Digital reconstruction method and device of porous graphite pore structure

Abstract

The invention discloses a digital reconstruction method and equipment for a porous graphite pore structure, and the method comprises the steps: firstly building an original input system based on the size, porosity, pore diameter mean value, standard deviation, distribution interval and other parameters of porous graphite, segmenting a medium in a three-dimensional space, and generating corresponding subspaces and random pore center points; and then, a Box-Muller algorithm is adopted to generate an aperture sequence obeying normal distribution, and a K-D Tree algorithm is utilized to carry out rapid neighbor search on the central point of the aperture, so that the matching of the position of the aperture and the aperture is realized. Furthermore, interaction force among pores is introduced, and the positions of the pores are adjusted through iterative optimization, so that the generated digital model is ensured to be consistent with actual porous graphite in pore distribution and overlapping degree.

Description

Technical Field

[0001] The present invention belongs to the technical field of porous media, and in particular relates to a method and device for digitally reconstructing the pore structure of porous graphite. Background Art

[0002] Due to its excellent material properties, porous graphite has been widely used in electronic cooling, aerospace and other fields, and has therefore attracted much attention from scholars.

[0003] Some studies adopted an idealized periodic topological structure, established a physical model of the pore structure based on the homogeneous assumption, and derived an empirical correlation for its effective thermal conductivity. However, the above model cannot reflect the randomness of the position and size of the porous graphite pore units, and there is a gap between the predicted thermal fluid transport phenomenon at the pore scale and the actual thermal fluid transport phenomenon in the porous graphite pores; another part of the research used optical or X-ray tomography methods to divide the porous graphite into plane cross-sectional slices according to the Cavalieri principle for three-dimensional structural reconstruction. However, this type of reconstruction technology is limited by the accuracy of the scanning instrument and the number of scanned sample sections, and it is difficult to achieve high-resolution reconstruction of porous graphite. If high-precision scanning is used as a premise, there are problems of huge data volume, high economic and time costs. In addition, when importing the model for meshing, further repair work is required on the edges and corners and intersection structures of the model. Summary of the Invention

[0004] The purpose of the present invention is to provide a method and equipment for digitally reconstructing the pore structure of porous graphite. The digital model of porous graphite generated by the present invention can be used for simulation, prediction of the heat transfer performance of porous graphite and research on the thermal fluid transport characteristics at the pore scale, providing a reference basis for the actual engineering application of porous graphite.

[0005] In order to achieve the above object, the present invention adopts the following technical solutions: A method for digitally reconstructing the pore structure of porous graphite comprises the following steps: Obtain the pore structure parameters of porous graphite, including graphite size, porosity, average pore size, pore size standard deviation and pore size distribution range; The geometric calculation domain is determined based on the graphite size and porosity, and the pore number and pore center coordinates are obtained according to the geometric calculation domain; Generate a pore size sequence based on the average pore size, pore size standard deviation and pore size distribution interval; Preliminary matching of the distance between the pore size sequence and the pore center coordinate points was performed to preliminarily determine the pore position; The interaction force between pores is introduced to adjust the pore position twice, and the pore center coordinates and the pore diameter data corresponding to the pore center coordinates are output; The pore structure of the porous graphite is reconstructed based on the pore structure parameters of the porous graphite, the output pore center coordinates and the pore diameter data corresponding to the pore center coordinates.

[0006] Furthermore, the pore center coordinates are determined by equally dividing the geometric calculation domain using an octree algorithm.

[0007] Furthermore, the steps of generating the pore size sequence according to the average pore size, pore size standard deviation and pore size distribution interval are as follows: Generate a uniform distribution sequence in [0,1] in advance based on the average pore size, pore size standard deviation and pore size distribution interval; The Box-Muller algorithm is used to transform the uniform distribution sequence into a two-dimensional standard normal distribution sequence, and then any dimension is intercepted and translated and scaled to obtain a normal distribution sequence that obeys the input radius and standard deviation.

[0008] Furthermore, the distance between the pore sequence and the pore center coordinate point is preliminarily matched to preliminarily determine the pore position. The process is as follows: According to the pore center coordinates, a three-dimensional spatial data structure is constructed for the coordinate array; The KNN nearest neighbor search algorithm was used to preliminarily match the pore center coordinates with the pore size sequence.

[0009] Furthermore, a three-dimensional spatial data structure is constructed for the coordinate array using a KD Tree algorithm.

[0010] Furthermore, the process of introducing the interaction force between pores, performing secondary adjustment on the pore positions, and outputting the pore center coordinates and the pore diameter data corresponding to the pore center coordinates is as follows: Iteratively calculate the center distance between any two pores, and obtain the pore interpenetration degree and interpenetration limitation coefficient based on the center distance between the two pores; Compare the pore interpenetration degree and interpenetration confinement coefficient; If the pore interpenetration degree is greater than zero and less than the interpenetration limit coefficient, the pore center coordinates and the pore diameter data corresponding to the pore center coordinates are output; otherwise, a force is applied to the pore and the pore position is updated until the output conditions are met.

[0011] Furthermore, in the iterative process, after each round of calculation of the new positions of all pores, the average moving distance of each pore is calculated and the average moving distance of the previous iteration is recorded. If the absolute value of the difference between the two exceeds the convergence threshold preset by Si Ao Yu, the iteration is stopped and the pore center coordinates and corresponding pore size data after the iteration are output.

[0012] In a second aspect, the present invention provides a digital reconstruction system for a porous graphite pore structure, comprising an acquisition module, a calculation module, a generation module, a matching module, an adjustment output module, and a reconstruction module, wherein: Acquisition module: used to obtain the pore structure parameters of porous graphite, including graphite size, porosity, average pore size, pore size standard deviation and pore size distribution range; Calculation module: Determine the geometric calculation domain based on graphite size and porosity, and obtain the number of pores and pore center coordinates according to the geometric calculation domain; Generation module: used to generate pore size sequence according to average pore size, pore size standard deviation and pore size distribution interval; Matching module: used to preliminarily match the distance between the pore sequence and the pore center coordinate point to preliminarily determine the pore position; An adjustment output module is used to introduce the interaction force between pores, perform secondary adjustment on the pore positions, and output the pore center coordinates and the pore diameter data corresponding to the pore center coordinates; The reconstruction module reconstructs the pore structure of the porous graphite based on the pore structure parameters of the porous graphite, the output pore center coordinates and the pore size data corresponding to the pore center coordinates.

[0013] According to a third aspect, a terminal device is provided, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the steps of the above method when executing the computer program.

[0014] In a fourth aspect, a computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the steps of the above method are implemented.

[0015] Compared with the prior art, the present invention has the following beneficial technical effects: The present invention provides a digital reconstruction method for the pore structure of porous graphite, which can generate a digital model of porous graphite with random pore distribution characteristics at a low time and economic cost. The digital model can be used to predict the heat transfer performance of porous graphite and study the thermal fluid transport function at the pore scale. The present invention pregenerates a pore size sequence and constructs a spatial data index for the pore center point, thereby realizing a fast nearest neighbor search for the pore center coordinate point and matching the radius sequence of the coordinate point. Then, the force between the pores is introduced to adjust the pore position, thereby realizing a random distribution of the pore position and pore size and random control of the number and degree of intersection between pores. According to the above method, the present invention can generate a random pore reconstruction digital model of porous graphite at a low time and computational cost to facilitate subsequent simulation calculations, thereby promoting the application of porous graphite in practical engineering.

[0016] The present invention first pregenerates pore size sequences and constructs a spatial data index of pore center points to effectively manage pore spatial distribution information. In particular, a Kd-dimensional tree algorithm is used to organize and query pore center point data, thereby achieving fast and efficient neighbor search and pairing operations. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] Figure 1 A flow chart of a method for digitally reconstructing the pore structure of porous graphite; Figure 2 Detailed flowchart of a method for digitally reconstructing the pore structure of porous graphite according to an embodiment of the present invention; Figure 3 Block diagram of the algorithm for iterative adjustment of random pore structure of porous graphite; Figure 4 To reconstruct the statistical diagram of random pore size distribution of digital model; Figure 5 Schematic diagram of random pore structure of the reconstructed digital model; Figure 6 Schematic diagram of the digital model for reconstructing random pores in porous graphite. DETAILED DESCRIPTION

[0018] In order to enable those skilled in the art to better understand the solutions of the present invention, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the embodiments described are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts should fall within the scope of protection of the present invention.

[0019] It should be noted that similar reference numerals and letters denote similar items in the following drawings, and therefore, once an item is defined in one drawing, it does not need to be further defined or explained in subsequent drawings.

[0020] It should be noted that the terms "first", "second", etc. in the description and claims of the present invention and the above-mentioned drawings are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that the numbers used in this way can be interchanged where appropriate, so that the embodiments of the present invention described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions. For example, a process, method, system, product or device that includes a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0021] The present invention is described in further detail below with reference to the accompanying drawings: like Figure 1 As shown, a method for digitally reconstructing the pore structure of porous graphite includes the following steps: Step 1: Obtaining the pore structure parameters of porous graphite, including graphite size, porosity, average pore size, pore size standard deviation and pore size distribution range; Step 2: Determine the geometric calculation domain based on the graphite size and porosity, and obtain the pore number and pore center coordinates according to the geometric calculation domain; First, the geometric calculation domain is determined according to the given porous graphite size division, and then the initial pore number and pore center position are determined within the geometric calculation domain according to the input porous graphite pore structure parameters.

[0022] When dividing the geometric calculation domain, the octree algorithm is used to evenly divide the geometric calculation domain into N subintervals. After the geometric calculation domain is divided into subintervals according to the octree algorithm, a random prepared pore center coordinate P that obeys a uniform distribution is generated in each corresponding subinterval. i (x i ,y i , z i ).

[0023] Step 3: Generate an pore size sequence based on the average pore size, pore size standard deviation and pore size distribution interval; Generate a uniform distribution sequence in [0,1] in advance based on the average pore size, pore size standard deviation and pore size distribution interval; The Box-Muller algorithm is used to transform the uniform distribution sequence into a two-dimensional standard normal distribution sequence, and then any dimension is intercepted and translated and scaled to obtain a normal distribution sequence that obeys the input radius and standard deviation.

[0024] Step 4: Preliminary matching of the pore sequence and the distance between the pore center coordinate points to preliminarily determine the pore position; The KD Tree algorithm is used to construct a three-dimensional spatial data structure for the randomly prepared pore center coordinate array P, and the KNN nearest neighbor search algorithm is used to search for a preliminary match between the pore center coordinates and the pore size sequence that obeys the normal distribution, and the array C after the coordinates and radius are matched is output. i (x i ,y i ,z i ,r i ).

[0025] Step 5: Introduce the interaction force between pores, perform secondary adjustment on the pore position, and output the pore center coordinates and the pore diameter data corresponding to the pore center coordinates; According to the constructed three-dimensional space data structure and the output radius sequence array C containing the matching results i (x i ,y i ,z i ,r i ), the pore position is iteratively adjusted by introducing the effect of inter-pore force, so that the distance between the pores is d satisfy d min <d< d max , d min and d max It is defined according to the interacting pore radius.

[0026] Step six, reconstructing the porous graphite pore structure based on the porous graphite pore structure parameters, the output pore center coordinates and the pore diameter data corresponding to the pore center coordinates.

[0027] This paper provides a method for generating a digital model of the random pore structure of porous graphite. This model accurately reflects the random and complex pore distribution characteristics within the porous graphite. The resulting high-fidelity digital model is then used to simulate the heat transfer performance of the porous graphite and the thermal fluid transport characteristics at the pore scale. This provides a strong numerical basis for a deeper understanding of the heat transfer and fluid behavior of porous graphite under different operating conditions. This provides reference data and theoretical support for the design and optimization of porous graphite in practical engineering projects.

[0028] In another embodiment of the present invention, a method for digitally reconstructing the pore structure of porous graphite is provided, such as Figure 2 As shown, the following steps are included: S1, setting the pore structure parameters of porous graphite: graphite size, porosity, average pore size, pore size standard deviation and pore size distribution range; Table 1 is a table of the physical pore structure parameters of porous graphite. According to these parameters, a digital model of the pore structure of porous graphite with a length, width and height of 5 mm is generated.

[0029] Table 1. Pore structure parameters of porous graphite

[0030] Specifically, according to the length L of the porous graphite input system x 、Width L y , high L z , in the three-dimensional space, the coordinate origin is used as the starting point, and the cubic interval constructed along the positive direction of the XYZ axis is used as the geometric calculation domain S0. The initial calculation space S0 can be expressed as:

[0031] (1), in the current subspace S k The center point P is calculated according to the following formula k :

[0032] (2) Then calculate the 8 subspaces S divided in the current space K+1 (i) , its boundaries are defined as follows:

[0033] Among them, x c ,y c , z c is the center coordinate of the current subspace, that is:

[0034] For each subspace Continue to execute substeps (1) and (2) until the termination condition is met, that is, the number of generated subspaces >Minimum number of pores N min , minimum number of pores N min Calculated according to the following formula:

[0035] in, N is the number of pores; Φ is the porosity; L x is the length of the medium, in mm; L y is the width of the medium, in mm; L z is the height of the medium, in mm; d p is the average diameter of the pores, in mm.

[0036] In each subinterval Generate a random point , whose coordinates are:

[0037] Among them, u x ,u y ,u z is a random number uniformly distributed in the interval [-0.5, 0.5], that is:

[0038] x c ,y c , z cis the center coordinate of the current subspace, that is:

[0039] Finally, all random point datasets Q are saved, which are composed of all subspaces S k Random points generated within Composition can be expressed as:

[0040] S2, divide the porous graphite geometric calculation domain and randomly generate the pore center coordinate points: According to the average pore size of porous graphite given in the input system d p and pore size standard deviation σ And interval, generate aperture sequence that obeys the corresponding normal distribution. First pre-generate two columns of uniform distribution sequence that obey [0,1] U 1, U 2. Then use the Box-Muller algorithm to convert the uniformly distributed sequence U 1, U 2 Convert to Standard normal distribution sequence, the transformation formula is as follows:

[0041] According to the given average pore size of porous graphite d p and pore size standard deviation σ , for the standard normal distribution sequence Z Perform a linear transformation to convert it into an average value , the normal distribution radius sequence R with standard deviation σ

[0042] S3, generate a porous graphite pore size sequence and pair them according to the distance between the pore center coordinate points, so that the pores are initially interconnected: According to the random pore center point set Q generated above, the KD Tree algorithm is used to construct the three-dimensional space data structure, and then the pore center point set Q is matched with the radius sequence R. First, according to the three-dimensional space data Q={Q1,Q2,……, Q n}, where each point Qi has (x, y, z) Coordinates. Recursively select dimension K (x, y, z in turn) and sort the point set according to this dimension. Select the median point as the current node, divide the point set into left and right subsets, and construct the left subtree and right subtree respectively. Then traverse the three-dimensional space data Q={Q 1 , Q 2 ,……, Q n} , and perform KNN nearest neighbor search as query points in turn. Starting from the root node of the KD Tree constructed above, recursively traverse the tree according to the coordinate value of the query point, and enter the subtree closer to the query point. When traversing to a leaf node, backtrack to check the nodes on the current path and maintain a priority queue of size 3-5 to store the nearest 3-5 points. Then calculate the hypersphere range to determine whether it is necessary to check another subtree. If there may be a closer point, continue traversing the subtree. Then return the coordinates P of the 3-5 nearest neighbors. i (x,y,z), and then calculate the query point P and P i Distance D i , randomly select a radius r from the radius sequence R for the query point P P Assignment, the neighboring point Pi randomly selects the radius r from the radius sequence R i , satisfying the following formula.

[0043]

[0044] For all points P i Traverse and match, and finally obtain a 4-dimensional array set C={C1, C2,……,C n}, where each point Ci It has the format of (x,y,z,r), which is a pairing of coordinates and corresponding radius.

[0045] S4, introduces the interaction force between pores and adjusts the pore positions so that the degree of interpenetration between pores is close to that of the real porous graphite pore structure: Iteratively calculate the center distance between any two pores, and obtain the pore interpenetration degree and interpenetration limitation coefficient based on the center distance between the two pores; Compare the pore interpenetration degree and interpenetration confinement coefficient; If the pore interpenetration degree is greater than zero and less than the interpenetration limit coefficient, the pore center coordinates and the pore diameter data corresponding to the pore center coordinates are output; otherwise, a force is applied to the pore and the pore position is updated until the output conditions are met.

[0046] Specifically, such as Figure 3 As shown, according to the 4-dimensional array set C={C1, C2,……,C n}, a method based on mechanical simulation is used to optimize the distribution of pores in a limited geometric calculation domain. In each iteration, for any two pores (pore center C i and , radius C i (r i ),C j (r j ), first calculate the distance d from the pore center ij: , and pore interpenetration

[0047]

[0048] In order to dynamically limit the adjustment distance, the intersection limit parameter is introduced ε , and its calculation formula is

[0049] where C randomly selects C i (r i ) or C j ( r j )one.

[0050] when When <0, an attractive force is exerted on the pores, forcing them to move closer together.

[0051]

[0052] when > ε When the pores are connected, a repulsive force is applied to the pores to keep them away from each other and prevent the pores from intersecting excessively.

[0053]

[0054] Finally, according to the unit direction vector F between the pores i And the learning rate η updates the pore position coordinates, that is,

[0055]

[0056] During the iteration process, after calculating the new positions of all pores in each round, the average moving distance of each pore is calculated.

[0057]

[0058] And record the average moving distance of the previous iteration , if the average moving difference of two consecutive iterations satisfies

[0059] in δ If the convergence threshold is reached, the system is considered to have reached a stable state and the iteration is terminated. Finally, the coordinate position of the pore center and the corresponding pore diameter after iteration are saved and output.

[0060] The above solution steps can be expressed by computer compiled language, and the solution value can be obtained using computing software such as MATLAB.

[0061] S5, output digital porous graphite size and pore structure information: S6, import the digitized porous graphite pore size information into the geometric modeling software to achieve the drawing of the solid phase and pores: By writing macro commands in the geometric modeling software, the saved file can be imported into the 3D visualization software, and a Boolean difference operation is performed on the cube geometry domain and the pore geometry domain to obtain the final porous graphite digital model.

[0062] The final pore size distribution diagram is as follows Figure 4 The final porous graphite digital model is shown in Figure 5 , as shown in 6.

[0063] The method and system for digitally reconstructing the pore structure of porous graphite involved in the present invention greatly reduces the workload of digitally reconstructing the pore structure of porous graphite. The generated pore structure is similar to the random distribution of the actual porous graphite pore structure. Using this method, a digital graphite model can be pre-generated during the engineering design phase, and numerical simulations can be performed on the digital model for actual engineering applications. This provides a strong numerical basis for in-depth understanding of the thermal transmission and fluid behavior of porous graphite under different working conditions. This provides reference data and theoretical support for the design and optimization of porous graphite in actual engineering, promoting the application of porous graphite in practical engineering.

[0064] The digital reconstruction method for the pore structure of porous graphite involved in the present invention greatly reduces the workload of digitally reconstructing the pore structure of porous graphite, and the generated pore structure is similar to the random distribution of the pore structure of actual porous graphite. Using this method, a digital graphite model can be pre-generated during the engineering design phase, and the digital model can be numerically simulated in actual engineering applications. This provides a strong numerical basis for in-depth understanding of the thermal transmission and fluid behavior of porous graphite under different working conditions. This provides reference data and theoretical support for the design and optimization of porous graphite in actual engineering, promoting the application of porous graphite in actual engineering.

[0065] In yet another embodiment of the present invention, a digital reconstruction system for a porous graphite pore structure is provided, comprising an acquisition module, a calculation module, a generation module, a matching module, an adjustment output module, and a reconstruction module, wherein: Acquisition module: used to obtain the pore structure parameters of porous graphite, including graphite size, porosity, average pore size, pore size standard deviation and pore size distribution range; Calculation module: Determine the geometric calculation domain based on graphite size and porosity, and obtain the number of pores and pore center coordinates according to the geometric calculation domain; Generation module: used to generate pore size sequence according to average pore size, pore size standard deviation and pore size distribution interval; Matching module: used to preliminarily match the distance between the pore sequence and the pore center coordinate point to preliminarily determine the pore position; An adjustment output module is used to introduce the interaction force between pores, perform secondary adjustment on the pore positions, and output the pore center coordinates and the pore diameter data corresponding to the pore center coordinates; The reconstruction module reconstructs the pore structure of the porous graphite based on the pore structure parameters of the porous graphite, the output pore center coordinates and the pore size data corresponding to the pore center coordinates.

[0066] It will be understood by those skilled in the art that embodiments of the present invention may be provided as methods, systems, or computer program products. Thus, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment, or an embodiment combining software and hardware. Furthermore, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to magnetic disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0067] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowcharts and / or block diagrams, as well as combinations of processes and / or blocks in the flowcharts and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowcharts and / or block diagrams. Figure 1 a process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0068] These computer program instructions may also be stored in a computer readable memory that can direct a computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 a process or multiple processes and / or boxes Figure 1 The function specified in one or more boxes.

[0069] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operational steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing the instructions executed on the computer or other programmable device for implementing the process. Figure 1a process or multiple processes and / or boxes Figure 1 A step that specifies a function in one or more boxes.

[0070] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit its scope of protection. Although the present invention has been described in detail with reference to the above embodiments, ordinary technicians in the field should understand that after reading the present invention, those skilled in the art may still make various changes, modifications or equivalent substitutions to the specific implementation methods of the invention, but these changes, modifications or equivalent substitutions are all within the scope of protection of the pending claims of the invention.

Claims

1. A method for digitally reconstructing the pore structure of porous graphite, characterized in that: The following steps are involved: Obtain the pore structure parameters of porous graphite, including graphite size, porosity, average pore size, pore size standard deviation and pore size distribution range; The geometric calculation domain is determined based on the graphite size and porosity, and the number of pores and the coordinates of the pore centers are obtained according to the geometric calculation domain; Generate a pore size sequence based on the average pore size, pore size standard deviation and pore size distribution interval; Preliminary matching of the distance between the pore sequence and the pore center coordinate points was performed to preliminarily determine the pore position; The interaction force between pores is introduced to adjust the pore position twice, and the pore center coordinates and the pore diameter data corresponding to the pore center coordinates are output; The pore structure of the porous graphite is reconstructed based on the pore structure parameters of the porous graphite, the output pore center coordinates and the pore diameter data corresponding to the pore center coordinates.

2. The method for digitally reconstructing the pore structure of porous graphite according to claim 1, characterized in that: The pore center coordinates are determined by equally partitioning the geometric calculation domain using an octree algorithm.

3. The method for digitally reconstructing the pore structure of porous graphite according to claim 1, characterized in that: The steps of generating the pore size sequence according to the average pore size, pore size standard deviation and pore size distribution interval are as follows: Generate a uniform distribution sequence in [0,1] in advance based on the average pore size, pore size standard deviation and pore size distribution interval; The Box-Muller algorithm is used to transform the uniform distribution sequence into a two-dimensional standard normal distribution sequence, and then any dimension is intercepted and translated and scaled to obtain a normal distribution sequence that obeys the input radius and standard deviation.

4. The method for digitally reconstructing the pore structure of porous graphite according to claim 1, characterized in that: The process of preliminarily matching the distance between the pore sequence and the pore center coordinate point to preliminarily determine the pore position is as follows: According to the pore center coordinates, a three-dimensional spatial data structure is constructed for the coordinate array; The KNN nearest neighbor search algorithm was used to preliminarily match the pore center coordinates with the pore size sequence.

5. The method for digitally reconstructing the pore structure of porous graphite according to claim 4, characterized in that: A three-dimensional spatial data structure is constructed for the coordinate array using a KD Tree algorithm.

6. The method for digitally reconstructing the pore structure of porous graphite according to claim 1, characterized in that: The process of introducing the interaction force between pores, performing secondary adjustment on the pore positions, and outputting the pore center coordinates and the pore diameter data corresponding to the pore center coordinates is as follows: Iteratively calculate the center distance between any two pores, and obtain the pore interpenetration degree and interpenetration limitation coefficient based on the center distance between the two pores; Compare the pore interpenetration degree and interpenetration confinement coefficient; If the pore interpenetration degree is greater than zero and less than the interpenetration limit coefficient, the pore center coordinates and the pore diameter data corresponding to the pore center coordinates are output; otherwise, a force is applied to the pore and the pore position is updated until the output conditions are met.

7. The method for digitally reconstructing the pore structure of porous graphite according to claim 6, characterized in that: During the iterative process, after calculating the new positions of all pores in each round, the average moving distance of each pore is calculated and the average moving distance of the previous iteration is recorded. If the absolute value of the difference between the two exceeds the convergence threshold preset by Si Ao Yu, the iteration is stopped and the pore center coordinates and corresponding pore size data after the iteration are output.

8. A digital reconstruction system for porous graphite pore structure, characterized in that: The digital reconstruction method of the porous graphite pore structure according to any one of claims 1 to 7 comprises an acquisition module, a calculation module, a generation module, a matching module, an adjustment output module and a reconstruction module, wherein: Acquisition module: used to obtain the pore structure parameters of porous graphite, including graphite size, porosity, average pore size, pore size standard deviation and pore size distribution range; Calculation module: Determine the geometric calculation domain based on graphite size and porosity, and obtain the number of pores and pore center coordinates according to the geometric calculation domain; Generation module: used to generate pore size sequence according to average pore size, pore size standard deviation and pore size distribution interval; Matching module: used to preliminarily match the distance between the pore sequence and the pore center coordinate point to preliminarily determine the pore position; An adjustment output module is used to introduce the interaction force between pores, perform secondary adjustment on the pore positions, and output the pore center coordinates and the pore diameter data corresponding to the pore center coordinates; The reconstruction module reconstructs the pore structure of the porous graphite based on the pore structure parameters of the porous graphite, the output pore center coordinates and the pore size data corresponding to the pore center coordinates.

9. A terminal device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 7 are implemented.

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