A method, apparatus, and storage medium for identifying pores in a three-dimensional reconstructed slurry hardened body.

Abstract

This invention belongs to the field of grouting material characterization technology, and relates to a method, device, and storage medium for identifying pores in a three-dimensional reconstructed grout hardened body. The method involves: acquiring pore regions and matrix regions in a microscopic image of the grouting material; calculating the pore diameter of each pore region in the microscopic image and obtaining the maximum pore diameter; calculating the grayscale difference between the maximum and minimum grayscale values ​​of the microscopic image; calculating a planar resolution parameter based on the length of the microscopic image and the number of pixels corresponding to that length; calculating an elevation conversion factor based on the maximum pore diameter and the grayscale difference; calculating a scaling factor based on the elevation conversion factor and the planar resolution parameter; mapping each pixel in the microscopic image using the scaling factor to obtain the elevation value of each pixel; obtaining point cloud data based on the pixel grid coordinates and elevation values ​​of all pixels; fitting the point cloud data to generate a continuous elevation surface, thereby constructing a three-dimensional model of the grouting material and calculating its porosity.

Description

Technical Field

[0001] This invention relates to the field of grouting material characterization technology, and in particular to a method, apparatus and computer-readable storage medium for identifying the pores of a three-dimensional reconstructed grout hardened body. Background Technology

[0002] In the field of grouting material research and development and performance optimization, accurately characterizing the pore structure of materials is of decisive significance for engineering applications. Porosity, as a key parameter for measuring material properties, directly affects the strength development, impermeability, and long-term durability of the grout.

[0003] Mercury intrusion porosimetry (MIP) is widely used for the quantitative determination of porosity in grouting materials due to its high precision. It involves gradually applying pressure to a dry sample to force non-wetting mercury into its pores and recording the volume of mercury entering the pores at different pressures to obtain a pressure-mercury intrusion curve for calculating porosity. However, this method has a long testing cycle, with a single test typically exceeding 24 hours, resulting in high operating costs. It also requires mercury contamination protection and specialized equipment maintenance. Furthermore, the intrusion of high-pressure mercury can cause distortion of the sample's pore structure. These limitations severely restrict the need for rapid screening for optimizing the mix proportions of grouting materials and the analysis of the dynamic evolution mechanism of pore structure during material consolidation and hardening.

[0004] With breakthroughs in microscale imaging technology, existing technologies have begun to use three-dimensional reconstruction methods based on microscopic images to characterize pore structures. These methods acquire spatial topological information of the pore network inside the material through high-resolution non-destructive scanning, and then reconstruct a three-dimensional pore model of the grout body from the micro to the macro scale using digital image processing algorithms. This allows for rapid non-destructive detection of porosity and dynamic tracking of the pore evolution of the grout material during the consolidation and hardening process. For example, focused ion beam scanning electron microscopy (FIB-SEM) uses a focused ion beam to bombard the sample surface, peeling away the surface material layer by layer. After each layer is milled, the scanning electron microscope scans the currently exposed sample surface. By detecting secondary or backscattered electrons emitted by the sample, a high-resolution 2D image of the sample surface is generated. Based on multiple milling operations, a set of 2D images arranged in depth order is generated. After image alignment, noise removal, and segmentation of all 2D images, a 3D model of the sample is reconstructed, and porosity is then calculated. However, FIB-SEM equipment is too expensive, and ion beam milling is an irreversible removal process that can damage the sample, making it unusable. Furthermore, ion beam bombardment can cause sample surface distortion, and milling debris can deposit on the sample surface, forming false structures. The scanning electron microscope can also cause charge accumulation, resulting in bright spots or distortions in the generated image, which in turn makes the generated 3D model inaccurate, leading to low accuracy in the calculated porosity.

[0005] In summary, existing methods for characterizing pore structure based on microscopic images for 3D reconstruction suffer from problems such as high equipment costs, significant sample damage, complex 3D model reconstruction process, and low reconstruction accuracy, leading to inaccurate calculated porosity. Summary of the Invention

[0006] Therefore, the technical problem to be solved by the present invention is to overcome the problems of high equipment cost, strong sample destructiveness, and low accuracy of the 3D model reconstruction process of the sample in the prior art, which leads to inaccurate porosity calculation.

[0007] To address the aforementioned technical problems, this invention provides a method for identifying the pores of a three-dimensional reconstructed slurry hardened body, comprising: Microscopic images of grouting materials are obtained, and the pore and matrix regions in the microscopic images are obtained based on the grayscale distribution characteristics of the pore pixels and matrix pixels of the grouting materials. Calculate the pore diameter of each pore region in the microscopic image and obtain the maximum pore diameter; based on the grayscale histogram of the microscopic image, obtain the maximum and minimum grayscale values ​​of the microscopic image, and calculate the grayscale difference between the maximum and minimum grayscale values. The planar resolution parameter is obtained based on the ratio of the length of the microscopic image to the number of pixels corresponding to that length; the elevation conversion factor is obtained based on the ratio of the maximum pore diameter to the grayscale difference; and the scaling correction factor is obtained based on the ratio of the elevation conversion factor to the planar resolution parameter. The elevation value of each pixel is obtained by multiplying the scaling factor with the gray value of each pixel in the microscopic image; discrete point cloud data is obtained based on the pixel grid coordinates and elevation values ​​of all pixels. A continuous elevation surface is generated by fitting the point cloud data; a three-dimensional visualization operation is performed on the elevation surface to obtain a three-dimensional model of the grouting material, thereby calculating the porosity of the grouting material.

[0008] Preferably, the point cloud data is fitted with a surface to generate a continuous elevation surface, including: Based on the distance from each point cloud data point to the interpolation location and the terrain complexity factor, the weight of each point cloud data point to the interpolation location is calculated. The elevation value of the location to be interpolated is calculated by weighting the elevation values ​​of each point cloud data point and the weight of the location to be interpolated. Based on all point cloud data and the elevation values ​​of the location to be interpolated, an elevation point set is obtained. A continuous elevation surface is generated by performing surface fitting on the set of elevation points.

[0009] Preferably, the formula for calculating the weight of each point cloud data point at the interpolation location is as follows: , in, This represents the weight of the i-th point cloud data at the interpolation position; This represents the distance from the i-th point cloud data point to the position to be interpolated; Indicates the terrain complexity factor; This indicates the number of point cloud data points.

[0010] Preferably, calculating the porosity of the grouting material includes: The 3D model is rasterized, and the total volume of the 3D model is obtained by multiplying the number of raster layers, the cross-sectional area of ​​a single raster layer, and the scaling factor. The upper surface of the 3D model is set as the reference plane, and the pore volume is obtained by accumulating the spatial volume differences of all spaces in the 3D model whose elevation values ​​are lower than the elevation values ​​of the reference plane. The porosity of the grouting material is obtained based on the ratio of pore volume to total volume.

[0011] Preferably, the upper surface of the 3D model is set as the reference plane, and the pore volume is obtained by accumulating the spatial volume differences of all elevation values ​​in the 3D model that are lower than the elevation value of the reference plane, including: Calculate the elevation difference between the elevation value of the reference plane and the elevation value of each pore in the 3D model; The volume of each pore is obtained by multiplying the elevation difference corresponding to each pore by the area of ​​the grid cell, and the pore volume is obtained by summing the volumes of all pores. In this context, pores are points in the 3D model whose elevation values ​​are lower than the elevation values ​​of the reference plane.

[0012] Preferably, the planar resolution parameter The calculation formula is: , in, Indicates the length of the microscopic image; This represents the number of pixels corresponding to the length of a microscopic image. Elevation conversion factor The calculation formula is: , in, Indicates the maximum pore diameter; Indicates the maximum grayscale value; Indicates the minimum grayscale value; Proportional correction factor The calculation formula is: ; The formula for calculating the elevation value of each pixel is: , in, This represents the elevation value of the i-th pixel. This represents the grayscale value of the i-th pixel; This represents the pixel grid coordinates of the i-th pixel. Indicates the base elevation benchmark value. .

[0013] Preferably, the total volume of the three-dimensional model The calculation formula is: , in, This represents the cross-sectional area of ​​a single grid layer obtained by scanning a completely white rasterized 3D model. Indicates the proportional correction factor; Pore ​​volume The calculation formula is: , in, This indicates the number of pores in a 3D model; The elevation value of the reference plane representing the three-dimensional model; This represents the elevation value of the j-th pore in the 3D model; This represents the area of ​​the raster cells after the 3D model has been rasterized. Porosity The calculation formula is: .

[0014] Preferably, a microscopic image of the grouting material is acquired, and based on the grayscale distribution characteristics of the pore pixels and matrix pixels of the grouting material, the pore region and matrix region in the microscopic image are obtained, including: The grouting material was scanned from multiple angles using a scanning electron microscope to obtain microscopic images of the grouting material; Pore ​​pixels are obtained based on pixels with gray values ​​in the range of 0 to 85 in the microscopic image; matrix pixels are obtained based on pixels with gray values ​​in the range of 170 to 255 in the microscopic image. The gray values ​​of the pore pixels are marked as 255, and the gray values ​​of the matrix pixels are marked as 0, thus converting the microscopic image into a binary image. Based on the independent connected regions with a gray value of 255 in the binarized image, the pore region of the microscopic image is obtained, and the matrix region of the microscopic image is obtained based on the region with a gray value of 0.

[0015] The present invention also provides a three-dimensional reconstruction slurry hardened body pore identification device, comprising: The image acquisition and region segmentation module is used to acquire microscopic images of grouting materials. Based on the grayscale distribution characteristics of pore pixels and matrix pixels of grouting materials, the pore region and matrix region in the microscopic image are acquired. The parameter acquisition module is used to calculate the pore diameter of each pore region in the microscopic image and obtain the maximum pore diameter; based on the grayscale histogram of the microscopic image, it obtains the maximum and minimum grayscale values ​​of the microscopic image and calculates the grayscale difference between the maximum and minimum grayscale values. The scaling factor calculation module is used to obtain the planar resolution parameter based on the ratio of the length of the microscopic image to the number of pixels corresponding to that length; to obtain the elevation conversion factor based on the ratio of the maximum pore diameter to the grayscale difference; and to obtain the scaling factor based on the ratio of the elevation conversion factor to the planar resolution parameter. The grayscale elevation mapping modeling module is used to obtain the elevation value of each pixel based on the product of the scaling correction factor and the grayscale value of each pixel in the microscopic image; and to obtain discrete point cloud data based on the pixel grid coordinates and elevation values ​​of all pixels. The porosity calculation module is used to perform surface fitting on point cloud data to generate a continuous elevation surface; and to perform three-dimensional visualization operations on the elevation surface to obtain a three-dimensional model of the grouting material, thereby calculating the porosity of the grouting material.

[0016] The present invention also provides a computer-readable storage medium storing a computer program, which, when executed by a processor, implements the steps of the above-described method for identifying pores in a three-dimensional reconstructed slurry hardened body.

[0017] The method for identifying pores in a three-dimensional reconstructed slurry hardened body provided in this application has the following beneficial effects: 1. By acquiring two-dimensional microscopic images of the grouting material, the pore and matrix regions are divided based on the grayscale characteristics of the pores and matrix in the two-dimensional images. Planar resolution parameters are determined based on the resolution of the microscopic images, thus establishing a pixel-to-micrometer conversion relationship. An elevation conversion factor is determined using the maximum pore diameter and the grayscale value range of the microscopic images, thus establishing a grayscale-to-micrometer conversion relationship. A scaling correction factor is calculated based on the planar resolution parameters and the elevation conversion factor, resulting in a direct pixel-to-grayscale conversion relationship. Scale coordination is achieved by introducing the actual physical length (in micrometers) of the microscopic images, thereby unifying the dimensions of height and pixel information and ensuring the proportional consistency of the subsequently constructed 3D model in the x, y, and z directions, thus ensuring the accuracy of the 3D model. Grayscale-elevation mapping modeling is used to transform the microscopic images... A mapping relationship is established between grayscale values ​​and three-dimensional elevation, realizing a linear conversion from the grayscale value of a pixel to the elevation value, thereby generating discrete point cloud data and bridging the conversion between two-dimensional images and three-dimensional models. Then, surface fitting is performed on the point cloud data to generate a continuous elevation surface, thereby obtaining a three-dimensional model of the grouting material and directly calculating its porosity. This application only requires the use of ordinary scanning equipment to acquire two-dimensional images of the grouting material, and combines the principle of Geographic Information System (GIS) Digital Elevation Model (DEM) to map the two-dimensional images to construct a three-dimensional model. At the same time, a scaling correction factor is constructed to unify the dimensions of the pixel information of the two-dimensional image and the elevation information of the three-dimensional model to ensure the accuracy of the three-dimensional model. Then, the porosity of the grouting material can be accurately calculated directly based on the three-dimensional model without the need for complex and high-cost equipment or destructive operations such as milling on the grouting material. 2. Considering the noise or uneven sampling during SEM scanning to acquire microscopic images, which may lead to missing or sparse distribution of point cloud data after modeling, this application employs an improved Kriging interpolation algorithm to fit the point cloud data to a surface, supplementing missing data and generating a continuous elevation surface. Specifically, since the elevation values ​​of point cloud data that are closer in distance are closer to the elevation values ​​of the missing locations, this application calculates the weight of each point cloud data point to the missing location based on its distance to the missing location. In addition, the introduction of a terrain complexity factor can dynamically adjust the attenuation intensity of the interpolation weights. When the pore structure of the grouting material is complex, a terrain complexity factor greater than 1 is used to strengthen the influence of neighboring point cloud data. When the pore structure of the grouting material is flat, a terrain complexity factor less than or equal to 1 is used to weaken the influence of neighboring point cloud data. Thus, the point cloud data after gray-level-elevation mapping modeling can be interpolated and fitted based on the characteristics of the grouting material, making the fitted elevation surface more consistent with the grouting material, thereby improving the accuracy of porosity calculation. Attached Figure Description

[0018] To make the content of this invention easier to understand, the invention will be further described in detail below with reference to specific embodiments and accompanying drawings, wherein: Figure 1 Flowchart of the three-dimensional reconstruction slurry hardened body pore identification method provided in this application; Figure 2 Images of the grouting material area before and after division provided in this application; wherein, Figure 2 Image (a) in the image is a microscopic image of the grouting material. Figure 2 (b) in the image is the binarized image after the microscopic image is transformed; Figure 3 A schematic diagram illustrating the grayscale elevation mapping modeling principle provided in this application; Figure 4 This is a schematic diagram illustrating the principle of the Kriging interpolation algorithm provided in this application; wherein, Figure 4 (a) in the figure is a schematic diagram showing the distance from each point cloud data point to the location to be interpolated. Figure 4 (b) in the figure is a schematic diagram showing the change of the weight of the interpolation location of each point cloud data with distance under different terrain complexity factors; Figure 5 The flowchart for identifying pores in the three-dimensional reconstructed slurry hardened body provided in this application; wherein, Figure 5 Image (a) in the image is a microscopic image of the grouting material. Figure 5 (b) in the figure represents discrete point cloud data. Figure 5 In the diagram, (c) represents the elevation surface fitted using the Kriging interpolation algorithm. Figure 5 (d) in the figure represents the three-dimensional morphology reconstruction result of the grouting material; Figure 6 This is a schematic diagram of a three-dimensional model of the grouting material. Detailed Implementation

[0019] The present invention will be further described below with reference to the accompanying drawings and specific embodiments, so that those skilled in the art can better understand and implement the present invention. However, the embodiments described are not intended to limit the present invention.

[0020] Please see Figure 1 , Figure 1 The diagram shows a flowchart of the three-dimensional reconstruction slurry hardening body pore identification method provided in this application. The method specifically includes steps S10 to S50: S10: Obtain a microscopic image of the grouting material. Based on the grayscale distribution characteristics of the pore pixels and matrix pixels of the grouting material, obtain the pore region and matrix region in the microscopic image.

[0021] Preferably, in order to eliminate artifacts caused by electronic noise and impurities on the surface of the grouting material during scanning, while preserving the edge details of pores and matrix in the microscopic image, median filtering can be used to filter the microscopic image.

[0022] Specifically, S10 includes S100~S103: S100: The grouting material is scanned from multiple angles using a scanning electron microscope (SEM) to obtain a microscopic image of the grouting material. For example, a scanning electron microscope (SEM) is used to scan the grouting material from multiple angles to cover the grouting material, thereby obtaining a microscopic image of the three-dimensional structure of the grouting material.

[0023] S101: Based on pixels with gray values ​​in the range of 0 to 85 in the microscopic image, obtain pore pixels; based on pixels with gray values ​​in the range of 170 to 255 in the microscopic image, obtain matrix pixels.

[0024] It should be noted that the gray values ​​of pore pixels are lower, while the gray values ​​of matrix pixels are higher. This is a characteristic of pore gray value distribution. However, the value ranges of pore pixels and matrix pixels are not exactly the same in the microscopic images of different samples.

[0025] S102: Mark the gray value of the pore pixels as 255 and the gray value of the matrix pixels as 0, thereby converting the microscopic image into a binary image.

[0026] S103: Based on the independent connected regions with a gray value of 255 in the binarized image, the pore region of the microscopic image is obtained, and based on the region with a gray value of 0, the matrix region of the microscopic image is obtained.

[0027] For example, such as Figure 2 The image shown is an image of the grouting material area before and after the division provided in this application; wherein, Figure 2 Image (a) in the image is a microscopic image of the grouting material. Figure 2 (b) in the image is the binarized image after the microscopic image is transformed.

[0028] Optionally, in some embodiments, multiple microscopic images of the grouting material can be acquired first. After filtering and denoising each microscopic image, based on the grayscale distribution characteristics of the grouting material's pores (grayscale value 0~85) and non-pores (grayscale value 170~255), the pore pixels and non-pore pixels in each microscopic image are labeled to construct a training set. The support vector machine algorithm is used to optimize the feature weights, and the classification model is obtained through iterative training. Thus, newly acquired microscopic images of the grouting material can be directly input into the classification model, and the pore pixels and non-pore pixels in the microscopic images can be directly output.

[0029] S20: Calculate the pore diameter of each pore region in the microscopic image and obtain the maximum pore diameter; based on the grayscale histogram of the microscopic image, obtain the maximum and minimum grayscale values ​​of the microscopic image, and calculate the grayscale difference between the maximum and minimum grayscale values.

[0030] S30: Based on the ratio of the length of the microscopic image to the number of pixels corresponding to that length, obtain the planar resolution parameter; based on the ratio of the maximum pore diameter to the grayscale difference, obtain the elevation conversion factor; based on the ratio of the elevation conversion factor to the planar resolution parameter, obtain the scaling correction factor.

[0031] Specifically, since the size of microscopic images is usually measured in pixels, while height information is represented by grayscale values, this inconsistency in units can introduce calculation errors. Therefore, this application uses the actual physical length of the microscopic image (in micrometers) for scale coordination, thereby unifying the dimensions of height information and pixel information.

[0032] Specifically, planar resolution parameters are determined based on the resolution of the microscopic image, thereby establishing the conversion relationship between pixels and micrometers. The calculation formula is: , in, Indicates the length of the microscopic image; This represents the number of pixels corresponding to the length of a microscopic image.

[0033] Furthermore, by using the maximum pore diameter and the grayscale range of the microscopic image, the elevation conversion factor can be determined, thereby establishing the conversion relationship between grayscale values ​​and micrometers. The calculation formula is: , in, Indicates the maximum pore diameter; Indicates the maximum grayscale value; This represents the minimum grayscale value.

[0034] Furthermore, based on the planar resolution parameters and elevation conversion factor, the scaling correction factor can be calculated, thereby obtaining the direct conversion relationship between pixels and grayscale values, ensuring the scaling consistency of the constructed 3D model in the x, y, and z directions, and thus ensuring the accuracy of the 3D model.

[0035] Specifically, the proportional correction factor The calculation formula is: .

[0036] S40: The elevation value of each pixel is obtained by multiplying the scaling correction factor with the gray value of each pixel in the microscopic image; discrete point cloud data is obtained based on the pixel grid coordinates and elevation values ​​of all pixels.

[0037] like Figure 3 The diagram illustrates the grayscale-elevation mapping modeling principle provided in this application. Based on the principles of Geographic Information System (GIS) Digital Elevation Model (DEM), the grayscale value matrix of a microscopic image has a similar structure to the elevation matrix of a DEM. Grayscale values ​​can be viewed as the distance of a particle surface relative to the observation surface: the larger the grayscale value, the closer it is to the observation surface, and the larger the corresponding elevation value. Based on this, this application establishes a mapping relationship between the grayscale values ​​of a microscopic image and three-dimensional elevation through grayscale-elevation mapping modeling, achieving a linear conversion from pixel grayscale values ​​to elevation values, thereby bridging the conversion between two-dimensional images and three-dimensional models.

[0038] Specifically, the formula for calculating the elevation value of each pixel is as follows: , in, This represents the elevation value of the i-th pixel. This represents the grayscale value of the i-th pixel; This represents the pixel grid coordinates of the i-th pixel. This represents the base elevation datum value, used to define the starting reference height in grayscale-elevation mapping. This is to ensure that elevation values ​​are calculated starting from a unified observation surface.

[0039] The x and y coordinates of each point cloud data obtained by mapping grayscale to elevation are the pixel grid coordinates of the pixel points before mapping, and the z coordinate is the elevation value after mapping, representing the initial three-dimensional information of the microstructure of the grouting material.

[0040] S50: Perform surface fitting on point cloud data to generate a continuous elevation surface; perform 3D visualization on the elevation surface to obtain a 3D model of the grouting material, thereby calculating the porosity of the grouting material.

[0041] Due to noise or uneven sampling during SEM scanning to acquire microscopic images, the point cloud data obtained after modeling may be missing or sparsely distributed. Therefore, this application uses an improved Kriging interpolation algorithm to fit the point cloud data to the surface, supplement the missing data, and thus generate a continuous elevation surface.

[0042] Specifically, the point cloud data is fitted with a surface using the Kriging interpolation algorithm to generate a continuous elevation surface, including S500~S502: S500: Calculate the weight of each point cloud data point at the interpolation location based on the distance from each point cloud data point to the location to be interpolated and the terrain complexity factor.

[0043] S501: Calculate the elevation value of the location to be interpolated based on the weighted average of the elevation values ​​of each point cloud data and the weight of the location to be interpolated. Based on all point cloud data and the elevation values ​​of the location to be interpolated, obtain the elevation point set.

[0044] S502: Perform surface fitting on the set of elevation points to generate a continuous elevation surface.

[0045] Specifically, the formula for calculating the weight of each point cloud data point at the interpolation location is as follows: , in, This represents the weight of the i-th point cloud data at the interpolation position; This represents the distance from the i-th point cloud data point to the position to be interpolated; Indicates the terrain complexity factor; This indicates the number of point cloud data points.

[0046] It should be noted that, since the elevation values ​​of point cloud data that are closer in distance are closer to the elevation values ​​of the missing locations, this application calculates the weight of each point cloud data point to the missing location based on its distance to the missing location; furthermore, the terrain complexity factor can dynamically adjust the attenuation intensity of the interpolation weights, when... This method enhances the influence of neighboring point cloud data, making it suitable for grouting materials with complex pore structures (where the elevation value of the missing location is close to that of the neighboring point cloud data, but differs significantly from that of the distant point cloud data). This reduces the influence of neighboring point cloud data, making it suitable for grouting materials with flat pore structures (where the elevation values ​​of almost all point cloud data are similar when the pore structure is flat); in some embodiments, The value can be calculated based on the variance of the elevation values ​​of the point cloud data. The weights of all point cloud data are scaled proportionally, and the sum is 1.

[0047] like Figure 4 The diagram shown illustrates the principle of the Kriging interpolation algorithm provided in this application; where, Figure 4 (a) in the figure is a schematic diagram showing the distance from each point cloud data point to the location to be interpolated. Figure 4 (b) in the figure is a schematic diagram showing the change of the weight of the interpolation location for each point cloud data under different terrain complexity factors as a function of distance.

[0048] Furthermore, the porosity of the grouting material is calculated, including S503~S505: S503: Rasterize the 3D model and obtain the total volume of the 3D model based on the product of the number of raster layers, the cross-sectional area of ​​a single raster layer, and the scaling factor.

[0049] Specifically, the total volume of the 3D model The calculation formula is: , in, This represents the cross-sectional area of ​​a single grid layer obtained by scanning a completely white rasterized 3D model. This represents the proportional correction factor.

[0050] S504: Set the upper surface of the 3D model as the reference plane, and accumulate the spatial volume differences of all elevation values ​​in the 3D model that are lower than the elevation value of the reference plane to obtain the pore volume. This specifically includes steps 1 and 2: Step 1: Calculate the elevation difference between the elevation value of the reference plane and the elevation value of each pore in the 3D model.

[0051] Step 2: Based on the product of the elevation difference corresponding to each pore and the area of ​​the grid cell, obtain the volume of each pore, and obtain the pore volume based on the sum of the volumes of all pores.

[0052] Specifically, pore volume The calculation formula is: , in, This indicates the number of pores in a 3D model; The elevation value of the reference plane representing the three-dimensional model; This represents the elevation value of the j-th pore in the 3D model; This represents the area of ​​the raster cells after the 3D model has been rasterized.

[0053] In this context, pores are points in the three-dimensional model whose elevation values ​​are lower than the elevation values ​​of the reference plane. Based on the description of the above embodiment, the larger the gray value, the closer it is to the observation surface, and the larger the corresponding elevation value. Therefore, points whose elevation values ​​are lower than the elevation values ​​of the reference plane are points far away from the reference plane, i.e., pores.

[0054] S505: The porosity of the grouting material is obtained based on the ratio of pore volume to total volume.

[0055] Specifically, porosity The calculation formula is: .

[0056] The above-described method for identifying pores in a three-dimensional reconstructed slurry hardened body will be further explained and illustrated through specific embodiments below.

[0057] The original SEM microscopic image of the grouting material obtained by scanning electron microscopy was saved as a TIFF file in grayscale format with a resolution of 1024*691. The image scale was set using ImageJ software: Click "Analyze" and select "Set Scale," setting the known distance to 10 μm and the unit of length to 268 pixels. The processed image was then imported into the plugin "Trainable Weka Segmentation." A classification model was established based on the distribution characteristics of pore pixels (grayscale value 0~85) and matrix pixels (grayscale value 170~255) to segment the pore boundaries, thereby dividing the microscopic image into a matrix region marked with a grayscale value of 0 and a pore region marked with a grayscale value of 255, resulting in a binarized image. Adhesive pores were further separated to obtain the maximum pore diameter: Click "Analyze Particles," set the size, and the maximum pore diameter selected after filtering was 12.932.

[0058] Determining the conversion relationship between pixels and actual length based on microscopic image resolution: ; In ImageJ software, click on Histogram in Analyze to obtain the grayscale histogram. This shows that the grayscale value range corresponding to the microscopic image is [31, 222]. Then, combining this with the maximum pore diameter, the conversion relationship between grayscale and actual length can be determined. ; Further determine the conversion relationship between pixels and grayscale: .

[0059] Import the data source (grayscale values ​​of each pixel in the microscopic image) through the graphical interface of the Map module of the GIS platform. Use the data format conversion tool in the ArcToolBox toolbox to convert the image file into ArcGIS raster file format - Grid file. At this time, the grayscale information of the pixels is automatically set as the elevation information of the DEM, completing the grayscale-elevation mapping modeling and obtaining point cloud data.

[0060] The point cloud data is fitted with a surface using the Kriging interpolation algorithm to generate a continuous elevation surface. Then, the system is switched to a 3D visualization environment, and the 3D terrain construction function is activated in the Scene module to display the created Grid file.

[0061] Launch the 3D analysis module and select area and volume. Select the reference plane as above, and enter the determined pixel-to-grayscale conversion relationship k value of 1.815 for the z factor. Then click Calculate.

[0062] The pore volume is 89,123,135 pixels, and the total volume of the grouting material is: The porosity is: .

[0063] like Figure 5 The diagram shown is a flowchart of the pore identification process for the three-dimensional reconstructed slurry hardened body in this embodiment; wherein, Figure 5 Image (a) in the image is a microscopic image of the grouting material. Figure 5 (b) in the figure represents discrete point cloud data. Figure 5 In the diagram, (c) represents the elevation surface fitted using the Kriging interpolation algorithm. Figure 5 In the diagram, (d) represents the three-dimensional morphology reconstruction result of the grouting material. For example... Figure 6 The figure shown is a schematic diagram of a three-dimensional model of the grouting material obtained in this embodiment.

[0064] Based on the three-dimensional reconstruction slurry hardened body pore identification method provided in the above embodiments, this application embodiment also provides a three-dimensional reconstruction slurry hardened body pore identification device, which specifically includes: The image acquisition and region segmentation module is used to acquire microscopic images of the grouting material. Based on the grayscale distribution characteristics of the pore pixels and matrix pixels of the grouting material, the pore region and matrix region in the microscopic image are acquired.

[0065] The parameter acquisition module is used to calculate the pore diameter of each pore region in the microscopic image and obtain the maximum pore diameter; based on the grayscale histogram of the microscopic image, it obtains the maximum and minimum grayscale values ​​of the microscopic image and calculates the grayscale difference between the maximum and minimum grayscale values.

[0066] The scaling factor calculation module is used to obtain the planar resolution parameter based on the ratio of the length of the microscopic image to the number of pixels corresponding to that length; to obtain the elevation conversion factor based on the ratio of the maximum pore diameter to the grayscale difference; and to obtain the scaling factor based on the ratio of the elevation conversion factor to the planar resolution parameter.

[0067] The grayscale elevation mapping modeling module is used to obtain the elevation value of each pixel based on the product of the scaling correction factor and the grayscale value of each pixel in the microscopic image; and to obtain discrete point cloud data based on the pixel grid coordinates and elevation values ​​of all pixels.

[0068] The porosity calculation module is used to perform surface fitting on point cloud data to generate a continuous elevation surface; and to perform three-dimensional visualization operations on the elevation surface to obtain a three-dimensional model of the grouting material, thereby calculating the porosity of the grouting material.

[0069] This application also provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the steps of the above-described method for identifying pores in a three-dimensional reconstructed slurry hardened body.

[0070] Those skilled in the art will understand that embodiments of this application can be provided as methods, systems, or computer program products. Therefore, this application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this application can take the form of a computer program product embodied on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0071] This application is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this application. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart... Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0072] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0073] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0074] Obviously, the above embodiments are merely illustrative examples for clear explanation and are not intended to limit the implementation. Those skilled in the art will recognize that other variations or modifications can be made based on the above description. It is neither necessary nor possible to exhaustively list all possible implementations here. However, obvious variations or modifications derived therefrom are still within the scope of protection of this invention.

Claims

1. A method for identifying pores in a three-dimensional reconstructed slurry hardened body, characterized in that, include: Microscopic images of grouting materials are obtained, and the pore and matrix regions in the microscopic images are obtained based on the grayscale distribution characteristics of the pore pixels and matrix pixels of the grouting materials. Calculate the pore diameter of each pore region in the microscopic image and obtain the maximum pore diameter; based on the grayscale histogram of the microscopic image, obtain the maximum and minimum grayscale values ​​of the microscopic image, and calculate the grayscale difference between the maximum and minimum grayscale values. The planar resolution parameter is obtained based on the ratio of the length of the microscopic image to the number of pixels corresponding to that length; the elevation conversion factor is obtained based on the ratio of the maximum pore diameter to the grayscale difference; and the scaling correction factor is obtained based on the ratio of the elevation conversion factor to the planar resolution parameter. The elevation value of each pixel is obtained by multiplying the scaling factor with the gray value of each pixel in the microscopic image; discrete point cloud data is obtained based on the pixel grid coordinates and elevation values ​​of all pixels. A continuous elevation surface is generated by fitting the point cloud data; a three-dimensional visualization operation is performed on the elevation surface to obtain a three-dimensional model of the grouting material, thereby calculating the porosity of the grouting material.

2. The method for identifying pores in a three-dimensional reconstructed slurry hardened body according to claim 1, characterized in that, Surface fitting is performed on point cloud data to generate continuous elevation surfaces, including: Based on the distance from each point cloud data point to the interpolation location and the terrain complexity factor, the weight of each point cloud data point to the interpolation location is calculated. The elevation value of the location to be interpolated is calculated by weighting the elevation values ​​of each point cloud data point and the weight of the location to be interpolated. Based on all point cloud data and the elevation values ​​of the location to be interpolated, an elevation point set is obtained. A continuous elevation surface is generated by performing surface fitting on the set of elevation points.

3. The method for identifying pores in a three-dimensional reconstructed slurry hardened body according to claim 2, characterized in that, The formula for calculating the weight of each point cloud data point at the interpolation location is as follows: ， in, This represents the weight of the i-th point cloud data at the interpolation position; This represents the distance from the i-th point cloud data point to the position to be interpolated; Indicates the terrain complexity factor; This indicates the number of point cloud data points.

4. The method for identifying pores in a three-dimensional reconstructed slurry hardened body according to claim 1, characterized in that, Calculating the porosity of grouting materials includes: The 3D model is rasterized, and the total volume of the 3D model is obtained by multiplying the number of raster layers, the cross-sectional area of ​​a single raster layer, and the scaling factor. The upper surface of the 3D model is set as the reference plane, and the pore volume is obtained by accumulating the spatial volume differences of all spaces in the 3D model whose elevation values ​​are lower than the elevation values ​​of the reference plane. The porosity of the grouting material is obtained based on the ratio of pore volume to total volume.

5. The method for identifying pores in a three-dimensional reconstructed slurry hardened body according to claim 3, characterized in that, The upper surface of the 3D model is set as the reference plane. The pore volume is obtained by summing the spatial volume differences of all spaces in the 3D model whose elevation values ​​are lower than the elevation values ​​of the reference plane, including: Calculate the elevation difference between the elevation value of the reference plane and the elevation value of each pore in the 3D model; The volume of each pore is obtained by multiplying the elevation difference corresponding to each pore by the area of ​​the grid cell, and the pore volume is obtained by summing the volumes of all pores. In this context, pores are points in the 3D model whose elevation values ​​are lower than the elevation values ​​of the reference plane.

6. The method for identifying pores in a three-dimensional reconstructed slurry hardened body according to claim 1, characterized in that, Planar resolution parameters The calculation formula is: ， in, Indicates the length of the microscopic image; This represents the number of pixels corresponding to the length of a microscopic image. Elevation conversion factor The calculation formula is: ， in, Indicates the maximum pore diameter; Indicates the maximum grayscale value; Indicates the minimum grayscale value; Proportional correction factor The calculation formula is: ； The formula for calculating the elevation value of each pixel is: ， in, This represents the elevation value of the i-th pixel. This represents the grayscale value of the i-th pixel; This represents the pixel grid coordinates of the i-th pixel. Indicates the base elevation benchmark value. .

7. The method for identifying pores in a three-dimensional reconstructed slurry hardened body according to claim 1, characterized in that, Total volume of the 3D model The calculation formula is: ， in, This represents the cross-sectional area of ​​a single grid layer obtained by scanning a completely white rasterized 3D model. Indicates the proportional correction factor; Pore ​​volume The calculation formula is: ， in, This indicates the number of pores in a 3D model; The elevation value of the reference plane representing the three-dimensional model; This represents the elevation value of the j-th pore in the 3D model; This represents the area of ​​the raster cells after the 3D model has been rasterized. Porosity The calculation formula is: 。 8. The method for identifying pores in a three-dimensional reconstructed slurry hardened body according to claim 1, characterized in that, Microscopic images of the grouting material are acquired. Based on the grayscale distribution characteristics of the pore pixels and matrix pixels of the grouting material, the pore region and matrix region in the microscopic image are obtained, including: The grouting material was scanned from multiple angles using a scanning electron microscope to obtain microscopic images of the grouting material; Pore ​​pixels are obtained based on pixels with gray values ​​in the range of 0 to 85 in the microscopic image; matrix pixels are obtained based on pixels with gray values ​​in the range of 170 to 255 in the microscopic image. The gray values ​​of the pore pixels are marked as 255, and the gray values ​​of the matrix pixels are marked as 0, thus converting the microscopic image into a binary image. Based on the independent connected regions with a gray value of 255 in the binarized image, the pore region of the microscopic image is obtained, and the matrix region of the microscopic image is obtained based on the region with a gray value of 0.

9. A three-dimensional reconstruction slurry hardened body pore identification device, characterized in that, include: The image acquisition and region segmentation module is used to acquire microscopic images of grouting materials. Based on the grayscale distribution characteristics of pore pixels and matrix pixels of grouting materials, the pore region and matrix region in the microscopic image are acquired. The parameter acquisition module is used to calculate the pore diameter of each pore region in the microscopic image and obtain the maximum pore diameter; based on the grayscale histogram of the microscopic image, it obtains the maximum and minimum grayscale values ​​of the microscopic image and calculates the grayscale difference between the maximum and minimum grayscale values. The scaling factor calculation module is used to obtain the planar resolution parameter based on the ratio of the length of the microscopic image to the number of pixels corresponding to that length; to obtain the elevation conversion factor based on the ratio of the maximum pore diameter to the grayscale difference; and to obtain the scaling factor based on the ratio of the elevation conversion factor to the planar resolution parameter. The grayscale elevation mapping modeling module is used to obtain the elevation value of each pixel based on the product of the scaling correction factor and the grayscale value of each pixel in the microscopic image; and to obtain discrete point cloud data based on the pixel grid coordinates and elevation values ​​of all pixels. The porosity calculation module is used to perform surface fitting on point cloud data to generate a continuous elevation surface; and to perform three-dimensional visualization operations on the elevation surface to obtain a three-dimensional model of the grouting material, thereby calculating the porosity of the grouting material.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program that, when executed by a processor, implements the steps of the three-dimensional reconstruction slurry hardening body pore identification method according to any one of claims 1 to 8.

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