Granular structure map generation method based on rock specimen and related equipment

Abstract

The invention provides a rock specimen-based particle structure map generation method and related equipment, and relates to the technical field of graphic data reading. Three-dimensional scanning is utilized to obtain a cross-section image sequence so as to obtain a cross-section image of the internal structure of the rock specimen, then seed points are determined based on an image gray value, and the seed points serve as initial voxels to serve as starting points. Due to the fact that different particles are different in image gray value, the seed point serves as the foundation, the adjacent voxels meeting the condition are classified into the same particle according to the preset growth threshold value condition by comparing the gray value of the seed point with the gray value of the adjacent voxels, and the process effectively simplifies the complexity of particle polymerization. According to the method, a plurality of interference factors confronted when the whole image is subjected to undifferentiated processing are avoided, and the generation efficiency of the structure atlas is improved.

Description

Technical Field

[0001] This application relates to the technical field of graphic data reading, and particularly to a method for generating a particle structure atlas based on rock specimens and related devices. Background Art

[0002] In the fields of geological research and engineering applications, thoroughly analyzing the particle structure of rock specimens is an important basis for deeply exploring the physical and chemical properties of rocks and predicting the behavior of rocks under different stress conditions. Precise particle structure analysis can provide strong support for mine exploitation planning, geological disaster prevention, etc.

[0003] The related technology uses electron microscope image analysis and three-dimensional reconstruction technology to analyze rock specimens. The electron microscope performs high-resolution imaging on the rock specimens to obtain a large number of microscopic images. Then, image analysis algorithms are used to process the microscopic images, extract information such as the boundaries and shapes of particles, and then through three-dimensional reconstruction algorithms, this information is integrated to construct a three-dimensional model of the rock particle structure, which is used as an atlas.

[0004] However, the integration calculation based on information such as boundaries and shapes is too large, resulting in too low an efficiency in generating the structure atlas. Summary of the Invention

[0005] This application provides a method for generating a particle structure atlas based on rock specimens and related devices, which is used to improve the efficiency of generating the atlas.

[0006] In a first aspect, this application provides a method for generating a particle structure atlas based on rock specimens, including: performing three-dimensional scanning on the rock specimens to obtain a sequence of sectional images of the internal structure of the rock specimens; identifying seed points from the sequence of sectional images based on the image gray values, and setting the seed points as initial voxels; comparing the gray values of the adjacent voxels of the initial voxels, and attributing the adjacent voxels that meet the preset growth threshold conditions to the same particle; performing feature analysis on all the obtained particles to obtain digital features, where the digital features include: morphological feature parameters of the particles, spatial distribution features of the particles, contact relationship network between particles, void network between particles; projecting the particles onto a two-dimensional plane according to the digital features to obtain a projection image; generating an analysis report and a visualization atlas, where the analysis report includes morphological feature parameters of the particles, spatial distribution features of the particles, contact relationship network between particles, void network between particles, and the visualization atlas includes the projection image.

[0007] By adopting the above technical solution, a sequence of sectional images is obtained through three-dimensional scanning to acquire sectional images of the internal structure of the rock specimen, and then seed points are determined based on the image gray values. These seed points serve as the starting points of the initial voxels. Since there are differences in the image gray values of different particles, with the seed points as the foundation, by comparing the gray values of adjacent voxels and classifying the adjacent voxels that meet the conditions into the same particle according to the preset growth threshold conditions, this process effectively simplifies the complexity of particle aggregation. It avoids many interference factors faced when processing the entire image without discrimination, and improves the generation efficiency of the structure atlas. Moreover, all particles are digitally processed, successfully converting the information of the solid particles into digital form, providing convenient conditions for the subsequent accurate generation of the atlas, and making the entire particle structure analysis process more efficient.

[0008] Combined with some embodiments of the first aspect, in some embodiments, the steps of identifying seed points from the sequence of sectional images based on the image gray values and setting the seed points as the initial voxels specifically include: taking the gray values in the sequence of sectional images as height values to construct a three-dimensional height field, where the position of each voxel point is determined by the corresponding spatial coordinates, and the height value of the voxel point is determined by the corresponding gray value; sorting the voxel points in the three-dimensional height field in ascending order; sequentially adding the voxel points as vertices to the simplicial complex in the ascending order, and constructing each voxel point into a zero-dimensional simplicial structure; searching for adjacent voxel points of the voxel points within the preset neighborhood range of the three-dimensional height field, and connecting the two voxel points with a distance less than the distance threshold to construct a one-dimensional simplex; when the endpoints of the one-dimensional simplex edges are connected end to end in sequence to form a closed loop, constructing the region enclosed by the closed loop into a two-dimensional simplex surface and adding the two-dimensional simplex surface to the simplicial complex; forming a nested sequence of simplicial complexes, where each layer of the simplicial complex sequence represents the topological structure with a gray value less than or equal to the gray value of the current voxel point; performing persistence calculation on the nested sequence of simplicial complexes, and calculating the local minimum points with persistence greater than the persistence threshold as the seed points.

[0009] By adopting the above technical solution, the gray values in the sequence of sectional images are converted into the height values of the three-dimensional height field, determining the corresponding relationship between the voxel point positions and the height values, laying a foundation for the subsequent topological structure construction. Sorting the voxel points in ascending order facilitates orderly processing. Sequentially constructing zero-dimensional, one-dimensional, and two-dimensional simplicial structures and forming a nested sequence of simplicial complexes, this process systematically integrates the spatial and gray information of the voxel points into topological structure information. By performing persistence calculation on the nested sequence of simplicial complexes and taking the local minimum points as the seed points, it can accurately screen out the voxel points that are of important significance in the topological structure evolution, excluding a large amount of irrelevant information interference, optimizing the accuracy and efficiency of seed point selection, and making the subsequent particle recognition and analysis based on the seed points more accurate and reliable.

[0010] In some embodiments in combination with some embodiments of the first aspect, the steps of performing persistence calculation on a nested sequence of simplicial complexes specifically include: new connected components are generated at local minimum points, and the local minimum points are denoted as the birth points of the connected components; the connected components are formed by the mutual connection of voxel points within the nested sequence of simplicial complexes, and the local minimum points are the points with the smallest gray value within a pre-divided local region; when two connected components are first connected and merged at the current gray value, the connected component with the higher birth value terminates, and the current gray value is denoted as the death point corresponding to the connected component with the higher birth value, where the birth value refers to the gray value corresponding to the birth point of the connected component; a persistence diagram is constructed based on the birth points and the corresponding death points, where the abscissa of the persistence diagram is the gray value of the birth point, and the ordinate of the persistence diagram is the gray value difference between the birth point and the corresponding death point.

[0011] By adopting the above technical solution, in the analysis process of the connected components, the birth points of the connected components are determined by local minimum points, the connected components are formed based on the connection of voxel points, and when connected and merged, the death points are determined according to the birth values and the persistence diagram is constructed. This recording method based on the key nodes of topological evolution deeply transforms the image gray information into a quantitative description of topological structure features. The persistence diagram intuitively shows the existence interval and stability of the connected components. By using the relevant information of the birth points and death points, the topological change law of the particle structure under different gray value changes can be effectively evaluated, making the selection of seed points more accurate.

[0012] In some embodiments in combination with some embodiments of the first aspect, the steps of performing feature analysis on all the obtained particles to obtain digital features specifically include: calculating the ratio of the surface area to the volume of each particle to obtain a sphericity parameter; calculating the ratio of the longest axis to the shortest axis of each particle to obtain an elongation parameter.

[0013] By adopting the above technical solution, the sphericity and elongation parameters of the particles are calculated. The sphericity parameter reflects the degree to which the particle approaches a sphere through the ratio of the surface area to the volume, and the elongation parameter reflects the elongation or flattening characteristics of the particle by the ratio of the longest axis to the shortest axis. These two key parameters simply and effectively quantify the morphological features of the particles, greatly reducing the amount of data processing and computational complexity compared to the processing of complex full shape information.

[0014] In some embodiments in combination with some embodiments of the first aspect, the steps of performing feature analysis on all the obtained particles to obtain digital features specifically include: statistically analyzing the particle size distribution; constructing a position density distribution map of the particles in three-dimensional space according to the coordinate information of the particles in three-dimensional space to obtain the spatial distribution characteristics of the particles.

[0015] By adopting the above technical solutions, the particle size distribution is statistically analyzed and a three-dimensional spatial position density distribution map is constructed. The particle size distribution intuitively presents the composition of particle sizes. Particles of different sizes play different roles in the rock mechanical structure, and its statistical results provide key data for evaluating the overall particle size characteristics and mechanical properties of the rock. The position density distribution map constructed based on the three-dimensional spatial coordinates of the particles clearly shows the density layout of the particles in space, which helps to deeply study the uniformity and local differences of the internal structure of the rock. By obtaining these two characteristics, it is possible to efficiently grasp the general situation of the particle spatial distribution without deeply analyzing the complex shape boundaries of the particles, reducing the data processing steps.

[0016] Combined with some embodiments of the first aspect, in some embodiments, the steps of performing feature analysis on all the obtained particles to obtain digital features specifically include: identifying the contact areas between adjacent particles; calculating the area and perimeter of the contact areas; classifying the contact areas as point contacts, line contacts, or surface contacts according to the area and perimeter; and constructing a contact relationship network between the particles based on the contact relationships between the particles.

[0017] By adopting the above technical solutions, the contact areas between adjacent particles are identified and a multi-dimensional analysis is performed to construct a contact relationship network. By accurately positioning the contact areas, calculating their areas and perimeters and classifying them into different contact types, there are significant differences in the force transmission methods and intensities of different contact types. The network constructed based on these contact relationships intuitively shows the connection topology structure between the particles. Compared with comprehensively integrating all particle information, focusing on the contact relationships reduces the processing of non-critical information and makes the interaction relationships between the particles clear and definite.

[0018] Combined with some embodiments of the first aspect, in some embodiments, the steps of performing feature analysis on all the obtained particles to obtain digital features specifically include: identifying the void areas between the particles; statistically analyzing the size distribution of the void areas; and constructing a void network between the particles.

[0019] By adopting the above technical solutions, the void areas between the particles are identified and the size distribution is statistically analyzed to construct a void network. The shape, size, and distribution of the void areas have a key impact on the integrity and functionality of the internal structure of the rock. Statistically analyzing the size distribution can accurately quantify the proportion and distribution law of voids of different sizes, and the constructed void network systematically sorts out the spatial association and connectivity between the voids. These accurate data and network structure information about the void areas can be incorporated into the final analysis report and visualization atlas.

[0020] In a second aspect, the present application provides a system for generating a particle structure atlas based on a rock specimen. The system for generating a particle structure atlas based on a rock specimen includes: one or more processors and a memory; the memory is coupled to the one or more processors, and the memory is used to store computer program code. The computer program code includes computer instructions, and the one or more processors call the computer instructions to cause the system for generating a particle structure atlas based on a rock specimen to execute the method described in the first aspect and any possible implementation manner in the first aspect.

[0021] In a third aspect, the present application provides a computer program product containing instructions. When the computer program product runs on a system for generating a particle structure atlas based on a rock specimen, it causes the system for generating a particle structure atlas based on a rock specimen to execute the method described in the first aspect and any possible implementation manner in the first aspect.

[0022] In a fourth aspect, the present application provides a computer-readable storage medium including instructions. When the instructions run on a system for generating a particle structure atlas based on a rock specimen, it causes the system for generating a particle structure atlas based on a rock specimen to execute the method described in the first aspect and any possible implementation manner in the first aspect.

[0023] One or more technical solutions provided in the embodiments of the present application have at least the following technical effects or advantages: 1. By using three-dimensional scanning to obtain a sequence of sectional images to acquire sectional images of the internal structure of a rock specimen, and then determining seed points based on the image gray values. These seed points serve as initial voxels and become the starting points. Since there are differences in the image gray values of different particles, based on the seed points, by comparing the gray values with adjacent voxels and classifying the adjacent voxels that meet the conditions into the same particle according to the preset growth threshold conditions, this process effectively simplifies the complexity of particle aggregation. It avoids many interference factors faced when processing the entire image without discrimination, improves the generation efficiency of the structure atlas. Moreover, by digitizing all particles, the information of the physical particles is successfully converted into digital form, providing convenient conditions for the accurate generation of subsequent atlases and making the entire particle structure analysis process more efficient.

[0024] 2. Convert the gray values in the cross-sectional image sequence into height values of a three-dimensional height field, determine the correspondence between voxel point positions and height values, and lay a foundation for subsequent topological structure construction. Sorting the voxel points in ascending order facilitates orderly processing. Sequentially construct zero-dimensional, one-dimensional, and two-dimensional simplex structures to form a nested sequence of simplicial complexes. This process systematically integrates the spatial and gray information of voxel points into topological structure information. By performing persistence calculations on the nested sequence of simplicial complexes and using local minimum points as seed points, voxel points that are significant in the topological structure evolution can be accurately screened out, excluding interference from a large amount of irrelevant information, optimizing the accuracy and efficiency of seed point selection, and making subsequent particle recognition and analysis based on seed points more accurate and reliable.

[0025] 3. During the analysis of connected components, use local minimum points to determine the birth points of connected components. Based on the connectivity of voxel points, connected components are formed. When connected components merge, determine the death points according to the birth values and construct a persistence diagram. This way of recording key nodes in topological evolution deeply transforms the image gray information into a quantitative description of topological structure features. The persistence diagram intuitively shows the existence intervals and stabilities of connected components. By using the relevant information of birth points and death points, the topological change rules of particle structures under different gray value changes can be effectively evaluated, making the selection of seed points more accurate. Description of the Drawings

[0026] Figure 1 is a flowchart of a method for generating a particle structure atlas based on a rock specimen in an embodiment of the present application; Figure 2 is another flowchart of a method for generating a particle structure atlas based on a rock specimen in an embodiment of the present application; Figure 3 is Figure 2 a specific flowchart of sub-step S2071 of step S207; Figure 4 is an exemplary hardware structure diagram of a system for generating a particle structure atlas based on a rock specimen in an embodiment of the present application. Detailed Embodiments

[0027] The terms used in the following embodiments of the present application are only for the purpose of describing specific embodiments and are not intended to limit the present application. As used in the specification and appended claims of the present application, the singular forms "a", "an", "the", "above-mentioned", "this", and "that" are also intended to include the plural forms unless the context clearly indicates otherwise. It should also be understood that the terms used in the present application and / or refer to and include any or all possible combinations of one or more of the listed items.

[0028] Hereinafter, the terms "first" and "second" are for descriptive purposes only and should not be construed as implying or suggesting relative importance or implicitly indicating the quantity of the indicated technical features. Thus, features defined with "first" and "second" may explicitly or implicitly include one or more of such features. In the description of the embodiments of the present application, unless otherwise specified, the meaning of "a plurality" is two or more.

[0029] Please refer to Figure 1 , Figure 1 which is a schematic flowchart of a method for generating a particle structure atlas based on a rock specimen in an embodiment of the present application; S101. Perform three-dimensional scanning on the rock specimen to obtain a sequence of sectional images of the internal structure of the rock specimen; Among them, three-dimensional scanning refers to a technical means of using a specific three-dimensional scanning device to perform non-contact data acquisition on the rock specimen from multiple angles to obtain information such as the shape and structure of the specimen in three-dimensional space. The sequence of sectional images refers to a set of two-dimensional images corresponding to each layer after the rock specimen is virtually stratified according to a certain thickness or interval. These images are arranged in sequence to form a sequence for representing the structural information of different depth levels inside the rock specimen.

[0030] The rock specimen is comprehensively scanned by a three-dimensional scanning device according to the preset accuracy and scanning range. During the scanning process, the device will record a large amount of spatial coordinate data and corresponding reflected light intensity and other information, and then use special image processing software to convert these data into a series of sectional image sequences. These images can clearly show the texture, particle distribution and other structural details of different levels inside the rock specimen.

[0031] S102. Identify seed points from the sequence of sectional images based on the image gray value, and set the seed points as initial voxels; Among them, the image gray value refers to the brightness information of each pixel point in the image, which is represented in numerical form and is used to distinguish the light and dark degrees of different regions in the image. It reflects the difference in the reflection or absorption of light by different substances or structures inside the rock specimen. The seed point refers to a point that serves as a starting reference point to initiate subsequent specific region recognition or growth algorithms in image analysis. In this step, it is the key point used to represent the starting recognition position of particles. The initial voxel is the extension of the concept of the seed point in three-dimensional space. It refers to the smallest unit with a certain volume constructed based on the seed point in three-dimensional image data, similar to a three-dimensional pixel.

[0032] Specifically, after obtaining the cross-sectional image sequence, in order to accurately identify the particle structure in the rock specimen, it is necessary to determine the starting point for particle recognition, that is, to execute this step. First, perform gray value analysis on each image in the cross-sectional image sequence. Since different particles may exhibit different gray characteristics in the image, by setting a specific gray value range or gray change rule, the pixel points that meet the requirements are selected as seed points, and then these seed points are defined as initial voxels, providing a basis for the subsequent growth and recognition of particles.

[0033] In some specific embodiments, first perform gray normalization processing on the cross-sectional image sequence to unify the gray value ranges of all images, then calculate the gray histogram of each image, determine the possible particle gray range according to the peaks and valleys of the histogram, and select the pixel points with relatively stable gray values and a high probability of being at the center of the particle in this range as seed points, and convert them into initial voxels, which is not limited here.

[0034] S103. Compare the gray values of the adjacent voxels of the initial voxel, and classify the adjacent voxels that meet the preset growth threshold condition as belonging to the same particle; Among them, the preset growth threshold condition refers to the pre-set limitation condition on the gray value difference range between the adjacent voxel and the initial voxel, which is used to judge whether the adjacent voxel belongs to the same particle structure.

[0035] After determining the initial voxel, in order to completely identify the range of the particle, this step needs to be carried out. By traversing the adjacent voxels around the initial voxel and comparing the gray value differences between them and the initial voxel one by one, when this difference is within the range of the preset growth threshold condition, it is considered that the adjacent voxel and the initial voxel belong to the same particle structure, and it is classified into this particle. In this way, it continuously expands outwards to gradually determine the range of the entire particle.

[0036] In some specific embodiments, first establish a three-dimensional voxel traversal queue, put the initial voxel into the queue, then take out a voxel from the queue, calculate the gray value differences between all its adjacent voxels and it, add the adjacent voxels that meet the preset growth threshold condition to the queue, and mark them as belonging to the same particle, and repeat this process until there are no new voxels added to the queue, which is not limited here.

[0037] S104. Perform feature analysis on all the obtained particles to obtain digital features, and the digital features include: the morphological feature parameters of the particles, the spatial distribution features of the particles, the contact relationship network between the particles, and the void network between the particles; Among them, the morphological feature parameters of the particles refer to the numerical indicators used to quantitatively describe the shape features of a single particle, such as sphericity, elongation, etc., which reflect the outline characteristics of the particle.

[0038] Specifically, after all the particles are identified, in order to gain an in-depth understanding of the particle structure characteristics of the rock specimen, a comprehensive feature analysis of these particles needs to be carried out, and this step is then executed. The morphological characteristic parameters of each particle are calculated through specialized algorithms. For example, the sphericity is calculated by the ratio of the surface area to the volume, and the elongation is calculated by the ratio of the longest axis to the shortest axis. The spatial distribution characteristics of the particles are statistically analyzed based on the coordinate information of the particles in three-dimensional space. For example, the density degree is determined by calculating the number of particles per unit volume. The contact relationship network is constructed by identifying the contact areas between adjacent particles and analyzing their characteristics. The void areas between the particles are identified and their relevant information is statistically analyzed to construct the void network, and finally these digital characteristics are obtained.

[0039] In some embodiments, the ratio of the surface area to the volume of each particle is calculated to obtain the sphericity parameter; the ratio of the longest axis to the shortest axis of each particle is calculated to obtain the elongation parameter.

[0040] It can be seen that the sphericity and elongation parameters of the particles are calculated. The sphericity parameter reflects the degree to which the particle approaches a sphere through the ratio of the surface area to the volume, and the elongation parameter reflects the elongation or flattening characteristics of the particle by the ratio of the longest axis to the shortest axis. These two key parameters simply and effectively quantify the morphological characteristics of the particles, and compared with the processing of complex full-shape information, the data processing volume and calculation complexity are greatly reduced.

[0041] In some embodiments, the particle size distribution is statistically analyzed; based on the coordinate information of the particles in three-dimensional space, a position density distribution map of the particles in three-dimensional space is constructed to obtain the spatial distribution characteristics of the particles.

[0042] It can be seen that the particle size distribution is statistically analyzed and the three-dimensional space position density distribution map is constructed. The particle size distribution intuitively presents the composition of the particle sizes. Particles of different sizes play different roles in the rock mechanical structure, and its statistical results provide key data for evaluating the overall particle size characteristics and mechanical properties of the rock. The position density distribution map constructed based on the three-dimensional space coordinates of the particles clearly shows the density layout of the particles in space, which helps to deeply study the uniformity and local differences of the internal structure of the rock. By obtaining these two characteristics, it is possible to efficiently grasp the general situation of the particle spatial distribution without deeply analyzing the complex shape boundaries of the particles, reducing the data processing link.

[0043] In some embodiments, the contact areas between adjacent particles are identified; the area and perimeter of the contact areas are calculated; the contact areas are classified as point contact, line contact or surface contact according to the area and perimeter; based on the contact relationships between the particles, a contact relationship network between the particles is constructed.

[0044] It can be seen that the contact area between adjacent particles is identified and multi-dimensional analysis is carried out to construct a contact relationship network. By accurately positioning the contact area, calculating its area and perimeter, and classifying it into different contact types, there are significant differences in the force transmission methods and intensities of different types of contacts. The network constructed based on these contact relationships intuitively displays the connection topology between particles. Compared with comprehensively integrating all particle information, focusing on contact relationships reduces the processing of non-critical information and makes the interaction relationships between particles clear and definite.

[0045] In some embodiments, the void area between particles is identified; the size distribution of the void area is statistically analyzed; and a void network between particles is constructed.

[0046] It can be seen that the void area between particles is identified and the size distribution is statistically analyzed to construct a void network. The shape, size, and distribution of the void area have a key impact on the integrity and functionality of the internal structure of the rock. Statistically analyzing the size distribution can accurately quantify the proportion and distribution law of voids of different sizes, and the constructed void network systematically sorts out the spatial correlation and connectivity between voids. These accurate data and network structure information about the void area can be incorporated into the final analysis report and visualization atlas.

[0047] S105: Project the particles onto a two-dimensional plane according to the digital features to obtain a projection image; After obtaining the digital features of the particles, in order to present the particle structure in a more intuitive way for easy observation and analysis, this step needs to be performed. According to a pre-set projection algorithm, such as orthogonal projection or perspective projection, the particles in three-dimensional space are projected onto a two-dimensional plane according to their digital features (such as coordinates, shape, etc.). During the projection process, the shape of the particles may undergo certain deformation, but their relative position relationships and main features can be retained, thus obtaining a projection image.

[0048] In some specific embodiments, the type of projection is determined to be orthogonal projection. Then, according to the coordinate range of the particles in three-dimensional space, the size and position of the two-dimensional projection plane are set. The three-dimensional coordinates of each particle are converted into two-dimensional coordinates. For the shape of the particles, according to its morphological characteristic parameters, simplification processing is performed, such as approximating a complex shape as an ellipse or a rectangle and then projecting it onto the two-dimensional plane. There is no limitation here.

[0049] In some specific embodiments, the perspective projection method is adopted. First, the viewpoint position and projection direction of the perspective projection are determined. According to the perspective principle, the projection coordinates of each particle are calculated. During the projection process, the projection size of the particle is adjusted according to its distance from the viewpoint to reflect the perspective effect. At the same time, features such as the contact relationship and void network of the particles are marked on the projection image with specific symbols or colors. There is no limitation here.

[0050] S106. Generate an analysis report and a visualization atlas, where the analysis report includes the morphological feature parameters of the particles, the spatial distribution characteristics of the particles, the contact relationship network between the particles, and the void network between the particles, and the visualization atlas includes projection images.

[0051] After completing the previous steps of particle recognition, feature analysis, and projection, this step is executed to comprehensively summarize and display the research results. The morphological feature parameters of the particles are organized into a data table form, the spatial distribution characteristics are described in text, the structure and characteristics of the contact relationship network and the void network are elaborated in detail to generate an analysis report; at the same time, the projection images are typeset, necessary annotations and legends are added, and combined with other possible visualization elements (such as particle feature distribution histograms, etc.) to generate a visualization atlas.

[0052] It can be seen that by using three-dimensional scanning to obtain a sequence of sectional images to obtain sectional images of the internal structure of the rock specimen, and then determining seed points based on the image gray values, these seed points serve as the starting points of the initial voxels. Since there are differences in the gray values of different particles, based on the seed points, by comparing the gray values with adjacent voxels and classifying the adjacent voxels that meet the conditions into the same particle according to the preset growth threshold conditions, this process effectively simplifies the complexity of particle aggregation. It avoids many interference factors faced when processing the entire image without discrimination, improves the generation efficiency of the structure atlas. And, by digitizing all the particles, the information of the solid particles is successfully converted into digital form, providing convenient conditions for the accurate generation of subsequent atlases, making the entire particle structure analysis process more efficient.

[0053] In the actual use process, if only relying on the magnitude of the gray value, a problem will occur, that is, because the gray values of different types of particles are different, if only relying on the gray value, the selection of seed points will be too one-sided and unstable. Since the various particle components in the rock specimen are complex and diverse, the distribution ranges of their gray values may overlap or intersect. Simply selecting seed points based on the gray value threshold is extremely likely to misjudge or miss the starting recognition points of some particles, resulting in large deviations in the subsequent particle recognition and analysis work based on these inaccurate seed points.

[0054] Please refer to Figure 2 , Figure 2 which is another process schematic diagram of the method for generating a particle structure atlas based on a rock specimen in an embodiment of the present application; In some embodiments, step S102 specifically includes: S201. Use the gray values in the sequence of sectional images as height values to construct a three-dimensional height field, where the position of each voxel point is determined by the corresponding spatial coordinates, and the height value of the voxel point is determined by the corresponding gray value; Specifically, after obtaining the sequence of sectional images, for each pixel point in each image, its gray value is extracted and converted into coordinates in three-dimensional space according to the position of the image in the sequence and the coordinate position of the pixel point in the image (the x and y coordinates correspond to the position on the image plane, and the z coordinate can correspond to the layer position of the image in the sequence). At the same time, the gray value of the pixel point is assigned to the voxel point corresponding to this coordinate position as its height value. In this way, a three-dimensional height field is constructed, enabling the original two-dimensional image gray information to be incorporated into the three-dimensional spatial structure, facilitating subsequent in-depth topological structure analysis.

[0055] S202. Ascendingly sort the voxel points in the three-dimensional height field; Specifically, after constructing the three-dimensional height field, in order to orderly utilize the voxel points to construct topological structures such as simplicial complexes, this step needs to be executed. Through a specific sorting algorithm, such as the quicksort algorithm or the bubble sort algorithm, etc., using the height value of the voxel points as the sorting basis, all the voxel points in the three-dimensional height field are traversed and compared, and they are rearranged in ascending order of height value. In this way, in subsequent operations, the voxel points can be processed in this order successively, facilitating the gradual construction of a topological structure that meets the requirements.

[0056] S203. In the order of ascending sorting, sequentially add the voxel points as vertices to the simplicial complex, and construct each voxel point into a zero-dimensional simplex structure; Among them, a simplicial complex is a combinatorial structure used to describe the spatial structure in the concept of topology. It is composed of multiple simplices combined according to certain rules, which can reflect the connection and topological relationship between elements in space and can be regarded as an abstract representation method of a complex spatial structure. The zero-dimensional simplex structure is the most basic and simplest structure form in the simplicial complex, which is just a single point. Here, specifically, each voxel point is regarded as a zero-dimensional simplex, which is the basic unit for constructing a more complex topological structure.

[0057] Specifically, after completing the ascending sorting of the voxel points, in order to gradually construct a simplicial complex that can reflect the topological structure related to the image gray value, this step needs to be executed. According to the sorted order of the voxel points, the voxel points are taken out one by one and added as vertices to the simplicial complex structure. Each time a voxel point is added, it is equivalent to constructing an independent zero-dimensional simplex structure in the simplicial complex. These zero-dimensional simplex structures will subsequently develop into more complex topological structures through certain connection rules.

[0058] S204. Search for adjacent voxel points of the voxel points within the preset neighborhood range of the three-dimensional height field, and connect and construct one-dimensional simplices between two voxel points with a distance less than the distance threshold; Among them, the preset neighborhood range refers to a local spatial area pre-set in the three-dimensional height field, which is used to define the range of searching for adjacent voxel points of the voxel point. Its size and shape can be set according to actual needs and image characteristics. For example, it can be set to a cube or sphere space area centered on the voxel point. Adjacent voxel points refer to other voxel points that are relatively close to the target voxel point in spatial position within the preset neighborhood range, and there is a certain spatial correlation between them. A one-dimensional simplex is a topological structure that is more complex than a zero-dimensional simplex structure. In topology, it represents a line segment. Here, it is a line segment structure formed by connecting two voxel points that meet the distance conditions, which is used to further enrich the topological relationship represented by the simplex complex.

[0059] Specifically, after constructing the zero-dimensional simplex structure, this step is performed in order to make the simplex complex reflect the spatial connection relationship between voxel points more carefully, and then mine the potential topological structure information in the image. For each voxel point, take it as the center, search for other voxel points in the preset neighborhood, calculate the spatial distance between the voxel point and the found adjacent voxel points (for example, it can be measured by calculating the Euclidean distance of the difference between the coordinates of the two points), and connect those adjacent voxel points whose distance is less than the distance threshold with the current voxel point, so that a one-dimensional simplex is constructed between them, that is, a line segment-shaped topological connection is formed, so that the simplex complex develops from isolated points (zero-dimensional simplex) to a structure with a connection relationship, and gradually constructs a more complex structure that can reflect the grayscale-related topological characteristics of the image.

[0060] S205, when the endpoints of the one-dimensional simplex edge are connected end to end in sequence to form a closed loop, constructing the area enclosed by the closed loop as a two-dimensional simplex surface, and adding the two-dimensional simplex surface to the simplex complex; A closed loop refers to a closed line shape without openings formed by connecting multiple one-dimensional simplex edges end to end, just like a circle surrounded by line segments. A two-dimensional simplex surface is equivalent to a triangle or a polygonal plane area composed of multiple triangles in topology. Here, it refers to the area surrounded by a closed loop. It is a planar topological structure further abstracted on the basis of one-dimensional simplex construction. It is used to more richly reflect the connection and spatial relationship between voxel points on the two-dimensional level. It is an important component of building more complex topological structures.

[0061] Specifically, on the basis of having constructed a one-dimensional simplex, as the edges of the one-dimensional simplex are continuously connected, when the endpoints are connected end to end in sequence to form a closed loop, in order to further improve the topological structure represented by the simplicial complex and enable it to more comprehensively reflect the spatial structure information related to the image gray value, this step needs to be executed. By detecting the connection situation of the edges of the one-dimensional simplex, once a closed loop is found, the area enclosed by this closed loop is identified, constructed as a two-dimensional simplex face, and then this two-dimensional simplex face is added to the simplicial complex, so that the simplicial complex develops from a structure containing line segments (one-dimensional simplexes) to a structure containing planar regions (two-dimensional simplex faces), continuously enriching its topological hierarchy for subsequent more in-depth exploration of the internal topological features of the image.

[0062] S206. Form a nested sequence of simplicial complexes, where each layer of the simplicial complex sequence represents the topological structure with a gray value less than or equal to the gray value of the current voxel. Among them, the nested sequence of simplicial complexes refers to a structure composed of multiple simplicial complexes nested layer by layer according to certain rules. Each layer of the simplicial complex is associated with a specific gray value range and contains simplexes of different dimensions from zero-dimensional to two-dimensional.

[0063] Specifically, in the process of continuously adding two-dimensional simplex faces to the simplicial complex and gradually enriching its topological structure, in order to overall and systematically present the topological structure evolution process corresponding to the change of the image gray value from low to high, this step needs to be executed. As the voxels are sorted in ascending order and participate in constructing the simplicial complex in turn, for each new voxel, a layer of simplicial complex sequence is determined according to its gray value. The simplicial complex already constructed, which is composed of voxels with gray values less than or equal to the gray value of this voxel, is taken as this layer and put into the nested sequence of simplicial complexes. In this way, by continuous accumulation, a nested sequence of simplicial complexes with a hierarchical structure is formed, which can clearly show the topological structure characteristics of the image at different gray value stages and their correlations, providing a comprehensive topological structure basis for subsequent analyses such as persistence calculation.

[0064] In some specific embodiments, an empty list is created to store the nested sequence of simplicial complexes. Then, in the order of ascending sorting of the voxels, each voxel is processed in turn. For each voxel, all the simplexes in the currently constructed simplicial complex that are composed of voxels with gray values less than or equal to the gray value of this voxel are selected according to its gray value. These simplexes are combined into a new simplicial complex as a layer and added to the list to form a nested structure, which is not limited here.

[0065] S207. Perform persistence calculation on the nested simplicial complex sequence, and calculate the local minimum points with persistence greater than the persistence threshold as seed points.

[0066] Among them, persistence calculation is a calculation method based on the analysis of topological structure evolution. It is mainly used to measure the stability and duration of different features (such as connected components, etc.) in a topological structure during a certain change process (here, with the change of image gray value). By quantifying, it reflects some key characteristics of the topological structure and helps to find points that are of great significance in the analysis of the entire image structure. The persistence threshold is a preset numerical boundary used to screen out those local minimum points that have sufficient stability in the persistence calculation and are worthy of being used as the starting basis for subsequent particle recognition.

[0067] Specifically, after forming the nested simplicial complex sequence, in order to screen out the key seed points from the complex topological structure that can be used as the basis for particle starting recognition, this step needs to be executed. Through a specific persistence calculation algorithm, analyze the nested simplicial complex sequence, calculate the persistence characteristics of the topological structure in each local area with the change of gray value, compare the obtained persistence values with the preset persistence threshold, and select those local minimum points with persistence greater than the threshold. It is determined that these points are the qualified seed points, which have relatively stable and important characteristics in terms of the topological structure and gray value distribution of the image. Starting from these seed points, subsequent particles can be more accurately identified and divided, and then in-depth analysis of the particle structure of the rock specimen can be carried out.

[0068] It can be seen that converting the gray value in the cross-section image sequence into the height value of the three-dimensional height field and determining the corresponding relationship between the voxel point position and the height value lay the foundation for subsequent topological structure construction. Sorting the voxel points in ascending order facilitates orderly processing. Sequentially construct zero-dimensional, one-dimensional, and two-dimensional simplex structures and form a nested simplicial complex sequence. This process systematically integrates the spatial and gray information of the voxel points into topological structure information. By performing persistence calculation on the nested simplicial complex sequence and using the local minimum points as seed points, voxel points that are of great significance in the topological structure evolution can be accurately screened out, excluding a large amount of interference from irrelevant information, optimizing the accuracy and efficiency of seed point selection, and making subsequent particle recognition and analysis based on seed points more accurate and reliable.

[0069] It should be noted that during this process, the gray value itself does not change. However, when constructing the nested simplicial complex sequence and performing persistence calculation, it is based on the gray value order (ascending order) of the voxel points to gradually construct the topological structure and observe its evolution.

[0070] When it is said that each layer of the simplicial complex sequence represents the topological structure with gray values less than or equal to the gray value of the current voxel point, it is not that the gray value has changed. Instead, during the construction process, as the voxel points are processed in ascending order of gray values, the gray value of each new voxel point is like a new stage. For example, initially, the voxel point with the smallest gray value is processed to construct a simple topological structure, which is the first layer. Then, when processing the next voxel point with a slightly larger gray value, the topological structure formed by all the voxel points with gray values less than or equal to the gray value of this new voxel point before is taken as a new layer. It is like building a topological structure that can reflect different gray value stages layer by layer along the ladder of gray values (the gray values of the voxel points sorted in ascending order), which is convenient for observing how the topological structure changes under different gray value ranges.

[0071] Please refer to Figure 3 , Figure 3 is Figure 2 the specific process schematic diagram of sub-step S2071 of step S207; In some specific embodiments, step S207 specifically includes: S2071. A new connected component is generated at a local minimum point, and the local minimum point is denoted as the birth point of the connected component; the connected component is formed by the mutual connection of voxel points within the nested simplicial complex sequence, and the local minimum point is the point with the smallest gray value in a pre-divided local area; Among them, a connected component refers to an independent part composed of mutually connected voxel points in a topological structure. Just like in a complex network, those node sets that are connected to each other by paths and can be regarded as a whole. Here, it is a relatively independent area formed by the connection relationship between voxel points in the nested simplicial complex sequence. A local minimum point refers to the point within a pre-divided local area range whose corresponding gray value is the smallest compared to the gray values of other points in this area. For example, after dividing the entire image into multiple small areas, in a certain small area, if the gray value of a voxel point is lower than the gray values of other voxel points in this small area, then it is the local minimum point of this local area. The birth point is used to represent the starting position where the connected component begins to form. Marking the local minimum point as the birth point of the connected component means that this connected component is gradually constructed starting from the position of this point with the smallest gray value.

[0072] Specifically, in the already constructed nested sequence of simplicial complexes, local regions are divided according to preset rules (such as dividing by a certain spatial range or the number of voxel points, etc.). Then, within each local region, the point with the minimum gray value is searched for, that is, the local minimum point. When it is found that some voxel points are connected to each other around this local minimum point, forming a relatively independent region, this region is a connected component, and this local minimum point is recorded as the birth point of this connected component. It marks the starting state of this connected component in the topological structure evolution process, and the subsequent development and changes of the connected component can be traced through this birth point.

[0073] In some specific embodiments, the space corresponding to the entire three-dimensional height field is divided with a cube region of a fixed side length as a unit, and each cube region is a local region. Then, the voxel points within each local region are traversed, and by comparing their gray values, the voxel point with the minimum gray value is found as the local minimum point. Next, starting from this local minimum point, using a graph search algorithm (such as breadth-first search or depth-first search algorithm), other voxel points connected to it are searched for, and the set composed of these mutually connected voxel points is determined as a connected component, and the found local minimum point is marked as the birth point of this connected component, which is not limited here.

[0074] S2072. When the two connected components are first connected and merged at the current gray value, the connected component with the higher birth value terminates, and the current gray value is recorded as the extinction point corresponding to the connected component with the higher birth value. The birth value refers to the gray value corresponding to the birth point of the connected component. Among them, the higher birth value means that in the two connected components to be merged, the gray values corresponding to their respective birth points are compared, and the larger gray value is the higher birth value. This birth value reflects a characteristic state when the connected component was initially formed.

[0075] Specifically, this step is executed when the birth points of all connected components have been determined and the changes of the connected components during the evolution of the topological structure are observed. With further analysis of the nested simplicial complex sequence based on the gray value (for example, during the continuous increase of the voxel point gray value, the topological structure continuously changes), when two originally independent connected components start to connect with each other and merge into a larger connected region, it is necessary to record this important topological change information. At this time, compare the gray values corresponding to the birth points of the two connected components, find the connected component corresponding to the higher birth value, and mark the current gray value at the time of their merger as the extinction point of this connected component, so as to clarify the existence interval of this connected component in the entire topological structure evolution, that is, starting from the gray value corresponding to the birth point to the gray value corresponding to this extinction point, which is convenient for subsequent operations such as constructing a persistence diagram to more comprehensively analyze the stability and change characteristics of the topological structure.

[0076] In some specific embodiments, the states of all connected components and their connectivity are monitored in real time. When it is detected that two connected components are connected and merged, the gray value data corresponding to the birth points of the two connected components are obtained, and the connected component corresponding to the higher birth value is determined by comparing the sizes. Then, record the gray value corresponding to the voxel point at this moment, that is, the current gray value, and mark it as the extinction point of the connected component with the higher birth value. The relevant information such as the birth point and extinction point of the connected component can be stored by establishing a special data structure for subsequent query and use, which is not limited here.

[0077] S2073. Construct a persistence diagram based on the birth point and the corresponding extinction point, where the abscissa of the persistence diagram is the gray value of the birth point, and the ordinate of the persistence diagram is the gray value difference between the birth point and the corresponding extinction point.

[0078] Specifically, after the birth points and corresponding extinction points of all connected components have been determined, take the gray value of the birth point of each connected component as the value of the abscissa, calculate the gray value difference between its birth point and the corresponding extinction point as the value of the ordinate, determine a point in the plane rectangular coordinate system to represent this connected component, and plot all the points corresponding to the connected components in the same coordinate system to form a persistence diagram.

[0079] It can be seen that during the analysis of connected components, the birth points of connected components are determined by local minimum points, and connected components are formed based on the connectivity of voxel points. When connected components are merged, the death points are determined according to the birth values, and a persistence diagram is constructed. This way of recording based on the key nodes of topological evolution deeply transforms the image gray information into a quantitative description of topological structure features. The persistence diagram intuitively shows the existence interval and stability of connected components. By using the relevant information of birth points and death points, the topological change rules of particle structures under different gray value changes can be effectively evaluated, making the selection of seed points more accurate.

[0080] Continuing with the above example, when performing persistence calculation, although the gray value of a single voxel point is fixed, what is concerned is the evolution of the topological structure at different gray value stages (determined by the ascending order of voxel points). For example, the topological structure in a local area may start to form at a lower gray value stage. As the gray value increases (that is, when processing voxel points with larger gray values), this topological structure may undergo changes such as merging and disappearing. Persistence calculation is to quantify the changes in the topological structure during the ascending processing of voxel point gray values, and find those points that are relatively stable within a local range (i.e., the topological structure corresponding to local minimum points) and meet certain stability requirements (persistence greater than the threshold) as seed points. The starting positions (local minimum points) of the topological structures in the local areas where these seed points are located are of great significance during the entire evolution process of gray values from low to high and are suitable as the starting references for particle recognition.

[0081] The following introduces the exemplary particle structure atlas generation system 400 based on rock specimens provided by the embodiments of the present application. Figure 4 FIG. is an exemplary hardware structure diagram of the particle structure atlas generation system 400 based on rock specimens provided by the embodiments of the present application.

[0082] In some embodiments, the particle structure atlas generation system 400 based on rock specimens is a computer device or the particle structure atlas generation system 400 based on rock specimens includes a computer device. The computer device includes a processor, a memory, and a network interface connected through a system bus. 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 data. The network interface of the computer device is used to communicate with other external terminals or servers through a network connection. In some embodiments, the network interface can be a wired network interface, and in some embodiments, the network interface can also be a wireless network interface. The computer program, when executed by the processor, implements the methods in the embodiments of the present application.

[0083] Those skilled in the art can understand that Figure 4 the structure shown in Figure 4 is only a block diagram of some structures related to the solution of this application, and does not constitute a limitation on the computer device to which the solution of this application is applied. The specific computer device may include more or fewer components than those shown in the figure, or combine some components, or have a different component layout.

[0084] As mentioned above, the above embodiments are only used to illustrate the technical solutions of this application, rather than to limit it; although this application has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that they can still modify the technical solutions recorded in the foregoing embodiments, or perform equivalent replacements on some of the technical features; and these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the scope of the technical solutions of the embodiments of this application.

[0085] In the above embodiments, according to the context, the term "when..." can be interpreted to mean "if..." or "after..." or "in response to determining..." or "in response to detecting...". Similarly, according to the context, the phrase "when determining..." or "if detecting (the stated condition or event)" can be interpreted to mean "if determining..." or "in response to determining..." or "when detecting (the stated condition or event)" or "in response to detecting (the stated condition or event)".

[0086] In the above embodiments, it can be implemented in whole or in part by software, hardware, firmware, or any combination thereof. When implemented using software, it can be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions. When the computer program instructions are loaded and executed on a computer, the processes or functions described in the embodiments of this application are generated in whole or in part. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable devices. The computer instructions can be stored in a computer-readable storage medium, or transmitted from one computer-readable storage medium to another computer-readable storage medium. For example, the computer instructions can be transmitted from one website, computer, server, or data center to another website, computer, server, or data center in a wired (such as coaxial cable, optical fiber, digital subscriber line) or wireless (such as infrared, wireless, microwave, etc.) manner. The computer-readable storage medium can be any available medium that the computer can access, or a data storage device such as a server or data center that includes one or more available media integrated. The available media can be magnetic media (such as floppy disks, hard disks, magnetic tapes), optical media (such as DVDs), or semiconductor media (such as solid-state drives), etc.

[0087] 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 relevant hardware instructed by a computer program. This program can be stored in a computer-readable storage medium. When the program is executed, it can include the processes of the above method embodiments. The foregoing storage medium includes various media that can store program codes, such as ROM, random access memory (RAM), magnetic disks, or optical discs.

Claims

1. A method for generating a particle structure atlas based on rock specimens, characterized in that Comprising: Performing three-dimensional scanning on a rock specimen to obtain a sequence of sectional images of the internal structure of the rock specimen; Identifying seed points from the sequence of sectional images based on image gray values, and setting the seed points as initial voxels; Comparing the gray values of adjacent voxels of the initial voxels, and attributing the adjacent voxels that meet the preset growth threshold condition to the same particle; Performing feature analysis on all the obtained particles to obtain digital features, where the digital features include: morphological feature parameters of the particles, spatial distribution features of the particles, contact relationship network between the particles, and void network between the particles; Projecting the particles onto a two-dimensional plane according to the digital features to obtain a projection image; Generating an analysis report and a visualization atlas, where the analysis report includes morphological feature parameters of the particles, spatial distribution features of the particles, contact relationship network between the particles, and void network between the particles, and the visualization atlas includes the projection image; 2. The method according to claim 1, wherein The step of identifying seed points from the sequence of sectional images based on image gray values and setting the seed points as initial voxels specifically includes: Taking the gray values in the sequence of sectional images as height values to construct a three-dimensional height field, where the position of each voxel point is determined by the corresponding spatial coordinates, and the height value of the voxel point is determined by the corresponding gray value; Performing ascending sorting on the voxel points in the three-dimensional height field; In the order of ascending sorting, adding the voxel points to the simplicial complex as vertices in sequence, and constructing each voxel point into a zero-dimensional simplicial structure; Searching for adjacent voxel points of the voxel points within a preset neighborhood range in the three-dimensional height field, and connecting two voxel points with a distance less than the distance threshold to construct a one-dimensional simplex; When the endpoints of the one-dimensional simplex edges are connected end to end in sequence to form a closed loop, constructing the region enclosed by the closed loop into a two-dimensional simplex face, and adding the two-dimensional simplex face to the simplicial complex; Forming a nested sequence of simplicial complexes, where each layer of the simplicial complex sequence represents the topological structure with a gray value less than or equal to the gray value of the current voxel point; Performing persistence calculation on the nested sequence of simplicial complexes, and calculating the local minimum points with persistence greater than the persistence threshold as seed points; 3. The method according to claim 2, wherein The step of performing persistence calculation on the nested sequence of simplicial complexes specifically includes: A new connected component is generated at the local minimum point, and the local minimum point is denoted as the birth point of the connected component; the connected component is formed by the mutual connection of voxel points within the nested sequence of simplicial complexes, and the local minimum point is the point with the smallest gray value in a pre-divided local area; When two connected components are first connected and merged at the current gray value, the connected component with the higher birth value terminates, and the current gray value is denoted as the death point corresponding to the connected component with the higher birth value, and the birth value refers to the gray value corresponding to the birth point of the connected component; Constructing a persistence diagram based on the birth point and the corresponding death point, where the abscissa of the persistence diagram is the gray value of the birth point, and the ordinate of the persistence diagram is the gray value difference between the birth point and the corresponding death point.

4. The method according to claim 1, wherein The step of performing feature analysis on all the obtained particles to obtain digital features specifically includes: Calculating the ratio of the surface area to the volume of each of the particles to obtain a sphericity parameter; Calculating the ratio of the longest axis to the shortest axis of each of the particles to obtain an elongation parameter.

5. The method according to claim 1, wherein The step of performing feature analysis on all the obtained particles to obtain digital features specifically includes: Statistical analysis of the particle size distribution of the particles; Based on the coordinate information of the particles in three-dimensional space, constructing a position density distribution map of the particles in three-dimensional space to obtain the spatial distribution characteristics of the particles.

6. The method according to claim 1, characterized in that, The step of performing feature analysis on all the obtained particles to obtain digital features specifically includes: Identifying the contact areas between adjacent particles; Calculating the area and perimeter of the contact areas; Classifying the contact areas as point contacts, line contacts, or surface contacts based on the area and the perimeter; Based on the contact relationships between the particles, constructing a contact relationship network between the particles.

7. The method according to claim 1, characterized in that The step of performing feature analysis on all the obtained particles to obtain digital features specifically includes: Identifying the void areas between the particles; Statistical analysis of the size distribution of the void areas; Constructing a void network between the particles.

8. A particle structure atlas generation system based on rock specimens, characterized in that, The particle structure atlas generation system based on a rock specimen includes: one or more processors and a memory; the memory is coupled to the one or more processors, the memory is used to store computer program code, the computer program code includes computer instructions, and the one or more processors call the computer instructions to cause the particle structure atlas generation system based on a rock specimen to execute the method according to any one of claims 1-7.

9. A computer program product comprising instructions, characterized in that, When the computer program product runs on the particle structure atlas generation system based on a rock specimen, it causes the particle structure atlas generation system based on a rock specimen to execute the method according to any one of claims 1-7.

10. A computer-readable storage medium, comprising instructions, characterized in that, When the instructions run on the particle structure atlas generation system based on a rock specimen, it causes the particle structure atlas generation system based on a rock specimen to execute the method according to any one of claims 1-7.

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