Power line defect estimation method, system and equipment based on ultrasonic guided waves and medium

Abstract

The invention discloses a power line defect estimation method, system and device based on ultrasonic guided waves and a medium, and belongs to the technical field of power line defect detection.The method comprises the steps that ultrasonic detection image data of a power line are obtained; performing signal processing based on the ultrasonic detection image data to obtain ultrasonic detection image data of a target mode; reconstructing a phase velocity frequency dispersion curve based on the ultrasonic detection image data of the target mode; and analyzing the phase velocity change according to the phase velocity frequency dispersion curve, and determining defect information of the power line. According to the method, the phase velocity frequency dispersion curve is reconstructed by using the ultrasonic detection image data of the target mode, and a defect detection criterion based on phase velocity change is established, so that inherent frequency dispersion effect interference in ultrasonic guided wave propagation is effectively avoided; the problem of misjudgment caused by natural energy attenuation due to frequency dispersion in a traditional signal amplitude attenuation-based method is solved, and the detection precision and reliability are improved.

Description

Technical Field

[0001] This invention relates to the field of power line defect detection technology, and in particular to a power line defect estimation method, system, device and medium based on ultrasonic guided waves. Background Technology

[0002] Currently, various non-destructive testing (NDT) techniques are used for defect detection and analysis of complex structures; however, the scale characteristics of power lines (such as long-distance cross-domain distribution) and the low sensitivity and poor adaptability of some traditional detection methods make it difficult to meet the needs of efficient on-site detection of power lines. Due to its advantages such as flexible sensor selection, sensitivity to minute defects, and ability to propagate over long distances along power lines, ultrasonic guided wave (UGW) technology is widely used in defect diagnosis of such structures. Lamb waves... Lamb waves are a special category of ultrasonic guided waves. Based on the product of the excitation frequency and the thickness of the propagation medium, they can be further divided into symmetrical modes (S0, S1) and asymmetrical modes (A0, A1). Under low-frequency conditions, there are fewer guided wave modes, making mode separation easier. Due to the high sensitivity of Lamb waves to defect areas, they are widely used to detect defects in power lines caused by environmental corrosion, mechanical fatigue, or external force damage, such as cracks, broken strands, and insulation aging. Despite the many advantages of ultrasonic guided waves, defect detection still faces severe challenges for targets like power lines, which have multi-segment connections, significant changes in cross-sectional dimensions, and can only be detected by contact on one side. In addition, the mutual coupling of wave dispersion, reflection, and mode conversion effects in the composite structure of the metal conductor and insulation layer of power lines further increases the complexity of signal evolution.

[0003] In existing research, defect assessment techniques based on ultrasonic guided wave interaction have achieved preliminary results, while a large amount of theoretical analysis focuses on the dispersion characteristics and multimodal behavior of guided waves. However, the attenuation and scattering effects of ultrasonic waves between the conductor and insulation layer of power lines can interfere with defect feature extraction. In addition, existing research has achieved experimental detection of power line defects using low-frequency ultrasonic guided waves, using changes in guided wave energy or amplitude to locate and quantify typical defects such as broken strands and corrosion. However, in actual operation of power lines, it has been found that the signal amplitude of ultrasonic guided waves attenuates with propagation distance due to dispersion effects and wavefront diffusion, and the traditional defect criterion based on amplitude decrease has the risk of false detection. Summary of the Invention

[0004] In view of the above-mentioned problems, the present invention is proposed.

[0005] Therefore, the technical problem solved by this invention is: how to address the issue that current power line defect detection technologies, which rely on signal amplitude changes for defect judgment, are susceptible to errors due to the transmission distance of the power line. Furthermore, during the propagation of low-frequency guided waves along the power line, wavefront diffusion and energy attenuation occur due to dispersion effects. The superposition interference from reflected signals at the interface between the metal conductor and the insulation layer further exacerbates the problem, making traditional amplitude feature extraction prone to failure. In addition, existing single-frequency single-mode analysis methods are ill-suited to the guided wave behavior of interfaces with different materials, resulting in high false negative rates and low quantitative accuracy when dealing with hidden defects such as broken strands and corrosion microcracks. By employing low-frequency ultrasonic technology, this invention develops a defect detection and accurate quantitative method that is not limited by propagation distance. This method can estimate the coordinates of defects without calibration or relying on the sample under investigation, providing a feasible technical solution for non-destructive testing of power lines and reducing the cost and time consumption of traditional detection methods.

[0006] To solve the above-mentioned technical problems, the present invention provides the following technical solution: a method for estimating electric field line defects based on ultrasonic guided waves, which includes the following steps:

[0007] Acquire ultrasonic inspection image data of the power line; perform signal processing based on the ultrasonic inspection image data to obtain ultrasonic inspection image data of the target mode; reconstruct the phase velocity dispersion curve based on the ultrasonic inspection image data of the target mode; analyze the phase velocity change according to the phase velocity dispersion curve to determine the defect information of the power line.

[0008] As a preferred embodiment of the electric power line defect estimation method based on ultrasonic guided waves described in this invention, the method involves: processing the ultrasonic detection image data to obtain ultrasonic detection image data of the target mode, including: converting the ultrasonic detection image data into a frequency-wavenumber domain spectrum; filtering the frequency-wavenumber domain spectrum to obtain a frequency-wavenumber domain spectrum after filtering out reflected signals; performing mode separation on the frequency-wavenumber domain spectrum after filtering out reflected signals to obtain the frequency-wavenumber domain spectrum of the target mode; and converting the frequency-wavenumber domain spectrum of the target mode into ultrasonic detection image data of the target mode. The beneficial effect of this preferred embodiment is that by converting the ultrasonic detection image data into a frequency-wavenumber domain spectrum and performing reflected signal filtering and mode separation processing, edge reflection interference and multi-mode signal aliasing in the electric power line structure can be effectively eliminated, and the pure A0 mode signal can be accurately extracted, improving the accuracy and reliability of subsequent defect analysis.

[0009] As a preferred embodiment of the electric field line defect estimation method based on ultrasonic guided waves described in this invention, the reconstruction of the phase velocity dispersion curve based on the ultrasonic detection image data of the target mode includes: extracting time-slice signals from the ultrasonic detection image data of the target mode; performing phase analysis on the time-slice signals to obtain the phase velocity at each frequency; and combining the phase velocities at each frequency to form the phase velocity dispersion curve of the target mode. The beneficial effect of this preferred embodiment is that by extracting time-slice signals from the ultrasonic detection image data of the target mode and performing phase analysis, a complete phase velocity dispersion curve can be reconstructed, establishing a dispersion model reflecting the intrinsic characteristics of the electric field line material, thus providing a reliable theoretical basis and judgment criterion for defect detection based on phase velocity changes.

[0010] As a preferred embodiment of the electric field line defect estimation method based on ultrasonic guided waves described in this invention, the method involves: analyzing the phase velocity change based on the phase velocity dispersion curve to determine the electric field line defect information, including: determining a phase velocity threshold based on the phase velocity dispersion curve; comparing the phase velocity in the phase velocity dispersion curve with the phase velocity threshold; and determining the location of the electric field line defect based on the comparison result.

[0011] As a preferred embodiment of the electric power line defect estimation method based on ultrasonic guided waves described in this invention, the method further includes: analyzing the phase velocity change based on the phase velocity dispersion curve to determine the defect information of the electric power line; obtaining a reference phase velocity dispersion curve of a healthy electric power line; comparing the phase velocity dispersion curve with the reference phase velocity dispersion curve to calculate the phase velocity change; and determining the type and severity of the electric power line defect based on the phase velocity change.

[0012] As a preferred embodiment of the electric power line defect estimation method based on ultrasonic guided waves described in this invention, the acquisition of ultrasonic inspection image data of the electric power line includes: emitting ultrasonic guided wave pulses on the surface of the electric power line; performing spatial scanning along the axial direction of the electric power line and acquiring time-domain signals at each scanning point; arranging the time-domain signals of each scanning point in spatial order to construct two-dimensional ultrasonic inspection image data.

[0013] As a preferred embodiment of the electric field defect estimation method based on ultrasonic guided waves described in this invention, the method involves: performing phase analysis on the time-slice signal to obtain the phase velocity at each frequency, including: selecting signals from adjacent spatial locations and determining the spectrum of adjacent signals based on the signals from the adjacent spatial locations; filtering the spectrum and reconstructing the filtered spectrum into a time-domain signal; determining the envelope of the time-domain signal and the time point corresponding to the maximum envelope value through Hilbert transform; determining the zero-crossing time point of the maximum envelope value based on zero-crossing detection technology; and calculating the phase velocity based on the zero-crossing time point. The beneficial effect of this preferred embodiment is that by using Hilbert transform combined with zero-crossing detection technology to calculate the phase velocity, the zero-crossing time point of the maximum envelope value can be accurately determined, effectively improving the phase velocity measurement accuracy, avoiding the problem of traditional amplitude attenuation methods being easily affected by dispersion effects, and achieving high-precision defect localization.

[0014] Another objective of this invention is to provide a power line defect estimation system based on ultrasonic guided waves.

[0015] To address the aforementioned technical problems, this invention provides the following technical solution: a power line defect estimation system based on ultrasonic guided waves, comprising: an image detection module for acquiring ultrasonic detection image data of the power line; a target extraction module for performing signal processing based on the ultrasonic detection image data to obtain ultrasonic detection image data of the target mode; a phase velocity module for reconstructing a phase velocity dispersion curve based on the ultrasonic detection image data of the target mode; and a defect estimation module for analyzing the phase velocity changes according to the phase velocity dispersion curve to determine the defect information of the power line.

[0016] The present invention provides a computer device, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the steps of the electric power line defect estimation method based on ultrasonic guided waves.

[0017] The present invention provides a computer-readable storage medium having a computer program stored thereon, wherein: when the computer program is executed by a processor, it implements the steps of the electric power line defect estimation method based on ultrasonic guided waves.

[0018] The beneficial effects of this invention are as follows: By converting ultrasonic inspection image data to the frequency-wavenumber domain and performing reflection signal filtering and A0 mode separation processing, this invention achieves accurate extraction of pure target mode signals, effectively eliminating interference from multi-mode guided wave signal aliasing and edge reflections in electric power line structures. This provides a high-quality signal foundation for long-distance defect detection in electric power lines and solves the problem of signal recognition difficulties in electric power line structures using traditional methods. Furthermore, by reconstructing the phase velocity dispersion curve using ultrasonic inspection image data of the target mode, a defect detection criterion based on phase velocity changes is established, effectively avoiding the inherent dispersion effect interference in ultrasonic guided wave propagation and overcoming the limitations of traditional methods. Traditional signal amplitude attenuation-based methods are prone to misjudgment due to energy decay caused by frequency dispersion. This method improves detection accuracy and reliability. By constructing a phase velocity threshold judgment standard and combining it with zero-crossing detection technology for phase velocity change analysis, defect identification is achieved without relying on sample calibration or empirical parameters. It is material-independent and the judgment standard can be generated by data-driven algorithms, simplifying the on-site inspection process of power lines and reducing deployment costs. Through coupled analysis of phase velocity distribution and spatial scanning coordinates, multi-parameter identification of the location, scale, and morphology of defects such as broken strands and corrosion can be achieved simultaneously, providing technical support for the safe and stable operation of power systems. Attached Figure Description

[0019] To more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings used in the following description of the embodiments will be briefly introduced. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort. Wherein:

[0020] Figure 1 The above is an overall flowchart of a power line defect estimation method based on ultrasonic guided waves provided in one embodiment of the present invention.

[0021] Figure 2 This is a schematic diagram of the structure of a computer device provided in one embodiment of the present invention. Detailed Implementation

[0022] To make the above-mentioned objects, features and advantages of the present invention more apparent and understandable, the specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings.

[0023] Many specific details are set forth in the following description in order to provide a full understanding of the invention. However, the invention may also be practiced in other ways different from those described herein, and those skilled in the art can make similar extensions without departing from the spirit of the invention. Therefore, the invention is not limited to the specific embodiments disclosed below.

[0024] Secondly, the term "one embodiment" or "embodiment" as used herein refers to a specific feature, structure, or characteristic that may be included in at least one implementation of the present invention. The phrase "in one embodiment" appearing in different places in this specification does not necessarily refer to the same embodiment, nor is it a single or selective embodiment that is mutually exclusive with other embodiments.

[0025] Example 1, referring to Figure 1 This is the first embodiment of the present invention, which provides a method for estimating electric field defects based on ultrasonic guided waves, including:

[0026] S100: Acquire ultrasonic inspection image data of power lines.

[0027] S200: Based on the ultrasonic detection image data, signal processing is performed to obtain the ultrasonic detection image data of the target mode.

[0028] S300: Reconstruction of phase velocity dispersion curves from ultrasonic detection image data based on target modes.

[0029] S400: Analyze the phase velocity changes based on the phase velocity dispersion curve to determine the defect information of the power line.

[0030] It should be noted that power lines, as a critical infrastructure for power transmission, are characterized by their long-distance, cross-regional distribution. Their multi-segment connections, significant variations in cross-sectional dimensions, and the limitation of single-sided contact testing make traditional non-destructive testing methods suffer from low sensitivity and poor adaptability in practical applications, failing to meet the demands for efficient on-site inspection of power lines. Furthermore, the dispersion, reflection, and mode conversion effects of ultrasonic guided waves in the composite structure of the metal conductor and insulation layer of power lines are mutually coupled. The attenuation and scattering effects of ultrasonic waves between the conductor and insulation layer interfere with defect feature extraction. Existing methods based on low-frequency ultrasonic guided waves... Although detection methods can simplify signal analysis through modal separation, their defect criteria, which rely on changes in signal amplitude, are susceptible to errors due to the transmission distance of electric power lines. Low-frequency guided waves propagate along electric power lines, causing wavefront diffusion and energy attenuation due to dispersion effects. In addition, the superposition interference of reflected signals at the interface between the metal conductor and the insulation layer makes traditional amplitude feature extraction prone to failure. Furthermore, the traditional phase dispersion velocity calculation method based on two-dimensional fast Fourier transform cannot provide coordinate and time information, resulting in high false negative rates and low defect quantitative accuracy when dealing with hidden defects such as broken strands and corrosion microcracks.

[0031] Therefore, to address the aforementioned challenges in balancing long-distance coverage, accurate defect identification, and resistance to environmental interference under actual working conditions, this paper proposes a solution through steps S100-S400. This involves using spatial filtering technology based on two-dimensional fast Fourier transform to filter out back-reflected signals, achieving precise separation of the A0 mode through a frequency-correlated bandpass filter, and reconstructing phase velocity changes using an improved hybrid method combining spectral decomposition and zero-crossing detection technology. A calibration-free defect assessment standard based on a phase velocity threshold is then established, enabling accurate identification and quantitative analysis of power line defects regardless of propagation distance. This provides a feasible technical solution for non-destructive testing of power lines, reducing the cost and time consumption of traditional testing methods.

[0032] Example 2, refer to Figure 1 This is the second embodiment of the present invention. Based on the above embodiments, a method for estimating electric field defects based on ultrasonic guided waves is provided.

[0033] In this embodiment of the invention, obtaining ultrasonic testing image data of the power line in step S100 includes the following steps A1-A3:

[0034] A1: Emit ultrasonic guided wave pulses on the surface of the electric field line.

[0035] A2: Perform a spatial scan along the electric field line axis and acquire time-domain signals at each scan point.

[0036] A3: Arrange the time-domain signals of each scanning point in spatial order to construct two-dimensional ultrasound detection image data.

[0037] Specifically, in step A1, ultrasonic guided wave pulses are emitted from the surface of the electric field line. The specific operation can be as follows:

[0038] Low-frequency ultrasonic guided wave pulses are coupled and emitted near a specific starting point on the surface of the electric field line using an ultrasonic transducer. Frequency points that are conducive to the excitation and separation of the A0 mode are selected. The typical frequency range of the low-frequency ultrasonic guided wave pulse is in the tens of kilohertz range, such as 20-50 kHz.

[0039] At another location on the power line (usually on the same side for easy on-site operation), another ultrasonic transducer (or sometimes the same transducer switched to receive mode after transmission) is coupled to the surface of the power line conductor to receive the propagating guided wave signal and convert it into an electrical signal.

[0040] It should be noted that the reason for using low-frequency ultrasonic guided wave pulses in step A1 is that: on the one hand, low-frequency guided waves have weaker attenuation and can cover a longer detection distance; on the other hand, there are fewer Lamb wave modes excited in the low-frequency band, which is beneficial to improving mode separation efficiency. Among them, Lamb waves are a special category of ultrasonic guided waves. According to the product of the excitation frequency and the thickness of the propagation medium, they can be further divided into symmetrical modes (S0, S1) and asymmetrical modes (A0, A1). Under low-frequency conditions, there are fewer guided wave modes, making it easier to achieve mode separation.

[0041] Specifically, in step A2, a spatial scan is performed along the electric field line axis. The specific operation can be as follows:

[0042] A one-dimensional spatial scan is performed along the length (axial direction) of the electric field conductor using a receiving transducer (or a single transducer in pulse echo mode), wherein the receiving point moves sequentially from the starting position to the far end (or the segment to be scanned as needed) according to a preset, fixed and small spatial step size (Δx).

[0043] At each receiving point (i.e., scanning point), a complete A-scan time-domain signal is acquired, and the change of the ultrasonic echo (or transmitted wave) signal at that receiving point over time is recorded to obtain the voltage sequence output by the receiving transducer at that receiving point.

[0044] Furthermore, in step A3, the two-dimensional ultrasound detection image data is constructed. The specific operations can be as follows:

[0045] After the receiving transducer sequentially completes the signal acquisition of a series of spatial location points (i.e., the distance along the electric field line axis), all the acquired A-scan time-domain signals are arranged in the corresponding spatial coordinate order: the horizontal axis (X-axis) represents the spatial position of the scanning point along the electric field line, the vertical axis (Y-axis) represents the time (or depth direction) of the received signal, and the brightness (or color) of the image pixel represents the amplitude of the ultrasonic signal received at the corresponding position and time, forming a two-dimensional spatiotemporal data matrix, i.e., ultrasonic guided wave B-scan image data.

[0046] In one optional implementation, the ultrasonic inspection image data of the power line is acquired in step S100. The signal quality can also be improved by optimizing the transducer selection and coupling method. Specifically, considering the characteristics of the composite structure of the metal conductor-insulator layer of the power line, a suitable transducer frequency response and beam angle are selected to ensure that the ultrasonic guided wave can effectively excite the A0 mode Lamb wave. At the same time, a special coupling agent and coupling pressure control device are used to ensure stable contact between the transducer and the surface of the power line, reducing signal distortion caused by poor contact. Through a multi-point excitation and reception configuration, detection is carried out simultaneously in multiple directions around the circumference of the power line (such as three points spaced 120 degrees apart), improving the comprehensiveness and accuracy of defect detection.

[0047] In another optional implementation, the ultrasonic testing image data of the power line acquired in step S100 can also be adapted to different types of power line structures through parameter adjustment technology. For example, the excitation frequency and scanning step size can be automatically adjusted according to the material (such as aluminum conductor, steel core, polyethylene sheath) and geometric parameters of the power line to establish a mapping relationship between the power line type and the testing parameters, ensuring that high-quality ultrasonic testing image data can be obtained under different working conditions. At the same time, a real-time signal quality assessment mechanism is introduced to monitor the signal-to-noise ratio and spectral characteristics. When poor signal quality is detected, the testing parameters are automatically adjusted or a retest is prompted to ensure the accuracy of subsequent signal processing and defect identification.

[0048] It should be noted that this invention solves the problems of non-standard data acquisition and inconsistent signal quality in traditional detection methods by acquiring ultrasonic inspection image data of power lines and adopting a standardized B-scan imaging process. Specifically, low-frequency ultrasonic guided wave pulse emission ensures long-distance signal propagation, axial spatial scanning enables precise defect localization, and the construction of two-dimensional image data provides a complete data foundation for subsequent signal processing and defect analysis. Compared with existing technologies that rely on single-point measurement or simple signal amplitude analysis, this invention, by establishing a complete space-time data matrix, not only improves the coverage and localization accuracy of defect detection but also provides reliable raw data for multi-modal signal separation and phase velocity analysis. Especially considering the characteristics of power lines with multi-segment connections, significant changes in cross-sectional dimensions, and the limitation of only unilateral contact detection, the data acquisition method of this invention has good adaptability and practicality, providing a feasible technical solution for the non-destructive testing of power lines.

[0049] In this embodiment of the invention, step S200 involves signal processing based on the ultrasonic detection image data to obtain the ultrasonic detection image data of the target mode, including the following steps B1-B4:

[0050] B1: Convert ultrasound image data into frequency-wavenumber domain spectrum.

[0051] B2: Perform reflection signal filtering on the frequency-wavenumber domain spectrum to obtain the frequency-wavenumber domain spectrum after filtering out the reflection signal.

[0052] B3: Perform mode separation on the frequency-wavenumber domain spectrum after filtering out the reflected signal to obtain the frequency-wavenumber domain spectrum of the target mode.

[0053] B4: Convert the frequency-wavenumber domain spectrum of the target mode into ultrasonic detection image data of the target mode.

[0054] Specifically, in step B1, the ultrasound image data is converted into a frequency-wavenumber domain spectrum. The specific operation can be as follows:

[0055] The ultrasonic guided wave B-scan image data was processed using spatial filtering technology based on two-dimensional fast Fourier transform (2D-FFT). The ultrasonic guided wave B-scan image data S(x,t) was transformed into the frequency-wavenumber domain to obtain the frequency-wavenumber domain spectrum F(k,f). By using two-dimensional fast Fourier transform, the signal in the time-space domain was converted into a frequency-wavenumber domain spectrum representation, which provides a basis for subsequent signal separation and filtering processing.

[0056] It should be noted that the reason for using two-dimensional fast Fourier transform in step B1 is that the frequency-wavenumber domain can effectively distinguish ultrasonic guided wave signals of different modes, because each mode has different dispersion characteristics and manifests as different energy distribution regions in the frequency-wavenumber domain, which facilitates mode separation and signal filtering.

[0057] Specifically, in step B2, the frequency-wavenumber domain spectrum is subjected to reflection signal filtering processing. The specific operation can be as follows:

[0058] In the frequency-wavenumber domain, defect edge reflection signals are identified and filtered out. The defect edge reflection wave is negative (k<0) in the wavenumber domain, while the forward propagation signal corresponds to a positive value (k>0). By directly applying a k>0 mask on the frequency-wavenumber domain spectrum, forward wave screening is achieved, effectively filtering out the interference of back reflection signals.

[0059] It should be noted that, since the A0 mode has distinctly different dispersion characteristics (i.e., the phase velocity varies with frequency) from other modes (especially S0 and A1), their energy is concentrated in different non-overlapping regions in the frequency-wavenumber domain. Therefore, by performing mode separation in the frequency-wavenumber (fk) domain, i.e., designing a frequency-correlated bandpass filter with a cosine conical window on the frequency-wavenumber curve, as a mode separation method, it is possible to extract only the Lamb wave signal of the A0 mode.

[0060] Specifically, in step B3, modal separation is performed on the frequency-wavenumber domain spectrum after filtering out the reflected signal. The specific operation can be as follows:

[0061] A two-dimensional filter H(f,k) is constructed in the frequency-wavenumber domain.

[0062] Multiply the frequency-wavenumber spectrum F(k,f) by the two-dimensional filter H(f,k), and retain the data in the region where the two-dimensional filter H(f,k) = 1, i.e., the A0 mode signal, to obtain the frequency-wavenumber spectrum of the target mode. This filters out other modal signals.

[0063] It should be noted that a frequency-correlated bandpass filter refers to a two-dimensional filter H(f,k) defined in the frequency-wavenumber domain. For each frequency f, the frequency-correlated bandpass filter represents a narrowband filter centered on the theoretical wavenumber k_{A0}(f) of the A0 mode at frequency f on the wavenumber axis. By setting the wavenumber passband Δk(f), the modes are effectively separated. Specifically, extracting only the A0 mode signal can be expressed as a two-dimensional filter H(f,k) = 1. The two-dimensional filter H(f,k) takes a value of 1 only in the range of [k_{A0}(f)-Δk(f), k_{A0}(f)+Δk(f)] (or uses a cosine window transition), and takes a value of 0 in other ranges.

[0064] Furthermore, in step B4, the frequency-wavenumber domain spectrum of the target mode is converted into ultrasound detection image data of the target mode. Specifically, this can be achieved through the following steps:

[0065] The frequency-wavenumber domain spectrum of the target mode is obtained using two-dimensional inverse Fourier transform (2D-IFFT). The inverse transformation back to the spatiotemporal domain yields an ultrasonic guided wave B-scan image containing only the A0 mode.

[0066] In one optional implementation, in step S200, signal processing is performed based on the ultrasonic detection image data to obtain the ultrasonic detection image data of the target mode. The mode separation effect can also be improved by filter design. For example, the theoretical dispersion curve of the A0 mode is calculated based on the specific structural parameters of the electric line (such as the layered structure and sheath thickness of the steel-cored aluminum stranded wire ACSR). The theoretical wavenumber of the A0 mode of the electric line structure is solved by the SAFE (Semi-Analytical Finite Element) method, and the theoretical curve is corrected based on the actual measured dispersion characteristics to ensure that the filter parameters match the actual propagation characteristics. At the same time, a multi-level filtering mechanism is introduced. First, coarse separation is performed to remove the main interference modes, and then fine filtering is performed to remove the traces of the A1 mode, finally obtaining a pure A0 mode signal.

[0067] In another optional implementation, in step S200, signal processing is performed based on the ultrasonic detection image data to obtain the ultrasonic detection image data of the target mode. The processing effect can also be ensured through signal quality assessment and optimization mechanisms. After each processing step, the signal-to-noise ratio and modal purity of the signal are evaluated. When the signal quality is found to be unsatisfactory, the filter parameters are automatically adjusted or the signal is re-acquired. The extraction effect of the A0 mode is verified by using frequency domain energy analysis. The target mode is confirmed to be correctly separated by comparing the spectral characteristics before and after filtering. At the same time, an optimization database of processing parameters is established to record the optimal processing parameters under different power line types and operating conditions, so as to realize the intelligence and standardization of the processing process.

[0068] It should be noted that this invention uses frequency domain signal processing technology based on two-dimensional fast Fourier transform to convert multimodal ultrasonic guided wave signals into a single A0 mode signal, solving the problem of signal analysis difficulties caused by mode aliasing in traditional methods. Compared with existing technologies that rely on time domain filtering or simple frequency filtering, this invention solves the problem of superimposed interference of reflected signals from the interface between the metal conductor and the insulation layer of the power line through joint frequency-wavenumber domain filtering. In particular, by designing a frequency-dependent bandpass filter, it can accurately match the dispersion characteristics of the A0 mode and effectively filter out the influence of other modes such as S0 and A1. This not only improves the purity of the signal and the accuracy of subsequent analysis, but also lays the foundation for accurate measurement of phase velocity. It makes the defect feature information that was originally difficult to extract accurately due to mode aliasing clear and distinguishable, thus improving the reliability and accuracy of power line defect detection.

[0069] In this embodiment of the invention, the reconstruction of the phase velocity dispersion curve based on the ultrasonic detection image data of the target mode in step S300 includes the following steps C1-C3:

[0070] C1: Extract time slice signals from the ultrasonic testing image data of the target modality.

[0071] C2: Perform phase analysis on the time-slice signal to obtain the phase velocity at each frequency.

[0072] C3: Combine the phase velocities of each frequency to form the phase velocity dispersion curve of the target mode.

[0073] Specifically, in step C1, the time slice signal is extracted from the ultrasound image data of the target modality. The specific operation can be as follows:

[0074] After reflection signal filtering and mode separation, an ultrasonic guided wave B-scan image S containing only the A0 mode is obtained. A0 (x,t) is used as input data.

[0075] Select a specific frequency f0 (e.g., 40kHz) for the ultrasonic guided wave B-scan image S containing only the A0 mode. A0 (x,t) is processed by time-frequency slicing to extract the corresponding time slice signal S. A0 (x).

[0076] Specifically, the extraction of time-slice signals in step C1 can be represented by the following formula:

[0077]

[0078] Among them, S A0 (x) represents the time slice signal; f0 represents a specific frequency.

[0079] It should be noted that in step C1, the two-dimensional spatiotemporal data is converted into a one-dimensional spatial signal through time-frequency slicing, providing basic data for subsequent phase analysis.

[0080] Specifically, step C2 involves performing phase analysis on the time-slice signal to obtain the phase velocity at each frequency, including the following steps C21-C25:

[0081] C21: Select signals from adjacent spatial locations and determine the spectrum of adjacent signals based on the signals from adjacent spatial locations.

[0082] C22: Filters the spectrum and reconstructs the filtered spectrum into a time-domain signal.

[0083] C23: Determine the envelope of the time-domain signal and the time point corresponding to the maximum value of the envelope by using Hilbert transform.

[0084] C24: Determine the zero-crossing time point of the envelope maximum value based on zero-crossing detection technology.

[0085] C25: Calculate the phase velocity based on the zero-crossing time point.

[0086] It should be noted that, to ensure accuracy, when selecting two signals at adjacent spatial locations in step C21, the time difference between the signals must be less than the lower half-cycle value of a specific frequency, and the distance Δx between the two spatial points must be less than half the wavelength corresponding to the specific frequency, so as to achieve reliable capture of the A0 mode.

[0087] Specifically, to capture the A0 mode, the distance Δx between two spatial points can be expressed as:

[0088]

[0089] in, To be in the defect-free region, frequency f r The theoretical phase velocity of the A0 mode at 43kHz; λ is the theoretical phase velocity of the A0 mode at frequency f. r The wavelength below.

[0090] Furthermore, in step C21, selecting signals from adjacent spatial locations and determining the spectrum of adjacent signals based on these signals refers to selecting signals u corresponding to two adjacent spatial locations in an ultrasound guided wave B-scan image containing only the A0 mode. x1 (t) and u x2 (t), calculate the spectrum of adjacent signals, which can be specifically expressed as:

[0091] U x1 (f)=FT[u x1 (t)],U x2 (f)=FT[u x2 (t)];

[0092] Where FT stands for Fourier transform; u x1 (t) and u x2 (t) represents the signal corresponding to two adjacent spatial positions in an ultrasonic guided wave B-scan image containing only the A0 mode; U x1 (f) and U x2 (f) represents u x1 (t) and u x2 (t) is the signal after Fourier transform.

[0093] Specifically, the filtering of the spectrum in step C22 refers to filtering the spectrum using a bandpass filter with predefined parameters, which can be expressed as follows:

[0094] S x1 (f)=U x1 (f)·B(f),S x2 (f)=U x2 (f)·B(f);

[0095] in, Let f be the frequency response of the Gaussian bandpass filter. C S is the center frequency of the filter, ΔB is the bandwidth of the filter; x1 (f) and S x2 (f) is the filtered spectrum.

[0096] Furthermore, in step C22, the filtered spectrum is reconstructed into a time-domain signal, which can be specifically represented as:

[0097] s x1 (t)=IFT[S x1 (f)],s x2 (t)=IFT[S x2 (f)];

[0098] Where IFT stands for Inverse Fourier Transform; s x1 (t) and s x2 (t) represents the reconstructed time-domain signal.

[0099] Specifically, in step C23, the envelope of the time-domain signal and the time point corresponding to the maximum envelope value are determined through Hilbert transform, which can be represented by the following formula:

[0100]

[0101] Among them, e x1 (t) and e x2 (t) represents the reconstructed signal s x1 (t) and s x2 The envelope of (t); e x1maxand e x2max For envelope e x1 (t) and e x2 The maximum value of (t); HT is the Hilbert transform; t m1 and t m2 This refers to the time point at which the maximum value of the envelope is calculated.

[0102] Furthermore, the zero-crossing time point for determining the maximum envelope value in step C24 based on zero-crossing detection technology can be specifically expressed as follows:

[0103]

[0104] in, and For two zero-crossing time points, it is closer to the maximum value e of the envelope. x1max and e x2max ; For the signal at the center frequency f c The half-cycle at that point.

[0105] Specifically, in step C25, the phase velocity is calculated based on the zero-crossing time point, which can be represented by the following formula:

[0106]

[0107] Among them, c ph x1 and x2 are the phase velocities of the Lamb wave and the x-coordinates of two points in space.

[0108] For example, in step C3, the phase velocities of each frequency are combined to form the phase velocity dispersion curve of the target mode. Specifically, this can be achieved by:

[0109] For the effective frequency band of 50-150kHz (power line A0 mode sensitive area), a cyclic operation is performed in 1kHz steps, including extracting the current frequency f using bandpass filtering. i The components are calculated, and the phase velocity value c at all frequency points is calculated. ph (x,f i ).

[0110] The phase velocity values ​​at each frequency point are aggregated to form the phase velocity dispersion distribution of the power line defect region.

[0111] Specifically, the phase velocity dispersion curve in step C3 can be represented by the following formula:

[0112]

[0113] in, The phase velocity dispersion distribution in the power line defect region is represented by N; N is the number of frequency sampling points.

[0114] In an optional implementation, the phase velocity dispersion curve reconstructed from the ultrasonic detection image data of the target mode in step S300 can also improve the stability of the dispersion curve through multi-point averaging and error correction mechanisms. The phase velocity calculation at each frequency point adopts the sliding window averaging method to reduce the influence of local noise on the results. A confidence evaluation system for phase velocity calculation is established to identify and eliminate abnormal calculation results. At the same time, a smoothing algorithm for the dispersion curve is introduced to ensure that the phase velocity change between adjacent frequency points conforms to physical laws. The rationality of the calculation results is verified by comparison with the theoretical dispersion curve, and a dispersion curve quality evaluation index is established to provide a reliable data foundation for subsequent defect analysis.

[0115] In another optional implementation, the reconstruction of the phase velocity dispersion curve based on the ultrasonic detection image data of the target mode in step S300 can also be optimized by a multi-scale analysis method. For example, wavelet transform can be used to decompose the ultrasonic detection image data of the target mode into multiple scales, extracting phase information at different scale levels. For the high-frequency part, refined phase analysis is used to capture local defect features, and for the low-frequency part, a wide-window phase calculation is used to obtain the overall trend. A multi-resolution phase velocity dispersion curve is constructed by information fusion between scales. At the same time, a parameter adjustment mechanism is established to adjust the extraction interval of time slices and the window size of phase analysis according to the local characteristics of the signal. When a region of signal change is detected, the sampling density is automatically increased, and the computational complexity is reduced in the region of stable signal. An iterative optimization algorithm is introduced to gradually improve the accuracy of the dispersion curve through multiple iterations. Each iteration is based on the result of the previous iteration for parameter optimization and error compensation, and finally a high-precision phase velocity dispersion curve is formed.

[0116] It should be noted that this invention reconstructs the phase velocity dispersion curve using a hybrid method based on Hilbert transform and zero-crossing detection, solving the problem that traditional phase dispersion velocity calculation methods based on two-dimensional fast Fourier transform cannot provide coordinate and time information. Compared with existing technologies that rely solely on amplitude changes or simple spectrum analysis, this invention, by accurately measuring the phase difference between signals at adjacent spatial locations, can reconstruct the local features of the Lamb wave phase velocity dispersion curve within the excitation signal bandwidth. In particular, by combining spectrum decomposition and zero-crossing detection techniques, it not only improves the accuracy and stability of phase velocity measurement but also effectively distinguishes the phase velocity difference between defective and undamaged regions, providing key characteristic parameters for subsequent defect identification and quantitative analysis, and enhancing the accuracy and reliability of power line defect detection.

[0117] In this embodiment of the invention, step S400 involves analyzing the phase velocity change based on the phase velocity dispersion curve to determine the defect information of the electric power line, including the following steps D1-D2:

[0118] D1: Determine the phase velocity threshold based on the phase velocity dispersion curve.

[0119] D2: Compare the phase velocity in the phase velocity dispersion curve with the phase velocity threshold, and determine the location of the power line defect based on the comparison result.

[0120] Specifically, in step D1, the phase velocity threshold is determined based on the phase velocity dispersion curve, which can be represented by the following formula:

[0121] c ph,thr =min(c ph (x))+0.5·Δc ph ,Δc ph =max(c ph (x))-min(c ph (x));

[0122] Among them, c ph,thr The phase velocity threshold; Δc ph The difference between the maximum and minimum phase velocities is given by ; max(·) and min(·) are the functions for finding the maximum and minimum values, respectively.

[0123] It should be noted that in step D1, the phase velocity threshold is used as the defect evaluation standard. The phase velocity threshold represents the median setting between the estimated maximum and minimum phase velocities. It has the characteristics of not requiring calibration and being independent of the test sample. At the same time, the coordinates of the defect can be calculated based on this criterion. In non-destructive testing (NDT), a halving of the signal amplitude usually indicates the location of the defect (e.g., at the level of 0.5 or -6 dB). Therefore, a multiplier of 0.5 is used in the phase velocity threshold calculation.

[0124] Furthermore, in step D2, the phase velocity in the phase velocity dispersion curve is compared with the phase velocity threshold, and the location of the electric field line defect is determined based on the comparison result. Specifically, this can be done as follows:

[0125] Defect identification is achieved by measuring the change in phase velocity along the scanning line of the receiving transducer. When the phase velocity is lower than the phase velocity threshold, the corresponding position is determined to be the location of the power line defect, that is, the position where the signal amplitude is halved is the location of the power line defect.

[0126] It should be noted that for ultrasonic guided waves, the signal amplitude will naturally attenuate due to the dispersion effect as the propagation distance changes. Therefore, the traditional criterion based on the halving of amplitude is not applicable. However, the change in phase velocity is not limited by the propagation distance, providing a more reliable defect criterion.

[0127] In one optional implementation, step S400 analyzes the phase velocity change based on the phase velocity dispersion curve to determine the defect information of the power line. The accuracy of defect identification can also be improved by establishing a defect feature database. This involves collecting phase velocity change patterns corresponding to different types and severity of defects to establish a feature fingerprint database of typical defects such as strand breakage, corrosion, and insulation aging. A pattern matching algorithm is used to compare the phase velocity change features of the area to be tested with standard patterns in the database, and the defect type is determined through similarity calculation. Simultaneously, a machine learning algorithm is introduced to train a defect classification model. A training sample set is constructed using historical detection data and expert annotation results. Defect features are automatically extracted through a deep learning network to achieve intelligent defect identification and severity assessment. Furthermore, a defect evolution prediction model is established to predict the development trend of defects based on their current state.

[0128] In another optional implementation, step S400 analyzes the phase velocity change based on the phase velocity dispersion curve to determine the defect information of the power line. The reliability of defect detection can also be enhanced by a multi-dimensional comprehensive analysis method. This involves combining multiple dimensions of information such as the amplitude, frequency distribution, and spatial location of the phase velocity change for comprehensive judgment. Statistical analysis methods are introduced to calculate statistical characteristic parameters such as variance, skewness, and kurtosis of the phase velocity change, establishing a multi-parameter joint criterion system. Uncertain information is processed using Bayesian inference or fuzzy logic methods. At the same time, the influence of environmental factors is considered, and a correction model for the phase velocity under external conditions such as temperature, humidity, and mechanical stress is established to eliminate the interference of environmental changes on the defect detection results. Furthermore, the confidence of the detection results is improved through cross-validation and repeated measurements.

[0129] In this embodiment of the invention, step S400, which analyzes the phase velocity change based on the phase velocity dispersion curve to determine the defect information of the electric field line, also includes the following steps F1-F3:

[0130] F1: Obtain the reference phase velocity dispersion curve of the healthy power line.

[0131] F2: Compare the phase velocity dispersion curve with the reference phase velocity dispersion curve and calculate the phase velocity change.

[0132] F3: Determine the type and severity of power line defects based on the phase velocity change.

[0133] Specifically, in step F1, the reference phase velocity dispersion curve of the healthy electric line is obtained. The specific operation can be as follows:

[0134] By comparing the phase velocity dispersion characteristics of known healthy power lines with those of the same type and specification through ultrasonic testing, the phase velocity dispersion curves within the same frequency range are obtained as the health benchmark.

[0135] Furthermore, in step F2, the phase velocity change is calculated. Specifically, this can be done as follows:

[0136] For the section to be tested (which may contain defects), the characteristic curve of its local phase velocity as a function of frequency is obtained by ultrasonic measurement, and the difference between the curve and the healthy baseline curve at the same frequency point is calculated to obtain the relative change.

[0137] Analyze the morphological characteristics of the characteristic curve of local phase velocity as a function of frequency, identify the sensitive frequency range where the phase velocity is abnormal (e.g., a high-frequency band of a specific kHz), and extract the maximum velocity drop and the cumulative amount of phase velocity deviation within the abnormal frequency band (i.e., the characteristic cumulative area).

[0138] Specifically, the specific operation for determining the type and severity of electric field line defects based on the phase velocity change in step F3 can be as follows:

[0139] Based on the extracted maximum velocity drop and the cumulative amount of phase velocity deviation within the abnormal frequency band, the type and severity of the defect are determined.

[0140] For example, when determining the type and severity of the power line defect in step F3, if the maximum speed drop exceeds a certain threshold and the change occurs in a higher frequency band, the power line defect is determined to be a strand breakage defect.

[0141] If the cumulative amount of phase velocity deviation within the abnormal frequency band is mainly characterized by the characteristic cumulative area, then the power line defect is determined to be a corrosion defect.

[0142] It should be noted that for strand breakage defects, the specific number of broken wires can be further estimated; for corrosion defects, the depth of corrosion defects can be estimated. In order to improve the comprehensiveness and accuracy of the detection, it is usually carried out simultaneously in multiple directions around the conductor circumference (such as three points 120 degrees apart).

[0143] It should be noted that this invention solves the problem of traditional defect detection methods based on signal amplitude changes being susceptible to errors due to propagation distance by establishing a defect criterion based on phase velocity thresholds and a severity assessment system for phase velocity changes. Compared with existing technologies that rely on empirical judgment or simple threshold comparisons, this invention, through a quantitative phase velocity change analysis method, can accurately identify and quantitatively assess long-distance power line defects without being affected by dispersion effects and wavefront diffusion. In particular, by comparing and analyzing the phase velocity dispersion curve with that of a healthy power line, it can not only accurately locate the defect but also distinguish different types of defects such as broken strands and corrosion, and assess their severity, thus improving the accuracy and reliability of power line defect detection and providing a scientific basis for preventive maintenance and safe operation of power lines.

[0144] In summary, this invention achieves accurate extraction of pure target modal signals by converting ultrasonic inspection image data to the frequency-wavenumber domain and performing reflection signal filtering and A0 mode separation processing. This effectively eliminates interference from multimodal guided wave signal aliasing and edge reflections in electric power line structures, providing a high-quality signal foundation for long-distance defect detection in electric power lines and solving the problem of signal recognition difficulties in electric power line structures using traditional methods. Furthermore, by reconstructing the phase velocity dispersion curve using ultrasonic inspection image data of the target mode, a defect detection criterion based on phase velocity changes is established, effectively avoiding the inherent dispersion effect interference in ultrasonic guided wave propagation and overcoming the limitations of traditional methods. To address the issue of misjudgments caused by natural energy attenuation due to frequency dispersion in signal amplitude attenuation methods, this method improves detection accuracy and reliability. By constructing a phase velocity threshold judgment standard and combining it with zero-crossing detection technology for phase velocity change analysis, defect identification is achieved without relying on sample calibration or empirical parameters. This method is material-independent and can generate judgment standards through data-driven algorithms, simplifying the on-site inspection process of power lines and reducing deployment costs. Through coupled analysis of phase velocity distribution and spatial scanning coordinates, multi-parameter identification of the location, scale, and morphology of defects such as broken strands and corrosion can be achieved simultaneously, providing technical support for the safe and stable operation of power systems.

[0145] Example 3 is the third embodiment of the present invention. This embodiment provides a power line defect estimation system based on ultrasonic guided waves, including: an image detection module for acquiring ultrasonic detection image data of the power line; a target extraction module for performing signal processing based on the ultrasonic detection image data to obtain ultrasonic detection image data of the target mode; a phase velocity module for reconstructing the phase velocity dispersion curve based on the ultrasonic detection image data of the target mode; and a defect estimation module for analyzing the phase velocity changes according to the phase velocity dispersion curve to determine the defect information of the power line.

[0146] Example 4 is the fourth embodiment of the present invention, which differs from the previous three embodiments in that: Figure 2As shown, if the function is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, or a part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of the present invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0147] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as a sequenced list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by, or in conjunction with, an instruction execution system, apparatus, or device (such as a computer-based system, a processor-included system, or other system that can fetch and execute instructions from, an instruction execution system, apparatus, or device). For the purposes of this specification, "computer-readable medium" can be any means that can contain, store, communicate, propagate, or transmit programs for use by, or in conjunction with, an instruction execution system, apparatus, or device.

[0148] More specific examples of computer-readable media (a non-exhaustive list) include: electrical connections (electronic devices) having one or more wires, portable computer disk drives (magnetic devices), random access memory (RAM), read-only memory (ROM), erasable and editable read-only memory (EPROM or flash memory), fiber optic devices, and portable optical disc read-only memory (CDROM). Furthermore, computer-readable media can even be paper or other suitable media on which the program can be printed, because the program can be obtained electronically, for example, by optically scanning the paper or other medium, followed by editing, interpreting, or otherwise processing as necessary, and then stored in computer memory.

[0149] It should be understood that various parts of the present invention can be implemented in hardware, software, firmware, or a combination thereof. In the above embodiments, multiple steps or methods can be implemented in software or firmware stored in memory and executed by a suitable instruction execution system. For example, if implemented in hardware, as in another embodiment, it can be implemented using any one or a combination of the following techniques known in the art: discrete logic circuits having logic gates for implementing logical functions on data signals, application-specific integrated circuits (ASICs) having suitable combinational logic gates, programmable gate arrays (PGAs), field-programmable gate arrays (FPGAs), etc.

[0150] Example 5, referring to Table 1, is the fifth embodiment of the present invention, which provides a method for estimating electric field defects based on ultrasonic guided waves. In order to verify the beneficial effects of the present invention, scientific demonstration is carried out through experiments.

[0151] This embodiment selects three typical power line models for testing: LGJ-400 / 35 steel-cored aluminum stranded wire, JL / G3A-900 overhead insulated conductor, and ACSR-720 / 50 steel-cored aluminum stranded wire, covering common conductor types in power systems. The experiment designed six test samples, including healthy baseline sample #1, broken strand defect samples #2 and #3, corrosion defect samples #4 and #5, and composite defect sample #6. Specific data are shown in Table 1.

[0152] Table 1. Defect estimation data for different power line models

[0153]

[0154] In Table 1, N represents the number of broken strands in the conductor, k1 and k2 are the broken strand inversion coefficients, h is the corrosion depth on the conductor surface, and the maximum relative phase velocity drop is not sensitive to corrosion and is only used for broken strand detection (this value reflects background noise in corrosion samples). The frequency domain integral area of ​​the defect feature is in the high-frequency band (70–100kHz) for broken strand samples and in the low-frequency band (5–50kHz) for corrosion samples. For the LGJ type, the broken strand inversion coefficients k1 = 0.08 and k2 = 0.002; for the JL / G3A type, η = 0.04.

[0155] As shown in Table 1, this invention has significant advantages in power line defect detection. In strand breakage detection, the measured true values ​​for samples #2 (2 broken strands) and #3 (5 broken strands) are 1.98 and 4.92 strands respectively, with relative errors of ≤1.6%, demonstrating the accuracy of strand breakage number inversion based on high-frequency phase velocity change analysis. In corrosion assessment, the corrosion depth accuracy for samples #4 (mild corrosion) and #5 (severe corrosion) reaches 96% and 95.1% respectively, verifying the sensitivity of the low-frequency phase velocity dispersion curve to continuous material stiffness degradation. It also indicates that the frequency band separation strategy effectively separates the two types of defect features, achieving a mutual exclusion rate of over 92%. However, for the composite defect sample #6, due to high-frequency corrosion noise interference, the relative error of strand breakage inversion rises to 13.3%, requiring further development of frequency domain decoupling algorithms (such as adaptive bandpass filtering) to improve robustness. Furthermore, in terms of overall technology, this invention has a reliability of >95% for single-type defect detection, proving the practicality and accuracy of the method in power line field defect detection.

[0156] 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, and all such modifications or substitutions should be covered within the scope of the claims of the present invention.

Claims

1. A method for estimating electric field line defects based on ultrasonic guided waves, characterized in that: include, Acquire ultrasonic inspection image data of power lines; Signal processing is performed on the ultrasonic detection image data to obtain ultrasonic detection image data of the target mode; Reconstruct the phase velocity dispersion curve based on the ultrasonic detection image data of the target mode; The phase velocity variation is analyzed based on the phase velocity dispersion curve to determine the defect information of the power line.

2. The electric field defect estimation method based on ultrasonic guided waves as described in claim 1, characterized in that: Signal processing is performed on the ultrasonic detection image data to obtain ultrasonic detection image data of the target modality, including: The ultrasound detection image data is converted into a frequency-wavenumber domain spectrum. The frequency-wavenumber domain spectrum is subjected to reflection signal filtering processing to obtain the frequency-wavenumber domain spectrum after the reflection signal is filtered out; Modal separation is performed on the frequency-wavenumber domain spectrum after filtering out the reflected signal to obtain the frequency-wavenumber domain spectrum of the target mode; The frequency-wavenumber domain spectrum of the target mode is converted into ultrasonic detection image data of the target mode.

3. The electric field defect estimation method based on ultrasonic guided waves as described in claim 2, characterized in that: Reconstructing the phase velocity dispersion curve from the ultrasonic detection image data of the target mode includes: Extract time-slice signals from the ultrasound detection image data of the target modality; Phase analysis is performed on the time-slice signal to obtain the phase velocity at each frequency; The phase velocities of each frequency are combined to form the phase velocity dispersion curve of the target mode.

4. The electric field defect estimation method based on ultrasonic guided waves as described in claim 3, characterized in that: Based on the phase velocity dispersion curve, the phase velocity variation is analyzed to determine the defect information of the power line, including: The phase velocity threshold is determined based on the phase velocity dispersion curve. The phase velocity in the phase velocity dispersion curve is compared with the phase velocity threshold, and the location of the power line defect is determined based on the comparison result.

5. The electric field defect estimation method based on ultrasonic guided waves as described in claim 4, characterized in that: Analyzing phase velocity changes based on the phase velocity dispersion curve to determine power line defect information also includes: Obtain the reference phase velocity dispersion curve of a healthy power line; The phase velocity dispersion curve is compared with the reference phase velocity dispersion curve to calculate the phase velocity change. The type and severity of power line defects are determined based on the phase velocity change.

6. The electric field defect estimation method based on ultrasonic guided waves as described in claim 5, characterized in that: The acquisition of ultrasonic inspection image data of the power line includes: Emitting ultrasonic guided wave pulses on the surface of an electric power line; Spatial scanning is performed along the power line axis, and time-domain signals are acquired at each scanning point; The time-domain signals of each scanning point are arranged in spatial order to construct two-dimensional ultrasound detection image data.

7. The electric field defect estimation method based on ultrasonic guided waves as described in claim 6, characterized in that: Phase analysis is performed on the time-slice signal to obtain the phase velocity at each frequency, including: Select signals from adjacent spatial locations, and determine the spectrum of the adjacent signals based on the signals from the adjacent spatial locations; The spectrum is filtered, and the filtered spectrum is reconstructed into a time-domain signal; The envelope of the time-domain signal and the time point corresponding to the maximum envelope value are determined by Hilbert transform. The zero-crossing time point of the envelope maximum value is determined based on the zero-crossing detection technique; Calculate the phase velocity based on the zero-crossing time point.

8. A power line defect estimation system based on ultrasonic guided waves, using the power line defect estimation method based on ultrasonic guided waves as described in any one of claims 1 to 7, characterized in that: include, The image detection module is used to acquire ultrasonic inspection image data of power lines; The target extraction module is used to perform signal processing based on ultrasonic detection image data to obtain ultrasonic detection image data of the target modality; Phase velocity module, used to reconstruct phase velocity dispersion curves based on ultrasonic detection image data of the target mode; The defect estimation module is used to analyze the phase velocity changes based on the phase velocity dispersion curve to determine the defect information of the power line.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that: When the processor executes the computer program, it implements the steps of the electric field defect estimation method based on ultrasonic guided waves as described in any one of claims 1 to 7.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by the processor, it implements the steps of the electric power line defect estimation method based on ultrasonic guided waves as described in any one of claims 1 to 7.

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