A production scheduling optimization method for cold-region open-pit mines based on rock mass quality and drilling efficiency

Abstract

The present invention proposes a production scheduling optimization method for cold-region open-pit mines based on rock mass quality and drilling efficiency. The method comprises collecting data from multiple sample points with different spatial coordinates within different areas of the cold-region open-pit mine to obtain the rock mass RMR parameter for each sample point; determining the optimal power of the inverse distance power method, and constructing an RMR spatial distribution model and a low-temperature RMR spatial distribution model based on the optimal power and the rock mass RMR parameter; obtaining the drilling efficiency of the drill rig at different sample points to obtain a drilling efficiency spatial distribution model and a low-temperature drilling efficiency spatial distribution model; dividing the cold-region open-pit mine into multiple mining units, obtaining the drilling fuel consumption of each mining unit, and obtaining a stripping plan optimization model. The stripping plan optimization model is used to optimize the production scheduling of the cold-region open-pit mine. The present invention can achieve refined mining in cold-region open-pit mines, reduce drilling fuel consumption, and lower mining costs.

Description

Technical Field

[0001] The invention belongs to the technical field of mining, and specifically discloses a production scheduling optimization method for a cold-region open-pit mine based on rock mass quality and drilling efficiency. Background Art

[0002] With my country's economic development and social progress, the demand for metal mineral resources continues to increase. However, resource availability in low-altitude areas is gradually decreasing, while the development and rational utilization of metal mineral resources in high-altitude, cold regions have become increasingly urgent. Furthermore, the development of mineral resources in these high-altitude, cold regions is progressing comprehensively and orderly. According to statistics, there are currently hundreds of mines in these regions that are viable, but the harsh, cold environment also poses a constraint on the development of mining activities.

[0003] Weather significantly impacts mining operations in cold-region open-pit mines, and rock mass quality is also affected by temperature. Rock mass contains various structural planes and weak interlayers, which weaken its strength. At low temperatures, moisture in these planes freezes, effectively increasing the rock mass's cohesion and thus its overall strength. Consequently, in cold, high-altitude regions, rock mass quality is often higher in winter than in other months, leading to higher drilling costs for mines during winter. To reduce costs, mining areas with lower rock mass quality can be considered in winter, while mining areas with higher rock mass in other months. Therefore, in low-grade mines, it is particularly important to refine drilling parameters to reduce drilling fuel consumption and costs. However, existing production optimization methods for cold-region open-pit mines rarely consider the impact of weather on mining activities. Therefore, it is crucial to research and design a new production optimization method for cold-region open-pit mines based on rock mass quality and drilling efficiency to address the challenges currently faced in cold-region open-pit mining. Summary of the Invention

[0004] In order to solve the problem that the influence of temperature on mining activities is rarely considered in existing open-pit mining, resulting in increased production costs, the present invention proposes a production scheduling optimization method for cold-region open-pit mines based on rock mass quality and drilling efficiency.

[0005] The present invention provides a production scheduling optimization method for a cold region open-pit mine based on rock mass quality and drilling efficiency, comprising the following steps:

[0006] S1. Collecting sample point data of multiple different spatial position coordinates in different areas of cold open-pit mines, and obtaining the rock mass RMR parameters of each sample point through the sample point data;

[0007] S2. Determine the optimal power required for spatial interpolation calculation using the inverse distance power method using the sample point data collected in step S1, construct an RMR spatial distribution model based on the optimal power and the rock mass RMR parameters of each sample point obtained in step S1, obtain the relationship between rock mass and temperature through rock mechanics strength tests under low temperature conditions, and construct a low-temperature RMR spatial distribution model based on the RMR spatial distribution model;

[0008] S3. Obtain the drilling efficiency at different sample points, obtain the drilling efficiency at different spaces by the RMR spatial distribution model, the low-temperature RMR spatial distribution model and the drilling efficiency obtained in step S2, and obtain the drilling efficiency spatial distribution model and the low-temperature drilling efficiency spatial distribution model;

[0009] S4. Divide the cold-region open-pit mine into multiple mining units, obtain the perforation oil consumption of each mining unit, and obtain a mining plan optimization model through the perforation efficiency spatial distribution model, low-temperature perforation efficiency spatial distribution model and perforation oil consumption of each mining unit obtained in step S3. Optimize the production schedule of the cold-region open-pit mine through the mining plan optimization model.

[0010] According to a production scheduling optimization method for a cold-region open-pit mine based on rock mass quality and drilling efficiency in some embodiments of the present application, in step S1, the sample point data includes the uniaxial compressive strength, RQD, joint spacing, joint conditions, groundwater conditions, and joint occurrence of the rock block;

[0011] The rock mass RMR parameter of each sample point is calculated using the sample point data, as shown in formula (1):

[0012] RMR=R1+R2+R3+R4+R5+R6(1)

[0013] Among them, R1 represents the uniaxial compressive strength score of the rock block, R2 represents the RQD score, R3 represents the joint spacing score, R4 represents the joint condition score, R5 represents the groundwater condition score, and R6 represents the joint occurrence score.

[0014] According to a production scheduling optimization method for a cold-region open-pit mine based on rock mass quality and drilling efficiency in some embodiments of the present application, step S2 includes the following steps:

[0015] S21. Construct a spatial interpolation calculation model as shown in formula (2):

[0016] x v =∑b a x a (2)

[0017] Among them, x v Indicates the overall space quality, x aIndicates the grade of the ath sample point falling within the influence range, b a Indicates the weight between the a-th sample point involved in the valuation and the valued point, 1≤a≤A, A>1, A represents the number of sample points collected;

[0018] The weight b between the a-th sample point involved in the valuation and the valued point a As shown in formula (3):

[0019]

[0020] Among them, h a It represents the distance between the a-th sample point involved in the estimation and the estimated point, and N represents the optimal power in the inverse distance power method;

[0021] S22. Constructing the three-dimensional geological entity model of the mine into a block model composed of blocks of the same size based on the octree algorithm;

[0022] S23. Constructing a rock uniaxial compressive strength attribute database, an RQD attribute database, and a joint spacing attribute database in the block model constructed in step S22;

[0023] S24. Input the uniaxial compressive strength and spatial position coordinates of the rock block at the sample point into the rock block uniaxial compressive strength attribute database, input the RQD and spatial position coordinates of the sample point into the RQD attribute database, input the joint spacing and spatial position coordinates of the sample point into the joint spacing attribute database, and obtain a spatial distribution block model using the spatial interpolation calculation model and the corresponding optimal power obtained in step S21;

[0024] S25. Construct a rock mass RMR parameter database in the spatially distributed block model obtained in step S24, input the rock mass RMR parameters into the rock mass RMR parameter database to obtain an RMR spatial distribution model, obtain the relationship between rock mass and temperature through rock mechanics strength tests under low temperature conditions, and construct a low-temperature RMR spatial distribution model based on the RMR spatial distribution model.

[0025] According to some embodiments of the present application, a method for optimizing production scheduling based on rock mass quality and drilling efficiency for a cold-region open-pit mine is provided. In step S21, the optimal power N of the sample point data is input into the spatial interpolation calculation model and obtained through cross-validation. The steps include:

[0026] S211. Given a power N k , 1≤k≤m; m is the total number of powers;

[0027] S212. For the j-th sample point p j, 1≤j≤A, and remove the j-th sample point p in turn j , the estimation is to get the jth sample point estimation

[0028] S213. Calculate the j-th sample point p j and the j-th sample point estimate The error is given by N k Root mean square error Re k , as shown in formula (4):

[0029]

[0030] in, represents the estimated value of the j-th sample point;

[0031] S214.k=k+1, repeat steps S211-S213 until k=m+1;

[0032] S215. Select the smallest root mean square error Re k The corresponding N k is the optimal power N.

[0033] According to a production scheduling optimization method for a cold-region open-pit mine based on rock mass quality and drilling efficiency in some embodiments of the present application, in step S25, it is found through a rock mechanical strength test under low-temperature conditions that the uniaxial compressive strength of the low-temperature rock block gradually increases with decreasing freezing temperature. The relationship between the uniaxial compressive strength of the low-temperature rock block and temperature is shown in formula (5):

[0034] Q=-0.17T+10.3 (5)

[0035] Where Q represents the uniaxial compressive strength of low-temperature rock blocks, and T represents temperature.

[0036] According to a production scheduling optimization method for a cold-region open-pit mine based on rock mass quality and drilling efficiency in some embodiments of the present application, step S3 includes:

[0037] S31. Obtaining drilling efficiency at different sample points;

[0038] S32. The correlation function between the rock mass RMR parameter and the drilling efficiency is obtained by using the rock mass RMR parameter and the drilling efficiency at different sample points, as shown in formula (6):

[0039] y=153.42x -0 .507 (6)

[0040] Where y represents the drilling efficiency of the drilling rig in meters per hour, and x represents the rock mass;

[0041] S33. Input the correlation function between the rock mass quality RMR parameter and the drilling rig drilling efficiency obtained in step S32 into the RMR spatial distribution model to obtain the drilling efficiency spatial distribution model. Input the correlation function between the rock mass quality RMR parameter and the drilling rig drilling efficiency obtained in step S32 into the low-temperature RMR spatial distribution model to obtain the low-temperature drilling efficiency spatial distribution model.

[0042] According to a production scheduling optimization method for a cold-region open-pit mine based on rock mass quality and drilling efficiency in some embodiments of the present application, step S4 includes:

[0043] S41. Divide the cold-region open-pit mine into a plurality of mining units, obtain the rock mass, perforation efficiency, metal grade, and bulk density of each mining unit, and obtain the perforation fuel consumption of the mining unit by the perforation efficiency of the mining unit, as shown in formula (7):

[0044]

[0045] Among them, DX i represents the perforation oil consumption of the i-th mining unit, V i represents the volume of the i-th mining unit, R represents the rated fuel consumption of the drilling rig, d represents the length of a single drill hole, S represents the hole spacing, L represents the row spacing, and U i represents the drilling speed of the drilling rig in the i-th mining unit, and h represents the step height;

[0046] S42. The mining plan optimization model is obtained by using the perforation efficiency spatial distribution model, the low-temperature perforation efficiency spatial distribution model, and the perforation oil consumption of each mining unit obtained in step S3. The objective function of the mining plan optimization model is shown in formula (8):

[0047]

[0048] Among them, n represents the number of mining units, T represents the mining cycle, t represents the tth mining time period, Q i represents the value of the ore in the i-th mining unit, H i represents the blasting, shoveling and transportation costs of the i-th mining unit, DR i represents the labor cost of drilling the i-th mining unit, DY i represents the equipment repair cost of the perforation of the i-th mining unit, β represents the discount rate, represents the rock mass quality of the i-th mining unit, The decision variable representing whether mining unit i is mined in the tth mining period is 1 if it is mined and 0 if it is not mined;

[0049] The production capacity constraint is shown in formula (9):

[0050]

[0051] Among them, MC min Represents the lower limit of ore mining capacity, ρ i represents the average weight of the i-th mining unit, MC max Indicates the upper limit of ore and rock mining capacity;

[0052] The ore processing capacity constraint is shown in formula (10):

[0053]

[0054] Among them, PC min Indicates the lower limit of ore and rock processing capacity, represents the ore proportion of the i-th mining unit, O i represents the average specific gravity of ore in the i-th mining unit, PC max Indicates the upper limit of ore and rock processing capacity;

[0055] The stripping ratio constraint condition is shown in formula (11):

[0056]

[0057] Among them, θ min Indicates the lower limit of production stripping ratio, represents the proportion of waste rock in the i-th production unit, W i represents the average weight of waste rock in the i-th mining unit, θ max Indicates the upper limit of production stripping ratio;

[0058] The average grade constraint is shown in formula (12):

[0059]

[0060] Among them, g i represents the ore grade in the i-th mining unit, G max Indicates the upper limit of the average grade of the ore, G min Indicates the lower limit of the average grade of the ore;

[0061] The spatial constraints are shown in formulas (13) and (14):

[0062] X x,y,z -X x,y,z+1 ≤0 (13)

[0063]

[0064] Among them, x, y, z represent the spatial coordinates of the mining unit, x, y represent the horizontal coordinates of the mining unit, z represents the step where the mining unit is located, X x,y,z Represents the mining unit located at x,y,z, X x,y,z+1 represents the mining unit at x,y,z+1, X(t) x,y,z The decision variable indicating whether the mining unit will be mined in the next planning period is 1 if it is mined and 0 if it is not mined;

[0065] The mining reserve constraint condition is shown in formula (15):

[0066]

[0067] This means that in cold-region open-pit mines, any mining unit can only be mined once;

[0068] S43. Optimize the production schedule of the cold-region open-pit mine through the objective function and constraint conditions of the mining and stripping plan optimization model.

[0069] Based on geological data and geostatistical methods, this paper establishes a three-dimensional, block-inhomogeneous model of rock mass quality that takes into account rock strength, structural surface characteristics, and water occurrence. This method also considers the relationship between rock mass quality and temperature. Combining multiple sets of field drilling data, it constructs a mapping relationship between rock mass quality and drilling efficiency, thereby establishing drilling efficiency distribution models for open-pit mines in summer and winter. Next, the three-dimensional geological model of the stope is divided into a series of mining units. By adjusting the mining sequence of each mining unit, the mining plan is optimized to achieve refined mining in cold-region open-pit mines, reduce drilling fuel consumption, and lower mining costs. BRIEF DESCRIPTION OF THE DRAWINGS

[0070] Figure 1 A schematic flow chart of a method for optimizing production scheduling in a cold-region open-pit mine based on rock mass quality and drilling efficiency, provided in Example 1 of the present invention;

[0071] Figure 2 is the root mean square error under different given optimal power values of Example 2 of the present invention;

[0072] Figure 3 is the RMR spatial distribution model of Example 2 of the present invention;

[0073] Figure 4 This is the low-temperature RMR spatial distribution model of Example 2 of the present invention;

[0074] Figure 5 Schematic diagram of the relationship between the rock mass RMR parameters and the drilling efficiency of the drilling rig at different sample points in Example 2 of the present invention;

[0075] Figure 6This is a schematic diagram of dividing a cold region open-pit mine into multiple mining units according to Example 2 of the present invention;

[0076] Figure 7 This is a schematic diagram comparing the fuel consumption during perforation in different months between the optimized solution and the original solution of Example 2 of the present invention. DETAILED DESCRIPTION

[0077] The following embodiments of the present invention are described in further detail with reference to the accompanying drawings and examples. The following examples are used to illustrate the present invention but are not intended to limit the scope of the present invention.

[0078] Example 1: This example provides a method for optimizing production scheduling based on rock mass quality and drilling efficiency in a cold region open-pit mine. Figure 1 As shown, the following steps are included:

[0079] S1. Collect data from multiple sample points with different spatial coordinates in different areas of a cold-region open-pit mine, and obtain the rock mass RMR parameters of each sample point through the sample point data;

[0080] S2. Determine the optimal power required for spatial interpolation calculation using the inverse distance power method using the sample point data collected in step S1, construct an RMR spatial distribution model based on the optimal power and the rock mass RMR parameters of each sample point obtained in step S1, and construct a low-temperature RMR spatial distribution model based on the relationship between rock mass and temperature obtained through rock mechanical strength tests under low temperature conditions;

[0081] S3. Obtain the drilling efficiency at different sample points, obtain the drilling efficiency at different spaces by the RMR spatial distribution model, the low-temperature RMR spatial distribution model and the drilling efficiency obtained in step S2, and obtain the drilling efficiency spatial distribution model and the low-temperature drilling efficiency spatial distribution model;

[0082] S4. Divide the cold-region open-pit mine into multiple mining units, obtain the perforation fuel consumption of each mining unit, and obtain a mining plan optimization model through the perforation efficiency spatial distribution model, low-temperature perforation efficiency spatial distribution model and perforation fuel consumption of each mining unit obtained in step S3. Optimize the production schedule of the cold-region open-pit mine through the mining plan optimization model.

[0083] Based on the analysis of extensive geological data, this example employed geostatistical methods to establish a three-dimensional, block-inhomogeneous model of rock mass quality that considers rock strength, structural surface characteristics, and water occurrence. Subsequently, combining multiple sets of field drilling data, a mapping relationship between rock mass quality and drilling efficiency was derived, and a drilling efficiency distribution model for open-pit mines was constructed. The three-dimensional geological model of the stope was then divided into a series of mining units. By adjusting the mining sequence of each unit and optimizing the stripping plan, refined mining in cold-region open-pit mines was achieved, thereby reducing drilling fuel consumption and lowering drilling costs.

[0084] Example 2: This example provides a method for optimizing production scheduling based on rock mass quality and drilling efficiency in a cold region open-pit mine, comprising the following steps:

[0085] S1. Collect data from multiple sample points at different spatial coordinates within different areas of a cold-region open-pit mine. The rock mass quality (RMR) parameters for each sample point are derived from this data. RMR is a rock quality evaluation and classification method. The RMR grading system primarily includes six parameters: rock mass strength, RQD value, joint spacing, joint condition, groundwater, and joint occurrence. Each parameter has a corresponding scoring criteria. The RMR value is then calculated by weightedly averaging the scores of the six parameters. Rock masses can be classified into different grades based on the RMR value.

[0086] As a preferred embodiment of the present invention, specifically, the sample point data includes the uniaxial compressive strength of the rock block, RQD, joint spacing, joint conditions, groundwater conditions and joint occurrence;

[0087] The rock mass RMR parameters of each sample point are calculated using the sample point data, as shown in formula (1):

[0088] RMR=R1+R2+R3+R4+R5+R6(1)

[0089] Here, R1 represents the uniaxial compressive strength score of the rock mass, which is based on rock strength; R2 represents the RQD score, which is assessed by measuring the integrity of the rock core; R3 represents the joint spacing score, which is based on measuring the distance between joints; R4 represents the joint condition score, which assesses the surface condition and filling of joints; R5 represents the groundwater condition score, which takes into account the influence of groundwater; and R6 represents the joint occurrence score, which takes into account the specific impact of the structural surface on the project. The uniaxial compressive strength score R1, the RQD value score R2, and the joint spacing score R3 are all obtained by consulting the rock mass quality parameter table.

[0090] S2. Determine the optimal power required for spatial interpolation using the inverse distance power method using the sample point data collected in step S1. Construct an RMR spatial distribution model based on the optimal power and the rock mass RMR parameters for each sample point obtained in step S1. Conduct rock mechanics strength tests under low-temperature conditions to determine the relationship between rock mass and temperature. Based on the RMR spatial distribution model, construct a low-temperature RMR spatial distribution model. The inverse method is a method used to express the spatial distribution of characteristics such as the geometry, geological structure, and physical and chemical properties of an ore body and its surrounding rock. In the inverse method, the properties of the estimated unit block are related to the properties of known points within a certain distance around it. This relationship is inversely proportional to the nth power of the distance from the known point to the center point of the estimated unit block.

[0091] As a preference of this embodiment, specifically, step S2 includes the following steps:

[0092] S21. Construct a spatial interpolation calculation model as shown in formula (2):

[0093] x v =∑b a x a (2)

[0094] Among them, x v Indicates the overall space quality, x a Indicates the grade of the ath sample point falling within the influence range, b a Indicates the weight between the a-th sample point involved in the valuation and the valued point, 1≤a≤A, A>1, A represents the number of sample points collected;

[0095] The weight b between the a-th sample point involved in the valuation and the valued point a As shown in formula (3):

[0096]

[0097] Among them, h a It represents the distance between the a-th sample point involved in the estimation and the estimated point, and N represents the optimal power in the inverse distance power method;

[0098] The optimal power N sample point data is input into the spatial interpolation calculation model and obtained through cross-validation. The steps include:

[0099] S211. Given a power N k , 1≤k≤m; m is the total number of powers;

[0100] S212. For the j-th sample point p j , 1≤j≤A, and remove the j-th sample point p in turn j , the estimation is to get the jth sample point estimation

[0101] S213. Calculate the j-th sample point p j and the jth sample point estimate The error is given by N k Root mean square error Re k , as shown in formula (4):

[0102]

[0103] in, represents the estimated value of the j-th sample point;

[0104] S214.k=k+1, repeat steps S211-S213 until k=m+1;

[0105] S215. Select the smallest root mean square error Re k The corresponding N k is the optimal power N, and the root mean square error under different powers N is as follows Figure 2 As shown;

[0106] S22. Using the octree algorithm, the 3D geological solid model of the mine is constructed as a block model consisting of blocks of equal size. Each block has independent spatial coordinates, and multiple attributes can be added to it in the attribute library. The subsequent rock mass quality model is constructed through spatial interpolation based on the block model.

[0107] S23. Construct a rock uniaxial compressive strength attribute database, an RQD attribute database, and a joint spacing attribute database in the block model constructed in step S22;

[0108] S24. Input the uniaxial compressive strength and spatial position coordinates of the sample point into the uniaxial compressive strength attribute database of the rock block; input the RQD and spatial position coordinates of the sample point into the RQD attribute database; input the joint spacing and spatial position coordinates of the sample point into the joint spacing attribute database; and obtain the spatial distribution block model using the spatial interpolation calculation model and the corresponding optimal power obtained in step S21;

[0109] S25. Construct a rock mass quality RMR parameter database in the spatially distributed block model obtained in step S24, and input the rock mass quality RMR parameters into the rock mass quality RMR parameter database, such as Figure 3 As shown in the figure, the RMR spatial distribution model is obtained. Through the rock mechanical strength test under low temperature conditions, the relationship between rock mass and temperature is obtained, as shown in the figure. Figure 4 As shown, a low-temperature RMR spatial distribution model is constructed based on the RMR spatial distribution model;

[0110] Through the rock mechanical strength test under low temperature conditions, it was found that the uniaxial compressive strength of low-temperature rock blocks gradually increases with the decrease of freezing temperature. The relationship between the uniaxial compressive strength of low-temperature rock blocks and temperature is shown in formula (5):

[0111] Q=-0.17T+10.3 (5)

[0112] Where Q represents the uniaxial compressive strength of low-temperature rock blocks, and T represents temperature.

[0113] S3. Obtain the drilling efficiency of the drill rig at different sample points. The drilling efficiency is the number of meters drilled per hour by the drill rig and is an important parameter that characterizes the fuel consumption of the drilling rig. Optimizing the fuel consumption of the drilling rig plays a significant role in reducing production costs. The drilling efficiency at different spatial locations is obtained by using the RMR spatial distribution model, the low-temperature RMR spatial distribution model, and the drilling rig drilling efficiency obtained in step S2. A spatial distribution model of the drilling efficiency and a spatial distribution model of the low-temperature drilling efficiency are obtained.

[0114] Step S3 includes:

[0115] S31. Obtaining drilling efficiency at different sample points;

[0116] S32. Figure 5 As shown in Figure 6, the correlation function between the rock mass RMR parameters and the drilling efficiency of the drilling rig is obtained by comparing the rock mass RMR parameters and the drilling efficiency of the drilling rig at different sample points, as shown in formula (6):

[0117] y=153.42x -0 .507 (6)

[0118] Where y represents the drilling efficiency of the drilling rig in meters per hour, and x represents the rock mass;

[0119] S33. Input the correlation function between the rock mass RMR parameters obtained in step S32 and the drilling rig's drilling efficiency into the RMR spatial distribution model to obtain a drilling efficiency spatial distribution model. Input the correlation function between the rock mass RMR parameters obtained in step S32 and the drilling rig's drilling efficiency into the low-temperature RMR spatial distribution model to obtain a low-temperature drilling efficiency spatial distribution model.

[0120] S4. Divide the cold-region open-pit mine into multiple mining units, obtain the drilling fuel consumption of each mining unit, and use the drilling efficiency spatial distribution model, low-temperature drilling efficiency spatial distribution model, and drilling fuel consumption of each mining unit obtained in step S3 to obtain a mining and stripping plan optimization model. The mining and stripping plan optimization model is used to optimize the production schedule of the cold-region open-pit mine. Open-pit production in high-altitude, cold-region mines is significantly affected by weather, and rock quality is also affected by temperature. The presence of various structural planes and weak interlayers in the rock mass weakens its strength. In low-temperature environments, the water in these structural planes freezes, which is equivalent to increasing the cohesion of the rock mass, thereby improving the overall strength of the rock mass. Therefore, in high-altitude, cold-region regions, rock quality is higher in winter than in summer, which results in higher drilling costs in the mine during winter than in other months. During the production schedule optimization design process, mining areas with low rock quality in winter and mining areas with high rock quality in other months can reduce drilling costs.

[0121] Step S4 includes:

[0122] S41. Divide the open-pit mine in cold regions into multiple mining units, such as Figure 6 As shown in the figure, mining units refer to the division of the ore body into sections that are convenient for recovering ore from it according to the requirements of the mining method. A three-dimensional geological model of the mine is constructed using three-dimensional modeling software, and then the three-dimensional geological model is divided into a series of mining units. The basis for the division can refer to the areas marked by the mine's blasting plan or the actual mining areas of the mine over the years. At the same time, the divided mining units are associated with the rock mass quality model and grade model to obtain the rock mass quality, perforation efficiency, metal grade and bulk density of each mining unit. The perforation fuel consumption of the mining unit is obtained through the perforation efficiency of the mining unit, as shown in formula (7):

[0123]

[0124] Among them, DX i represents the perforation oil consumption of the i-th mining unit, V i represents the volume of the i-th mining unit, R represents the rated fuel consumption of the drilling rig, d represents the length of a single drill hole, S represents the hole spacing, L represents the row spacing, and U i represents the drilling speed of the drilling rig in the i-th mining unit, and h represents the step height;

[0125] S42. The mining plan optimization model is obtained by the perforation efficiency spatial distribution model, the low-temperature perforation efficiency spatial distribution model, and the perforation oil consumption of each mining unit obtained in step S3. The objective function of the mining plan optimization model is shown in formula (8):

[0126]

[0127] Among them, n represents the number of mining units, T represents the mining cycle, t represents the tth mining time period, Q i It represents the value of the ore in the i-th mining unit, in yuan, H i represents the blasting, shoveling and transportation costs of the i-th mining unit, in yuan, DR i It represents the labor cost of drilling the i-th mining unit, in yuan, DY i represents the equipment repair cost of the perforation of the i-th mining unit, in yuan, β represents the discount rate, represents the rock mass of the i-th mining unit in tons, The decision variable representing whether mining unit i is mined in the tth mining period is 1 if it is mined and 0 if it is not mined;

[0128] The production capacity constraint is shown in formula (9):

[0129]

[0130] Among them, MC0 min Indicates the lower limit of ore mining capacity, in tons, ρ i Represents the average density of the i-th mining unit, in t / m 3 , MC max Indicates the upper limit of ore and rock mining capacity, in tons;

[0131] The ore processing capacity constraint is shown in formula (10):

[0132]

[0133] Among them, PC min Indicates the lower limit of ore and rock processing capacity, in tons. represents the ore proportion of the i-th mining unit, O i Indicates the average specific gravity of ore in the i-th mining unit, in t / m 3 , PC max Indicates the upper limit of ore and rock processing capacity, in tons;

[0134] The stripping ratio constraint condition is shown in formula (11):

[0135]

[0136] Among them, θ min Indicates the lower limit of production stripping ratio, represents the proportion of waste rock in the i-th production unit, W i The average weight of waste rock in the i-th mining unit, in t / m 3 ,θ max Indicates the upper limit of production stripping ratio;

[0137] The average grade constraint is shown in formula (12):

[0138]

[0139] Among them, g i represents the ore grade in the i-th mining unit, G max Indicates the upper limit of the average grade of the ore, G min Indicates the lower limit of the average grade of the ore;

[0140] The spatial constraints are shown in formulas (13) and (14):

[0141] X x,y,z -X x,y,z+1 ≤0 (13)

[0142]

[0143] Among them, x, y, z represent the spatial coordinates of the mining unit, x, y represent the horizontal coordinates of the mining unit, z represents the step where the mining unit is located, X x,y,z Represents the mining unit located at x,y,z, X x,y,z+1 represents the mining unit at x,y,z+1, X(t) x,y,z The decision variable indicating whether the mining unit will be mined in the next planning period is 1 if it is mined and 0 if it is not mined;

[0144] The mining reserve constraint condition is shown in formula (15):

[0145]

[0146] This means that in cold-region open-pit mines, any mining unit can only be mined once;

[0147] S43. Optimize the production schedule of cold-region open-pit mines through the objective function and constraints of the mining plan optimization model.

[0148] In the objective function of the optimized stripping plan in this embodiment, the fuel consumption cost of drilling per mining unit is no longer fixed but varies with the season. The improved rock quality in winter also increases the fuel consumption cost of drilling per mining unit. The mining plan is optimized by incorporating production capacity constraints, ore processing capacity constraints, stripping ratio constraints, average grade constraints, spatial constraints, and mined reserves constraints from the traditional stripping plan optimization model as constraints. This embodiment first determines the drilling efficiency of each mining unit based on the average temperature of each month and estimates the drilling fuel consumption of each mining unit accordingly. A production plan is then developed and the production scheduling optimization mathematical model is used to determine the mining units to be mined each month to minimize the total drilling fuel consumption for the entire production plan. During this process, a regression model is used to link rock quality parameters with the data from each drilling run to estimate the drilling efficiency and fuel consumption of each mining unit, thereby obtaining a corresponding drilling efficiency model. Ultimately, mining units with lower rock quality are prioritized in winter, while mining units with higher rock quality are prioritized in other months, achieving an optimized stripping plan.

[0149] Comparison of optimization results: The mining plan was optimized using Mineplan software, which can efficiently divide the three-dimensional geological model into mining units. This embodiment formulated a one-year mining plan, and used Mineplan software to compare the mine's original mining plan with the mining plan optimized based on rock mass quality in this embodiment. The results showed that the ore mining volume of the optimized scheme and the original scheme of this embodiment were both 11.184 million tons, and the rock stripping volume was both 89.444 million tons. The average perforation fuel consumption per ton of ore rock in the original scheme was 0.0338 kg / ton, consuming 3,398,195 kg of diesel; while the average perforation fuel consumption per ton of ore rock in the optimized scheme of this embodiment was reduced to 0.0323 kg / ton, consuming 3,250,284 kg of diesel, saving 147.9 tons of diesel, and reducing the perforation diesel cost by 1.0354 million yuan. The specific comparison results are shown in Tables 1 and Figure 7 shown.

[0150] Table 1 Comparison of mining and stripping plans

[0151]

[0152] Based on the analysis of extensive geological data, this example employed geostatistical methods to establish a three-dimensional, block-inhomogeneous model of rock mass quality that considers rock strength, structural surface characteristics, and water occurrence. Subsequently, combining multiple sets of field drilling data, a mapping relationship between rock mass quality and drilling efficiency was derived, and a drilling efficiency distribution model for open-pit mines was constructed. The three-dimensional geological model of the stope was then divided into a series of mining units. By adjusting the mining sequence of each unit and optimizing the stripping plan, refined mining in cold-region open-pit mines was achieved, thereby reducing drilling fuel consumption and lowering drilling costs.

[0153] The embodiments of the present invention are presented for purposes of illustration and description and are not intended to be exhaustive or to limit the invention to the disclosed forms. Many modifications and variations will be apparent to those skilled in the art. The embodiments are chosen and described in order to better illustrate the principles of the invention and its practical application and to enable those skilled in the art to understand the invention and design various embodiments with various modifications as suited for specific applications.

Claims

1. A production scheduling optimization method for cold-region open-pit mines based on rock mass quality and drilling efficiency, characterized in that: The steps include: S1. Collecting sample point data of multiple different spatial position coordinates in different areas of cold open-pit mines, and obtaining the rock mass RMR parameters of each sample point through the sample point data; S2. Determine the optimal power required for spatial interpolation calculation using the inverse distance power method using the sample point data collected in step S1, construct an RMR spatial distribution model based on the optimal power and the rock mass RMR parameters of each sample point obtained in step S1, obtain the relationship between rock mass and temperature through rock mechanics strength tests under low temperature conditions, and construct a low-temperature RMR spatial distribution model based on the RMR spatial distribution model; S3. Obtain the drilling efficiency at different sample points, obtain the drilling efficiency at different spaces by the RMR spatial distribution model, the low-temperature RMR spatial distribution model and the drilling efficiency obtained in step S2, and obtain the drilling efficiency spatial distribution model and the low-temperature drilling efficiency spatial distribution model; S4. Divide the cold-region open-pit mine into multiple mining units, obtain the perforation oil consumption of each mining unit, and obtain a mining plan optimization model through the perforation efficiency spatial distribution model, low-temperature perforation efficiency spatial distribution model and perforation oil consumption of each mining unit obtained in step S3. Optimize the production schedule of the cold-region open-pit mine through the mining plan optimization model.

2. The method for optimizing production scheduling based on rock mass quality and drilling efficiency in cold region open-pit mines according to claim 1, characterized in that: In step S1, the sample point data includes the uniaxial compressive strength, RQD, joint spacing, joint conditions, groundwater conditions and joint occurrence of the rock block; The rock mass RMR parameter of each sample point is calculated using the sample point data, as shown in formula (1): RMR=R1+R2+R3+R4+R5+R6 (1) Among them, R1 represents the uniaxial compressive strength score of the rock block, R2 represents the RQD score, R3 represents the joint spacing score, R4 represents the joint condition score, R5 represents the groundwater condition score, and R6 represents the joint occurrence score.

3. The method for optimizing production scheduling based on rock mass quality and drilling efficiency in cold region open-pit mines according to claim 2, characterized in that: The step S2 comprises the following steps: S21. Construct a spatial interpolation calculation model as shown in formula (2): x v =∑b a x a (2) Among them, x v Indicates the overall space quality, x a Indicates the grade of the ath sample point falling within the influence range, b a Indicates the weight between the a-th sample point involved in the valuation and the valued point, 1≤a≤A, A>1, A represents the number of sample points collected; The weight b between the a-th sample point involved in the valuation and the valued point a As shown in formula (3): Among them, h a It represents the distance between the a-th sample point involved in the estimation and the estimated point, and N represents the optimal power in the inverse distance power method; S22. Constructing the three-dimensional geological entity model of the mine into a block model composed of blocks of the same size based on the octree algorithm; S23. Constructing a rock uniaxial compressive strength attribute database, an RQD attribute database, and a joint spacing attribute database in the block model constructed in step S22; S24. Input the uniaxial compressive strength and spatial position coordinates of the rock block at the sample point into the rock block uniaxial compressive strength attribute database, input the RQD and spatial position coordinates of the sample point into the RQD attribute database, input the joint spacing and spatial position coordinates of the sample point into the joint spacing attribute database, and obtain a spatial distribution block model using the spatial interpolation calculation model and the corresponding optimal power obtained in step S21; S25. Construct a rock mass RMR parameter database in the spatially distributed block model obtained in step S24, input the rock mass RMR parameters into the rock mass RMR parameter database to obtain an RMR spatial distribution model, obtain the relationship between rock mass and temperature through rock mechanics strength tests under low temperature conditions, and construct a low-temperature RMR spatial distribution model based on the RMR spatial distribution model.

4. The method for optimizing production scheduling based on rock mass quality and drilling efficiency in cold region open-pit mines according to claim 3, characterized in that: In step S21, the optimal power N of the sample point data is input into the spatial interpolation calculation model and obtained through cross-validation, and the steps include: S211. Given a power N k , 1≤k≤m; m is the total number of powers; S212. For the j-th sample point p j , 1≤j≤A, and remove the j-th sample point p in turn j , the estimation is to get the jth sample point estimation S213. Calculate the j-th sample point p j and the j-th sample point estimate The error is given by N k Root mean square error Re k , as shown in formula (4): in, represents the estimated value of the j-th sample point; S214.k=k+1, repeat steps S211-S213 until k=m+1; S215. Select the smallest root mean square error Re k The corresponding N k is the optimal power N.

5. The method for optimizing production scheduling based on rock mass quality and drilling efficiency in cold region open-pit mines according to claim 3, characterized in that: In step S25, the rock mechanical strength test under low temperature conditions shows that the uniaxial compressive strength of the low-temperature rock block gradually increases with the decrease of the freezing temperature. The relationship between the uniaxial compressive strength of the low-temperature rock block and the temperature is shown in formula (5): Q=-0.17T+10.3 (5) Where Q represents the uniaxial compressive strength of low-temperature rock blocks, and T represents temperature.

6. The method for optimizing production scheduling based on rock mass quality and drilling efficiency in cold region open-pit mines according to claim 1, characterized in that: The step S3 comprises: S31. Obtaining drilling efficiency at different sample points; S32. The correlation function between the rock mass RMR parameter and the drilling efficiency is obtained by using the rock mass RMR parameter and the drilling efficiency at different sample points, as shown in formula (6): y=153.42x -0 .507 (6) Where y represents the drilling efficiency of the drilling rig in meters per hour, and x represents the rock mass; S33. Input the correlation function between the rock mass quality RMR parameter and the drilling rig drilling efficiency obtained in step S32 into the RMR spatial distribution model to obtain the drilling efficiency spatial distribution model. Input the correlation function between the rock mass quality RMR parameter and the drilling rig drilling efficiency obtained in step S32 into the low-temperature RMR spatial distribution model to obtain the low-temperature drilling efficiency spatial distribution model.

7. The method for optimizing production scheduling based on rock mass quality and drilling efficiency in cold region open-pit mines according to claim 1, characterized in that: The step S4 comprises: S41. Divide the cold-region open-pit mine into a plurality of mining units, obtain the rock mass, perforation efficiency, metal grade, and bulk density of each mining unit, and obtain the perforation fuel consumption of the mining unit by the perforation efficiency of the mining unit, as shown in formula (7): Among them, DX i represents the perforation oil consumption of the i-th mining unit, V i represents the volume of the i-th mining unit, R represents the rated fuel consumption of the drilling rig, d represents the length of a single drill hole, S represents the hole spacing, L represents the row spacing, and U i represents the drilling speed of the drilling rig in the i-th mining unit, and h represents the step height; S42. The mining plan optimization model is obtained by using the perforation efficiency spatial distribution model, the low-temperature perforation efficiency spatial distribution model, and the perforation oil consumption of each mining unit obtained in step S3. The objective function of the mining plan optimization model is shown in formula (8): Among them, n represents the number of mining units, T represents the mining cycle, t represents the tth mining time period, Q i represents the value of the ore in the i-th mining unit, H i represents the blasting, shoveling and transportation costs of the i-th mining unit, DR i represents the labor cost of drilling the i-th mining unit, DY i represents the equipment repair cost of the perforation of the i-th mining unit, β represents the discount rate, represents the rock mass quality of the i-th mining unit, The decision variable representing whether mining unit i is mined in the tth mining period is 1 if it is mined and 0 if it is not mined; The production capacity constraint is shown in formula (9): Among them, MC min Represents the lower limit of ore mining capacity, ρ i represents the average weight of the i-th mining unit, MC max Indicates the upper limit of ore and rock mining capacity; The ore processing capacity constraint is shown in formula (10): Among them, PC min Indicates the lower limit of ore and rock processing capacity, represents the ore proportion of the i-th mining unit, O i represents the average specific gravity of ore in the i-th mining unit, PC max Indicates the upper limit of ore and rock processing capacity; The stripping ratio constraint condition is shown in formula (11): Among them, θ min Indicates the lower limit of production stripping ratio, represents the proportion of waste rock in the i-th production unit, W i represents the average weight of waste rock in the i-th mining unit, θ max Indicates the upper limit of production stripping ratio; The average grade constraint is shown in formula (12): Among them, g i represents the ore grade in the i-th mining unit, G max Indicates the upper limit of the average grade of the ore, G min Indicates the lower limit of the average grade of the ore; The spatial constraints are shown in formulas (13) and (14): X x,y,z -X x,y,z+1 ≤0(13) Among them, x, y, z represent the spatial coordinates of the mining unit, x, y represent the horizontal coordinates of the mining unit, z represents the step where the mining unit is located, X x,y,z Represents the mining unit located at x,y,z, X x,y,z+1 represents the mining unit at x,y,z+1, X(t) x,y,z The decision variable indicating whether the mining unit will be mined in the next planning period is 1 if it is mined and 0 if it is not mined; The mining reserve constraint condition is shown in formula (15): This means that in cold-region open-pit mines, any mining unit can only be mined once; S43. Optimize the production schedule of the cold-region open-pit mine through the objective function and constraint conditions of the mining and stripping plan optimization model.

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