Nondestructive detection methods, devices, equipment, media and products for pore structure characteristics

Abstract

The present application discloses a method, device, equipment, medium and product for non-destructive detection of pore structure characteristics, which relates to the field of non-destructive detection of ores. The method includes performing tomographic scanning on a bulk body of ore particles with different particle diameters to obtain tomographic images of the bulk body of ore particles with different particle diameters; slicing the tomographic images of the bulk body of ore particles to determine multi-layer slice images; performing three-dimensional reconstruction on the multi-layer slice images to construct a three-dimensional pore model; and non-destructively detecting the pore structure characteristics of the bulk body of ore particles based on the three-dimensional pore model. The present application can effectively image during the non-destructive detection process, is not restricted by samples, and improves resolution.

Description

Technical Field

[0001] The present application relates to the field of non-destructive detection of ores, and in particular to a method, device, equipment, medium and product for non-destructive detection of pore structure characteristics. Background Art

[0002] The loose ore in the mining area is composed of rock particles of different shapes, regularities, and sizes, and its pore structure is very complex. Currently, research on pore structure mainly focuses on the study of porous media, including soil, rock, coal seams, oil layers, etc. The research methods for porous media are mainly divided into two categories: one is the study of pore throats, which mainly includes mercury intrusion, semi-permeable partition method, and centrifuge method; the other is the study of the actual pores themselves, which mainly includes quantitative stereology, thin-section microscopy statistics, and non-destructive detection methods. However, whether it is the saturation method, pore casting method, thin section method, or mercury intrusion method, a certain material must be injected into the pores of the medium, which is bound to cause certain damage to the internal structure of the medium, thereby affecting the measurement results.

[0003] There are certain differences between the loose ore in the mining area and the porous medium. The loose ore is a non-consolidated body composed of a large number of ore particles with different shapes and sizes. The solid skeleton composed of the particles is unstable. As the leaching proceeds, the skeleton will undergo certain changes and the pore structure will also continue to change.

[0004] Porosity measurement methods for porous media are even less suitable for the pore structure of bulk mineral media, requiring the use of non-destructive detection techniques. CT scanning is used to obtain structural images, which are then reconstructed in 3D using Avizo software to visualize the pores and calculate and analyze relevant pore structure characteristics such as pores and throats.

[0005] With the continuous development of computer image processing, non-destructive detection technology has also been widely used. Today, more mature non-destructive detection technologies include computerized tomography imaging technology, magnetic resonance imaging (MRI) technology, ultrasonic detection, etc.

[0006] MRI technology is only effective for samples containing fluids. MRI relies on hydrogen protons, and low or no water content in minerals results in weak signals, making effective imaging impossible. CT scanning has limitations on sample size and may not be able to resolve small pores. Ultrasonic detection has low resolution and requires the use of empirical models. Summary of the Invention

[0007] The purpose of this application is to provide a method, device, equipment, medium and product for non-destructive detection of pore structure characteristics to solve the problems of ineffective imaging, sample limitations and low resolution.

[0008] To achieve the above objectives, this application provides the following solutions.

[0009] In a first aspect, the present application provides a method for non-destructive detection of pore structure characteristics, comprising the following steps.

[0010] Tomographic scanning is performed on ore particles with different particle diameters to obtain tomographic images of ore particles with different particle diameters.

[0011] The ore particle bulk tomographic image is sliced to determine a multi-layer slice image.

[0012] The multi-layer slice images are reconstructed in three dimensions to construct a three-dimensional pore model.

[0013] The pore structure characteristics of the ore particle bulk are non-destructively detected based on the three-dimensional pore model.

[0014] In a second aspect, the present application provides a non-destructive detection device for pore structure characteristics, comprising the following modules.

[0015] The ore particle three-body tomographic image determination module is used to perform tomographic scanning on ore particle bulks with different particle diameters to obtain tomographic images of ore particle bulks with different particle diameters.

[0016] The slicing processing module is used to perform slicing processing on the ore particle bulk tomographic image to determine a multi-layer slicing image.

[0017] The three-dimensional pore model construction module is used to perform three-dimensional reconstruction of multi-layer slice images and construct a three-dimensional pore model.

[0018] The pore structure feature non-destructive detection module is used to non-destructively detect the pore structure features of the ore particle bulk based on the three-dimensional pore model.

[0019] In a third aspect, the present application provides a computer device comprising: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement any of the above-described methods for non-destructive detection of pore structure characteristics.

[0020] In a fourth aspect, the present application provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements any of the above-described methods for non-destructive detection of pore structure characteristics.

[0021] In a fifth aspect, the present application provides a computer program product, comprising a computer program, which, when executed by a processor, implements any of the above-mentioned methods for non-destructive detection of pore structure characteristics.

[0022] According to the specific embodiments provided in the present application, the present application discloses the following technical effects: the present application performs tomographic scanning on ore particle bulks of different particle diameters, and slices the obtained ore particle bulk tomographic images, so as to accurately detect the pore structure characteristics of each layer, and reconstructs the multi-layer slice images after slicing into a three-dimensional pore model. When performing non-destructive detection on ore particle bulks of any particle diameter, the three-dimensional pore model is directly called to obtain the pore structure characteristics. The entire non-destructive detection process is not affected by the ore moisture content, sample size and detection resolution, and is no longer limited to the influence of particle size on macroscopic seepage. The influence of ore particle diameter on pore structure characteristics is analyzed from a microscopic perspective, thereby improving the accuracy of the detection results and providing a theoretical basis for seepage and leaching effects. BRIEF DESCRIPTION OF THE DRAWINGS

[0023] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0024] Figure 1 This is a diagram of the application environment of a non-destructive detection method for pore structure characteristics in one embodiment of the present application.

[0025] Figure 2 A schematic flow chart of a method for non-destructive detection of pore structure characteristics provided in one embodiment of the present application.

[0026] Figure 3 CT scan two-dimensional images of different particle size models G1-G6 corresponding to ore particle bulks with different particle diameters.

[0027] Figure 4 Schematic diagram of the three-dimensional pore model after removing isolated pores.

[0028] Figure 5 It is a stick chart of G1-G6.

[0029] Figure 6 This is a graph showing the relationship between the bulk porosity and average particle size of single-stage ore particles.

[0030] Figure 7 Schematic diagram of the pore equivalent diameter of G1.

[0031] Figure 8 Schematic diagram of the pore equivalent diameter of G2.

[0032] Figure 9 Schematic diagram of the pore equivalent diameter of G3.

[0033] Figure 10 Schematic diagram of the pore equivalent diameter of G4.

[0034] Figure 11 Schematic diagram of the pore equivalent diameter of G5.

[0035] Figure 12 Schematic diagram of the pore equivalent diameter of G6.

[0036] Figure 13 This is the pore equivalent diameter distribution relationship diagram of G1-G6.

[0037] Figure 14 Schematic diagram of the equivalent throat diameter of G1.

[0038] Figure 15 Schematic diagram of the equivalent throat diameter of G2.

[0039] Figure 16 Schematic diagram of the equivalent throat diameter of G3.

[0040] Figure 17 Schematic diagram of the equivalent throat diameter of G4.

[0041] Figure 18 Schematic diagram of the equivalent throat diameter of G5.

[0042] Figure 19 Schematic diagram of the equivalent throat diameter of G6.

[0043] Figure 20 This is the distribution relationship diagram of the throat equivalent diameter of G1-G6.

[0044] Figure 21 Schematic diagram of the throat length of G1.

[0045] Figure 22 Schematic diagram of the throat length of G2.

[0046] Figure 23 Schematic diagram of the throat length of G3.

[0047] Figure 24 Schematic diagram of the throat length of G4.

[0048] Figure 25 Schematic diagram of the throat length of G5.

[0049] Figure 26 Schematic diagram of the throat length of G6.

[0050] Figure 27 This is the throat length distribution relationship diagram of G1-G6.

[0051] Figure 28 Schematic diagram of the pore coordination number of G1.

[0052] Figure 29 Schematic diagram of the pore coordination number of G2.

[0053] Figure 30 Schematic diagram of the pore coordination number of G3.

[0054] Figure 31 Schematic diagram of the pore coordination number of G4.

[0055] Figure 32 Schematic diagram of the pore coordination number of G5.

[0056] Figure 33 Schematic diagram of the pore coordination number of G6.

[0057] Figure 34 Schematic diagram of the coordination number distribution of G1-G6. DETAILED DESCRIPTION

[0058] The following will be combined with the drawings in the embodiments of this application to clearly and completely describe the technical solutions in the embodiments of this application. Obviously, the embodiments described are only part of the embodiments of this application, not all of the embodiments. Based on the embodiments in this application, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of this application.

[0059] In order to make the above-mentioned purposes, features and advantages of the present application more obvious and easy to understand, the present application is further described in detail below with reference to the accompanying drawings and specific implementation methods.

[0060] To analyze the effects of bulk ore particle size, shape, and gradation on pore structure, experiments were conducted using single-grade copper ore particles of varying sizes, glass balls, and ore particles of varying gradations. Ore samples were obtained from a mine, crushed with a jaw crusher, and then sieved and grouped by particle size. Glass balls were purchased online in sizes ranging from 1 to 20 mm.

[0061] To analyze the influence of particle size and shape on the porosity of bulk ore, single-grade copper ore particles and standard spherical particles were used to compare and analyze the effects of particle size and shape on the pore structure characteristics within the fill. The ore particles used were crushed, screened, cleaned, and air-dried copper ore. The ore particles were angular and irregular, resulting in diverse interparticle contacts in the fill samples, which contrasted well with the standard spherical particle samples.

[0062] Spherical particles have a regular shape and relatively simple interparticle contact. Glass and metal spheres are readily available. CT scanning experiments revealed that the pore boundaries of metal sphere samples were blurred and had interfering bright spots in the scanned images. In contrast, the pore boundaries of glass sphere samples were clear and easily discernible, meeting the requirements of CT scanning experiments and research needs. Therefore, high-precision translucent glass spheres were used in this experiment. The same particle size was used for both the ore particles and the glass spheres. The particle sizes of the samples are shown in Table 1.

[0063] Table 1 Particle size of various ore particles

[0064]

[0065] The present invention provides a method for non-destructive detection of pore structure characteristics. Figure 1 As shown, the method is executed by a computer device, specifically, it can be executed by a computer device such as terminal 102 or server 104 alone, or it can be executed by the terminal and the server together. In the embodiment of the present application, as shown in FIG. Figure 2 As shown, the method includes the following steps.

[0066] S1: Perform tomographic scanning on ore particles with different particle diameters to obtain tomographic images of the ore particles with different particle diameters.

[0067] S2: Slicing the scattered tomographic image of the ore particles to determine a multi-layer slice image.

[0068] S3: Perform three-dimensional reconstruction on the multi-layer slice images to construct a three-dimensional pore model.

[0069] S4: non-destructively detecting the pore structure characteristics of the ore particle bulk according to the three-dimensional pore model.

[0070] In an exemplary embodiment, Figure 3 As shown, each sample is loaded into a glass column in order from small to large particle size. The sample is a bulk ore particle. A CT scanning test device is used to scan the bulk ore particle tomographic image, i.e., a CT scanning two-dimensional image. S1 can be replaced by the following steps.

[0071] S11: Use a CT machine to perform tomographic scanning on a bulk body of ore particles with different particle diameters to obtain tomographic images of the bulk body of ore particles with different particle diameters; the CT machine is an industrial X-ray CT machine, which collects 2 layers of images per second, has a voxel size of 139 μm, a tomographic thickness of 139 μm, a resolution of 794 pixels × 794 pixels, a scanning voltage of 420 kV, and a current of 1400 μm.

[0072] The leaching column used for the experiment was a transparent plexiglass column, 600mm high and 100mm in inner diameter. The experiment was divided into three groups, each undergoing CT scanning tests. The first and second groups consisted of glass balls and single-grade ore particles, respectively. Samples of these single-grade ore particles were loaded into the glass column in ascending order of particle size, with each sample approximately 100mm long. The third group consisted of graded ore particles. Screened graded ore samples were loaded into the leaching column in sequence, according to their number, with each sample approximately 100mm long. The test was completed in two separate runs. In both groups, the maximum particle size was less than one-fifth of the leaching column inner diameter to minimize wall effects.

[0073] The CT scanner used for the scans was an industrial X-ray CT machine, model Phoenix v|tome|xc. This model can scan large samples up to 500 mm in diameter and 1000 mm in length, weighing 50 kg. For high-density samples requiring high-power penetration, it was equipped with a 450 kV X-ray tube and a high-performance linear array detector to reduce image artifacts caused by scattered radiation from fan-beam CT. In this experiment, the CT scan acquired two slices per second, with a voxel size of 139 μm, a slice thickness of 139 μm, and a resolution of 794 × 794 pixels. The CT scanner was set at a scan voltage of 420 kV and a current of 1400 μA. The model's image results were saved in horizontal cross-sections. This means that the CT scan scanned the sample slice by slice, with a slice thickness of 139 μm, and saved as a two-dimensional image. For example, for a sample 100 mm high, 720 grayscale images with a resolution of 794 × 794 pixels were obtained for each sample.

[0074] In an exemplary embodiment, before S2, the following steps are further included:

[0075] S21: performing filtering processing on the scattered tomographic image of the ore particles to determine a filtered tomographic image.

[0076] S22: performing binarization processing on the filtered tomographic image to determine a binarized image.

[0077] In an exemplary embodiment, S3 may be replaced by the following steps.

[0078] S31: Preprocessing the slice image based on Avizo software to determine the preprocessed slice image.

[0079] In practical applications, the images obtained during CT scanning may be affected by noise. During 3D reconstruction, the images must first be filtered to reduce the effects of random and system noise and improve the signal-to-noise ratio. The scanned images are grayscale images. To make the objects and background more prominent, the images must be binarized. Binarization allows for faster image processing, reduces computational complexity, and improves overall computational efficiency.

[0080] S32: Using Volume Edit to crop the pre-processed slice image to determine a region of interest image.

[0081] S33: using an interactive threshold module to identify pores in the region of interest image, segmenting the identified pores, and determining a segmented pore image.

[0082] S34: Use the Volume rendering module to perform three-dimensional structural mapping on the segmented pore image and construct a three-dimensional pore model.

[0083] In practical applications, quantitative analysis of pores in a stope requires further exploration of the connection between pores, namely, pore connectivity, based on the understanding of pore distribution characteristics.

[0084] The calculation results show that the connectivity of the six particle size models is above 97%, indicating that the vast majority of pores in the six models maintain good connectivity, with only a small number of isolated pore morphologies. Figure 4 This is a schematic diagram of the three-dimensional pore model after removing isolated pores, where spheres represent pores, and larger spheres indicate larger pores; the sticks between spheres represent connecting channels between pores, and longer and thinner sticks indicate longer and thinner channels between the two pores.

[0085] Figure 4 and Figure 5 It can be seen that in the G1-G6 three-dimensional models, the number of spheres decreases significantly, and the number of spheres increases with increasing particle size. This indicates that pore size increases with increasing particle size, consistent with the pore size distribution trend analysis. However, the number of pores decreases relatively. This is because as particle size increases, the proportion of large pores increases, while the number of small pores decreases significantly. Furthermore, due to the edge wall effect, the large pores at the edge of the model are much larger than the pores within. The pores and interpore channels in the G6 model are significantly larger than those in the other models and are more dispersed, resulting in larger interpore sizes.

[0086] like Figure 5The stick plots for G1-G6 show that the internal pores (spheres) of the models increase with particle size, and the throats also widen accordingly. In the G1 model, the spheres are smaller, the sticks are thinner, and there are fewer sticks connecting the spheres. In the G2 and G3 models, the sticks connecting the spheres are also primarily thin strips. After G4, the spheres become significantly larger, but their number decreases, and the sticks are significantly thicker than in the previous three models, indicating a wider throat, which is conducive to seepage.

[0087] Figure 5 This indicates that large pores can enhance local pore connectivity. The larger the pore, the greater the number of pores connected to it, meaning the coordination number increases. Furthermore, the throat size connecting to the large pores also increases accordingly. Larger local pore and throat sizes increase the fluidity of the solution within them. However, when pore size distribution is uneven, large pores can also be the primary cause of dominant flow, reducing the uniformity of solution flow.

[0088] In an exemplary embodiment, S4 may be replaced by the following steps.

[0089] The pore structure characteristics include surface porosity, porosity and pore-related parameters; the pore-related parameters include pore equivalent diameter, pore coordination number, throat equivalent diameter and throat length.

[0090] S41: Determine the total cross-sectional area of the three-dimensional pore model and the area of the ore particles within the cross-sectional area.

[0091] S42: Calculate the surface porosity based on the total area of the cross section and the area of the ore particles in the cross section.

[0092] In practical applications, in order to quantitatively analyze the spatial distribution characteristics of pores in loose ore, the surface porosity is used as an indicator for analysis. The surface porosity is defined as the pore area of the cross section divided by the total cross-sectional area.

[0093] ;in, is the interfacial porosity, %; S s is the total area of the cross section, m 2 ; S z is the area of ore particles in the cross section, m 2 .

[0094] S43: Determine the porosity of the mineral particle bulk according to the surface porosity.

[0095] In practice, the porosity of each of the six single-grade bulk ore samples in the experiment can be calculated by taking the average porosity of each image. The total number of images for each sample is approximately 720. To ensure the accuracy of the calculation results, we excluded images of the interface between two adjacent samples. We selected 400 images for each sample for porosity calculation and took the average value as the porosity of each sample.

[0096] In order to analyze the relationship between the pore structure and seepage performance of different samples, the absolute permeability in the vertical direction was calculated using Avizo software. k z The results are listed in Table 2. Based on Table 2, the relationship between the porosity and particle size of the single-stage ore particles can be drawn and linear regression can be performed, such as Figure 6 As shown, y=1.71525x+21.99203, R 2 =0.97736, where y is the porosity, x is the particle size, and R 2 is the degree of agreement between the test data and the fitting function.

[0097] Table 2 Porosity and permeability parameters of single-stage ore particle bulk samples

[0098]

[0099] Depend on Figure 6 It can be seen that for single-level ore particles within a certain scale range, the porosity is linearly positively correlated with the particle size. The porosity increases with the increase of particle size, and the correlation coefficient of the regression equation reaches 0.9774.

[0100] In practical applications, porosity is a macroscopic description of the pore structure. In order to quantify the pore characteristic distribution in depth, it is necessary to further analyze the three-dimensional pore size characteristic distribution.

[0101] The analysis is carried out with the equivalent diameter of the pore as the parameter. ;in, V p is the volume of a single pore after segmentation, mm 3 ; r is the equivalent radius, mm.

[0102] S44: Based on the three-dimensional pore model, determine the pore surface area and the throat surface area according to the total number of pore voxels.

[0103] S45: Determine the pore equivalent diameter according to the pore surface area.

[0104] In practical applications, the calculation of pore surface area and pore equivalent diameter specifically includes: first measuring the surface area of each pore, and calculating the pore equivalent diameter based on the definition of pore equivalent diameter, that is, a sphere with the same surface area as the pore.

[0105] ;in, S is the pore surface area; N is the total number of pore voxels; i For the i pore voxels; is the number of exposed faces of the voxel in x, y, and z; is the actual physical size of the voxel.

[0106] ;in, D is the pore equivalent diameter; S is the pore surface area.

[0107] In practical applications, the equivalent diameter distribution of three-dimensional pores in different particle size models is as follows: Figure 7-12 As shown, the equivalent pore diameters are mostly distributed between 2 and 11 mm. The average pore size of each model increases with increasing particle size. The equivalent diameters of G1 and G2 are concentrated between 1 and 3 mm, with average values of 1.94 and 3.12 mm, respectively. The equivalent diameters of G3 and G4 are concentrated between 3 and 6 mm, with average values of 4.64 and 4.7 mm, respectively. The equivalent diameters of G5 and G6 are concentrated between 3 and 10 mm, with average values of 5.12 and 6.29 mm, respectively. The pore data are shown in Table 3.

[0108] Table 3 Schematic table of pore data

[0109]

[0110] Depend on Figure 13 It can be seen that the peak of the pore size distribution curve for models G1-G6 shifts to the right as the particle size increases. The distribution range of the curve for G1-G4 is smaller, and the maximum percentage value gradually decreases, indicating that the size distribution is relatively concentrated. However, the pore size distribution becomes more dispersed as the particle size increases. Compared with the first four, the pore size distribution range of the G5-G6 model is wider, and the peak of the curve is significantly lower. The peak of the curve still shifts to the right as the particle size increases, indicating that larger particle size increases the number and percentage of pore size in the dispersed body, which is beneficial to solution seepage.

[0111] S46: Determine the throat equivalent diameter according to the throat surface area, and measure the throat length.

[0112] In practical applications, the surface area of each throat is measured , and then calculate the equivalent throat diameter .

[0113] in, is the throat surface area; K is the total number of pore voxels; is the number of exposed faces of the voxel in x, y, and z; is the actual physical size of the voxel; is the throat equivalent diameter; is the throat surface area.

[0114] In practical applications, the throat equivalent diameter distribution of different particle size models and the cumulative percentage of each size throat are as follows: Figures 14-20 As shown in Table 4 and Figures 14-19 It can be seen that the number of throats decreases with particle size, while the average throat size of each model increases with increasing particle size. The peak of the throat size distribution curve shifts to the right with increasing particle size. The equivalent diameters of G1-G2 are concentrated between 0.2-1.6 mm, with average values of 0.58 mm and 1.31 mm, respectively. The equivalent diameters of G3-G5 are concentrated between 0.8-2.4 mm, with average values of 1.60 mm, 1.71 mm, and 2.15 mm, respectively. The equivalent diameter of G6 is concentrated between 1.6-3.2 mm, with an average value of 2.55 mm. Large throats larger than 4.8 mm are present in the G5 and G6 models, accounting for 1.86% and 2.69% of the total, respectively.

[0115] Depend on Figure 20 It can be seen that the cumulative curves of the G1-G6 models shift to the right as the particle size increases, indicating that as the particle size increases, the proportion of small-sized throats decreases, while the proportion of large-sized throats increases. The curve of the G1 model rises the fastest, followed by G2, indicating that throats are mostly concentrated in small sizes. The cumulative change curves of the throat size of G3 and G4 are similar, but the cumulative change curves of the throat size of G5 and G6 differ significantly, indicating that the change in throat size is more obvious as the particle size increases, and larger particle size can increase the number and percentage of large throats.

[0116] Table 4 Throat parameters of each model

[0117]

[0118] In practical applications, the throat length distribution of different particle size models and the cumulative percentage of throats of each length are as follows: Figure 21-Figure 27 As shown in Table 5 and Figures 21-26It can be seen that the average throat length of the G1-G4 models increases slowly with increasing particle size, while the average lengths of the G5 and G6 models are significantly larger than those of the first four. The throat length of G1 is concentrated between 2 mm and 4 mm, that of G2-G4 is concentrated between 4 mm and 8 mm, and that of G5-G6 is concentrated between 6 mm and 11 mm, with a few throats exceeding 15 mm. The number and percentage of long throats increase significantly with increasing particle size. Throats exceeding 11 mm are almost nonexistent in the G1 and G2 models, only approximately 5% each in the G3-G4 models, and greater than 20% in the G5 and G6 models. Long throats increase the flow time of the solution within them, hindering solution penetration. However, a comprehensive assessment must be made based on both throat and pore size.

[0119] Table 5 Throat parameters of each model

[0120]

[0121] S47: Based on the three-dimensional pore model, determine the pore coordination number according to the number of throats.

[0122] In practical applications, the pore coordination number indicates how many other pores each pore is directly connected to, and the pore coordination number is equal to the number of throats connected to it.

[0123] In practical applications, the pore coordination numbers in the G1~G6 pore network models are statistically calculated respectively. The coordination numbers of each model are as follows: Figures 28-33 Since isolated pores have been eliminated when the pore network model was established, there is no case where the pore coordination number is 0 in the statistical diagram.

[0124] Depend on Figures 28-33 It can be seen that the distribution of pore coordination numbers in the pore network models of different particle sizes first increases and then decreases. The coordination numbers are mainly distributed between 4 and 19, with a small number of pores having coordination numbers greater than 25. The average and maximum coordination numbers of models G1 to G6 are shown in Table 6.

[0125] Table 6 Model coordination number statistics

[0126]

[0127] From Table 6 and Figures 28-33Comprehensive analysis shows that, in the early stages, the average coordination number increases with increasing particle size. The average coordination numbers for the G1-G4 models range from 10.38, 10.79, 1.94, to 11.06, and the maximum coordination number also increases with increasing particle size. The coordination numbers for the G1 model are concentrated between 6 and 10, with the maximum occurring at a coordination number of 8. The coordination numbers for the G2 and G3 models are concentrated between 6 and 12, with the maximum occurring at a coordination number of 9. However, in the G3 model, pores with coordination numbers of 10-12 account for 26.93%, while in the G2 model, the proportion of pores with coordination numbers of 10-12 is only 21.54%. The G3 model has a higher concentration of pores with large coordination numbers. The G4 model has a concentrated distribution between 7 and 11, with the maximum occurring at a coordination number of 10. Pores with large coordination numbers (25-27) account for 3.07%, exceeding those of the other models. This is because as the particle size increases, the pores between the particles also increase accordingly, resulting in an increase in the number of pores connected to the large pores. However, as the particle size continues to increase, the average coordination number and the maximum value actually show a downward trend. The average and maximum coordination numbers in the G5 and G6 models are both smaller than those in the G4 model. This is because as the particle size increases, the size of the pores formed between the particles increases, the throat also widens accordingly, and the number of pores connecting the same pore decreases due to the increase in size. In summary, the coordination number and particle size only show a linear growth relationship within a certain range. Therefore, comprehensive considerations should be made when setting the gradation.

[0128] comprehensive Figure 34 It can be seen that for the G1-G6 models, the cumulative trend of the pore coordination number shifts to the right as the ore particle size increases, indicating that the coordination number of the model pores also increases with increasing particle size. This is because as the particle size increases, the pores between the particles also increase accordingly, resulting in an increase in the number of pores connected to the large pores. In summary, the large pores have good connectivity with nearby pores and a high coordination number. Analyzing only from the perspective of the pore coordination number, the local connectivity between the pores within the small-particle model is reduced, which is not conducive to the uniformity of the solution distribution. However, the cumulative pore coordination number curves for the G5 and G6 models do not change significantly, and most of them overlap, indicating that when the particle size increases to a certain extent, the trend of the pore coordination number increase slows down or even remains unchanged.

[0129] In an exemplary embodiment, pores with a pore coordination number not equal to 0 are regarded as connected pores, and the connected pores are extracted, and S4 and subsequent steps further include:

[0130] S51: Calculating the connected porosity based on the total volume of the connected pores and the total volume of the loose ore particles.

[0131] In practical applications, ;in, is the connected porosity; V Cis the total volume of connected pores; V t is the total volume of the sample, that is, the total volume of the ore particles.

[0132] S52: Calculate the connected porosity based on the number of pores whose pore coordination numbers are higher than the set pore coordination number threshold and the total number of pores.

[0133] In practical applications, ;in, is the number of high-coordination pores, that is, the number of pores with a pore coordination number higher than the set pore coordination number threshold; P t is the total number of pores.

[0134] This application uses CT scanning to obtain clear tomographic images. Using Avizo software to construct a 3D model, we can quantitatively determine the pore size distribution characteristics, pore coordination number distribution, throat size distribution characteristics, and throat length distribution of different particle size models. This allows us to analyze the impact of ore particle size on pore structure characteristics. This analysis goes beyond the influence of particle size on macroscopic seepage and analyzes the effect of particle size on pore structure from a microscopic perspective, providing a theoretical basis for seepage and leaching effects.

[0135] Based on the same inventive concept, embodiments of the present application also provide a nondestructive pore structure detection device for implementing the aforementioned nondestructive pore structure detection method. The solution provided by this device is similar to the solution described in the aforementioned method. Therefore, the specific limitations of one or more embodiments of the nondestructive pore structure detection device provided below can be found in the above-mentioned limitations of the nondestructive pore structure detection method and will not be further elaborated here.

[0136] In an exemplary embodiment, a non-destructive detection device for pore structure characteristics is provided, comprising:

[0137] The ore particle three-body tomographic image determination module is used to perform tomographic scanning on ore particle bulks with different particle diameters to obtain tomographic images of ore particle bulks with different particle diameters.

[0138] The slicing processing module is used to perform slicing processing on the ore particle bulk tomographic image to determine a multi-layer slicing image.

[0139] The three-dimensional pore model construction module is used to perform three-dimensional reconstruction of multi-layer slice images and construct a three-dimensional pore model.

[0140] The pore structure feature non-destructive detection module is used to non-destructively detect the pore structure features of the ore particle bulk based on the three-dimensional pore model.

[0141] In an exemplary embodiment, a computer device is provided, which may be a server or a terminal. The computer device includes a processor, memory, an input / output (I / O) interface, and a communication interface. The processor, memory, and I / O interface are connected via a system bus, and the communication interface is connected to the system bus via the I / O interface. The processor of the computer device provides computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and internal memory. The non-volatile storage medium stores an operating system, a computer program, and a database. The internal memory provides an environment for the operation of the operating system and computer program in the non-volatile storage medium. The database of the computer device stores non-destructive detection data of pore structure characteristics. The I / O interface of the computer device is used to exchange information between the processor and external devices. The communication interface of the computer device is used to communicate with external terminals via a network connection. When executed by the processor, the computer program implements a method for non-destructive detection of pore structure characteristics.

[0142] In an exemplary embodiment, a computer device is provided, including a memory and a processor. The memory stores a computer program, and the processor implements the above method when executing the computer program.

[0143] In an exemplary embodiment, a computer-readable storage medium is provided, storing a computer program, which implements the above method when executed by a processor.

[0144] In an exemplary embodiment, a computer program product is provided, including a computer program, which implements the above method when executed by a processor.

[0145] Those skilled in the art will appreciate that all or part of the processes in the above-mentioned embodiments can be implemented by instructing the relevant hardware through a computer program. The computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the above-mentioned embodiments. In particular, any reference to memory, database, or other media used in the embodiments provided in this application can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM may be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM).

[0146] In this application, all actions to obtain signals, information or data are carried out in compliance with the relevant data protection laws and policies of the country where they are located and with the authorization given by the owner of the corresponding device.

[0147] The databases involved in the various embodiments provided herein may include at least one of a relational database and a non-relational database. Non-relational databases may include, but are not limited to, distributed databases based on blockchains. The processors involved in the various embodiments provided herein may include, but are not limited to, general-purpose processors, central processing units, graphics processing units, digital signal processors, programmable logic units, data processing logic units based on quantum computing, and the like.

[0148] The technical features of the above embodiments can be combined arbitrarily. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0149] This document uses specific examples to illustrate the principles and implementation methods of this application. The description of the above examples is only intended to help understand the method and core concept of this application. At the same time, for those skilled in the art, based on the concept of this application, there may be changes in the specific implementation methods and application scope. In summary, the content of this specification should not be understood as limiting this application.

Claims

1. A non-destructive detection method for pore structure characteristics, characterized in that: include: Perform tomographic scanning on ore particles with different particle diameters to obtain tomographic images of ore particles with different particle diameters, specifically including: A CT machine is used to perform tomographic scanning on a bulk body of ore particles with different particle diameters to obtain tomographic images of the bulk body of ore particles with different particle diameters; the CT machine is an industrial X-ray CT machine, which acquires two layers of images per second, has a voxel size of 139 μm, a tomographic thickness of 139 μm, a resolution of 794 pixels×794 pixels, a scanning voltage of 420 kV, and a current of 1400 μm; Slicing the scattered tomographic image of the ore particles to determine a multi-layer slice image; Perform three-dimensional reconstruction on multi-layer slice images to construct a three-dimensional pore model; non-destructively detecting the pore structure characteristics of the ore particle bulk according to the three-dimensional pore model; the pore structure characteristics include surface porosity, porosity and pore-related parameters; the pore-related parameters include pore equivalent diameter, pore coordination number, throat equivalent diameter and throat length; Determining the total cross-sectional area of the three-dimensional pore model and the area of the ore particles within the cross-sectional area; Calculating the porosity based on the total area of the cross section and the area of the ore particles within the cross section; Determining the porosity of the ore particle bulk according to the surface porosity; Based on the three-dimensional pore model, determining the pore surface area and the throat surface area according to the total number of pore voxels; Determining a pore equivalent diameter based on the pore surface area; Determine the throat equivalent diameter according to the throat surface area, and measure the throat length; Based on the three-dimensional pore model, the pore coordination number is determined according to the number of throats.

2. The non-destructive detection method of pore structure characteristics according to claim 1, characterized in that: Slicing the scattered tomographic image of the ore particles to determine a multi-layer slice image, further comprising: performing filtering processing on the scattered tomographic image of the ore particles to determine a filtered tomographic image; The filtered tomographic image is binarized to determine a binarized image.

3. The non-destructive detection method of pore structure characteristics according to claim 1, characterized in that: Perform 3D reconstruction on multi-layer slice images and construct a 3D pore model, including: Preprocessing the slice image based on Avizo software to determine a preprocessed slice image; Using Volume Edit to crop the pre-processed slice image to determine the region of interest image; using an interactive threshold module to identify pores in the region of interest image, segment the identified pores, and determine a segmented pore image; The volume rendering module is used to perform three-dimensional structural drawing on the segmented pore image and construct a three-dimensional pore model.

4. The non-destructive detection method of pore structure characteristics according to claim 1, characterized in that: Non-destructively detecting the pore structure characteristics of the ore particle bulk according to the three-dimensional pore model, and then further comprising: Calculating the connected porosity based on the total volume of the connected pores and the total volume of the loose ore particles; or, The connected porosity is calculated based on the number of pores with a pore coordination number higher than the set pore coordination number threshold and the total number of pores.

5. A non-destructive detection device for pore structure characteristics, characterized in that: The nondestructive detection device for pore structure characteristics adopts the nondestructive detection method for pore structure characteristics according to claims 1 to 4, and the nondestructive detection device for pore structure characteristics comprises: The ore particle three-body tomographic image determination module is used to perform tomographic scanning on ore particles of different particle diameters to obtain tomographic images of ore particles of different particle diameters; a slicing processing module, configured to perform slicing processing on the ore particle bulk tomographic image to determine a multi-layer slicing image; A three-dimensional pore model construction module is used to perform three-dimensional reconstruction of multi-layer slice images and construct a three-dimensional pore model; The pore structure feature non-destructive detection module is used to non-destructively detect the pore structure features of the ore particle bulk based on the three-dimensional pore model.

6. A computer device comprising: A memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the non-destructive detection method for pore structure characteristics according to any one of claims 1 to 4.

7. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the method for non-destructive detection of pore structure characteristics according to any one of claims 1 to 4 is implemented.

8. A computer program product comprising a computer program, characterized in that When the computer program is executed by a processor, the method for non-destructive detection of pore structure characteristics according to any one of claims 1 to 4 is implemented.

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