Mine cluster type in-pit drilling hole distribution design method and system based on analytic geometry

Abstract

The invention relates to the technical field of data processing, and discloses a mine cluster type in-pit drilling hole arrangement design method and system based on analytic geometry. The method comprises the steps that ore body occurrence parameters are collected, ore body surface normal vectors are calculated, a geometric parameter database is obtained, evaluation units are divided, chamber adaptability indexes are calculated, chamber position coordinates are screened, drilling parameters are optimized through a particle swarm algorithm, and a control ellipsoid is constructed to recognize exploration blind area distribution. And blind area centroid coordinates are obtained through clustering, drilling parameters are supplemented through calculation, and a final hole arrangement scheme is obtained. According to the method, the problems of lack of quantitative evaluation of underground chamber site selection, low drilling parameter optimization efficiency and inaccurate exploration blind area recognition in the existing tunnel drilling hole arrangement design are solved.

Description

Technical Field

[0001] This application relates to the field of data processing technology, and in particular to a method and system for designing borehole layout in clustered pits in mines based on analytical geometry. Background Technology

[0002] In mine exploration, tunnel drilling offers significant advantages over traditional surface drilling. Drilling within underground tunnels or stopes allows for precise control of the target ore body, shortening the drilling cycle, reducing costs, and effectively exploring deep and peripheral resources. Traditional tunnel drilling layout design relies on geological engineers manually calculating borehole parameters using spatial analytical geometry formulas based on ore body occurrence data and exploration experience. The borehole trajectory is designed from a single chamber location towards multiple target ore-bearing points. Borehole parameters are determined by calculating the spatial distance, azimuth, and dip angle from the chamber coordinates to the target points. This cluster-style layout of multiple boreholes per chamber reduces the amount of tunnel excavation work and improves exploration efficiency.

[0003] However, existing methods for designing borehole layouts in tunnel exploration have several shortcomings: First, the selection of chamber locations mainly relies on engineers' experience and judgment, lacking a quantitative comprehensive evaluation of multiple factors such as ore body stability, surrounding rock integrity, and radiation coverage efficiency. This results in the selected chamber locations often not being optimal, affecting the overall layout effect of subsequent borehole groups. Second, borehole parameter design uses manual trial calculations, requiring designers to repeatedly adjust borehole dip and azimuth angles to meet grid uniformity requirements. However, it is difficult to quickly obtain the globally optimal combination of borehole group parameters under complex constraints (such as avoiding fracture zones, ensuring safe borehole spacing, and adapting to ore body dip angles), leading to low design efficiency and susceptibility to human input errors. Third, the visualization of design results is insufficient, making it impossible to intuitively assess the spatial control effect of the borehole group on the ore body and hindering the timely detection of exploration blind spots or borehole conflicts. Summary of the Invention

[0004] This application provides a method and system for designing borehole layout in clustered pits in mines based on analytical geometry. It is used to establish a multi-dimensional quantitative evaluation system for the location of the chamber, adopt a global optimization method for borehole parameters coupled with particle swarm optimization and spatial analytical geometry, construct a borehole control ellipsoid model to quantitatively identify blind spots and automatically generate supplementary borehole parameters. This solves the problems of lack of quantitative evaluation of chamber site selection, low efficiency of borehole parameter optimization, and inaccurate identification of exploration blind spots in existing tunnel drilling layout design, and improves the scientific nature, optimization efficiency and resource control accuracy of clustered pit drilling layout design.

[0005] This application provides a method for designing borehole layout in clustered pits in mines based on analytical geometry. The method includes: Step S1: Collect the strike angle, dip angle, and dip angle of the ore body, calculate the surface normal vector of the ore body based on the dip angle and dip angle, and obtain the ore body geometric parameter database; Step S2: Divide the orebody in the orebody geometric parameter database into evaluation units and calculate the chamber adaptability index. Use the chamber adaptability index to filter and obtain the chamber location coordinates. Step S3: Based on the location coordinates of the chamber and the strike angle, arrange the coordinates of the target ore-seeking point, and use the particle swarm optimization algorithm to iteratively optimize the borehole azimuth and dip angle to obtain the optimal borehole parameters; Step S4: Calculate the coordinates of the ore-encrusted points based on the optimal borehole parameters and the ore body surface normal vector, and construct a control ellipsoid from the ore-encrusted point coordinates to obtain the distribution of exploration blind zones; Step S5: Cluster the distribution of the exploration blind zone to obtain the centroid coordinates of the blind zone, calculate the supplementary drilling parameters based on the chamber location coordinates and the centroid coordinates of the blind zone, and obtain the final borehole layout scheme.

[0006] Secondly, this application provides a drilling layout design system for clustered pits in mines based on analytical geometry, the drilling layout design system for clustered pits in mines based on analytical geometry includes: The acquisition module is used to acquire the strike angle, dip angle and dip angle of the ore body, calculate the surface normal vector of the ore body based on the dip angle and dip angle, and obtain a database of ore body geometric parameters; The partitioning module is used to divide the ore body in the ore body geometric parameter database into evaluation units and calculate the chamber adaptability index, and obtain the chamber location coordinates by filtering the chamber adaptability index; The iteration module is used to arrange the target ore-seeking point coordinates according to the chamber location coordinates and the strike angle, and to use the particle swarm optimization algorithm to iteratively optimize the borehole azimuth and dip angle to obtain the optimal borehole parameters; The module is used to calculate the coordinates of the ore-encrusted points based on the optimal borehole parameters and the ore body surface normal vector, and to construct a control ellipsoid from the coordinates of the ore-encrusted points to obtain the distribution of exploration blind zones; The calculation module is used to cluster the distribution of the exploration blind zone to obtain the centroid coordinates of the blind zone, calculate supplementary drilling parameters based on the chamber location coordinates and the centroid coordinates of the blind zone, and obtain the final borehole layout scheme.

[0007] Thirdly, a drilling layout design device for clustered mine pits based on analytical geometry is provided, comprising: a memory and at least one processor, wherein the memory stores instructions; the at least one processor invokes the instructions in the memory to cause the drilling layout design device for clustered mine pits based on analytical geometry to execute the aforementioned drilling layout design method for clustered mine pits based on analytical geometry.

[0008] Fourthly, a computer-readable storage medium is provided, wherein instructions are stored therein, which, when executed on a computer, cause the computer to execute the above-described method for designing borehole layout in clustered pits in mines based on analytical geometry.

[0009] The technical solution provided in this application establishes a three-dimensional rectangular coordinate system in the mining area and collects the strike angle, dip angle, and dip angle of the ore body. Based on the dip angle and dip angle, the normal vector of the ore body surface is calculated to obtain a database of ore body geometric parameters. Spatial analytical geometry methods are applied to the precise mathematical expression of the ore body's occurrence, enabling a quantitative description of the ore body's geometric characteristics in three-dimensional space. This provides accurate geometric basis data for subsequent chamber site selection evaluation and borehole parameter optimization. Furthermore, the ore body in the ore body geometric parameter database is divided into evaluation units, and chamber suitability is calculated. The index, which uses the chamber suitability index to screen and obtain chamber location coordinates, changes the traditional method of relying on experience to determine chamber location. It establishes a quantitative evaluation system encompassing multiple dimensions, including attitude stability, surrounding rock integrity, radiation cover efficiency, engineering economy, and multi-vein synergistic control. Through weighted summation calculations, different geological conditions and engineering constraints are comprehensively weighed, ensuring that the selected chamber location is optimal under multiple constraints and avoiding the bias of subjective judgment. After arranging the target ore-bearing point coordinates based on the chamber location coordinates and strike angle, a particle size distribution is used. Subswarm optimization (SSO) iteratively optimizes borehole azimuth and dip angles to obtain optimal borehole parameters. Particle swarm optimization (PSO) demonstrates global optimization capabilities in the specific application of borehole layout design in clustered mine pits. By constructing a multi-objective fitness function that integrates grid uniformity, total workload, dip angle adaptability, fracture zone avoidance, borehole spacing constraints, and boundary control, the professional constraints of mine geological exploration are transformed into evaluation criteria for the algorithm. This ensures that the particle search process in the parameter space always follows geological laws and engineering safety requirements. Compared to the traditional manual trial calculation method that requires repeated parameter adjustments, PSO can automatically explore the optimal combination of borehole parameters in hundreds of iterations, significantly reducing the repetitive labor intensity of designers and shortening the overall cycle from chamber site selection to completion of borehole cluster design. The speed update mechanism in the algorithm integrates individual particle experience and group optimal information, enabling the optimization process to maintain exploration diversity while quickly converging to the global optimum. Especially when dealing with complex parameter coupling optimization problems involving dozens of boreholes, the parallel search characteristics of the algorithm have a significant efficiency advantage compared to serial trial calculation methods.

[0010] The coordinates of the ore-encrusted points are calculated based on optimal borehole parameters and the ore body surface normal vector. A control ellipsoid is then constructed from these coordinates to obtain the distribution of exploration blind zones. This approach combines trajectory equation solving in spatial analytical geometry with 3D geometric modeling. The effective control range of a single borehole over the ore body is precisely defined using the ellipsoid mathematical model. The control length and radius of the ellipsoid are dynamically determined based on the ore body thickness and occurrence stability coefficient, ensuring that the control range setting conforms to actual exploration patterns under different geological conditions. Spatial Boolean operations merge the coverage areas of multiple control ellipsoids. A 3D voxel meshing method is used to determine whether each voxel in the ore body space is controlled by a borehole, achieving precise quantitative identification of exploration blind zones. Compared to the traditional method of estimating blind zones using 2D profiles, the 3D voxel method can accurately calculate the volume of blind zones and statistically analyze their spatial distribution characteristics in the three dimensions of elevation, strike, and dip, providing a clear objective for supplementary borehole design. The system identifies target areas; clusters the distribution of exploration blind zones to obtain the centroid coordinates of the blind zones; calculates supplementary borehole parameters based on the chamber location coordinates and the centroid coordinates of the blind zones to obtain the final borehole layout scheme; and uses a density clustering algorithm to divide spatially dispersed blind zone voxels into continuous blind zone sub-regions according to their proximity. For the centroid position of each blind zone sub-region, the system automatically generates the azimuth, dip, and depth parameters of supplementary boreholes, realizing a closed-loop automated process from blind zone identification to supplementary borehole design. This avoids the tedious process of manually judging blind zone locations and manually designing supplementary boreholes, ensuring that the final borehole layout scheme fully covers and controls the ore body. The overall technical solution organically combines the precise calculation capabilities of spatial analytical geometry with the global optimization capabilities of particle swarm optimization, forming a complete technical chain from geometric modeling, chamber evaluation, parameter optimization, blind zone identification to scheme iteration. This significantly improves the automation level, optimization efficiency, and resource control accuracy of borehole layout design in clustered pits of mines. Attached Figure Description

[0011] To more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings used in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0012] Figure 1 This is a schematic diagram of an embodiment of the mine cluster-type pit drilling layout design method based on analytical geometry in this application; Figure 2 This is a schematic diagram showing the distribution of the chamber adaptability index as a function of the evaluation unit in an embodiment of this application. Figure 3 This is a schematic diagram of an embodiment of the mine cluster-type pit drilling layout design system based on spatial analytical geometry in this application. Figure 4This is a schematic block diagram of the structure of a mine cluster-type pit drilling layout design device based on spatial analytical geometry in an embodiment of the present invention. Detailed Implementation

[0013] This application provides a method and system for designing borehole layout in clustered pits in mines based on analytical geometry. The terms "first," "second," "third," "fourth," etc. (if present) in the specification, claims, and accompanying drawings of this application are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments described herein can be implemented in a sequence other than that illustrated or described herein. Furthermore, the terms "comprising" or "having" and any variations thereof are intended to cover a non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0014] For ease of understanding, the specific process of the embodiments of this application is described below. Please refer to [link / reference]. Figure 1 One embodiment of the drilling layout design method for clustered pits in mines based on analytical geometry in this application includes: Step S1: Collect the strike angle, dip angle, and dip angle of the ore body, calculate the surface normal vector of the ore body based on the dip angle and dip angle, and obtain the ore body geometric parameter database; Step S2: Divide the orebody in the orebody geometric parameter database into evaluation units and calculate the chamber fit index. Use the chamber fit index to filter and obtain the chamber location coordinates. Step S3: Based on the location coordinates and strike angle of the chamber, determine the coordinates of the target ore-seeking point. Use the particle swarm optimization algorithm to iteratively optimize the borehole azimuth and dip angle to obtain the optimal borehole parameters. Step S4: Calculate the coordinates of the ore-encrusted points based on the optimal borehole parameters and the ore body surface normal vector. Construct a control ellipsoid from the ore-encrusted point coordinates to obtain the distribution of exploration blind zones. Step S5: Cluster the distribution of exploration blind areas to obtain the centroid coordinates of the blind areas, calculate the supplementary drilling parameters based on the chamber location coordinates and the centroid coordinates of the blind areas, and obtain the final borehole layout scheme.

[0015] It is understood that the executing entity of this application can be a mine cluster-type pit drilling layout design system based on spatial analytical geometry, or it can be a terminal or a server; the specific implementation is not limited here. This application's embodiment uses a server as an example for illustration.

[0016] Specifically, a three-dimensional rectangular coordinate system is established in the mining area, with a fixed reference point as the origin, the X-axis pointing due east, the Y-axis pointing due north, and the Z-axis vertically upward to represent elevation. Spatial coordinate data of the ore body is obtained by collecting spatial coordinates of the ore body at different exploration points. The strike angle, dip angle, and dip angle are obtained by measuring the spatial coordinate data of the ore body. The strike angle represents the angle between the extension direction of the ore body and the due north direction, the dip angle represents the angle between the dip direction of the ore body and the due north direction, and the dip angle represents the angle between the ore body surface and the horizontal plane. The sine and cosine values ​​of the dip angle and dip angle are calculated respectively. The first component of the ore body surface normal vector is obtained by multiplying the sine value of the dip angle and the cosine value of the dip angle. The second component of the ore body surface normal vector is obtained by multiplying the cosine value of the dip angle and the cosine value of the dip angle. The third component of the ore body surface normal vector is obtained by calculating the sine value of the dip angle and the first, second, and third components of the ore body surface normal vector with the spatial coordinate data of the ore body to obtain the spatial plane coefficient database of the ore body. The orebody in the orebody geometric parameter database is spatially divided along the strike direction at preset intervals to obtain multiple evaluation units. The dip angle standard deviation, surrounding rock fracture rate, and unit center point coordinates are extracted from each evaluation unit. The following scores are calculated: attitude stability coefficient, surrounding rock integrity, radiation cover efficiency, engineering tunneling economy, and multi-vein collaborative control. The attitude stability coefficient score is calculated based on the dip angle standard deviation; a smaller dip angle standard deviation indicates a more stable orebody attitude and a higher score. The surrounding rock integrity score is calculated based on the surrounding rock fracture rate; a lower fracture rate indicates a more intact surrounding rock and a higher score. The radiation cover efficiency score is based on the coverage that can be achieved from the unit center point as the chamber location within the allowable dip angle range. The ore body area is calculated, with a larger coverage area resulting in a higher score. The engineering tunneling economy score is calculated based on the distance from the unit to the nearest existing roadway; the closer the distance, the higher the score. The multi-vein collaborative control score is calculated based on the vertical distance difference between the unit and each parallel vein; the smaller the distance difference, the higher the score, indicating that multiple veins can be controlled simultaneously. The dwelling suitability index of each evaluation unit is obtained by weighted summation of the occurrence stability coefficient score, surrounding rock integrity score, radiation coverage efficiency score, engineering tunneling economy score, and multi-vein collaborative control score. The dwelling suitability indexes of each evaluation unit are sorted in descending order of numerical value, and the coordinates of the center point of the top-ranked evaluation units are selected as the dwelling location coordinates.Based on the chamber location coordinates and strike angle, target control points are arranged at different elevations of the ore body according to the exploration grid spacing to obtain the coordinates of the target mineralization point. The particle swarm is initialized, and borehole opening offset, borehole azimuth, and borehole dip angle are used as particle position parameters. A multi-objective fitness function is constructed, including grid uniformity evaluation, total engineering quantity evaluation, dip angle adaptability evaluation, fracture zone avoidance evaluation, borehole spacing constraint, and boundary control evaluation. The grid uniformity evaluation is achieved by calculating the area variance of the Voronoi polygon projected by the borehole control points on the ore body surface; a smaller area variance indicates a more uniform grid distribution. The total engineering quantity evaluation is achieved by calculating the sum of all borehole lengths; a smaller total length indicates less engineering work. The dip angle adaptability evaluation determines whether the borehole dip angle is within the allowable range based on the ore body dip angle. Within the specified range, penalties are applied if the borehole trajectory exceeds the limit. The fracture zone avoidance evaluation item determines whether the borehole trajectory crosses a high fracture rate fracture zone; if it does, penalties are applied. The borehole spacing constraint item detects the minimum spatial distance between boreholes; if the distance is less than the safety threshold, penalties are applied. The boundary control evaluation item increases the weight of target points within the ore body boundary range. The borehole spatial distance is calculated based on the chamber location coordinates and the target ore-bearing point coordinates. The borehole spatial distance is substituted into the multi-objective fitness function for particle fitness evaluation. The particle velocity and particle position are updated for iterative optimization. The particle velocity update is calculated based on the current velocity, the individual's historical best position, and the global best position. The particle position update is the current position plus the updated velocity. The iteration terminates when the particle swarm reaches a preset number of generations, and the position parameters of the globally optimal particle are extracted as the optimal borehole parameters.

[0017] Based on the borehole azimuth and dip angles in the optimal borehole parameters, a borehole spatial trajectory parameter equation is constructed. The intersection parameters of the borehole spatial trajectory parameter equation and the spatial plane equation corresponding to the ore body surface normal vector are solved simultaneously to obtain the coordinates of the ore-bearing point. Taking the coordinates of the ore-bearing point as the center, the control length and control radius of the ellipsoid are determined according to the ore body thickness and the occurrence stability coefficient. The larger the ore body thickness and the higher the occurrence stability coefficient, the larger the control length and control radius of the ellipsoid. A control ellipsoid corresponding to each borehole is constructed. A spatial Boolean union operation is performed on multiple control ellipsoids to obtain the total control range. The ore body space is divided into a three-dimensional voxel grid. The ore body space and control range attribution are determined for the coordinates of the center point of each voxel. The number of voxels that simultaneously satisfy the condition of belonging to the ore body space but not to the total control range is counted. The volume of the exploration blind zone is obtained by multiplying the number of voxels in the blind zone by the unit volume of the voxels. The voxels corresponding to the volume of the exploration blind zone are spatially distributed according to elevation, strike, and dip to obtain the distribution of the exploration blind zone. Density clustering analysis is performed on the distribution of exploration blind zones to divide spatially continuous blind zone voxels into multiple blind zone sub-regions. Density clustering analysis determines whether voxels belong to the same cluster based on the spatial distance between them. Voxels whose distance is less than the neighborhood radius and whose number of voxels in the neighborhood is greater than the minimum cluster size are classified into the same blind zone sub-region. The average value of the coordinates of the center points of all voxels in each blind zone sub-region is calculated to obtain the centroid coordinates of each blind zone sub-region. The azimuth and inclination of the supplementary boreholes are calculated based on the chamber location coordinates and the centroid coordinates of the blind zone. The azimuth of the supplementary boreholes is calculated based on the horizontal projection direction of the centroid coordinates of the blind zone relative to the chamber location coordinates. The inclination of the supplementary boreholes is calculated based on the arctangent of the ratio of the vertical height difference between the centroid coordinates of the blind zone and the chamber location coordinates to the horizontal distance. The depth of the supplementary boreholes is calculated based on the spatial distance between the chamber location coordinates and the centroid coordinates of the blind zone to obtain the supplementary borehole parameters. The supplementary borehole parameters are added to the optimal borehole parameters to obtain the final borehole layout scheme.

[0018] Figure 2 This is a schematic diagram showing the distribution of the chamber adaptability index as a function of the evaluation unit in an embodiment of this application. Figure 2 This is a schematic diagram showing the distribution of the chamber adaptability index as a function of the evaluation units in this application embodiment. The horizontal axis represents the evaluation unit number divided along the strike of the ore body at a preset interval, and the vertical axis represents the chamber adaptability index value of each evaluation unit. This index is obtained by weighted summation of the occurrence stability coefficient score, surrounding rock integrity score, radiation coverage efficiency score, engineering tunneling economic score, and multi-vein collaborative control score. The broken line shows the distribution trend of the chamber adaptability index of 15 evaluation units. The data points mark the specific index value of each unit. It can be seen from the figure that the chamber adaptability index of the 10th evaluation unit reaches the maximum value of 85.4. After sorting in descending order, the coordinates of the center point of this unit are selected as the chamber location coordinates.

[0019] In one specific embodiment, step S1 includes: A three-dimensional rectangular coordinate system was established in the mining area, and the spatial coordinates of the ore body at different exploration points were collected to obtain the spatial coordinate data of the ore body. The spatial coordinate data of the ore body are used to measure its attitude, and the strike angle, dip angle, and dip angle are obtained. Calculate the sine and cosine values ​​of the dip angle and dip angle respectively. Multiply the sine value of the dip angle by the cosine value of the dip angle to obtain the first component of the ore body surface normal vector. Multiply the cosine value of the dip angle by the cosine value of the dip angle to obtain the second component of the ore body surface normal vector. Calculate the sine value of the dip angle to obtain the third component of the ore body surface normal vector. The first, second, and third components of the ore body surface normal vector are used to solve for the spatial plane coefficients with the ore body spatial coordinate data to obtain the ore body geometric parameter database.

[0020] Specifically, when establishing a three-dimensional rectangular coordinate system in the mining area, a fixed reference point in the mining area is selected as the origin of the coordinate system. The X-axis points due east, the Y-axis points due north, and the Z-axis points vertically upward to represent the elevation. The spatial coordinates of the ore body at different exploration points are collected using geological exploration equipment. The X-coordinate, Y-coordinate, and Z-coordinate values ​​of each exploration point are recorded. The coordinate data of all exploration points are summarized to form a spatial coordinate data set of the ore body. When performing attitude measurement on the spatial coordinate data of the ore body, the attitude measurement includes measuring three key parameters of the ore body: strike angle, dip angle, and dip angle. The strike angle represents the angle between the projection line of the ore body's extension direction on the horizontal plane and the due north direction. The dip angle represents the angle between the projection line of the ore body's dip direction on the horizontal plane and the due north direction. The dip angle represents the angle between the ore body surface and the horizontal plane. These three angle values ​​are obtained by measuring with a compass or electronic inclinometer.

[0021] When calculating the normal vector of the ore body surface based on the dip angle and dip angle, the normal vector is a vector perpendicular to the ore body surface. Its three components are along the X-axis, Y-axis, and Z-axis, respectively. To calculate the first component, the sine of the dip angle is multiplied by the cosine of the dip angle. To calculate the second component, the cosine of the dip angle is multiplied by the cosine of the dip angle. To calculate the third component, the sine of the dip angle is taken directly. These three components together constitute the normal vector of the ore body surface. When solving for the spatial plane coefficients of the first, second, and third components of the ore body surface normal vector with the spatial coordinate data of the ore body, the general equation of the spatial plane contains four coefficients. The first three coefficients are the three components of the normal vector. The fourth coefficient is obtained by substituting the coordinates of a known exploration point on the ore body into the plane equation and solving in reverse. The first component is multiplied by the X coordinate of the exploration point, the second component is multiplied by the Y coordinate of the exploration point, and the third component is multiplied by the Z coordinate of the exploration point. Taking the negative value of the summation result gives the fourth plane coefficient.

[0022] In one specific embodiment, step S2 includes: The orebody in the orebody geometric parameter database is spatially divided along the strike direction according to a preset interval to obtain multiple evaluation units; Extract the standard deviation of ore body dip angle, surrounding rock fracture rate and unit center point coordinates in each evaluation unit, and calculate the occurrence stability coefficient score, surrounding rock integrity score, radiation coverage efficiency score, engineering tunneling economy score and multi-vein collaborative control score respectively. The stability coefficient of occurrence, the integrity of surrounding rock, the radiation coverage efficiency, the economic efficiency of engineering tunneling, and the synergistic control of multiple veins are weighted and summed to obtain the chamber adaptability index of each evaluation unit. The chamber adaptability indices of each evaluation unit are sorted in descending order of numerical value, and the coordinates of the center point of the top-ranked evaluation units are selected as the chamber location coordinates.

[0023] Specifically, when spatially dividing the orebody in the orebody geometric parameter database along the strike direction according to a preset interval, the preset interval is determined based on the orebody size and exploration accuracy requirements. Starting from the strike starting point of the orebody, a dividing surface is set at every preset interval. The dividing surface is perpendicular to the strike direction of the orebody, dividing the orebody into multiple continuous evaluation units along the strike extension direction. Each evaluation unit contains the orebody space and its occurrence data within that interval range. When extracting the standard deviation of the dip angle of the orebody within each evaluation unit, the dip angle values ​​measured at all exploration points within that unit are statistically analyzed, and the average of these dip angle values ​​is calculated. The values ​​are then calculated, and the square of the difference between each dip angle value and the average value is calculated. The sum of all squared differences is divided by the number of dip angle values, and the square root of the result is taken to obtain the standard deviation of the dip angle of the ore body in that unit. The smaller the standard deviation of the dip angle, the more stable the occurrence of the ore body in that unit. When extracting the fracture rate of the surrounding rock, the number of fractures per unit length in the core samples of the borehole in that unit is counted. The lower the fracture rate, the more intact the surrounding rock. When extracting the coordinates of the center point of the unit, the midpoint of the start and end positions of the unit in the strike direction is calculated. Combined with the spatial coordinates of the ore body at that position, the X, Y, and Z coordinates of the center point of the unit are obtained.

[0024] When calculating the stability coefficient score, the standard deviation of the ore body dip angle is multiplied by -10 and then added to 100 as the scoring benchmark. A score higher than a certain threshold when the dip angle standard deviation is less than a certain threshold indicates a high-stability zone; a score lower than zero when the dip angle standard deviation is greater than a certain threshold indicates a zone of abrupt change in attitude and requires a negative score. When calculating the surrounding rock integrity score, the ratio of the surrounding rock fracture rate to a certain percentage threshold is subtracted from 1 and then multiplied by 100. A score higher than a certain threshold when the fracture rate is less than a certain percentage indicates high-integrity surrounding rock; a score higher than a certain threshold when the fracture rate is greater than a certain percentage indicates high-integrity surrounding rock. A time score of less than or equal to zero indicates severely fractured surrounding rock. When calculating the radiation coverage efficiency score, virtual borehole rays are emitted from the center point of the unit as candidate chamber locations towards different directions of the ore body. The allowable range of borehole dip angle is determined based on the dip angle of the ore body. For gently dipping ore bodies with a dip angle less than a certain degree, the borehole dip angle range is from a certain negative degree to a certain positive degree. For steeply dipping ore bodies with a dip angle greater than a certain degree, the borehole dip angle range is limited to a certain negative degree to a certain positive degree. The ore body surface that the virtual borehole can cover within the allowable dip angle range is then statistically analyzed. The radiation coverage efficiency score is calculated by multiplying the ratio of the covered area to the total area of ​​the ore body in the unit by 100. When calculating the economic efficiency score of the tunneling project, the straight-line distance from the center point of the unit to the nearest roadway in the existing mine roadway system is measured. The distance is divided by a certain number of meters, and the negative natural index is taken and then multiplied by 100. If the distance is less than a certain meter, the score is higher than a certain value, indicating that the existing roadway can be used. If the distance is greater than a certain meter, the score is lower than a certain value, indicating that a new long exploration roadway needs to be excavated. When calculating the multi-vein collaborative control score, for the case of parallel dense vein groups, the vertical distance from the center point of the unit to each parallel vein is calculated. For two parallel veins, the vertical distances to the first vein and the second vein are obtained respectively. The ratio of the absolute value of the difference between the two distances to the sum of the two distances is calculated. The ratio is subtracted from one and then multiplied by 100 to obtain the collaborative control score. When the center point of the unit is located in the middle of two veins, the two vertical distances are close to equal. A collaborative control score close to 100 indicates that two veins can be controlled efficiently at the same time.

[0025] When performing a weighted summation of the attitude stability coefficient score, surrounding rock integrity score, radiation cover efficiency score, engineering tunneling economy score, and multi-vein synergistic control score, weight coefficients are assigned to each of the five scores, and the sum of the five weight coefficients equals one. The attitude stability coefficient score, surrounding rock integrity score, radiation cover efficiency score, engineering tunneling economy score, and multi-vein synergistic control score are then multiplied by their respective weight coefficients. The sum of these five products yields the chamber fit for the evaluation unit. When sorting the chamber adaptability indices of each evaluation unit in descending order of their numerical values, all evaluation units are arranged in descending order of their chamber adaptability indices. The larger the chamber adaptability index value, the more suitable the unit is as a chamber location. When selecting the top-ranked evaluation units, several evaluation units with the highest chamber adaptability indices are selected from the ranking results, and the coordinates of the center points of these evaluation units are extracted as candidate chamber location coordinates. From the candidate chamber locations, the coordinates of the center point of the unit with the highest chamber adaptability index are selected as the final determined chamber location coordinates.

[0026] In one specific embodiment, step S3 includes: Based on the location coordinates and strike angle of the chamber, target control points are arranged at different elevations of the ore body according to the exploration grid spacing to obtain the coordinates of the target ore-bearing point; Initialize the particle swarm, and use the borehole opening offset, borehole azimuth angle, and borehole inclination angle as particle position parameters to construct a multi-objective fitness function that includes evaluation items for grid uniformity, total engineering volume, inclination angle adaptability, fracture zone avoidance, borehole spacing constraint, and boundary control. The borehole spatial distance is calculated based on the coordinates of the chamber location and the target ore-bearing point. The borehole spatial distance is then substituted into the multi-objective fitness function to evaluate the particle fitness. The particle velocity and particle position are updated for iterative optimization. The iteration terminates when the particle swarm reaches a preset number of iterations, and the position parameters of the globally optimal particle are extracted as the optimal drilling parameters.

[0027] Specifically, when setting up target control points at different elevations of the ore body according to the exploration grid spacing based on the chamber location coordinates and strike angle, the exploration grid spacing is determined according to the resource level requirements. Starting from the chamber location coordinates, a control line is set along the strike angle of the ore body at every exploration grid spacing. On each control line, a target control point is set along the dip direction of the ore body at every exploration grid spacing. Target control points are set up according to the distribution of the ore body at different elevations. The X, Y, and Z coordinates of each target control point in three-dimensional space are recorded to form a set of target ore-encounter point coordinates. When initializing the particle swarm, the particle swarm size is set to several particles, and each particle represents a complete borehole group design scheme. The particle position parameters include borehole opening offset and borehole azimuth. The system includes three types of parameters: borehole angle, borehole inclination angle, and borehole opening offset. The borehole opening offset represents the X and Y direction offsets of the borehole opening position relative to the center point of the chamber on the horizontal plane. The offset range is set to ± a certain number of meters to adjust the opening position within the chamber floor area. The borehole azimuth angle represents the angle between the borehole's projection direction on the horizontal plane and true north, ranging from 0 degrees to 360 degrees. The borehole inclination angle represents the angle between the borehole trajectory line and the horizontal plane. The allowable range of the inclination angle is dynamically adjusted according to the dip angle of the ore body. The position parameter values ​​are randomly initialized for each particle. The multi-objective fitness function includes six evaluation terms. The network uniformity evaluation term is achieved by calculating the area variance of the Voronoi polygon at the intersection of the borehole and the ore body on the ore body surface. First, based on the opening angle of each borehole... The coordinates of the intersection points between the borehole trajectory line and the ore body surface are calculated using borehole offset, azimuth, and dip angle. All intersection points are projected onto the ore body surface to construct a Voronoi diagram. The Voronoi diagram divides the ore body surface into multiple polygonal regions. Within each polygon, the distance from all points to their corresponding intersection point is less than the distance to other intersection points. The area of ​​each Voronoi polygon is calculated, and the variance of all polygon areas is calculated. A smaller variance indicates a more uniform and regular distribution of borehole control points. The total engineering quantity evaluation is achieved by calculating the sum of all borehole lengths. The actual opening coordinates of each borehole are obtained by adding the opening offset to the chamber location coordinates. The spatial straight-line distance between the opening coordinates and the corresponding target ore-seeking point coordinates is calculated as the design borehole depth. All boreholes are then... The total engineering volume is obtained by summing the designed hole depths. The smaller the total engineering volume, the lower the drilling cost. The dip angle adaptability evaluation item determines whether the borehole dip angle is within the allowable range based on the ore body dip angle. The dip angle value of the ore body segment where the target ore point is located is extracted from the ore body geometric parameter database. When the ore body dip angle is less than a certain threshold, it is a gently dipping ore body, and the allowable range of the borehole dip angle is from -0.05 degrees to +0.05 degrees. When the ore body dip angle is greater than a certain threshold, it is a steeply dipping ore body, and the allowable range of the borehole dip angle is limited to -0.05 degrees to +0.05 degrees to control the risk of trajectory deviation. If the borehole dip angle exceeds the allowable range of its corresponding ore body segment, the square of the excess is calculated as a penalty value. The fracture zone avoidance evaluation item determines whether the borehole trajectory crosses a high fracture rate fracture zone. The borehole trajectory line is discretized into sampling points every several meters.The rock fracture rate of each sampling point is obtained by querying the ore body geometric parameter database. When the fracture rate is greater than a certain percentage threshold, the segment is determined to be a fracture zone. The length of the borehole crossing the fracture zone is calculated and multiplied by the fracture rate of that segment to obtain the fracture zone risk coefficient. The higher the risk coefficient, the higher the drilling risk and the heavier the penalty. The borehole spacing constraint is used to detect the minimum spatial distance between different boreholes in the same chamber. For any two boreholes, sampling points are taken every meter within the trajectory segment of the first few meters after drilling. The Euclidean distance between the corresponding sampling points of the two boreholes is calculated. The distance between all sampling point pairs is taken. The minimum distance between two boreholes is used as the minimum spacing between them. When the minimum spacing is less than a certain safety threshold, it is considered an interference risk. The square of the difference between the safety threshold and the actual minimum spacing is calculated as the penalty value. The boundary control evaluation item adds control weight to the target mineralization point in the orebody boundary area. It determines the shortest distance from the target mineralization point to the edge of the orebody. When the distance is less than a certain meter, the point is considered to be within the orebody boundary. The control accuracy requirement for the boundary target point is higher, so its weight coefficient is set to several times that of the conventional target point. The six evaluation items are multiplied by their respective weight coefficients and then summed to form a multi-objective fitness function.

[0028] When calculating the borehole spatial distance based on the chamber location coordinates and the target ore-bearing point coordinates, the X-coordinate of the chamber location coordinates is added to the borehole's X-direction opening offset within the current particle, and the Y-coordinate of the chamber location coordinates is added to the borehole's Y-direction opening offset within the current particle. The Z-coordinate of the chamber location coordinates remains unchanged. This yields the actual three-dimensional coordinates of the borehole opening. The difference between the opening X-coordinate and the target ore-bearing point X-coordinate is squared, the difference between the opening Y-coordinate and the target ore-bearing point Y-coordinate is squared, and the difference between the opening Z-coordinate and the target ore-bearing point Z-coordinate is squared. The difference between the Z coordinates of the points is squared, and the square root of the sum of the three squared values ​​is taken to obtain the borehole spatial distance. This borehole spatial distance is used as the design borehole depth and substituted into the multi-objective fitness function calculation. The fitness function value of the particle is calculated by combining parameters such as the coordinates of the intersection of the borehole trajectory and the ore body, the area variance of the Voronoi polygon, the risk coefficient of the fractured zone, and the minimum distance between boreholes. A larger fitness function value indicates a better borehole group design scheme corresponding to the particle. When updating the particle velocity, the current velocity of the particle and the particle's historical best velocity are considered. The optimal position and the global optimal position are calculated. The particle's current velocity is multiplied by the inertia weight coefficient as the velocity maintenance term. The difference between the particle's individual historical best position and the particle's current position is multiplied by the learning factor and a random number as the individual cognition term. The difference between the global optimal position and the particle's current position is multiplied by the learning factor and a random number as the social cognition term. The velocity maintenance term, the individual cognition term, and the social cognition term are added together to obtain the particle's updated velocity. When updating the particle's position, the particle's current position is added to the updated velocity to obtain the particle's new position. The hole offset, drilling azimuth angle, and drilling inclination angle parameters in the new position are the updated drilling group design scheme. The fitness function value of the updated particles is recalculated. If the new fitness value is greater than the particle's historical best fitness value, the particle's individual historical best position is updated. If the new fitness value is greater than the current global best fitness value, the global optimal position is updated. After all particles complete one position and velocity update, the iteration number is increased by one. The particle fitness evaluation, velocity update, and position update process is repeated to iteratively optimize.

[0029] The particle swarm optimization (PSO) terminates when it reaches a preset number of iterations. This preset number is set to balance the optimization accuracy requirements with computation time; more iterations result in more accurate optimization but also longer computation time. After termination, the globally optimal particle is extracted from all particles. The position parameter corresponding to the globally optimal particle is the position of the particle with the highest fitness function value that has appeared throughout the entire iteration process. This position parameter includes the opening offset, azimuth, and inclination values ​​of all boreholes. These parameters are output as the optimal borehole parameters. The optimal borehole parameters ensure that the borehole group forms a uniform control network on the ore body surface, minimizes the total drilling workload, adapts the borehole inclination to the ore body's occurrence characteristics, avoids fracture zones in the borehole trajectory to reduce construction risks, maintains a safe distance between boreholes to avoid mutual interference, and achieves key control over the ore body boundary area. The PSO algorithm uses multiple particles to search in parallel in the parameter space and gradually approaches the globally optimal solution through individual experience and group information sharing mechanisms. Compared with traditional manual trial calculation methods, it can quickly obtain the optimal combination of borehole group parameters under complex constraints.

[0030] In one specific embodiment, step S4 includes: Construct the borehole spatial trajectory parameter equation based on the borehole azimuth and borehole inclination angle in the optimal borehole parameters; Solve the intersection point parameters by simultaneously solving the parametric equations of the borehole spatial trajectory and the spatial plane equations corresponding to the ore body surface normal vector, and obtain the coordinates of the ore-seeping point. Using the coordinates of the ore-bearing point as the center, the control length and control radius of the ellipsoid are determined according to the ore body thickness and the occurrence stability coefficient, and the control ellipsoid corresponding to each borehole is constructed. The total control range is obtained by performing a spatial Boolean union operation on multiple control ellipsoids. The volume of the total control range is then subtracted from the volume of the ore body to obtain the distribution of exploration blind zones.

[0031] Specifically, when constructing the borehole spatial trajectory parametric equations based on the borehole azimuth and borehole inclination angles in the optimal borehole parameters, the borehole spatial trajectory is represented in the form of parametric equations in a three-dimensional coordinate system. The X-coordinate parametric equation is the chamber X-coordinate plus the opening X-offset plus the advance parameter multiplied by the sine of the borehole inclination angle and the cosine of the borehole azimuth angle. The Y-coordinate parametric equation is the chamber Y-coordinate plus the opening Y-offset plus the advance parameter multiplied by the sine of the borehole inclination angle and the sine of the borehole azimuth angle. The Z-coordinate parametric equation is the chamber Z-coordinate plus the advance parameter multiplied by the cosine of the borehole inclination angle. The advance parameter starts from zero to represent the value from... The distance from the borehole opening point along the drilling direction is used to solve for the intersection point parameters by simultaneously solving the borehole spatial trajectory parametric equation and the spatial plane equation corresponding to the ore body surface normal vector. The spatial plane equation contains the first, second, and third components of the ore body surface normal vector and the plane coefficient. Substituting the X, Y, and Z coordinate expressions from the borehole trajectory parametric equation into the spatial plane equation, a linear equation in one variable regarding the advance parameters is obtained. Solving this equation yields the advance parameter values ​​corresponding to the intersection point. Substituting these advance parameter values ​​into the borehole trajectory parametric equation, the X, Y, and Z coordinates of the intersection point are calculated, which are the coordinates of the ore-encrusted point.

[0032] When constructing a control ellipsoid centered on the ore-bearing point coordinates, the ore body thickness and attitude stability coefficient at that location are extracted from the ore body geometric parameter database. Ore body thickness represents the ore body's thickness dimension perpendicular to the ore body surface, and the attitude stability coefficient is the standard deviation of the ore body's dip angle at that location. The ellipsoid control length and control radius are determined based on the ore body thickness and attitude stability coefficient. When the ore body thickness is greater than a certain threshold and the attitude stability coefficient is less than a certain threshold, the ore body is relatively thick and its attitude is stable; the ellipsoid control length is set to a larger value. When the ore body thickness is less than a certain threshold or the attitude stability coefficient is greater than a certain threshold, the ore body is relatively thin or its attitude is unstable; the ellipsoid control length is set to a smaller value. The ellipsoid control length represents the distance from the ore-bearing point to the ore-bearing point. The control ellipsoid is defined as the control distance extending forward and backward along the borehole trajectory. The ellipsoid control radius represents the control radius perpendicular to the borehole trajectory on the ore body surface. The control radius is determined based on the dip angle of the ore body. When the dip angle is small, the control radius is set to a larger value, and when the dip angle is large, the control radius is set to a smaller value. When constructing the control ellipsoid, the coordinates of the ore-encrusting point are taken as the center. The control lengths of the ellipsoid extending forward and backward along the borehole trajectory form the major axis of the ellipsoid, and the radius of the minor axis perpendicular to the borehole trajectory is the control radius. The mathematical expression of the ellipsoid is: the square of the projected distance from the spatial point to the ore-encrusting point along the borehole direction divided by the square of the control length, plus the square of the distance perpendicular to the borehole direction divided by the square of the control radius, which is less than or equal to one.

[0033] When performing a spatial Boolean union operation on multiple control ellipsoids, the spatial Boolean union operation calculates the spatial union of the multiple ellipsoids, i.e., the total spatial range covered by all ellipsoids. The ore body space is divided into a three-dimensional voxel grid, where each voxel is a cube unit with a side length of one meter. The coordinates of the center point of each voxel are used to determine the ore body space affiliation. The coordinates of the voxel center point are substituted into the ore body surface space plane equation and ore body boundary constraints to determine whether the voxel belongs to the ore body space. The coordinates of the center point of each voxel are then used to determine the control range affiliation. The coordinates of the voxel center point are substituted into the mathematical expressions of all control ellipsoids. If the point satisfies any... An inequality constraint on an ellipsoid determines that a voxel belongs to the total control area. The number of voxels that simultaneously satisfy the condition of belonging to the ore body space but not to the total control area is counted. The volume of each voxel is one cubic meter. The volume of the exploration blind zone is obtained by multiplying the number of voxels in the blind zone by one cubic meter. The voxels corresponding to the volume of the exploration blind zone are statistically analyzed according to the Z coordinate to obtain the distribution of the blind zone at different elevations. The voxels in the blind zone are projected according to the strike direction of the ore body to obtain the distribution segment of the blind zone along the strike. The voxels in the blind zone are projected according to the dip direction of the ore body to obtain the positional characteristics of the blind zone in the hanging wall, footwall, or middle, forming a complete spatial description of the distribution of the exploration blind zone.

[0034] In one specific embodiment, a spatial Boolean union operation is performed on multiple control ellipsoids to obtain the total control range. The volume of the total control range is then subtracted from the volume of the ore body to obtain the distribution of exploration blind zones, including: Perform a spatial Boolean union operation on multiple control ellipsoids to obtain the total control range; The space of the ore body is divided into a three-dimensional voxel grid, and the coordinates of the center point of each voxel are used to determine the spatial ownership and control range of the ore body. The number of voxels that simultaneously satisfy the conditions of belonging to the ore body space but not to the total control area is counted, and the product of the number of voxels in the blind zone and the unit volume of the voxels is calculated to obtain the volume of the exploration blind zone. The spatial distribution of the exploration blind zone is obtained by statistically analyzing the voxels corresponding to the volume of the exploration blind zone according to the elevation, strike and dip.

[0035] Specifically, when performing a spatial Boolean union operation on multiple control ellipsoids, the spatial Boolean union operation calculates the spatial union of multiple three-dimensional geometries to obtain the total spatial range covered by all control ellipsoids. Each control ellipsoid is defined by its center point coordinates, control length, and control radius. Any point in space that satisfies the spatial constraints of any ellipsoid belongs to the total control range. When dividing the ore body space into a three-dimensional voxel grid, the voxel grid is a grid structure that regularly divides the three-dimensional space into cubic units. Each voxel is a cube with a side length of one meter. A voxel is uniquely identified by its center point coordinates. The voxel grid of the ore body space covers the maximum extension range of the ore body in the X, Y, and Z directions. When determining the ore body space attribution based on the center point coordinates of each voxel, the center point coordinates of the voxel are substituted into the spatial plane equation of the ore body surface. The spatial plane equation is obtained by the ore body surface method. The vector is calculated by multiplying the first component by the X-coordinate, the second component by the Y-coordinate, and the third component by the Z-coordinate, plus a plane coefficient. A result close to zero indicates that the point is on the ore body surface. Simultaneously, it is determined whether the point is within the strike and dip extension range of the ore body. Voxels satisfying the plane equation constraints and boundary range constraints are considered to belong to the ore body space. When determining the control range attribution for the center point coordinates of each voxel, the center point coordinates are sequentially substituted into the mathematical expression of each control ellipsoid. The ellipsoid mathematical expression calculates the square of the projected distance from the point to the center point along the borehole direction divided by the square of the control length, plus the square of the distance perpendicular to the borehole direction divided by the square of the control radius. If the calculation result is less than or equal to one, the point is inside the ellipsoid. Traversing all control ellipsoids, if any ellipsoid contains the voxel, the voxel is determined to belong to the total control range.

[0036] When counting the number of voxels that simultaneously belong to the ore body space but are not within the total control area, a voxel determination result record table is established. Each voxel in the table records its ore body space attribution determination result and control area attribution determination result. Voxels with a true ore body space attribution determination result and a false control area attribution determination result are selected. These voxels are the blind zone voxels located inside the ore body but not controlled by any borehole. The total number of blind zone voxels is accumulated to obtain the blind zone voxel count. When calculating the product of the blind zone voxel count and the voxel unit volume, the voxel unit volume is the volume of each voxel, i.e., the volume of a cube with a side length of one meter is one cubic meter. Multiplying the blind zone voxel count by one cubic meter yields the exploration blind zone volume. The exploration blind zone volume represents the total space in the ore body not effectively controlled by boreholes. When spatially distributing the voxels corresponding to the exploration blind zone volume according to elevation, the Z-coordinate value (elevation value) of each blind zone voxel is extracted. Blind zone voxels are grouped according to elevation values, and the number of blind zone voxels within each elevation interval is counted to obtain the blind zone volume. In terms of vertical distribution characteristics, when the voxels corresponding to the volume of the exploration blind zone are spatially distributed according to the strike, the coordinates of each voxel in the blind zone are projected onto the strike direction of the ore body. The strike direction is determined by the strike angle of the ore body. The projection position of each voxel in the strike direction is calculated. The voxels in the blind zone are segmented according to the strike extension direction to obtain the distribution segment of the blind zone in the strike extension direction of the ore body. When the voxels corresponding to the volume of the exploration blind zone are spatially distributed according to the dip, the coordinates of each voxel in the blind zone are projected onto the dip direction of the ore body. The dip direction is determined by the dip angle of the ore body. It is determined whether each voxel is located in the hanging wall, footwall, or middle of the ore body. The distribution of the number of voxels in the hanging wall, footwall, and middle of the blind zone is statistically analyzed to obtain the positional characteristics of the blind zone in the dip direction of the ore body. The information of elevation distribution, strike distribution, and dip distribution is combined to form a complete three-dimensional spatial description of the distribution of exploration blind zones. The distribution of exploration blind zones clearly indicates which areas in the ore body are not effectively controlled by the existing borehole group and need to be supplemented with additional boreholes for intensified exploration.

[0037] In one specific embodiment, step S5 includes: Density clustering analysis was performed on the distribution of exploration blind zones to divide the spatially continuous blind zone voxels into multiple blind zone sub-regions; Calculate the average value of the coordinates of the center points of all voxels in each blind zone sub-region to obtain the coordinates of the centroid of the blind zone in each blind zone sub-region; The azimuth and inclination of the supplementary borehole are calculated based on the location coordinates of the chamber and the centroid coordinates of the blind zone. The depth of the supplementary borehole is calculated based on the spatial distance between the location coordinates of the chamber and the centroid coordinates of the blind zone, thus obtaining the supplementary borehole parameters. Add the supplementary drilling parameters to the optimal drilling parameters to obtain the final hole layout scheme.

[0038] Specifically, when performing density clustering analysis on the distribution of exploration blind zones, a density-based spatial clustering algorithm is used. This algorithm aggregates densely distributed blind zone voxels into the same category based on the spatial proximity relationship between voxels. Two key parameters are set during the clustering process: the neighborhood radius and the minimum cluster size. The neighborhood radius is defined as a value in meters representing the maximum spatial distance between two voxels considered neighbors. The minimum cluster size is defined as a number of voxels representing the minimum number of voxels required to form an effective cluster. For each blind zone voxel, the number of other blind zone voxels within its neighborhood radius is calculated. When the number of blind zone voxels in its neighborhood is greater than or equal to the minimum cluster size, that voxel is marked as a core voxel. The core voxel and all voxels in its neighborhood belong to the same blind zone sub-region. Through the connectivity between core voxels, all blind zone voxels are divided into several spatially continuous sub-regions. The blind zone sub-regions are spatially adjacent to each other, forming a continuous distribution. Voxels in different blind zone sub-regions are spatially separated. After dividing the spatially continuous blind zone voxels into multiple blind zone sub-regions, each sub-region is assigned a unique region number. When calculating the average value of the center point coordinates of all voxels in each blind zone sub-region, the center point coordinates of all voxels belonging to the same blind zone sub-region are extracted. The average X-coordinate is obtained by summing the X-coordinate values ​​of all voxels and dividing it by the total number of voxels in the sub-region. The average Y-coordinate is obtained by summing the Y-coordinate values ​​of all voxels and dividing it by the total number of voxels in the sub-region. The average Z-coordinate is obtained by summing the Z-coordinate values ​​of all voxels and dividing it by the total number of voxels in the sub-region. The three average coordinate values ​​form the blind zone centroid coordinates of the blind zone sub-region, which represent the geometric center position of the blind zone sub-region.

[0039] When calculating the azimuth of the supplementary borehole based on the chamber location coordinates and the blind zone centroid coordinates, the difference between the X-coordinate of the blind zone centroid coordinates and the X-coordinate of the chamber location coordinates is used as the horizontal X-direction distance component. The difference between the Y-coordinate of the blind zone centroid coordinates and the Y-coordinate of the chamber location coordinates is used as the horizontal Y-direction distance component. The arctangent of the horizontal Y-direction distance component is obtained by dividing the horizontal X-direction distance component by the horizontal X-direction distance component. The quadrant of the azimuth is determined based on the signs of the horizontal X-direction and horizontal Y-direction distance components. The initial azimuth is adjusted to the range of 0 to 360 degrees to obtain the supplementary borehole azimuth. The supplementary borehole azimuth represents the horizontal angle from the chamber location to the blind zone centroid. When calculating the inclination of the supplementary borehole based on the chamber location coordinates and the blind zone centroid coordinates, the difference between the Z-coordinate of the blind zone centroid coordinates and the Z-coordinate of the chamber location coordinates is used as the vertical height difference. The square of the horizontal X-direction distance component is calculated. The horizontal distance is obtained by taking the square root of the sum of the squares of the horizontal Y-direction distance components. The arctangent of the vertical height difference divided by the horizontal distance is used to obtain the supplementary borehole inclination angle. A positive inclination angle indicates that the borehole is inclined upwards, and a negative inclination angle indicates that the borehole is inclined downwards. When calculating the supplementary borehole depth based on the spatial distance between the chamber location coordinates and the blind zone centroid coordinates, the square root of the sum of the squares of the horizontal X-direction distance components, the squares of the horizontal Y-direction distance components, and the squares of the vertical height difference is taken to obtain the spatial straight-line distance. The spatial straight-line distance is the Euclidean distance from the chamber location to the blind zone centroid, which is used as the supplementary borehole depth. The supplementary borehole depth represents the total length that the supplementary borehole needs to drill to reach the blind zone centroid. The supplementary borehole azimuth angle, supplementary borehole inclination angle, and supplementary borehole depth are combined to form the supplementary borehole parameters corresponding to the blind zone sub-region. The above calculation process is repeated for all blind zone sub-regions to obtain multiple supplementary borehole parameters.

[0040] When supplementary borehole parameters are added to the optimal borehole parameters, the optimal borehole parameters include the opening offset, azimuth, inclination, and designed depth of all boreholes obtained from the original particle swarm optimization algorithm. Supplementary borehole parameters include the azimuth, inclination, and depth of supplementary boreholes designed for the blind zone. Setting the opening offset of supplementary boreholes to zero indicates drilling from the center of the chamber. The azimuth, inclination, depth, and opening offset of each supplementary borehole are added to the end of the optimal borehole parameter list according to a uniform format, forming a complete set of borehole parameters including the original designed boreholes and supplementary boreholes. The complete borehole parameter set is then statistically analyzed. The total number of boreholes in the combined drilling is calculated by summing the designed borehole depths of all boreholes to obtain the total drilling workload. Based on the complete set of borehole parameters, the control range coverage of the borehole group over the ore body is recalculated. It is verified whether the exploration blind zone volume is reduced to below the specified threshold after adding supplementary boreholes. The complete set of parameters, including the original design borehole parameters and the supplementary borehole parameters, is output as the final borehole layout plan. The final borehole layout plan clarifies the detailed parameters of all boreholes that need to be drilled from the selected chamber location, including the opening position, drilling direction, drilling angle, and drilling depth of each borehole, guiding the on-site drilling construction to achieve comprehensive three-dimensional control of the ore body.

[0041] The above describes the borehole layout design method for clustered mine pits based on analytical geometry in the embodiments of this application. The following describes the borehole layout design system for clustered mine pits based on spatial analytical geometry in the embodiments of this application. Please refer to [link to relevant documentation]. Figure 3 One embodiment of the mine cluster-type pit drilling layout design system based on spatial analytical geometry in this application includes: The acquisition module is used to acquire the strike angle, dip angle and dip angle of the ore body, calculate the surface normal vector of the ore body based on the dip angle and dip angle, and obtain a database of ore body geometric parameters; The partitioning module is used to divide the ore body in the ore body geometric parameter database into evaluation units and calculate the chamber adaptability index, and obtain the chamber location coordinates by filtering the chamber adaptability index; The iteration module is used to arrange the target ore-seeking point coordinates according to the chamber location coordinates and the strike angle, and to use the particle swarm optimization algorithm to iteratively optimize the borehole azimuth and dip angle to obtain the optimal borehole parameters; The module is used to calculate the coordinates of the ore-encrusted points based on the optimal borehole parameters and the ore body surface normal vector, and to construct a control ellipsoid from the coordinates of the ore-encrusted points to obtain the distribution of exploration blind zones; The calculation module is used to cluster the distribution of the exploration blind zone to obtain the centroid coordinates of the blind zone, calculate supplementary drilling parameters based on the chamber location coordinates and the centroid coordinates of the blind zone, and obtain the final borehole layout scheme.

[0042] above Figure 3The drilling layout design system for clustered mine pits based on spatial analytical geometry in this embodiment of the invention is described in detail from the perspective of modular functional entities. The drilling layout design equipment for clustered mine pits based on spatial analytical geometry in this embodiment of the invention is described in detail from the perspective of hardware processing.

[0043] Reference Figure 4 This invention also provides a drilling layout design device for clustered pits in mines based on spatial analytical geometry. This device can be a server, and its internal structure can be as follows: Figure 4 As shown, the mine cluster drilling layout design equipment based on spatial analytical geometry includes a processor, memory, display screen, input device, network interface, and database connected via a system bus. The processor provides computational and control capabilities. The memory of the mine cluster drilling layout design equipment includes a non-volatile storage medium and internal memory. The non-volatile storage medium stores the operating system, computer programs, and database. The internal memory provides an environment for the operation of the operating system and computer programs in the non-volatile storage medium. The database of the mine cluster drilling layout design equipment based on spatial analytical geometry stores the data corresponding to this embodiment. The network interface of the mine cluster drilling layout design equipment based on spatial analytical geometry is used for communication with external terminals via a network connection. When the computer program is executed by the processor, it implements the above-described method.

[0044] Those skilled in the art will understand that Figure 4 The structure shown is merely a block diagram of a portion of the structure related to the present invention and does not constitute a limitation on the mine cluster-type pit drilling layout design equipment based on spatial analytical geometry applied thereto.

[0045] The present invention also provides a computer-readable storage medium, which can be a non-volatile computer-readable storage medium or a volatile computer-readable storage medium, wherein the computer-readable storage medium stores instructions that, when the instructions are executed on a computer, cause the computer to perform the steps of the analytical geometry-based method for designing borehole layout in a clustered pit in a mine.

[0046] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working processes of the systems and units described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here.

[0047] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a mining cluster-type pit drilling and hole layout design device (which can be a personal computer, server, or network device, etc.) based on spatial analytical geometry to execute all or part of the steps of the methods described in the various embodiments of the present invention. The aforementioned storage medium includes: USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, optical disks, and other media capable of storing program code.

[0048] The above embodiments are only used to illustrate the technical solutions of the present invention, and are not intended to limit it. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for designing borehole layout in clustered pit drilling in mines based on analytical geometry, characterized in that, The method includes: Step S1: Collect the strike angle, dip angle, and dip angle of the ore body, calculate the surface normal vector of the ore body based on the dip angle and dip angle, and obtain the ore body geometric parameter database; Step S2: Divide the orebody in the orebody geometric parameter database into evaluation units and calculate the chamber adaptability index. Use the chamber adaptability index to filter and obtain the chamber location coordinates. Step S3: Based on the location coordinates of the chamber and the strike angle, arrange the coordinates of the target ore-seeking point, and use the particle swarm optimization algorithm to iteratively optimize the borehole azimuth and dip angle to obtain the optimal borehole parameters; Step S4: Calculate the coordinates of the ore-encrusted points based on the optimal borehole parameters and the ore body surface normal vector, and construct a control ellipsoid from the ore-encrusted point coordinates to obtain the distribution of exploration blind zones; Step S5: Cluster the distribution of the exploration blind zone to obtain the centroid coordinates of the blind zone, calculate the supplementary drilling parameters based on the chamber location coordinates and the centroid coordinates of the blind zone, and obtain the final borehole layout scheme.

2. The method for designing borehole layout in clustered pits in mines based on analytical geometry according to claim 1, characterized in that, Step S1 includes: A three-dimensional rectangular coordinate system was established in the mining area, and the spatial coordinates of the ore body at different exploration points were collected to obtain the spatial coordinate data of the ore body. The spatial coordinate data of the ore body are subjected to attitude measurement to obtain the strike angle, dip angle and dip angle; The sine and cosine values ​​are calculated based on the dip angle and dip angle, respectively. The first component of the normal vector of the ore body surface is obtained by multiplying the sine value of the dip angle with the cosine value of the dip angle. The second component of the normal vector of the ore body surface is obtained by multiplying the cosine value of the dip angle with the cosine value of the dip angle. The third component of the normal vector of the ore body surface is obtained by calculating the sine value of the dip angle. The first, second, and third components of the surface normal vector of the ore body are used to solve for the spatial plane coefficients with the spatial coordinate data of the ore body to obtain the geometric parameter database of the ore body.

3. The method for designing borehole layout in clustered pits in mines based on analytical geometry according to claim 1, characterized in that, Step S2 includes: The orebody in the orebody geometric parameter database is spatially divided along the strike direction according to a preset interval to obtain multiple evaluation units; Extract the standard deviation of ore body dip angle, surrounding rock fracture rate and unit center point coordinates in each evaluation unit, and calculate the occurrence stability coefficient score, surrounding rock integrity score, radiation coverage efficiency score, engineering tunneling economy score and multi-vein collaborative control score respectively. The stability coefficient of occurrence, the integrity of surrounding rock, the radiation coverage efficiency, the economic efficiency of engineering tunneling, and the multi-vein collaborative control score are weighted and summed to obtain the chamber adaptability index of each evaluation unit. The chamber adaptability indices of each evaluation unit are sorted in descending order of their numerical values, and the coordinates of the center point of the top-ranked evaluation units are selected as the chamber location coordinates.

4. The method for designing borehole layout in mine cluster-type pit drilling based on analytical geometry according to claim 1, characterized in that, Step S3 includes: Based on the location coordinates of the chamber and the strike angle, target control points are arranged at different elevations of the ore body according to the exploration grid spacing to obtain the coordinates of the target ore-encounter point; Initialize the particle swarm, and use the borehole opening offset, borehole azimuth angle, and borehole inclination angle as particle position parameters to construct a multi-objective fitness function that includes evaluation items for grid uniformity, total engineering volume, inclination angle adaptability, fracture zone avoidance, borehole spacing constraint, and boundary control. The borehole spatial distance is calculated based on the coordinates of the chamber location and the coordinates of the target ore-bearing point. The borehole spatial distance is then substituted into the multi-objective fitness function to evaluate particle fitness, and the particle velocity and particle position are updated for iterative optimization. The iteration terminates when the particle swarm reaches a preset number of iterations, and the position parameters of the globally optimal particle are extracted as the optimal drilling parameters.

5. The method for designing borehole layout in mine cluster-type pit drilling based on analytical geometry according to claim 4, characterized in that, Step S4 includes: Construct a borehole spatial trajectory parameter equation based on the borehole azimuth and borehole inclination angle in the optimal borehole parameters; Solve the intersection point parameters by simultaneously solving the spatial trajectory parametric equation of the borehole and the spatial plane equation corresponding to the normal vector of the ore body surface to obtain the coordinates of the ore-encrusting point; Centered on the coordinates of the ore-encrusted point, the control length and control radius of the ellipsoid are determined according to the ore body thickness and the occurrence stability coefficient, and the control ellipsoid corresponding to each borehole is constructed. The total control range is obtained by performing a spatial Boolean union operation on multiple control ellipsoids. The volume of the total control range is then subtracted from the volume of the ore body to obtain the distribution of the exploration blind zone.

6. The method for designing borehole layout in mine cluster-type pit drilling based on analytical geometry according to claim 5, characterized in that, The step of performing a spatial Boolean union operation on multiple control ellipsoids to obtain the total control range, and subtracting the volume of the total control range from the spatial volume of the ore body to obtain the distribution of the exploration blind zone, includes: The total control range is obtained by performing a spatial Boolean union operation on multiple control ellipsoids. The space of the ore body is divided into a three-dimensional voxel grid, and the coordinates of the center point of each voxel are used to determine the spatial ownership and control range of the ore body. The number of voxels that simultaneously satisfy the conditions of belonging to the ore body space but not belonging to the total control range is counted, and the product of the number of voxels in the blind zone and the unit volume of the voxels is calculated to obtain the exploration blind zone volume. The spatial distribution of the exploration blind zone is obtained by statistically analyzing the voxels corresponding to the volume of the exploration blind zone according to their elevation, direction, and dip.

7. The method for designing borehole layout in clustered pits in mines based on analytical geometry according to claim 1, characterized in that, Step S5 includes: Density clustering analysis was performed on the distribution of the exploration blind zone to divide the spatially continuous blind zone voxels into multiple blind zone sub-regions; Calculate the average value of the coordinates of all voxel center points within each of the aforementioned blind zone sub-regions to obtain the centroid coordinates of each of the aforementioned blind zone sub-regions; The azimuth and inclination of the supplementary borehole are calculated based on the location coordinates of the chamber and the centroid coordinates of the blind zone, and the depth of the supplementary borehole is calculated based on the spatial distance between the location coordinates of the chamber and the centroid coordinates of the blind zone, thus obtaining the parameters of the supplementary borehole. The supplementary drilling parameters are added to the optimal drilling parameters to obtain the final hole layout scheme.

8. A mine cluster-type in-pit drilling layout design system based on spatial analytical geometry, characterized in that, For implementing the mine cluster-type pit drilling layout design method based on analytical geometry as described in any one of claims 1-7, the mine cluster-type pit drilling layout design system based on spatial analytical geometry includes: The acquisition module is used to acquire the strike angle, dip angle, and dip angle of the ore body, calculate the surface normal vector of the ore body based on the dip angle and dip angle, and obtain a database of ore body geometric parameters. The partitioning module is used to divide the ore body in the ore body geometric parameter database into evaluation units and calculate the chamber adaptability index, and obtain the chamber location coordinates by filtering the chamber adaptability index; The iteration module is used to arrange the target ore-seeking point coordinates according to the chamber location coordinates and the strike angle, and to use the particle swarm optimization algorithm to iteratively optimize the borehole azimuth and dip angle to obtain the optimal borehole parameters; The module is used to calculate the coordinates of the ore-encrusted points based on the optimal borehole parameters and the ore body surface normal vector, and to construct a control ellipsoid from the coordinates of the ore-encrusted points to obtain the distribution of exploration blind zones; The calculation module is used to cluster the distribution of the exploration blind zone to obtain the centroid coordinates of the blind zone, calculate supplementary drilling parameters based on the chamber location coordinates and the centroid coordinates of the blind zone, and obtain the final borehole layout scheme.

9. A mine cluster-type pit drilling layout design device based on spatial analytical geometry, characterized in that, The invention includes a memory and a processor, the memory storing a computer program that can run on the processor, and the processor executing the computer program to implement the drilling layout design method for clustered pits in mines based on analytical geometry as described in any one of claims 1 to 7.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is run by the processor, it causes the processor to execute the drilling layout design method for clustered pits in mines based on analytical geometry as described in any one of claims 1 to 7.

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