Nondestructive detection method, device and equipment for pore structure characteristics, medium and product

Abstract

The invention discloses a pore structure characteristic nondestructive detection method, device and equipment, a medium and a product, and relates to the field of ore nondestructive detection.The method comprises the steps that tomography is conducted on ore particle dispersions with different particle diameters, and ore particle dispersions with different particle diameters are obtained; performing slicing processing on the ore particle discrete body cross-sectional image, and determining a multi-layer slice image; performing three-dimensional reconstruction on the multi-layer slice image to construct a three-dimensional pore model; and according to the three-dimensional pore model, performing nondestructive detection on the pore structure characteristics of the ore particle bulk according to the three-dimensional pore model. According to the invention, effective imaging can be realized in a nondestructive detection process, sample limitation is avoided, and the resolution is improved.

Description

Technical Field

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

[0002] The ore bulk in the stope is composed of rock particles with different shapes, rules and sizes, and the pore structure is very complex. At present, the research on the pore structure mainly focuses on the research of porous media, including soil, rock, coal seam, oil reservoir, etc. The research methods for porous media are mainly divided into two categories: one is the research on pore throats, mainly including mercury intrusion method, semi-permeable baffle method, centrifuge method; the other is the research on the real pores themselves, mainly including quantitative stereology method, statistical method under thin section microscope and non-destructive detection method. However, whether it is the saturation method, pore casting method, thin section method or mercury intrusion method, certain materials must be injected into the pores of the medium, which will inevitably cause certain damage to the internal structure of the medium, thus affecting the measurement results.

[0003] There are also certain differences between the ore bulk in the stope and porous media. The ore bulk is an unconsolidated body composed of a large number of ore particles with different shapes and sizes. The solid skeleton composed of particles is unstable, and with the progress of leaching, the skeleton will change to a certain extent, and the pore structure is also constantly changing.

[0004] The measurement methods for the pores of porous media are more inapplicable to the pore structure of ore bulk media, and non-destructive detection technology must be adopted. By using CT scanning to obtain the structural image, and then using Avizo software for three-dimensional reconstruction to visualize the pores, and calculating and analyzing the 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 applied. Now relatively 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 fluid-containing samples. MRI depends on hydrogen protons. Since the water content in ores is low or there is no water, the signal is weak and effective imaging cannot be achieved. CT scanning has limitations on the sample size. When the pore size is small, it may not be distinguishable. Ultrasonic detection has low resolution and needs to be combined with an empirical model. 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 limitation and low resolution.

[0008] To achieve the above object, the present application provides the following solutions.

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

[0010] Perform tomographic scanning on ore particle dispersions with different particle diameters to obtain tomographic images of ore particle dispersions with different particle diameters.

[0011] Perform slicing processing on the tomographic images of the ore particle dispersions to determine multiple layers of sliced images.

[0012] Perform three-dimensional reconstruction on the multiple layers of sliced images to construct a three-dimensional pore model.

[0013] Non-destructively detect the pore structure characteristics of the ore particle dispersions 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, including the following modules.

[0015] An ore particle three-body tomographic image determination module for performing tomographic scanning on ore particle dispersions with different particle diameters to obtain tomographic images of ore particle dispersions with different particle diameters.

[0016] A slicing processing module for performing slicing processing on the tomographic images of the ore particle dispersions to determine multiple layers of sliced images.

[0017] A three-dimensional pore model construction module for performing three-dimensional reconstruction on the multiple layers of sliced images to construct a three-dimensional pore model.

[0018] A non-destructive detection module for pore structure characteristics for non-destructively detecting the pore structure characteristics of the ore particle dispersions based on the three-dimensional pore model.

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

[0020] In a fourth aspect, the present application provides a computer-readable storage medium, on which a computer program is stored, and when the computer program is executed by a processor, it implements the non-destructive detection method for pore structure characteristics described in any one of the above.

[0021] In a fifth aspect, the present application provides a computer program product, including a computer program, and when the computer program is executed by a processor, it implements the non-destructive detection method for pore structure characteristics described in any one of the above.

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

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

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

[0025] Figure 2 It is a schematic flowchart of a non-destructive detection method for pore structure characteristics provided in an embodiment of the present application.

[0026] Figure 3 It is a two-dimensional CT scan diagram of different particle size models G1 - G6 corresponding to ore particle dispersions with different particle diameters.

[0027] Figure 4 It is a schematic diagram of a three-dimensional pore model after removing isolated pores.

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

[0029] Figure 6 It is a relationship diagram between the porosity and average particle diameter of single-stage ore particle dispersions.

[0030] Figure 7 It is a schematic diagram of the pore equivalent diameter of G1.

[0031] Figure 8 It is a schematic diagram of the pore equivalent diameter of G2.

[0032] Figure 9 It is a schematic diagram of the pore equivalent diameter of G3.

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

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

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

[0036] Figure 13 Distribution relationship diagram of the equivalent pore diameter 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 Distribution relationship diagram of the equivalent throat 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 Distribution relationship diagram of the throat length of G1 - G6.

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

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

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

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

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

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

[0057] Figure 34 Schematic diagram of coordination number distribution of G1 - G6. Specific implementation manners

[0058] Next, the technical solutions in the embodiments of the present application will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present application. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present application.

[0059] To make the above - mentioned objects, features, and advantages of the present application more obvious and understandable, the present application will be further described in detail below in conjunction with the accompanying drawings and specific implementation manners.

[0060] To analyze the influence of the particle size, shape, gradation, etc. of ore bulk particles on the pore structure, experiments are carried out using single - sized copper ore particles with different particle sizes, glass balls, and ore particles with different gradations. The ore samples for the ore particles are taken from a certain mine. After being crushed by a jaw crusher, they are screened by a screening machine and grouped according to the particle size. Glass balls with samples of each particle size from 1 - 20 mm are purchased through the Internet.

[0061] To analyze the influence of particle size and shape on the pores of ore bulk, single - sized copper ore particles and standard spherical particles are used to comparatively analyze the influence of particle size and particle shape on the internal pore structure characteristics of the filling body. The ore particles used are copper ores after being crushed, screened, cleaned, and air - dried. The ore particles are multi - angled and irregular, and the inter - particle contacts of the filling specimens are diverse, which has strong comparability with the standard spherical particle specimens.

[0062] The spherical particles have regular shapes and relatively simple inter-particle contacts. Readily available spherical particles include glass balls and metal balls. Through CT scanning attempts, it was found that in the scanned images of metal ball specimens, the pore boundaries were blurred and there were interfering bright spots; while in the images of glass ball specimens, the pore boundaries were clear and easy to identify, meeting the requirements of CT scanning tests and research needs. Therefore, high-precision translucent glass balls were used in the tests. The ore particles and glass balls had the same particle size, and the particle sizes of each sample are shown in Table 1.

[0063] Table 1 Particle Size Table of Each Ore Particle Dispersoid

[0064] The embodiment of the present application provides a non-destructive detection method for pore structure characteristics, as Figure 1 shown. This method is executed by a computer device, specifically, it can be executed alone by a computer device such as a terminal 102 or a server 104, or jointly executed by a terminal and a server. In the embodiment of the present application, as Figure 2 shown, this method includes the following steps.

[0065] S1: Perform tomographic scanning on ore particle dispersoids with different particle diameters to obtain tomographic images of ore particle dispersoids with different particle diameters.

[0066] S2: Perform slicing processing on the tomographic images of the ore particle dispersoids to determine multiple layers of sliced images.

[0067] S3: Perform three-dimensional reconstruction on the multiple layers of sliced images to construct a three-dimensional pore model.

[0068] S4: Non-destructively detect the pore structure characteristics of the ore particle dispersoids according to the three-dimensional pore model.

[0069] In an exemplary embodiment, as Figure 3 shown, each sample is sequentially loaded into a glass column from smallest to largest in terms of particle size. This sample is the ore particle dispersoid. Use a CT scanning test device to perform scanning to obtain a tomographic image of the ore particle dispersoid, that is, a CT scan two-dimensional image. S1 can be replaced by the following steps.

[0070] S11: Use a CT machine to perform tomographic scanning on ore particle dispersoids with different particle diameters to obtain tomographic images of ore particle dispersoids with different particle diameters; the CT machine is an industrial X-ray CT machine. The CT machine acquires 2 layers of images per second, the voxel size is 139μm, the tomographic thickness is 139μm, the resolution is 794 pixels × 794 pixels, the scanning voltage is 420kV, and the current is 1400μm.

[0071] The leaching column used for loading ore in the experiment is a transparent plexiglass column, 600 mm in height and 100 mm in inner diameter. The experiment is divided into three major groups for CT scanning tests respectively. The first group and the second group are single-stage ore particle dispersions of glass beads and ore. Each single-stage ore particle dispersion sample is loaded into the glass column in ascending order of particle size. The length of each sample is about 100 mm. The third group is the ore graded dispersion. The screened ore samples of each grade are loaded into the leaching column in sequence according to the number. The length of each sample is about 100 mm, and the test is completed in two times. In both groups of tests, the maximum particle size is less than one-fifth of the inner diameter of the leaching column to minimize the side wall effect.

[0072] The CT machine used for scanning is an industrial X-ray CT machine, and the equipment model is Phoenix v|tome|x c. This CT machine can scan large-size samples with a diameter of up to 500 mm × length of 1000 mm and a weight of 50 KG. For high-density samples that require high-power penetration, a 450 kV X-ray tube is equipped, and a high-performance linear array detector is equipped to reduce image artifacts caused by scattered radiation in fan-beam CT. In this experiment, the CT acquires 2 layers of images per second, the voxel size is 139 μm, the slice thickness is 139 μm, the resolution is 794×794 pixels, and the scanning voltage of the CT instrument is set to 420 KV and the current is 1400 μA. The model saves the image results in the horizontal cross-section direction, that is, the CT scans the sample in slices with a slice thickness of 139 μm, which will be saved as a two-dimensional photo. For example, if the sample is 100 mm high, each sample can obtain 720 grayscale images with a resolution of 794×794 pixels.

[0073] In an exemplary embodiment, before S2, it further includes: S21: Filter the tomographic image of the ore particle dispersion to determine the filtered tomographic image.

[0074] S22: Binarize the filtered tomographic image to determine the binarized image.

[0075] In an exemplary embodiment, S3 can be replaced by the following steps.

[0076] S31: Based on Avizo software, preprocess the slice image to determine the preprocessed slice image.

[0077] In practical applications, during the CT scanning process, the obtained images may be affected by noise. During 3D reconstruction, it is necessary to first perform filtering on the images to reduce the influence of random noise and system noise in the images and improve the signal-to-noise ratio of the images. The images obtained by scanning are grayscale images. To make the image targets and backgrounds more prominent, it is also necessary to perform binarization on the images. Moreover, the processed images after binarization have a faster processing speed and a smaller computational amount, which can improve the overall computational efficiency.

[0078] S32: Use Volume edit to crop the preprocessed slice images to determine the region-of-interest images.

[0079] S33: Use the Interactive threshold module to identify the pores in the region-of-interest images and segment the identified pores to determine the segmented pore images.

[0080] S34: Use the Volume rendering module to draw a three-dimensional structure of the segmented pore images to construct a three-dimensional pore model.

[0081] In practical applications, for the quantitative analysis of the pores in the stope, on the basis of mastering the pore distribution characteristics, it is necessary to further explore the connection relationship between the pores, that is, the pore connectivity.

[0082] The calculation results show that in the 6 particle size models, the connectivity degrees are all above 97%, indicating that most of the pores in the 6 models maintain good connectivity, and only a small number of isolated pore morphologies exist. Figure 4 It is a schematic diagram of the three-dimensional pore model after removing the isolated pores. Among them, the spheres represent the pores, and the larger the sphere, the larger the pore; the stick-like between the spheres represents the connection channels between the pores, and the longer and thinner the stick-like, the longer and thinner the channel between the two pores.

[0083] Figure 4 and Figure 5 It can be seen that in the G1-G6 three-dimensional models, the number of spheres gradually decreases significantly, and the spheres show an increasing trend with the increase of the granularity of the granular material. This shows that with the increase of the granularity, the pore size shows an increasing trend, which is consistent with the analysis of the pore size distribution trend. However, the number of pores decreases relatively. This is because with the increase of the granularity, the proportion of large pores increases, and the number of small pores decreases significantly. And due to the influence of the side-wall effect, the large pores at the edge of the model are much larger than the pores inside it. The channels between the pores in the G6 model are significantly larger in size and more dispersed than those in other models, so the size between the pores is larger.

[0084] Such as Figure 5As can be seen from the bar charts of G1 - G6, the internal pores (spheres) of the model show an increasing trend with the increase of particle size, and the throats also become wider accordingly. In the G1 model, the spheres are smaller, the rods are thinner, and there are fewer rods connecting the spheres. Also, the rods connecting the spheres in the G2 and G3 models are mainly thin strips. After the G4 model, the spheres increase significantly in size but decrease in number, and the rods are significantly thicker than the previous three, indicating that the widening of the throats is beneficial to seepage.

[0085] Figure 5 It shows that large pores can improve the connectivity of local pores. The larger the pores, the corresponding increase in the number of pores connected to them around, that is, the pore coordination number increases. Secondly, the size of the throats connected to the large pores is also correspondingly larger. The relatively large local pore size and throat size will increase the fluidity of the solution in them. However, when the pore size distribution is uneven, large pores are also the main reason for the formation of preferential flow, which will reduce the balance of solution flow.

[0086] In an exemplary embodiment, S4 can be replaced by the following steps.

[0087] The pore structure characteristics include the surface porosity, porosity, and pore - related parameters; the pore - related parameters include the equivalent pore diameter, pore coordination number, equivalent throat diameter, and throat length.

[0088] S41: Determine the total cross - sectional area of the three - dimensional pore model and the area of ore particles within the cross - section.

[0089] S42: Calculate the surface porosity based on the total cross - sectional area and the area of ore particles within the cross - section.

[0090] In practical applications, in order to quantitatively analyze the pore space distribution characteristics of ore bulk, the surface porosity is used as an index for analysis. The surface porosity is defined as the pore area of the cross - section divided by the total cross - sectional area.

[0091] ; where is the surface porosity, %; S s is the total cross - sectional area, m 2 ; S z is the area of ore particles within the cross - section, m 2 .

[0092] S43: Determine the porosity of the ore particle bulk based on the surface porosity.

[0093] In practical applications, by taking the average of the porosity of each image face, the porosity of the 6 single-stage ore particle bulk samples in the experiment can be calculated respectively. The total number of images of each single-stage ore particle bulk sample is approximately 720. To ensure the accuracy of the calculation results, we excluded the images at the interface between two adjacent samples. 400 images were selected from each sample as the calculation objects for porosity, and finally the average value was taken as the porosity of each sample.

[0094] 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 , and the results are all listed in Table 2. Based on Table 2, the relationship diagram between the porosity and particle size of the single-stage ore particle bulk can be plotted and linearly regressed, as Figure 6 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 coincidence between the experimental data and the fitting function.

[0095] Table 2 Parameters of porosity and permeability of single-stage ore particle bulk samples

[0096] From Figure 6 it can be seen that for the single-stage ore particle bulk within a certain scale range, its porosity shows a linear positive correlation with the particle size, and the porosity increases with the increase of the particle size. The correlation coefficient of the regression equation reaches 0.9774.

[0097] In practical applications, porosity is a macroscopic description of the pore structure. To further quantify the pore characteristic distribution, it is necessary to further analyze the three-dimensional pore size characteristic distribution.

[0098] Analysis is carried out with the equivalent diameter of the pore as a parameter, ; where V p is the volume of a single pore after segmentation, mm 3 ; r is the equivalent radius, mm.

[0099] 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.

[0100] S45: Determine the equivalent diameter of the pore according to the pore surface area.

[0101] In practical applications, calculating the pore surface area and the equivalent diameter of the pore specifically includes: first measuring the surface area of each pore, and calculating the equivalent diameter of the pore according to the definition of the equivalent diameter of the pore, that is, a sphere with the same surface area as the pore.

[0102] ; wherein, S is the pore surface area; N is the total number of pore voxels; i is the i th pore voxel; is the number of faces exposed by the voxel in the x, y, and z directions; is the actual physical size of the voxel.

[0103] ; wherein, D is the equivalent diameter of the pore; S is the pore surface area.

[0104] In practical applications, the equivalent diameter distribution of three-dimensional pores with different particle size models is as Figures 7 - 12 shown. It can be seen that the equivalent pore diameters are mostly distributed between 2 and 11 mm. The average pore size of each model increases with the increase of the particle size. The equivalent diameters of G1 - G2 are concentrated between 1 mm and 3 mm, with average values of 1.94 mm and 3.12 mm respectively; the equivalent diameters of G3 - G4 are concentrated between 3 mm and 6 mm, with average values of 4.64 mm and 4.7 mm respectively; the equivalent diameters of G5 - G6 are concentrated between 3 mm and 10 mm, with average values of 5.12 mm and 6.29 mm respectively. The pore data is shown in Table 3.

[0105] Table 3 Schematic Table of Pore Data

[0106] From Figure 13 it can be seen that the peak of the pore size distribution curve of the G1 - G6 models shows a rightward shift trend as the particle size increases. The distribution range of the G1 - G4 curves is smaller, and the maximum percentage value gradually decreases, indicating that the size distribution is relatively concentrated. However, as the particle size increases, the pore size distribution becomes more dispersed. For the G5 - G6 models, compared with the previous four, the pore size distribution range is wider, and the peak of the curve is significantly reduced. The peak of the curve still shows a rightward shift trend as the particle size increases, indicating that larger particle sizes will increase the number and percentage of pore sizes in the granular material, which will be beneficial to the seepage of the solution.

[0107] S46: Determine the equivalent diameter of the throat according to the throat surface area and measure the throat length.

[0108] In practical applications, measure the surface area of each throat , and then calculate the equivalent diameter of the throat .

[0109] Wherein, is the throat surface area; K is the total number of pore voxels; is the number of exposed faces of the voxel in the x, y, and z directions; is the actual physical size of the voxel; is the equivalent diameter of the throat; is the surface area of the throat.

[0110] In practical applications, the equivalent diameter distribution of the throats of different particle size models and the cumulative percentage of throats of each size are as Figures 14 - 20 shown. From Table 4 and Figures 14 - 19 it can be seen that the number of throats decreases with the increase of the particles, and the average throat size of each model increases with the increase of the particle size. The peak of the throat size distribution curve shifts to the right with the increase of the particle size. The equivalent diameters of G1 - G2 are concentrated in the range of 0.2 - 1.6 mm, and the average values are 0.58 mm and 1.31 mm respectively; the equivalent diameters of G3 - G5 are concentrated in the range of 0.8 - 2.4 mm, and the average values are 1.60 mm, 1.71 mm, and 2.15 mm respectively; the equivalent diameter of G6 is concentrated in the range of 1.6 - 3.2 mm, and the average value is 2.55 mm. There are large - sized throats above 4.8 mm in the G5 and G6 models, accounting for 1.86% and 2.69% respectively.

[0111] From Figure 20 it can be seen that the cumulative curves of the G1 - G6 models shift to the right with the increase of the particles, indicating that with the increase of the particles, the proportion of the number of small - sized throats decreases, and the proportion of large - sized throats increases continuously. The curve of the G1 model rises the fastest, followed by G2, indicating that the throats are mostly concentrated in small sizes. The cumulative change curves of the throat sizes of G3 and G4 are similar, but the cumulative change curves of the throat sizes of G5 and G6 are quite different, indicating that with the increase of the particle size, the change of the throat size is more obvious, and large particle sizes can increase the number and percentage of large throats.

[0112] Table 4 Throat parameter table of each model

[0113] In practical applications, the throat length distribution of different particle size models and the cumulative percentage of throats of each length are as Figures 21 - 27 shown. From Table 5 and Figures 21 - 26It can be seen that the average throat length of the G1-G4 models shows a slow increasing trend with the increase of particle size, while the average size of the G5 and G6 models is significantly larger than the former four. The throat length of G1 is concentrated between 2 mm and 4 mm, the throat lengths of G2-G4 are concentrated between 4 mm and 8 mm, while the throat lengths of G5-G6 are all concentrated between 6 mm and 11 mm, and there are a few throat lengths greater than 15 mm. With the increase of particle size, the number and percentage of long throats both increase significantly. For throats above 11 mm, there are almost none in the G1 and G2 models, each of the G3-G4 models only accounts for about 5%, while the content of the G5 and G6 models is greater than 20%. Long throats will increase the flow time of the solution in them, which is not conducive to solution penetration, but a comprehensive judgment should be made in combination with the throat size and pore size.

[0114] Table 5 Throat parameter table of each model

[0115] S47: Based on the three-dimensional pore model, determine the pore coordination number according to the number of throats.

[0116] In practical applications, the pore coordination number represents how many other pores each pore is directly connected to, and the pore coordination number is equal to the number of throats (Throat) connected to it.

[0117] In practical applications, the pore coordination numbers in the G1-G6 pore network models are respectively statistically calculated, and the coordination numbers of each model are as Figures 28 - 33 shown. Since isolated pores have been removed when establishing the pore network model, there is no case where the pore coordination number is 0 in the statistical chart.

[0118] From Figures 28 - 33 it can be seen that the distribution of the pore coordination numbers in the pore network models with different particle sizes all follows the rule of increasing first and then decreasing, and the coordination numbers are mainly distributed between 4 and 19, and there are a small number of cases where the pore coordination number is greater than 25. The average coordination number and the maximum coordination number of the G1-G6 models are shown in Table 6.

[0119] Table 6 Statistical table of model coordination numbers

[0120] From Table 6 and Figures 28 - 33Comprehensive analysis shows that in the early stage, as the particle size increases, the average coordination number also increases. Among the G1 - G4 models, the average coordination numbers are 10.38, 10.79, 1.94, and 11.06 respectively, and the maximum coordination number also increases with the increase in particle size. The coordination numbers of the G1 model are concentrated between 6 and 10, and the maximum content appears when the coordination number is 8. The coordination numbers of the G2 and G3 models are both concentrated between 6 and 12, and the maximum content appears when the coordination number is 9. However, for the G3 model, the proportion of pores with coordination numbers between 10 and 12 is 26.93%, while for the G2 model, the proportion of pores with coordination numbers between 10 and 12 is only 21.54%. The G3 model has more pores with large coordination numbers. The coordination numbers of the G4 model are concentrated between 7 and 11, and the maximum content appears when the coordination number is 10. Moreover, the proportion of pores with large coordination numbers (25 - 27) is 3.07%, which is greater than that of other models. This is because after the particle size increases, the pores formed between particles also increase correspondingly, resulting in an increase in the number of pores connected to large pores. However, as the particle size continues to increase, the average coordination number and the maximum value show a downward trend instead. The average coordination number and the maximum value in the G5 and G6 models are both smaller than those in the G4 model. This is because as the particles increase, the pore size formed between particles increases, the throat also becomes wider accordingly, and the number of pores connecting the same pore decreases due to the increase in size. In summary, the coordination number and the particle size show a linear growth relationship within a certain range. Therefore, when setting the grading, comprehensive consideration should be taken into account.

[0121] Comprehensive Figure 34 It can be seen that for the G1 - G6 models, as the particle size of the ore particles increases, the cumulative trend of the pore coordination number shifts to the right, indicating that as the particle size increases, the coordination number of the pores in the model also increases. This is because after the particle size increases, the pores formed between particles also increase correspondingly, resulting in an increase in the number of pores connected to large pores. From the above analysis, large pores have good connectivity with nearby pores and have a higher coordination number. Only from the perspective of the pore coordination number, the local connectivity between the pores inside the small - particle - size model decreases, which is not conducive to the balance of solution distribution. However, the cumulative curves of the pore coordination numbers of the G5 and G6 models change insignificantly and mostly overlap, indicating that when the particle size increases to a certain extent, the increasing trend of the pore coordination number slows down or even remains unchanged.

[0122] In an exemplary embodiment, pores with non - zero pore coordination numbers are regarded as connected pores, and these connected pores are extracted. After S4, it further includes: S51: Calculate the connected porosity according to the total volume of the connected pores and the total volume of the ore particle bulk.

[0123] In practical applications, ; where, is the connected porosity; V C is the total volume of the connected pores;V t is the total sample volume, i.e., the total volume of the ore particle bulk.

[0124] S52: Calculate the connected porosity 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.

[0125] In practical applications, ; where is the number of high - coordination - number pores, i.e., 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.

[0126] This application obtains clear tomographic images through CT scanning. After the three - dimensional model construction by Avizo software, the pore size distribution characteristics, pore coordination number distribution of different particle - size models, as well as the throat size distribution characteristics and throat length distribution can be quantitatively obtained, so as to analyze the influence of ore particle size on the pore structure characteristics. It is no longer limited to the influence of particle size on macroscopic seepage. From a microscopic perspective, it analyzes the influence of particle size on the pore structure, providing a theoretical basis for seepage effect and leaching effect.

[0127] Based on the same inventive concept, the embodiment of this application also provides a non - destructive detection device for pore structure characteristics for implementing the non - destructive detection method of pore structure characteristics involved above. The solution provided by this device to solve the problem is similar to the solution recorded in the above - mentioned method. Therefore, the specific limitations in one or more embodiments of the non - destructive detection device for pore structure characteristics provided below can refer to the limitations on the non - destructive detection method of pore structure characteristics in the above text, and will not be elaborated here.

[0128] In an exemplary embodiment, a non - destructive detection device for pore structure characteristics is provided, including: An ore particle three - body tomographic image determination module, configured to perform tomographic scanning on the ore particle bulk with different particle diameters to obtain tomographic images of the ore particle bulk with different particle diameters.

[0129] A slicing processing module, configured to perform slicing processing on the tomographic image of the ore particle bulk to determine multiple layers of sliced images.

[0130] A three - dimensional pore model construction module, configured to perform three - dimensional reconstruction on the multiple layers of sliced images to construct a three - dimensional pore model.

[0131] A non - destructive detection module for pore structure characteristics, configured to non - destructively detect the pore structure characteristics of the ore particle bulk according to the three - dimensional pore model.

[0132] In an exemplary embodiment, a computer device is provided, which may be a server or a terminal. The computer device includes a processor, a memory, an input / output interface (Input / Output, abbreviated as I / O), and a communication interface. Among them, the processor, the memory, and the input / output interface are connected through a system bus, and the communication interface is connected to the system bus through the input / output interface. Among them, the processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an 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 the computer program in the non-volatile storage medium. The database of the computer device is used to store non-destructive detection data of pore structure characteristics. The input / output 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 an external terminal through a network connection. When the computer program is executed by the processor, a non-destructive detection method for pore structure characteristics is implemented.

[0133] In an exemplary embodiment, a computer device is provided, including a memory and a processor. A computer program is stored in the memory, and when the processor executes the computer program, the above method is implemented.

[0134] In an exemplary embodiment, a computer-readable storage medium is provided, storing a computer program, and when the computer program is executed by a processor, the above method is implemented.

[0135] In an exemplary embodiment, a computer program product is provided, including a computer program, and when the computer program is executed by a processor, the above method is implemented.

[0136] Those of ordinary skill in the art can understand that all or part of the processes in the methods of the above embodiments can be completed by instructing 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 embodiments of the above methods. Among them, any reference to a memory, database, or other medium used in the embodiments provided in the present application can include at least one of non-volatile and volatile memories. Non-volatile memories can include read-only memory (ROM), magnetic tapes, floppy disks, flash memories, optical memories, high-density embedded non-volatile memories, resistive random access memories (ReRAM), magnetoresistive random access memories (MRAM), ferroelectric random access memories (FRAM), phase change memories (PCM), graphene memories, etc. Volatile memories can include random access memory (RAM) or external cache memories, etc. By way of illustration and not limitation, RAM can be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM), etc.

[0137] In this application, all actions of obtaining signals, information, or data are carried out on the premise of complying with the corresponding data protection regulations and policies of the country where it is located and obtaining authorization from the owner of the corresponding device.

[0138] The databases involved in the embodiments provided in the present application can include at least one of relational databases and non-relational databases. Non-relational databases can include distributed databases based on blockchain, etc., without limitation. The processors involved in the embodiments provided in the present application can be general-purpose processors, central processors, graphics processors, digital signal processors, programmable logics, data processing logics based on quantum computing, etc., without limitation.

[0139] The technical features of the above embodiments can be combined arbitrarily. For the sake of brevity of description, 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, it should be considered as the scope described in this specification.

[0140] In this text, specific examples are used to elaborate on the principles and implementation manners of this application. The description of the above embodiments is only used to help understand the method and its core idea of this application; at the same time, for those of ordinary skill in the art, according to the idea of this application, there will be changes in the specific implementation manners and application scopes. To sum up, the content of this specification should not be construed as a limitation to this application.

Claims

1. A non-destructive detection method for pore structure characteristics, characterized in that: include: Performing tomographic scanning on ore particles with different particle diameters to obtain tomographic images of ore particles with different particle diameters; 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; The pore structure characteristics of the ore particle bulk are non-destructively detected based on the three-dimensional pore model.

2. The nondestructive detection method of pore structure characteristics according to claim 1, characterized in that: The ore particles with different particle diameters are subjected to tomographic scanning to obtain tomographic images of the 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 collects 2 layers of images per second, has a voxel size of 139 μm, a slice thickness of 139 μm, a resolution of 794 pixels×794 pixels, a scanning voltage of 420 kV, and a current of 1400 μm.

3. The nondestructive detection method of pore structure characteristics according to claim 1, characterized in that: The ore particle bulk tomographic image is sliced ​​to determine a multi-layer slice image, which also includes: 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.

4. The nondestructive 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 the preprocessed slice image; Using Volume Edit to crop the preprocessed slice image to determine an image of a region of interest; Using an interactive threshold module to identify pores in the region of interest image, and segmenting the identified pores to determine a segmented pore image; The volume rendering module is used to draw the three-dimensional structure of the segmented pore image and construct a three-dimensional pore model.

5. The nondestructive detection method of pore structure characteristics according to claim 1, characterized in that: Nondestructively detecting the pore structure characteristics of the ore particle bulk according to the three-dimensional pore model, specifically including: 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 surface porosity based on the total area of ​​the cross section and the area of ​​the ore particles in 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.

6. The nondestructive detection method of pore structure characteristics according to claim 5, characterized in that: The method further comprises the following steps: non-destructively detecting the pore structure characteristics of the ore particle bulk according to the three-dimensional pore model; and then: Calculating the connected porosity based on the total volume of connected pores and the total volume of the ore particles; or, The connected porosity is calculated based on the number of pores with pore coordination numbers higher than the set pore coordination number threshold and the total number of pores.

7. A non-destructive detection device for pore structure characteristics, characterized in that: include: The ore particle three-body tomographic image determination module is used to perform tomographic scanning on the ore particle bulk with different particle diameters to obtain the tomographic images of the ore particle bulk with different particle diameters; A slicing processing module, used for slicing 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 according to the three-dimensional pore model.

8. 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 method for nondestructive detection of pore structure characteristics according to any one of claims 1 to 6.

9. 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 nondestructive detection of pore structure characteristics described in any one of claims 1 to 6 is implemented.

10. A computer program product, comprising a computer program, characterized in that When the computer program is executed by a processor, the method for nondestructive detection of pore structure characteristics described in any one of claims 1 to 6 is implemented.

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