Method, device and equipment for measuring degree of rock fragment crushing and storage medium

Abstract

This application discloses a method, apparatus, device, and storage medium for measuring the degree of rock fragmentation, relating to the field of image data processing technology. The method for measuring the degree of rock fragmentation includes: acquiring three-dimensional spatial information of rock fragmentation; performing spatial mapping based on the three-dimensional spatial information to obtain a cell adjacency mapping table; obtaining an independent fragment set based on the cell adjacency mapping table and rock fragmentation cells; and calculating the volume and morphological parameters of the rock fragmentation material based on the independent fragment set to determine the degree of rock fragmentation. This application effectively reduces the complexity of neighborhood search when processing large-scale fragmentation cells and significantly improves computational efficiency.

Description

Technical Field

[0001] This application relates to the field of image data processing technology, and in particular to a method, apparatus, device and storage medium for measuring the degree of rock chip fragmentation. Background Technology

[0002] With the continuous development of blasting engineering, tunnel engineering, and geotechnical engineering technologies, rock materials generate a large number of irregularly shaped fragments during construction. The size and morphological characteristics of these fragments are core indicators for evaluating the fragmentation effect, optimizing construction parameters, and calculating energy utilization efficiency. Therefore, rapid and accurate quantitative measurement of the degree of rock fragmentation is of great guiding significance for engineering practice.

[0003] Existing technologies for extracting rock cuttings information from massive discrete element data generally employ a global traversal approach for neighborhood search. When dealing with large-scale fractured elements, it is necessary to compare all elements one by one to determine their spatial adjacency, resulting in excessively high computational complexity for neighborhood search. As the number of elements increases, the computational load grows exponentially, making it difficult to meet the real-time analysis requirements of engineering projects. Furthermore, some methods use octrees or 3D meshes for spatial partitioning, but errors can easily occur at large differences in element size or at boundaries, leading to inaccurate neighborhood identification.

[0004] Therefore, how to reduce the complexity of neighborhood search and improve the efficiency of large-scale data processing is an urgent problem to be solved. Summary of the Invention

[0005] The main objective of this application is to provide a method, apparatus, device, and storage medium for measuring the degree of rock fragmentation, aiming to solve the technical problems of high complexity and low computational efficiency in neighborhood search under large-scale unit data processing scenarios.

[0006] To achieve the above objectives, this application provides a method for measuring the degree of rock fragmentation, comprising: Obtain three-dimensional spatial information of rock fragmentation; Based on the three-dimensional spatial information of the rock fragments, a spatial mapping is performed to obtain a unit adjacency mapping table; Based on the unit adjacency mapping table and the rock fragmentation unit, an independent fragment set is obtained; The volume and morphological parameters of the rock fragments are calculated based on the independent fragment set to determine the degree of rock fragmentation.

[0007] In one embodiment, the three-dimensional spatial information of rock fragmentation includes rock fragmentation volume information and rock fragmentation node information, and the step of obtaining the three-dimensional spatial information of rock fragmentation includes: Obtain simulated values ​​of rock cuttings breakage; Based on the simulated rock fragmentation values, the rock fragmentation coordinate information is obtained; Based on the rock fragmentation coordinate information, the rock fragmentation volume information is obtained; The rock fragmentation coordinate information is meshed and aligned to obtain the aligned node coordinates; The information of the rock fragmentation nodes is obtained based on the aligned node coordinates.

[0008] In one embodiment, the step of spatial mapping based on the three-dimensional spatial information of the rock fragments to obtain a cell adjacency mapping table includes: The spatial range of the rock fragments is determined based on the coordinate information of the fragments, and a cell network is established; Based on the rock fragmentation node information and the cell network, the cell adjacency mapping table is obtained.

[0009] In one embodiment, obtaining an independent fragment set based on the cell adjacency mapping table and the rock fragmentation cell includes: Based on the unit adjacency mapping table, a local neighborhood query of the rock cuttings breaking unit is performed to obtain the rock cuttings breaking unit to be determined. The connection relationship of the rock cuttings fragmentation units to be determined is determined to obtain the connected rock cuttings fragmentation units. The connected rock fragmentation units are merged to obtain an independent set of fragments.

[0010] In one embodiment, determining the connection relationship of the rock fragmentation units to be determined includes: Obtain the rock fragmentation unit to be determined and the three-dimensional spatial information of rock fragmentation; Based on the rock fragmentation unit to be determined and the three-dimensional spatial information of rock fragmentation, if the rock fragmentation unit has a geometric common surface, the connection of the rock fragmentation unit to be determined is determined.

[0011] In one embodiment, determining the connection relationship of the rock fragmentation units to be determined further includes: If the rock cutting unit does not have a geometric common surface, obtain the failure state parameters of the bonding unit between the rock cutting units; If the failure state parameters of the bonding unit between the rock cuttings crushing units are not failed, the connection of the rock cuttings crushing units to be determined is confirmed.

[0012] In one embodiment, the step of merging the connected rock fragmentation units to obtain an independent fragment set includes: Obtain the root node of the rock cuttings breaking unit; If the root nodes of the rock fragmentation units are different, rock fragmentation units to be merged are obtained; The rock fragmentation units to be merged are merged according to rank to obtain an independent fragment set.

[0013] In addition, to achieve the above objectives, this application also provides a measuring device for the degree of rock cuttings fragmentation, comprising: The acquisition module acquires three-dimensional spatial information of rock fragmentation. The mapping module performs spatial mapping based on the three-dimensional spatial information of the broken rock fragments to obtain a unit adjacency mapping table. The merging module obtains an independent fragment set based on the unit adjacency mapping table and the rock fragmentation unit; The results module calculates the volume and morphological parameters of the rock fragments based on the independent fragment set, and determines the degree of rock fragmentation.

[0014] In addition, to achieve the above objectives, this application also proposes a device for measuring the degree of rock fragmentation, the device comprising: a memory, a processor, and a computer program stored in the memory and executable on the processor, the computer program being configured to implement the steps of the method for measuring the degree of rock fragmentation as described above.

[0015] In addition, to achieve the above objectives, this application also proposes a storage medium, which is a computer-readable storage medium, on which a computer program is stored, and when the computer program is executed by a processor, it implements the steps of the method for measuring the degree of rock fragmentation as described above.

[0016] This application provides a method for measuring the degree of rock fragmentation. By converting the rock fragmentation material into three-dimensional spatial information and constructing a cell adjacency mapping table through a spatial three-dimensional mesh mapping mechanism, this application transforms the global neighborhood search, which requires comparing all cells one by one in traditional methods, into a local fast retrieval that only needs to query the mapping table. This significantly reduces the computational complexity of the neighborhood search and effectively solves the technical problems of high complexity and low computational efficiency of the neighborhood search when dealing with large-scale fragmented cells. Attached Figure Description

[0017] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with this application and, together with the description, serve to explain the principles of this application.

[0018] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, for those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0019] Figure 1 This is a schematic flowchart of the first embodiment for measuring the degree of rock fragmentation in this application; Figure 2This is a flowchart illustrating the second embodiment of the measurement of the degree of rock fragmentation in this application; Figure 3 This is a flowchart illustrating the third embodiment of the measurement of the degree of rock fragmentation in this application; Figure 4 This is a structural block diagram of the first embodiment of the rock cuttings fragmentation degree measuring device of this application; Figure 5 This is a schematic diagram of the structure of a device for measuring the degree of rock fragmentation in the hardware operating environment involved in the embodiments of this application.

[0020] The purpose, features, and advantages of this application will be further explained in conjunction with the embodiments and with reference to the accompanying drawings. Detailed Implementation

[0021] It should be understood that the specific embodiments described herein are merely illustrative of the technical solutions of this application and are not intended to limit this application.

[0022] To better understand the technical solution of this application, a detailed description will be provided below in conjunction with the accompanying drawings and specific implementation methods.

[0023] The main solution of this application is as follows: First, obtain the three-dimensional spatial information of rock fragmentation; perform spatial mapping based on the three-dimensional spatial information of rock fragmentation to obtain a unit adjacency mapping table; obtain an independent fragment set based on the unit adjacency mapping table and the rock fragmentation unit; finally, calculate the volume and morphological parameters of the rock fragmentation material based on the independent fragment set to determine the degree of rock fragmentation.

[0024] In this embodiment, for ease of description, the following description focuses on the measurement of the degree of rock fragmentation.

[0025] Current technologies, when dealing with large-scale fragmented cells, employ a global traversal approach for neighborhood search, requiring the comparison of each cell individually to determine their spatial adjacency. This results in excessively high computational complexity for neighborhood search. Therefore, reducing the complexity of neighborhood search and improving the efficiency of large-scale data processing is a pressing issue that needs to be addressed.

[0026] This application transforms rock fragmentation material into three-dimensional spatial information and constructs a spatial three-dimensional mesh mapping mechanism, transforming global neighborhood search into local fast query based on the mapping table. This effectively reduces the neighborhood search complexity when processing large-scale fragmentation units and significantly improves computational efficiency.

[0027] It should be noted that the executing entity in this embodiment can be a computing service device with data processing, network communication, and program execution functions, such as a tablet computer, personal computer, or mobile phone, or an electronic device capable of performing the above functions, a rock cuttings fragmentation measurement program, etc. The following description uses a computing device as an example to illustrate this embodiment and the subsequent embodiments.

[0028] Based on this, this application proposes a method for measuring the degree of rock fragmentation according to the first embodiment. Please refer to [link / reference]. Figure 1 The method for measuring the degree of rock fragmentation includes steps S10 to S40: Step S10: Obtain three-dimensional spatial information of rock fragmentation.

[0029] Understandably, in the fields of blasting engineering, tunnel engineering, and geotechnical engineering, the quantitative analysis of the volume and morphology of rock fragments after rock mass fracturing requires first obtaining basic data. Numerical simulation technology can simulate the fracturing process of rock mass under external loads and output calculation result files containing a large amount of discrete element information. Therefore, it is necessary to extract the three-dimensional spatial basic data related to the fracturing analysis from the simulation result file to provide data input for subsequent operations such as node deduplication, topology identification, and connectivity determination.

[0030] It should be noted that the three-dimensional spatial information of rock fragmentation refers to the complete set of data extracted from the three-dimensional numerical simulation calculation results file, used to describe the spatial geometric characteristics and physical state of the fractured rock mass. Taking blasting rock breaking simulation as an example, this information includes, but is not limited to: node spatial coordinates, tetrahedral element set information, element edge length information, element volume information, and fracture criterion-related parameters. Among them, node spatial coordinates refer to the three-dimensional coordinates (x, y, z) of the vertices of each tetrahedral element in the numerical model. For example, the coordinates of a node are (1.25, 3.67, -0.82), describing the precise location of the point in space. The tetrahedral element set information refers to which four nodes each tetrahedral element consists of. For example, the element... By node , , , The structure records the topological connections of the elements; the element side length information refers to the length of the six sides of the tetrahedron calculated from the node coordinates, which is the basis for calculating the characteristic scale and dynamic tolerance of the elements; the element volume information refers to the volume value of each tetrahedral element calculated from the node coordinates using the vector hybrid product method or the determinant method; the fracture criterion related parameters include, but are not limited to, mechanical parameters such as displacement discontinuity, strain mutation, or damage variable. For example, if the damage variable D of a certain interface is equal to 0.8, exceeding the critical value of 0.7, then it is determined that fracture has occurred at that point.

[0031] Understandably, by reading and structurally storing key data from the numerical simulation results in one go, the original simulation data was transformed into a data format specifically for rock cuttings analysis. This clearly defines the data range and content required for subsequent processing, avoiding the redundant computational overhead of repeatedly reading the original files in later steps – a single read allows for full reuse. Secondly, by unifying and integrating data such as node coordinates, element topology, and mechanical parameters, complete and consistent data input is provided for dynamic tolerance mesh alignment, node deduplication, and fracture criterion determination in subsequent steps, ensuring the accuracy of the calculation results from the source. Finally, the pre-processed element volume and side length information are obtained and can be directly reused in subsequent steps for rock cuttings accumulation and characteristic scale calculations, reducing redundant computations and improving the overall efficiency of the computational process.

[0032] refer to Figure 2 In one feasible implementation, step S10 may include steps A11 to A15: Step A11: Obtain simulated values ​​of rock cuttings breakage.

[0033] It should be noted that rock fragmentation simulation values ​​refer to the complete result data obtained after calculating the process of rock mass fracturing under external loads using numerical simulation methods. These simulation values ​​are usually stored in the form of result files and are first-hand raw data that has not undergone any extraction or processing. Taking blasting rock breaking simulation as an example, the simulation values ​​contain complete information describing the entire state of the fractured rock mass.

[0034] Understandably, by directly reading the numerical simulation result file to obtain the simulated values ​​of rock cuttings fracturing, the actual physical fracturing process is mapped into digital information. Because the simulated values ​​contain both geometric and mechanical information, subsequent analysis can repeatedly extract fracturing data under different working conditions and at different times in a single simulation. It can also accurately distinguish between the real fracture surface and the internal interface of the continuum based on the fracture criteria, improving the accuracy of fragment division. Furthermore, the ability to repeatedly extract fracturing data under different working conditions and at different times in a single simulation supports comparative analysis of multiple schemes. This step provides a reliable data input source for the entire fracturing degree measurement process.

[0035] Step A12: Obtain the rock fragmentation coordinate information based on the simulated rock fragmentation values.

[0036] It should be noted that the rock fragmentation coordinate information refers to the set of three-dimensional spatial coordinate data of all entity element nodes extracted from the rock fragmentation simulation values. This information accurately describes the geometric position of each mesh node in the numerical model in three-dimensional space. Taking a tetrahedral element mesh as an example, the simulation model may contain tens of thousands or even hundreds of thousands of nodes. Each node is uniquely determined by a three-dimensional coordinate (x, y, z). For example, the coordinates of a node are (3.52, -0.47, 1.83), which means that the node is 3.52 units away from the origin in the X-axis direction, -0.47 units away from the origin in the Y-axis direction, and 1.83 units away from the origin in the Z-axis direction. The coordinates of all nodes together constitute the spatial geometric framework of the fractured rock mass numerical model, which is an indispensable basic data for any subsequent geometric calculations.

[0037] Understandably, by selectively extracting node coordinate information from the complex simulation results, data simplification and focus are achieved. This significantly reduces the amount of data for subsequent processing and improves computational efficiency by removing redundant data irrelevant to the current analysis. Furthermore, accurate node coordinates form the data foundation for key steps such as volume calculation, dynamic tolerance setting, node deduplication, and topological surface identification, ensuring the geometric accuracy and computational reliability of the entire rock cuttings quantitative analysis process from the source.

[0038] Step A13: Obtain the rock fragmentation volume information based on the rock fragmentation coordinate information.

[0039] It is understandable that after obtaining the node coordinate information, the volume of each tetrahedral element needs to be pre-calculated as the basis for subsequent volume accumulation. If this step is skipped and the process proceeds directly to the topology analysis stage, calculating the volume of each element only during the final statistics will lead to repeated calculations, a chaotic process, and an increased probability of errors. Completing the element volume calculation and storing it in advance allows for direct retrieval in subsequent steps.

[0040] It should be noted that the volume information of broken rock fragments refers to the set of volume values ​​for each tetrahedral element in the numerical model. A tetrahedron is a three-dimensional geometric solid enclosed by four triangular faces, and its volume can be accurately calculated using geometric formulas based on the three-dimensional coordinates of the four vertices.

[0041] The above calculation of solid element volume based on vertex-based 3D coordinates can be performed using the vector hybrid product method, which is as follows:

[0042]

[0043] in, The coordinates of point A of the tetrahedron; The coordinates of point B on the tetrahedron; The coordinates of point C of the tetrahedron; The coordinates of point D of the tetrahedron; The volume of a tetrahedral solid unit; Let A be the vector from point A to point B; Let A be the vector from point A to point C; Let be the vector from point A to point D.

[0044] The volume of a solid element calculated based on vertex-based 3D coordinates can also be calculated using the determinant method, as follows:

[0045] in, The coordinates of point A of the tetrahedron; The coordinates of point B on the tetrahedron; The coordinates of point C of the tetrahedron; The coordinates of point D of the tetrahedron; The volume of a tetrahedral solid unit.

[0046] The above-mentioned calculation of solid element volume based on vertex-based 3D coordinates can also be performed using the Cayley-Menger determinant, the calculation method of which is as follows:

[0047] The subscript numbers 1, 2, 3, and 4 correspond to the four nodes A, B, C, and D of the tetrahedron, respectively. This represents the side length between node 1 and node 2. This represents the side length between node 1 and node 3. This represents the side length between node 1 and node 4. This represents the side length between node 2 and node 3. This represents the side length between node 2 and node 4. This represents the side length between node 3 and node 4.

[0048] Understandably, the calculation based directly on the node coordinates using the vector hybrid product method or the determinant method is an accurate analytical solution with no numerical accumulation error, ensuring the high accuracy of the final rock cuttings volume statistics. Furthermore, the calculation results are bound to the node coordinates for storage, providing directly callable values ​​for rock cuttings volume accumulation in subsequent steps.

[0049] Step A14: Align the rock fragmentation coordinate information into a grid to obtain the aligned node coordinates.

[0050] Understandably, during numerical simulations, node coordinates are stored and calculated in floating-point form. Due to the limitations of floating-point precision in computers, nodes that should be located in the same spatial position may exhibit slight deviations due to numerical errors. For example, two nodes originally designed to be in the same location might have coordinates of (1.5000001, 2.0, 3.0) and (1.4999999, 2.0, 3.0) respectively. While such small errors have limited impact on the calculation of a single element, they can lead to two nodes that should overlap being misidentified as different nodes during subsequent node deduplication and topological surface identification. This results in failed coplanar identification, incorrect element connectivity, and ultimately structural deviations in the fragmentation of rock debris. Therefore, a mesh alignment method must be used to normalize the node coordinates, eliminating the adverse effects of floating-point errors on topological identification while preserving the effective geometric accuracy of the model.

[0051] It should be noted that mesh alignment refers to the process of normalizing node coordinates to the intersections of a virtual 3D mesh based on dynamic tolerance, so that nodes that should overlap obtain exactly the same coordinate values. First, the arithmetic mean of the six side lengths of each tetrahedral element is calculated to obtain the characteristic scale of the element; then, the tolerance coefficient is multiplied by the characteristic scale of the element to obtain the dynamic tolerance value of the element; finally, each node coordinate is divided by the dynamic tolerance value of the element, rounded to the nearest integer, and then multiplied by the dynamic tolerance value of the element to obtain the aligned coordinates.

[0052] The above calculation method for mesh alignment is as follows:

[0053]

[0054]

[0055]

[0056]

[0057] in, This is the tolerance factor; This is the characteristic scale of the unit; For the tetrahedral unit i The length of each side; the initial coordinates are ( , (z) The dynamic tolerance value, after alignment, has coordinates of ( , , ), This is the floor function.

[0058] Understandably, by adopting dynamic tolerance instead of fixed tolerance, the tolerance value adapts to the cell size, which ensures the effectiveness of the tolerance for large cells and avoids incorrect node merging due to excessive tolerance for small cells, thus balancing accuracy and robustness. Secondly, after meshing and alignment, the coordinates of nodes that should overlap are completely consistent, removing numerical obstacles to node deduplication and enabling the accuracy of node deduplication to reach the theoretically optimal level.

[0059] Step A15: Obtain the rock fragmentation node information based on the aligned node coordinates.

[0060] Understandably, after mesh alignment, the node coordinates, which originally had slight differences due to floating-point errors, are now aligned to a unified mesh vertex, and nodes with identical spatial positions have obtained completely consistent coordinate values. However, a large number of duplicates still exist in the node data: the same spatial location may correspond to multiple original node numbers. If these duplicate nodes are directly used for subsequent topology face decomposition and face identifier generation, the same face will be misjudged as different faces due to different node numbers, thus disrupting the topological consistency between elements. Therefore, it is necessary to deduplicate and merge the aligned node coordinates, uniformly mapping nodes with completely overlapping spatial positions to globally unique standard node numbers, constructing a concise and non-redundant set of node information, and laying an accurate foundation for subsequent topology relationship construction.

[0061] It should be noted that the rock fragmentation node information refers to a set of standard node data that is unique in both spatial location and number after deduplication and merging. By traversing all aligned node coordinates, a dictionary mapping mechanism is used to establish a mapping relationship between coordinate values ​​as keys and standard node numbers as values: if a coordinate appears for the first time, a new standard node number is assigned to it; if a mapping record already exists for that coordinate, the node is pointed to the existing standard node number, thus completing the deduplication; and the deduplicated nodes are then merged to obtain the rock fragmentation node information.

[0062] The formula for calculating the above standard node number is:

[0063] The aligned coordinates are ( , , ), Standard node numbering, This is a mapping function.

[0064] Understandably, using dictionary mapping to deduplicate and merge spatially overlapping nodes eliminates node redundancy caused by meshing or model splicing in numerical models, significantly reducing the amount of node data and lowering the computational overhead of subsequent topological surface decomposition and hash operations. Furthermore, since a unified mapping relationship is established from the original node number to the standard node number, the construction of subsequent tetrahedral topological surfaces is entirely based on the standard node number, ensuring the uniqueness and consistency of topological relationships from the root. Finally, dictionary mapping uses coordinate values ​​as keys, resulting in constant-level query efficiency. Even with large-scale models of hundreds of thousands of nodes, it can complete the deduplication of all nodes in a very short time, ensuring the high efficiency of the entire process.

[0065] Step S20: Perform spatial mapping based on the three-dimensional spatial information of the rock fragments to obtain a unit adjacency mapping table.

[0066] Understandably, after acquiring the 3D spatial information of rock fragmentation, a neighborhood search is needed for all tetrahedral elements to determine which elements are spatially adjacent. Traditional methods employ a global traversal approach, comparing each element with every other element, resulting in high computational complexity. For example, in a medium-sized model containing 100,000 elements, a global traversal would require approximately 5 billion comparisons, consuming enormous computational time and failing to meet the demands of real-time engineering analysis. Therefore, an efficient spatial indexing mechanism is needed to transform the global search into a fast local retrieval, significantly reducing the computational complexity of the neighborhood search.

[0067] It should be noted that spatial mapping refers to the process of assigning cells to a pre-constructed three-dimensional cell network based on their spatial location, thereby establishing a mapping relationship between cells and the spatial grid. The mapping function can be a hash function or a dictionary function. First, the three-dimensional spatial range of the entire computational domain is determined based on the coordinate range of all nodes. Then, the grid edge length is determined with reference to the cell characteristic scale, dividing the space into a regular three-dimensional cell network. Finally, all tetrahedral cells are traversed, and each cell is mapped to its corresponding cell based on its center coordinates or node coordinates. The cell adjacency mapping table refers to the data records established based on this mapping. Each cell corresponds to a cell list, recording all tetrahedral cells contained within that cell. When searching for the neighbors of a cell, only the cell it contains and its adjacent cells need to be searched; it is not necessary to traverse all cells.

[0068] Understandably, by constructing a cell adjacency mapping table through spatial mapping, the global neighborhood search is transformed into a fast local retrieval, which greatly reduces the computational complexity of the neighborhood search. The cell adjacency mapping table supports fast indexing and dynamic updates, so that adjacent cell pairs can be queried as needed in subsequent processes.

[0069] In one feasible implementation, step S20 further includes determining the spatial range of the rock fragmentation coordinates based on the information and establishing a cell network; and obtaining the cell adjacency mapping table based on the rock fragmentation node information and the cell network.

[0070] Understandably, the core of spatial mapping lies in allocating tetrahedral elements to a reasonable spatial grid, thus limiting neighborhood searches to a local area. To achieve this, it is first necessary to determine the spatial extent of the entire model and establish a regular cell network to provide a framework for mapping; secondly, each element needs to be assigned to a corresponding cell based on its spatial location, establishing the membership relationship between elements and cells. If the cell network extent is unreasonable, some elements may not be covered or a large number of empty cells may be generated; if the mapping relationship is inaccurate, neighboring elements may be missed or non-adjacent elements may be misclassified as neighbors during neighborhood searches.

[0071] It should be noted that a cell network refers to dividing the aforementioned spatial range into a regular three-dimensional mesh structure with uniform dimensions. The side length of a cell is generally a certain multiple of the maximum value of the characteristic scale of all tetrahedral elements to ensure that the adjacent cells of any cell are within the query range. The cell adjacency mapping table is an index data structure built on the cell network. By traversing all tetrahedral elements, the center point of each cell is calculated based on its standard node coordinates, or its cell is determined directly using the node coordinates, and the cell index is added to the list of the corresponding cells.

[0072] Understandably, this step achieves the construction of a complete spatial index by determining the spatial range, establishing a cell network, and building a cell adjacency mapping table. Furthermore, the cell side length is adaptively set with reference to the cell feature scale, minimizing the query range while ensuring retrieval completeness.

[0073] Step S30: Obtain an independent fragment set based on the unit adjacency mapping table and the rock fragmentation unit.

[0074] It should be noted that the rock fragmentation element refers to each tetrahedral element extracted from the numerical simulation results; it is the smallest discrete unit constituting the fractured rock mass. Connectivity determination refers to the process of checking whether each pair of adjacent elements recorded in the element adjacency mapping table is truly connected. The determination criteria include, but are not limited to: if two elements have a geometrically common surface and no fracture has occurred at that surface, they are considered connected; or if two elements are connected by a non-failed bonding element, they are also considered connected. Fracture determination uses fracture criteria, such as when the displacement discontinuity at the interface exceeds a threshold, the strain mutation exceeds a threshold, or the damage variable exceeds a critical value, fracture is determined to have occurred at that point, and the two elements are not connected.

[0075] Understandably, by introducing a fracture criterion to determine connectivity based on spatial adjacency, fragment division not only relies on geometric adjacency but also on the actual physical fracture state, effectively avoiding false connectivity misjudgments and ensuring that the fragment division results are consistent with physical reality. Secondly, each independent fragment maintains complete tetrahedral unit set information, which facilitates accurate statistical analysis of the volume and morphological parameters of each fragment in subsequent steps.

[0076] refer to Figure 3 In one feasible implementation, step S30 further includes steps B11 to B13: Step B11: Perform a local neighborhood query of the rock cuttings fragmentation unit based on the unit adjacency mapping table to obtain the rock cuttings fragmentation unit to be determined.

[0077] It should be noted that the local neighborhood query of rock fragmentation elements refers to the process of identifying potential adjacent element pairs by using an element adjacency mapping table to search for other elements only within the cell containing the rock fragmentation element and its adjacent cells for each tetrahedral element. The rock fragmentation elements to be determined refer to the set of element pairs that are spatially adjacent but have not yet undergone connection determination, filtered through the local neighborhood query. These element pairs are candidate objects for subsequent connection relationship determination.

[0078] Understandably, this step utilizes a mapping table for local neighborhood lookup, transforming global pairing into local retrieval. The mapping table supports direct location via cell index, resulting in high query efficiency; neighborhood lookup for each cell can be completed in constant time. Local lookup doesn't miss any potentially adjacent cell pairs, while eliminating many spatially impossible cell combinations, providing a concise and complete candidate set for subsequent connection determination, further improving the overall computational efficiency.

[0079] Step B12: Determine the connection relationship of the rock cuttings fragmentation units to be determined, and obtain the connected rock cuttings fragmentation units.

[0080] Understandably, the rock fragmentation element pairs obtained through local neighborhood queries only indicate that the two elements are spatially close, but whether they are actually connected is uncertain. During rock fracturing, elements in the numerical model may fracture at the interface due to external loads, and two elements that originally shared the same topological surface may have broken apart and belong to different fragments. If all spatially adjacent elements are directly merged without determining connectivity, fractured and broken elements will be incorrectly aggregated, resulting in a fragment set that does not match the physical reality. Therefore, it is necessary to introduce a fracture criterion to determine each element individually to distinguish between truly connected and fractured element pairs.

[0081] It should be noted that connectivity determination refers to the process of judging whether two rock fragmentation elements are truly physically connected based on the mechanical state at their interface. The determination criteria include both geometric and mechanical conditions: first, it checks whether the two elements have a geometrically common surface; if there is no common surface, they are directly determined to be disconnected; if there is a common surface, it is further checked whether fracture has occurred at that surface according to fracture criteria. Fracture criteria include at least one of displacement discontinuity, strain mutation, or damage variable threshold. Connected rock fragmentation elements refer to all element pairs confirmed as truly physically connected after the above determination; these element pairs will serve as direct input for subsequent union-lookup set operations.

[0082] Understandably, this step, by introducing a fracture criterion based on geometric adjacency to determine physical connectivity, achieves precise filtering from spatial adjacency to actual connectivity. Because the fracture criterion integrates multiple mechanical indicators such as displacement, strain, and damage, it can accurately distinguish between the actual fracture surface and the internal interface of the continuum, avoiding fragmentation errors caused by misclassifying fractured elements as connected. Since connectivity is established only for adjacent elements that meet the unbroken condition, the interference of pseudo-connectivity is eliminated at the source, ensuring that the independent fragment sets generated by subsequent union of lookup sets are highly consistent with the actual physical fracture state. Connectivity determination is performed on a simplified candidate set after spatial mapping, with far fewer determinations than the global pairing count, ensuring both accuracy and computational efficiency.

[0083] In one feasible implementation, step B12 further includes obtaining the rock fragmentation unit to be determined and the three-dimensional spatial information of rock fragmentation; and determining the connection of the rock fragmentation unit to be determined if the rock fragmentation unit has a geometric common surface, based on the rock fragmentation unit to be determined and the three-dimensional spatial information of rock fragmentation.

[0084] Understandably, the presence of a pair of fragmentation elements to be determined only indicates that the two elements are spatially adjacent within adjacent cells, but whether they actually share a topological surface still needs to be confirmed. In a tetrahedral mesh model, two elements may be adjacent cells but not in direct contact; for example, they may be separated by a small gap, or they may only share a node or an edge instead of the entire surface. Only two tetrahedral elements that share the same topological surface (i.e., a triangular surface composed of three identical standard nodes) possess the basic geometric premise of physical connectivity. Therefore, it is necessary to first check whether the pair of elements to be determined has a geometrically common surface, excluding element pairs without a common surface, to ensure that subsequent fracture criteria are only applied to truly geometrically adjacent element pairs.

[0085] It should be noted that a geometric common face refers to a triangular topological face shared by two tetrahedral elements, consisting of three identical standard nodes. Each tetrahedron is enclosed by four triangular faces, and when two tetrahedrons are spatially adjacent, they will share one of these triangular faces. The method for determining whether two elements have a geometric common face is as follows: decompose the four topological faces of each tetrahedron, sort the three standard node numbers of each face in ascending order to generate a face identifier, and then compare whether the two elements have the same face identifier.

[0086] The calculation method for constructing the above tetrahedral topological surfaces is as follows:

[0087] in, A set of faces; the first face Depend on node, node, Node composition; second face Youyou node, node, Node composition; third face Youyou node, node, Node composition; fourth face Youyou node, node, Node composition.

[0088] The method for calculating the node number sorting is as follows:

[0089] in, This is an ascending sorting function; These are three nodes on a topological surface.

[0090] The face identifier is calculated as follows:

[0091] in, For face identifiers; This is a hash function.

[0092] Understandably, by examining geometric common surfaces, a preliminary screening of unit pairs to be judged is achieved. The scope of connectivity judgment is further narrowed from all spatially adjacent unit pairs to unit pairs that truly share topological surfaces, excluding non-face adjacent units that only share nodes or edges, thus reducing the number of subsequent breakage criteria. The surface identifier is generated through hash operations based on sorted standard node numbers, resulting in extremely high efficiency for surface matching queries, enabling rapid retrieval of common surfaces even when faced with a large number of unit pairs. Using geometric common surfaces as a prerequisite for connectivity judgment logically ensures that only truly closely contacting units can establish a connection, providing an accurate geometric basis for subsequent physical connectivity judgment based on breakage criteria.

[0093] In another feasible implementation, step B12 further includes: if the rock cutting unit does not have a geometric common surface, obtaining the failure state parameters of the bonding unit between the rock cutting units; if the failure state parameters of the bonding unit between the rock cutting units are not failed, determining the connection of the rock cutting unit to be determined.

[0094] Understandably, in numerical simulations, the modeling of rock mass fracturing processes is not limited to tetrahedral elements directly sharing geometric common surfaces. Some numerical methods, when simulating rock mass fracturing, insert a special bonding element between adjacent tetrahedral elements to simulate the internal bonding forces of the material. In this case, adjacent tetrahedral elements do not directly share topological surfaces geometrically, but are indirectly connected through the bonding element. If the connection relationship is determined solely based on the geometric common surface, such element pairs connected by unfailed bonding elements will be missed as disconnected, resulting in excessive fragmentation and discrepancies with physical reality. Therefore, for element pairs to be judged that do not have a geometric common surface, it is necessary to further check whether there is a bonding element between them, and determine whether they remain connected through that bonding element based on the failure state of the bonding element.

[0095] It should be noted that a bonding element is a special interface element placed between adjacent solid elements in numerical simulation to simulate the bonding force or cementation effect within the material. A bonding element has its own mechanical constitutive model and can deform and accumulate damage under external loads. When the damage reaches a critical value, the bonding element fails, indicating that the material has broken at that point. For example, in the simulation of rock breaking by a tunnel boring machine's cutterhead, potential fracture surfaces within the rock mass are pre-embedded with bonding elements to simulate the cementation effect between rock particles. Failure state parameters are quantitative indicators describing whether a bonding element has currently fractured. Substituting the values ​​of the failure state parameters into the rules of the comprehensive fracture criterion yields a conclusion on whether fracture has occurred. The comprehensive fracture criterion is that when any one of the displacement discontinuity, strain mutation, or damage variable exceeds its corresponding critical threshold, the interface is determined to have fractured, and the two elements are no longer connected; when none of the three exceed their respective critical thresholds, the interface is determined not to have fractured, and the two elements remain connected.

[0096] The calculation method for the above comprehensive failure criterion is as follows:

[0097] in, This is a parameter representation of the overall fracture process; This represents the amount of displacement discontinuity. This represents the critical threshold for displacement discontinuity. This refers to the abrupt change in strain. This is the critical threshold for the sudden change in strain. For damage variables; This represents the critical threshold for damage variables.

[0098] Understandably, by introducing the failure state determination of bonding elements as a supplement to the determination of geometric common surfaces, the problem of missed connection relationships caused by differences in modeling methods in numerical simulation is solved. First, it takes into account both direct shared surface connections and indirect connections through bonding elements, making the connection relationship determination more universal and adaptable to different numerical simulation methods. Second, the failure state of bonding elements originates from the mechanical calculation results of the numerical simulation itself, and the connection determination strictly corresponds to the physical state of the simulation results, avoiding errors caused by human assumptions. Finally, the combined use of the two determination methods comprehensively covers the possible connection forms between elements from both geometric and mechanical dimensions, ensuring the completeness and accuracy of the final fragmentation results.

[0099] Step B13: Merge the connected rock fragmentation units to obtain an independent fragment set.

[0100] It's important to note that merging refers to the process of dynamically aggregating connected rock fragmentation units using a disjoint-set data structure. A disjoint-set data structure is used to manage disjoint sets, and its core operations include searching and merging. Initially, each tetrahedral unit is treated as an independent set, with its own number as the root node. When traversing all connected unit pairs, a search function is used to trace the root nodes of the sets to which the two units belong: if the root nodes are different, it means the two units currently belong to different sets, and a merge operation is performed, pointing the root node of one set to the root node of the other set, thus merging them into one; if the root nodes are the same, it means the two units have already been merged into the same set in a previous merge, and no repeat operation is needed. An independent fragment set refers to the 3D rock fragment entity corresponding to each independent subset in the disjoint-set data structure after the full mesh traversal. Each independent subset contains several tetrahedral units connected together through connectivity relationships, forming a complete 3D rock fragment.

[0101] Understandably, this step efficiently merges connected units using a disjoint-set data structure, transforming scattered connections into a complete set of fragments. Secondly, after traversal, all connected relationships are automatically aggregated into independent sets, requiring no additional secondary processing. The output directly corresponds to the rock fragments, with clear logic and concise implementation. Finally, each independent fragment retains a complete list of its contained tetrahedral units, providing clear data boundaries for subsequent steps such as volume statistics, morphological parameter calculation, and exporting visualization files.

[0102] In one feasible implementation, step B13 further includes obtaining the root node of the rock fragmentation unit; if the root nodes of the rock fragmentation units are different, a rock fragmentation unit to be merged is obtained; the rock fragmentation units to be merged are merged by rank to obtain an independent fragment set.

[0103] It's important to note that in a disjoint-set data structure, the root node represents the element of each set. A disjoint-set data structure organizes set relationships in a tree structure, where each node points to its parent node, and the root node is the only node that points to itself, representing the entire set. By tracing the parent node, the root node of any unit's set can be found. A pair of rock fragmentation units to be merged refers to a pair of rock fragmentation units that, after comparing their root nodes, are confirmed to belong to two different sets and need to be merged. If the root nodes of the two units are the same, it means they are already in the same set and do not need to be merged. Merging by rank is a strategy to optimize the merging operation: each set maintains a rank value, initially all sets have a rank of 0; during merging, the ranks of the root nodes of the two sets are compared, and the root node with the smaller rank points to the root node with the larger rank; if the ranks of the two sets are equal, one is chosen as the new root node, and its rank is incremented by 1.

[0104] Based on the disjoint-set data structure algorithm, the search function is:

[0105]

[0106]

[0107] in, This is the entity unit currently being searched; The parent node of the current element, that is, in the disjoint-set data structure, each element points to its direct parent node; To recursively call the search function, trace upwards along the parent node chain until the root node is found; To make elements and elements Connected components are merged into one connected component; For elements The root node of the connected component. For elements The root node of the connected component.

[0108] Understandably, by comparing root nodes before merging and employing a rank-based merging strategy, efficient and stable connected component aggregation is achieved. Prioritizing root node comparison avoids repeatedly performing merging operations on units already belonging to the same set, reducing unnecessary computation. Rank-based merging ensures that the tree structure of the disjoint-set data structure remains relatively low; even after numerous merging operations, the path length for a single search operation remains logarithmic, and when combined with path compression optimization, it approaches constant length, ensuring search efficiency during large-scale data processing.

[0109] Step S40: Calculate the volume and morphological parameters of the rock fragments based on the independent fragment set, and determine the degree of rock fragmentation.

[0110] Understandably, the set of independent fragments obtained through the aforementioned steps only completes the task of dividing discrete tetrahedral units into several independent rock fragments, but the volume, morphological characteristics, and overall degree of fragmentation of each rock fragment have not yet been quantified. In practical engineering scenarios such as blasting, tunnel boring, and rock breaking with cutting tools, the particle size distribution, shape characteristics, and volume distribution of rock fragments are core indicators for evaluating rock mass fragmentation effects, construction efficiency, and energy utilization levels. For example, the particle size distribution curve of the fragmented rock fragments can intuitively reflect the degree of fragmentation uniformity and fragmentation ratio, providing a basis for optimizing construction parameters. Therefore, based on the fragment division, it is necessary to perform volume accumulation and morphological parameter calculations on each independent fragment, and to summarize and statistically analyze all fragments to obtain a quantitative indicator that can characterize the overall degree of fragmentation.

[0111] It should be noted that the volume parameter refers to the sum of the volumes of all tetrahedral units contained in each independent fragment. The volume of each tetrahedral unit has already been pre-calculated in the steps mentioned above; here, the total volume of the fragment is obtained by summing the volumes of the corresponding units one by one according to the list of units contained in the fragment. Morphological parameters are quantitative indicators used to describe the three-dimensional geometric characteristics of rock fragments, including but not limited to equivalent sphere diameter, aspect ratio, and flatness. The degree of rock fragment fragmentation refers to a quantitative indicator that comprehensively reflects the overall grain size distribution after the rock mass is fragmented, including but not limited to the number of fragments, fragment volume distribution, cumulative volume percentage curve, average grain size, and fragmentation ratio.

[0112] The formula for calculating the volume parameter is:

[0113] in, This refers to the volume parameter of the fragment; For the first The volume of each fragment unit.

[0114] The calculation method for the equivalent sphere diameter in the aforementioned morphological parameters is as follows:

[0115] in, The equivalent sphere diameter; This refers to the volume parameter of the fragment.

[0116] Understandably, this step, by accumulating volume and calculating morphological parameters of independent fragment sets, achieves the output of quantitative indicators of fragmentation degree from fragment division results; secondly, this step is also the final output link of the entire rock cuttings fragmentation degree measurement method, transforming all previous processing results into quantitative indicators that can be directly applied in engineering; and the volume calculation directly reuses the pre-calculated unit volume data, making the accumulation process simple and efficient, without the need for repeated calculations.

[0117] This application also provides a device for measuring the degree of rock cuttings fragmentation, please refer to... Figure 4 The device for measuring the degree of rock fragmentation includes: Module 10 acquires three-dimensional spatial information of rock fragmentation; Mapping module 20 performs spatial mapping based on the three-dimensional spatial information of the rock fragments to obtain a unit adjacency mapping table; The merging module 30 obtains an independent fragment set based on the unit adjacency mapping table and the rock fragmentation unit; Result module 40 calculates the volume and morphological parameters of the rock fragments based on the independent fragment set, and determines the degree of rock fragmentation.

[0118] The rock fragmentation degree measuring device provided in this application, employing the rock fragmentation degree measuring method in the above embodiments, solves the technical problems of high neighborhood search complexity and low computational efficiency in the prior art when processing large-scale fragmentation units. Compared with the prior art, the beneficial effects of the rock fragmentation degree measuring device provided in this application are the same as those of the rock fragmentation degree measuring method provided in the above embodiments, and other technical features in the rock fragmentation degree measuring device are the same as those disclosed in the methods of the above embodiments, and will not be repeated here.

[0119] This application provides a device for measuring the degree of rock fragmentation. The device includes: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, which are executed by the at least one processor to enable the at least one processor to perform the rock fragmentation measurement method in Embodiment 1 above.

[0120] The following is for reference. Figure 5 The diagram illustrates a structural schematic of a measuring device suitable for implementing the rock fragmentation degree of the embodiments of this application. The measuring device for the rock fragmentation degree in the embodiments of this application may include, but is not limited to, mobile terminals such as mobile phones, laptops, digital radio receivers, PDAs (Personal Digital Assistants), PADs (Portable Application Description), PMPs (Portable Media Players), in-vehicle terminals (e.g., in-vehicle navigation terminals), and fixed terminals such as digital TVs and desktop computers. Figure 5 The illustrated device for measuring the degree of rock fragmentation is merely an example and should not be construed as limiting the functionality and scope of the embodiments of this application.

[0121] like Figure 5As shown, the device for measuring the degree of rock fragmentation may include a processing unit 1001 (e.g., a central processing unit, a graphics processor, etc.), which can perform various appropriate actions and processes according to a program stored in a read-only memory (ROM) 1002 or a program loaded from a storage device 1003 into a random access memory (RAM) 1004. The RAM 1004 also stores various programs and data required for the operation of the rock fragmentation measuring device. The processing unit 1001, ROM 1002, and RAM 1004 are interconnected via a bus 1005. An input / output (I / O) interface 1006 is also connected to the bus. Typically, the following systems can be connected to I / O interface 1006: input devices 1007 including, for example, touchscreens, touchpads, keyboards, mice, image sensors, microphones, accelerometers, gyroscopes, etc.; output devices 1008 including, for example, liquid crystal displays (LCDs), speakers, vibrators, etc.; storage devices 1003 including, for example, magnetic tapes, hard disks, etc.; and communication devices 1009. Communication device 1009 allows the rock cuttings fragmentation measurement device to communicate wirelessly or wiredly with other devices to exchange data. Although rock cuttings fragmentation measurement devices with various systems are shown in the figure, it should be understood that it is not required to implement or possess all the systems shown. More or fewer systems can be implemented alternatively.

[0122] Specifically, according to the embodiments disclosed in this application, the processes described above with reference to the flowcharts can be implemented as computer software programs. For example, embodiments disclosed in this application include a computer program product comprising a computer program carried on a computer-readable medium, the computer program containing program code for performing the methods shown in the flowcharts. In such embodiments, the computer program can be downloaded and installed from a network via a communication device, or installed from storage device 1003, or installed from ROM 1002. When the computer program is executed by processing device 1001, it performs the functions defined in the methods of the embodiments disclosed in this application.

[0123] The rock fragmentation degree measuring device provided in this application, employing the rock fragmentation degree measuring method described in the above embodiments, solves the technical problems of high neighborhood search complexity and low computational efficiency in the prior art when processing large-scale fragmented units. Compared with the prior art, the beneficial effects of the rock fragmentation degree measuring device provided in this application are the same as those of the rock fragmentation degree measuring method provided in the above embodiments, and other technical features of this rock fragmentation degree measuring device are the same as those disclosed in the previous embodiment method, and will not be repeated here.

[0124] It should be understood that the various parts disclosed in this application can be implemented using hardware, software, firmware, or a combination thereof. In the description of the above embodiments, specific features, structures, materials, or characteristics can be combined in any suitable manner in one or more embodiments or examples.

[0125] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

[0126] This application provides a computer-readable storage medium having computer-readable program instructions (i.e., a computer program) stored thereon, the computer-readable program instructions being used to execute the method for measuring the degree of rock fragmentation in the above embodiments.

[0127] The computer-readable storage medium provided in this application may be, for example, a USB flash drive, but is not limited to, electrical, magnetic, optical, electromagnetic, infrared, or semiconductor systems, devices, or any combination thereof. More specific examples of computer-readable storage media may include, but are not limited to: electrical connections having one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination thereof. In this embodiment, the computer-readable storage medium may be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system, system, or device. The program code contained on the computer-readable storage medium may be transmitted using any suitable medium, including but not limited to: wires, optical cables, RF (Radio Frequency), etc., or any suitable combination thereof.

[0128] The aforementioned computer-readable storage medium may be included in the device for measuring the degree of rock fragmentation; or it may exist independently and not assembled into the device for measuring the degree of rock fragmentation.

[0129] The aforementioned computer-readable storage medium carries one or more programs that, when executed by the rock cuttings fragmentation measuring device, cause the rock cuttings fragmentation measuring device to: Obtain two-dimensional spatial information of rock cuttings fragmentation; Based on the two-dimensional spatial information of the rock fragments, a candidate connection relationship diagram is obtained by mapping. The candidate connection graph is merged to obtain an independent fragment set; Based on the aforementioned set of independent fragments, the area and morphological parameters of the rock debris fragments are obtained; The degree of rock fragmentation is obtained based on the area and morphological parameters of the fragmented rock material.

[0130] Computer program code for performing the operations of this application can be written in one or more programming languages ​​or a combination thereof, including object-oriented programming languages ​​such as Java, Smalltalk, and C++, and conventional procedural programming languages ​​such as the "C" language or similar programming languages. The program code can be executed entirely on the user's computer, partially on the user's computer, as a standalone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving remote computers, the remote computer can be connected to the user's computer via any type of network—including a Local Area Network (LAN) or a Wide Area Network (WAN)—or can be connected to an external computer (e.g., via the Internet using an Internet service provider).

[0131] The flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various embodiments of this application. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of code containing one or more executable instructions for implementing a specified logical function. It should also be noted that in some alternative implementations, the functions indicated in the blocks may occur in a different order than those indicated in the drawings. For example, two consecutively indicated blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in the block diagrams and / or flowcharts, and combinations of blocks in the block diagrams and / or flowcharts, can be implemented using a dedicated hardware-based system that performs the specified function or operation, or using a combination of dedicated hardware and computer instructions.

[0132] The modules described in the embodiments of this application can be implemented in software or hardware. The names of the modules do not necessarily limit the functionality of the unit itself.

[0133] The readable storage medium provided in this application is a computer-readable storage medium that stores computer-readable program instructions (i.e., a computer program) for executing the above-described method for measuring the degree of rock fragmentation. This solves the technical problems of high complexity and low computational efficiency in neighborhood search when processing large-scale fragmented units in existing technologies. Compared with existing technologies, the beneficial effects of the computer-readable storage medium provided in this application are the same as those of the rock fragmentation measurement method provided in the above embodiments, and will not be repeated here.

[0134] The above are merely preferred embodiments of this application and do not limit the patent scope of this application. Any equivalent structural or procedural transformations made using the content of this application's specification and drawings, or direct or indirect applications in other related technical fields, are similarly included within the patent scope of this application.

Claims

1. A method of measuring the degree of rock fragment breakage, characterized by, include: Obtain three-dimensional spatial information of rock fragmentation; Based on the three-dimensional spatial information of the rock fragments, a spatial mapping is performed to obtain a unit adjacency mapping table; Based on the unit adjacency mapping table and the rock fragmentation unit, an independent fragment set is obtained; The volume and morphological parameters of the rock fragments are calculated based on the independent fragment set to determine the degree of rock fragmentation.

2. The method of measuring the degree of rock fragment breakage according to claim 1, characterized by, The three-dimensional spatial information of rock fragmentation includes rock fragmentation volume information and rock fragmentation node information. The acquisition of the three-dimensional spatial information of rock fragmentation includes: Obtain simulated values ​​of rock cuttings breakage; Based on the simulated rock fragmentation values, the rock fragmentation coordinate information is obtained; Based on the rock fragmentation coordinate information, the rock fragmentation volume information is obtained; The rock fragmentation coordinate information is meshed and aligned to obtain the aligned node coordinates; The information of the rock fragmentation nodes is obtained based on the aligned node coordinates.

3. The method of measuring the degree of rock fragment breakage according to claim 2, characterized in that, The step of spatial mapping based on the three-dimensional spatial information of the broken rock fragments to obtain a cell adjacency mapping table includes: The spatial range of the rock fragments is determined based on the coordinate information of the fragments, and a cell network is established; Based on the rock fragmentation node information and the cell network, the cell adjacency mapping table is obtained.

4. The method for measuring the degree of rock fragmentation according to claim 3, characterized in that, The process of obtaining an independent fragment set based on the unit adjacency mapping table and the rock fragmentation unit includes: Based on the unit adjacency mapping table, a local neighborhood query of the rock cuttings breaking unit is performed to obtain the rock cuttings breaking unit to be determined. The connection relationship of the rock cuttings fragmentation units to be determined is determined to obtain the connected rock cuttings fragmentation units. The connected rock fragmentation units are merged to obtain an independent fragment set.

5. The method for measuring the degree of rock fragmentation according to claim 4, characterized in that, The step of determining the connection relationship of the rock fragmentation units to be judged includes: Obtain the rock fragmentation unit to be determined and the three-dimensional spatial information of rock fragmentation; Based on the rock fragmentation unit to be determined and the three-dimensional spatial information of rock fragmentation, if the rock fragmentation unit has a geometric common surface, the connection of the rock fragmentation unit to be determined is determined.

6. The method for measuring the degree of rock cutting fragmentation according to claim 4, characterized in that, The step of determining the connection relationship of the rock fragmentation units to be judged also includes: If the rock cutting unit does not have a geometric common surface, obtain the failure state parameters of the bonding unit between the rock cutting units; If the failure state parameters of the bonding unit between the rock cuttings crushing units are not failed, the connection of the rock cuttings crushing units to be determined is confirmed.

7. The method for measuring the degree of rock fragmentation according to claim 4, characterized in that, The process involves merging the connected rock fragmentation units to obtain independent fragment sets, including: Obtain the root node of the rock cuttings breaking unit; If the root nodes of the rock fragmentation units are different, rock fragmentation units to be merged are obtained; The rock fragmentation units to be merged are merged according to rank to obtain an independent fragment set.

8. A device for measuring the degree of rock fragmentation, characterized in that, include: The acquisition module acquires three-dimensional spatial information of rock fragmentation. The mapping module performs spatial mapping based on the three-dimensional spatial information of the broken rock fragments to obtain a unit adjacency mapping table. The merging module obtains an independent fragment set based on the unit adjacency mapping table and the rock fragmentation unit; The results module calculates the volume and morphological parameters of the rock fragments based on the independent fragment set, and determines the degree of rock fragmentation.

9. A device for measuring the degree of rock fragmentation, characterized in that, The device includes: a memory, a processor, and a rock fragmentation measurement program stored in the memory and executable on the processor, the rock fragmentation measurement program being configured to implement the steps of the rock fragmentation measurement method as described in any one of claims 1 to 7.

10. A storage medium, characterized in that, The storage medium stores a program for measuring the degree of rock fragmentation, which, when executed by a processor, implements the steps of the method for measuring the degree of rock fragmentation as described in any one of claims 1 to 7.

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