A method and system for optimizing a survey net
By calculating the intensity of parameter variation in the exploration grid, the problem of lack of dynamic feedback in grid degree setting in existing technologies is solved, grid degree optimization is achieved, and the accuracy and economy of exploration are improved.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- GUANGDONG PROVINCE DABAOSHAN MINING CO LTD
- Filing Date
- 2026-03-30
- Publication Date
- 2026-07-10
AI Technical Summary
Existing technologies lack features that reflect changes in grid spacing when determining exploration grid density, making it impossible to quantitatively determine whether the grid density is sufficient to capture ore body variations. This leads to blindly increasing or decreasing the density, wasting funds, or risking missed ore deposits.
By calculating the parameter variation intensity of multiple exploration grids and using the parameter variation intensity as a convergence criterion, the grid degree is gradually adjusted and the corresponding index is calculated. When the parameter variation intensity drops below the preset threshold, it is determined that the current grid degree has met the requirements.
This establishes a direct correlation between grid density settings and ore body variations, avoiding over-densification or over-sparseness of the grid, and improving the accuracy and economy of exploration.
Smart Images
Figure CN122365829A_ABST
Abstract
Description
Technical Field
[0001] This invention relates to the field of mineral exploration, specifically to a method for optimizing exploration network density. Background Technology
[0002] In solid mineral exploration, determining the exploration grid density aims to balance the accuracy of reserve estimation with engineering costs. Current processes typically follow a "analyze orebody morphology—check specifications to determine spacing" model. For ore bodies with significant anisotropy, although theoretically rectangular grids can be designed to match the orebody's strike, existing evaluation indicators (such as grade variance and adjacent sample difference) only reflect the inherent heterogeneity within the geological body when determining whether a specific grid density meets accuracy requirements; they are static constants independent of grid spacing.
[0003] In other words, regardless of whether the exploration grid is set to a sparse 100m×50m or a dense 20m×10m, the grade variation calculated by the traditional method is exactly the same. This is because existing algorithms only focus on the attribute differences between samples, but lack a feature that can compare the sampling distance with the designed grid spacing.
[0004] The lack of such features means that the calculated variation values do not dynamically change with the adjustment of the grid spacing, and therefore cannot serve as a feedback signal to engineers indicating whether "the current grid spacing is too sparse" or "it is already dense enough." More importantly, since the indicator does not change with the grid spacing, it is impossible to establish a convergence threshold based on grid spacing adjustments. Consequently, it is difficult to determine whether the current rectangular grid spacing is sufficient to capture the key variations of the ore body through numerical quantification, and engineers can only blindly rely on experience to make conservative encryption (leading to wasted funds) or risky relaxation (leading to the risk of missing ore). Summary of the Invention
[0005] To address the shortcomings of existing technologies, this invention provides an exploration grid optimization method that solves the technical problems mentioned in the background art by analyzing the parameter variation intensity of multiple exploration grids.
[0006] To achieve the above objectives, the present invention provides the following technical solution: In a first aspect, the present invention discloses a method for optimizing exploration grid density, comprising the following steps: An exploration grid optimization method includes: S1. Read the N original boundary sampling coordinates of the target mining area; S2. Based on the N original boundary sampling coordinates, construct the mining area constraint boundary of the target mining area; S3. Obtain the current grid spacing pairs of the constraint boundaries of the mining area; S4. Using the current grid spacing as the step size, the boundary of the mining area is divided into grids to obtain J candidate exploration grids; S5. Extract the grid coordinates of J candidate exploration grids; S6. Based on the grid coordinates of the J candidate exploration grids, calculate the parameter variation intensity of the current grid spacing pair; S7. Iteratively update the current degree spacing pair with a preset step size, and simultaneously calculate the parameter variation intensity after the update; S8. If the intensity of parameter variation after the update is lower than the set threshold, the updated current degree spacing pair is determined as the optimal degree spacing pair.
[0007] In some specific embodiments, the mining area constraint boundary of the target mining area is constructed based on N original boundary sampling coordinates, including: S2-1. Identify the coordinates of M turning points in the mining area from N original boundary sampling coordinates; S2-2. Sort the coordinates of the M turning points of the mining area by polar angle to obtain M coordinate numbers; S2-3. Based on M coordinate numbers, arrange the coordinates of M mining area turning points sequentially on the electronic map to obtain the mining area constraint boundary of the target mining area.
[0008] In some specific embodiments, the coordinates of the M mining area inflection points are sorted by polar angle to obtain M coordinate indices, including: S2-2-1. Calculate the mean coordinates of the inflection points of the M mining areas; S2-2-2, Using the mean coordinates as the poles, calculate the polar angle of the inflection point coordinates of each mining area relative to the polar axis; S2-2-3. Based on the polar angle of the inflection point coordinates of each mining area relative to the polar axis, the inflection point coordinates of the M mining areas are arranged in ascending or descending order to generate the corresponding ordered coordinate sequence. S2-2-4. Based on the arrangement of each polar angle in the ordered coordinate sequence, assign consecutive integers from 1 to M to obtain the M coordinate numbers.
[0009] In some specific embodiments, obtaining the current grid spacing pairs of the mining area constraint boundaries includes: S3-1. Perform principal component analysis on the coordinates of the M turning points of the mining area to extract the first principal direction vector and the second principal direction vector that characterize the extension trend of the ore body. S3-2. Based on the first principal direction vector and the second principal direction vector representing the extension trend of the ore body, determine the anisotropy ratio representing the morphology of the underground ore body. S3-3. Using the anisotropy ratio as an index, match the current grid spacing pairs of the target mining area in the preset grid rule library.
[0010] In some specific embodiments, the extraction of the first principal direction vector and the second principal direction vector characterizing the extension trend of the ore body includes: S3-1-1, Anchoring the horizontal and vertical coordinate components in the mean coordinates; S3-1-2. Subtract the horizontal and vertical coordinate components from the coordinate components of the M mining area inflection points to obtain M decentralized coordinates. S3-1-3. Based on M decentralized coordinates, construct the covariance matrix of the inflection point coordinates in the mining area; S3-1-4. Perform eigenvalue decomposition on the covariance matrix of the inflection point coordinates of the mining area to obtain the first eigenvalue and the second eigenvalue, as well as their corresponding eigenvectors. S3-1-5. Compare the magnitudes of the first eigenvalue and the second eigenvalue; S3-1-6. Define the eigenvector corresponding to the larger eigenvalue as the first principal direction vector, and the eigenvector corresponding to the smaller eigenvalue as the second principal direction vector.
[0011] In some specific embodiments, determining the anisotropy ratio characterizing the morphology of underground ore bodies includes: S3-2-1. Multiply the first principal direction vector and the second principal direction vector by the coordinates of the M mining area inflection points to obtain the M first direction projection values and second direction projection values of the coordinates of the inflection points of each mining area. S3-2-2. Among the M projection values in the first direction and the projection values in the second direction, select the maximum and minimum projection values for each. S3-2-3. Pair the respective maximum and minimum projection values to form a first directional extremum pair and a second directional extremum pair; S3-2-4. Based on the first and second directional extreme value pairs, anchor the corresponding major axis endpoint coordinate pairs and minor axis endpoint coordinate pairs in the coordinates of the M mining area inflection points. S3-2-5. Calculate the Euclidean distance between the major axis endpoint coordinate pairs and the minor axis endpoint coordinate pairs to obtain the major axis distance and minor axis distance of the ore body shape. S3-2-6. Calculate the ratio of the major axis distance to the minor axis distance to determine the anisotropy ratio that characterizes the morphology of the underground ore body.
[0012] In some specific embodiments, calculating the parameter variation intensity of the current degree interval pair includes: S6-1. Select a target exploration grid from J candidate exploration grids; S6-2. Calculate the Euclidean distance between the target exploration grid and the remaining J-1 candidate exploration grids; S6-3. Among the J-1 candidate exploration grids, select K neighboring exploration grids whose distances are below the threshold. S6-4. Obtain the target exploration grid and its K neighboring exploration grids' exploration target parameter values; S6-5. Based on the target exploration parameter values and their corresponding grid coordinates, calculate the parameter variation intensity of the target exploration grid; S6-6. Traverse the J candidate exploration grids and calculate the global mean of the J parameter variation intensities, which is used as the parameter variation intensity of the current grid spacing pair.
[0013] This invention provides a method for optimizing exploration grid density, which has the following beneficial effects: This invention introduces a comparison calculation between the actual sampling distance and the designed grid spacing, so that the intensity of parameter variation directly depends on the grid setting. It exhibits a monotonic change characteristic that the sparser the grid, the larger the value, and the denser the grid, the smaller the value, thus truly reflecting the degree of control of the current grid on the variation of the ore body.
[0014] Furthermore, based on the convergence criterion of grid degree based on the intensity of parameter variation, in the design of exploration schemes, it is only necessary to gradually adjust the grid degree and calculate the corresponding indicators. When the intensity of parameter variation drops below the preset threshold, it can be determined that the current grid degree has met the requirements, effectively avoiding over-densification or over-sparseness caused by the lack of quantitative standards.
[0015] In a second aspect, the present invention discloses an exploration network optimization system for executing the exploration network optimization method described in the first aspect, the system comprising: The sampling coordinate loading unit is used to read the N original boundary sampling coordinates of the target mining area; The constraint boundary construction unit is used to construct the mining area constraint boundary of the target mining area based on N original boundary sampling coordinates; The current degree acquisition unit is used to acquire the current degree spacing pairs of the constraint boundary of the mining area; An exploration grid division unit is used to divide the boundary of the mining area into grids with the current grid spacing as the step size, so as to obtain J candidate exploration grids. The grid coordinate extraction unit is used to extract the grid coordinates of J candidate exploration grids; The variation intensity calculation unit is used to calculate the parameter variation intensity of the current grid spacing pair based on the grid coordinates of J candidate exploration grids. The mutation intensity update unit is used to iteratively update the current degree spacing pair with a preset step size, and simultaneously calculate the mutation intensity of the updated parameters; The optimal degree determination unit is used to determine whether the updated parameter variation intensity is lower than a set threshold, and then the updated current degree spacing pair is determined as the optimal degree spacing pair.
[0016] Compared with the prior art, the beneficial effects of the exploration network optimization system of the present invention are the same as those of the exploration network optimization method described above, and therefore will not be repeated here. Attached Figure Description
[0017] Figure 1 This is a flowchart illustrating an exploration grid optimization method according to the present invention. Figure 2 This is a schematic diagram illustrating the process of obtaining the mining area constraint boundary as described in this invention. Figure 3 This is a schematic diagram of the coordinate number allocation process described in this invention; Figure 4 This is a schematic diagram of the calculation process for the anisotropy ratio described in this invention; Figure 5 This is a schematic diagram of the calculation process for the parameter variation intensity described in this invention; Figure 6 This is a structural block diagram of an exploration grid optimization system according to the present invention. Detailed Implementation
[0018] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.
[0019] Example 1: Please refer to Figures 1 to 5 This invention provides a method for optimizing exploration grid density, comprising the following steps: S1. Read the N original boundary sampling coordinates of the target mining area; Specifically, the original boundary sampling coordinates are derived from geological surveying data, remote sensing image interpretation contours, or manually drawn trajectories. They are a set of discrete points continuously collected along the edge of the mining area, including key inflection points that characterize the geometry of the mining area and redundant collinear points located on the edge.
[0020] S2. Based on the N original boundary sampling coordinates, construct the mining area constraint boundary of the target mining area; S3. Obtain the current grid spacing pairs of the constraint boundaries of the mining area; S4. Using the current grid spacing as the step size, the boundary of the mining area is divided into grids to obtain J candidate exploration grids; Specifically, using existing regular grid generation algorithms, the orientation spacing in the current degree spacing pair is ( ) and tidal spacing ( These are respectively used as the generation step sizes for the mesh in the two principal axis directions; within the area enclosed by the constraint boundary of the mining area, meshes are generated with side lengths respectively... and A set of regular grids composed of rectangular or parallelogram units; when Not equal to At that time, the generated grid cells exhibit a non-square geometric shape, thus forming the J candidate exploration grids.
[0021] S5. Extract the grid coordinates of J candidate exploration grids; Specifically, the grid coordinates are the coordinates of the geometric center point of each candidate exploration grid unit; after generating regularly arranged grid nodes by dividing the grid in step S4, the geometric center position of the grid unit is determined by calculating the arithmetic mean of the coordinates of the four corner points constituting each grid unit, thereby obtaining the grid coordinates of J candidate exploration grids.
[0022] S6. Based on the grid coordinates of the J candidate exploration grids, calculate the parameter variation intensity of the current grid spacing pair; S7. Iteratively update the current degree spacing pair with a preset step size, and simultaneously calculate the parameter variation intensity after the update; S8. If the intensity of parameter variation after the update is lower than the set threshold, the updated current degree spacing pair is determined as the optimal degree spacing pair.
[0023] In this embodiment, the current grid spacing pair is updated iteratively and the parameter variation intensity is recalculated in real time until the parameter variation intensity value drops below the set threshold. The current grid spacing pair at this time is directly locked as the optimal grid spacing pair, thereby realizing the automatic determination of the optimal exploration grid spacing pair that adapts to the difference between the strike and dip extension of the ore body.
[0024] In this embodiment, step S2 further includes: S2-1. Identify the coordinates of M turning points in the mining area from N original boundary sampling coordinates; Specifically, the Douglas-Peucker curve simplification algorithm is adopted: a vertical distance threshold ϵ is set, and the vertical distance from each point in the N original boundary sampling coordinates to the line connecting adjacent key points is recursively calculated; if the vertical distance of a certain coordinate point is greater than ϵ, it is retained as the inflection point coordinate of the mining area; if it is less than or equal to ϵ, it is discarded as a redundant collinear point; finally, the coordinates of M inflection points of the mining area that constitute the key inflection points of the geometric shape of the mining area are extracted.
[0025] S2-2. Sort the coordinates of the M turning points of the mining area by polar angle to obtain M coordinate numbers; S2-3. Based on M coordinate numbers, arrange the coordinates of M mining area turning points sequentially on the electronic map to obtain the mining area constraint boundary of the target mining area.
[0026] In this embodiment, by eliminating redundant collinear points and connecting key inflection points in topological order, the interference of noise in the original data on the boundary contour is eliminated, and a high-precision closed polygon of the mining area is constructed.
[0027] Furthermore, step S2-2 also includes: S2-2-1. Calculate the mean coordinates of the inflection points of the M mining areas; Specifically, the x-coordinate of the mean coordinate is the arithmetic mean of the x-coordinates of the M inflection points, and the y-coordinate is the arithmetic mean of the y-coordinates of the M inflection points.
[0028] S2-2-2, Using the mean coordinates as the poles, calculate the polar angle of the inflection point coordinates of each mining area relative to the polar axis; The formula for calculating the polar angle is:
[0029] in, This represents the polar angle of the inflection point coordinates of the i-th mining area. and These represent the x and y coordinates of the inflection point of the i-th mining area, respectively. and These represent the x and y coordinates of the mean coordinates, respectively. This represents the arctangent function in the four quadrants, used to return the radian value in the interval (-π, π]. That is, with the mean coordinate as the center, the coordinates of each mining area's inflection point are rotated counterclockwise relative to the due east direction (positive X-axis). It can uniquely determine the orientation order of each mining area's inflection point coordinates in the polar coordinate system, avoiding sorting chaos caused by quadrant judgment errors.
[0030] S2-2-3. Based on the polar angle of the inflection point coordinates of each mining area relative to the polar axis, the inflection point coordinates of the M mining areas are arranged in ascending or descending order to generate the corresponding ordered coordinate sequence. S2-2-4. Based on the arrangement of each polar angle in the ordered coordinate sequence, assign consecutive integers from 1 to M to obtain the M coordinate numbers.
[0031] In this embodiment, by calculating the polar angles of the M mining area inflection point coordinates relative to the mean coordinates and arranging them in ascending order, the spatially disordered M mining area inflection point coordinates are transformed into an ordered sequence connected in clockwise or counterclockwise directions, thus achieving the topological correct sorting of polygon vertices.
[0032] In this embodiment, step S3 further includes: S3-1. Perform principal component analysis on the coordinates of the M turning points of the mining area to extract the first principal direction vector and the second principal direction vector that characterize the extension trend of the ore body. S3-2. Based on the first principal direction vector and the second principal direction vector representing the extension trend of the ore body, determine the anisotropy ratio representing the morphology of the underground ore body. S3-3. Using the anisotropy ratio as an index, match the current grid spacing pairs of the target mining area in the preset grid rule library.
[0033] Wherein, the current grid spacing represents the spacing of the first direction exploration lines along the strike of the ore body ( ) and the distance between the exploration works in the second direction along the dip of the ore body ( The two-dimensional vector parameters, namely the strike spacing and dip spacing at the current iteration step, are used to guide the differentiated layout of non-square grids. Specifically, the grid degree rule base represents a pre-constructed "anisotropy ratio-optimal grid degree spacing" mapping table. This mapping table, based on the geostatistical variation characteristics of different mineral types and industry exploration standards, specifies the spacing combinations that can effectively control the ore body morphology and achieve the optimal economic cost under different aspect ratios.
[0034] Furthermore, step S3-1 also includes: S3-1-1, Anchoring the horizontal and vertical coordinate components in the mean coordinates; S3-1-2. Subtract the horizontal and vertical coordinate components from the coordinate components of the M mining area inflection points to obtain M decentralized coordinates. S3-1-3. Based on M decentralized coordinates, construct the covariance matrix of the inflection point coordinates in the mining area; The expression for the covariance matrix is: ; in, The covariance matrix representing the coordinates of the inflection points in the mining area. and Let represent the variances of the x-axis and y-axis components, respectively. and Represents the covariance of the x and y coordinate components; The x-axis component represents the decentralized coordinate. This represents the ordinate component of the decentralized coordinates.
[0035] S3-1-4. Perform eigenvalue decomposition on the covariance matrix of the inflection point coordinates of the mining area to obtain the first eigenvalue and the second eigenvalue, as well as their corresponding eigenvectors. Specifically, in this embodiment, the singular value decomposition (SVD) algorithm is used to perform eigenvalue decomposition, and the principal component eigenvectors representing the direction of maximum variance of the data and their corresponding eigenvalues are obtained.
[0036] S3-1-5. Compare the magnitudes of the first eigenvalue and the second eigenvalue; S3-1-6. Define the eigenvector corresponding to the larger eigenvalue as the first principal direction vector, and the eigenvector corresponding to the smaller eigenvalue as the second principal direction vector.
[0037] In this embodiment, singular value decomposition is performed on the covariance matrix of the coordinates of the M mining area inflection points to determine the principal axis direction of the distribution of the coordinates of the M mining area inflection points, thereby quantifying the direction and dip of the ore body extension and providing a criterion for calculating the anisotropy ratio.
[0038] Step S3-2 further includes: S3-2-1. Multiply the first principal direction vector and the second principal direction vector by the coordinates of the M mining area inflection points to obtain the M first direction projection values and second direction projection values of the coordinates of the inflection points of each mining area. S3-2-2. Among the M projection values in the first direction and the projection values in the second direction, select the maximum and minimum projection values for each. S3-2-3. Pair the respective maximum and minimum projection values to form a first directional extremum pair and a second directional extremum pair; S3-2-4. Based on the first and second directional extreme value pairs, anchor the corresponding major axis endpoint coordinate pairs and minor axis endpoint coordinate pairs in the coordinates of the M mining area inflection points. S3-2-5. Calculate the Euclidean distance between the major axis endpoint coordinate pairs and the minor axis endpoint coordinate pairs to obtain the major axis distance and minor axis distance of the ore body shape. S3-2-6. Calculate the ratio of the major axis distance to the minor axis distance to determine the anisotropy ratio that characterizes the morphology of the underground ore body.
[0039] In this embodiment, by obtaining the anisotropy ratio, the length ratio of the area enclosed by the coordinates of the M mining area inflection points is reflected in the two principal axis directions, thereby transforming the geometric morphological features of the ore body into specific numerical indexes, driving the grid rule library to match the current grid spacing pairs that are suitable for the shape of the ore body.
[0040] In this embodiment, step S6 further includes: S6-1. Select a target exploration grid from J candidate exploration grids; S6-2. Calculate the Euclidean distance between the target exploration grid and the remaining J-1 candidate exploration grids; S6-3. Among the J-1 candidate exploration grids, select K neighboring exploration grids whose distances are below the threshold. S6-4. Obtain the target exploration grid and its K neighboring exploration grids' exploration target parameter values; The exploration target parameter values represent key attribute indicators of the economic value or geological characteristics of the ore body, which may be ore grade (such as the average content of gold and copper) or ore body thickness (such as true thickness or vertical thickness).
[0041] S6-5. Based on the target exploration parameter values and their corresponding grid coordinates, calculate the parameter variation intensity of the target exploration grid; The formula for calculating the intensity of parameter variation is: ; in, Indicates the target exploration grid. This represents the target parameter values for the target exploration grid. This represents the exploration parameters of the k-th neighboring exploration grid. The squared difference of the target exploration parameter values is used to characterize the local fluctuation energy of ore body properties (such as grade and thickness) between the target exploration grid and its neighboring exploration grids at the current sampling density. The larger the value, the more drastic the change in geological conditions between the two points and the higher the degree of property fluctuation between adjacent grids.
[0042] This represents the Euclidean distance between the target exploration grid and its k-th neighboring exploration grid. This represents the geometric mean scalar calculated based on the current degree spacing. Specifically, if the current degree spacing includes the orientation spacing and directional spacing ,but This value represents the equivalent square side length of the current non-square mesh, used to eliminate the influence of mesh aspect ratio differences on distance normalization calculations, and to ensure that the intensity of parameter variation is comparable under different mesh spacings. It represents the normalized scaling factor of the actual sampling distance relative to the theoretical grid spacing, used to characterize the uncertainty amplification effect of spatial inference under the current grid degree.
[0043] Specifically, when A value of approximately 1 indicates that neighboring points are within the effective sampling range of the current grid degree, and their parameter differences contribute significantly to the overall variation; a value less than 1 indicates that neighboring points are too close, and their differences may originate from local noise; a value greater than 1 (such as due to grid irregularities) indicates the existence of "over-range" sampling, in which case the variation is amplified, suggesting that the current grid degree spacing is insufficient to control the ore body changes in this direction. S6-6. Traverse the J candidate exploration grids and calculate the global mean of the J parameter variation intensities, which is used as the parameter variation intensities of the current grid degree interval pair.
[0044] In this embodiment, by obtaining the parameter variation intensity of the current grid spacing pair, the spatial inference uncertainty of ore body attributes (such as grade) under the current grid density can be quantitatively evaluated, and the parameter variation intensity is used as a convergence criterion to determine whether the iteration process should stop and whether the current grid spacing pair meets the standard.
[0045] Example 2: See Figure 6The technical solution of Embodiment 2 differs from Embodiment 1 in that this embodiment of the invention also provides an exploration network optimization system. This system is used to implement the above-described method embodiments, and details already described will not be repeated. The terms "module," "unit," and "subunit" used below refer to combinations of software and / or hardware that achieve a predetermined function. Although the system described in the following embodiments is preferably implemented in software, hardware implementation, or a combination of software and hardware, is also possible and contemplated.
[0046] like Figure 6 As shown, Figure 6 This is a structural block diagram of an exploration grid optimization system according to the present invention. The system includes: The sampling coordinate loading unit is used to read the N original boundary sampling coordinates of the target mining area; The constraint boundary construction unit is used to construct the mining area constraint boundary of the target mining area based on N original boundary sampling coordinates; The current degree acquisition unit is used to acquire the current degree spacing pairs of the constraint boundary of the mining area; An exploration grid division unit is used to divide the boundary of the mining area into grids with the current grid spacing as the step size, so as to obtain J candidate exploration grids. The grid coordinate extraction unit is used to extract the grid coordinates of J candidate exploration grids; The variation intensity calculation unit is used to calculate the parameter variation intensity of the current grid spacing pair based on the grid coordinates of J candidate exploration grids. The mutation intensity update unit is used to iteratively update the current degree spacing pair with a preset step size, and simultaneously calculate the mutation intensity of the updated parameters; The optimal degree determination unit is used to determine whether the updated parameter variation intensity is lower than a set threshold, and then the updated current degree spacing pair is determined as the optimal degree spacing pair.
[0047] In the above system, the sampling coordinate loading unit loads N original boundary sampling coordinates; the constraint boundary construction unit constructs the mining area constraint boundary of the target mining area; the current grid degree acquisition unit obtains the current grid degree spacing pair; the exploration grid division unit divides the mining area constraint boundary into grids to obtain J candidate exploration grids; the grid coordinate extraction unit extracts the grid coordinates of the J candidate exploration grids; the variation intensity calculation unit calculates the parameter variation intensity of the current grid degree spacing pair; the variation intensity update unit calculates the updated parameter variation intensity; and the optimal grid degree determination unit determines the updated current grid degree spacing pair as the optimal grid degree spacing pair, thus solving the problem of insufficient capture of key variations of the ore body.
[0048] The above embodiments can be implemented, in whole or in part, by software, hardware, firmware, or any other combination thereof. When implemented using software, the above embodiments can be implemented, in whole or in part, as a computer program product. The computer program product includes one or more computer instructions or computer programs. When the computer instructions or computer program are loaded or executed on a computer, all or part of the processes or functions described in the embodiments of this application are generated. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another. For example, the computer instructions can be transmitted from one website, computer, server, or data center to another website, computer, server, or data center via wired (e.g., infrared, wireless, microwave, etc.) means.
[0049] The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate, and the components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs. Those skilled in the art can understand and implement this without any creative effort.
[0050] Through the above description of the embodiments, those skilled in the art can clearly understand that each embodiment can be implemented by means of software plus necessary general-purpose hardware platforms, and of course, it can also be implemented by hardware. Based on this understanding, the above technical solutions, in essence or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, magnetic disk, optical disk, etc., and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute the methods described in the various embodiments or some parts of the embodiments.
[0051] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any changes or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application.
Claims
1. A method for optimizing exploration grid density, characterized in that, include: S1. Read the N original boundary sampling coordinates of the target mining area; S2. Based on the N original boundary sampling coordinates, construct the mining area constraint boundary of the target mining area; S3. Obtain the current grid spacing pairs of the constraint boundaries of the mining area; S4. Using the current grid spacing as the step size, the boundary of the mining area is divided into grids to obtain J candidate exploration grids; S5. Extract the grid coordinates of J candidate exploration grids; S6. Based on the grid coordinates of the J candidate exploration grids, calculate the parameter variation intensity of the current grid spacing pair; S7. Iteratively update the current degree spacing pair with a preset step size, and simultaneously calculate the parameter variation intensity after the update; S8. If the intensity of parameter variation after the update is lower than the set threshold, the updated current degree spacing pair is determined as the optimal degree spacing pair.
2. The exploration grid optimization method according to claim 1, characterized in that, Based on N original boundary sampling coordinates, construct the mining area constraint boundary of the target mining area, including: S2-1. Identify the coordinates of M turning points in the mining area from N original boundary sampling coordinates; S2-2. Sort the coordinates of the M turning points of the mining area by polar angle to obtain M coordinate numbers; S2-3. Based on M coordinate numbers, arrange the coordinates of M mining area turning points sequentially on the electronic map to obtain the mining area constraint boundary of the target mining area.
3. The exploration grid optimization method according to claim 2, characterized in that, Sort the coordinates of the M inflection points in the mining area by polar angle to obtain M coordinate indices, including: S2-2-1. Calculate the mean coordinates of the inflection points of the M mining areas; S2-2-2, Using the mean coordinates as the poles, calculate the polar angle of the inflection point coordinates of each mining area relative to the polar axis; S2-2-3. Based on the polar angle of the inflection point coordinates of each mining area relative to the polar axis, the inflection point coordinates of the M mining areas are arranged in ascending or descending order to generate the corresponding ordered coordinate sequence. S2-2-4. Based on the arrangement of each polar angle in the ordered coordinate sequence, assign consecutive integers from 1 to M to obtain the M coordinate numbers.
4. The exploration grid optimization method according to claim 1, characterized in that, Obtaining the current grid spacing pairs of the mining area constraint boundaries includes: S3-1. Perform principal component analysis on the coordinates of the M turning points of the mining area to extract the first principal direction vector and the second principal direction vector that characterize the extension trend of the ore body. S3-2. Based on the first principal direction vector and the second principal direction vector representing the extension trend of the ore body, determine the anisotropy ratio representing the morphology of the underground ore body. S3-3. Using the anisotropy ratio as an index, match the current grid spacing pairs of the target mining area in the preset grid rule library.
5. The exploration grid optimization method according to claim 4, characterized in that, The extraction of the first principal direction vector and the second principal direction vector characterizing the extension trend of the ore body includes: S3-1-1, Anchoring the horizontal and vertical coordinate components in the mean coordinates; S3-1-2. Subtract the horizontal and vertical coordinate components from the coordinate components of the M mining area inflection points to obtain M decentralized coordinates. S3-1-3. Based on M decentralized coordinates, construct the covariance matrix of the inflection point coordinates in the mining area; S3-1-4. Perform eigenvalue decomposition on the covariance matrix of the inflection point coordinates of the mining area to obtain the first eigenvalue and the second eigenvalue, as well as their corresponding eigenvectors. S3-1-5. Compare the magnitudes of the first eigenvalue and the second eigenvalue; S3-1-6. Define the eigenvector corresponding to the larger eigenvalue as the first principal direction vector, and the eigenvector corresponding to the smaller eigenvalue as the second principal direction vector.
6. The exploration grid optimization method according to claim 4, characterized in that, The determination of the anisotropy ratio characterizing the morphology of underground ore bodies includes: S3-2-1. Multiply the first principal direction vector and the second principal direction vector by the coordinates of the M mining area inflection points to obtain the M first direction projection values and second direction projection values of the coordinates of the inflection points of each mining area. S3-2-2. Among the M projection values in the first direction and the projection values in the second direction, select the maximum and minimum projection values for each. S3-2-3. Pair the respective maximum and minimum projection values to form a first directional extremum pair and a second directional extremum pair; S3-2-4. Based on the first and second directional extreme value pairs, anchor the corresponding major axis endpoint coordinate pairs and minor axis endpoint coordinate pairs in the coordinates of the M mining area inflection points. S3-2-5. Calculate the Euclidean distance between the major axis endpoint coordinate pairs and the minor axis endpoint coordinate pairs to obtain the major axis distance and minor axis distance of the ore body shape. S3-2-6. Calculate the ratio of the major axis distance to the minor axis distance to determine the anisotropy ratio that characterizes the morphology of the underground ore body.
7. The exploration grid optimization method according to claim 6, characterized in that, The calculation of the parameter variation intensity of the current degree interval pair includes: S6-1. Select a target exploration grid from J candidate exploration grids; S6-2. Calculate the Euclidean distance between the target exploration grid and the remaining J-1 candidate exploration grids; S6-3. Among the J-1 candidate exploration grids, select K neighboring exploration grids whose distances are below the threshold. S6-4. Obtain the target exploration grid and its K neighboring exploration grids' exploration target parameter values; S6-5. Based on the target exploration parameter values and their corresponding grid coordinates, calculate the parameter variation intensity of the target exploration grid; S6-6. Traverse the J candidate exploration grids and calculate the global mean of the J parameter variation intensities, which is used as the parameter variation intensity of the current grid spacing pair.
8. An exploration grid optimization system, used to execute an exploration grid optimization method according to any one of claims 1 to 7, comprising: The sampling coordinate loading unit is used to read the N original boundary sampling coordinates of the target mining area; The constraint boundary construction unit is used to construct the mining area constraint boundary of the target mining area based on N original boundary sampling coordinates; The current degree acquisition unit is used to acquire the current degree spacing pairs of the constraint boundary of the mining area; An exploration grid division unit is used to divide the boundary of the mining area into grids with the current grid spacing as the step size, so as to obtain J candidate exploration grids. The grid coordinate extraction unit is used to extract the grid coordinates of J candidate exploration grids; The variation intensity calculation unit is used to calculate the parameter variation intensity of the current grid spacing pair based on the grid coordinates of J candidate exploration grids. The mutation intensity update unit is used to iteratively update the current degree spacing pair with a preset step size, and simultaneously calculate the mutation intensity of the updated parameters; The optimal degree determination unit is used to determine whether the updated parameter variation intensity is lower than a set threshold, and then the updated current degree spacing pair is determined as the optimal degree spacing pair.