Rock mass quality and perforation efficiency-based production scheduling optimization method for strip mine in cold region

Abstract

The invention provides a production scheduling optimization method for a cold region strip mine based on rock mass quality and perforation efficiency. The method comprises the following steps: acquiring sample point data of a plurality of different spatial position coordinates in different regions of the cold region strip mine to obtain rock mass quality RMR parameters of each sample point; the optimal power of the distance power inverse proportion method is determined, and an RMR spatial distribution model and a low-temperature RMR spatial distribution model are constructed based on the optimal power and rock mass quality RMR parameters; drilling machine perforating efficiency at different sample points is obtained, and a perforating efficiency space distribution model and a low-temperature perforating efficiency space distribution model are obtained; the cold region strip mine is divided into a plurality of mining units, the perforation oil consumption of each mining unit is obtained, a mining and stripping plan optimization model is obtained, and the production scheduling of the cold region strip mine is optimized through the mining and stripping plan optimization model. Fine mining of the strip mine in the cold region can be achieved, perforation oil consumption is reduced, and the mining cost is reduced.

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 perforation efficiency. Background Art

[0002] With the development of my country's economy and the progress of society, the demand for metal mineral resources continues to increase. However, the amount of resources in low-altitude areas is gradually decreasing, and the development and rational utilization of metal mineral resources in high-altitude and cold areas has become very urgent. In addition, the development of mineral resources in the high-altitude and cold areas in the west is being promoted in an all-round and orderly manner. According to statistics, the number of mines that can be mined in high-altitude and cold areas has reached hundreds, but the harsh high-altitude environment also restricts the development of mining activities.

[0003] When producing in open-pit mines in cold regions, weather has a great impact on mining activities, and rock mass quality is also affected by temperature. There are various structural planes and weak interlayers in the rock mass, which will weaken its strength. In a low temperature environment, the water in the structural plane freezes, which is equivalent to increasing the cohesion of the rock mass, thereby increasing the overall strength of the rock mass. Therefore, in high-cold and high-altitude areas, the rock mass quality in winter is usually higher than in other months, which will cause the drilling cost of the mine to be higher in winter than in other months. In order to reduce costs, it is possible to consider mining areas with lower rock mass quality in winter and mining areas with higher rock mass quality in other months. Therefore, in low-grade mines, it becomes particularly important to finely characterize the drilling parameters to reduce drilling oil consumption and reduce drilling costs, but the existing cold open-pit mine scheduling optimization methods rarely consider the impact of weather on mining activities. Therefore, it is very necessary to study and design a new type of cold open-pit mine scheduling optimization method based on rock mass quality and drilling efficiency to solve the problems existing in the existing cold 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 perforation efficiency.

[0005] The present invention provides a method for optimizing production scheduling of a cold-region open-pit mine based on rock mass quality and perforation 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 calculating spatial interpolation by the inverse distance power method through 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 rig perforation efficiency at different sample points, obtain the perforation efficiency at different spaces through the RMR spatial distribution model, low-temperature RMR spatial distribution model and drilling rig perforation efficiency obtained in step S2, and obtain the perforation efficiency spatial distribution model and low-temperature perforation efficiency spatial distribution model;

[0009] 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.

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

[0011] The rock mass RMR parameter of each sample point is calculated by 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 method for optimizing production scheduling based on rock mass quality and perforation efficiency in a cold region open-pit mine 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 quality of the overall space, x arepresents the grade of the ath sample point falling into 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 ath sample point involved in the valuation and the valued point a As shown in formula (3):

[0019]

[0020] Among them, h a represents the distance between the ath sample point involved in the estimation and the estimated point, and N represents the best 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 block 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 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 through the spatial interpolation calculation model obtained in step S21 and the corresponding optimal power;

[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 a method for optimizing production scheduling based on rock mass quality and perforation efficiency in a cold-region open-pit mine in some embodiments of the present application, in step S21, the sample point data of the optimal power N is input into the spatial interpolation calculation model and obtained through cross-validation, and 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 jth sample point is estimated

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

[0029]

[0030] in, represents the estimate of the jth 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 method for optimizing production scheduling based on rock mass quality and drilling efficiency in a cold-region open-pit mine in some embodiments of the present application, in step S25, through a rock mechanics strength test under low temperature conditions, it is obtained that the uniaxial compressive strength of the low-temperature rock block gradually increases with the decrease of the freezing temperature, and the relationship between the uniaxial compressive strength of the low-temperature rock block and the 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, and T represents temperature.

[0036] According to a method for optimizing production scheduling based on rock mass quality and perforation efficiency in a cold region open-pit mine 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 of the drilling rig at different sample points, as shown in formula (6):

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

[0040] Among them, 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 method for optimizing production scheduling based on rock mass quality and perforation efficiency in a cold region open-pit mine 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 through 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 fuel 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] Where 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 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 the mining capacity of mineral rock;

[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 the ore in the i-th mining unit, PC max Indicates the upper limit of ore and rock processing capacity;

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

[0056]

[0057] Among them, θ min represents the lower limit of production stripping ratio, represents the waste rock proportion of 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, and x x,y,z represents the mining unit at x,y,z, X x,y,z+1 represents the mining unit located 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 plan optimization model.

[0069] Based on geological data and geostatistical methods, the present invention establishes a three-dimensional block non-uniform model of rock mass quality that takes into account rock strength, structural surface characteristics and water occurrence. At the same time, the method also considers the relationship between rock mass quality and temperature, and combines multiple sets of field perforation data to construct a mapping relationship between rock mass quality and perforation efficiency, thereby establishing a perforation efficiency distribution model for open-pit mines in summer and winter temperatures. Next, the three-dimensional geological model of the stope is divided into a series of mining units, and the mining plan is optimized by adjusting the mining sequence of each mining unit to achieve refined mining of open-pit mines in cold regions, reduce perforation fuel consumption, and reduce mining costs. BRIEF DESCRIPTION OF THE DRAWINGS

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

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

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

[0073] Figure 4 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 of 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 in conjunction with 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 perforation efficiency in a cold region open-pit mine. Figure 1 As shown, the following steps are included:

[0079] 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;

[0080] S2. Determine the optimal power required for calculating spatial interpolation by the inverse distance power method through the sample point data collected in step S1, build 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 build a low-temperature RMR spatial distribution model based on the RMR spatial distribution model;

[0081] S3. Obtain the drilling rig perforation efficiency at different sample points, obtain the perforation efficiency at different spaces through the RMR spatial distribution model, low-temperature RMR spatial distribution model and drilling rig perforation efficiency obtained in step S2, and obtain the perforation efficiency spatial distribution model and low-temperature perforation 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 a large amount of geological data, this embodiment uses geostatistical methods to establish a three-dimensional block non-uniform model of rock mass quality that takes into account rock strength, structural surface characteristics, and water occurrence. Then, combined with multiple sets of field perforation data, the mapping relationship between rock mass quality and perforation efficiency is obtained, and a perforation efficiency distribution model for open-pit mines is constructed. Then, 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 and optimizing the stripping plan, refined mining of open-pit mines in cold regions is achieved, thereby reducing perforation fuel consumption and reducing perforation 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 sample point data of multiple different spatial position coordinates in different areas of cold open-pit mines, and obtain the RMR parameters of rock mass quality at each sample point through the sample point data; RMR is a rock quality evaluation and classification method. The RMR classification system mainly includes six parameters: rock strength, RQD value, joint spacing, joint conditions, groundwater, joint occurrence, etc. Each parameter has a corresponding scoring standard, and finally the scores of the six parameters are weighted averaged to obtain the RMR value. According to the RMR value, the rock mass can be divided into different levels.

[0086] As a preferred embodiment of this invention, specifically, the sample point data include 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] Among them, R1 represents the uniaxial compressive strength score of the rock block, and R1 is scored according to the strength of the rock; R2 represents the RQD score, and R2 is evaluated by measuring the integrity of the rock core; R3 represents the joint spacing score, and R3 is scored by measuring the distance between joints; R4 represents the joint condition score, and R4 is used to evaluate the surface state and filling condition of the joints; R5 represents the groundwater condition score, and R5 considers the influence of groundwater; R6 represents the joint occurrence score, and R6 considers the specific influence of the structure on the project. The uniaxial compressive strength score R1 of the rock block, the RQD value score R2, and the joint spacing score R3 are all obtained by referring to the rock mass quality parameter table.

[0090] S2. Determine the optimal power required for calculating spatial interpolation by the inverse distance power method through the sample point data collected in step S1, build 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 build a low-temperature RMR spatial distribution model based on the RMR spatial distribution model; the inverse method is a method for expressing the spatial distribution of the geometric morphology, geological structure, physical and chemical properties of the ore body and its surrounding rock. In the inverse method, the attributes of the estimated unit block are related to the attributes of the known points within a certain distance around it, and 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 preferred embodiment of this invention, 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 quality of the overall space, x a represents the grade of the ath sample point falling into 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 ath sample point involved in the valuation and the valued point a As shown in formula (3):

[0096]

[0097] Among them, h a represents the distance between the ath sample point involved in the estimation and the estimated point, and N represents the best 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 jth sample point is estimated

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

[0102]

[0103] in, represents the estimate of the jth 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. Based on the octree algorithm, the three-dimensional geological entity model of the mine is constructed into a block model composed of blocks of the same 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 based on the block model through spatial interpolation;

[0107] S23. Constructing a rock block 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 rock block 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 through 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 parameter 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 mechanics strength test under low temperature conditions, the relationship between rock mass and temperature is obtained, as shown in 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 is 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, and T represents temperature.

[0113] S3. Obtain the drilling efficiency of the drilling rig at different sample points. The drilling efficiency is the number of meters drilled per hour by the drilling rig, and is an important parameter to characterize the fuel consumption of drilling. The optimization of the fuel consumption of drilling plays a very important role in reducing the production cost. The RMR spatial distribution model, the low-temperature RMR spatial distribution model and the drilling rig drilling efficiency obtained in step S2 are used to obtain the drilling efficiency at different spaces, and the drilling efficiency spatial distribution model and the low-temperature drilling efficiency spatial distribution model 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 using 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] Among them, 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 the 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 the low-temperature drilling efficiency spatial distribution model.

[0120] S4. Divide the open-pit mine in the cold region into multiple mining units, obtain the perforation fuel consumption of each mining unit, obtain the perforation efficiency spatial distribution model obtained in step S3, the low-temperature perforation efficiency spatial distribution model and the perforation fuel consumption of each mining unit to obtain the mining and stripping plan optimization model, and optimize the production scheduling of the open-pit mine in the cold region through the mining and stripping plan optimization model; open-pit production in high-cold and high-altitude mines is greatly affected by the weather, and the quality of the rock mass is also affected by the temperature. The presence of various structural planes and weak interlayers in the rock mass will weaken its strength. In a low-temperature environment, 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, the rock mass quality in winter in high-cold and high-altitude areas will be higher than in summer, which will cause the rock drilling cost of the mine in winter to be higher than in other months. In the process of production scheduling optimization design, mining areas with low rock mass quality in winter and mining areas with high rock mass quality in other months can reduce rock 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, mining units refer to the division of the ore body into sections that are convenient for recovering ore from the ore body according to the requirements of the mining method. The 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 area marked by the mine's blasting plan or the actual mining area 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 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):

[0126]

[0127] Where 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 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 Represents the lower limit of ore mining capacity, in tons, ρ i Represents the average specific gravity of the i-th mining unit, in t / m 3 , M.C. max It indicates the upper limit of the mining capacity of mineral rock, 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 Represents the average specific gravity of the ore in the i-th mining unit, in t / m 3 , PC max It indicates the upper limit of the ore and rock processing capacity, in tons;

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

[0135]

[0136] Among them, θ min represents the lower limit of production stripping ratio, represents the waste rock proportion of the i-th production unit, W i It represents 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, and x x,y,z represents the mining unit at x,y,z, X x,y,z+1 represents the mining unit located 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 open-pit mines in cold regions through the objective function and constraint conditions of the mining plan optimization model.

[0148] In the objective function of the mining plan after optimization in this embodiment, the fuel consumption cost of drilling of each mining unit is no longer fixed, but will change with the season. The rock mass quality improves in winter, which also increases the fuel consumption cost of drilling of each mining unit. At the same time, the production capacity constraint, ore processing capacity constraint, stripping ratio constraint, average grade constraint, space constraint and mining reserve constraint in the traditional mining plan optimization mathematical model are used as constraint conditions to optimize the mining plan. In this embodiment, the drilling efficiency of each mining unit is first determined according to the average temperature of different months, and the drilling fuel consumption of each mining unit is estimated accordingly. Then, a production plan is formulated, and the production scheduling optimization mathematical model is used to determine the mining units that need to be mined each month so that the total drilling fuel consumption of the entire production plan is minimized. In this process, based on the regression model, the rock mass quality parameters are linked to each drilling data, and the drilling efficiency and fuel consumption of each mining unit are estimated, thereby obtaining the corresponding drilling efficiency model. Finally, mining units with lower rock mass quality are preferentially mined in winter, while mining units with higher rock mass quality are preferentially mined in other months to achieve the optimization of the mining 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 show that the ore mining volume of the optimized scheme and the original scheme of this embodiment are both 11.184 million tons, and the rock stripping volume is 89.444 million tons. The average perforation fuel consumption per ton of ore rock in the original scheme is 0.0338kg / ton, consuming 3398195kg of diesel; while the average perforation fuel consumption per ton of ore rock in the optimized scheme of this embodiment is reduced to 0.0323kg / ton, consuming 3250284kg 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 a large amount of geological data, this embodiment uses geostatistical methods to establish a three-dimensional block non-uniform model of rock mass quality that takes into account rock strength, structural surface characteristics, and water occurrence. Then, combined with multiple sets of field perforation data, the mapping relationship between rock mass quality and perforation efficiency is obtained, and a perforation efficiency distribution model for open-pit mines is constructed. Then, 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 and optimizing the stripping plan, refined mining of open-pit mines in cold regions is achieved, thereby reducing perforation fuel consumption and reducing perforation costs.

[0153] The embodiments of the present invention are given for the purpose 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 of ordinary skill in the art. The embodiments are selected and described in order to better illustrate the principles and practical applications of the present invention and to enable those of ordinary skill in the art to understand the present invention and thereby design various embodiments with various modifications suitable for specific uses.

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 calculating spatial interpolation by the inverse distance power method through 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 rig perforation efficiency at different sample points, obtain the perforation efficiency at different spaces through the RMR spatial distribution model, low-temperature RMR spatial distribution model and drilling rig perforation efficiency obtained in step S2, and obtain the perforation efficiency spatial distribution model and low-temperature perforation efficiency spatial distribution model; 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.

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 is 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 by 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 is 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 quality of the overall space, x a represents the grade of the ath sample point falling into 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 ath sample point involved in the valuation and the valued point a As shown in formula (3): Among them, h a represents the distance between the ath sample point involved in the estimation and the estimated point, and N represents the best 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 block 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 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 through the spatial interpolation calculation model obtained in step S21 and the corresponding optimal power; 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 is characterized in that: In the 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: 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 The root mean square error Re k , as shown in formula (4): in, represents the estimate of the jth 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 is 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, 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 is 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 of the drilling rig at different sample points, as shown in formula (6): y=153.42x -0 .507 (6) Among them, 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 is 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 through 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, + i represents the drilling speed of the drilling rig in the i-th mining unit, and 6 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 fuel consumption of each mining unit obtained in step S3. The objective function of the mining plan optimization model is shown in formula (8): Where 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 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 the mining capacity of mineral rock; 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 the ore in the i-th mining unit, PC max Indicates the upper limit of ore and rock processing capacity; The stripping ratio constraint is shown in formula (11): Among them, θ min represents the lower limit of production stripping ratio, represents the waste rock proportion of 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, and x x,y,z represents the mining unit at x,y,z, X x,y,z+1 represents the mining unit located 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 plan optimization model.

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