A method, system and storage medium for ore blending in open-pit mine production

Abstract

The present invention belongs to the technical field of open-pit ore matching, and discloses an open-pit ore matching method, system and storage medium, wherein the method comprises: obtaining the positions of each mining point, each matching point and the transportation routes between each mining point and each matching point; taking the positions of each mining point as the input of a pre-constructed grade prediction model, predicting the ore grade of each mining point; constructing an ore matching target function based on the ore grade of each mining point, the actual grade and transportation route required by each matching point for the ore participating in the matching; solving the ore matching target function based on a preset optimization algorithm to obtain the optimal ore source of each matching point; determining the optimal total mining volume of each mining point based on the ore transportation volume transported from each optimal mining point to the matching point. The present invention can quickly generate an ore matching plan for a mine, improve the ore matching efficiency, and the rationality of the ore matching plan is higher, which can greatly reduce the ore matching production cost.

Description

Technical Field

[0001] The present invention belongs to the technical field of open-pit mine blending, and in particular relates to an open-pit mine production blending method, system and storage medium. Background Art

[0002] Open-pit mining refers to the mining of mineral resources using open-pit mining. This mining method is carried out directly on the ground without the need for underground excavation. It is suitable for mineral resources with shallow burial depth, uniform quality and high value.

[0003] In the process of open-pit mining, it is necessary to reasonably distribute and mix the ore according to factors such as the geological conditions of the ore deposit, mining technical conditions and market demand, so as to meet the production needs of the ore dressing plant and improve resource utilization.

[0004] In theory, there is an optimal plan for open-pit mine production planning and organization that meets the requirements of ore matching. However, due to the huge total amount of mining and stripping in the mine, variables in the production efficiency of auxiliary operations and engineering equipment will cause differences in ore mining plans, ultimately leading to huge changes in the absolute quantity of ore matching grades. Therefore, it is very necessary to develop mine matching planning and short-term ore matching plans that meet the mine supply quality requirements and work characteristics.

[0005] In the prior art, the ore blending technicians obtain the ore grade by making a comprehensive comparison of the geological report, and then measure the transportation distance of each ore blending point according to the topographic geological map or the distance meter, and then use the trial and error method to try out the input amount for each ore blending point until the target ore blending plan is finally obtained. However, the trial and error method has low work efficiency in practical applications, low accuracy of the obtained ore grade, and the target ore blending plan obtained by the trial and error method is not the optimal plan, which leads to high ore blending costs. Summary of the invention

[0006] The purpose of the present invention is to provide an open-pit mine production ore matching method, system and storage medium to solve the problems that the existing trial and error method has low working efficiency and low accuracy of the ore grade obtained in practical applications, and the target ore matching plan obtained by the trial and error method is not the optimal plan, which leads to high ore matching costs.

[0007] In order to achieve the above object, the present invention adopts the following technical solutions:

[0008] In a first aspect, the present invention provides a method for ore blending in an open-pit mine, the method comprising:

[0009] Obtain the location of each mining point, the location of each ore distribution point, and the transportation routes between each mining point and each ore distribution point;

[0010] The location of each mining point is used as the input of the pre-built grade prediction model to predict the ore grade of each mining point;

[0011] Based on the ore grade of each mining point, the actual grade and transportation route required by each ore blending point for the ore participating in the ore blending, the ore blending objective function is constructed;

[0012] Based on the preset optimization algorithm, the ore distribution objective function is solved to obtain the optimal ore source for each ore distribution point. The optimal ore source for each ore distribution point includes: at least one optimal mining point and the ore transportation volume from each optimal mining point to the ore distribution point;

[0013] Based on the ore transportation volume from each optimal mining point to the ore distribution point, the optimal total mining volume of each mining point is determined.

[0014] Preferably, the method further comprises: constructing a grade prediction model, comprising:

[0015] Constructing a distribution map of the mining area, gridding the distribution map to obtain a grid distribution map, wherein the grid distribution map has a plurality of grid points, the grid points are intersections between grid lines of the grid distribution map, and taking any position coordinate point of a non-grid point in the grid distribution map as a prediction point;

[0016] Based on the ore sampling method, the ore grade value of each grid point is calculated;

[0017] In the grid distribution map, at least two grid points adjacent to the prediction point are selected as adjacent grid points of the prediction point;

[0018] Calculate the distance between the prediction point and each adjacent grid point;

[0019] Based on the ore grade values ​​of each adjacent grid point and the distances between the prediction point and each adjacent grid point, a grade prediction model for the prediction point is constructed.

[0020] Preferably, the ore sampling method comprises:

[0021] A preset number of grid points are selected in the grid distribution diagram as grid points to be tested, and unselected grid points are regarded as unknown grid points;

[0022] Drilling sampling is carried out at the mine location corresponding to the grid point to be tested to obtain the ore grade value of each grid point to be tested.

[0023] Preferably, based on the ore sampling method, the ore grade value of each grid point includes:

[0024] Select one grid point to be tested from among all the grid points to be tested, and search for the grid point to be tested in the search area which is centered at the selected grid point to be tested and has a preset length as a radius;

[0025] When there are at least three grid points to be measured in the search area, the selected grid points to be measured and all grid points to be measured in the search area are taken as a group of grid points to be measured;

[0026] Reselect a grid point to be tested from the grid points to be tested that are not included in the grid point group to be tested, update the center of the search area with the grid point, and repeatedly search for the grid points to be tested in the search area to obtain a plurality of grid point groups to be tested;

[0027] Clustering a plurality of groups of grid point groups to be tested to obtain a plurality of grid point clusters, each grid point cluster having a plurality of grid point groups to be tested;

[0028] Determine the grade calculation weight of each grid point cluster according to the ore grade values ​​of all grid points to be measured in each grid point cluster;

[0029] Among all unknown grid points, determine at least three grid points to be tested that are adjacent to each unknown grid point;

[0030] The ore grade value of each unknown grid point is determined according to the ore grade values ​​of at least three grid points to be measured adjacent to each unknown grid point and the grade calculation weight of the grid point cluster corresponding to the at least three grid points to be measured adjacent to each unknown grid point.

[0031] Preferably, the preset length L for: , where d is the grid length in the grid distribution diagram.

[0032] Preferably, the ore matching objective function includes: a function model and constraints of the function model;

[0033] The function model includes: a grade deviation model and an operation energy consumption model, wherein the grade deviation model is used to characterize the total difference between the actual grade required by the ore blending point for the ore participating in the ore blending and the ore grade of each mining point, and the operation energy consumption model is used to characterize the total energy consumption in the mining operation, transportation operation and crushing operation;

[0034] The constraints include at least: capacity constraints of mining sites, capacity constraints of ore distribution sites, total output constraints and transportation volume constraints of mining sites.

[0035] Preferably, the expression of the grade deviation model is:

[0036] ;

[0037] In the formula, F 1 is the total grade deviation, J is the number of mining points, I is the number of mining sites, α i For thei The predicted ore grade of each mining site, β j For the j The actual grade of the ore required by each blending point for the ore to be blended. x ij For i Transport from mining site to j The ore transportation volume of each distribution point, k is a constant.

[0038] Preferably, the expression of the operation energy consumption model is:

[0039] ;

[0040] In the formula, F 2 is the total energy consumption, E ci For the i Unit mining energy consumption of mining operations at each mining site, E d is the energy consumption per unit transport distance of the transport operation, D ij For the i Mining sites and j The length of the transportation line between the distribution points, E pi It is the unit crushing energy consumption of the crushing operation at the ore distribution point.

[0041] In a second aspect, the present invention provides an open-pit mine production ore matching system, which is used to implement the above-mentioned open-pit mine production ore matching method, and the system comprises:

[0042] The data acquisition module is used to obtain the location of each mining point, the location of each ore distribution point and the transportation routes between each mining point and each ore distribution point;

[0043] The grade prediction module is used to predict the ore grade of each mining point by taking the location of each mining point as the input of the pre-built grade prediction model;

[0044] Function construction module, used to construct the ore blending objective function based on the ore grade of each mining point, the actual grade and transportation route required by each ore blending point for the ore participating in the ore blending;

[0045] The function solving module is used to solve the ore distribution objective function based on the preset optimization algorithm to obtain the optimal ore source for each ore distribution point. The optimal ore source for each ore distribution point includes: at least one optimal mining point and the ore transportation volume from each optimal mining point to the ore distribution point;

[0046] The mining quantity determination module is used to determine the optimal total mining volume of each mining point based on the ore transportation volume transported from each optimal mining point to the ore distribution point.

[0047] In a fourth aspect, the present invention provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the above-mentioned open-pit mine production ore blending method.

[0048] Beneficial effects:

[0049] The present invention constructs a grade prediction model to accurately predict the ore grade of each mining point in the mine. At the same time, an ore matching objective function is constructed based on the ore grade of each mining point, the actual grade required by each matching point for the ore involved in the matching, and the transportation route. The ore matching objective function is solved by a preset optimization algorithm to obtain the optimal ore source for each matching point. The ore matching plan for the mine can be quickly generated, thereby improving the ore matching efficiency. The ore matching plan is more reasonable and can significantly reduce the ore matching production cost. BRIEF DESCRIPTION OF THE DRAWINGS

[0050] The accompanying drawings are used to provide a further understanding of the embodiments of the present invention and constitute a part of the specification. Together with the following specific implementations, they are used to explain the embodiments of the present invention, but do not constitute a limitation on the embodiments of the present invention. In the accompanying drawings:

[0051] Figure 1 It is a flow chart of an open-pit mine production ore blending method provided by one embodiment of the present invention;

[0052] Figure 2 It is a block diagram of an open-pit mine production ore blending system provided by one embodiment of the present invention. DETAILED DESCRIPTION

[0053] In order to more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the present invention will be briefly introduced below in combination with the drawings and the description of the embodiments or the prior art. Obviously, the following description of the structure of the drawings is only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work. It should be noted that the description of these embodiments is used to help understand the present invention, but does not constitute a limitation of the present invention.

[0054] Embodiment 1

[0055] Figure 1 is a flow chart of an open-pit mine production ore blending method provided by one embodiment of the present invention, such as Figure 1 As shown, this embodiment provides an open-pit mine production ore blending method, the method comprising:

[0056] Step S10: Acquire the location of each mining point, the location of each ore distribution point, and the transportation routes between each mining point and each ore distribution point.

[0057] In this embodiment, the locations of each mining point and each ore distribution point in the mine can be obtained through the GPS system; after obtaining the locations of each mining point and each ore distribution point, an electronic map of the mine area can be obtained through electronic map software, and the locations of each mining point and each ore distribution point can be marked on the electronic map, and the transportation routes between each mining point and each ore distribution point can be planned on the electronic map.

[0058] Step S20: using the location of each mining point as the input of the pre-built grade prediction model to predict the ore grade of each mining point.

[0059] Step S30: constructing an ore blending objective function based on the ore grade of each mining point, the actual grade and transportation route required by each ore blending point for the ore participating in the ore blending.

[0060] Specifically, the ore matching objective function includes: a function model and constraints of the function model;

[0061] The function model includes: a grade deviation model and an operation energy consumption model, wherein the grade deviation model is used to characterize the total difference between the actual grade required by the ore blending point for the ore participating in the ore blending and the ore grade of each mining point, and the operation energy consumption model is used to characterize the total energy consumption in the mining operation, transportation operation and crushing operation;

[0062] The constraints include at least: capacity constraints of mining sites, capacity constraints of ore distribution sites, total output constraints and transportation volume constraints of mining sites.

[0063] The capacity constraints of mining points are: in order to ensure that each mining point can produce ore and avoid equipment idleness, each mining point needs to participate in ore allocation; and the production capacity of each mining point is limited, and the mining volume of each mining point cannot exceed the device limit.

[0064] Capacity constraints of ore distribution points: The ore grade required by each ore distribution point is quite different from the grade of ore mined by each mining point. Each ore distribution point needs to accept ore mined from mining points in different locations. Moreover, the production capacity of each ore distribution point is limited, and the total amount of ore accepted by each ore distribution point cannot exceed its limit.

[0065] Total output constraint: In each production cycle, the total amount of ore produced by all ore distribution points should be greater than the total production target established for that production cycle.

[0066] Mining point transportation volume constraint: The ore mined at each mining point must be transported.

[0067] In this embodiment, the expression of the grade deviation model is:

[0068] ;

[0069] In the formula, F 1 is the total grade deviation, J is the number of mining points, I is the number of mining sites, α i For the i The predicted ore grade of each mining site, β j For the j The actual grade of the ore required by each blending point for the ore to be blended. x ij For i Transport from mining site to j The ore transportation volume of each distribution point, k is a constant, a constant k The value is usually 2.

[0070] In this embodiment, the expression of the operation energy consumption model is:

[0071] ;

[0072] In the formula, F 2 is the total energy consumption, E ci For the i Unit mining energy consumption of mining operations at each mining site, E d is the energy consumption per unit transport distance of the transport operation, D ij For the i Mining sites and j The length of the transportation line between the distribution points, E pi It is the unit crushing energy consumption of the crushing operation at the ore distribution point.

[0073] Step S40: Based on a preset optimization algorithm, the ore distribution objective function is solved to obtain the optimal ore source for each ore distribution point. The optimal ore source for each ore distribution point includes: at least one optimal mining point and the ore transportation volume from each optimal mining point to the ore distribution point.

[0074] In this embodiment, the preset optimization algorithm can be a genetic algorithm. Under the constraints of the production capacity constraints of the mining point, the production capacity constraints of the ore distribution point, the total output constraints and the transportation volume constraints of the mining point, the genetic algorithm is used to optimize and solve the ore distribution objective function so that the total energy consumption of the operation energy consumption model is minimized and the total grade deviation of the grade deviation model is minimized.

[0075] In this embodiment, a genetic algorithm can be used to optimize and solve the grade deviation model and the operating energy consumption model respectively, or the values ​​of the grade deviation model and the operating energy consumption model can be normalized, and then the normalized values ​​of the grade deviation model and the operating energy consumption model are fused to obtain a fused model, and then a genetic algorithm is used to optimize and solve the fused model.

[0076] The solution process of the genetic algorithm is:

[0077] Initialization: randomly generate a certain number of individuals (chromosomes representing solutions) and evaluate the fitness of each individual (the value of the fusion model);

[0078] Selection: Select excellent individuals to enter the next generation based on their fitness;

[0079] Crossover: Perform crossover operations on selected individuals to generate new individuals. You can use methods such as single-point crossover and multi-point crossover.

[0080] Mutation: Perform mutation operations on newly generated individuals to maintain the diversity of the population; for example, this can be achieved by changing certain genes on individual chromosomes;

[0081] Replacement: Replace individuals with lower fitness in the population with newly generated individuals;

[0082] Termination condition: Determine whether the termination condition is met, such as reaching the preset number of iterations, finding a solution that meets the requirements, or the fitness no longer improves; if the termination condition is met, output the optimal solution; otherwise, repeat the above process.

[0083] The output optimal solution is the optimal ore source for each ore distribution point, including at least one optimal mining point for each ore distribution point and the ore transportation volume from each optimal mining point to the ore distribution point.

[0084] Step S50: Determine the optimal total mining volume of each mining point based on the ore transportation volume from each optimal mining point to the ore distribution point.

[0085] When the total energy consumption of the operating energy consumption model is the lowest and the total grade deviation of the grade deviation model is the smallest, the optimal ore source for each distribution point can be obtained; then, based on the amount of ore transported from each optimal mining point to the distribution point, the optimal total mining volume of each mining point is determined, and the distribution plan is based on the optimal ore source for each distribution point and the optimal total mining volume of each mining point.

[0086] The present invention can quickly generate an ore distribution plan for a mine, thereby improving ore distribution efficiency. The ore distribution plan is more reasonable and can significantly reduce ore distribution production costs.

[0087] As a further optimization of this embodiment, the method further includes: constructing a grade prediction model, including:

[0088] Step a01: construct a distribution map of the mining area, grid the distribution map to obtain a grid distribution map, wherein the grid distribution map has a plurality of grid points, the grid points are intersections between grid lines of the grid distribution map, and any position coordinate point of a non-grid point in the grid distribution map is used as a prediction point.

[0089] In this embodiment, an electronic map may be used to construct a distribution map of the mining area, and the electronic map may be gridded, for example, the electronic map may be divided into a number of 100m*100m square grids, and the vertex of each square grid is a grid point.

[0090] Step a02: Based on the ore sampling method, calculate the ore grade value of each grid point.

[0091] In this embodiment, the ore sampling method includes: selecting a preset number of grid points in the grid distribution map as grid points to be tested, and unselected grid points as unknown grid points; drilling and sampling the mine locations corresponding to the grid points to be tested to obtain the ore grade value of each grid point to be tested.

[0092] Since actual sampling and measurement are performed on all grid points, the sampling and measurement intensity is high, which is not conducive to the embodiment; therefore, the preset number of this embodiment can be 40%~60% of the total number of grid points, and 40%~60% of the grid points need to be drilled and sampled; for example: drilling holes are performed at the mine locations actually corresponding to the grid points, and the boreholes are about 200~300 m deep, so as to measure the ore grade value of the grid point.

[0093] The remaining grid points are regarded as unknown grid points. The ore grade values ​​of the unknown grid points can be calculated according to the ore grade values ​​of the grid points to be measured, so as to determine the ore grade value of each grid point.

[0094] Specifically, based on the ore sampling method, the ore grade value of each grid point includes:

[0095] Step a021: Select a grid point to be tested from all grid points to be tested, and search for the grid point to be tested in the search area with the selected grid point to be tested as the center and a preset length as the radius; in this embodiment, the preset length L for: , where d is the grid length in the grid distribution diagram.

[0096] Step a022: when there are at least three grid points to be measured in the search area, the selected grid points to be measured and all grid points to be measured in the search area are taken as a group of grid points to be measured.

[0097] In this embodiment, when a selected grid point to be tested does not contain other grid points to be tested in the search area or the number of existing grid points to be tested is less than three, a new grid point to be tested is reselected from the remaining grid points to be tested, and then the reselected grid point to be tested is used as the center of the search area, and it is determined again whether there are at least three grid points to be tested in the search area.

[0098] Step a023: reselect a grid point to be tested from the grid points to be tested that are not included in the grid point group to be tested, update the center of the search area with the grid point, and repeat the search for the grid points to be tested in the search area to obtain several groups of grid points to be tested.

[0099] Step a024: clustering several groups of grid point groups to be tested to obtain multiple grid point clusters, each grid point cluster having multiple grid point groups to be tested; the algorithm for clustering several groups of grid point groups to be tested in this embodiment may adopt K-means clustering algorithm.

[0100] Step a025: Determine the grade calculation weight of each grid point cluster according to the ore grade values ​​of all grid points to be measured in each grid point cluster.

[0101] The expression of the grade calculation weight of each grid point cluster is:

[0102] ;

[0103] In the formula, For the m The grade calculation weight of the grid point cluster, For the m The number of grid point groups to be tested in the grid point clusters, t For the m The grid point cluster t A group of grid points to be tested, For the m The grid point cluster t The center of the group of grid points to be tested is the grid point to be tested. For The adjacent n There are grid points to be measured, N is The total number of adjacent grid points to be tested.

[0104] Step a026: among all unknown grid points, determine at least three grid points to be tested that are adjacent to each unknown grid point;

[0105] Step a027: Determine the ore grade value of each unknown grid point according to the ore grade values ​​of at least three grid points to be measured adjacent to each unknown grid point and the grade calculation weight of the grid point cluster corresponding to at least three grid points to be measured adjacent to each unknown grid point.

[0106] In this embodiment, three grid points to be measured adjacent to the unknown grid point are preferably selected, and the expression of the ore grade value of the unknown grid point is:

[0107] ;

[0108] In the formula, For the v The ore grade value of unknown grid points, For The first adjacent grid point to be tested, for The grade calculation weight of the grid point cluster, For The second adjacent grid point to be tested, for The grade calculation weight of the grid point cluster, For The third adjacent grid point to be tested, for The grade calculation weight of the grid point cluster.

[0109] Step a03: In the grid distribution map, at least two grid points adjacent to the prediction point are selected as adjacent grid points of the prediction point.

[0110] In this embodiment, four grid points adjacent to the predicted point are preferably selected as adjacent grid points.

[0111] Step a04: Calculate the distance between the prediction point and each adjacent grid point.

[0112] Step a05: Based on the ore grade values ​​of each adjacent grid point and the distances between the prediction point and each adjacent grid point, a grade prediction model for the prediction point is constructed.

[0113] In this embodiment, the grade prediction model of the prediction point is expressed as:

[0114] ;

[0115] In the formula, For the u The quality of the predicted points, For The first adjacent grid point, For The second adjacent grid point, For The third adjacent grid point, For The fourth adjacent grid point, for and The distance between for and The distance between for and The distance between for and The distance between for and The smaller distance in for and The smaller distance in .

[0116] In this embodiment, the present invention constructs a grid distribution map, and the ore grade corresponding to each grid point on the grid distribution map is known. Therefore, by inputting the mining point location of the prediction point into the grade prediction model, the ore grade of each mining point in the mine can be accurately predicted.

[0117] In this embodiment, when the mining point is located exactly on a grid point of the grid distribution map, the ore grade of the mining point is determined directly according to the ore grade corresponding to the grid point.

[0118] Embodiment 2

[0119] Figure 2 is a block diagram of an open-pit mine production and ore matching system provided by an embodiment of the present invention, such as Figure 2 As shown, this embodiment provides an open-pit mine production ore matching system, the system is used to implement the open-pit mine production ore matching method of embodiment 1, and the system includes:

[0120] The data acquisition module is used to obtain the location of each mining point, the location of each ore distribution point and the transportation routes between each mining point and each ore distribution point;

[0121] The grade prediction module is used to predict the ore grade of each mining point by taking the location of each mining point as the input of the pre-built grade prediction model;

[0122] Function construction module, used to construct the ore blending objective function based on the ore grade of each mining point, the actual grade and transportation route required by each ore blending point for the ore participating in the ore blending;

[0123] The function solving module is used to solve the ore distribution objective function based on the preset optimization algorithm to obtain the optimal ore source for each ore distribution point. The optimal ore source for each ore distribution point includes: at least one optimal mining point and the ore transportation volume from each optimal mining point to the ore distribution point;

[0124] The mining quantity determination module is used to determine the optimal total mining volume of each mining point based on the ore transportation volume transported from each optimal mining point to the ore distribution point.

[0125] This embodiment also provides a computer-readable storage medium on which a computer program is stored. When the program is executed by a processor, the above-mentioned open-pit mine production ore blending method is implemented.

[0126] The present invention constructs a grade prediction model to accurately predict the ore grade of each mining point in the mine. At the same time, an ore matching objective function is constructed based on the ore grade of each mining point, the actual grade required by each matching point for the ore involved in the matching, and the transportation route. The ore matching objective function is solved by a preset optimization algorithm to obtain the optimal ore source for each matching point. The ore matching plan for the mine can be quickly generated, thereby improving the ore matching efficiency. The ore matching plan is more reasonable and can significantly reduce the ore matching production cost.

[0127] Those skilled in the art will appreciate that the embodiments of the present application may be provided as methods, systems, or computer program products. Therefore, the present application may adopt the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Moreover, the present application may adopt the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program codes.

[0128] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0129] The above are only embodiments of the present application and are not intended to limit the present application. For those skilled in the art, the present application may have various changes and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application should be included within the scope of the claims of the present application.

Claims

1. A method for ore blending in open-pit mine production, characterized in that: The method comprises: Obtain the location of each mining point, the location of each ore distribution point, and the transportation routes between each mining point and each ore distribution point; The location of each mining point is used as the input of the pre-built grade prediction model to predict the ore grade of each mining point; Based on the ore grade of each mining point, the actual grade and transportation route required by each ore blending point for the ore participating in the ore blending, the ore blending objective function is constructed; Based on the preset optimization algorithm, the ore distribution objective function is solved to obtain the optimal ore source for each ore distribution point. The optimal ore source for each ore distribution point includes: at least one optimal mining point and the ore transportation volume from each optimal mining point to the ore distribution point; Determine the optimal total mining volume of each mining point based on the ore transportation volume from each optimal mining point to the ore distribution point; The method further comprises: constructing a grade prediction model, including: Constructing a distribution map of the mining area, gridding the distribution map to obtain a grid distribution map, wherein the grid distribution map has a plurality of grid points, the grid points are intersections between grid lines of the grid distribution map, and taking any position coordinate point of a non-grid point in the grid distribution map as a prediction point; Based on the ore sampling method, the ore grade value of each grid point is calculated; wherein the ore sampling method includes: selecting a preset number of grid points in the grid distribution map as grid points to be tested, and unselected grid points as unknown grid points; drilling and sampling the mine locations corresponding to the grid points to be tested to obtain the ore grade value of each grid point to be tested; In the grid distribution map, at least two grid points adjacent to the prediction point are selected as adjacent grid points of the prediction point; Calculate the distance between the prediction point and each adjacent grid point; Based on the ore grade values ​​of each adjacent grid point and the distances between the prediction point and each adjacent grid point, a grade prediction model for the prediction point is constructed; Among them, based on the ore sampling method, the ore grade value of each grid point includes: Select one grid point to be tested from among all the grid points to be tested, and search for the grid point to be tested in the search area which is centered at the selected grid point to be tested and has a preset length as a radius; When there are at least three grid points to be measured in the search area, the selected grid points to be measured and all grid points to be measured in the search area are taken as a group of grid points to be measured; Reselect a grid point to be tested from the grid points to be tested that are not included in the grid point group to be tested, update the center of the search area with the grid point, and repeatedly search for the grid points to be tested in the search area to obtain a plurality of grid point groups to be tested; Clustering a plurality of groups of grid point groups to be tested to obtain a plurality of grid point clusters, each grid point cluster having a plurality of grid point groups to be tested; Determine the grade calculation weight of each grid point cluster according to the ore grade values ​​of all grid points to be measured in each grid point cluster; Among all unknown grid points, determine at least three grid points to be tested that are adjacent to each unknown grid point; Determine the ore grade value of each unknown grid point according to the ore grade values ​​of at least three grid points to be measured adjacent to each unknown grid point and the grade calculation weight of the grid point cluster corresponding to the at least three grid points to be measured adjacent to each unknown grid point; Among them, the expression of the grade prediction model of the prediction point is: ； In the formula, For the u The quality of the predicted points, For The first adjacent grid point, For The second adjacent grid point, For The third adjacent grid point, For The fourth adjacent grid point, for and The distance between for and The distance between for and The distance between for and The distance between for and The smaller distance in for and The smaller distance in .

2. The method for ore blending in open-pit mine production according to claim 1, characterized in that: The preset length L for: , where d is the grid length in the grid distribution diagram.

3. The method for ore blending in open-pit mine production according to claim 1, characterized in that: The ore matching objective function includes: a function model and constraints of the function model; The function model includes: a grade deviation model and an operation energy consumption model, wherein the grade deviation model is used to characterize the total difference between the actual grade required by the ore blending point for the ore participating in the ore blending and the ore grade of each mining point, and the operation energy consumption model is used to characterize the total energy consumption in the mining operation, transportation operation and crushing operation; The constraints include at least: capacity constraints of mining sites, capacity constraints of ore distribution sites, total output constraints and transportation volume constraints of mining sites.

4. The method for ore blending in open-pit mine production according to claim 3, characterized in that: The expression of the grade deviation model is: ； In the formula, F 1 is the total grade deviation, J is the number of mining points, I is the number of mining sites, α i For the i The predicted ore grade of each mining site, β j For the j The actual grade of the ore required by each blending point for the ore to be blended. x ij For i Transport from mining site to j The ore transportation volume of each distribution point, k is a constant.

5. The method for ore blending in open-pit mine production according to claim 4, characterized in that: The expression of the operation energy consumption model is: ； In the formula, F 2 is the total energy consumption, E ci For the i Unit mining energy consumption of mining operations at each mining site, E d is the energy consumption per unit transport distance of the transport operation, D ij For the i Mining sites and j The length of the transportation line between the ore distribution points, E pi It is the unit crushing energy consumption of the crushing operation at the ore distribution point.

6. An open-pit mine production ore matching system, used to implement the open-pit mine production ore matching method according to any one of claims 1 to 5, characterized in that: The system comprises: The data acquisition module is used to obtain the location of each mining point, the location of each ore distribution point and the transportation routes between each mining point and each ore distribution point; The grade prediction module is used to predict the ore grade of each mining point by taking the location of each mining point as the input of the pre-built grade prediction model; Function construction module, used to construct the ore blending objective function based on the ore grade of each mining point, the actual grade and transportation route required by each ore blending point for the ore participating in the ore blending; The function solving module is used to solve the ore distribution objective function based on the preset optimization algorithm to obtain the optimal ore source for each ore distribution point. The optimal ore source for each ore distribution point includes: at least one optimal mining point and the ore transportation volume from each optimal mining point to the ore distribution point; The ore quantity determination module is used to determine the optimal total mining volume of each mining point based on the ore transportation volume transported from each optimal mining point to the ore distribution point.

7. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the program is executed by a processor, the open-pit mine production ore matching method described in any one of claims 1 to 5 is implemented.

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