Nondestructive testing ultrasonic imaging method based on spatial frequency spectrum coherence coefficient and coherence energy coefficient

Abstract

The invention relates to a nondestructive testing ultrasonic imaging method based on a spatial frequency spectrum coherence coefficient and a coherence energy coefficient, and belongs to the technical field of ultrasonic nondestructive testing. The technical problem that high resolution and high contrast cannot be both considered in a traditional delay superposition algorithm is solved. According to the technical scheme, the method comprises the steps of preprocessing ultrasonic echo signals; extracting a spatial spectrum coherence coefficient through discrete Fourier transform; fusing symbol coherence and energy distribution characteristics to generate a coherent energy coefficient; weighting a delay superposition result by using double coefficients; and outputting an optimized scanning line signal through adaptive FIR band-pass filtering. The method has the technical effects that the traditional balancing bottleneck of imaging resolution and contrast is broken through, interference artifacts in a metal homogeneous medium are remarkably inhibited, the micro-defect identification capability is enhanced, and the method is adaptive to diversified industrial detection scenes.

Description

Technical Field

[0001] The invention belongs to the technical field of ultrasonic non-destructive testing, and relates to a non-destructive testing ultrasonic imaging method based on spatial spectrum coherence coefficient and coherence energy coefficient. Background Art

[0002] Ultrasonic imaging technology exploits the reflection, scattering, and attenuation properties of ultrasound waves in a medium to generate a visual image of an object's internal structure by receiving and processing echo signals. In the field of industrial nondestructive testing, this technology uses the interaction of high-frequency sound waves with the internal structure of metal materials, combined with signal processing and image reconstruction methods, to achieve nondestructive detection and visualization of defect location, morphology, and size. It is widely used in industrial quality control and equipment safety assessment.

[0003] B-mode ultrasound imaging is the most widely used technology in industrial nondestructive testing (NDT). It generates two-dimensional grayscale images through multi-beam scanning. The delay-addition algorithm, a core fundamental algorithm in this field, has become a mainstream method for industrial inspection due to its simple principle and efficient computation. This algorithm consists of two phases: the transmit focusing phase, which precisely controls the delay of each element of the array transducer to achieve spatial superposition and enhancement of the ultrasonic beams in the target area; the receive phase, which dynamically compensates for the delay and coherently adds the multi-channel echoes to ultimately reconstruct an acoustic image of the object's internal structure.

[0004] However, the traditional delay and sum algorithm (DAS) has significant defects:

[0005] Insufficient beamforming quality: Mainlobe divergence leads to reduced lateral resolution, and sidelobe energy leakage severely reduces image contrast;

[0006] Weak noise suppression capability: Coherent clutter interference can easily produce artifacts in complex media, affecting defect recognition accuracy.

[0007] These defects make it difficult for the algorithm to meet the dual requirements of high resolution and high contrast, seriously restricting its application value in precision non-destructive testing and micro-defect identification.

[0008] Current improved algorithms, such as the generalized coherence coefficient method, are effective for scenarios with non-uniform sound velocity, but their ability to suppress artifacts in uniform metallic media is limited. While the coherence coefficient method improves contrast, it still suffers from insufficient resolution. Therefore, developing a new beamforming algorithm that can simultaneously overcome the bottlenecks of resolution and contrast has become an urgent technical need in the field of industrial ultrasonic testing. Summary of the Invention

[0009] In view of this, the purpose of the present invention is to provide a non-destructive testing ultrasonic imaging method based on spatial spectrum coherence coefficient and coherence energy coefficient, which can effectively improve the algorithm imaging contrast while maintaining the good resolution performance of the traditional high-resolution adaptive filtering beamforming algorithm, thereby improving the overall imaging quality of the algorithm.

[0010] In order to achieve the above object, the present invention provides the following technical solutions:

[0011] A non-destructive testing ultrasonic imaging method based on spatial spectrum coherence coefficient and coherence energy coefficient specifically comprises the following steps:

[0012] S1: Amplify, A / D convert, delay focus and digitally filter the echo signal received by the ultrasonic array element to obtain ultrasonic echo data x(n);

[0013] S2: Perform discrete Fourier transform on the delayed echo data, calculate the spectrum energy of each spatial frequency after discrete Fourier transform, and multiply the low-frequency spectrum energy by the total spectrum energy to obtain the spatial spectrum coherence coefficient;

[0014] S3: Calculate the coherent energy coefficient of the echo data of each imaging point after focusing delay;

[0015] S4: summing the delayed focused echo data of the N channels to obtain a delayed superposition result, and performing weighted processing on the delayed summation result of the N channel echo data using the solved spatial spectrum coherence coefficient and coherence energy coefficient;

[0016] S5: The weighted data is subjected to a 32-order FIR bandpass filter to obtain a scan line signal for adaptive beamforming, and the optimized scan line signal is used for final imaging;

[0017] Furthermore, in S2, discrete Fourier transform is performed on the delayed echo data to calculate the spectrum energy of each spatial frequency after discrete Fourier transform and multiply the low-frequency spectrum energy by the total spectrum energy to obtain the spatial spectrum coherence coefficient, which specifically includes the following steps:

[0018] S21: For a sensor array with N equally spaced elements, the discrete Fourier transform of the echo data after focusing delay is:

[0019]

[0020] Where k = 0 to N-1 is the spatial frequency index, j is the imaginary unit, i is the array number ranging from 0 to N-1, and d is the center distance between adjacent array elements.

[0021] S22: Multiply the low-frequency energy in the obtained echo data spatial spectrum by the total spatial spectrum energy to obtain the spatial spectrum coherence coefficient (SSCF):

[0022]

[0023] Where M is the low-frequency cutoff frequency, which is generally between 3 and 6.

[0024] Furthermore, in S3, the coherent energy coefficient of the echo data of each imaging point after the focus delay is solved, which specifically includes the following steps:

[0025] S31: Calculate the energy distribution coefficient of the echo data after focusing delay of a sensor array with N equally spaced elements:

[0026]

[0027] where x i (n) is the echo data of the ith channel of the nth imaging point, and sig() is the sign operation.

[0028] S32: Calculate the coherence coefficient of the echo data after focusing delay of a sensor array with N equally spaced elements:

[0029]

[0030] S33: Multiply the energy distribution coefficient by the coherence coefficient to obtain the coherence energy coefficient:

[0031]

[0032] Furthermore, in S4, the N-channel echo data are summed to obtain a delayed superposition result, and the obtained spatial spectrum coherence coefficient and coherence energy coefficient are used to perform weighted processing on the delayed summation result of the N-channel echo data, which specifically includes the following steps:

[0033] S41: Sum the echo data of N ultrasonic arrays to obtain a delay superposition result:

[0034]

[0035] Where n is the imaging point number, x i (n-Δ i )(i=1,...,N) represents the ultrasonic signal after focusing and delaying the N channel echoes, Δ i Indicates the delay time applied to each array element signal;

[0036] S42: Use the obtained spatial spectrum coherence coefficient and coherence energy coefficient to weight the delayed summation result of the echo data of the N channels:

[0037]

[0038] Furthermore, in S5, the 32nd-order FIR bandpass filter coefficient with a center frequency of 5.26 MHz and a bandwidth of 1.4 MHz (the frequency parameters of the imaging system of the present invention need to be adjusted accordingly for different imaging systems) is used as a weighting factor and multiplied by the output of the improved adaptive filtering structure to obtain the scan line signal of adaptive beamforming. The optimized scan line signal is used for final imaging:

[0039]

[0040] c n Represents the filter coefficient, which is the impulse response of the ideal filter multiplied by a window function. n In the frequency domain, it is expressed as a window function added to a specific frequency interval. The value outside this interval is 0 or a minimum value. Multiplying it with the original signal in the frequency domain, that is, performing convolution calculation in the time domain, can remove the value of the original signal outside the specific frequency interval in the frequency domain and filter out the clutter outside the passband frequency in the time domain.

[0041] Furthermore, in S1, the sensor array is a 128-element ultrasonic phased array detector, the element spacing is 0.75 mm, and the center frequency is 5.26 MHz;

[0042] In the S5, the center frequency of the 32nd-order FIR bandpass filter is 5.26 MHz, and the bandwidth is 1.4 MHz.

[0043] Furthermore, in the step S22 , the low-frequency cutoff frequency M has a value ranging from 3 to 6.

[0044] The beneficial effects of the present invention are that, compared to existing high-resolution adaptive filtering algorithms, the present invention can improve imaging contrast while maintaining high resolution. The present invention can significantly suppress noise and clutter in nondestructive testing ultrasonic imaging, overcoming the difficult trade-off between high image resolution and high contrast.

[0045] Other advantages, objects, and features of the present invention will be described in part in the following description and, in part, will be apparent to those skilled in the art upon examination of the following description or may be learned from practice of the present invention. The objects and other advantages of the present invention may be realized and obtained through the following description. BRIEF DESCRIPTION OF THE DRAWINGS

[0046] In order to make the purpose, technical solutions and advantages of the present invention more clear, the present invention will be described in detail below with reference to the accompanying drawings, in which:

[0047] Figure 1 is a flow chart of the ultrasonic imaging method of the present invention;

[0048] Figure 2 The overall structural framework diagram of the non-destructive testing ultrasonic imaging method based on spatial spectrum coherence coefficient and coherence energy coefficient;

[0049] Figure 3 This is a schematic diagram of the imaging area of the 20# steel test block;

[0050] Figure 4 This is a comparison image of 20# steel test block at 10mm depth;

[0051] Figure 5 This is the lateral resolution curve of 20# steel test block at 10mm depth;

[0052] Figure 6 This is a comparison chart of array performance index and contrast of different algorithms for 20# steel test blocks;

[0053] Figure 7 Schematic diagram of the imaging area of the aluminum test block;

[0054] Figure 8 This is a comparison image of an aluminum test block at a depth of 65mm;

[0055] Figure 9 This is the lateral resolution curve of the aluminum test block at a depth of 65 mm;

[0056] Figure 10 This is the lateral resolution curve of the imaging point of the aluminum test block (-18.5, 65). DETAILED DESCRIPTION

[0057] The following describes the embodiments of the present invention by means of specific examples, and those skilled in the art can easily understand other advantages and effects of the present invention from the contents disclosed in this specification. The present invention can also be implemented or applied through other different specific embodiments, and the details in this specification can also be modified or changed in various ways based on different viewpoints and applications without departing from the spirit of the present invention. It should be noted that the illustrations provided in the following embodiments are only schematic illustrations of the basic concept of the present invention, and the following embodiments and features in the embodiments can be combined with each other without conflict.

[0058] Among them, the accompanying drawings are only for illustrative purposes and represent only schematic diagrams rather than actual pictures, and should not be understood as limiting the present invention. In order to better illustrate the embodiments of the present invention, some parts of the accompanying drawings may be omitted, enlarged or reduced, and do not represent the dimensions of actual products. For those skilled in the art, it is understandable that some well-known structures and their descriptions may be omitted in the accompanying drawings.

[0059] The same or similar numbers in the drawings of the embodiments of the present invention correspond to the same or similar parts; in the description of the present invention, it should be understood that if there are terms such as "upper", "lower", "left", "right", "front", "back", etc. indicating directions or positional relationships, they are based on the directions or positional relationships shown in the drawings. They are only for the convenience of describing the present invention and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific direction, be constructed and operate in a specific direction. Therefore, the terms describing the positional relationship in the drawings are only used for illustrative purposes and cannot be understood as limiting the present invention. For ordinary technicians in this field, the specific meanings of the above terms can be understood according to specific circumstances.

[0060] See also Figures 1 to 10 , Figure 1 It is a flow chart of the method of the present invention, as shown in Figure 1 As shown, the present invention provides a non-destructive testing ultrasonic imaging method based on spatial spectrum coherence coefficient and coherence energy coefficient, comprising the following steps:

[0061] S1: Amplify, A / D convert, delay focus, and digitally filter the 32-channel echo signals received by the ultrasonic array element to obtain ultrasonic echo data x(n);

[0062] S2: Performing a discrete Fourier transform on the delayed echo data, calculating the spectrum energy of each spatial frequency after the discrete Fourier transform, and multiplying the low-frequency spectrum energy by the total spectrum energy to obtain the spatial spectrum coherence coefficient. Specifically, the following steps are included:

[0063] S21: For a sensor array with N equally spaced elements, the discrete Fourier transform of the echo data after focusing delay is:

[0064]

[0065] Where k = 0 to N-1 is the spatial frequency index, j is the imaginary unit, i is the array number ranging from 0 to N-1, and d is the center distance between adjacent array elements.

[0066] S22: Multiply the low-frequency energy in the obtained echo data spatial spectrum by the total spatial spectrum energy to obtain the spatial spectrum coherence coefficient (SSCF):

[0067]

[0068] Where M is the low-frequency cutoff frequency, which is generally between 3 and 6.

[0069] S3: Calculating the coherent energy coefficient of the echo data of each imaging point after the focus delay, specifically including the following steps:

[0070] S31: Calculate the energy distribution coefficient of the echo data after focusing delay of a sensor array with N equally spaced elements:

[0071]

[0072] where x i (n) is the echo data of the ith channel of the nth imaging point, and sig() is the sign operation.

[0073] S32: Calculate the coherence coefficient of the echo data after focusing delay of a sensor array with N equally spaced elements:

[0074]

[0075] S33: Multiply the energy distribution coefficient by the coherence coefficient to obtain the coherence energy coefficient:

[0076]

[0077] S4: summing the echo data of the N channels to obtain a delayed superposition result, and performing weighted processing on the delayed summation result of the echo data of the N channels using the obtained spatial spectrum coherence coefficient and coherence energy coefficient, specifically comprising the following steps:

[0078] S41: Sum the echo data of N ultrasonic arrays to obtain a delay superposition result:

[0079]

[0080] Where n is the imaging point number, x i (n-Δ i )(i=1,...,N) represents the ultrasonic signal after focusing and delaying the N channel echoes, Δ i Indicates the delay time applied to each array element signal;

[0081] S42: Use the obtained spatial spectrum coherence coefficient and coherence energy coefficient to weight the delayed summation result of the echo data of the N channels:

[0082]

[0083] S5: The 32nd-order FIR bandpass filter coefficient with a center frequency of 5.26 MHz and a bandwidth of 1.4 MHz (the frequency parameters of the imaging system of the present invention need to be adjusted accordingly for different imaging systems) is used as a weighting factor and multiplied by the output of the improved adaptive filtering structure to obtain the scan line signal of adaptive beamforming. The optimized scan line signal is used for final imaging:

[0084]

[0085] cn Represents the filter coefficient, which is the impulse response of the ideal filter multiplied by a window function. n In the frequency domain, it is expressed as a window function added to a specific frequency interval. The value outside this interval is 0 or a minimum value. Multiplying it with the original signal in the frequency domain, that is, performing convolution calculation in the time domain, can remove the value of the original signal outside the specific frequency interval in the frequency domain and filter out the clutter outside the passband frequency in the time domain.

[0086] Experimental verification:

[0087] This experiment used a 128-element ultrasonic phased array probe to inspect 20# steel and aluminum test blocks with side holes. Ultrasonic signals were transmitted and acquired using B-mode imaging mode to verify the imaging method. The ultrasonic probe used a 5L128-0.75x10-C58-P-110-2.0-D1 phased array transducer. This transducer has a center frequency of 5.26 MHz, an element spacing of 0.75 mm, and a total of 128 elements. The data acquisition equipment was a self-designed data acquisition system with a sampling frequency of 50 MHz. It supports 128-element B-mode sliding line scan data acquisition and real-time imaging. The experimental parameters are shown in Table 1.

[0088] Table 1 Experimental parameter settings

[0089]

[0090]

[0091] This paper uses a 20# steel test block for ultrasonic imaging verification. 20# steel is a commonly used low-carbon steel with a low carbon content of about 0.2%. Therefore, it has good plasticity, toughness and welding properties, making it widely used in construction, machinery manufacturing and engineering structures. The longitudinal wave speed of the 20# steel experimental test block is 5900m / s. Figure 3 As shown, the depth of the imaging area focused in this experiment is 10mm, the simulated defect point is a circular through-hole with a diameter of 1mm, and the through-hole spacing is 3mm, with a total of 11 points to be detected. During the experiment, B-type sliding line scanning is used to collect raw imaging data. Each time, 32 array elements are excited to form a scan line, and different 32 array elements are excited in turn, and a total of 193 scan lines are excited to form a frame of raw image data. The raw imaging data is collected and processed using the traditional time delay superposition (DAS) algorithm, the generalized coherence coefficient (GCF) algorithm, the coherence coefficient (CF) algorithm, and the adaptive weighting algorithm proposed in this paper to obtain the imaging image as shown in the figure. Figure 4 shown.

[0092] Imaging results such as Figure 4As shown in the figure, the traditional time-lapse focusing imaging method only utilizes the amplitude information of the scattered echo signal, and does not fully utilize the phase information contained in the scattered signal. It is easily affected by noise interference, resulting in a large number of artifacts in the imaging image and reducing the image quality. The GCF imaging method uses the spatial spectrum coherence of the echo data to weight the imaging data, effectively improving the imaging resolution and suppressing background clutter. However, the GCF algorithm has a good suppression effect on the focusing error caused by the unevenness of the sound velocity. In the metal medium scenario with uniform sound velocity, the improvement of the imaging quality is limited, and some clutter information and artifacts still exist in the imaging image. CF imaging is based on the amplitude information of the scattered signal and introduces a phase coherence factor to weight the scattered signal, so that the coherence of the scattered signal at the defect is higher and the coherence of the scattered signal at the non-defect is lower, effectively reducing the number of artifacts in the imaging results and improving the image quality. The CEF algorithm uses symbol coherence and energy distribution characteristics to weight the scattered signals. This effectively preserves the scattered signals at defects with strong coherent energy and fully suppresses the scattered signals at non-defect locations with weaker coherent energy. This effectively reduces the number of artifacts in the imaging results, improves image resolution, and achieves superior imaging quality improvements compared to the CF algorithm. The SSCF algorithm uses the differences in the spatial spectrum between defects and non-defects to weight the imaging data, achieving the best resolution improvement while also effectively suppressing clutter and artifacts. The SSCF-CRF algorithm combines the coherent energy characteristics with the differences in the spatial spectrum to achieve the best imaging results in the control group. As can be seen from the comparison images, clutter and artifacts are completely suppressed while maintaining the imaging information of the defect area. The array performance index and contrast values for the selected imaging areas are shown in Table 2.

[0093] Table 2 Comparison of imaging indicators

[0094] method DAS GCF CF CEF SSCF SSCF-CEF Array Performance Index 1.3990 1.0959 0.9359 0.8098 0.6861 0.7395 Contrast 2.0559 8.3355 9.1130 9.1636 9.4582 11.0661

[0095] As can be seen, compared with DAS, the GCF and CF algorithms significantly improve image quality. The GCF algorithm's array performance index decreases by 21.67% while contrast increases by 305.44%, while the CF algorithm's array performance index decreases by 33.10% while contrast increases by 343.26%. Furthermore, the proposed CEF, SSCF, and SSCF-CEF algorithms reduce their column performance index by 42.12%, 50.96%, and 47.14%, respectively, while contrast increases by 345.72%, 360.05%, and 438.26%, respectively. The SSCF-CEF algorithm achieves the greatest improvement in imaging quality.

[0096] In order to systematically evaluate the contrast enhancement performance of the algorithm proposed in this paper in non-destructive testing ultrasonic imaging, this section uses a standard aluminum test block for experimental verification. Figure 7 As shown in the figure, the experiment uses an imaging area with a depth of 65mm, and sets up multiple groups of artificial simulated defects inside the test block: including 5 circular through holes with a diameter of 2mm arranged at equal intervals (spacing 5mm), and two groups of through holes of the same size with a spacing of 10mm. This defect layout is designed to evaluate the algorithm's ability to resolve adjacent defects and the difference in imaging contrast under different spacing conditions. The experimental data is collected in B-type linear scanning mode, with ultrasonic signals transmitted and received by a phased array probe, and the sampling frequency is set to 50MHz to ensure signal integrity. In order to fully verify the performance of the algorithm, different imaging methods are applied to the same data set. By comparing the imaging results of different algorithms, key indicators such as lateral resolution and background clutter suppression level can be quantitatively analyzed.

[0097] As can be seen from the imaging results, consistent with the experimental results in the previous section, the DAS algorithm introduces a large amount of clutter during the imaging process and results in low resolution. The GCF and CF algorithms, on the other hand, effectively suppress background noise by leveraging coherence, thereby improving imaging quality to a certain extent. The CEF algorithm proposed in this paper introduces symbolic coherence and energy distribution characteristics to weight the scattered signal, significantly suppressing background noise and artifacts and improving overall image quality. The SSCF algorithm utilizes spatial spectral differences to weight the imaging data, further reducing clutter artifacts and improving imaging resolution. The SSCF-CEF algorithm combines the spatial spectral differences and coherent energy characteristics of the echo data to achieve optimal imaging results. Figure 9 and Figure 10 The lateral resolution curve at a depth of 65 mm is shown. It is clear that the DAS algorithm achieves a full width at half maximum of 3.86 mm and the highest sidelobe level, while the SSCF-CEF algorithm exhibits the lowest sidelobe level and the lowest full width at half maximum of 1.78 mm, further verifying the effectiveness of the proposed algorithm in improving imaging resolution.

[0098] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not limiting. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention can be modified or replaced by equivalents without departing from the purpose and scope of the technical solutions, which should all be included in the scope of the claims of the present invention.

Claims

1. A non-destructive testing ultrasonic imaging method based on spatial spectrum coherence coefficient and coherence energy coefficient, characterized by: The specific steps include: S1: Amplify, A / D convert, delay focus and digitally filter the echo signal received by the ultrasonic array element to obtain ultrasonic echo data x(n); S2: Perform discrete Fourier transform on the delayed echo data, calculate the spectrum energy of each spatial frequency after discrete Fourier transform, and multiply the low-frequency spectrum energy by the total spectrum energy to obtain the spatial spectrum coherence coefficient; S3: Calculate the coherent energy coefficient of the echo data of each imaging point after focusing delay; S4: summing the delayed focused echo data of the N channels to obtain a delayed superposition result, and performing weighted processing on the delayed summation result of the N channel echo data using the solved spatial spectrum coherence coefficient and coherence energy coefficient; S5: The weighted data is subjected to a 32-order FIR bandpass filter to obtain a scan line signal for adaptive beamforming, and the optimized scan line signal is used for final imaging.

2. The nondestructive testing ultrasonic imaging method based on spatial spectrum coherence coefficient and coherence energy coefficient according to claim 1, characterized in that: In S2, discrete Fourier transform is performed on the delayed echo data, the spectrum energy of each spatial frequency after discrete Fourier transform is solved, and the low-frequency spectrum energy is multiplied by the total spectrum energy to obtain the spatial spectrum coherence coefficient, which specifically includes the following steps: S21: For a sensor array with N equally spaced elements, the discrete Fourier transform of the echo data after focusing delay is: Where k = 0 to N-1 is the spatial frequency index, j is the imaginary unit, i is the array number, ranging from 0 to N-1, and d is the center distance between adjacent array elements; S22: Multiply the low-frequency energy in the obtained echo data spatial spectrum by the total energy of the spatial spectrum to obtain the spatial spectrum coherence coefficient (SSCF): Where M is the low-frequency cutoff frequency.

3. The nondestructive testing ultrasonic imaging method based on spatial spectrum coherence coefficient and coherence energy coefficient according to claim 1, characterized in that: In S3, solving the coherent energy coefficient of the echo data of each imaging point after the focus delay specifically includes the following steps: S31: Calculate the energy distribution coefficient of the echo data after focusing delay of a sensor array with N equally spaced elements: where x i (n) is the echo data of the ith channel of the nth imaging point, and sig() is the sign operation; S32: Calculate the coherence coefficient of the echo data after focusing delay of a sensor array with N equally spaced elements: S33: Multiply the energy distribution coefficient by the coherence coefficient to obtain the coherence energy coefficient:

4. The non-destructive testing ultrasonic imaging method based on spatial spectrum coherence coefficient and coherence energy coefficient according to claim 1, characterized in that: In S4, the N-channel echo data are summed to obtain a delayed superposition result, and the delayed summation result of the N-channel echo data is weighted using the obtained spatial spectrum coherence coefficient and coherence energy coefficient, which specifically includes the following steps: S41: Sum the echo data of N ultrasonic arrays to obtain a delay superposition result: Where n is the imaging point number, x i (n-Δ i )(i=1,...,N) represents the ultrasonic signal after focusing and delaying the N channel echoes, Δ i Indicates the delay time applied to each array element signal; S42: Use the obtained spatial spectrum coherence coefficient and coherence energy coefficient to weight the delayed summation result of the echo data of the N channels:

5. The non-destructive testing ultrasonic imaging method based on spatial spectrum coherence coefficient and coherence energy coefficient according to claim 1, characterized in that: In S5, the 32nd-order FIR bandpass filter coefficient with a center frequency of 5.26 MHz and a bandwidth of 1.4 MHz is used as a weighting factor to multiply the output of the improved adaptive filter structure to obtain the scan line signal of adaptive beamforming. The optimized scan line signal is used for final imaging: c n Represents the filter coefficient, which is the impulse response of the ideal filter multiplied by a window function; c n In the frequency domain, it is expressed as a window function added to a specific frequency interval. The value outside this interval is 0 or a minimum value. It is multiplied with the original signal in the frequency domain, that is, convolution calculation is performed in the time domain. The value of the original signal outside the specific frequency interval is removed in the frequency domain, and the clutter outside the passband frequency is filtered out in the time domain.

6. The non-destructive testing ultrasonic imaging method based on spatial spectrum coherence coefficient and coherence energy coefficient according to claim 1, characterized in that: In S1, the sensor array is a 128-element ultrasonic phased array detector with an element spacing of 0.75 mm and a center frequency of 5.26 MHz; In the S5, the center frequency of the 32nd-order FIR bandpass filter is 5.26 MHz, and the bandwidth is 1.4 MHz.

7. The nondestructive testing ultrasonic imaging method based on spatial spectrum coherence coefficient and coherence energy coefficient according to claim 2, characterized in that: In the step S22 , the low-frequency cutoff frequency M has a value range of 3 to 6.

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