Array ultrasonic-based mortar layer bonding state detection method and system

Abstract

The present application relates to the mortar layer detection technical field, especially in kind of mortar layer bonding state detection method and system based on array ultrasonic, method includes: using two-dimensional ultrasonic phased array probe to the mortar layer carries out full matrix capture scanning, obtains original data;The original data is handled to total focusing algorithm, obtains the reconstruction image containing mortar matrix interface, and extracts the interface echo signal corresponding to the interface line on the test point;The interface echo signal is continuously wavelet transformed, and the energy form graph of signal energy distribution in time-frequency plane is obtained;The time-frequency transient distortion degree index is constructed, which is used for characterizing the distortion degree of the energy form graph relative to the preset reference form graph;According to the time-frequency transient distortion degree index, the bonding defect of the mortar layer is identified. The present application improves the accuracy and reliability of detection.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of mortar layer detection, and in particular to a mortar layer bonding state detection method and system based on array ultrasonic waves. BACKGROUND

[0002] In building engineering quality detection, the bonding quality of the mortar layer and the base body (such as concrete, brick wall) is a key factor determining the safety and durability of the structure. The current detection methods mainly include: manual knocking method, core drilling sampling method and traditional single-point ultrasonic detection method. The manual knocking method is highly subjective, low in efficiency and cannot accurately locate minor defects; the core drilling method is a destructive detection method that will damage the structural integrity and can only be used for local sampling inspection; and the traditional ultrasonic method usually uses a single probe for A-scan or B-scan, and determines the bonding state by analyzing the amplitude of the interface echo.

[0003] Chinese patent document with publication number CN115436880B discloses an adaptive ultrasonic detection method and system based on a microphone array. It includes constructing a microphone array, wherein the microphone array includes N array-distributed microphones, and the detection end faces of all microphones are located on the same plane; using the above-mentioned constructed microphone array to perform spatial ultrasonic scanning; for any position point in the spatial ultrasonic scanning, determining the position point direction spectrum corresponding to the position point; and for the position point direction spectrum of all spatial scanning position points, selecting the position point corresponding to the maximum position point direction spectrum as the spatial sound source position.

[0004] The traditional ultrasonic-based mortar layer bonding state detection method relies too much on the amplitude of the interface echo. The echo amplitude will be attenuated and disturbed by various factors unrelated to the bonding state during propagation, such as uneven thickness of the coupling agent between the probe and the mortar surface, small pores or aggregate distribution within the mortar layer, etc. This leads to serious problems in distinguishing subtle state differences such as weak bonding and tight bonding, i.e., similar amplitudes corresponding to different bonding states, and different amplitudes corresponding to the same bonding state, resulting in high miss rate and misjudgment rate, which cannot meet the requirements of modern engineering for detection accuracy and reliability. SUMMARY

[0005] In order to solve the problem of low accuracy of detection results when using ultrasonic waves to detect the bonding state of the mortar layer, the present application provides a mortar layer bonding state detection method and system based on array ultrasonic waves.

[0006] In the first aspect, the present application provides a mortar layer bonding state detection method based on array ultrasonic waves, which adopts the following technical scheme:

[0007] A mortar layer bonding state detection method based on array ultrasonic waves, comprising the steps of: using a two-dimensional ultrasonic phased array probe to perform full matrix capture scanning on the mortar layer to obtain original data; performing total focusing algorithm processing on the original data to obtain a reconstructed image containing a mortar matrix interface, and extracting interface echo signals corresponding to the to-be-detected points on the interface line; performing continuous wavelet transform on the interface echo signals to obtain an energy pattern graph representing the energy distribution of the signals in the time-frequency plane; constructing a time-frequency transient distortion degree index for representing the distortion degree of the energy pattern graph relative to a preset reference pattern graph, the construction method comprising: calculating the energy deviation degree of the energy pattern graph and the reference pattern graph, and a distortion energy graph, the distortion energy graph being positively correlated with the energy deviation degree, and performing weighted aggregation on the distortion energy graph to obtain the time-frequency transient distortion degree index, wherein the weight used in the weighted aggregation depends on both the core area of the reference signal and the abnormal area of the distortion energy graph; and identifying the bonding defects of the mortar layer according to the time-frequency transient distortion degree index.

[0008] By using the two-dimensional ultrasonic phased array probe to perform full matrix capture scanning and combining the total focusing algorithm, a high-resolution reconstructed image is obtained. The present application constructs a time-frequency transient distortion degree index. The index comprehensively analyzes the distortion of the signal in the time-frequency domain from multiple dimensions of energy pattern and energy deviation degree by performing continuous wavelet transform on the echo signal, and uses a composite weighting method considering the core area of the reference signal and the abnormal area of the distortion to aggregate. It can deeply excavate the subtle features related to the bonding defects in the signal, effectively overcome the misjudgment and omission problems caused by the interference of coupling agents, material internal structure and other factors in the traditional method, and thus improve the identification accuracy and reliability of the bonding defects of the mortar layer, especially the subtle state differences such as weak bonding.

[0009] Preferably, the extraction of the interface echo signal comprises: setting a key area in the reconstructed image; identifying the mortar matrix interface line in the key area using the Canny edge detection algorithm; taking each pixel point on the interface line as a to-be-detected point, and extracting the echo signal of each to-be-detected point as the interface echo signal.

[0010] The Canny edge detection algorithm can automatically, accurately and repeatedly identify the mortar matrix interface line. The automation and efficiency of signal extraction are improved, the accuracy and consistency of the data used for subsequent analysis are ensured, and a solid foundation is provided for the reliability of the entire detection method.

[0011] Preferably, the detection method further comprises: calculating the time-frequency entropy of the to-be-detected point, and performing normalization processing on the time-frequency entropy to obtain a time-frequency scattering factor, the expression of the time-frequency entropy being:

[0012]

[0013] wherein, represents the The first online interface The time-frequency entropy of the test point; Indicates the first The first online interface The test points at time and frequency points Energy pattern diagram at the location; It is a very small positive number.

[0014] By calculating the time-frequency entropy of the signal energy morphology diagram and performing normalization, the time-frequency scattering factor is obtained, transforming the ultrasonic scattering effect caused by bonding defects from a vague qualitative concept into a precise quantitative value.

[0015] Preferably, the expression for the distortion energy map is:

[0016]

[0017] in, Indicates the first The first online interface The test points at time and frequency points Distortion energy map at the location; Indicates the first The first online interface The test points at time and frequency points Energy deviation at the location; Indicates the first The first online interface The time-frequency scattering factor of the test point.

[0018] By multiplying the energy deviation, which characterizes the difference in energy values, with the time-frequency scattering factor, which characterizes the disorder of energy distribution, a more robust distortion evaluation model is established. Compared to considering either factor alone, this combined approach can effectively distinguish between genuine defect signals and ordered signal changes caused by non-defects, thereby reducing misjudgments caused by fluctuations in a single indicator and improving the specificity of defect identification.

[0019] Preferably, the detection method further includes: calculating an information fidelity weighted operator, the expression of which is:

[0020]

[0021] in, Indicates the first The first online interface The test points at time and frequency points Information fidelity weighted operator; It is a reference energy fingerprint obtained using a standard test block; It is the transition sharpness coefficient; It is a preset importance threshold, and max represents the maximum value function.

[0022] Preferably, the detection method further includes: calculating a dynamic anomaly weighted operator, the expression of which is:

[0023] ;

[0024] in, Representing time and frequency points Dynamic anomaly weighted operator; Indicates the first The first online interface The test points at time and frequency points Distortion energy map at the location; It is the distortion sensitivity coefficient.

[0025] This weighting dynamically assigns higher analytical weight to regions with high distortion energy values ​​(i.e., anomalous regions). This design ensures that newly generated anomalous signals caused by defects (which may appear in non-core regions) are not ignored. Compared to methods that only focus on the core region of the reference signal, this approach can sensitively capture various defect signals, improving detection sensitivity.

[0026] Preferably, before calculating the time-frequency transient distortion index, the method further includes calculating the composite weight, expressed as:

[0027]

[0028] in, Representing time and frequency points Composite weights, Indicates the first The first online interface The test points at time and frequency points Information fidelity weighted operator at the location, Representing time and frequency points Dynamic anomaly weighted operator.

[0029] By fusing the information fidelity weighting operator and the dynamic anomaly weighting operator into a composite weight, high weights are assigned to both distortions located in the core signal region and newly emerging anomaly regions. Compared to using a single weight, this composite weight balances the robustness (noise suppression) and sensitivity (anomaly detection) of the analysis, making the subsequent weighted aggregation process more comprehensive and accurate.

[0030] Preferably, the expression for the time-frequency transient distortion index is:

[0031]

[0032] in, Indicates the first The first online interface The time-frequency transient distortion index of each test point Representing time and frequency points Composite weights, Indicates the first The first online interface The test points at time and frequency points The distortion energy map at that location. This represents a minimum value.

[0033] By performing a composite weighted average of the distortion energy map, the complex time-frequency analysis results are ultimately condensed into a single, normalized numerical index. This index intuitively reflects the degree of adhesion anomaly at each test point. Compared to existing technologies that require manual interpretation of complex waveforms or images, this invention provides clear and quantitative evaluation results, facilitating subsequent automated processing and threshold determination.

[0034] Preferably, the method for identifying bonding defects in the mortar layer based on the time-frequency transient distortion index is as follows: the calculated time-frequency transient distortion index of each test point is arranged according to its physical position on its respective interface line to form a two-dimensional bonding quality index matrix; using The drawing library renders the bonding quality index matrix as Scanned image;

[0035] Will Pixels in the scanned image with an adhesion quality index higher than a preset threshold are initially marked as defective pixels, while those below the preset threshold are marked as healthy pixels, thus converting the pseudo-color image into a black-and-white binary image; using The clustering algorithm clusters the binary image, and identifies clusters with more than a preset threshold of white pixels as defect regions.

[0036] By rendering the distortion index into a C-scan image and processing the binarized image using the DBSCAN clustering algorithm, this invention achieves automatic identification of defect regions. Compared to simple threshold segmentation, the DBSCAN algorithm can identify defect clusters of arbitrary shapes and effectively filter out isolated noise points, thereby more accurately delineating the actual contour and extent of defects and providing users with more intuitive and precise detection results.

[0037] Secondly, the present invention provides a mortar layer bonding state detection system based on array ultrasonic waves, which adopts the following technical solution:

[0038] A mortar layer bonding state detection system based on array ultrasonic waves includes a processor and a memory. The memory stores computer program instructions, which, when executed by the processor, implement the mortar layer bonding state detection method based on array ultrasonic waves described above.

[0039] The above-mentioned method for detecting the bonding state of mortar layers based on array ultrasonic waves is generated into a computer program and stored in a memory so that it can be loaded and executed by a processor. Thus, a system can be made based on the memory and processor for convenient use.

[0040] The present invention has the following technical effects:

[0041] This invention evaluates the bonding state of mortar layers using a time-frequency transient distortion index based on time-frequency analysis. By comprehensively evaluating the energy deviation and scattering degree of ultrasonic signals in the time-frequency domain, and employing a composite weighting strategy that combines information fidelity and dynamic anomalies, it can effectively suppress noise interference, accurately capture the essential signal distortion caused by bonding defects, improve the accuracy and reliability of detection, and has a good effect in identifying subtle defects such as weak bonding. Attached Figure Description

[0042] Figure 1 This is a flowchart of a method for detecting the bonding state of mortar layers based on array ultrasonic waves according to the present invention. Detailed Implementation

[0043] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0044] This invention discloses a method for detecting the bonding state of mortar layers based on array ultrasonic waves, referring to... Figure 1 The process includes the following steps, as detailed below:

[0045] S1: High-dimensional wave field data acquisition and preprocessing.

[0046] A two-dimensional ultrasonic phased array probe is placed on the surface of the mortar layer to be tested. Exemplarily, the probe includes... Each array element has a preferred center frequency. This frequency provides sufficient resolution to identify minute defects while ensuring detection depth. Then, full matrix capture is performed. Scanning mode. In this mode, each element in the array is activated sequentially as a transmitter, and simultaneously, all... Each array element acts as a receiver, synchronously recording the complete time-domain echo signal. To achieve comprehensive coverage of the detection area, the probe moves along a preset scanning path and performs continuous scanning. Second data collection.

[0047] For the Second-rate( The system collects and outputs a three-dimensional raw data cube, denoted as... .in, For the index of the transmitting array element, For the index of the receiving array element, This refers to the time sampling point. In this embodiment, to fully capture the interface echo, the length of the time window is set to... Each sampling unit has a dimension of 1. Therefore, the original data generated in each sampling session has a dimension of 1. Each data element Representative by the array element The emitted ultrasonic waves, after propagating and reflecting within the structure, are absorbed by the array elements. At any moment The amplitude of the received signal.

[0048] S2: Image reconstruction and interface echo extraction.

[0049] For each raw data cube obtained from collection Using total focusing ( The algorithm is used to reconstruct the image and obtain the reconstructed image. The algorithm generates a high-resolution physically reconstructed image at each scan position by precisely compensating for the time delay and coherently superimposing the signals of all transmit-receive pairs based on the acoustic path to each pixel in the image. The collection yielded a total of [number] samples. Zhang reconstructed image.

[0050] Based on prior knowledge of the approximate thickness of the mortar layer, a key area requiring focused analysis is defined in each reconstructed image. Within this area, [the following method is used]. The edge detection algorithm automatically searches for and identifies the line segment with the strongest energy and most continuous continuity. This line segment is defined as the physical interface line of the mortar matrix. It should be noted that each reconstructed image corresponds to one line segment.

[0051] The first The first on the interface line of the reconstructed image Each pixel is defined as a point to be measured, and using... The algorithm extracts the interface echo signal corresponding to the point to be measured, denoted as . .

[0052] S3: Construct a time-frequency transient distortion index.

[0053] Extracted interface echo signal Perform standard continuous wavelet transform ( This is then mapped onto a two-dimensional time-frequency plane to obtain wavelet transform coefficients. Subsequently, the wavelet transform coefficients are squared to obtain a result that intuitively reflects the signal energy over time. and frequency The energy fingerprint of the distribution is obtained. Finally, the energy fingerprint is probabilistically processed, i.e., normalized, so that its integral over the entire time-frequency plane is 1, to obtain the first... The first online interface Energy pattern diagram of each test point To perform quantitative comparisons, a benchmark is needed. Therefore, steps S1 and S2 are repeated on a standard test block whose bonding condition has been confirmed to be intact to generate an energy reference pattern representing an ideal healthy state, denoted as . .

[0054] S31: Construct the distortion energy map.

[0055] Distortion energy maps are used to quantify the energy pattern of the signal under test. Relative to energy reference pattern The degree of deviation. The calculation process includes the following steps:

[0056] (1) Calculate the energy deviation.

[0057] The energy deviation is obtained by calculating the squared difference between the energy of the signal under test and the reference signal at each time frequency point. The expression is as follows:

[0058]

[0059] in, Indicates the first The first online interface Energy deviation at each test point Indicates the first The first online interface The test points at time and frequency points Energy pattern diagram at the location; This is an energy reference shape diagram. The formula calculates both at each time frequency point. The square of the difference in energy values ​​not only reflects the magnitude of the deviation, but also amplifies significant differences through squaring, while eliminating the influence of the direction of deviation.

[0060] (2) Calculate the time-frequency entropy.

[0061] Bonding defects (such as microvoids) cause scattering of ultrasonic waves, resulting in a shift from concentrated to diffuse energy distribution in the time-frequency plane, i.e., increased disorder. The degree of disorder in the energy distribution of the measured signal is quantified by calculating the time-frequency entropy, expressed as:

[0062]

[0063] in, Indicates the first The first online interface The time-frequency entropy of each test point is used to quantify the degree of disorder in the energy distribution of the test signal; Indicates the first The first online interface The test points at time and frequency points Energy pattern diagram at the location; It is a very small positive number, for example This is used to prevent the independent variable in the logarithmic function from being zero. According to information theory, the information content of an event is the negative of the logarithm of its probability of occurrence. Here, the entire energy pattern diagram is considered as a probability distribution, and the time-frequency entropy... That is, all time and frequency points The entropy is the sum of the expected information content. A larger entropy value indicates a more diffuse and disordered energy distribution. Similarly, the reference entropy can be calculated. .

[0064] (3) Calculate the time-frequency scattering factor.

[0065] The expression for the time-frequency scattering factor is:

[0066]

[0067] in, Indicates the first The first online interface Time-frequency scattering factor of each test point; Indicates the first The first online interface The time-frequency entropy of the test point; It is the reference entropy; This is the time-frequency sharpness coefficient, used to determine the response speed of the scattering factor to changes in the entropy ratio. In this embodiment, The value is .

[0068] When the disorder level of the signal under test is similar to that of the reference signal, The entropy ratio is close to The independent variable is close to , The value approaches When the signal under test becomes more chaotic due to defect scattering, If the value increases, the independent variable becomes positive. The value approaches Conversely, if the signal is more ordered, Approaching .therefore, The higher the value, the more diffuse the signal energy, and the greater the possibility of defects.

[0069] (4) Generate a distortion energy map.

[0070]

[0071] in, Indicates the first The first online interface The test points at time and frequency points Distortion energy map at the location; Indicates the first The first online interface The test points at time and frequency points Energy deviation at the location; Indicates the first The first online interface The time-frequency scattering factor of the test point.

[0072] The distortion at each time-frequency point is determined by multiplying the energy deviation by the degree of macroscopic disorder in the energy distribution. Only when a time-frequency point exhibits both a significant energy deviation and a disordered energy distribution across the entire signal is the corresponding distortion energy value determined. This is significant, which effectively suppresses misjudgments caused by ordered energy changes due to non-defect factors.

[0073] S32: Weighted aggregation of core areas.

[0074] Distortion energy map Although all discrepancies are identified, they contain a significant amount of unreliable information caused by noise and located outside the core energy region of the signal. To obtain a robust single numerical metric that can quantify the severity of the defect, the distortion energy at all time and frequency points must be weighted and aggregated.

[0075] (1) Calculate the information fidelity weighted operator.

[0076] In an ideal, defect-free environment, signal energy concentrates in specific time-frequency core regions, where the signal-to-noise ratio is highest and the information is most reliable. Therefore, these regions should be given higher weight, as expressed by:

[0077]

[0078] in, Indicates the first The first online interface The test points at time and frequency points Information fidelity weighted operator; It is a reference energy fingerprint obtained using a standard test block; It is the transition sharpness coefficient, preferably... This is used to control the steepness of the transition of weights from the non-core area to the core area; It is a preset importance threshold, preferably This is used to define the energy threshold of the core region, where `max` represents the maximum value function. For time-frequency points with higher energy in the reference energy fingerprint, their normalized energy value is larger, and the information fidelity weighting operator... The closer the value is to Conversely, for background noise regions, the value tends to be... .

[0079] (2) Calculate the dynamic anomaly weighted operator.

[0080] Information fidelity operators only focus on the core region of the reference signal, potentially ignoring the nascent anomaly energy generated by certain defects in non-core regions. Therefore, a dynamic anomaly weighting operator is needed to assign higher weights to the distortion itself, expressed as:

[0081]

[0082] in, Representing time and frequency points Dynamic anomaly weighted operator; Indicates the first The first online interface The test points at time and frequency points Distortion energy map at the location; It is the distortion sensitivity coefficient, preferably This is used to control the response speed to distortion changes. When the distortion energy at a certain time frequency point... When the value is very small, the value of the operator approaches 1. As the distortion energy increases, the value of this operator rapidly approaches [value missing]. .

[0083] (3) Weighted aggregation generates time-frequency transient distortion index.

[0084] First, construct a composite weight. By integrating the aforementioned information fidelity weighting operator and dynamic anomaly weighting operator, it is ensured that only time-frequency regions simultaneously identified as both reliable and distorted are assigned high weights. The construction method is as follows:

[0085]

[0086] in, Representing time and frequency points Composite weights, Indicates the first The first online interface The test points at time and frequency points Information fidelity weighted operator at the location, Representing time and frequency points Dynamic anomaly weighted operator.

[0087] This formula is equivalent to the logical "OR" operation, ensuring that as long as either of the two operators has a larger value, the composite weight will be larger.

[0088] Finally, by weighted averaging the distortion energy maps, the final time-frequency transient distortion index is obtained, expressed as:

[0089]

[0090] in, Indicates the first The first online interface The time-frequency transient distortion index of each test point Representing time and frequency points Composite weights, Indicates the first The first online interface The test points at time and frequency points The distortion energy map at that location. This represents a very small value to prevent the denominator from being 0; for example, its value is 0.001.

[0091] This formula calculates the total distortion energy after weighting by the composite weights, and divides it by the total weights to obtain a normalized index representing the average distortion level. The larger the value, the stronger the distortion at the measured point. The higher the likelihood and severity of bonding defects, the better.

[0092] S4: Defect location and assessment.

[0093] The calculated values ​​for each test point The values ​​are arranged according to their physical positions on their respective interface lines to form a two-dimensional adhesion quality index (AQI). ) matrix. Then, using standard plotting libraries, such as , will The matrix is ​​rendered into a two-dimensional pseudo-color image, which represents the final bonding quality. Scan image. In this embodiment, a color spectrum from blue to red is used, where blue represents... Low value indicates good adhesion; red indicates High value indicates poor adhesion.

[0094] Set one The threshold, for example, is set to... .Will Scanning image Pixels with values ​​above a threshold are initially labeled as defective pixels, while those below the threshold are labeled as healthy pixels, thus converting the pseudo-color image into a black-and-white binary image, where white areas represent potential defects. Utilizing... The clustering algorithm clusters the binary image, and identifies the defective regions by selecting clusters with more than a preset threshold of white pixels.

[0095] This invention also discloses a mortar layer bonding state detection system based on array ultrasonic waves, including a processor and a memory. The memory stores computer program instructions, which, when executed by the processor, implement a mortar layer bonding state detection method based on array ultrasonic waves according to this invention.

[0096] The system also includes other components well known to those skilled in the art, such as communication buses and communication interfaces, the settings and functions of which are known in the art and will not be described in detail here.

[0097] The above are all preferred embodiments of the present invention and are not intended to limit the scope of protection of the present invention. Therefore, all equivalent changes made in accordance with the structure, shape and principle of the present invention should be covered within the scope of protection of the present invention.

Claims

1. A method for detecting the bonding state of mortar layers based on array ultrasonic waves, characterized in that, The steps include: using a two-dimensional ultrasonic phased array probe to perform full matrix capture scanning of the mortar layer to obtain raw data; processing the raw data with a total focusing algorithm to obtain a reconstructed image containing the mortar matrix interface, and extracting the interface echo signal corresponding to the test point on the interface line; A continuous wavelet transform is performed on the interface echo signal to obtain an energy pattern diagram that characterizes the distribution of signal energy in the time-frequency plane. A time-frequency transient distortion index is constructed to characterize the degree of distortion of the energy pattern map relative to a preset reference pattern map. The construction method includes: constructing a distorted energy map. ; Indicates the first The first online interface The test points at time and frequency points Distortion energy map at the location; Indicates the first The first online interface The test points at time and frequency points Energy deviation at the location; Indicates the first The first online interface The time-frequency scattering factor of each test point is calculated; the energy deviation between the energy pattern diagram and the reference energy pattern diagram is calculated. , Indicates the first The first online interface The test points at time and frequency points Energy pattern diagram at the location; This is an energy reference shape diagram; calculate the time-frequency scattering factor: ; Indicates the first The first online interface The time-frequency entropy of the test point; It is the reference entropy; It is the time-frequency sharpness coefficient. The distortion energy map is positively correlated with the energy deviation. The time-frequency transient distortion index is obtained by weighted aggregation of the distortion energy map. The weights used in the weighted aggregation depend on both the core region of the reference signal and the abnormal region of the distortion energy map. The bonding defects of the mortar layer are identified based on the time-frequency transient distortion index. The detection method also includes: calculating the information fidelity weighted operator: Indicates the first The first online interface The test points at time and frequency points Information fidelity weighted operator; It is a reference energy fingerprint obtained using a standard test block; It is the transition sharpness coefficient; It is a preset importance threshold, and max represents the maximum value function; The dynamic anomaly weighted operator is calculated using the following expression: ; Representing time and frequency points Dynamic anomaly weighted operator; Indicates the first The first online interface The test points at time and frequency points Distortion energy map at the location; This represents the preset distortion sensitivity coefficient; Calculate the composite weights: Representing time and frequency points The composite weights; The expression for the time-frequency transient distortion index is: Indicates the first The first online interface The time-frequency transient distortion index of each test point This represents a minimum value.

2. The method for detecting the bonding state of mortar layers based on array ultrasonic waves according to claim 1, characterized in that, Extracting the interface echo signal includes: setting key regions in the reconstructed image; using the Canny edge detection algorithm within the key regions to identify the mortar matrix interface line; taking each pixel on the interface line as a test point, and extracting the echo signal of each test point as the interface echo signal.

3. The method for detecting the bonding state of mortar layers based on array ultrasonic waves according to claim 1, characterized in that, The detection method also includes: calculating the time-frequency entropy of the point to be measured, and normalizing the time-frequency entropy to obtain the time-frequency scattering factor. The expression for the time-frequency entropy is: in, Indicates the first The first online interface The time-frequency entropy of the test point; Indicates the first The first online interface The test points at time and frequency points Energy pattern diagram at the location; It is a very small positive number.

4. The method for detecting the bonding state of mortar layers based on array ultrasonic waves according to claim 1, characterized in that, The method for identifying bonding defects in mortar layers based on the time-frequency transient distortion index is as follows: the calculated time-frequency transient distortion index of each test point is arranged according to its physical position on its respective interface line to form a two-dimensional bonding quality index matrix. use The drawing library renders the bonding quality index matrix as Scanned image; Will Pixels in the scanned image with an adhesion quality index higher than a preset threshold are initially marked as defective pixels, while those below the preset threshold are marked as healthy pixels, thus converting the pseudo-color image into a black-and-white binary image; using The clustering algorithm clusters the binary image, and identifies clusters with more than a preset threshold of white pixels as defect regions.

5. A mortar layer bonding state detection system based on array ultrasonic waves, characterized in that, include: The processor and memory, wherein the memory stores computer program instructions that, when executed by the processor, implement the method for detecting the bonding state of mortar layers based on array ultrasonic waves as described in any one of claims 1 to 4.

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