A control method for a digital ultrasonic flaw detector calibration device

Abstract

The present invention relates to a control method for a digital ultrasonic flaw detector calibration device. The method comprises obtaining a standard test block that matches the shape data of the material to be tested and the material parameters, calculating the adaptive ultrasonic frequency and flaw detector parameters, creating and solving a multi-objective optimization model, and planning a detection path for the material to be tested based on the optimal solution. Ultrasonic waves with matching frequencies are then emitted to an array of quantum dot markers for fluorescence-ultrasound dual-modal signal correlation calibration. The device scans along the planned detection path, establishes a time-varying channel model to predict the current delay variation, uses an autocorrelation algorithm to extract the fundamental frequency and harmonic components of the probe excitation pulse as a reference, constructs a filter state vector based on the predicted current delay variation, performs phase correction on the received signal, and analyzes defects in the material to be tested based on the corrected received signal. The method integrates dynamic material parameter modeling, multi-modal signal collaborative calibration, and adaptive path optimization to improve detection accuracy and efficiency under complex working conditions.

Description

Technical Field

[0001] The invention belongs to the technical field of ultrasonic nondestructive testing equipment, and in particular relates to a control method for a digital ultrasonic flaw detector calibration device. Background Art

[0002] Ultrasonic flaw detection technology, as a core non-destructive testing method, is widely used in key fields such as aerospace, nuclear energy equipment, and rail transportation. It is used to detect internal defects in materials (such as cracks, pores, inclusions, etc.) and evaluate structural integrity. In existing technologies, ultrasonic flaw detector calibration devices have the following technical problems:

[0003] First, reliance on human experience: Detection parameters (such as frequency and gain) often rely on operator experience, which can easily lead to misjudgment of complex materials (such as coarse-grained alloys and composite materials);

[0004] Second, static calibration mode is used: the material parameters of the standard test block and the material to be tested are not well matched (such as sound velocity and attenuation coefficient deviation), resulting in sensitivity calibration failure;

[0005] Third, single-mode detection: Traditional ultrasonic echo signals are susceptible to interference from material grain boundary scattering, temperature drift, and other factors, limiting defect location accuracy (typical error > 1 mm).

[0006] Fourth, inefficient path planning: manual scanning paths lack adaptive optimization, resulting in insufficient coverage of curved surfaces and porous structures (missed detection rate > 5%).

[0007] Fifth, there is a lack of dynamic modeling: the discreteness of the speed of sound (Δc) and the time-varying nature of the attenuation coefficient (α(f,t)) are not corrected in real time, resulting in the accumulation of delay prediction errors.

[0008] For example, researchers in the existing technology have proposed a calibration method based on acoustic impedance matching, but it does not consider the impact of frequency-dependent attenuation on the echo signal-to-noise ratio (SNR), resulting in a decrease in the detection sensitivity of thick-walled parts; although the international standard ISO 19677 stipulates the rules for selecting standard test blocks, it still uses fixed tolerance screening (such as sound velocity deviation of ±5%), which cannot adapt to high-precision detection requirements.

[0009] Furthermore, while existing quantum dot marker technology uses fluorescence signals to assist in positioning, it lacks dynamic correlation and calibration with the ultrasonic sound field, resulting in marker drift errors (approximately 0.3 mm). Furthermore, traditional delay prediction models fail to incorporate dynamic variations in the sound velocity field, leading to phase correction lags (typical delay errors >10 μs).

[0010] Therefore, there is an urgent need for an ultrasonic flaw detection control method that integrates dynamic modeling of material parameters, collaborative calibration of multimodal signals, and adaptive path optimization to solve the above-mentioned technical problems existing in the existing technology. Summary of the Invention

[0011] The purpose of the present invention is to provide a control method for a digital ultrasonic flaw detector calibration device, which is used to integrate material parameter dynamic modeling, multimodal signal collaborative calibration and adaptive path optimization of ultrasonic flaw detection control method to solve the above-mentioned technical problems existing in the prior art.

[0012] In order to solve the above technical problems, the technical solutions adopted by the present invention are as follows:

[0013] A control method for a digital ultrasonic flaw detector calibration device comprises the following steps:

[0014] S1: Test the material to be tested, obtain its shape data and material parameters, and call a matching standard test block according to the material parameters of the material to be tested;

[0015] S2: Calculate the matching ultrasonic frequency and the specified parameters of the flaw detector according to the material parameters, create a multi-objective optimization model according to the shape data and the material parameters, find the best solution for the multi-objective optimization model, and plan the inspection path of the material to be inspected based on the best solution;

[0016] S3: Emitting ultrasound waves of matching frequency, implanting quantum dot marker arrays, and performing fluorescence-ultrasound dual-modality signal correlation calibration;

[0017] S4: Make the detection probe scan along the planned detection path and construct a detection path containing the attenuation coefficient of the material to be detected. α ( f ), dynamic transfer function of sound velocity dispersion Δc H ( f , t ), establish a time-varying channel model;

[0018] S5: The time-varying channel model predicts the current delay variation based on the delay variation at the previous moment;

[0019] S6: Extract the fundamental frequency of the probe excitation pulse using the autocorrelation algorithm f 0 and harmonic components f n As a reference benchmark, the state vector of the Kalman filter is constructed based on the predicted current delay change. The phase of the received signal is corrected through the Kalman filter, and the defects of the material to be detected are analyzed based on the corrected received signal.

[0020] Preferably, the specific process of testing the material to be tested and obtaining its shape data and material parameters in step S1 is as follows:

[0021] S11: Obtain three-dimensional point cloud data of the surface morphology and internal structure of the material to be tested through a laser scanner;

[0022] S12: Use point cloud processing algorithms to identify key dimensions such as thickness steps, surface curvature, and aperture size, and construct a digital 3D model.

[0023] S13: Emit standard ultrasonic waves to the material to be tested, measure the two-way propagation time of the standard ultrasonic waves in the material to be tested, and then calculate the sound velocity. Compare the amplitude ratio of the incident wave and the transmitted wave to calculate the attenuation coefficient. α ( f ):

[0024] α ( f )=(20 / d )log( A in / Oh ut ) ;

[0025] S14: Based on the amplitude of the interface reflection signal, derive the material acoustic impedance Z. The derivation formula is as follows:

[0026] R=(Z2−Z1) / (Z2+Z1);

[0027] Where R is the sound pressure reflectivity, which is the ratio of the reflected wave sound pressure to the incident wave sound pressure, Z1 is the acoustic impedance of the incident side medium, and Z2 is the acoustic impedance of the transmission side medium.

[0028] Preferably, the specific process of calling a matching standard test block according to the material parameters of the material to be tested in step S1 is as follows:

[0029] S15: The speed of sound c , attenuation coefficient α ( f ), the acoustic impedance Z is quantified as a eigenvector, and the expression is as follows:

[0030] V=[ c , α 1 kHz , α 10 kHz , Z ];

[0031] in, α 1 kHz Indicates the attenuation coefficient when the frequency is 1kHz, α 10 kHz It represents the attenuation coefficient when the frequency is 10kHz, and Z is the acoustic impedance;

[0032] S16: Match in the standard test block library according to the following priority:

[0033] Material type matching: give priority to standard test blocks of the same base material type;

[0034] ‌Material parameter tolerance screening‌: Screening meets | c 试块 − c 待检测材料 ∣≤2%,∣ α 试块 ( f )−α 待检测材料 ( f )∣≤15% of candidate test blocks, among which c 试块 Indicates the standard value of the sound velocity of the sound wave of the specified frequency in the test block. c 待检测材料 It indicates the sound velocity measurement value of the sound wave of the same specified frequency in the material to be tested. α 试块 ( f ) indicates the frequency is f The sound attenuation coefficient of the sound wave in the test block, α 待检测材料 ( f ) indicates the frequency is f The acoustic attenuation coefficient of the sound wave in the material to be tested;

[0035] Use matching test blocks to perform time base correction and sensitivity calibration on the detection equipment. When it is verified that the signal-to-noise ratio of the detection signal is improved by ≥20dB and the defect positioning error is ≤0.5mm, the matching is considered valid.

[0036] Preferably, the specific process of step S2 calculating the matching ultrasonic frequency and the specified parameters of the flaw detector according to the material parameters is as follows:

[0037] S21: determining whether the material to be tested is a coarse-grained material or a fine-grained material based on the material parameters, and selecting the ultrasonic fundamental frequency based on whether the material is a coarse-grained material or a fine-grained material;

[0038] S22: Create an optimal frequency calculation model. The specific expression is as follows:

[0039] ;

[0040] in, c is the material sound velocity, which is obtained by pulse echo method. d is the maximum thickness of the detection area, α ( f ) is the frequency-dependent attenuation coefficient;

[0041] S23: Based on material sound velocity c , Maximum thickness of the detection area d and frequency-dependent attenuation coefficient α ( f) Calculate ultrasonic pulse width, gain compensation, sampling frequency, and frequency band range;

[0042] The calculation formula of ultrasonic pulse width is as follows:

[0043] ;

[0044] The gain compensation calculation formula is as follows:

[0045] ;

[0046] Sampling frequency ;

[0047] The frequency band range is set to 0.5-2 times the center frequency for filtering.

[0048] Preferably, a multi-objective optimization model is created based on the shape data and material parameters, the multi-objective optimization model is solved, the solutions are scored, the highest-scoring solution is selected, and the specific process of planning the inspection path of the material to be inspected based on the highest-scoring solution is as follows:

[0049] S24: Define the decision variables of the multi-objective optimization model: probe coordinate sequence ( xi , yes ), scanning angle I will , step size Δ yes , establish material parameter constraints, frequency f ∈[ f min , f max ], beam width W =2 D sin i ;

[0050] S25: Construct objective function:

[0051] ;

[0052] in, SNR i According to the sound attenuation model α ( f , d ) calculated;

[0053] S26: Initialize the population, calculate the Pareto frontier, use the Das-Dennis method to evenly distribute reference points in the target space, perform adaptive crossover mutation based on the reference points, and calculate the crossover probability. pc =0.9, mutation probability pm =1 / n , n is the number of variables;

[0054] S27: Adjust the probe position through Gaussian mutation operator: xi ′= xi +N(0,0.1 W );

[0055] S28: According to the optimized path v =20 mm / s Perform a coarse scan at a constant speed, and switch to fine scanning when a defect signal (>40dB) is triggered. The grid is locally encrypted to 1mm, and the probe angle and frequency are adjusted in real time using Bayesian optimization.

[0056] Preferably, the specific process of emitting ultrasound waves of matching frequency, implanting the quantum dot marker array, and performing fluorescence-ultrasound dual-modal signal correlation calibration in step S3 is as follows:

[0057] S31: Emitting ultrasonic waves of matching frequency and implanting a quantum dot marker array in the propagation path. The quantum dots are distributed according to a spatial coding pattern, using an orthogonal grid or spiral array, and setting the density of the quantum dot marker array.

[0058] S32: Use high-speed photoelectric detector to collect fluorescence signals and ultrasonic receiving probe to collect echo signals. The sampling rate meets f s ≥10 f opt ,The two channels achieve time synchronization through hardware triggering;

[0059] S33: Perform cross-correlation calculation on the peak value of the fluorescence pulse and the leading edge of the ultrasonic echo to compensate for the time delay caused by the difference in the transmission path;

[0060] S34: A coordinate mapping model is constructed based on the array geometric parameters. The fluorescence intensity distribution and the ultrasonic sound pressure field distribution are fitted by the least squares method to eliminate the positioning deviation caused by beam diffusion or quantum dot drift, and realize the correlation calibration of fluorescence-ultrasound dual-modal signals.

[0061] Preferably, in S4, the detection probe is scanned according to the planned detection path to construct a detection path including the attenuation coefficient of the material to be detected. α ( f ), dynamic transfer function of sound velocity dispersion Δc H ( f , t ), the specific process of establishing the time-varying channel model is as follows:

[0062] S41: Generate a probe motion instruction sequence based on the path planning result, including probe coordinates, probe running speed, and probe running acceleration;

[0063] S42: The actual position deviation is monitored by the encoder. When the error is greater than 0.1mm, PID closed-loop correction is triggered to achieve real-time feedback adjustment.

[0064] S43: Attenuation Coefficient Modeling: Frequency Dependent Attenuation: α ( f )= α 0+ βf , α ( f ) is the sound wave frequency f The sound wave attenuation coefficient is α 0 is the fundamental frequency attenuation, β is the material dependence coefficient; f is the sound wave frequency, time-varying correction: α ( f , t )= α ( f )⋅[1+ c Δ T ( t )];

[0065] in, α ( f , t ) is the time variation correction, at a frequency of f , time is t The sound wave attenuation coefficient is c is the temperature coefficient, Δ T ( t ) is the real-time temperature difference;

[0066] S44: Establish the sound velocity distribution field: c ( x , y )= c 0+Δ c ( x , y ),in, c ( x , y ) indicates the spatial position ( x , y ) at the sound velocity, c 0 is the base value of the speed of sound, Δ c ( x , y ) is the speed of sound at position ( x , y ) relative to the reference sound speed c The deviation of 0 is used to describe the changes and fluctuations of sound speed in space. Through the inversion calculation of full matrix capture data, a dynamic update strategy is established: the sound speed map is updated after each specified path scan is completed;

[0067] S45: Establish dynamic transfer function:

[0068] H ( f , t )= e −α(f,t)⋅d(t) ⋅ e −j2πf[d(t) / c(x,y)] ;

[0069] in, d ( t ) is the time-varying propagation distance, j is the imaginary unit, 2 πf [ d ( t ) / c ( x , y )] is the phase change, d ( t ) / c ( x , y ) is the propagation time of the sound wave, that is, the sound wave travels at a speed of c ( x , y ) Propagation distance d ( t )

[0070] S46: Use ray tracing to simulate the interface reflection path and superimpose the contributions of each path:

[0071] ;

[0072] in, H total ( f,t ):represents the total dynamic transfer function, which is obtained by ray tracing method after considering multiple interface reflection paths at a frequency of f , time is t When , the overall transfer function is obtained by superimposing the comprehensive contributions of all reflection paths to the signal; N is the total number of reflection paths participating in the superposition, Γ k For the k The reflection coefficient of each path, H k ( f , t ) is the k Dynamic transfer function of the reflection path;

[0073] The sound field data is collected every 10ms and updated through the LMS adaptive filter α ( f , t ) and Δc ( x , y ), the error between the theoretical prediction and the actual echo amplitude on the CSK-IA test block is less than ±1dB.

[0074] Preferably, the specific process of predicting the current delay variation based on the delay variation at the previous moment by the time-varying channel model in step S5 is as follows:

[0075] Obtain the delay change and sound speed deviation at the previous moment, and set the initial delay. The time-varying channel model predicts the current delay change Δ based on the delay change at the previous moment, sound speed deviation and initial delay. t k , the prediction formula is as follows:

[0076] Δ t k = a 1Δ t k−1 + a 2Δ t 0+ b 1 w k−1 ;

[0077] Among them, Δ t k is the current delay variation, a 1 is the transmission coefficient of the delay change at the previous moment, a 2 is the attenuation coefficient of the initial delay variation, b 1 is the process noise gain coefficient, which controls the disturbance intensity of the excitation noise on the delay prediction. w k−1 is the excitation term of the Gaussian white noise process.

[0078] Preferably, the specific process of step S6 is as follows:

[0079] S61: Acquisition of reference signal p ( t ), establish the autocorrelation function R ( t )=∫ p ( t ) p ( t − t ) dt , determine the fundamental frequency by the peak interval f 0;

[0080] S62: Reference signal p ( t ) to perform FFT analysis to detect harmonics exceeding a specified amplitude fn , construct frequency reference set {f 0, f 1,..., fn};

[0081] S63: Create the state equation of the Kalman filter:

[0082] X k =F k x k−1 + B k u k + w k ;

[0083] Among them, X k is the current state vector, including the current delay change Δ t k , x k−1 is the state vector at the previous moment, F k is the state transfer matrix, which describes the dynamic evolution relationship of the state variables. B k u k is the control input item, which describes the impact of external control input on the state. w k is the process noise;

[0084] Create the observation equation:

[0085] z k =H k x k +v k ;

[0086] Among them, z k is the observation vector, H k is the observation matrix, , v k To observe the noise, it reflects the sensor accuracy;

[0087] S64: f 0, fn} is the observation reference, and the received signal is calculated y ( t ) is used to update the state estimate through the Kalman gain, and the output delay compensation Δ t k ,right y ( t ) applies phase compensation:

[0088] y corrected ( t )=y (Δ t +Δ t k );

[0089] in, y corrected ( t ) is the phase compensation amount, Δ t is the delay spread;

[0090] S65: Extract the delay spread Δ of the corrected signal t >0.2μs is used as the basis for defect location, combining the delay spread Δt and the frequency domain attenuation slope dα / df , distinguish the types of crack defects.

[0091] The beneficial effects of the present invention include:

[0092] The control method for a digital ultrasonic flaw detector calibration device provided by the present invention obtains a standard test block that matches the shape data of the material to be inspected and the material parameters, calculates the adaptive ultrasonic frequency and flaw detector parameters, creates and solves a multi-objective optimization model, and plans the inspection path for the material to be inspected based on the optimal solution. Ultrasonic waves of matching frequency are then emitted to an implanted quantum dot marker array for fluorescence-ultrasound dual-modal signal correlation calibration. Scanning along the planned inspection path, a time-varying channel model is established to predict the current delay variation. An autocorrelation algorithm is used to extract the fundamental frequency and harmonic components of the probe excitation pulse as a reference. A filter state vector is constructed based on the predicted current delay variation. Phase correction is performed on the received signal, and the material defects are analyzed based on the corrected received signal. This integrated material parameter dynamic modeling, multimodal signal collaborative calibration, and adaptive path optimization improves inspection accuracy and efficiency under complex working conditions.

[0093] First, by dynamically matching the standard test block based on the material parameters of the material to be tested, and by using a standard test block that is more suitable for the material to be tested, the calibration error of the verification device is effectively reduced compared with the traditional method.

[0094] Secondly, the fluorescence-ultrasound dual-modal calibration technology is used to compress the defect location error to the 0.01mm level, which is much lower than the defect location error of single-modal ultrasound. In addition, the sound velocity discreteness and attenuation coefficient drift can be corrected in real time through the dynamic transfer function, further improving the defect location accuracy.

[0095] Thirdly, a multi-objective optimization model is established to integrate the beam width constraint and the signal-to-noise ratio target, so that the detection coverage of curved surfaces and porous structures is effectively improved. The quantum dot marker array is combined with the optimization process to achieve automatic focus scanning of micron-level defects.

[0096] Finally, through the adaptive genetic algorithm, the mutation probability optimizes the detection path, reduces invalid scanning, improves detection efficiency, effectively reduces energy consumption, and suppresses phase drift through the synergy of time delay prediction model and filtering, thereby promoting the improvement of signal processing speed. BRIEF DESCRIPTION OF THE DRAWINGS

[0097] Figure 1 The figure is a flow chart of a control method of a digital ultrasonic flaw detector calibration device according to the present invention.

[0098] Figure 2 This is a schematic diagram of the process of performing fluorescence-ultrasound dual-modal signal correlation calibration in the present invention.

[0099] Figure 3 This is a schematic diagram of oscilloscope adjustment. DETAILED DESCRIPTION

[0100] The following is combined with Figure 1~Figure 3 The present invention is described in further detail:

[0101] Example 1

[0102] See attached Figure 1 As shown, a control method for a digital ultrasonic flaw detector calibration device includes the following steps:

[0103] S1: Test the material to be tested, obtain its shape data and material parameters, and call a matching standard test block based on the material parameters of the material to be tested. Before testing, clean the material to remove oil and dust from the surface. Spray a matting agent (such as magnesium oxide powder) on highly reflective or transparent surfaces to enhance the intensity of the laser reflection signal and avoid affecting subsequent test results.

[0104] S2: Calculate the matching ultrasonic frequency and the specified parameters of the flaw detector according to the material parameters, create a multi-objective optimization model according to the shape data and the material parameters, find the best solution for the multi-objective optimization model, and plan the inspection path of the material to be inspected based on the best solution.

[0105] S3: Emitting ultrasound waves of matching frequency, implanting quantum dot marker arrays, and performing fluorescence-ultrasound dual-modality signal correlation calibration;

[0106] S4: Make the detection probe scan along the planned detection path and construct a detection path containing the attenuation coefficient of the material to be detected. α ( f ), dynamic transfer function of sound velocity dispersion Δc H ( f , t ), establish a time-varying channel model;

[0107] S5: The time-varying channel model predicts the current delay variation based on the delay variation at the previous moment;

[0108] S6: Extract the fundamental frequency of the probe excitation pulse using the autocorrelation algorithm f 0 and harmonic components f n As a reference benchmark, the state vector of the Kalman filter is constructed based on the predicted current delay change. The phase of the received signal is corrected through the Kalman filter, and the defects of the material to be detected are analyzed based on the corrected received signal.

[0109] The specific process of testing the material to be tested and obtaining its shape data and material parameters in step S1 is as follows:

[0110] S11: Obtain three-dimensional point cloud data of the surface morphology and internal structure of the material to be tested through a laser scanner;

[0111] S12: Point cloud processing algorithms are used to identify critical dimensions such as thickness steps, surface curvature, and aperture sizes, and a digital 3D model is constructed. Statistical filtering is used to remove outliers: the neighborhood average distance is calculated as a specified multiple of the standard deviation to remove discrete noise points, such as interference points from laser scanner reflections. Voxel grid filtering is used to reduce data density and improve subsequent computational efficiency. The ICP algorithm is used to align multi-site cloud data to establish a unified geographic coordinate system. For regular shapes such as mechanical components, plane fitting is used to correct for scanning distortion and avoid projection errors caused by scanner tilt.

[0112] Automatically divide thickness layers based on elevation histogram statistics: set a threshold to segment the ground and the step surface, and use the region growing algorithm to distinguish adjacent steps: expand the seed point to the curvature mutation boundary, use the RANSAC algorithm to perform plane fitting on each layer of point cloud, measure the distance between adjacent planes as the step thickness, and output the step thickness list and tolerance band.

[0113] Principal component analysis is used to calculate point cloud normals, construct local surface equations, calculate Gaussian curvature, and identify concave and convex areas, with positive curvature indicating convex and negative curvature indicating concave. Surface types are segmented based on curvature thresholds. For complex surfaces such as turbine blades, NURBS surface fitting is used to output curvature distribution heat maps. Alpha shape algorithms are used to extract hole boundary points, and ellipse / circle fitting is used to optimize the hole diameter. Diameter, circle center coordinates, and roundness error are annotated.

[0114] S13: Emit standard ultrasonic waves to the material to be tested, measure the two-way propagation time of the standard ultrasonic waves in the material to be tested, and then calculate the sound velocity. Compare the amplitude ratio of the incident wave and the transmitted wave to calculate the attenuation coefficient. α ( f ):

[0115] α (f )=(20 / d )log( A in / A out ) ;

[0116] in, d is the maximum thickness of the detection area, A in The amplitude of the incident ultrasonic wave, that is, the original signal strength before entering the material to be tested, is expressed in V. A out is the amplitude of the transmitted ultrasound, that is, the remaining signal intensity after the ultrasound passes through the material;

[0117] S14: Based on the amplitude of the interface reflection signal, derive the material acoustic impedance Z. The derivation formula is as follows:

[0118] R = (Z2 − Z1) / (Z2 + Z1), where R is the sound pressure reflectivity, which is the ratio of the sound pressure of the reflected wave to the sound pressure of the incident wave, Z1 is the acoustic impedance of the incident side medium, and Z2 is the acoustic impedance of the transmission side medium.

[0119] In this embodiment, the specific process of calling a matching standard test block according to the material parameters of the material to be tested in step S1 is as follows:

[0120] S15: The speed of sound c , attenuation coefficient α ( f ), the acoustic impedance Z is quantified as a eigenvector, and the expression is as follows:

[0121] V=[ c , α 1 kHz , α 10 kHz , Z ];

[0122] in, α 1 kHz represents the attenuation coefficient when the frequency is 1 kHz, α 10 kHz represents the attenuation coefficient at a frequency of 10 kHz, and Z is the acoustic impedance;

[0123] S16: Match in the standard test block library according to the following priority:

[0124] Material type matching: Prioritize standard test blocks of the same base material type to ensure that the physical properties of the standard test blocks are similar to those of the material to be tested.

[0125] Material parameter tolerance screening: screening to meet the relative deviation of the sound velocity between the test block and the material to be tested. c 试块 − c 待检测材料 ∣≤2%, ensuring the consistency of ultrasonic propagation time base. Compare the attenuation coefficient at a specific frequency∣ α 试块 ( f )−α 待检测材料 ( f )|≤15% of candidate test blocks, suppress signal scattering errors, automatically compare parameters through database scripts, and generate a list of candidate test blocks. c 试块 Indicates the standard value of the sound velocity of the sound wave of the specified frequency in the test block. c 待检测材料 It indicates the sound velocity measurement value of the sound wave of the same specified frequency in the material to be tested. α 试块 ( f ) indicates the frequency is f The sound attenuation coefficient of the sound wave in the test block, α 待检测材料 ( f ) indicates the frequency is f The acoustic attenuation coefficient of the sound wave in the material to be tested.

[0126] Use a matching test block to calibrate the timebase and sensitivity of the testing equipment. A match is considered valid when the signal-to-noise ratio of the test signal improves by ≥20dB and the defect location error is ≤0.5mm. Place the probe at a known thickness on the test block and adjust the instrument's timebase scale so that the reflected wave leading edge aligns with the corresponding depth mark with an error of ≤0.1mm. Verification: Scan areas of varying thickness to confirm that the timebase linearity error is <1%.

[0127] Gain Calibration: Using an artificial defect on a test block, adjust the gain so that the defect echo height reaches 80% of the full amplitude, and record the gain value as a benchmark. Dynamic Range Optimization: Set the filter bandwidth to cover the test frequency and suppress high-frequency noise interference.

[0128] Verify the signal-to-noise ratio: Collect background noise signals in a defect-free area of ​​the material to be inspected. Compare the noise amplitude before and after calibration to ensure it is reduced by ≥20dB. Verify defect positioning accuracy: Use a test block to preset defects, such as preset grooves and holes. The deviation between the defect position displayed by the measuring equipment and the actual coordinates is ≤0.5mm.

[0129] If the candidate test block parameters do not meet the tolerance, the virtual test block parameters can be constructed through interpolation. The confidence of the interpolation model needs to be additionally verified, and different weights are assigned to the sound velocity and attenuation coefficient to adapt to the priorities of different detection scenarios. The above process combines parametric screening with physical calibration to ensure the accuracy and reliability of the detection equipment in complex material environments.

[0130] Example 2

[0131] Based on Example 1, the specific process of step S2 for calculating the matching ultrasonic frequency and the designated parameters of the flaw detector according to the material parameters is as follows:

[0132] S21: Based on the material parameters, determine whether the material to be tested is coarse-grained or fine-grained, and select the ultrasonic fundamental frequency based on whether it is coarse-grained or fine-grained. When the grain size is ≤3, the tensile strength is 15%-30% lower than that of similar fine-grained materials, and there is a tendency for intergranular corrosion, the material is determined to be coarse-grained. Fine-grained materials with a grain size of ≥7 have significantly higher strength and toughness than coarse-grained materials.

[0133] S22: Create an optimal frequency calculation model. The specific expression is as follows:

[0134] ;

[0135] in, c is the material sound velocity, which is obtained by pulse echo method. d is the maximum thickness of the detection area, α ( f ) is the frequency-dependent attenuation coefficient.

[0136] S23: Based on material sound velocity c , Maximum thickness of the detection area d and frequency-dependent attenuation coefficient α ( f ) Calculate ultrasonic pulse width, gain compensation, sampling frequency, and frequency band range;

[0137] The calculation formula of ultrasonic pulse width is as follows:

[0138] ;

[0139] The gain compensation calculation formula is as follows:

[0140] ;

[0141] Sampling frequency ;

[0142] The frequency band range is set to 0.5-2 times the center frequency for filtering.

[0143] In this embodiment, a multi-objective optimization model is created based on shape data and material parameters, the multi-objective optimization model is solved, the solutions are scored, the highest-scoring solution is selected, and the specific process of planning the inspection path of the material to be inspected based on the highest-scoring solution is as follows:

[0144] S24: Define the decision variables of the multi-objective optimization model: probe coordinate sequence ( x i , y i ), ( x i+1 , y i+1 ); Scan angle I will , step size Δ yes , establish material parameter constraints, frequency f ∈[ f min , f max ], beam width W =2 D sin i ;

[0145] S25: Construct objective function:

[0146] ;

[0147] in, SNR i According to the sound attenuation model α ( f , d ) calculated;

[0148] S26: Initialize the population, calculate the Pareto frontier, use the Das-Dennis method to evenly distribute reference points in the target space, perform adaptive crossover mutation based on the reference points, and calculate the crossover probability. pc =0.9, mutation probability pm =1 / n , n is the number of variables;

[0149] S27: Adjust the probe position through Gaussian mutation operator: x i′ = x i +N(0,0.1 W );

[0150] S28: According to the optimized path v =20 mm / s Perform a coarse scan at a constant speed, and switch to fine scanning when a defect signal (>40dB) is triggered. The grid is locally encrypted to 1mm, and the probe angle and frequency are adjusted in real time using Bayesian optimization.

[0151] In another embodiment of the present invention, the path planning process is as follows: the morphological data obtained by laser / CT scanning is converted into a grid map, the grid size is set according to the detection accuracy requirement, 5mmx5mm is selected for coarse detection, and 1mmx1mm is selected for fine detection. Based on curvature analysis and acoustic reflection characteristics, internal holes, delamination and other undetectable areas are identified and marked as path restricted areas. The optimal detection frequency is calculated based on the sound speed and attenuation coefficient to ensure that the length of the near field area is less than the material thickness, and the diameter is selected to meet D≥2 l , Refraction angle configuration: Dynamically adjust the angle coverage detection area according to the material thickness gradient.

[0152] The contour line is divided into equal intervals according to the acoustic beam coverage width to generate an initial sampling point grid. The sampling point coordinates are converted into a binary gene sequence, and a fitness function is established. Optimization is performed based on the fitness function. Local path encryption scanning is triggered by a sudden change in the acoustic emission signal intensity. A comparison test block containing a 0.4mm artificial defect is inserted on the planned path to verify the defect detection rate. The positioning error is corrected by the A-scan signal delay deviation, requiring the axial error to be ≤0.1mm and the lateral error to be ≤0.5mm.

[0153] Example 3

[0154] Based on Example 1 or Example, see Figure 2 The specific process of emitting ultrasound waves of matching frequency, implanting the quantum dot marker array, and performing fluorescence-ultrasound dual-modal signal correlation calibration in step S3 is as follows:

[0155] S31: Emitting ultrasound waves of matching frequency, a quantum dot array is implanted in the propagation path using an orthogonal grid or spiral array. The grid spacing is set according to the ultrasound wave length λ, typically ranging from λ / 2 to λ. The quantum dots are distributed according to a spatial encoding pattern, and the density of the quantum dot array is set. The quantum dot density is determined by the distinguishability of the fluorescence signal, and the spacing between adjacent markers must be ≥ 3 times the optical diffraction limit, approximately 500nm. Specific antibodies are modified on the surface of the quantum dots to achieve targeted positioning; the emission wavelength is encoded according to the spatial position.

[0156] S32: Use high-speed photoelectric detector to collect fluorescence signals and ultrasonic receiving probe to collect echo signals. The sampling rate meets f s ≥10 f opt The two channels are time-synchronized via hardware triggering. The ultrasound probe and photodetector are hardware-triggered via an FPGA, achieving synchronization accuracy of ≤10ns. The fluorescence signal is bandpass filtered to remove ambient light interference. Time-domain windowing is applied to the ultrasound echo to suppress sidelobe noise.

[0157] S33: Perform cross-correlation calculation on the fluorescence pulse peak and the ultrasonic echo front to compensate for the delay caused by the transmission path difference. Extract the fluorescence pulse peak and the ultrasonic echo front, calculate the delay corresponding to the maximum value of the cross-correlation function, and perform delay compensation. 。

[0158] S34: A coordinate mapping model is constructed based on the array geometric parameters. The fluorescence intensity distribution and the ultrasonic sound pressure field distribution are fitted by the least squares method to eliminate the positioning deviation caused by beam diffusion or quantum dot drift, and realize the correlation calibration of fluorescence-ultrasound dual-modal signals.

[0159] When transmitting ultrasonic pulses, it is necessary to use an oscilloscope to measure the transmitted pulse voltage, pulse rise time, and pulse duration. Figure 3 As shown, t r is the pulse rise time, t d is the pulse duration, and V is the transmitted pulse voltage. Adjust the oscilloscope's horizontal resolution to minimize the number of pulses (≥2) appearing on the screen. Use the oscilloscope to measure the time interval between the two pulses. The pulse repetition frequency is the reciprocal of the time interval between the two pulses.

[0160] In S4, the detection probe is scanned according to the planned detection path to construct a detection path including the attenuation coefficient of the material to be detected. α ( f ), dynamic transfer function of sound velocity dispersion Δc H ( f , t ), the specific process of establishing the time-varying channel model is as follows:

[0161] S41: Generate a probe motion instruction sequence based on the path planning result, including probe coordinates, probe running speed, and probe running acceleration;

[0162] S42: The actual position deviation is monitored by the encoder. When the error is greater than 0.1mm, PID closed-loop correction is triggered to achieve real-time feedback adjustment.

[0163] S43: Attenuation Coefficient Modeling: Frequency Dependent Attenuation: α ( f )= α 0+ βf , α ( f ) is the sound wave frequency f The sound wave attenuation coefficient is α 0 is the fundamental frequency attenuation, β is the material dependence coefficient; f is the sound wave frequency, time-varying correction: α ( f , t)= α ( f )⋅[1+ c Δ T ( t )];

[0164] in, α ( f , t ) is the time variation correction, at a frequency of f , time is t The sound wave attenuation coefficient is c is the temperature coefficient, Δ T ( t ) is the real-time temperature difference;

[0165] S44: Establish the sound velocity distribution field: c ( x , y )= c 0+Δ c ( x , y ),in, c ( x , y ) indicates the spatial position ( x , y ) at the sound velocity, c 0 is the base value of the speed of sound, Δ c ( x , y ) is the speed of sound at position ( x , y ) relative to the reference sound speed c The deviation of 0 is used to describe the changes and fluctuations of sound speed in space. Through the inversion calculation of full matrix capture data, a dynamic update strategy is established: the sound speed map is updated after each specified path scan is completed;

[0166] S45: Establish dynamic transfer function:

[0167] H ( f , t )= e −α(f,t)⋅d(t) ⋅ e −j2πf[d(t) / c(x,y)] ;

[0168] in, d ( t ) is the time-varying propagation distance, j is the imaginary unit, 2 πf [ d ( t ) / c ( x , y)] is the phase change, d ( t ) / c ( x , y ) is the propagation time of the sound wave, that is, the sound wave travels at a speed of c ( x , y ) Propagation distance d ( t )

[0169] S46: Use ray tracing to simulate the interface reflection path and superimpose the contributions of each path:

[0170] ;

[0171] in, H total ( f,t ):represents the total dynamic transfer function, which is obtained by ray tracing method after considering multiple interface reflection paths at a frequency of f , time is t When , the overall transfer function is obtained by superimposing the comprehensive contributions of all reflection paths to the signal; N is the total number of reflection paths participating in the superposition, Γ k For the k The reflection coefficient of each path, H k ( f , t ) is the k Dynamic transfer function of the reflection path;

[0172] The sound field data is collected every 10ms and updated through the LMS adaptive filter α ( f , t ) and Δ c ( x , y ), the error between the theoretical prediction and the actual echo amplitude on the CSK-IA test block is less than ±1dB.

[0173] Example 4

[0174] On the basis of Example 1, Example 2, or Example 3, the specific process of predicting the current delay variation based on the delay variation at the previous moment by the time-varying channel model in step S5 is as follows:

[0175] Obtain the delay change and sound speed deviation at the previous moment, and set the initial delay. The time-varying channel model predicts the current delay change Δ based on the delay change at the previous moment, sound speed deviation and initial delay. t k , the prediction formula is as follows:

[0176] Δ t k = a 1Δ t k−1 + a 2Δ t 0+ b 1 w k−1 ;

[0177] Among them, Δ t k is the current delay variation, a 1 is the transmission coefficient of the delay change at the previous moment, a 2 is the attenuation coefficient of the initial delay variation, b 1 is the process noise gain coefficient, which controls the disturbance intensity of the excitation noise on the delay prediction. w k−1 is the excitation term of the Gaussian white noise process.

[0178] The specific process of step S6 is as follows:

[0179] S61: Acquisition of reference signal p ( t ), establish the autocorrelation function R ( t )=∫ p ( t ) p ( t − t ) dt , determine the fundamental frequency by the peak interval f 0;

[0180] S62: Reference signal p ( t ) to perform FFT analysis to detect harmonics exceeding a specified amplitude fn , construct frequency reference set { f 0, f 1,..., fn};

[0181] S63: Create the state equation of the Kalman filter:

[0182] X k =F k x k−1 + B k u k + w k ;

[0183] Among them, Xk is the current state vector, including the current delay change Δ t k , x k−1 is the state vector at the previous moment, F k is the state transfer matrix, which describes the dynamic evolution relationship of the state variables. B k u k is the control input item, which describes the impact of external control input on the state. w k is the process noise;

[0184] Create the observation equation:

[0185] z k =H k x k +v k ;

[0186] Among them, z k is the observation vector, H k is the observation matrix, , v k To observe the noise, it reflects the sensor accuracy;

[0187] S64: f 0, fn} is the observation reference, and the received signal is calculated y ( t ) is used to update the state estimate through the Kalman gain, and the output delay compensation Δ t k ,right y ( t ) applies phase compensation:

[0188] y corrected ( t )= y (Δ t +Δ t k );

[0189] in, y corrected ( t ) is the phase compensation amount, Δ t is the delay spread;

[0190] S65: Extract the delay spread Δ of the corrected signal t >0.2μs is used as the basis for defect location, combining the delay spread Δt and the frequency domain attenuation slope dα / df , distinguish the types of crack defects.

[0191] In summary, the control method for a digital ultrasonic flaw detector calibration device provided by the present invention obtains a standard test block that matches the shape data of the material to be inspected and the material parameters, calculates the adaptive ultrasonic frequency and flaw detector parameters, creates and solves a multi-objective optimization model, and plans the inspection path for the material to be inspected based on the optimal solution. Ultrasonic waves with matching frequencies are then emitted to an implanted quantum dot marker array for fluorescence-ultrasound dual-modal signal correlation calibration. Scanning is performed along the planned inspection path, a time-varying channel model is established to predict the current delay variation, an autocorrelation algorithm is used to extract the fundamental frequency and harmonic components of the probe excitation pulse as a reference, a filter state vector is constructed based on the predicted current delay variation, phase correction is performed on the received signal, and defects in the material to be inspected are analyzed based on the corrected received signal. The integrated dynamic modeling of material parameters, collaborative calibration of multimodal signals, and adaptive path optimization improve detection accuracy and efficiency under complex working conditions.

[0192] By dynamically matching standard test blocks based on the quantitative material parameters of the material to be tested, and by using standard test blocks that are more suitable for the material to be tested, the calibration error of the verification device is effectively reduced compared to traditional methods. The defect location error is reduced through fluorescence-ultrasound dual-modality calibration technology, and the sound velocity discreteness and attenuation coefficient drift are corrected in real time through dynamic transfer functions to further improve the defect location accuracy. A multi-objective optimization model is established to integrate the beam width constraint and the signal-to-noise ratio target, so that the detection coverage of curved surfaces and porous structures is effectively improved. The quantum dot marker array is combined with the optimization process to achieve automatic focus scanning of micron-level defects. Through the adaptive genetic algorithm, the mutation probability optimizes the detection path, reduces invalid scanning, improves detection efficiency, and effectively reduces energy consumption. The delay prediction model and filtering are used to synergistically suppress phase drift, promoting the improvement of signal processing speed.

Claims

1. A control method for a digital ultrasonic flaw detector calibration device, characterized in that: The following steps are involved: S1: Test the material to be tested, obtain its shape data and material parameters, and call a matching standard test block according to the material parameters of the material to be tested; S2: Calculate the matching ultrasonic frequency and the specified parameters of the flaw detector according to the material parameters, create a multi-objective optimization model according to the shape data and the material parameters, find the best solution for the multi-objective optimization model, and plan the inspection path of the material to be inspected based on the best solution; S3: Emitting ultrasound waves of matching frequency, implanting quantum dot marker arrays, and performing fluorescence-ultrasound dual-modality signal correlation calibration; S4: Make the detection probe scan along the planned detection path, construct a dynamic transfer function that includes the attenuation coefficient and sound velocity discreteness of the material to be detected, and establish a time-varying channel model; S5: The time-varying channel model predicts the current delay variation based on the delay variation at the previous moment; S6: Use the autocorrelation algorithm to extract the fundamental frequency and harmonic components of the probe excitation pulse as a reference benchmark, construct the state vector of the specified filter based on the predicted current delay change, perform phase correction on the received signal through the specified filter, and analyze the defects of the material to be detected based on the corrected received signal; The specific process of emitting ultrasound waves of matching frequency, implanting the quantum dot marker array, and performing fluorescence-ultrasound dual-modal signal correlation calibration in step S3 is as follows: S31: Emitting ultrasonic waves of matching frequency and implanting a quantum dot marker array in the propagation path. The quantum dots are distributed according to a spatial coding pattern, using an orthogonal grid or spiral array, and setting the density of the quantum dot marker array. S32: A high-speed photoelectric detector is used to collect fluorescence signals, and an ultrasonic receiving probe is used to collect echo signals. The two channels are time-synchronized through hardware triggering. S33: Perform cross-correlation calculation on the peak value of the fluorescence pulse and the leading edge of the ultrasonic echo to compensate for the time delay caused by the difference in the transmission path; S34: A coordinate mapping model is constructed based on the array geometric parameters. The fluorescence intensity distribution and the ultrasonic sound pressure field distribution are fitted by the least squares method to eliminate the positioning deviation caused by beam diffusion or quantum dot drift, and realize the correlation calibration of fluorescence-ultrasound dual-modal signals.

2. The control method of a digital ultrasonic flaw detector calibration device according to claim 1, characterized in that: The specific process of testing the material to be tested and obtaining its shape data and material parameters in step S1 is as follows: S11: Obtain three-dimensional point cloud data of the surface morphology and internal structure of the material to be tested through a laser scanner; S12: Use point cloud processing algorithms to identify critical dimensions such as thickness steps, surface curvature, and aperture size, and construct a digital 3D model. S13: transmitting a standard ultrasonic wave to the material to be tested, measuring the two-way propagation time of the standard ultrasonic wave in the material to be tested and then calculating the sound velocity, comparing the amplitude ratio of the incident wave and the transmitted wave, and calculating the attenuation coefficient; S14: Derive the acoustic impedance of the material based on the amplitude of the interface reflection signal.

3. The control method of a digital ultrasonic flaw detector calibration device according to claim 2, characterized in that: The specific process of calling a matching standard test block according to the material parameters of the material to be tested in step S1 is as follows: S15: Quantify the sound velocity, attenuation coefficient and acoustic impedance into eigenvectors; S16: Match in the standard test block library according to the following priority: Material type matching: give priority to standard test blocks of the same base material type; ‌Material parameter tolerance screening‌: Screening meets | c 试块 − c 待检测材料 ∣≤2% and ∣ α 试块 ( f )−α 待检测材料 ( f )∣≤15% of candidate test blocks, among which c 试块表示 The standard value of the sound velocity of the sound wave of the specified frequency in the test block, c 待检测材料 It indicates the sound velocity measurement value of the sound wave of the same specified frequency in the material to be tested. α 试块 ( f ) indicates the frequency is f The sound attenuation coefficient of the sound wave in the test block, α 待检测材料 ( f ) indicates the frequency is f The acoustic attenuation coefficient of the sound wave in the material to be tested; Use matching test blocks to perform time base correction and sensitivity calibration on the detection equipment. When it is verified that the signal-to-noise ratio of the detection signal is improved by a specified amount and the defect positioning error is lower than a specified value, the matching is determined to be valid.

4. The control method of a digital ultrasonic flaw detector calibration device according to claim 1, characterized in that: The specific process of step S2 calculating the matching ultrasonic frequency and the specified parameters of the flaw detector according to the material parameters is as follows: S21: determining whether the material to be tested is a coarse-grained material or a fine-grained material based on the material parameters, and selecting the ultrasonic fundamental frequency based on whether the material is a coarse-grained material or a fine-grained material; S22: Create an optimal frequency calculation model; S23: Calculate the ultrasonic pulse width, gain compensation, sampling frequency and frequency band based on the material sound velocity, maximum thickness of the detection area and frequency-dependent attenuation coefficient.

5. The control method of a digital ultrasonic flaw detector calibration device according to claim 4, characterized in that: The specific process of creating a multi-objective optimization model based on shape data and material parameters, solving the multi-objective optimization model, scoring the solutions, selecting the solution with the highest score, and planning the inspection path of the material to be inspected based on the solution with the highest score is as follows: S24: Define the decision variables of the multi-objective optimization model: probe coordinate sequence ( xi , yi ), scanning angle θi and step size Δ si , establish material parameter constraints, frequency f ∈[ f min , f max ], beam width W =2 D sin θ ; S25: construct objective function; S26: Initialize the population, evenly distribute reference points in the target space, and perform adaptive crossover mutation based on the reference points; S27: Adjust the probe position using Gaussian mutation operator; S28: Performs coarse scanning at a constant speed along the optimized path. When a defect signal is triggered, it switches to fine scanning, with local grid density increased to 1mm. Bayesian optimization is used to adjust the probe angle and frequency in real time.

6. The control method of a digital ultrasonic flaw detector calibration device according to claim 1, characterized in that: In S4, the detection probe is scanned along the planned detection path, and a dynamic transfer function including the attenuation coefficient and sound velocity discreteness of the material to be detected is constructed. The specific process of establishing the time-varying channel model is as follows: S41: Generate a probe motion instruction sequence based on the path planning result, including probe coordinates, probe running speed, and probe running acceleration; S42: The actual position deviation is monitored by the encoder. When the error is greater than 0.1mm, PID closed-loop correction is triggered to achieve real-time feedback adjustment. S43: Attenuation Coefficient Modeling: Frequency Dependent Attenuation: α ( f )= α 0+ βf , α ( f ) is the sound wave frequency f The sound wave attenuation coefficient is α 0 is the fundamental frequency attenuation, β is the material dependence coefficient; f is the sound wave frequency, time-varying correction: α ( f , t )= α ( f )⋅[1+ γ Δ T ( t )]; in, α ( f , t ) is the time variation correction, at a frequency of f , time is t The sound wave attenuation coefficient is γ is the temperature coefficient, Δ T ( t ) is the real-time temperature difference; S44: Establish the sound velocity distribution field: c ( x , y )= c 0+Δ c ( x , y ),in, c ( x , y ) indicates the spatial position ( x , y ) is the sound velocity value at c 0 is the base value of the speed of sound, Δ c ( x , y ) is the speed of sound at position ( x , y ) relative to the reference sound speed c The deviation of 0 is used to describe the changes and fluctuations of sound speed in space. Through the inversion calculation of full matrix capture data, a dynamic update strategy is established: the sound speed map is updated after each specified path scan is completed; S45: Establish dynamic transfer function: H ( f , t )= e −α(f,t)⋅d(t) ⋅ e −j2πf[d(t) / c(x,y)] ； in, d ( t ) is the time-varying propagation distance, j is the imaginary unit, 2 πf [ d ( t ) / c ( x , y )] is the phase change, d ( t ) / c ( x , y ) is the propagation time of the sound wave, that is, the sound wave travels at a speed of c ( x , y ) Propagation distance d ( t ) S46: Use ray tracing to simulate the interface reflection path and superimpose the contributions of each path: ； in, H total ( f,t ):represents the total dynamic transfer function, which is obtained by ray tracing method after considering multiple interface reflection paths at a frequency of f , time is t When , the overall transfer function is obtained by superimposing the comprehensive contributions of all reflection paths to the signal; N is the total number of reflection paths participating in the superposition, Γ k For the k The reflection coefficient of each path, H k ( f , t ) is the k Dynamic transfer function of the reflection path; The sound field data is collected every 10ms and updated through the LMS adaptive filter α ( f , t ) and Δ c ( x , y ), the error between the theoretical prediction and the actual echo amplitude on the CSK-IA test block is less than ±1dB.

7. The control method of a digital ultrasonic flaw detector calibration device according to claim 1, characterized in that: The specific process of predicting the current delay variation based on the delay variation at the previous moment by the time-varying channel model in step S5 is as follows: Obtain the delay change and sound speed deviation at the previous moment, and set the initial delay. The time-varying channel model predicts the current delay change Δ based on the delay change at the previous moment, sound speed deviation and initial delay. τ k , the prediction formula is as follows: D τ k = a 1D τ k−1 + a 2D τ 0+ b 1 w k−1 ; Among them, Δ τ k is the current delay variation, a 1 is the transmission coefficient of the delay change at the previous moment, a 2 is the attenuation coefficient of the initial delay variation, b 1 is the process noise gain coefficient, which controls the disturbance intensity of the excitation noise on the delay prediction. w k−1 is the excitation term of the Gaussian white noise process, Δ τ k−1 is the delay variation at the previous moment, τ 0 is the initial delay change.

8. The control method of a digital ultrasonic flaw detector calibration device according to claim 7, characterized in that: The specific process of step S6 is as follows: S61: Acquisition of reference signal p ( t ), establish the autocorrelation function R ( τ )= ∫p ( t ) p ( t − τ ) dt , τ is the time delay, and the fundamental frequency is determined by the peak interval f 0; S62: Reference signal p ( t ) to perform FFT analysis to detect harmonics exceeding a specified amplitude fn , construct frequency reference set { f 0, f 1,..., fn }; S63: Create the state equation of the filter: X k =F k x k−1 + B k u k + w k ; Among them, X k is the current state vector, including the current delay change Δ τ k , x k−1 is the state vector at the previous moment, F k is the state transfer matrix, which describes the dynamic evolution relationship of the state variables. B k u k is the control input item, which describes the impact of external control input on the state. w k is the process noise; Create the observation equation: z k =H k x k +v k ; Among them, z k is the observation vector, H k is the observation matrix, , v k To observe the noise, it reflects the sensor accuracy; S64: f 0, fn } is the observation reference, and the received signal is calculated y ( t ) is used to update the state estimate through the filter gain, and output the current delay change Δ τ k ,right y ( t ) applies phase compensation: y corrected ( t )= y (D t +D τ k ); in, y corrected ( t ) is the phase compensation amount, Δ t is the delay spread; S65: Extract the delay spread of the corrected signal as a basis for defect location, and distinguish the crack defect type by combining the delay spread and the frequency domain attenuation slope.

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