The resonant frequency point and corresponding impedance amplitude value based on the impedance spectrum are compared with typical fault modes in the fault feature database to obtain the fault type and fault location of the target cable, including: Identify resonance peaks whose amplitudes exceed a preset threshold from the impedance spectrum, and record the frequency value and corresponding impedance amplitude value of the resonance peaks to obtain the feature set of the resonance peaks to be analyzed. The matching degree of the feature set to be analyzed is calculated between the benchmark feature set corresponding to the typical fault modes in the fault feature database. Based on the matching degree calculation results, the candidate fault modes with the highest matching degree are selected, wherein the matching degree is calculated as follows: ； In the formula, The degree of matching between the feature set to be analyzed and the typical fault patterns in the fault feature database is given. An adaptive weighting system is determined by the saliency index of the benchmark feature set at key resonant frequency points. The initial matching degree between the feature set to be analyzed and the typical fault modes in the fault feature database is given. This is a normalized value of the relative error of impedance amplitude between the feature set to be analyzed and the reference feature set at a preset key resonant frequency point; Based on the fault location calculation rules corresponding to the candidate fault modes and the resonant frequency points in the feature set to be analyzed, the fault location of the target cable is determined, and the candidate fault mode with the highest matching degree is identified as the fault type.

8. The cable fault diagnosis method as described in claim 7, characterized in that, The determination of the final fault location of the target cable based on the fault location calculation rules corresponding to the candidate fault modes and the resonant frequency points in the feature set to be analyzed includes: The fault location calculation rule corresponding to the candidate fault mode is called from the pre-stored rule base; The resonant frequency points in the feature set to be analyzed are sorted according to their frequency values ​​from high to low to obtain the resonant frequency point sequence of the feature set to be analyzed. The fault locations of the resonant frequency point sequence are derived based on the mapping relationship of the fault location calculation rules.

9. A cable fault diagnosis system, characterized in that, include: A broadband excitation module is used to apply a broadband excitation signal to a target cable to excite the target cable to generate a first response signal; The fault template matching module is used to extract multi-dimensional time-frequency features of the first response signal after adaptive wavelet threshold denoising, and match the fault template of the preset cable reference model according to the energy distribution features in the multi-dimensional time-frequency features. The fault area determination module is used to determine the fault area in the fault template corresponding to the target cable based on the propagation time and propagation speed of the first response signal. A narrowband excitation module is used to apply a narrowband excitation signal to the fault region of the target cable to obtain the impedance spectrum of the target cable; The impedance spectrum comparison module is used to compare the resonant frequency point and the corresponding impedance amplitude value of the impedance spectrum with typical fault modes in the fault feature database to obtain the fault type and fault location of the target cable. The diagnostic report generation module is used to generate a diagnostic report for the target cable based on the fault type and the fault location.

10. A terminal, characterized in that, The steps of implementing the method according to any one of claims 1 to 8.

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