Multi-pulse joint modulation and feature entropy fusion defect detection method and related device

Abstract

The embodiment of the invention relates to the field of data processing, and provides a multi-pulse joint modulation and feature entropy fusion defect detection method and a related device, and the method comprises the steps: generating N high-voltage pulse signals; extracting feedback signals of the N high-voltage pulse signals output to the to-be-detected tower pole to obtain defect response signals; performing wavelet packet decomposition energy entropy calculation on the defect response signal to obtain a wavelet packet decomposition energy entropy; and inputting the wavelet packet decomposition energy entropy into a preset defect detection model for defect detection to obtain a defect detection result, so that more accurate and more efficient tower pole defect detection can be realized.

Description

Technical Field

[0001] This application relates to the field of data processing technology, specifically to a defect detection method and related apparatus based on multi-pulse joint modulation and feature entropy fusion. Background Technology

[0002] In the field of tower defect detection, existing technologies have many limitations. From the perspective of excitation methods, traditional detection methods often use single pulses with fixed parameters, resulting in a narrow spectral coverage and difficulty in exciting high-frequency resonant responses to minute defects such as microcracks, leading to insufficient detection capability for micron-level defects. From the signal perspective, the defect signal is significantly attenuated due to factors such as tower grounding impedance fluctuations, resulting in a low signal-to-noise ratio, making the identification of minute defects difficult and leading to significant missed detections. Regarding algorithms, existing technologies either rely on simple threshold judgments, resulting in a high false positive rate, or employ deep learning models that require extensive sample training, making them difficult to adapt to rapid deployment and application in complex field environments. Therefore, how to achieve more accurate and efficient tower defect detection has become an urgent problem to be solved. Summary of the Invention

[0003] This application provides a defect detection method and related apparatus based on multi-pulse joint modulation and characteristic entropy fusion, which can achieve more accurate and efficient tower defect detection.

[0004] The first aspect of this application provides a defect detection method based on multi-pulse joint modulation and feature entropy fusion, the method comprising: Generate N high-voltage pulse signals; The feedback signals of the N high-voltage pulse signals output to the tower to be tested are extracted to obtain the defect response signal; The wavelet packet decomposition energy entropy is calculated by performing wavelet packet decomposition on the defect response signal to obtain the wavelet packet decomposition energy entropy; The wavelet packet decomposition energy entropy is input into a preset defect detection model for defect detection to obtain the defect detection result.

[0005] In one possible implementation, the step of extracting the feedback signal of the N high-voltage pulse signals output to the tower to be tested to obtain the defect response signal includes: The feedback signal of the N high-voltage pulse signals output to the tower to be tested is extracted using the method shown in the following formula to obtain the defect response signal: ; in, This is a defect response signal. It is a superposition signal of N high-voltage pulse signals. The frequency of the signal actually acquired by the receiving probe used to receive the N high-voltage pulse signals output by the tower under test is given. This is the frequency domain representation of the signal actually acquired by the receiving probe used to receive N high-voltage pulse signals output by the tower under test.

[0006] In one possible implementation, the step of calculating the wavelet packet decomposition energy entropy of the defect response signal to obtain the wavelet packet decomposition energy entropy includes: The wavelet packet decomposition energy entropy is calculated by performing wavelet packet decomposition on the defect response signal using the method shown in the following formula: ; ; in, The wavelet packet decomposition energy entropy, E i It is the energy of the i-th subband out of the 16 subband energies.

[0007] In one possible implementation, the phase difference between the N high-voltage pulse signals is: ; in, This represents the phase difference.

[0008] In one possible implementation, after inputting the wavelet packet decomposition energy entropy into a preset defect detection model for defect detection and obtaining the defect detection result, the method further includes: If the defect detection result indicates the presence of a defect, then the defect location and defect type are extracted; Based on the defect location and the defect type, a defect risk analysis is performed to obtain the risk analysis results. Risk warning information is generated based on the risk analysis results; The aforementioned risk warning information is displayed.

[0009] A second aspect of this application provides a defect detection device based on multi-pulse joint modulation and feature entropy fusion, the device comprising: The generation unit is used to generate N high-voltage pulse signals; The extraction unit is used to extract the feedback signals of the N high-voltage pulse signals output to the tower to be inspected, and obtain the defect response signal; The calculation unit is used to perform wavelet packet decomposition energy entropy calculation on the defect response signal to obtain the wavelet packet decomposition energy entropy. The detection unit is used to input the wavelet packet decomposition energy entropy into a preset defect detection model to perform defect detection and obtain the defect detection result.

[0010] In one possible implementation, the extraction unit is specifically used for: The feedback signal of the N high-voltage pulse signals output to the tower to be tested is extracted using the method shown in the following formula to obtain the defect response signal: ; in, This is a defect response signal. It is a superposition signal of N high-voltage pulse signals. The frequency of the signal actually acquired by the receiving probe used to receive the N high-voltage pulse signals output by the tower under test is given. This is the frequency domain representation of the signal actually acquired by the receiving probe used to receive N high-voltage pulse signals output by the tower under test.

[0011] In one possible implementation, the computing unit is specifically used for: The wavelet packet decomposition energy entropy is calculated by performing wavelet packet decomposition on the defect response signal using the method shown in the following formula: ; ; in, The wavelet packet decomposition energy entropy, E i It is the energy of the i-th subband out of the 16 subband energies.

[0012] In one possible implementation, the phase difference between the N high-voltage pulse signals is: ; in, This represents the phase difference.

[0013] In one possible implementation, after inputting the wavelet packet decomposition energy entropy into a preset defect detection model for defect detection and obtaining the defect detection result, the device is further configured to: If the defect detection result indicates the presence of a defect, then the defect location and defect type are extracted; Based on the defect location and the defect type, a defect risk analysis is performed to obtain the risk analysis results. Risk warning information is generated based on the risk analysis results; The aforementioned risk warning information is displayed.

[0014] A third aspect of this application provides a terminal including a processor, an input device, an output device, and a memory, wherein the processor, input device, output device, and memory are interconnected, wherein the memory is used to store a computer program, the computer program including program instructions, and the processor is configured to invoke the program instructions to execute the step instructions as described in the first aspect of this application.

[0015] A fourth aspect of this application provides a computer-readable storage medium storing a computer program for electronic data interchange, wherein the computer program causes a computer to perform some or all of the steps described in the first aspect of this application.

[0016] A fifth aspect of this application provides a computer program product, wherein the computer program product includes a non-transitory computer-readable storage medium storing a computer program operable to cause a computer to perform some or all of the steps described in the first aspect of this application. The computer program product may be a software installation package.

[0017] Implementing the embodiments of this application has the following beneficial effects: By generating N high-voltage pulse signals, extracting the feedback signals of the N high-voltage pulse signals output to the tower to be tested, a defect response signal is obtained; wavelet packet decomposition energy entropy is calculated on the defect response signal to obtain the wavelet packet decomposition energy entropy; and the wavelet packet decomposition energy entropy is input into a preset defect detection model for defect detection to obtain the defect detection result. Therefore, multiple high-voltage pulse signals can be applied to the tower to be tested and the corresponding defect response signals can be extracted. After calculating the wavelet packet decomposition energy entropy using the defect response signals, defect detection is performed, thus improving the accuracy of defect detection. Attached Figure Description

[0018] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0019] Figure 1 This application provides a flowchart illustrating a defect detection method based on multi-pulse joint modulation and feature entropy fusion. Figure 2 This is a schematic diagram of the structure of a terminal provided in an embodiment of this application; Figure 3 This application provides a schematic diagram of the structure of a defect detection device that combines multi-pulse joint modulation and feature entropy fusion. Detailed Implementation

[0020] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0021] The terms "first," "second," etc., in the specification, claims, and accompanying drawings of this application are used to distinguish different objects, not to describe a specific order. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion. For example, a process, method, system, product, or apparatus that includes a series of steps or units is not limited to the listed steps or units, but may optionally include steps or units not listed, or may optionally include other steps or units inherent to these processes, methods, products, or apparatuses.

[0022] In this application, the reference to "embodiment" means that a specific feature, structure, or characteristic described in connection with an embodiment may be included in at least one embodiment of this application. The appearance of this phrase in various places throughout the specification does not necessarily refer to the same embodiment, nor is it a mutually exclusive, independent, or alternative embodiment. It will be explicitly and implicitly understood by those skilled in the art that the embodiments described in this application can be combined with other embodiments.

[0023] To better understand the defect detection method based on multi-pulse joint modulation and feature entropy fusion provided in this application, a brief introduction to existing solutions is given below. Traditional single-pulse impulse methods (such as those used in the IEC 62478 standard) are limited to a fixed 8 / 20μs waveform, with a spectral range of only 0-5MHz. This fails to effectively excite the characteristic signals of microcracks (especially those with high-frequency resonant responses >10MHz), resulting in insufficient spectral coverage. Furthermore, the tower grounding impedance is prone to fluctuations of ±30%, causing defect signal attenuation exceeding 40dB. For cracks smaller than 0.5mm, the false detection rate is as high as 25%. At the algorithm level, threshold detection methods (such as CN110852201B) have a false positive rate exceeding 15%, while deep learning models require training on tens of thousands of samples, making them difficult to deploy in the field and exhibiting poor adaptability. Furthermore, while existing related patents (such as CN117092595A) propose multi-pulse phase coding, they fail to address the issues of wideband excitation and weak feature enhancement, particularly lacking a model for controlling the resonant characteristics of tower materials in the 20-50MHz frequency band, resulting in a significant technological gap. These problems severely restrict the accuracy, efficiency, and applicability of tower defect detection.

[0024] To address the aforementioned issues, this application provides a defect detection method that integrates multi-pulse joint modulation and feature entropy fusion. It proposes a multi-pulse dynamic joint modulation model for the first time, overcoming the limitations of single-pulse spectrum (compared to CN117092595A which only optimizes phase coding). It integrates wavelet packet energy entropy and CNN time-frequency classification to solve the problem of weak microcrack response (compared to CN110852201B which relies on envelope spectrum matching). The detection accuracy reaches 0.1 mm level, with a 10-fold speed improvement, supporting online monitoring in complex environments (verified by CEC 20232039 standard), enabling more accurate and efficient tower defect detection.

[0025] Please see Figure 1 , Figure 1 This document provides a flowchart illustrating a defect detection method based on multi-pulse joint modulation and feature entropy fusion, as described in an embodiment of this application. Figure 1 As shown, the method includes: S10 generates N high-voltage pulse signals.

[0026] After generating N high-voltage pulse signals, when testing the tower, these N high-voltage pulse signals need to be input to the tower. After inputting them to the tower, the superimposed signal of the N high-voltage pulse signals can be characterized by the following formula: ; in, This is a superposition signal of N high-voltage pulse signals, where N is the number of pulses, and A... k For amplitude (0.5-10kV), f k The center frequency (100kHz-50MHz), ɸ k For the initial phase (adjustable from 0 to 2π), β k Gaussian decay factor (pulse width T) k =0.1-5μЅ), Rect(˙) is a rectangular window function (time series T) k (Ensure pulses do not overlap).

[0027] The phase difference between N high-voltage pulse signals is: ; in, This represents the phase difference.

[0028] S20. Extract the feedback signals of the N high-voltage pulse signals output to the tower to be tested to obtain the defect response signal.

[0029] The feedback signal of the N high-voltage pulse signals output to the tower to be tested is extracted using the method shown in the following formula to obtain the defect response signal: ; in, This is a defect response signal. It is a superposition signal of N high-voltage pulse signals. The signal actually acquired by the receiving probe used to receive N high-voltage pulse signals output by the tower under test. This is the frequency domain representation of the signal actually acquired by the receiving probe used to receive N high-voltage pulse signals output by the tower under test.

[0030] S30. Perform wavelet packet decomposition energy entropy calculation on the defect response signal to obtain the wavelet packet decomposition energy entropy.

[0031] The wavelet packet decomposition energy entropy is calculated by performing wavelet packet decomposition on the defect response signal using the method shown in the following formula: ; ; in, The wavelet packet decomposition energy entropy, E i It is the energy of the i-th subband out of the 16 subband energies.

[0032] S40. Input the wavelet packet decomposition energy entropy into the preset defect detection model to perform defect detection and obtain the defect detection result.

[0033] The preset defect detection model is a pre-trained model used for defect detection. This preset defect detection model can be a CNN (Convolutional Neural Network) model.

[0034] In one possible implementation, after inputting the wavelet packet decomposition energy entropy into a preset defect detection model for defect detection and obtaining the defect detection result, the method further includes: A1. If the defect detection result indicates the presence of a defect, then extract the defect location and defect type; A2. Perform a defect risk analysis based on the defect location and defect type to obtain the risk analysis results; A3. Generate risk warning information based on the risk analysis results; A4. Display the aforementioned risk warning information.

[0035] The defect location can be a coordinate range representing the depth of the defect and the distance from the surface to the crack. Defect types can include cracks, degradation, corrosion, etc. Then, a lookup table method is used to determine the defect risk analysis results corresponding to the defect location and type. The table used in the lookup method includes a pre-defined mapping relationship between defect location, defect type, and defect risk analysis results.

[0036] Different risk analysis results have corresponding risk warning information. Risk warning information could be, for example: high risk level, requiring immediate verification; low risk level, requiring periodic verification, etc. This is merely an example and not a specific limitation.

[0037] When displaying risk warning information, common display methods can be used, such as displaying it on a monitor.

[0038] One possible implementation is illustrated by using the multi-frequency pulse energy entropy detection method to detect weld cracks in steel towers.

[0039] 1. Detailed Explanation of Test Conditions (1) Sample preparation Material: Q345B low-alloy high-strength steel (yield strength ≥345MPa), widely used in power transmission towers.

[0040] Crack design: 20 artificial cracks were pre-made by laser cutting, with depth gradients of 0.1mm, 0.3mm, 0.5mm, 0.8mm, and 1.0mm (4 samples per group).

[0041] Crack location: Heat-affected zone (HAZ) of weld, simulating the actual stress concentration area.

[0042] Pulse parameters: .

[0043] Judgment threshold: 30MHz energy decay >20% and E>1.8.

[0044] (2) Definition and physical meaning of core parameters (3) Threshold determination principle 30MHz power decay: in, It can represent the energy received by a crack-free region, indicating the energy received by the detection area in a defect-free state; It can represent the energy received by the cracked area, that is, the energy received when there is a crack defect in the detection area.

[0045] Physical significance: High-frequency waves scatter more intensely when they encounter cracks, resulting in a significant reduction in received energy.

[0046] Energy entropy E: Where E can represent energy entropy, which measures the degree of dispersion of energy in the frequency domain; i can represent the index of the frequency component; Pi can represent the energy proportion of the i-th frequency component. k can represent the energy of the i-th frequency component (the energy value corresponding to each frequency is obtained after decomposing the signal by Fast Fourier Transform (FFT); k can represent the summation index, similar to the index i of the frequency component, both are for traversing different frequency components.

[0047] Physical meaning: The higher the entropy value, the more dispersed the energy distribution in the frequency domain, indicating that crack disturbance leads to multi-frequency energy redistribution.

[0048] 2. Experimental Procedures and Precautions (1) Flowchart [Sample surface polishing] → [Coupler application] → [Probe positioning] → [Pulse emission] → [Signal acquisition] ↓↓ [Degreasing with Cleaning Agent][Multi-Frequency Energy Separation] → [E-Value Calculation] → [30MHz Attenuation Analysis] ↓↓ [Crack Depth Calibration][Dual Threshold Judgment] → [Output Results] (2) Explanation of key steps Sample pretreatment: The weld surface must be polished to Ra≤3.2μm (roughness lower than the ultrasonic wavelength) to avoid noise interference.

[0049] Use a glycerol-based coupling agent with a thickness ≤0.1mm to uniformly cover the detection area and eliminate air gaps.

[0050] Probe positioning and scanning: A dual-crystal focusing probe is used with a tilt angle of 15° (to avoid obstruction by weld excess height).

[0051] The scanning path moves in a zigzag pattern along the weld direction, with a step of 0.5 mm (less than the minimum crack length).

[0052] Signal acquisition precautions: Four sets of pulses (N=4) are emitted at each detection point, with a 10ms interval between each set to avoid residual vibration interference.

[0053] The ambient temperature is maintained at 20±5℃ (temperature changes affect the speed of sound and require real-time calibration).

[0054] Key points of data processing: Wavelet denoising is performed on the received signal to remove the background noise of the equipment.

[0055] The 30MHz component requires a bandpass filter (28-32MHz) to accurately extract its energy.

[0056] 3. In-depth analysis of experimental results (1) Relationship between detection rate and energy entropy (2) Comparison with traditional radiographic testing 4. Technological advantages and applicable scenarios (1) Core Innovation Points Multi-frequency collaborative detection: low frequency (1MHz) penetrates the substrate → medium frequency (10-15MHz) locates the defect area → high frequency (30MHz) accurately characterizes the crack.

[0057] Dual-indicator fusion judgment: energy decay (sensitivity) + energy entropy (noise resistance), reducing false alarm rate.

[0058] (2) Engineering application scenarios Online inspection of weld seams on high-voltage transmission towers (no disassembly required).

[0059] Monitoring of microcracks in inaccessible structures such as bridge anchors and wind turbine towers.

[0060] 5. Conclusion This method solves the problem of high false negative rate for microcracks (<0.3mm) in traditional X-ray methods by using multi-frequency pulse energy entropy analysis.

[0061] The key technology lies in: Phase-controlled pulse group ( The method enhances the defect scattering signal; quantifies the spectral distortion caused by cracks using energy entropy; and uses a 30MHz high-frequency component as a sensitivity scale. Experiments show that a detection rate of ≥98% can be achieved for cracks larger than 0.3mm, and the on-site inspection time is reduced by 70% compared to the X-ray method (≤10 minutes for a single weld).

[0062] One possible implementation is illustrated by taking the detection of degradation of composite insulator core rods as an example.

[0063] 1. Dual-pulse quadrature modulation strategy (Measure the rate of change of impedance) ) (Measure the tangent of the dielectric loss angle δ) determination: and Core principle: Simultaneously transmit two orthogonal signals of different frequencies (f1 and f2 mentioned above) (without interference), and measure the rate of change of impedance ( ) and dielectric loss tangent ( A comprehensive assessment of the core rod's deterioration was conducted.

[0064] Parameter definition and physical meaning: 2. Derivation of key formulas and measurement principles (1) Dielectric loss tangent ( Measurement formula: in, ω can represent the dielectric loss tangent, a parameter measuring the magnitude of dielectric loss in insulating materials. It reflects the degree of energy loss within the insulating material due to polarization, conductivity, and other factors under the influence of an alternating electric field. ω can represent the angular frequency, used to describe the rate of change of a sinusoidal alternating current, and its relationship with the frequency f is... ( C can represent the equivalent capacitance of an insulator, which is the equivalent capacitance value when the insulator is regarded as a capacitor element, reflecting the insulator's ability to store charge. It can represent the equivalent parallel resistance, reflecting the leakage current of the material. The larger the leakage current, the lower the resistance. The smaller the size, the greater the energy loss; I R It can represent the current in a resistive branch, representing the active current generated due to factors such as the conductivity of the insulating material; I C It can represent the current in a capacitor branch, representing the reactive current generated due to the capacitance effect of the insulating material.

[0065] Measurement method: Inject a 10MHz sinusoidal voltage U into the insulator and measure the current phase difference θ. Wherein, the current phase difference θ is the current I in the resistor branch. R With capacitor branch current I C The phase difference between the phases, when θ < 5, can be used to calculate the dielectric loss tangent using the approximate relationship described by the above formula. .

[0066] (2) Rate of change of impedance ( Calculation formula: in, Z can represent the impedance change rate, used to measure the degree of impedance change between a field-tested insulator and a new insulator of the same type (reference insulator), reflecting the aging, cracks, moisture and other defects of the insulator; Zsample can represent the field-tested impedance, which is the impedance value of the field insulator measured at a frequency of 100kHz, reflecting the current impedance characteristics of the field insulator; Zref can represent the reference impedance of the new insulator of the same type, which is the impedance value of the new insulator of the same type obtained by laboratory calibration, and is used as a reference standard to compare the impedance change of the field insulator.

[0067] Physical basis: Cracks increase air gaps → impedance increases; moisture forms ion channels → impedance decreases. 3. On-site testing operation procedures and precautions Sample: 110kV line insulators (10 normal, 15 deteriorated) Accuracy: 96.7% (78% for traditional bridge method) Please see Figure 3 , Figure 3 An exemplary flowchart for detecting the degradation of composite insulator core rods is shown.

[0068] Step 1: Equipment Calibration operate: Connect the probe to the standard impedance module (simulating a new insulator). Automatically calibrate the zero-phase points of f1 (100kHz) and f2 (10MHz). Precautions: The ambient temperature needs to be kept stable at 25±5℃. A change in temperature of 10℃ will cause a δ drift of approximately 3%. After calibration, the sample must be verified using a validation sample (an insulator with a known δ=0.015). The error should be <5%. Step 2: Dual-frequency signal transmission and acquisition Signal acquisition: High-frequency sampling rate ≥ 40 MS / s (satisfying the Nyquist criterion) Use an FIR digital filter to separate the f1 / f2 response signals. Step 3: Parameter Calculation and Logical Determination graph TD A [Acquire dual-frequency response signal] --> B {Separate f1 signal} A -->C {Separate f2 signal} B -->D[Calculate ΔZ = |Z_sample / Z_ref -1|×100%] C -->E[Calculate δ = tanθ ≈ θ] D --> F{ΔZ>15%?} E --> G{δ>0.02?} F -->|Yes| H [Triggering structure degradation flag] G -->|Yes| I [Triggers chemical degradation indicator] H&I -->J [Judged as "Deteriorated Insulator"] Step 4: Output Results The display interface simultaneously shows: Trend chart (comparison with historical data) Spectrum curve (peak frequency offset indicates aging type) 4. Comparison of technological advantages with traditional methods The reason why this solution achieves an accuracy rate of 96.7% is as follows: Simultaneously satisfy (Structural failure) and Deterioration is only judged when (chemical failure) occurs; Avoid misjudgment based on a single indicator: only High → Possibly surface contamination (not a core rod issue) Only high δ → may be momentarily damp (poor reproducibility) 5. Key Precautions Probe contact problem: A constant pressure spring probe (pressure ≥ 5N) must be used to ensure that there is no air gap at the electrode-silicone rubber interface; poor contact will cause δ to be artificially high (actual case: δ can reach 0.035 when poor contact → after adjustment it drops to 0.01).

[0069] Temperature compensation: Built-in NTC temperature sensor, automatically corrected according to formula: Where δcorr can represent the corrected dielectric loss tangent value, which is the dielectric loss tangent result that better reflects the actual situation after temperature compensation; δmeas can represent the measured dielectric loss tangent value, that is, the value obtained by direct measurement without temperature compensation; T can represent the actual ambient temperature, usually in degrees Celsius, and 25° is used as the reference temperature for correction in the formula.

[0070] Degradation type diagnosis: This embodiment employs dual-frequency orthogonal decoupled measurement, taking into account both the macroscopic structure and microscopic chemical changes of the insulator, making it more reliable than single-parameter methods. In practical applications, it is recommended to perform testing every six months, focusing on insulators with a continuously increasing delta (δ) value (even if it does not exceed the threshold).

[0071] One possible implementation is illustrated by taking the detection of corrosion of steel bars in concrete towers as an example.

[0072] 1. Parameter optimization and optimization principle Low-frequency penetration: Inducing rust eddies High-frequency coupling: Detecting surface cracks Judgment: E>1.6 and phase shift ° (1) Dual-frequency eddy current detection technology Low-frequency penetration (50-500Hz): Function: Low-frequency electromagnetic waves penetrate the concrete protective layer (depth 20-100 mm) and induce eddy currents in the steel reinforcement.

[0073] Corrosion response: The conductivity of the corrosion zone decreases, which leads to distortion of the eddy current path, resulting in an increase in the real part of the impedance.

[0074] Formula derivation: in, R can represent probe impedance, which is the impedance presented by the probe in eddy current testing, reflecting the characteristics of the object being tested, such as its resistance to eddy currents; R can represent resistance, which is the resistive part of the basic impedance, reflecting the loss of electrical energy converted into heat energy; ω can represent angular frequency, which describes the rate of change of sinusoidal alternating current; L can represent inductance, which is the inductor part of the basic impedance, reflecting the characteristics of magnetic field energy storage.

[0075] Changes in resistance during corrosion: ΔR can represent the change in resistance during corrosion, and is used to measure the degree of resistance change caused by corrosion. It can represent the concrete attenuation coefficient (empirical value 0.8~1.2), reflecting the attenuation effect of concrete on related physical quantities (such as electrical conductivity); It can represent the electrical conductivity (S / m) of the steel bar after corrosion, indicating the ability of the steel bar to conduct current after corrosion; Excitation frequency (Hz) is the alternating current frequency used to excite eddy currents in eddy current detection.

[0076] High-frequency coupling (5-20kHz): Function: Focuses on the concrete surface (depth <10mm) to identify electromagnetic field leakage caused by cracks.

[0077] Crack response: Cracks cause the magnetic field to diverge, and the phase angle shift increases.

[0078] 2. Corrosion judgment criteria: E>1.6 and phase offset>15° Parameter definition: Explanation of the determination formula: Corrosion caused the impedance amplitude to rise to more than 1.6 times the reference value (the reference is uncorroded steel bars).

[0079] Phase shift >15°: Cracks or corrosion products cause eddy current phase lag of more than 15°.

[0080] Physical meaning: The electrical conductivity of the corrosion product (Fe2O3) is only 1 / 100 that of the steel bar, which significantly increases the resistivity (R↑).

[0081] The crack increases magnetic resistance, causing a change in the inductive reactance component (ωL) and a phase angle shift.

[0082] 3. Comparison with microwave method (CEC 2023-2039 standard) 4. Operating Procedures and Precautions (1) Preparation before testing Surface treatment: Grind the concrete until it is smooth (roughness <1mm) to avoid air gaps affecting high-frequency coupling.

[0083] Note: If a waterproof layer is present, it needs to be removed locally (high frequencies cannot penetrate non-conductive layers).

[0084] (2) Dual-frequency scanning flowchart.

[0085] (3) Key points to note: Rebar depth calibration: If the thickness of the protective layer is unknown, it must first be measured with a radar thickness gauge; otherwise, the low-frequency penetration depth will be insufficient.

[0086] Temperature compensation: The electrical conductivity of the reinforcing steel varies with temperature (coefficient ≈ 0.4% / ℃), so an ambient temperature correction parameter needs to be input. Among them, σ calibration can represent the electrical conductivity of the steel bar after temperature calibration. It is a value that can more accurately reflect the actual electrical conductivity of the steel bar after taking into account the influence of on-site temperature; σ0 can represent the electrical conductivity of the steel bar at the reference temperature, which serves as the benchmark value for temperature calibration. It can represent the temperature coefficient, which is used to quantify the degree to which conductivity changes with temperature, reflecting the relative change in conductivity for every 1°C change; It can represent the ambient temperature, in °C, which is the actual temperature of the environment during testing, and is used to calculate the effect of temperature on conductivity.

[0087] Influence of rebar spacing: When the spacing between adjacent reinforcing bars is less than 3 times the thickness of the protective layer, an array probe must be used to suppress interference.

[0088] 5. Summary of Technical Features (1) Dual-frequency separation: Low frequency penetration depth ( (skin depth), at 100Hz (Concrete).

[0089] At high frequency 5kHz It accurately captures surface cracks.

[0090] (2) Speed ​​advantage: The realization of 5ms / point relies on FPGA parallel processing: synchronous demodulation of low frequency amplitude / high frequency phase.

[0091] (3) Anti-interference design: Gradient coil probes are used to suppress the stray eddy currents of carbon fibers in concrete.

[0092] 6. Supplementary Explanation Applicable scenarios: Suitable for tower structures with concrete protective layer ≤80mm and steel bar diameter ≥8mm, and has the highest sensitivity to chloride corrosion (coastal areas).

[0093] Limitations: When the steel mesh density is greater than 150mm×150mm, microwave imaging-assisted positioning is required.

[0094] Through the optimization steps described above in this embodiment, the detection efficiency can be increased to 360,000 times that of traditional methods (30 min / point → 5 ms / point) while ensuring accuracy, and the false alarm rate can be reduced to below 5%.

[0095] For examples consistent with the above embodiments, please refer to... Figure 2 , Figure 2 This is a schematic diagram of the structure of a terminal provided in an embodiment of this application, such as... Figure 2 As shown, it includes a processor, an input device, an output device, and a memory, which are interconnected. The memory is used to store a computer program, which includes program instructions. The processor is configured to call the program instructions. The program includes instructions for performing the following steps. Generate N high-voltage pulse signals; The feedback signals of the N high-voltage pulse signals output to the tower to be tested are extracted to obtain the defect response signal; The wavelet packet decomposition energy entropy is calculated by performing wavelet packet decomposition on the defect response signal to obtain the wavelet packet decomposition energy entropy; The wavelet packet decomposition energy entropy is input into a preset defect detection model for defect detection to obtain the defect detection result.

[0096] The above mainly describes the solutions of the embodiments of this application from the perspective of the method execution process. It is understood that, in order to achieve the above functions, the terminal includes the corresponding hardware structure and / or software modules for executing each function. Those skilled in the art should readily recognize that, in conjunction with the units and algorithm steps of the various examples described in the embodiments provided herein, this application can be implemented in hardware or a combination of hardware and computer software. Whether a function is executed in hardware or by computer software driving hardware depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0097] This application embodiment can divide the terminal into functional units according to the above method example. For example, each function can be divided into a separate functional unit, or two or more functions can be integrated into one processing unit. The integrated unit can be implemented in hardware or as a software functional unit. It should be noted that the unit division in this application embodiment is illustrative and only represents one logical functional division. In actual implementation, there may be other division methods.

[0098] For those consistent with the above, please refer to Figure 3 , Figure 3 This application provides a schematic diagram of the structure of a defect detection device that combines multi-pulse joint modulation and feature entropy fusion. Figure 3 As shown, the device includes: The generation unit 101 is used to generate N high-voltage pulse signals; Extraction unit 102 is used to extract the feedback signal of N high-voltage pulse signals output to the tower to be tested, and obtain the defect response signal; The calculation unit 103 is used to perform wavelet packet decomposition energy entropy calculation on the defect response signal to obtain the wavelet packet decomposition energy entropy. The detection unit 104 is used to input the wavelet packet decomposition energy entropy into a preset defect detection model to perform defect detection and obtain defect detection results.

[0099] In one possible implementation, the extraction unit 102 is specifically used for: The feedback signal of the N high-voltage pulse signals output to the tower to be tested is extracted using the method shown in the following formula to obtain the defect response signal: ; in, This is a defect response signal. It is a superposition signal of N high-voltage pulse signals. The frequency of the signal actually acquired by the receiving probe used to receive the N high-voltage pulse signals output by the tower under test is given. This is the frequency domain representation of the signal actually acquired by the receiving probe used to receive N high-voltage pulse signals output by the tower under test.

[0100] In one possible implementation, the computing unit 103 is specifically used for: The wavelet packet decomposition energy entropy is calculated by performing wavelet packet decomposition on the defect response signal using the method shown in the following formula: ; ; in, The wavelet packet decomposition energy entropy, E i It is the energy of the i-th subband out of the 16 subband energies.

[0101] In one possible implementation, the phase difference between the N high-voltage pulse signals is: ; in, This represents the phase difference.

[0102] In one possible implementation, after inputting the wavelet packet decomposition energy entropy into a preset defect detection model for defect detection and obtaining the defect detection result, the device is further configured to: If the defect detection result indicates the presence of a defect, then the defect location and defect type are extracted; Based on the defect location and the defect type, a defect risk analysis is performed to obtain the risk analysis results. Risk warning information is generated based on the risk analysis results; The aforementioned risk warning information is displayed.

[0103] This application also provides a computer storage medium storing a computer program for electronic data interchange, which causes a computer to perform some or all of the steps of any of the multi-pulse joint modulation and feature entropy fusion defect detection methods described in the above method embodiments.

[0104] This application also provides a computer program product, which includes a non-transitory computer-readable storage medium storing a computer program that causes a computer to perform some or all of the steps of any of the multi-pulse joint modulation and feature entropy fusion defect detection methods described in the above method embodiments.

[0105] It should be noted that, for the sake of simplicity, the foregoing method embodiments are all described as a series of actions. However, those skilled in the art should understand that this application is not limited to the described order of actions, as some steps may be performed in other orders or simultaneously according to this application. Furthermore, those skilled in the art should also understand that the embodiments described in the specification are preferred embodiments, and the actions and modules involved are not necessarily essential to this application.

[0106] In the above embodiments, the descriptions of each embodiment have different focuses. For parts not described in detail in a certain embodiment, please refer to the relevant descriptions in other embodiments.

[0107] In the several embodiments provided in this application, it should be understood that the disclosed apparatus can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection between devices or units may be electrical or other forms.

[0108] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0109] Furthermore, the functional units in the various embodiments of the application can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software program module.

[0110] If the integrated unit is implemented as a software program module and sold or used as an independent product, it can be stored in a computer-readable storage device (CMD). Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a memory 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 this application. The aforementioned memory includes various media capable of storing program code, such as USB flash drives, read-only memory (ROM), random access memory (RAM), portable hard drives, magnetic disks, or optical disks.

[0111] Those skilled in the art will understand that all or part of the steps in the various methods of the above embodiments can be implemented by a program instructing related hardware. The program can be stored in a computer-readable storage device, which may include: a flash drive, a read-only memory, a random access memory, a magnetic disk, or an optical disk, etc.

[0112] The embodiments of this application have been described in detail above. Specific examples have been used to illustrate the principles and implementation methods of this application. The description of the above embodiments is only for the purpose of helping to understand the method and core ideas of this application. At the same time, for those skilled in the art, there will be changes in the specific implementation methods and application scope based on the ideas of this application. Therefore, the content of this specification should not be construed as a limitation of this application.

Claims

1. A defect detection method of multi-pulse combined modulation and feature entropy fusion, characterized by, The method comprises: generating N high-voltage pulse signals; extracting feedback signals of the N high-voltage pulse signals output by the tower to be detected to obtain a defect response signal; performing wavelet packet decomposition energy entropy calculation on the defect response signal to obtain wavelet packet decomposition energy entropy; inputting the wavelet packet decomposition energy entropy into a preset defect detection model to perform defect detection and obtain a defect detection result.

2. The method according to claim 1, wherein, The method of extracting feedback signals of the N high-voltage pulse signals output by the tower to be detected to obtain a defect response signal comprises: extracting feedback signals of the N high-voltage pulse signals output by the tower to be detected to obtain a defect response signal by the method shown in the following formula: ； wherein, is a defect response signal, is a superposition signal of N high voltage pulse signals, is the frequency of the signal actually acquired by the receiving probe for receiving the N high voltage pulse signals output by the tower to be detected, is the frequency domain representation of the signal actually acquired by the receiving probe for receiving the N high voltage pulse signals output by the tower to be detected.

3. The method according to claim 2, wherein, The method of performing wavelet packet decomposition energy entropy calculation on the defect response signal to obtain wavelet packet decomposition energy entropy comprises: performing wavelet packet decomposition energy entropy calculation on the defect response signal to obtain wavelet packet decomposition energy entropy by the method shown in the following formula: ； ； wherein, E is the wavelet packet decomposition energy entropy, i is the i-th subband energy of the 16 subband energies.

4. The defect detection method of claim 3, wherein, The phase difference of the N high-voltage pulse signals is: ； wherein is the phase difference.

5. The method according to any one of claims 1-4, characterized in that, After inputting the wavelet packet decomposition energy entropy into a preset defect detection model to perform defect detection and obtain a defect detection result, the method further comprises: if the defect detection result is that there is a defect, extracting a defect position and a defect type; performing defect risk analysis according to the defect position and the defect type to obtain a risk analysis result; generating risk warning information according to the risk analysis result; displaying the risk warning information.

6. A multi-pulse combined modulation and feature entropy fusion based defect detection apparatus, characterized in that, The device comprises: a generating unit configured to generate N high-voltage pulse signals; an extracting unit configured to extract feedback signals of the N high-voltage pulse signals output by the tower to be detected to obtain a defect response signal; a calculating unit configured to perform wavelet packet decomposition energy entropy calculation on the defect response signal to obtain wavelet packet decomposition energy entropy; a detecting unit configured to input the wavelet packet decomposition energy entropy into a preset defect detection model to perform defect detection and obtain a defect detection result.

7. The method according to claim 6, wherein, The extracting unit is specifically configured to: extract feedback signals of the N high-voltage pulse signals output by the tower to be detected to obtain a defect response signal by the method shown in the following formula: ； wherein, is a defect response signal, is a superposition signal of N high voltage pulse signals, is a frequency of the signal actually acquired by the receiving probe for receiving the N high voltage pulse signals output by the tower to be detected, is a frequency domain representation of the signal actually acquired by the receiving probe for receiving the N high voltage pulse signals output by the tower to be detected.

8. The method according to claim 7, wherein, The calculating unit is specifically configured to: perform wavelet packet decomposition energy entropy calculation on the defect response signal to obtain wavelet packet decomposition energy entropy by the method shown in the following formula: ； ； wherein, E is the wavelet packet decomposition energy entropy, i is the i-th subband energy of the 16 subband energies.

9. A terminal, characterized by comprising: The device comprises a processor, an input device, an output device, and a memory, which are connected to each other, wherein the memory is used to store a computer program, the computer program comprises program instructions, the processor is configured to invoke the program instructions, and perform the defect detection method of multi-pulse joint modulation and feature entropy fusion according to any one of claims 1-5.

10. A computer-readable storage medium, characterized in that, The computer readable storage medium stores a computer program, the computer program comprises program instructions, and the program instructions make the processor execute the defect detection method of multi-pulse joint modulation and feature entropy fusion according to any one of claims 1-5 when executed by the processor.

Images