Cable fault point positioning method, device and equipment

Abstract

The invention relates to the technical field of electronic measurement, and discloses a cable fault point positioning method, device and equipment, and the method comprises the steps: controlling a time domain reflection unit to generate a pulse signal with preset phase deviation, and collecting a reflection signal generated by the pulse signal in a to-be-detected cable; processing data points in the reflected signal according to a preset signal processing algorithm to obtain a denoised signal; determining an initial fault point of the to-be-detected cable based on the denoised signal; and acquiring temperature data of the to-be-detected cable, and performing position compensation on the initial fault point based on the temperature data and a preset temperature compensation algorithm to obtain a target fault point. Through fusion of multi-phase pulse collaborative acquisition and a multistage signal processing algorithm, physical sampling rate limitation of traditional equipment is broken through, a temperature dynamic compensation mechanism is combined, the problem of positioning drift caused by thermal expansion and cold contraction of a cable and dielectric constant change is solved, and high-precision positioning of a fault point in a complex environment is realized.

Description

Technical Field

[0001] The present application relates to the field of electronic measurement technology, and in particular to a method, device and equipment for locating a cable fault point. Background Art

[0002] When existing FPGA (Field Programmable Gate Array) algorithms address the bottleneck of traditional TDR (Time Domain Reflectometry), full-channel parallel processing consumes a large amount of lookup table resources, resulting in high latency, making it difficult to deploy lightweight models and unable to meet online detection needs. Summary of the Invention

[0003] The main purpose of this application is to provide a cable fault point location method, device and equipment, aiming to solve the technical problem that the existing FPGA algorithm full-channel parallel processing consumes a large amount of lookup table resources and cannot meet the online detection needs.

[0004] To achieve the above objectives, the present application proposes a cable fault point locating method, which includes:

[0005] Controlling the time domain reflectometry unit to generate a pulse signal with a preset phase offset, and collecting a reflection signal generated by the pulse signal in the cable to be detected;

[0006] Processing the data points in the reflected signal according to a preset signal processing algorithm to obtain a noise-reduced signal;

[0007] Determining an initial fault point of the cable to be detected based on the noise-reduced signal;

[0008] The temperature data of the cable to be detected is obtained, and the position compensation of the initial fault point is performed based on the temperature data and a preset temperature compensation algorithm to obtain a target fault point.

[0009] Optionally, the step of obtaining temperature data of the cable to be detected, and performing position compensation on the initial fault point based on the temperature data and a preset temperature compensation algorithm to obtain a target fault point includes:

[0010] Acquiring temperature data of the cable to be detected, and processing the temperature data through a Kalman filter algorithm to generate a fused temperature value;

[0011] Inputting the fusion temperature value into a preset temperature compensation algorithm to obtain a corrected cable length, wherein the preset temperature compensation algorithm is constructed by using the conductor thermal expansion coefficient and the insulation dielectric constant temperature coefficient;

[0012] The initial fault point is position-compensated according to the cable length to obtain a target fault point.

[0013] Optionally, the preset temperature compensation algorithm is:

[0014]

[0015] Where, L true is the actual length of the cable, L meas is the length of the cable before correction, ΔT is the difference between the current temperature of the cable and the reference temperature, α is the linear expansion coefficient of the conductor, and β is the temperature coefficient of the dielectric constant of the insulation material.

[0016] Optionally, the step of processing the data points in the reflected signal according to a preset signal processing algorithm to obtain a noise-reduced signal includes:

[0017] Smoothing the unevenly spaced data points in the reflection signal by cubic spline interpolation to obtain observation data;

[0018] Reconstructing the observation data according to a compressed sensing enhanced reconstruction algorithm to generate a high-resolution signal;

[0019] Adaptive wavelet noise reduction processing is performed on the high-resolution signal to obtain a noise-reduced signal.

[0020] Optionally, the step of smoothing the unevenly spaced data points in the reflected signal by cubic spline interpolation to obtain observation data includes:

[0021] The reflection signal data collected multiple times are interleaved and arranged in a preset phase offset order to obtain a phase-aligned interpolated sequence;

[0022] A cubic spline interpolation operation is performed on the non-uniformly spaced data points of the interpolated sequence to generate observation data.

[0023] Optionally, the step of reconstructing the observation data according to a compressed sensing enhanced reconstruction algorithm to generate a high-resolution signal includes:

[0024] Mapping the observation data into an observation vector and inputting the vector into a compressed sensing enhanced reconstruction model, wherein the compressed sensing enhanced reconstruction model is constructed by a Gaussian random matrix;

[0025] The compressed sensing enhanced reconstruction model is solved by an iterative soft threshold algorithm to obtain a reconstructed high-resolution signal, wherein each iteration of the iterative soft threshold algorithm includes a gradient descent and a soft threshold processing.

[0026] Optionally, the step of performing adaptive wavelet noise reduction processing on the high-resolution signal to obtain a noise-reduced signal includes:

[0027] Determine the wavelet basis that has the highest matching degree with the high-resolution signal;

[0028] Performing wavelet decomposition on the high-resolution signal based on the wavelet basis to generate detail coefficients of a preset number of decomposition layers;

[0029] Calculating a target threshold of the detail coefficient of each layer, and performing soft threshold processing on the detail coefficient based on the target threshold to obtain a processed coefficient;

[0030] The processed coefficients are reconstructed into a time domain signal using an inverse wavelet transform to obtain a noise-reduced signal.

[0031] Optionally, the time domain reflection unit includes a high-voltage pulse generator, an analog-to-digital converter and a timing controller; the high-voltage pulse generator generates a pulse signal with a phase shift characteristic, the analog-to-digital converter collects the reflected signal generated after the pulse signal is injected into the cable to be detected, the timing controller controls the phase shift sequence of the high-voltage pulse generator, and cooperates with the analog-to-digital converter to perform multi-phase interleaved collection of the reflected signal.

[0032] In addition, to achieve the above-mentioned purpose, the present application also proposes a cable fault point locating device, which includes:

[0033] A signal acquisition module, configured to control the time domain reflectometry unit to generate a pulse signal with a preset phase offset, and to acquire a reflection signal generated by the pulse signal in the cable to be detected;

[0034] A signal processing module, configured to process the data points in the reflection signal according to a preset signal processing algorithm to obtain a noise-reduced signal;

[0035] A fault prediction module, configured to determine an initial fault point of the cable to be detected based on the noise-reduced signal;

[0036] The position compensation module is used to obtain temperature data of the cable to be detected, and perform position compensation on the initial fault point based on the temperature data and a preset temperature compensation algorithm to obtain a target fault point.

[0037] In addition, to achieve the above-mentioned purpose, the present application also proposes a cable fault point locating device, which includes: a memory, a processor, and a computer program stored on the memory and executable on the processor, wherein the computer program is configured to implement the steps of the cable fault point locating method as described above.

[0038] This application discloses a method for controlling a time domain reflection unit to generate a pulse signal with a preset phase offset, and collecting the reflected signal generated by the pulse signal in the cable to be detected; processing the data points in the reflected signal according to a preset signal processing algorithm to obtain a noise-reduced signal; determining the initial fault point of the cable to be detected based on the noise-reduced signal; obtaining the temperature data of the cable to be detected, and performing position compensation on the initial fault point based on the temperature data and a preset temperature compensation algorithm to obtain the target fault point. This application breaks through the physical sampling rate limitations of traditional TDR equipment by integrating multi-phase pulse collaborative acquisition with a multi-level signal processing algorithm, and combines a temperature dynamic compensation mechanism to solve the positioning drift problem caused by cable thermal expansion and contraction and dielectric constant changes, thereby achieving high-precision positioning of fault points in complex environments. BRIEF DESCRIPTION OF THE DRAWINGS

[0039] The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the present application and, together with the description, serve to explain the principles of the present application.

[0040] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, for ordinary technicians in this field, other drawings can be obtained based on these drawings without any creative work.

[0041] Figure 1 This is a flow chart of the first embodiment of the cable fault point locating method of the present application;

[0042] Figure 2 This is the hardware architecture diagram of the cable fault point location method of this application;

[0043] Figure 3 This is a flow chart of a second embodiment of the cable fault point locating method of the present application;

[0044] Figure 4 This is a flow chart of a third embodiment of the cable fault point locating method of the present application;

[0045] Figure 5 This is the algorithm flow chart of the multi-pulse collaborative interpolation algorithm of this application;

[0046] Figure 6 This is the algorithm flow chart of the adaptive wavelet domain denoising algorithm of this application;

[0047] Figure 7 This is a schematic diagram of the module structure of the cable fault point locating device according to an embodiment of the present application;

[0048] Figure 8This is a schematic diagram of the device structure of the hardware operating environment involved in the cable fault point locating method in the embodiment of the present application.

[0049] The purpose, features and advantages of this application will be further explained in conjunction with the embodiments and with reference to the accompanying drawings. DETAILED DESCRIPTION

[0050] It should be understood that the specific embodiments described herein are merely used to explain the technical solutions of the present application and are not intended to limit the present application.

[0051] In order to better understand the technical solution of the present application, a detailed description will be given below in conjunction with the accompanying drawings and specific implementation methods.

[0052] Traditional cable fault detection is limited by the insufficient physical sampling rate of the ADC (less than or equal to 500MSPS), long-distance signal attenuation to the ambient noise level, and temperature drift-induced propagation velocity distortion. This significantly increases the positioning error (±10m) over a 10km cable. Existing time-domain reflectometry cannot distinguish reflections from branch nodes and fault points. After a 200V pulse is transmitted over 10km, the reflected signal amplitude is less than 1mV, drowning out the ambient noise. Therefore, FPGA algorithms are used for processing. However, existing FPGA algorithms for full-channel parallel processing (such as FFT interpolation) consume more than 80% of LUTs (Look Up Tables), making it difficult to deploy lightweight models. Furthermore, compressed sensing reconstruction latency exceeds 100μs, making it unsuitable for online detection.

[0053] Therefore, this application provides a high-precision cable status detection technology based on multi-algorithm fusion. Through a phased processing mechanism (such as pulse generation, signal processing, temperature compensation, etc.), it simultaneously solves the problems of insufficient time domain resolution and long-distance attenuation distortion in a single detection process. The preset offset design of the multi-phase pulse circumvents the ADC sampling rate limitation, and the temperature compensation process eliminates the influence of material thermal effects on wave velocity, ensuring that the fault point location results have both spatial accuracy stability and environmental adaptability.

[0054] It should be noted that the execution subject of this embodiment can be a computing service device with signal processing, data calculation, and program execution functions, such as a computer, or an electronic device capable of performing the above functions. This embodiment and the following embodiments are described below using a cable status detection system as an example.

[0055] Based on this, the embodiment of the present application provides a method for locating a cable fault point, referring to Figure 1 , Figure 1 This is a flow chart of the first embodiment of the cable fault point locating method of the present application.

[0056] In this embodiment, the cable fault point locating method includes:

[0057] Step S10: Control the time domain reflectometry unit to generate a pulse signal with a preset phase offset, and collect a reflection signal generated by the pulse signal in the cable to be detected.

[0058] It's important to note that a time domain reflectometry unit (TDRU) is a hardware system consisting of a high-voltage pulse generator (such as an avalanche transistor circuit), an ADC sampling module, and an FPGA timing controller. Its core function is to control pulse transmission timing through the FPGA, generating multiple sets of high-voltage pulses with strict phase offsets (such as 0°, 90°, 180°, and 270°) and simultaneously acquiring the cable's reflected signals.

[0059] It should be understood that the time domain reflectometry unit achieves hardware coordination between multi-phase pulse emission and high-resolution signal acquisition, and can include a high-voltage pulse generator, an analog-to-digital converter, and a timing controller. The high-voltage pulse generator, based on an avalanche transistor circuit architecture, can generate a pulse signal with a phase-shift characteristic; the analog-to-digital converter is used to acquire the reflected signal generated after the pulse signal is injected into the cable to be tested, supporting the synchronous acquisition and storage of multi-phase data; the timing controller, based on FPGA hardware logic, can accurately control the phase offset sequence of the high-voltage pulse generator (0°, 90°, 180°, 270°), and cooperate with the analog-to-digital converter to achieve multi-phase interleaved acquisition of the reflected signal, forming a multi-dimensional raw data link.

[0060] As you can see, the FPGA generates four trigger signals, each offset by 1 / 4 cycle (i.e., 1 ns) relative to the 250 MHz sampling clock, to drive the high-voltage circuit to transmit a pulse train. An ADC (such as the AD9481) captures 1000 points of reflection data after each pulse trigger. These data are stored in BRAM partitions ordered by phase offset, forming four sets of complementary time-domain sampling sequences.

[0061] It should be understood that clock jitter must be less than 50ps, corresponding to a ±0.45° phase error. Otherwise, waveform distortion will occur during interpolation. Avalanche transistor pulse circuit: The ZETEX FMMT417 achieves a 200V / 10ns pulse (rising edge 1.5ns) with a 40:1 step-up ratio. The ADL5330 VGA chip achieves 0-60dB adaptive gain (response time <20ns).

[0062] In one example, reference Figure 2 , Figure 2 This is the hardware architecture diagram of the cable fault point locating method of this application.

[0063] The architecture utilizes the Cortex-A7 i.MX6ULL as the core control unit, offering low power consumption and high computational efficiency. It handles global task scheduling and integrates temperature sensing and signal acquisition data for comprehensive computation, ensuring coordinated operation across all system modules. The XCTA100T FPGA (Field Programmable Gate Array) communicates with the core control unit via UART / SPI, receiving control commands and providing status feedback. It performs high-speed digital processing on analog signals (high-voltage pulse generator output and cable reflection signals) collected by the AD9481 ADC, including filtering and time / frequency domain conversion, providing the core unit with precise, pre-processed digital signals, improving detection efficiency and accuracy. The AD9481 ADC (Analog-to-Digital Converter) connects to the FPGA via an LVDS (Low Voltage Differential Signaling) interface to convert analog signals (from the high-voltage pulse generator and cable reflection voltage signals) into digital signals.

[0064] The 10.1-inch resistive screen serves as the human-computer interaction interface, supports touch input and graphical display, receives detection data and system status (such as temperature and communication connection status) sent by Cortex-A7, presents them in an intuitive interface, and transmits user touch commands (parameter settings, function triggers) back to the core control unit to implement detection process intervention and system configuration adjustment.

[0065] The wireless communication module integrates Bluetooth BLE (Bluetooth Low Energy) and 4G communication technology. BLE is used for short-distance, low-power data interaction; the 4G module supports wide-area networking to enable remote uploading of detection data. The temperature sensing module collects environmental or cable temperature data based on the temperature-resistance characteristics of the PT100 resistor. The high-voltage pulse generator generates a 200V pulse signal according to system instructions and injects it into the cable to be tested connected to the "cable interface" as a detection excitation source, causing the cable fault point (such as a breakpoint, short circuit) to generate a reflection signal, providing a "detection signal" for subsequent acquisition and analysis. The cable interface is physically connected to the cable to be tested to achieve high-voltage pulse injection and reflected signal return. Among them, the core hardware parameters of this embodiment are shown in the following table:

[0066] Table 1 Hardware parameters

[0067] Module Model / Parameters Features Main control processor i.MX6ULL (Cortex-A7) 800MHz, industrial temperature range (-40~85℃) FPGA XilinxXC7A100T-2FGG484I 101K LUTs,240DSP48E1,4.8Mb BRAM ADC AD9481BCPZ-250 8-bit, 250MSPS, LVDS output High-voltage pulse generator Custom avalanche transistor circuit FMMT417+ energy storage capacitor bank, 200V@10ns Temperature sensor PT100+ADS1248 ±0.1℃ accuracy, SPI interface

[0068] Step S20: Process the data points in the reflected signal according to a preset signal processing algorithm to obtain a noise-reduced signal.

[0069] It should be noted that the preset signal processing algorithm is a pre-designed digital signal processing algorithm, such as filtering and denoising models, which can perform calculations on the original data points of the cable reflection signal collected by the ADC (including noise, such as electromagnetic interference, hardware thermal noise, etc.), suppress noise components, retain effective signal characteristics, and output a purer noise-reduced signal, providing high-quality data for subsequent cable fault analysis (such as breakpoint location, impedance anomaly identification, etc.).

[0070] It should be understood that during data acquisition and preprocessing, the AD9481 ADC captures the analog signal reflected from the cable, converts it into digital data points (timed discrete values), and transmits it to the XCTA100T FPGA via LVDS. The FPGA performs preliminary preprocessing (such as outlier removal and formatting) to provide standardized data input for the noise reduction algorithm. Algorithm execution may include wavelet decomposition, thresholding, and adaptive filtering enhancement, which are not specifically limited in this embodiment. The FPGA uses a preset wavelet transform kernel to decompose the raw data points into high-frequency and low-frequency components. The high-frequency component is shrunk by a preset threshold to suppress small coefficients dominated by noise while retaining coefficients related to signal mutations. In combination with the cable signal model, the reconstructed signal is adaptively filtered to further offset residual noise and output a noise-reduced signal.

[0071] It can be understood that the data points in the reflected signal can be processed according to the preset signal processing algorithm. The reflected signal data points can be first decomposed by wavelet to obtain wavelet coefficients at different scales. For the high-frequency coefficients containing noise, a threshold method (such as soft threshold, hard threshold) is used to perform shrinkage processing, retaining the coefficients of the effective signal, and then the signal is restored through wavelet reconstruction; an adaptive filtering algorithm can also be used to automatically adjust the filter parameters according to the statistical characteristics of the reflected signal, filter out noise data points that are greatly different from the signal characteristics, and finally integrate the processed data points to output the noise-reduced signal.

[0072] Step S30: determining the initial fault point of the cable to be detected based on the noise-reduced signal.

[0073] It should be noted that the purer cable reflection signal after noise reduction processing can be used to locate the first identified position of cable faults (such as breakpoints, short circuits, impedance anomalies, etc.) by analyzing the signal time domain characteristics (such as the arrival time and amplitude change of the reflected wave) or frequency domain characteristics based on signal propagation characteristics, such as the propagation speed and reflection law of electromagnetic waves in the cable.

[0074] It's understandable that the propagation speed of electromagnetic waves in a cable can be calculated based on the cable type, characteristic impedance, and signal frequency. The initial moment the high-voltage pulse generator injects the signal is marked. The waveform in the signal that is opposite to the incident pulse (or has a sudden change in characteristics) is detected to determine the arrival time of the reflected wave. Based on the principle of round-trip electromagnetic wave propagation, the distance to the initial fault point is calculated and marked as the "initial fault point" of the cable.

[0075] Step S40 , obtaining temperature data of the cable to be detected, and performing position compensation on the initial fault point based on the temperature data and a preset temperature compensation algorithm to obtain a target fault point.

[0076] It can be understood that by obtaining cable environment or conductor temperature data through temperature sensors, the initial fault point location can be calibrated using a preset temperature compensation algorithm to eliminate the deviation in cable length and signal propagation speed caused by temperature changes, and ultimately obtain a more accurate target fault point location.

[0077] In this embodiment, a time domain reflectometer is controlled to generate a pulse signal with a preset phase offset, and the reflected signal generated by the pulse signal in the cable to be detected is collected; the data points in the reflected signal are processed according to a preset signal processing algorithm to obtain a noise-reduced signal; the initial fault point of the cable to be detected is determined based on the noise-reduced signal; the temperature data of the cable to be detected is obtained, and the position of the initial fault point is compensated based on the temperature data and a preset temperature compensation algorithm to obtain the target fault point. By integrating multi-phase pulse collaborative acquisition with a multi-level signal processing algorithm, the physical sampling rate limitations of traditional TDR equipment are broken through, and combined with a dynamic temperature compensation mechanism, the positioning drift problem caused by cable thermal expansion and contraction and dielectric constant changes is solved, achieving high-precision positioning of fault points in complex environments.

[0078] Reference Figure 3 , Figure 3 This is a flow chart of the second embodiment of the cable fault point locating method of the present application. Based on the above-mentioned first embodiment, the second embodiment of the cable fault point locating method of the present application is proposed.

[0079] In the second embodiment, step S40 includes:

[0080] Step S401: Acquire temperature data of the cable to be detected, and process the temperature data using a Kalman filter algorithm to generate a fused temperature value.

[0081] As you can understand, a PT100 temperature sensor (accuracy ±0.1°C) senses the cable environment or conductor temperature in real time. Its resistance changes with temperature, and the ADS1248 analog-to-digital converter (SPI interface) converts this data into a digital signal, outputting the raw temperature value. This raw temperature data contains Gaussian white noise, which requires filtering to reduce.

[0082] Step S402: inputting the fusion temperature value into a preset temperature compensation algorithm to obtain a corrected cable length, wherein the preset temperature compensation algorithm is constructed by the conductor thermal expansion coefficient and the insulation dielectric constant temperature coefficient.

[0083] It should be understood that the change in cable length with temperature is primarily influenced by two factors: 1. Thermal expansion and contraction of the conductor, resulting in a length change of ΔL = αL0ΔT, where L0 is the initial length and ΔT is the temperature difference, calculated between the current temperature T and the reference temperature T0. 2. Changes in the dielectric constant of the insulation layer, which causes changes in signal propagation speed.

[0084] It should be understood that the formula for cable length variation with temperature is:

[0085] L=L0·[1+α(T-T0)]

[0086] Changes in the dielectric constant of insulating materials affect propagation speed:

[0087]

[0088] Where α is the linear expansion coefficient of the conductor (such as copper 17×10 -6 / ℃), β is the dielectric constant temperature coefficient of the insulating material (such as XLPE: 80×10 -6 / ℃), v0 is the standard propagation speed.

[0089] Combining the two effects, the preset temperature compensation algorithm is:

[0090]

[0091] Among them, L true is the actual length of the cable, L meas is the length of the cable before correction, and ΔT is the difference between the current temperature of the cable and the reference temperature.

[0092] Step S403: performing position compensation on the initial fault point according to the cable length to obtain a target fault point.

[0093] In one example, system performance verification is shown in Table 2 and Table 3.

[0094] Table 2 Length detection accuracy test table

[0095] True length (m) Measurement results (m) Error (m) Error after compensation (m) 10000.0 10020.5 +20.5 +0.8 5500.0 5502.3 +2.3 +0.3 3200.0 3198.7 -1.3 -0.2 8765.4 8770.2 +4.8 +0.5

[0096] As you can understand, by measuring the total cable length through the hardware system or directly inputting it, the distance from the initial fault point to the test end can be calculated based on the noise-reduced signal. A PT100 temperature sensor is used to obtain the current temperature T, and a Kalman filter algorithm is used to generate a fused temperature value to suppress noise interference. Based on the physical characteristics of the cable, a dual-factor compensation model for temperature, length, and signal propagation speed is established.

[0097] Table 3 Fault point location test table

[0098] Fault type Actual location (m) Measurement location (m) Error (m) Open circuit fault 3245.6 3245.1 -0.5 Short circuit fault 7853.2 7853.4 +0.2 Water immersion failure 5567.8 5567.5 -0.3 Joint oxidation 9214.7 9214.9 +0.2

[0099] The effects of temperature on length and speed are combined to derive a formula for the distance to the target fault point. The compensation algorithm is executed by the Cortex-A7 processor, and the results are displayed on a 10.1-inch resistive screen. The results are synchronized to a remote monitoring system via a Bluetooth Low Energy (BLE) / 4G module, supporting real-time calibration and recording of the fault point location.

[0100] This embodiment discloses obtaining temperature data from the cable to be tested, processing the data using a Kalman filter algorithm to generate a fused temperature value, inputting the fused temperature value into a preset temperature compensation algorithm constructed using the conductor's thermal expansion coefficient and the insulation's dielectric constant temperature coefficient to obtain a corrected cable length, and performing position compensation on the initial fault point based on the cable length to obtain the target fault point. Kalman filtering is used to fuse multi-source temperature data to eliminate single-point temperature measurement errors. A joint compensation model based on the conductor's thermal expansion coefficient and the insulation's dielectric constant is used to accurately correct for temperature-induced changes in radio wave propagation velocity, reducing positioning errors in long-distance cables.

[0101] Reference Figure 4 , Figure 4 This is a flow chart of the third embodiment of the cable fault point locating method of the present application. Based on the above embodiments, the third embodiment of the cable fault point locating method of the present application is proposed.

[0102] In the third embodiment, step S20 includes:

[0103] Step S201 : Smoothing the unevenly spaced data points in the reflection signal by cubic spline interpolation to obtain observation data.

[0104] It should be noted that cubic spline interpolation constructs a cubic polynomial function piecewise, ensuring that the function in adjacent intervals satisfies the continuity conditions of the first and second derivatives at the nodes, thereby generating a smooth curve. In cable reflection signal processing, it is used to convert unevenly spaced data points (such as those caused by sampling rate fluctuations or signal distortion) into uniform, smooth observation data, improving the accuracy of subsequent fault feature extraction.

[0105] Furthermore, in order to construct equivalent oversampled data based on the staggered arrangement of the phase offset sequence, the non-uniformly spaced data points are processed by combining cubic spline interpolation, and the effective sampling rate is increased to an integer multiple of the physical sampling rate without improving the ADC hardware performance. The step S201 may include:

[0106] The reflected signal data collected multiple times are interleaved and arranged in a preset phase offset order to obtain a phase-aligned interpolated sequence; a cubic spline interpolation operation is performed on the non-uniformly spaced data points of the interpolated sequence to generate observation data.

[0107] In one example, reference Figure 5 , Figure 5 This is the algorithm flow chart of the multi-pulse collaborative interpolation algorithm of this application. The TDR module is controlled by FPGA to transmit 4 pulses. The trigger time of each pulse is offset by 0°, 90°, 180°, and 270° relative to the ADC sampling clock phase, which is equivalent to the start time offset of each acquisition by 0ns, 1ns, 2ns, and 3ns (250MSPS corresponds to a 4ns sampling interval). The 1000 points of data collected 4 times are interleaved in chronological order to obtain 4000 points of data, and the equivalent sampling rate is increased to 1GSPS. They are stored in 4 BRAMs (memory) respectively.

[0108] Assume the original sampling sequence is s i [n], i=0,1,2,3 correspond to 4 acquisitions respectively, n=0,1,...,999. Then the interpolated sequence is:

[0109] s interp [4n+i]=s i [n],i=0,1,2,3; n=0,1,...,999.

[0110] The unevenly spaced data points are then smoothed using cubic spline interpolation.

[0111] Phase 0°: The acquisition start time is 0 ns, and the data sequence is s0[n], where n = 0, 1, ..., 999;

[0112] Phase 90°: The starting time is offset by 1 ns, and the sequence is s1[n], where n = 0, 1, ..., 999 (4 ns interval, but the overall right shift is 1 ns).

[0113] Phase 180°: start time offset 2 ns, sequence s2[n], n = 0, 1, ..., 999;

[0114] Phase 270°: The start time offset is 3 ns, and the sequence is s3[n], where n = 0, 1, ..., 999.

[0115] The interleaved sequence is evenly spaced on the time axis (1ns intervals). However, due to the phase-shifted acquisition mechanism, the original data points belong to a non-uniform sampling sequence with different phases before merging. Phase alignment is required before interpolation to ensure temporal continuity.

[0116] Step S202: reconstruct the observation data according to a compressed sensing enhanced reconstruction algorithm to generate a high-resolution signal.

[0117] As you can understand, compressed sensing theory shows that if a signal is sparse in a certain transform domain, the original signal can be reconstructed using a small number of linear projections. Reflected signals are sparse in the time domain (pulses are present only at the fault location), so compressed sensing methods can be used to improve resolution. In cable inspection scenarios, reflected signals only have pulses at the fault location, making them naturally sparse in the time domain and suitable for compressed sensing reconstruction.

[0118] Furthermore, in order to construct an incomplete measurement system using a Gaussian random matrix, the L1 norm minimization problem is efficiently solved at the FPGA hardware level through iterative optimization of gradient descent and soft threshold shrinkage, thereby achieving high-resolution reconstruction of the signal with low computational complexity. The step S202 may include:

[0119] The observation data is mapped into an observation vector and input into a compressed sensing enhanced reconstruction model, wherein the compressed sensing enhanced reconstruction model is constructed by a Gaussian random matrix; the compressed sensing enhanced reconstruction model is solved by an iterative soft threshold algorithm to obtain a reconstructed high-resolution signal, wherein each iteration of the iterative soft threshold algorithm includes a gradient descent and a soft threshold processing.

[0120] In one example, the observed data y∈R generated by cubic spline interpolation M (For example, a 4000-point uniform sequence, M = 4000), it is necessary to first use a sparse basis transformation (such as identity transformation, because the signal is already sparse in the time domain) to express it as: y = Φx, where Φ∈R N×M is the measurement matrix (N is much smaller than M, such as N=1000), which can be a Gaussian random matrix, and x is the high-resolution signal to be reconstructed (only the fault point position is non-zero).

[0121] The objective function of the compressed sensing enhanced reconstruction model is expressed as follows:

[0122]

[0123] Where ε is the observation noise tolerance, and the iterative formula is expressed as:

[0124] x k+1 =S λ (x k +μΦ T (y-Φx k ))

[0125] Where μ is the step size, μ=1 / ||Φ T Φ|| ensures convergence, and the soft threshold function is:

[0126]

[0127] The iterative soft threshold algorithm (ISTA) is used for solution. Each iteration consists of two steps:

[0128] 1. Gradient descent: x k+1 =x k -μΦ T (Φx k -y);

[0129] 2. Soft threshold: x k+1 =sign(x k+1 )max(|x k+1 |-λμ,0).

[0130] The termination condition is that the number of iterations reaches the preset value or the residual ||y-Φx||2 is less than the threshold.

[0131] Through the above method, the system uses compressed sensing theory to break through the traditional ADC sampling rate limitation and combines FPGA hardware acceleration to achieve rapid reconstruction of sparse signals.

[0132] Step S203 : performing adaptive wavelet noise reduction processing on the high-resolution signal to obtain a noise-reduced signal.

[0133] It can be understood that performing a five-layer wavelet decomposition on a high-resolution signal (such as a 4000-point sequence) decomposes the signal into approximation coefficients (low-frequency component cA5) and detail coefficients (high-frequency components cD1-cD5). Each layer of decomposition halves the signal bandwidth, with cD1 corresponding to the highest frequency band (1 / 2 to 1 / 4 of the sampling rate) and cA5 corresponding to the lowest frequency band (<1 / 32 of the sampling rate).

[0134] Furthermore, in order to dynamically match the optimal wavelet basis through the entropy minimization criterion, combined with the hierarchical threshold strategy to distinguish noise and effective signal components, the soft and hard threshold compromise processing retains the impact characteristics of the fault point while suppressing broadband noise, significantly improving the detection robustness under low signal-to-noise ratio conditions. The step S203 may include:

[0135] Determine the wavelet basis that has the highest matching degree with the high-resolution signal; perform wavelet decomposition on the high-resolution signal based on the wavelet basis to generate detail coefficients of a preset decomposition layer; calculate the target threshold of the detail coefficients of each layer, and perform soft threshold processing on the detail coefficients based on the target threshold to obtain processed coefficients; use an inverse wavelet transform to reconstruct the processed coefficients into a time domain signal to obtain a denoised signal.

[0136] It should be understood that dynamic basis function selection can be to calculate the Shannon entropy of the detail coefficients after decomposition under different wavelet bases (such as db4, sym5, coif3), select the basis function with the smallest entropy value (the smaller the entropy, the better the signal sparsity and the lower the noise ratio), calculate the entropy value of each basis function in parallel in FPGA, and output the optimal basis function index after comparison, such as 2-bit code corresponding to 3 basis functions.

[0137] In one example, reference Figure 6 , Figure 6 This is the algorithm flow chart of the adaptive wavelet domain denoising algorithm of this application. The adaptive wavelet domain denoising algorithm flow in the figure includes:

[0138] 1. Wavelet decomposition: Perform three-layer wavelet decomposition on the signal to obtain detail coefficients (cD1-cD3).

[0139] 2. Dynamic wavelet basis selection: Calculate the Shannon entropy under different wavelet bases (db4, sym5, coif3) and select the basis function with the minimum entropy.

[0140] 3. Layered threshold calculation: For each layer of detail coefficient, calculate the noise variance σ n , signal variance σ x , threshold

[0141] 4. Soft and hard threshold compromise: Apply threshold processing to the detail coefficients.

[0142] 5. Wavelet reconstruction: obtain the denoised signal.

[0143] In layered threshold calculation and coefficient processing, noise variance can be estimated using the median absolute deviation of each layer's detail coefficients. Signal variance is the standard deviation of each layer's coefficients, characterizing the signal energy distribution. Soft and hard thresholding suppresses noise by zeroing small-amplitude coefficients while retaining large-amplitude coefficients to reflect signal characteristics. Finally, an optimal wavelet basis is used to perform an inverse wavelet transform on the processed detail coefficients and generate the de-noised signal.

[0144] This embodiment discloses smoothing the unevenly spaced data points in the reflected signal using cubic spline interpolation to obtain observation data; reconstructing the observation data using a compressed sensing enhanced reconstruction algorithm to generate a high-resolution signal; and performing adaptive wavelet denoising on the high-resolution signal to obtain a denoised signal. Interpolation smoothing eliminates uneven sampling distortion, compressed sensing reconstruction overcomes the Nyquist limit to improve time-domain resolution, and adaptive wavelet denoising suppresses ambient noise, all of which together enable reliable extraction of weak reflected signals.

[0145] It should be noted that the above examples are only used to understand the present application and do not constitute a limitation on the cable fault point locating method of the present application. More simple transformations based on this technical concept are all within the scope of protection of the present application.

[0146] This application also provides a cable fault point locating device, please refer to Figure 7 , the cable fault point locating device comprises:

[0147] The signal acquisition module 10 is used to control the time domain reflectometry unit to generate a pulse signal with a preset phase offset, and to acquire a reflection signal generated by the pulse signal in the cable to be detected;

[0148] A signal processing module 20 is configured to process the data points in the reflection signal according to a preset signal processing algorithm to obtain a noise-reduced signal;

[0149] A fault prediction module 30 is configured to determine an initial fault point of the cable to be detected based on the noise-reduced signal;

[0150] The position compensation module 40 is configured to obtain temperature data of the cable to be detected, and perform position compensation on the initial fault point based on the temperature data and a preset temperature compensation algorithm to obtain a target fault point.

[0151] The cable fault point locating device provided in this application utilizes the cable fault point locating method described in the aforementioned embodiments, resolving the technical issue that existing FPGA algorithms, which process all channels in parallel, consume a large amount of lookup table resources and are unable to meet online detection requirements. Compared to the prior art, the cable fault point locating device provided in this application achieves the same beneficial effects as the cable fault point locating method described in the aforementioned embodiments. Other technical features of the cable fault point locating device are the same as those disclosed in the aforementioned embodiments and are not further elaborated here.

[0152] The present application provides a cable fault point locating device, which includes: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions that can be executed by the at least one processor, and the instructions are executed by the at least one processor so that the at least one processor can execute the cable fault point locating method in the above-mentioned embodiment 1.

[0153] Reference below Figure 8, which shows a schematic diagram of the structure of a cable fault point locating device suitable for implementing an embodiment of the present application. The cable fault point locating device in the embodiment of the present application may include, but is not limited to, mobile terminals such as mobile phones, laptop computers, digital broadcast receivers, PDAs (Personal Digital Assistants), PADs (Portable Application Descriptions), PMPs (Portable Media Players), vehicle-mounted terminals (such as vehicle-mounted navigation terminals), and fixed terminals such as digital TVs and desktop computers. Figure 8 The cable fault point locating device shown is merely an example and should not limit the functions and scope of use of the embodiments of the present application.

[0154] like Figure 8 As shown, the cable fault point locating device may include a processing device 1001 (e.g., a central processing unit, a graphics processing unit, etc.), which can perform various appropriate actions and processes based on programs stored in a read-only memory 1002 or programs loaded from a storage device 1003 into a random access memory 1004. The random access memory 1004 also stores various programs and data required for the operation of the cable fault point locating device. The processing device 1001, the read-only memory 1002, and the random access memory 1004 are interconnected via a bus 1005. An input / output interface 1006 is also connected to the bus. Typically, the following systems can be connected to the input / output interface 1006: an input device 1007 including, for example, a touch screen, touchpad, keyboard, mouse, image sensor, microphone, accelerometer, gyroscope, etc.; an output device 1008 including, for example, a liquid crystal display (LCD), speaker, vibrator, etc.; a storage device 1003 including, for example, a magnetic tape, hard disk, etc.; and a communication device 1009. The communication device 1009 can allow the cable fault locating device to communicate with other devices wirelessly or by wire to exchange data. Although the figure shows a cable fault locating device with various systems, it should be understood that it is not required to implement or have all of the systems shown. More or fewer systems can be implemented or have alternatively.

[0155] In particular, according to the embodiments disclosed in the present application, the processes described above with reference to the flowcharts can be implemented as computer software programs. For example, the embodiments disclosed in the present application include a computer program product comprising a computer program carried on a computer-readable medium, the computer program comprising program code for executing the method shown in the flowchart. In such an embodiment, the computer program can be downloaded and installed from a network via a communication device, or installed from a storage device 1003, or installed from a read-only memory 1002. When the computer program is executed by the processing device 1001, the above-mentioned functions defined in the method of the embodiment disclosed in the present application are performed.

[0156] The cable fault point locating device provided in this application utilizes the cable fault point locating method described in the aforementioned embodiment, resolving the technical issue that existing FPGA algorithms, which process all channels in parallel, consume a large amount of lookup table resources and are unable to meet online detection requirements. Compared to the prior art, the cable fault point locating device provided in this application achieves the same beneficial effects as the cable fault point locating method described in the aforementioned embodiment. Other technical features of this cable fault point locating device are the same as those disclosed in the aforementioned embodiment and are not further elaborated here.

[0157] It should be understood that the various parts disclosed in this application can be implemented using hardware, software, firmware, or a combination thereof. In the description of the above embodiments, specific features, structures, materials, or characteristics can be combined in any one or more embodiments or examples in a suitable manner.

[0158] The above description is merely a specific embodiment of the present application, but the scope of protection of the present application is not limited thereto. Any changes or substitutions that can be easily conceived by a person skilled in the art within the technical scope disclosed in this application should be included in the scope of protection of this application. Therefore, the scope of protection of this application should be based on the scope of protection of the claims.

[0159] The above description is only part of the embodiments of the present application and does not limit the patent scope of the present application. All equivalent structural transformations made by using the contents of the present application specification and drawings under the technical concept of the present application, or direct / indirect application in other related technical fields are included in the patent protection scope of the present application.

Claims

1. A cable fault point location method, characterized in that: The cable fault point locating method comprises: Controlling the time domain reflectometry unit to generate a pulse signal with a preset phase offset, and collecting a reflection signal generated by the pulse signal in the cable to be detected; Processing the data points in the reflected signal according to a preset signal processing algorithm to obtain a noise-reduced signal; Determining an initial fault point of the cable to be detected based on the noise-reduced signal; The temperature data of the cable to be detected is obtained, and the position compensation of the initial fault point is performed based on the temperature data and a preset temperature compensation algorithm to obtain a target fault point.

2. The cable fault point locating method according to claim 1, wherein: The step of obtaining the temperature data of the cable to be detected, and performing position compensation on the initial fault point based on the temperature data and a preset temperature compensation algorithm to obtain the target fault point includes: Acquiring temperature data of the cable to be detected, and processing the temperature data through a Kalman filter algorithm to generate a fused temperature value; Inputting the fusion temperature value into a preset temperature compensation algorithm to obtain a corrected cable length, wherein the preset temperature compensation algorithm is constructed by using the conductor thermal expansion coefficient and the insulation dielectric constant temperature coefficient; The initial fault point is position-compensated according to the cable length to obtain a target fault point.

3. The cable fault point locating method according to claim 2, characterized in that: The preset temperature compensation algorithm is: Where, L true is the actual length of the cable, L meas is the length of the cable before correction, ΔT is the difference between the current temperature of the cable and the reference temperature, α is the linear expansion coefficient of the conductor, and β is the temperature coefficient of the dielectric constant of the insulation material.

4. The cable fault point locating method according to claim 1, wherein: The step of processing the data points in the reflected signal according to a preset signal processing algorithm to obtain a noise-reduced signal includes: Smoothing the unevenly spaced data points in the reflection signal by cubic spline interpolation to obtain observation data; Reconstructing the observation data according to a compressed sensing enhanced reconstruction algorithm to generate a high-resolution signal; Adaptive wavelet noise reduction processing is performed on the high-resolution signal to obtain a noise-reduced signal.

5. The cable fault point locating method according to claim 4, characterized in that: The step of smoothing the unevenly spaced data points in the reflection signal by cubic spline interpolation to obtain observation data comprises: The reflection signal data collected multiple times are interleaved and arranged in a preset phase offset order to obtain a phase-aligned interpolated sequence; A cubic spline interpolation operation is performed on the non-uniformly spaced data points of the interpolated sequence to generate observation data.

6. The cable fault point locating method according to claim 4, characterized in that: The step of reconstructing the observation data according to the compressed sensing enhanced reconstruction algorithm to generate a high-resolution signal includes: Mapping the observation data into an observation vector and inputting the vector into a compressed sensing enhanced reconstruction model, wherein the compressed sensing enhanced reconstruction model is constructed by a Gaussian random matrix; The compressed sensing enhanced reconstruction model is solved by an iterative soft threshold algorithm to obtain a reconstructed high-resolution signal, wherein each iteration of the iterative soft threshold algorithm includes a gradient descent and a soft threshold processing.

7. The cable fault point locating method according to claim 4, characterized in that: The step of performing adaptive wavelet noise reduction processing on the high-resolution signal to obtain a noise-reduced signal includes: Determine the wavelet basis that has the highest matching degree with the high-resolution signal; Performing wavelet decomposition on the high-resolution signal based on the wavelet basis to generate detail coefficients of a preset number of decomposition layers; Calculating a target threshold of the detail coefficient of each layer, and performing soft threshold processing on the detail coefficient based on the target threshold to obtain a processed coefficient; The processed coefficients are reconstructed into a time domain signal using an inverse wavelet transform to obtain a noise-reduced signal.

8. The cable fault point locating method according to claim 1, wherein: The time domain reflection unit includes a high-voltage pulse generator, an analog-to-digital converter and a timing controller; the high-voltage pulse generator generates a pulse signal with a phase shift characteristic, the analog-to-digital converter collects the reflected signal generated after the pulse signal is injected into the cable to be detected, the timing controller controls the phase shift sequence of the high-voltage pulse generator, and cooperates with the analog-to-digital converter to perform multi-phase interleaved collection of the reflected signal.

9. A cable fault point locating device, characterized in that: The device comprises: A signal acquisition module, configured to control the time domain reflectometry unit to generate a pulse signal with a preset phase offset, and to acquire a reflection signal generated by the pulse signal in the cable to be detected; A signal processing module, configured to process the data points in the reflection signal according to a preset signal processing algorithm to obtain a noise-reduced signal; A fault prediction module, configured to determine an initial fault point of the cable to be detected based on the noise-reduced signal; The position compensation module is used to obtain temperature data of the cable to be detected, and perform position compensation on the initial fault point based on the temperature data and a preset temperature compensation algorithm to obtain a target fault point.

10. A cable fault point locating device, characterized in that: The device comprises: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the computer program is configured to implement the steps of the cable fault point locating method according to any one of claims 1 to 8.

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