Precast concrete T-beam bubble detection method and system

Abstract

The invention relates to the technical field of image processing, and discloses a precast concrete T-beam bubble detection method and system. The method comprises the following steps: carrying out multiband reflection data acquisition and calibration on the surface of a T-beam, and obtaining optimized data through light intensity compensation and band screening; performing spatial registration and noise reduction processing on the data, and fusing the data into an image; edge detection and image segmentation are carried out, and pore morphological features are extracted; calculating depth data and performing spatial reconstruction to obtain pore distribution data; performing statistical analysis on the distribution data to obtain a defect evaluation result; and finally generating a detection report. According to the method, accurate positioning and quantitative evaluation of bubble defects are realized through multi-source data fusion and deep analysis, a complete early warning and evaluation mechanism is established, and the reliability and practicability of a detection result are improved.

Description

Technical Field

[0001] The present application relates to the field of image processing, and in particular to a method and system for detecting bubbles in precast concrete T-beams. Background Art

[0002] Precast concrete T-beams are widely used in construction and bridge engineering, and their quality control directly affects the safety and durability of the structure. At present, bubble detection of precast concrete T-beams mainly relies on traditional methods such as manual visual inspection and local tapping, or single detection methods such as ultrasound and X-rays. These detection methods have accumulated rich experience in practical applications and provide a basic basis for the identification and evaluation of bubble defects. At the same time, with the development of computer vision and multi-source data fusion technology, methods such as image processing and deep learning have also begun to be applied to bubble detection, improving the level of automation of detection.

[0003] However, the existing detection methods have some obvious shortcomings: first, a single detection method is difficult to fully reflect the spatial distribution characteristics of bubble defects, especially the detection effect of deep bubbles is not ideal; second, the detection results lack a unified quantitative evaluation standard, and the judgment of the degree of harm of bubble defects mainly relies on experience and lacks scientific basis; third, the interference of environmental factors during the detection process (such as light changes, surface contamination, etc.) will affect the accuracy of the detection results, and the existing methods lack an effective compensation mechanism; finally, the processing and analysis of detection data lacks systematicity, and it is difficult to form a complete quality assessment system. Summary of the invention

[0004] The present application provides a precast concrete T-beam bubble detection method and system, which is used to achieve accurate positioning and quantitative evaluation of bubble defects through multi-source data fusion and in-depth analysis, and establishes a complete early warning and evaluation mechanism, thereby improving the reliability and practicality of the detection results.

[0005] In a first aspect, the present application provides a method for detecting bubbles in a precast concrete T-beam, the method comprising: According to the multi-band reflection data of the surface of the precast concrete T-beam, the collected reflection data is calibrated by position mapping according to the calibration plate parameters, the calibrated reflection data of each band is subjected to light intensity compensation processing and signal-to-noise ratio optimization, and the optimal band combination is selected by cross-validation to obtain the compensated multi-band reflection data; Perform spatial registration based on the compensated multi-band reflection data, perform noise reduction and smoothing on the registered data, perform principal component calculation and data integration on the smoothed multi-band data, and obtain a fused image of the reflection data; Edge detection and image segmentation are performed based on the fused image of the reflection data. The segmented image area is converted into numerical values ​​according to the contour features. The converted numerical values ​​are jointly analyzed with the texture features. The pore boundaries are refined and extracted using an adaptive threshold to obtain the morphological parameter data of the pores. The depth is calculated based on the morphological parameter data of the pores, the calculated depth data is transformed into spatial coordinates, the transformed spatial coordinate data and the surface reflection intensity are reconstructed with dual constraints, and the data are corrected in combination with the concrete density parameters to obtain the spatial distribution data of the pores; Perform numerical statistics based on the spatial distribution data of pores, classify and divide the statistical results according to the preset threshold, and perform correlation operation on the division results and the pore characteristic parameters to obtain quantitative evaluation data of pore defects; The warning level is determined based on the quantitative evaluation data of the pore defects, the determination results are automatically labeled and visualized, and the processing results are compared and analyzed with the quality parameters of the prefabricated components to obtain the test report data.

[0006] In a second aspect, the present application provides a precast concrete T-beam bubble detection system, the precast concrete T-beam bubble detection system comprising: The acquisition module is used to collect multi-band reflection data from the surface of the precast concrete T-beam, perform position mapping calibration on the collected reflection data according to the calibration plate parameters, perform light intensity compensation processing and signal-to-noise ratio optimization on the calibrated reflection data of each band, select the optimal band combination by cross-validation, and obtain compensated multi-band reflection data; A registration module is used to perform spatial registration based on the compensated multi-band reflection data, perform noise reduction and smoothing on the registered data, perform principal component calculation and data integration on the smoothed multi-band data, and obtain a fused image of the reflection data; The segmentation module is used to perform edge detection and image segmentation based on the fused image of the reflection data, perform numerical conversion on the segmented image area according to the contour features, jointly analyze the converted numerical values ​​with the texture features, and use the adaptive threshold to extract the pore boundaries in a refined manner to obtain the morphological parameter data of the pores; A calculation module is used to perform depth calculation based on the morphological parameter data of the pores, perform spatial coordinate conversion on the calculated depth data, perform dual constraint reconstruction on the converted spatial coordinate data and the surface reflection intensity, and perform correction in combination with the concrete density parameter to obtain the spatial distribution data of the pores; A statistical module is used to perform numerical statistics based on the spatial distribution data of pores, classify and divide the statistical results according to a preset threshold, and associate the division results with the pore characteristic parameters to obtain quantitative evaluation data of pore defects; The determination module is used to determine the warning level based on the quantitative evaluation data of the pore defects, automatically mark and visualize the determination results, compare and analyze the processing results with the quality parameters of the prefabricated components, and obtain the test report data.

[0007] In the technical solution provided by the present application, by using multi-band reflection data for acquisition and position mapping calibration of calibration plate parameters, accurate spatial positioning and light intensity compensation are achieved in the bubble detection process, thereby improving the accuracy of data acquisition; by performing spatial registration and noise reduction and smoothing processing on the compensated multi-band reflection data, combined with principal component calculation and data integration technology, the quality and reliability of the reflection data fusion image are effectively improved; by using edge detection and image segmentation methods, combined with the numerical conversion of contour features and the joint analysis of texture features, the pore boundaries are refined and extracted through adaptive thresholds, thereby achieving accurate acquisition of pore morphological parameters; by performing deep calculation and spatial coordinate conversion on the pore morphological parameter data, combined with the dual-constraint reconstruction of the surface reflection intensity and the correction of the concrete density parameters, the accuracy of the pore spatial distribution data is improved; by using numerical statistics and classification technology, combined with the correlation operation of pore characteristic parameters, quantitative evaluation of pore defects is achieved; based on the quantitative evaluation data of pore defects, the warning level is determined, and through automatic labeling and visualization processing, combined with the comparative analysis of prefabricated component quality parameters, complete test report data is formed, providing a scientific basis for quality control. The entire detection process has achieved full automation and standardization from data collection to result output, significantly improving the efficiency and accuracy of bubble detection in precast concrete T-beams, and providing strong technical support for quality control in engineering practice. Through the fusion analysis of multi-source data and a multi-level evaluation system, the limitations of traditional single detection methods have been overcome, and accurate identification and quantitative evaluation of bubble defects have been achieved, improving the reliability and practical value of the detection results. BRIEF DESCRIPTION OF THE DRAWINGS

[0008] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings required for use in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other accompanying drawings can be obtained based on these accompanying drawings without paying creative work.

[0009] Figure 1 A schematic diagram of an embodiment of a method for detecting bubbles in a precast concrete T-beam in an embodiment of the present application; Figure 2 This is a schematic diagram of an embodiment of a precast concrete T-beam bubble detection system in an embodiment of the present application. DETAILED DESCRIPTION

[0010] The embodiments of the present application provide a method and system for detecting bubbles in a precast concrete T-beam. The terms "first", "second", "third", "fourth", etc. (if any) in the specification and claims of the present application and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchangeable where appropriate, so that the embodiments described here can be implemented in an order other than that illustrated or described here. In addition, the terms "including" or "having" and any variations thereof are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device that includes a series of steps or units is not necessarily limited to those steps or units that are clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0011] For ease of understanding, the specific process of the embodiment of the present application is described below. Figure 1 , an embodiment of the method for detecting bubbles in precast concrete T beams in the embodiment of the present application includes: Step S101, collecting multi-band reflection data from the surface of the precast concrete T-beam, calibrating the collected reflection data by position mapping according to the calibration plate parameters, performing light intensity compensation processing and signal-to-noise ratio optimization on the calibrated reflection data of each band, selecting the optimal band combination by cross-validation, and obtaining compensated multi-band reflection data; Step S102, performing spatial registration according to the compensated multi-band reflection data, performing noise reduction and smoothing processing on the registered data, performing principal component calculation and data integration on the smoothed multi-band data, and obtaining a fused image of the reflection data; Step S103, edge detection and image segmentation are performed based on the fused image of the reflection data, the segmented image area is numerically converted according to the contour features, the converted values ​​are jointly analyzed with the texture features, and the pore boundaries are refined and extracted using an adaptive threshold to obtain the morphological parameter data of the pores; Step S104, performing depth calculation according to the morphological parameter data of the pores, performing spatial coordinate transformation on the calculated depth data, performing dual constraint reconstruction on the transformed spatial coordinate data and the surface reflection intensity, and performing correction in combination with the concrete density parameter to obtain spatial distribution data of the pores; Step S105, performing numerical statistics according to the spatial distribution data of the pores, classifying and dividing the statistical results according to a preset threshold, and performing correlation operation on the classification results and the pore characteristic parameters to obtain quantitative evaluation data of the pore defects; Step S106: Determine the warning level based on the quantitative evaluation data of the pore defects, automatically mark and visualize the determination results, compare and analyze the processing results with the quality parameters of the prefabricated components, and obtain the test report data.

[0012] It is understandable that the execution subject of the present application may be a precast concrete T-beam bubble detection system, or a terminal or a server, which is not limited here. The present application embodiment is described by taking a server as the execution subject as an example.

[0013] Specifically, during the data collection phase, a multispectral imaging system is used to perform an all-round scan of the T-beam surface. The multi-band reflection data collection process is carried out by equipping a sensor array with different wavelengths, including visible light, near-infrared and far-infrared bands, and each band is used to collect different reflection characteristics of the concrete surface. In the actual collection process, the determination of the calibration plate parameters is crucial. Several calibration points with known spacing are set on the calibration plate, and these calibration points have clear reflection characteristics under different bands. The calibration plate parameters contain the spatial position information and reflection intensity information of the calibration points, which are used to establish the mapping relationship between the image space and the actual space. Position mapping calibration calculates the correspondence between the calibration points in the image space and the actual space, establishes a coordinate transformation matrix, and ensures the spatial consistency of images in different bands.

[0014] Light intensity compensation processing corrects for changes in ambient light and differences in sensor response. By real-time monitoring of ambient light intensity, a compensation model is established to dynamically adjust the reflection data of each band. Signal-to-noise ratio optimization uses filtering algorithms to improve data quality by analyzing the signal strength and noise level of each band data. When cross-validating and screening the optimal band combination, the data set is divided into a training set and a validation set. The detection effects of different band combinations are evaluated through multiple iterations, and the band combination with the highest detection accuracy is selected. The spatial registration link first matches the feature points of multi-band data, uses SIFT feature descriptors to extract the key points of each band image, and establishes a point-to-point correspondence. The denoising and smoothing process uses the wavelet transform method to decompose and reconstruct noise of different scales and retain effective signals. Principal component analysis calculates the covariance matrix of multi-band data, extracts the main feature directions, and achieves data dimensionality reduction and information concentration.

[0015] In the image segmentation and edge detection stage, the Canny operator and the region growing algorithm are combined to identify the boundary of the bubble area. The numerical conversion of contour features includes calculating geometric parameters such as area, perimeter, and circularity. The grayscale co-occurrence matrix method is used for texture feature analysis to extract statistical features such as energy, contrast, and entropy. The determination of the adaptive threshold is based on the grayscale distribution characteristics of the local area, and the segmentation parameters are adjusted dynamically. In the depth calculation and spatial reconstruction stage, the bubble depth is estimated by using the difference in multi-band reflection intensity. The spatial coordinate transformation converts the image coordinate system into the actual physical coordinate system. The dual constraint reconstruction optimizes the three-dimensional morphology of the bubble by combining the surface reflection intensity and spatial position information. The concrete density parameter is used to correct the depth estimation result, considering the influence of material properties on the detection. In the numerical statistics and classification stage, the spatial distribution characteristics of the bubble are quantitatively analyzed, including density, depth distribution, size distribution, etc. The preset threshold is determined based on engineering specifications and empirical data and is used for the classification of bubble defects. The association operation takes into account the morphological characteristics and spatial distribution characteristics of the bubble to form a comprehensive evaluation index. In the early warning judgment and result visualization stage, the defect level is determined based on the quantitative evaluation data, and different colors and logos are used for visual annotation. The test report data includes bubble distribution diagrams, statistical data, evaluation results and treatment suggestions, providing a basis for quality control.

[0016] For example, on a precast concrete T-beam production line, a batch of newly produced T-beams were tested for bubbles. When collecting multi-band data, sampling and analysis were performed on typical bubble areas to obtain reflection intensity data. Through data processing, it was found that the near-infrared band contributed most significantly to bubble detection, and the detection effect was best when used in conjunction with the visible light band. After spatial registration, the bubble boundary is clearer, which is convenient for subsequent feature extraction. The depth calculation results show that the bubble depth distribution is mainly concentrated in the range of 2-5mm on the surface. After correction based on the concrete density parameters, the actual spatial distribution of the bubbles was determined. The final test report clearly marks the bubble location, depth and degree of hazard.

[0017] In the embodiment of the present application, by using multi-band reflection data for acquisition and position mapping calibration of calibration plate parameters, accurate spatial positioning and light intensity compensation are achieved in the bubble detection process, and the accuracy of data acquisition is improved; by performing spatial registration and noise reduction smoothing processing on the compensated multi-band reflection data, combined with principal component calculation and data integration technology, the quality and reliability of the reflection data fusion image are effectively improved; by using edge detection and image segmentation methods, combined with the numerical conversion of contour features and the joint analysis of texture features, the pore boundaries are refined and extracted through adaptive thresholds, and the accurate acquisition of pore morphological parameters is achieved; by performing deep calculation and spatial coordinate conversion on the pore morphological parameter data, combined with the dual-constraint reconstruction of the surface reflection intensity and the correction of the concrete density parameters, the accuracy of the pore spatial distribution data is improved; by using numerical statistics and classification technology, combined with the correlation operation of pore characteristic parameters, the quantitative evaluation of pore defects is achieved; based on the quantitative evaluation data of pore defects, the warning level is determined, and through automatic labeling and visualization processing, combined with the comparative analysis of prefabricated component quality parameters, a complete test report data is formed, which provides a scientific basis for quality control. The entire detection process has achieved full automation and standardization from data collection to result output, significantly improving the efficiency and accuracy of bubble detection in precast concrete T-beams, and providing strong technical support for quality control in engineering practice. Through the fusion analysis of multi-source data and a multi-level evaluation system, the limitations of traditional single detection methods have been overcome, and accurate identification and quantitative evaluation of bubble defects have been achieved, improving the reliability and practical value of the detection results.

[0018] In a specific embodiment, the process of executing step S101 may specifically include the following steps: (1) Collect multi-band reflection data on the surface of the precast concrete T-beam. Use a multi-band sensor to continuously scan the surface of the precast concrete T-beam to obtain original multi-band reflection data. (2) The original multi-band reflection data is spatially aligned with the position reference points of the calibration plate parameters, and a position mapping relationship is established through spatial mapping calculation to obtain the initial position mapping data; (3) Calculate the light intensity deviation of each band of reflection data in the initial position mapping data, compare the calculated deviation value with the real-time ambient light intensity, and obtain the light intensity correction factor; (4) Numerically correct the light intensity correction factor and the initial position mapping data, calculate the signal-to-noise ratio of the corrected multi-band reflection data through data calculation, and obtain the band quality assessment value; (5) The band quality assessment values ​​are grouped according to the numerical range, and each group of multi-band reflectance data is cross-validated through data analysis to obtain the band combination score; (6) The band combination scores are arranged in descending order, and the band combination with the highest score is selected by numerical comparison. The multi-band reflection data of the band combination is normalized to obtain the compensated multi-band reflection data.

[0019] Specifically, a multi-band sensor array is used for data collection. The multi-band sensor array includes detectors in three bands: visible light, near infrared, and far infrared. Each detector responds to electromagnetic waves in a specific wavelength range. This multi-band collaborative collection method makes full use of the differences in the reflection characteristics of the concrete surface in different bands, providing rich feature information for subsequent bubble detection. During the continuous scanning process, the multi-band sensor array moves at a uniform speed along the surface of the T-beam. The scanning speed and sampling frequency need to be set to ensure that the spatial resolution meets the detection requirements. The original multi-band reflection data records the reflection intensity value of each band at each sampling point, forming a three-dimensional data matrix containing spatial position and spectral information.

[0020] The position reference point of the calibration board parameters plays a key role in the spatial mapping process. A regularly distributed array of calibration points is set on the calibration board, and each calibration point has precise physical coordinates and known reflection characteristics. The spatial transformation matrix is ​​calculated by establishing a correspondence between the calibration point image in the original multi-band reflection data and the actual position on the calibration board. This process is optimized using the least squares method to ensure the accuracy of the mapping transformation. The initial position mapping data contains the corrected spatial coordinate information, providing a unified spatial reference for subsequent processing. The calculation of light intensity deviation is an important step in correcting for changes in ambient illumination and differences in sensor response. For the reflection data of each band, the difference between the theoretical reflection intensity and the measured value at the calibration point is first calculated to establish a light intensity correction model. At the same time, the changes in ambient light intensity are monitored in real time, and the ambient light intensity is used as a reference for dynamic compensation. By comparing the calculated light intensity correction factor, accurate correction of the reflection data is achieved.

[0021] Signal-to-noise ratio calculation is the core indicator for evaluating data quality, and its calculation formula is: ; in: is the signal-to-noise ratio, is the target signal strength matrix; is the reference signal strength matrix; is the noise component; , is the signal weight coefficient; is the gain factor; is the noise modulation function; is the background noise term; M, N are the signal matrix dimensions; L is the number of noise components.

[0022] The calculation of the band quality assessment value comprehensively considers multiple indicators such as signal-to-noise ratio, contrast and spatial resolution, and obtains the comprehensive score of each band through weighted average. The band grouping process divides the assessment value according to the preset interval boundaries to form different band combinations. Each band combination must be cross-validated to evaluate its effectiveness in bubble detection. The cross-validation operation uses the K-fold cross-validation method to divide the data set into K subsets, select one subset as the validation set each time, and the rest as the training set, and repeat K times to obtain the average performance index. This method effectively avoids the overfitting problem and improves the reliability of the evaluation results.

[0023] The descending order of band combination scores helps to screen out the optimal band combination. The multi-band reflection data of the optimal band combination is normalized to unify the data range of different bands to the [0, 1] interval for subsequent processing and analysis. The compensated multi-band reflection data not only retains the effective information of the original data, but also eliminates the influence of various interference factors.

[0024] For example, in the bubble detection of a precast concrete T-beam, a multi-band sensor is first used to scan the surface of the T-beam. After the original data is collected, the nine position reference points on the calibration plate are used for spatial mapping. Through the calculation of light intensity deviation, it is found that the data in the near-infrared band is greatly affected by the ambient light and requires a larger correction factor. The signal-to-noise ratio calculation results show that the combination of visible light and near-infrared bands has the highest band quality assessment value. After cross-validation, it is confirmed that this band combination performs best in bubble detection. The compensated multi-band reflection data obtained clearly shows the location and morphological characteristics of the bubbles.

[0025] In a specific embodiment, the process of executing step S102 may specifically include the following steps: (1) Extract feature points from the compensated multi-band reflection data, mark control points of each band of the multi-band reflection data through spatial analysis, calculate the coordinate transformation matrix according to the spatial distribution of the marked points, and obtain the registration reference data; (2) Calculate the error of the marker points in the registration reference data according to the least squares criterion, perform iterative optimization on the calculation results, and obtain the spatial transformation parameters; (3) Using the spatial transformation parameters to coordinate map the compensated multi-band reflection data, and obtaining the initial registration data through resampling calculation; (4) Perform wavelet decomposition on the initial registration data, sort the decomposition coefficients according to energy through data analysis, select the main coefficients for reconstruction calculation, and obtain the denoised and smoothed data of the registered data; (5) Perform covariance calculation on each band component in the denoised and smoothed data, determine the principal component weights based on eigenvalue decomposition through feature analysis, and obtain the data fusion weight coefficient; (6) The data fusion weight coefficient and the denoised smoothed data are weightedly superimposed, and the contrast enhancement calculation is performed on the superposition result through data processing to obtain a fused image of the reflection data.

[0026] Specifically, feature point extraction is performed on the compensated multi-band reflection data. Feature point extraction uses a method based on corner point detection to identify significant feature points in the reflection data of each band. Corner point detection finds points where grayscale changes dramatically by calculating the grayscale change gradient in the local area. These feature points include typical features such as the inflection point of the bubble edge and the mutation point of the surface texture. During the spatial analysis process, the extracted feature points are screened and matched to determine the position of the control point markers. The selection of control points needs to consider the uniformity of spatial distribution and the stability of features. By calculating the spatial relationship between feature points, the topological structure of the marker points is constructed to provide a basis for subsequent coordinate transformation. The spatial distribution of the marker points is evaluated through neighborhood analysis and distance calculation to ensure the calculation accuracy of the transformation matrix.

[0027] The coordinate transformation matrix is ​​calculated using the following formula: ; in: is the transformation matrix element; , , is the scale factor; is the nonlinear transformation function in the x direction; is the nonlinear transformation function in the y direction; is the nonlinear transformation function in the z direction.

[0028] The generation of registration benchmark data is based on the spatial correspondence of the marker points. The least squares criterion is used to optimize the transformation parameters, and the residual sum of squares is calculated iteratively. In each iteration, the mapping error under the current transformation parameters is calculated, and the parameters are adjusted until the error converges below the preset threshold. The spatial transformation parameters contain basic transformation information such as translation, rotation and scaling. In the process of coordinate mapping conversion, the spatial transformation parameters are applied to the original data points to calculate the new coordinate position. The resampling calculation uses the bicubic interpolation method to ensure the continuity and smoothness of the data. The initial registration data retains the detailed features of the original data and achieves accurate correspondence of the spatial position. The wavelet decomposition operation adopts a multi-level decomposition strategy to analyze the data at different scales. After the decomposition coefficients are sorted according to the energy size, the coefficients with a larger energy share are selected for reconstruction, which effectively removes noise interference. The denoising and smoothing data retains the main structural features and improves the stability of subsequent processing. The covariance operation analyzes the correlation between the components of each band, constructs the covariance matrix, and determines the main change direction through eigenvalue decomposition. The size of the eigenvalue reflects the significance of the data change in each direction, and the weight coefficient of data fusion is determined accordingly. The weighted overlay process multiplies the weight coefficient with the data of each band and sums them to obtain the fused image. Contrast enhancement is achieved through histogram equalization, which improves the visual effect of the image.

[0029] For example, when a precast concrete T-beam is tested for bubbles, feature points are first extracted from the compensated multi-band reflection data. A large number of edge feature points are identified in the visible light band data through the corner detection algorithm. These feature points are mainly distributed at the bubble edges and in areas where the surface texture changes significantly. The control point markers select points with uniform spatial distribution and stable features, and construct a control point network covering the entire detection area. During the least squares optimization process, the initial error is large, and after multiple iterations, the error gradually decreases and tends to be stable. The spatial transformation parameters reflect the geometric correspondence between data in different bands, including slight deformation and distortion. Coordinate mapping transformation and resampling processing ensure the spatial consistency of the data. After wavelet decomposition, the main coefficients are selected according to the energy distribution for reconstruction to obtain denoised smooth data. The covariance analysis of the data in each band shows that the near-infrared and visible light bands have a strong correlation, while the far-infrared band provides complementary information. Based on this correlation feature, a reasonable fusion weight is determined, and the fused image clearly shows the location and morphology of the bubble.

[0030] In a specific embodiment, the process of executing step S103 may specifically include the following steps: (1) Perform gradient calculation and analysis on the fused image of the reflection data, construct an edge feature point set based on the gradient intensity value through data calculation, perform connectivity analysis and calculation on the edge feature point set, and obtain the initial edge contour; (2) The initial edge contour is segmented and calculated according to the grayscale distribution. The boundary tracking analysis of the segmented area is performed through data processing. The closure judgment of the tracking result is performed to screen the effective area to obtain the candidate pore area. (3) Calculate the perimeter and area of ​​the contour features of the candidate pore area, compare the calculated results with the circularity index through numerical analysis, and analyze the shape characteristics of the compared results to obtain the initial morphological data; (4) Performing spatial correspondence analysis on the region of the initial morphological data and the fused image of the reflection data, extracting the grayscale distribution characteristics and texture direction characteristics of the corresponding region through data, and obtaining texture feature data; (5) Refine the boundary of the candidate pore area according to the texture feature data, calculate and analyze the local contrast through numerical calculation to determine the dynamic threshold range, and obtain the pore boundary point set; (6) The stomatal boundary point set is subjected to curve fitting operation. The geometric parameters of the fitting curve are extracted and the feature quantification calculation is performed through data processing to obtain the morphological parameter data of the stoma.

[0031] Specifically, the gradient calculation is performed on the fused image of the reflection data. The gradient calculation uses a multi-directional gradient operator to calculate the grayscale change rate of each pixel in the horizontal and vertical directions. Through the gradient intensity threshold screening, the pixels with significant changes are marked as edge candidate points. These edge candidate points are subjected to connectivity analysis to determine the topological relationship between adjacent points and form an initial edge contour. The regional segmentation of the initial edge contour uses a watershed algorithm based on grayscale distribution to divide the image into different regions. The watershed algorithm starts from the local minimum point and gradually grows the regional boundaries until the adjacent regions meet. In the boundary tracking process, the boundary integrity check is performed on each segmented area to evaluate the degree of closure and continuity of the boundary. The closure judgment is based on the connection relationship and gap size of the boundary points, and the closed and complete areas are screened out as candidate pore areas.

[0032] The contour feature analysis of the candidate pore area includes the calculation of geometric parameters. The perimeter is calculated by accumulating the Euclidean distance between the boundary points, and the area is calculated using the Green formula. The circularity index reflects the similarity between the shape of the area and the standard circle, and the calculation formula is: ; in: is the region shape matrix; is the roundness weight factor; is the correction factor; is the boundary smoothness; is the shape feature vector; is the standard circle eigenvector; is the normalization factor; R, S are the dimensions of the region matrix; T is the dimension of the eigenvector.

[0033] Spatial correspondence analysis aligns the initial morphological data with the fused image and extracts the grayscale features of the corresponding area. Grayscale distribution features include statistics such as mean, variance, skewness and kurtosis. Texture directional features are obtained by calculating the directional gradient histogram of the local area, which reflects the directional characteristics of the surface structure.

[0034] The boundary refinement calculation is based on local contrast analysis and uses an adaptive threshold method. By analyzing the grayscale change trend near the stomatal boundary, the appropriate threshold range is determined to achieve accurate boundary positioning. The stomatal boundary point set records the refined boundary position coordinates.

[0035] The curve fitting adopts the B-spline interpolation method to smoothly fit the boundary point set. The geometric parameter extraction includes characteristic quantities such as curvature and eccentricity, and the standardized morphological parameter data is obtained through feature quantification.

[0036] For example, in the bubble detection on the surface of a precast concrete T-beam, the fused image shows obvious edge features after gradient calculation. Connectivity analysis connects adjacent edge points into continuous contour lines. After region segmentation, multiple closed regions are found, some of which are close to circular in shape. The real bubble regions are screened out by perimeter and area calculations combined with circularity index analysis. The grayscale distribution of these regions shows typical concave features, and the texture direction features show that the edges have regular radial distribution. After boundary refinement, the precise bubble contour is obtained, and the curve fitting results show that the bubble shape is regular and the curvature changes gently. The final morphological parameter data accurately describes the geometric characteristics of the bubbles.

[0037] In a specific embodiment, the process of executing step S104 may specifically include the following steps: (1) Perform depth conversion calculation on the grayscale distribution characteristics in the morphological parameter data of the stomata, establish a depth mapping relationship through data analysis, perform boundary constraint operation on the mapping results, and obtain the initial depth data; (2) The initial depth data is transformed into coordinates according to the spatial distribution law, the depth value is relocated and analyzed through numerical calculation, and the relocation result is error corrected to obtain the spatial coordinate data of the initial depth data; (3) Perform a joint operation on the spatial coordinate data and the surface reflection intensity, calculate and analyze the reflection intensity gradient through data processing to determine the depth correction coefficient, and perform a weighted combination of the correction coefficient and the spatial coordinate data to obtain the initial reconstructed data; (4) Compare and analyze the initial reconstructed data with the surface reflection intensity of the local area, adjust the spatial distribution parameters of the neighborhood features through numerical calculation, verify the consistency of the adjusted parameters, and obtain the reconstructed corrected data; (5) Correlation calculation is performed on the reconstructed correction data and the concrete density parameters, compensation processing is performed on the spatial coordinate data through data analysis, and the compensation results are compared with the theoretical distribution to obtain the correction parameter set; (6) The correction parameter set is used to perform spatial calibration calculation on the reconstructed corrected data. The pore positions are located by multi-dimensional feature fusion through data processing. The positioning results are statistically analyzed to obtain the spatial distribution data of the pores.

[0038] Specifically, the morphological parameter data of the pores are subjected to depth conversion calculation. The depth conversion calculation is based on the grayscale distribution characteristics, and the corresponding relationship between the grayscale value and the actual depth is established to achieve a quantitative description of the surface morphology. The formula for the depth conversion calculation is as follows: ; in: is the depth conversion value; is the grayscale distribution matrix; is the depth map weight; is the depth conversion index; is the calibration curve parameter; is the reference depth value; is the depth offset constant; B and H are the grayscale matrix dimensions; J is the number of calibration parameters, a and b is the index parameter.

[0039] The establishment of the depth mapping relationship adopts a piecewise linear mapping method, and different mapping coefficients are set for different depth ranges. The boundary constraint operation ensures the rationality of the depth value in the physical sense and prevents abnormal depth estimation results. The spatial coordinate transformation converts the initial depth data into a three-dimensional physical space. It should be noted that the depth value in the embodiment of the present application refers to the distance measurement value of the pore extending from the concrete surface to the inside, which is a key parameter to characterize the position of the pore in three-dimensional space. Specifically, the depth value reflects the spatial position information of the pore in the direction perpendicular to the concrete surface, and the unit is usually millimeter (mm). The coordinate transformation process takes into account the geometric characteristics and scanning direction of the T-beam surface, and establishes a conversion relationship between the local coordinate system and the global coordinate system. The depth value repositioning analysis optimizes and adjusts the single-point depth value by considering the depth distribution of neighboring points. The error correction process adopts a statistical filtering method to eliminate random errors and systematic errors in depth measurement.

[0040] The joint calculation of spatial coordinate data and surface reflection intensity is the key link in the reconstruction process. The concrete density correlation calculation adopts the following formula: ; Where: D is the density correlation coefficient, is the density distribution matrix; is the spatial weight function; is the depth compensation factor; is the standard density component; is the nonlinear correction index; is the scale factor; is the compensation constant; U, V are the dimensions of the distribution matrix; W is the number of compensation factors.

[0041] The surface reflection intensity gradient calculation determines the depth correction coefficient by analyzing the reflection intensity change rate of the local area. The weighted combination process fuses the correction coefficient with the spatial coordinate data to generate the initial reconstruction data. The comparative analysis of the initial reconstruction data and the surface reflection intensity of the local area mainly focuses on the spatial distribution law of the reflection intensity. The spatial distribution parameter adjustment of the neighborhood features is based on the principle of local consistency to ensure the continuity and smoothness of the reconstruction results. The correlation calculation between the reconstruction correction data and the concrete density parameters takes into account the influence of material properties on bubble formation. The spatial coordinate data is compensated by establishing a corresponding relationship between density and bubble distribution. The comparison operation of the compensation result with the theoretical distribution helps to identify abnormal areas and ensure the accuracy of the reconstruction results.

[0042] The spatial calibration calculation of the modified parameter set adopts a multi-level optimization strategy. The multi-dimensional feature fusion positioning comprehensively considers the depth information, reflection intensity and density characteristics, which improves the accuracy of pore location determination. The statistical analysis focuses on evaluating the regularity and concentration of pore distribution.

[0043] For example, when performing bubble detection on a precast concrete T-beam, the grayscale distribution characteristics of the surface are first analyzed. Through depth conversion calculation, the mapping relationship between grayscale value and actual depth is established. The spatial coordinate transformation converts the depth data into the actual physical space, and error correction is performed at the same time. The gradient analysis of the surface reflection intensity shows that the bubble area has obvious intensity change characteristics. In the joint operation process, the depth correction coefficient is determined according to the change of reflection intensity, and the initial reconstruction result is optimized. The correlation analysis of concrete density parameters shows that there is an obvious correlation between the formation of bubbles and the density of the local area. The reliability of the reconstruction results is ensured by spatial parameter adjustment and consistency verification. The final spatial distribution data clearly reflects the distribution characteristics of bubbles on the surface of the T-beam, including key information such as depth, position and density. This measurement and analysis process fully considers the material properties and structural characteristics of the precast concrete T-beam, and realizes the precise positioning and characterization of bubble defects through the fusion and analysis of multi-source data. The processing results not only reflect the morphological characteristics of a single bubble, but also reveal the distribution law of bubble groups.

[0044] In a specific embodiment, the process of executing step S105 may specifically include the following steps: (1) Calculate the frequency of the depth values ​​and position coordinates in the spatial distribution data of stomata, perform clustering analysis on the stomata distribution density by interval division, normalize the clustering data, and obtain initial statistical data; (2) The initial statistical data is numerically classified according to spatial division, the number of stomata in each area is counted by density distribution, and the statistical value is compared with the preset threshold to obtain the stomata classification parameter; (3) The stomatal classification parameters are hierarchically processed, the distribution characteristics of the stomatal groups are determined according to the spatial position relationship, and the regional connectivity analysis of the distribution characteristic data is performed to obtain the classification statistical results; (4) Data matching is performed between the classification statistical results and the stomatal characteristic parameters. The association matrix of stomatal morphology and distribution is established through parameter mapping. The association matrix is ​​numerically calculated to obtain characteristic association data. (5) The feature-related data are weighted according to the spatial distribution law, and an evaluation index system is constructed through multi-dimensional feature calculation. The evaluation index is analyzed hierarchically to obtain an evaluation parameter set; (6) Perform numerical calculations on the evaluation parameter set, determine the evaluation value of the pore defect by integrating multiple indicators, compare and analyze the evaluation value with the standard range, and obtain quantitative evaluation data of the pore defect.

[0045] Specifically, the spatial distribution data of pores are analyzed in depth. The frequency calculation of pore depth values ​​and position coordinates uses the statistical histogram method to divide the entire detection area into regular spatial units and count the number of pores in each unit. The interval division process takes into account the typical size of bubbles and divides the depth range into several equally spaced segments to facilitate the observation of the distribution of bubbles at different depths. Aggregation analysis evaluates the concentration of bubble groups by calculating the bubble density in the local area. Normalization processing unifies the statistical data of different regions and depths to the same numerical range for easy comparison and analysis. The spatial division of initial statistical data uses the adaptive grid method to dynamically adjust the grid size according to the density of pore distribution. Smaller grid units are used in dense pore areas, while larger grid units are used in sparse areas. This division method not only ensures the accuracy of the analysis, but also improves the calculation efficiency. The weighted counting method is used to count the number of pores in each area, taking into account the influence of pore size on the statistical results. The preset thresholds are reference values ​​determined based on engineering experience and quality standards. The interval comparison process compares the statistical values ​​with these thresholds to determine the severity of pore distribution.

[0046] The hierarchical processing of pore classification parameters established a multi-level classification system. According to the characteristics of pore depth, size and density, the pores were divided into different levels. The spatial position relationship analysis focused on the relative position and clustering phenomenon between pores. The distribution characteristics of pore groups include the size, shape and directionality of the group. The regional connectivity analysis uses graph theory to connect adjacent pores into a network to evaluate the overall distribution characteristics of the pore group. The classification statistics and the data matching process of pore characteristic parameters established the correlation between morphological characteristics and spatial distribution. The parameter mapping uses a multidimensional mapping method to map morphological parameters and distribution parameters to the same feature space. The correlation matrix records the degree of correlation between different features, and the dependency relationship between features is determined by numerical calculation. Specifically, the pore characteristic parameters include: morphological characteristic parameters: including quantitative indicators such as the diameter, area, perimeter, circularity index, and major-minor axis ratio of the pores to describe the geometric shape of the pores. Position characteristic parameters: including the three-dimensional coordinates of the pores in the concrete T-beam. Spatial distribution characteristic parameters: including the degree of pore aggregation, spatial density, and spacing between adjacent pores, which characterize the relative position relationship of multiple pores. Physical characteristic parameters: including pore surface reflectivity, edge clarity, internal filling state, and other parameters that reflect the physical properties of pores.

[0047] Data matching is achieved by establishing a corresponding relationship between the classification statistics results and the stomatal characteristic parameters. It can be expressed by the following formula: ; in: is the element of the correlation matrix; is the classification statistics result matrix; is the feature mapping function; is the matching weight index; D and E are the statistical matrix dimensions.

[0048] The feature mapping function J(i,j,d,e) defines the correspondence between the statistical results and the feature parameters, which can be expressed as: ; in: is the characteristic parameter of pores; is the classification statistical weight; A is the normalization coefficient; B is the feature dimension. The feature association data can be obtained by the following calculation: ; in: is feature-related data; is the i-th component of the eigenvector; is the eigenvalue; G is the number of eigenvalues.

[0049] The weight configuration of feature-related data takes into account the importance of different features for the evaluation of pore defects. Multi-dimensional feature calculation integrates morphological features, distribution features and material property indicators to build a complete evaluation index system. Specifically, the multi-dimensional feature calculation to build an evaluation index system is to comprehensively calculate the characteristic indicators of multiple dimensions such as pore morphological features, distribution features, material properties and environmental factors to form a complete evaluation system. This process can be represented by the following mathematical model: ; in: To evaluate the indicator system; is the feature category weight; is the characteristic component; is the dimension coefficient; is the number of features in each dimension; u represents the feature category (1-morphological features, 2-distribution features, 3-material properties, 4-environmental factors); n is the evaluation index index. First, the feature components in each feature category are weighted and summed, and then normalized; then, the calculation results of different feature categories are weighted and fused again to form the final evaluation index system.

[0050] Characteristic Component The calculation of takes into account the difference between the relative value of the feature and the baseline value, as well as the adaptability weight, and its calculation formula is: ; in: is the original eigenvalue; is the characteristic reference value; is the standardization factor; is the adaptability weight.

[0051] The analytic hierarchy process uses the analytic hierarchy process to determine the weight coefficients of each indicator. The evaluation parameter set contains quantitative evaluation indicators and corresponding weight values. The numerical calculation process weights and fuses multiple indicators to obtain a comprehensive evaluation value. These evaluation values ​​are compared with the pre-set standard range to determine the severity of the pore defect. The quantitative evaluation data not only reflects the defect status of a single area, but also reflects the overall quality level. Specifically, the fusion of multiple indicators to determine the evaluation value of pore defects is a process of integrating the indicators in each evaluation parameter set into a single evaluation value through weighted calculation. The specific implementation includes indicator normalization, indicator weight allocation, weighted fusion calculation and evaluation value calibration. Indicator normalization is to convert various indicators of different dimensions and different numerical ranges (such as pore diameter, depth, density, aggregation, etc.) into a unified numerical interval [0,1] to ensure that the indicators are comparable. This is usually achieved by the maximum and minimum value normalization method or the z-score normalization method.

[0052] The indicator weight allocation is to allocate different weight coefficients according to the importance of each indicator on the performance of concrete structure. For example, the influence of pore diameter on structural strength may be greater than that of pore depth, so the weight of pore diameter indicator will increase accordingly. The weight allocation can be determined by expert scoring method, hierarchical analysis method or historical data regression analysis.

[0053] Weighted fusion calculation uses linear weighting or nonlinear weighting to multiply all normalized indicators by their corresponding weights and then sum them to obtain a single evaluation value. If there is interaction between some indicators, cross terms will be introduced for calculation to more comprehensively reflect the mutual influence relationship between indicators. Evaluation value calibration is to calibrate the preliminary evaluation values ​​based on historical data and engineering experience to ensure that the evaluation results are consistent with the actual situation. The calibration process often needs to be carried out through comparative verification method, comparing the algorithm results with the expert evaluation results and making necessary adjustments. The process of comparing and analyzing the evaluation values ​​with the standard range to obtain quantitative evaluation data mainly includes standard range definition, interval mapping conversion, defect impact factor calculation, defect distribution feature synthesis, quantitative evaluation data generation and result reliability verification. Standard range definition is based on national standards, industry specifications and engineering experience to determine the evaluation value range corresponding to defects of different levels. For example, the evaluation values ​​can be divided into three level intervals of 0.0-0.3 (minor defects), 0.3-0.6 (medium defects), and 0.6-1.0 (serious defects) to form an evaluation standard. Interval mapping conversion is to map a single evaluation value to the corresponding defect grade interval to determine the severity level of the pore defect. This process is usually implemented by table lookup or function mapping to ensure that the evaluation result has a clear grade attribute.

[0054] The defect impact factor calculation is to calculate the defect impact factor based on the defect grade and the importance of the position of the pore in the component, and quantify the degree of influence of the defect on the overall performance of the component. The impact factor of the pore defect located in the stress concentration area is often given a higher weight. The defect distribution characteristics comprehensively consider the spatial distribution characteristics of the pore defects, such as clustered distribution, chain distribution or uniform distribution, and adjust the defect impact factor. There are significant differences in the impact of different distribution characteristics on structural performance, which need to be fully considered in the evaluation process. Quantitative evaluation data generation is to comprehensively evaluate the numerical value, defect grade, impact factor and distribution characteristics to generate multi-dimensional quantitative evaluation data, including defect severity index, structural impact coefficient, quality grade assessment and safety risk degree. These data are expressed in specific numerical form to facilitate subsequent analysis and decision-making. The reliability verification of the results is to verify the accuracy and reliability of the evaluation data by comparing with actual engineering cases and test results, and to fine-tune the parameters when necessary. The verification process helps to optimize the evaluation algorithm and improve the accuracy and practicality of the evaluation results.

[0055] For example, in the bubble detection of a precast concrete T-beam, the surface pore distribution data was first obtained. Frequency calculation shows that the pore depth is mainly concentrated in the range of 2-5 mm on the surface, and the position distribution shows the characteristics of local aggregation. Aggregation analysis found that the pore density in the stress concentration area of ​​the beam is significantly higher than that in other areas. After normalization, the data in different areas are comparable. After spatial division, it was found that the distribution of pores showed obvious regional differences. The number of pores in some areas exceeded the preset threshold, and these areas were marked as key areas of concern. Through hierarchical processing, the pores were divided into three levels: mild, moderate and severe. Regional connectivity analysis showed that some pores formed a continuous chain distribution, and this distribution feature had a more significant impact on the structure. The correlation analysis between pore morphology and distribution showed that larger pores tended to be concentrated in specific areas, while the distribution of small pores was relatively uniform. These features were quantified as numerical values ​​in the correlation matrix. When configuring the weight, considering that large-sized pores have a greater impact on structural performance, their weight coefficients were increased accordingly. The final evaluation results clearly reflect the distribution characteristics and severity of porosity defects, providing an important basis for quality control.

[0056] In a specific embodiment, the process of executing step S106 may specifically include the following steps: (1) Perform numerical interval analysis on the quantitative evaluation data of pore defects, classify the pore defects in each interval according to the distribution characteristics, establish early warning index data based on the classification results, and obtain the initial early warning parameters; (2) The initial warning parameters are classified hierarchically according to the spatial distribution, the hazard level coefficient is determined by defect degree calculation, and each level of defects is identified and mapped to obtain the warning judgment result; (3) Regionally mark the warning judgment results, spatially locate the defect information through position coordinates, convert the location data into visual marks, and obtain the labeled data set; (4) Graphically process the annotated data set, construct a visualization interface through multi-dimensional feature expression, optimize the layout of interface elements, and obtain visualization results; (5) Match the visualization results with the quality parameters of prefabricated components, establish a quality assessment correlation matrix through parameter mapping, and perform comprehensive calculations on the correlation data to obtain quality comparison data; (6) Analyze and organize the quality comparison data according to the evaluation standards, generate the test evaluation content by integrating multiple indicators, format the evaluation content, and obtain the test report data.

[0057] Specifically, numerical interval analysis is performed on the quantitative evaluation data of pore defects. The numerical interval analysis adopts a hierarchical classification method to set different evaluation intervals according to the characteristics of pore size, depth and distribution density. The distribution characteristic analysis considers the degree of aggregation and continuity of pores in space, and determines the characteristic parameters of pore distribution by statistical methods. The grading adopts a multi-level classification system to divide pore defects into different hazard levels. The early warning indicator data contains thresholds in multiple dimensions such as pore size, depth, and density, which are determined based on engineering standards and practical experience. The spatial distribution analysis of the initial early warning parameters focuses on the location characteristics of the defects. The hierarchical classification process adopts a top-down classification strategy, first determining a large range of hazard areas, and then gradually refining them to specific locations. The calculation of the defect degree comprehensively considers the geometric characteristics and material properties of the pores, and the hazard level coefficient is obtained by weighted calculation. The identification mapping represents defects of different levels with different identifiers to form a clear early warning judgment result.

[0058] The regional annotation of the early warning judgment results adopts a multi-level annotation strategy. The spatial positioning of the position coordinates adopts a combination of absolute coordinates and relative coordinates to ensure the accuracy of the annotation. The visual marking of the defect information adopts different graphic symbols and color coding to intuitively express the type and severity of the defect. The annotation data set contains multidimensional data such as location information, defect characteristics and warning level. The graphical processing of the annotation data set adopts a variety of visualization technologies. The multi-dimensional feature expression uses visual elements such as color, shape, and size to show different data dimensions. The construction of the visualization interface takes into account the hierarchical structure and association relationship of the data, and adopts a combination of hierarchical layout and grid layout. The layout optimization of the interface elements ensures the clear display and intuitive understanding of the information. The data matching process of the prefabricated component quality parameters and the visualization results establishes the mapping relationship between the defect characteristics and the quality indicators. The quality assessment association matrix records the mutual influence between different parameters, and determines the weight and correlation of each indicator through matrix operation. The comprehensive calculation process weights and fuses multiple quality indicators to form an overall quality assessment result.

[0059] The analysis and collation of quality comparison data is based on the preset evaluation criteria, comparing the test results with the quality requirements. The integration of multiple indicators adopts the hierarchical analysis method to reasonably allocate the weights of each indicator. The test evaluation content includes multiple parts such as numerical analysis results, graphical display and quality suggestions. Formatting ensures the standardization and readability of the report.

[0060] For example: In the bubble detection of a precast concrete T-beam, numerical interval analysis was performed based on quantitative evaluation data, and it was found that the distribution of pores has obvious regularity. Larger pores (diameter greater than 5mm) are mainly distributed in the web area of ​​the beam, while small pores are more scattered. Through the analysis of distribution characteristics, a two-dimensional classification system based on position and size was established. Spatial distribution analysis showed that pores formed a continuous chain distribution in some areas, which was marked as a high hazard level. The calculation of the hazard level coefficient takes into account the size, depth and continuity of the pores, and the weighted average method is used to obtain a comprehensive score. Defects of different levels are mapped with markers of different colors and shapes, with red indicating severe defects, yellow indicating moderate defects, and green indicating minor defects.

[0061] The visualization process uses a three-dimensional display method, with different shades of color representing the depth distribution of pores, and the bubble size represented by circular marks of different sizes, forming an intuitive spatial distribution map. The interface layout uses a partitioned display method, with the overall distribution map displayed on the left and detailed parameters and statistical data displayed on the right. During the quality assessment process, the test data is compared with quality parameters such as the strength requirements and appearance requirements of prefabricated components. Through correlation analysis, it was found that there is a significant correlation between pore density and concrete strength, and this finding is used to optimize the production process. The final test report contains quantitative analysis results, distribution maps, quality assessments, and improvement suggestions, providing comprehensive technical support for quality control.

[0062] The above describes the bubble detection method for precast concrete T beams in the embodiment of the present application. The following describes the bubble detection system for precast concrete T beams in the embodiment of the present application. Figure 2 In the embodiment of the present application, an embodiment of the bubble detection system for precast concrete T beams includes: The acquisition module 201 is used to collect multi-band reflection data from the surface of the precast concrete T-beam, perform position mapping calibration on the collected reflection data according to the calibration plate parameters, perform light intensity compensation processing and signal-to-noise ratio optimization on the calibrated reflection data of each band, select the optimal band combination by cross-validation, and obtain compensated multi-band reflection data; The registration module 202 is used to perform spatial registration according to the compensated multi-band reflection data, perform noise reduction and smoothing processing on the registered data, perform principal component calculation and data integration on the smoothed multi-band data, and obtain a fused image of the reflection data; The segmentation module 203 is used to perform edge detection and image segmentation based on the fused image of the reflection data, perform numerical conversion on the segmented image area according to the contour features, jointly analyze the converted numerical values ​​with the texture features, and use an adaptive threshold to perform fine extraction on the pore boundaries to obtain the morphological parameter data of the pores; The calculation module 204 is used to perform depth calculation according to the morphological parameter data of the pores, perform spatial coordinate conversion on the calculated depth data, perform dual constraint reconstruction on the converted spatial coordinate data and the surface reflection intensity, and perform correction in combination with the concrete density parameter to obtain the spatial distribution data of the pores; The statistical module 205 is used to perform numerical statistics based on the spatial distribution data of the pores, classify and divide the statistical results according to a preset threshold, and perform correlation calculations on the classification results and the pore characteristic parameters to obtain quantitative evaluation data of the pore defects; The determination module 206 is used to determine the warning level according to the quantitative evaluation data of the pore defects, automatically mark and visualize the determination results, compare and analyze the processing results with the quality parameters of the prefabricated components, and obtain the test report data.

[0063] Through the coordinated cooperation of the above-mentioned components, by using multi-band reflection data for acquisition and position mapping calibration of calibration plate parameters, accurate spatial positioning and light intensity compensation in the bubble detection process are achieved, and the accuracy of data acquisition is improved; by spatial registration and noise reduction and smoothing of the compensated multi-band reflection data, combined with principal component calculation and data integration technology, the quality and reliability of the reflection data fusion image are effectively improved; by using edge detection and image segmentation methods, combined with the numerical conversion of contour features and the joint analysis of texture features, the pore boundaries are refined and extracted through adaptive thresholds, and the accurate acquisition of pore morphological parameters is achieved; by performing deep calculation and spatial coordinate conversion of pore morphological parameter data, combined with the dual-constraint reconstruction of surface reflection intensity and the correction of concrete density parameters, the accuracy of pore spatial distribution data is improved; by using numerical statistics and classification technology, combined with the correlation operation of pore characteristic parameters, quantitative evaluation of pore defects is achieved; based on the quantitative evaluation data of pore defects, the warning level is determined, and through automatic labeling and visualization processing, combined with the comparative analysis of prefabricated component quality parameters, complete inspection report data is formed, providing a scientific basis for quality control. The entire detection process has achieved full automation and standardization from data collection to result output, significantly improving the efficiency and accuracy of bubble detection in precast concrete T-beams, and providing strong technical support for quality control in engineering practice. Through the fusion analysis of multi-source data and a multi-level evaluation system, the limitations of traditional single detection methods have been overcome, and accurate identification and quantitative evaluation of bubble defects have been achieved, improving the reliability and practical value of the detection results.

[0064] As described above, the above embodiments are only used to illustrate the technical solutions of the present application, rather than to limit them. Although the present application has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present application.

Claims

1. A method for detecting bubbles in precast concrete T-beams, characterized in that: The precast concrete T-beam bubble detection method comprises: According to the multi-band reflection data of the surface of the precast concrete T-beam, the collected reflection data is calibrated by position mapping according to the calibration plate parameters, the calibrated reflection data of each band is subjected to light intensity compensation processing and signal-to-noise ratio optimization, and the optimal band combination is selected by cross-validation to obtain the compensated multi-band reflection data; Perform spatial registration based on the compensated multi-band reflection data, perform noise reduction and smoothing on the registered data, perform principal component calculation and data integration on the smoothed multi-band data, and obtain a fused image of the reflection data; Edge detection and image segmentation are performed based on the fused image of the reflection data. The segmented image area is converted into numerical values ​​according to the contour features. The converted numerical values ​​are jointly analyzed with the texture features. The pore boundaries are refined and extracted using an adaptive threshold to obtain the morphological parameter data of the pores. The depth is calculated based on the morphological parameter data of the pores, the calculated depth data is transformed into spatial coordinates, the transformed spatial coordinate data and the surface reflection intensity are reconstructed with dual constraints, and the data are corrected in combination with the concrete density parameters to obtain the spatial distribution data of the pores; Perform numerical statistics based on the spatial distribution data of pores, classify and divide the statistical results according to the preset threshold, and perform correlation operation on the division results and the pore characteristic parameters to obtain quantitative evaluation data of pore defects; The warning level is determined based on the quantitative evaluation data of the pore defects, the determination results are automatically labeled and visualized, and the processing results are compared and analyzed with the quality parameters of the prefabricated components to obtain the test report data.

2. The method for detecting bubbles in precast concrete T beams according to claim 1, characterized in that: The method collects multi-band reflection data from the surface of the precast concrete T-beam, performs position mapping calibration on the collected reflection data according to the calibration plate parameters, performs light intensity compensation processing and signal-to-noise ratio optimization on the calibrated reflection data of each band, selects the optimal band combination by cross-validation, and obtains compensated multi-band reflection data, including: Collecting multi-band reflection data on the surface of the precast concrete T-beam, and continuously scanning the surface of the precast concrete T-beam with a multi-band sensor to obtain original multi-band reflection data; The original multi-band reflection data is spatially corresponded to the position reference point of the calibration plate parameter, and a position mapping relationship is established through spatial mapping calculation to obtain initial position mapping data; Performing light intensity deviation calculation on each band reflection data in the initial position mapping data, comparing the calculated deviation value with the real-time ambient light intensity to obtain a light intensity correction factor; Numerically correcting the light intensity correction factor and the initial position mapping data, and calculating the signal-to-noise ratio of the corrected multi-band reflection data through data calculation to obtain a band quality assessment value; The band quality assessment values ​​are grouped into bands according to the numerical intervals, and a cross-validation operation is performed on each group of multi-band reflection data through data analysis to obtain a band combination score; The band combination scores are arranged in descending order, the band combination with the highest score is selected by numerical comparison, and the multi-band reflection data of the band combination is normalized to obtain the compensated multi-band reflection data.

3. The method for detecting bubbles in precast concrete T-beams according to claim 1, characterized in that: The method of performing spatial registration according to the compensated multi-band reflection data, performing noise reduction and smoothing processing on the registered data, and performing principal component calculation and data integration on the smoothed multi-band data to obtain a fused image of the reflection data includes: Extracting feature points from the compensated multi-band reflection data, marking control points of the multi-band reflection data of each band through spatial analysis, calculating a coordinate transformation matrix according to the spatial distribution of the marking points, and obtaining registration reference data; The error calculation of the marked points in the registration reference data is performed according to the least squares criterion, and the calculation results are iteratively optimized to obtain the spatial transformation parameters; Using the spatial transformation parameters to perform coordinate mapping conversion on the compensated multi-band reflection data, and obtaining initial registration data through resampling calculation; Performing wavelet decomposition operation on the initial registration data, sorting the decomposition coefficients according to energy size through data analysis, selecting main coefficients for reconstruction calculation, and obtaining denoised and smoothed data of the registered data; Performing covariance calculation on each band component in the noise reduction and smoothing data, determining the principal component weights based on eigenvalue decomposition through feature analysis, and obtaining data fusion weight coefficients; The data fusion weight coefficient and the noise reduction smoothing data are weightedly superimposed, and contrast enhancement calculation is performed on the superposition result through data processing to obtain a fused image of the reflection data.

4. The method for detecting bubbles in precast concrete T-beams according to claim 1, characterized in that: The method performs edge detection and image segmentation based on the fused image of the reflection data, performs numerical conversion on the segmented image area according to the contour features, jointly analyzes the converted numerical values ​​with the texture features, and uses an adaptive threshold to extract the pore boundaries in a refined manner to obtain the morphological parameter data of the pores, including: Performing gradient calculation analysis on the fused image of the reflection data, constructing an edge feature point set according to the gradient intensity value through data calculation, and performing connectivity analysis calculation on the edge feature point set to obtain an initial edge contour; The initial edge contour is segmented and calculated according to the grayscale distribution, the segmented area is tracked and analyzed by data processing, and the tracking result is judged for closure to select the effective area to obtain the candidate pore area; Calculating the perimeter and area of ​​the contour features of the candidate pore area, comparing the calculated results with the circularity index through numerical analysis, and analyzing the shape features of the compared results to obtain initial morphological data; Performing spatial correspondence analysis on the region of the initial morphological data and the fused image of the reflection data, and extracting grayscale distribution features and texture direction features of the corresponding region through data to obtain texture feature data; Perform boundary refinement calculation on the candidate pore area according to the texture feature data, calculate and analyze the local contrast through numerical calculation to determine the dynamic threshold range, and obtain a pore boundary point set; The pore boundary point set is subjected to a curve fitting operation, and geometric parameter extraction and feature quantification calculation are performed on the fitting curve through data processing to obtain the morphological parameter data of the pores.

5. The method for detecting bubbles in precast concrete T beams according to claim 1, characterized in that: The depth calculation is performed according to the morphological parameter data of the pores, the calculated depth data is converted into spatial coordinates, the converted spatial coordinate data and the surface reflection intensity are double-constrained reconstructed, and the spatial distribution data of the pores is obtained by combining the concrete density parameter for correction, including: Performing depth conversion calculation on the grayscale distribution features in the morphological parameter data of the pores, establishing a depth mapping relationship through data analysis, performing boundary constraint calculation on the mapping results, and obtaining initial depth data; The initial depth data is subjected to coordinate transformation calculation according to the spatial distribution law, the depth value is relocated and analyzed through numerical calculation, and the relocation result is subjected to error correction processing to obtain the spatial coordinate data of the initial depth data; Performing a joint operation on the spatial coordinate data and the surface reflection intensity, calculating and analyzing the reflection intensity gradient through data processing to determine the depth correction coefficient, and performing a weighted combination of the correction coefficient and the spatial coordinate data to obtain initial reconstructed data; Comparing and analyzing the initial reconstructed data with the surface reflection intensity of the local area, adjusting the spatial distribution parameters of the neighborhood features through numerical calculation, and verifying the consistency of the adjusted parameters to obtain reconstructed corrected data; The reconstructed correction data and the concrete density parameter are correlated and calculated, the spatial coordinate data is compensated through data analysis, and the compensation result is compared with the theoretical distribution to obtain a correction parameter set; The correction parameter set is used to perform spatial calibration calculation on the reconstructed correction data, the pore positions are located by multi-dimensional feature fusion through data processing, and the positioning results are statistically analyzed to obtain the spatial distribution data of the pores.

6. The method for detecting bubbles in precast concrete T-beams according to claim 1, characterized in that: The method of performing numerical statistics based on the spatial distribution data of the pores, classifying and dividing the statistical results according to a preset threshold, and correlating the division results with the pore characteristic parameters to obtain quantitative evaluation data of the pore defects includes: Performing frequency calculation on the depth values ​​and position coordinates in the spatial distribution data of the pores, performing aggregation analysis on the pore distribution density by interval division, and normalizing the aggregation data to obtain initial statistical data; The initial statistical data is numerically classified according to spatial division, the number of stomata in each region is counted by density distribution, and the statistical value is compared with a preset threshold value to obtain a stomata classification parameter; The stomatal classification parameters are processed hierarchically, the distribution characteristics of the stomatal groups are determined according to the spatial position relationship, and the regional connectivity analysis is performed on the distribution characteristic data to obtain the classification statistical results; Performing data matching on the classification statistical results and the stomatal characteristic parameters, establishing a correlation matrix of stomatal morphology and distribution through parameter mapping, performing numerical calculation on the correlation matrix, and obtaining characteristic correlation data; The feature-related data are weighted according to the spatial distribution law, an evaluation index system is constructed through multi-dimensional feature calculation, and a hierarchical analysis is performed on the evaluation index to obtain an evaluation parameter set; Numerical calculation is performed on the evaluation parameter set, and the evaluation value of the pore defect is determined by fusing multiple indicators. The evaluation value is compared and analyzed with the standard range to obtain quantitative evaluation data of the pore defect.

7. The method for detecting bubbles in precast concrete T-beams according to claim 1, characterized in that: The method of determining the warning level based on the quantitative evaluation data of the pore defects, automatically marking and visualizing the determination results, and comparing and analyzing the processing results with the quality parameters of the prefabricated components to obtain the test report data includes: Performing numerical interval analysis on the quantitative evaluation data of the pore defects, classifying the pore defects in each interval according to the distribution characteristics, establishing early warning index data for the classification results, and obtaining initial early warning parameters; The initial warning parameters are hierarchically classified according to spatial distribution, the hazard level coefficient is determined by defect degree calculation, and each level of defects is identified and mapped to obtain a warning judgment result; Performing regional annotation on the warning determination result, spatially locating the defect information through position coordinates, converting the positioning data into visual marks, and obtaining annotated data sets; Graphically processing the annotated data set, constructing a visualization interface through multi-dimensional feature expression, optimizing the layout of interface elements, and obtaining a visualization result; Performing data matching between the visualization result and the quality parameters of the prefabricated component, establishing a quality assessment association matrix through parameter mapping, and performing comprehensive calculation on the associated data to obtain quality comparison data; The quality comparison data is analyzed and sorted according to the evaluation criteria, and the detection evaluation content is generated by integrating multiple indicators. The evaluation content is formatted to obtain the detection report data.

8. A precast concrete T-beam bubble detection system, used to implement the precast concrete T-beam bubble detection method according to any one of claims 1 to 7, characterized in that: The precast concrete T-beam bubble detection system comprises: The acquisition module is used to collect multi-band reflection data from the surface of the precast concrete T-beam, perform position mapping calibration on the collected reflection data according to the calibration plate parameters, perform light intensity compensation processing and signal-to-noise ratio optimization on the calibrated reflection data of each band, select the optimal band combination by cross-validation, and obtain compensated multi-band reflection data; A registration module is used to perform spatial registration based on the compensated multi-band reflection data, perform noise reduction and smoothing on the registered data, perform principal component calculation and data integration on the smoothed multi-band data, and obtain a fused image of the reflection data; The segmentation module is used to perform edge detection and image segmentation based on the fused image of the reflection data, perform numerical conversion on the segmented image area according to the contour features, jointly analyze the converted numerical values ​​with the texture features, and use the adaptive threshold to extract the pore boundaries in a refined manner to obtain the morphological parameter data of the pores; A calculation module is used to perform depth calculation based on the morphological parameter data of the pores, perform spatial coordinate conversion on the calculated depth data, perform dual constraint reconstruction on the converted spatial coordinate data and the surface reflection intensity, and perform correction in combination with the concrete density parameter to obtain the spatial distribution data of the pores; A statistical module is used to perform numerical statistics based on the spatial distribution data of pores, classify and divide the statistical results according to a preset threshold, and associate the division results with the pore characteristic parameters to obtain quantitative evaluation data of pore defects; The determination module is used to determine the warning level based on the quantitative evaluation data of the pore defects, automatically mark and visualize the determination results, compare and analyze the processing results with the quality parameters of the prefabricated components, and obtain the test report data.

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