A method for optimizing the mining boundary of open-pit mines based on the characterization of rock mass quality and mining cost

Abstract

The present invention proposes an open-pit mine boundary optimization method based on the characterization of rock mass quality mining cost. First, the sample point data of multiple spatial position coordinates of the open-pit mine are collected to determine the optimal power N required for calculating spatial interpolation by the inverse distance power method, and a rock mass quality RMR calculation model is constructed to obtain the spatial distribution model of RMR. Secondly, an explosive unit consumption calculation model is constructed, and the spatial distribution model of RMR is used to obtain the spatial model of explosive unit consumption and the spatial model of blasting cost. Finally, a value mining calculation model is constructed, and the spatial model of explosive unit consumption and the spatial model of blasting cost are used to obtain the spatial distribution model of mining cost, and further obtain the spatial distribution model of mining value for open-pit mine boundary estimation. Based on the regression model, the present invention links the rock mass quality parameter RMR with the progress of each blasting, estimates the explosive unit consumption of each area, and then obtains the corresponding blasting cost model and mining value model, and finally performs boundary optimization.

Description

Technical Field

[0001] The invention belongs to the field of mining technology and blasting, and specifically discloses an open-pit mine boundary optimization method based on rock mass quality mining cost characterization.

[0002] Background

[0003] In recent years, with the increase in the depth of open-pit mining, the geological conditions in the area to be blasted have become more complex, with multiple rock and mineral interlayers coexisting, making it difficult to distinguish specific types of ore and rock. Designing blasting parameters based on past experience has resulted in large blocks and roots being easily produced after blasting, resulting in poor blasting effects and increased production costs. Therefore, it is very important to finely characterize blasting parameters in low-grade mines to improve blasting effects and reduce blasting costs.

[0004] Based on this, 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 based on the analysis of a large amount of geological data and based on geostatistical methods, and then combines multiple sets of on-site blasting data to obtain the mapping relationship between rock mass quality and explosive unit consumption, and constructs an explosive unit consumption and blasting cost distribution model for open-pit mines. Finally, the final state optimization is carried out to achieve refined mining of low-grade open-pit mines, improve production efficiency, and reduce mining costs. Summary of the invention

[0005] In order to solve the problem that blasting parameters are designed according to past experience in existing open-pit mining, which leads to the generation of large blocks and roots after blasting, poor blasting effect and increased production cost, the present invention proposes an open-pit mine boundary optimization method based on rock mass quality mining cost characterization.

[0006] The present invention provides an open-pit mine boundary optimization method based on rock mass quality mining cost characterization, comprising the following steps:

[0007] S1. Collect sample point data of multiple spatial position coordinates of open-pit mines;

[0008] S2. Determine the optimal power N required for calculating spatial interpolation by the inverse distance power method using the sample point data collected in step S1, construct a rock mass quality RMR calculation model based on the optimal power N and the sample point data, and obtain the spatial distribution model of the RMR;

[0009] S3. Constructing a calculation model for explosive unit consumption, using the spatial distribution model of the RMR obtained in step S2 to obtain a spatial model of explosive unit consumption and a spatial model of blasting cost;

[0010] S4. Construct a value mining calculation model, use the spatial model of the explosive unit consumption and the spatial model of the blasting cost obtained in step S3 to obtain a spatial distribution model of the mining cost, and further obtain a spatial distribution model of the mining value for open-pit mine boundary estimation.

[0011] According to an open-pit mine boundary optimization method based on rock mass quality mining cost characterization in some embodiments of the present application, the sample point data in step S1 is used to calculate the rock mass quality RMR, and the RMR is calculated by formula (1):

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

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

[0014] According to an open-pit mine boundary optimization method based on rock mass quality mining cost characterization in some embodiments of the present application, the step S2 of constructing the rock mass quality RMR calculation model includes:

[0015] S21. Construct a spatial interpolation calculation model as shown in formula (2) and formula (3) and write a spatial interpolation calculation program.

[0016]

[0017] Where b i is the weight between the i-th sample point involved in the valuation and the valued point, 1≤i≤n, n>1, is the number of sample points collected in step S1, h i is the distance between the i-th sample point involved in the estimation and the estimated point, x v is the grade of the calculation module, x i is the grade of the i-th sample point falling into the influence range, N is the best power in the inverse distance power method;

[0018] 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;

[0019] S23. Based on the block model constructed in step S22, a rock block uniaxial compressive strength attribute database S1, an RQD attribute database S2 and a joint spacing attribute database S3 are constructed;

[0020] S24. Input the sample points and spatial position coordinates of the uniaxial compressive strength of the rock block into the uniaxial compressive strength attribute database S1 of the rock block, call the spatial interpolation calculation program constructed in step S21, and input the corresponding optimal power N to obtain the spatial distribution block model of the uniaxial compressive strength of the rock block;

[0021] S25. Input the sample points and spatial position coordinates of the RQD into the RQD attribute database S2, call the spatial interpolation calculation program constructed in step S21, and input its corresponding optimal power N to obtain the spatial distribution block model of the RQD;

[0022] S26. Input the sample points and spatial position coordinates of the joint spacing into the joint spacing attribute database S3, call the spatial interpolation calculation program constructed in step S21, and input its corresponding optimal power N to obtain the spatial distribution block model of the joint spacing;

[0023] S27. Based on the block model constructed in step S22, a rock block uniaxial compressive strength score attribute database SR1 is constructed to store the rock block uniaxial compressive strength score R1, an RQD score attribute database SR2 is constructed to store the RQD value score R2, and a joint spacing score attribute database SR3 is constructed to store the joint spacing score R3, and a score distribution model corresponding to each block is obtained through a segmented expression;

[0024] S28. Based on the block model constructed in step S22, a joint condition attribute database SR4 is constructed to store the joint condition R4, a groundwater condition attribute database SR5 is constructed to store the groundwater condition R5, and a joint occurrence attribute database SR6 is constructed to store the joint occurrence R6;

[0025] S29. Based on the block model constructed in step S22, an attribute database SR7 of RMR is constructed to store the RMR, and the RMR of each block is calculated using formula (1) to obtain a spatial distribution model of the RMR.

[0026] According to an open-pit mine boundary optimization method based on rock mass quality mining cost characterization in some embodiments of the present application, the optimal power N is obtained by inputting the sample point data collected in step S1 into the rock mass quality RMR calculation model and cross-validating, and the steps include:

[0027] S211. Given a power N k , 1≤k≤m; m is the total number of powers;

[0028] S212. For the j-th sample point p j , 1≤j≤n, and remove the jth sample point p in turnj , the jth sample point is estimated

[0029] S213. Calculate the j-th sample point p j and the j-th sample point estimate The error is calculated by using formula (4) to obtain the given N k The root mean square error Re k ,

[0030]

[0031] In the formula, represents the estimate of the jth sample point, p j represents the measured value of the jth sample point;

[0032] S214.k=k+1, repeat steps S211-S213 until k=m+1;

[0033] S215. Select the smallest root mean square error Re k The corresponding N k is the optimal power N.

[0034] According to an open-pit mine boundary optimization method based on rock mass quality mining cost characterization in some embodiments of the present application, step S3 includes:

[0035] S31. Constructing explosive consumption attribute database SR8 and blasting cost attribute database SR9 based on the block model constructed in step S22;

[0036] S32. Calculate the explosive consumption of each block using formula (5) and store it in the explosive consumption attribute database SR8 to obtain the spatial distribution model of the explosive consumption.

[0037] y=0.23+0.02e ((RMR-25) / 17.7) (5)

[0038] In the formula, y represents the unit consumption of explosives, in kilograms per ton;

[0039] S33. Calculate the blasting cost of each block using cost formula (6) and store it in the blasting cost attribute database SR9 to obtain the spatial distribution model of the blasting cost.

[0040] C B =P B y(6)

[0041] In the formula, C B is the blasting cost, in yuan / ton; y is the explosive consumption, in kilograms / ton; P Bis the price corresponding to the unit consumption of explosives, Yuan / kg.

[0042] According to an open-pit mine boundary optimization method based on rock mass quality mining cost characterization in some embodiments of the present application, the step S4 of constructing a value mining model includes:

[0043] S41. Constructing a mining cost attribute database SR based on the block model constructed in step S22 10 and mining value attribute database SR 11 ;

[0044] S42. Calculate the mining cost of each block using formula (7) and store it in the mining cost attribute database SR 10 , and obtain the spatial distribution model of the mining cost,

[0045] C M =C B +C O (7)

[0046] In the formula, C M is the total mining cost of ore or waste rock, in yuan / ton, C B is the blasting cost, in RMB / ton, C O is other production costs, in Yuan / ton;

[0047] S42. Calculate the mining value of each block using formula (8) and formula (9) and store it in the mining value attribute database SR 10 , and obtain the spatial distribution model of the mining value,

[0048] V ore =g Au p Au +g Cu p Cu -C ore (8)

[0049] Where V ore is the mining value of the ore, in yuan / ton, g Au is the grade of Au, in grams per ton, g Cu is the grade of Cu, in grams per ton, p Au is the price of Au, in yuan / g, p Cu is the price of Cu, in yuan / gram, C ore is the mining cost of the ore, in yuan / ton, calculated by formula (7),

[0050] V faste =-C faste (9)

[0051] Where Vfaste is the mining value of waste rock, in yuan / ton, C faste is the mining cost of waste rock, in Yuan / ton, calculated by formula (7).

[0052] According to an open-pit mine boundary optimization method based on rock mass quality mining cost characterization in some embodiments of the present application, the step S4 further includes:

[0053] S51. Using 3DMine software to construct a spatial distribution model of the mining cost of rock mass quality;

[0054] S52. Using 3DMine software to construct a spatial distribution model of the mining value of rock mass quality;

[0055] S53. Compare the mining cost and the mining value under different RMR values ​​to determine the optimal RMR value.

[0056] According to an open-pit mine boundary optimization method based on rock quality mining cost characterization in some embodiments of the present application, the rock block uniaxial compressive strength score R1, the RQD value score R2 and the joint spacing score R3 in step S26 are all obtained by referring to the rock quality parameter table, and the score distribution model corresponding to each block is obtained through a segmented expression.

[0057] The technical solution provided by the present invention establishes a three-dimensional block inhomogeneous model of rock mass quality taking into account rock strength, structural surface characteristics and water occurrence status based on a geostatistical method, and obtains a mapping relationship between rock mass quality and unit consumption of explosives by combining multiple sets of on-site blasting data, thereby constructing a distribution model of unit consumption of explosives and blasting costs for open-pit mines, and ultimately achieving open-pit mine boundary optimization, realizing refined mining of low-grade open-pit mines, improving production efficiency and reducing mining costs. BRIEF DESCRIPTION OF THE DRAWINGS

[0058] Figure 1 A schematic flow chart of an open-pit mine boundary optimization method based on rock mass quality mining cost characterization provided in Example 1 of the present invention;

[0059] Figure 2 The root mean square error under different given optimal power values ​​provided in Embodiment 3 of the present invention;

[0060] Figure 3 The spatial distribution model of the uniaxial compressive strength of rock blocks provided in Example 3 of the present invention;

[0061] Figure 4 The spatial distribution model of the uniaxial compressive strength score R1 of the rock block provided in Example 3 of the present invention;

[0062] Figure 5The spatial distribution model of RQD provided in Example 3 of the present invention;

[0063] Figure 6 The RQD value scoring R2 spatial distribution model provided in Example 3 of the present invention;

[0064] Figure 7 The spatial distribution model of RMR provided in Example 3 of the present invention;

[0065] Figure 8 Correlation coefficients between different geological parameters and explosive unit consumption provided in Example 3 of the present invention;

[0066] Fig. 9 The corresponding relationship between rock strength and explosive consumption per unit provided in Example 3 of the present invention;

[0067] Fig.10 The corresponding relationship between rock mass quality and explosive consumption per unit provided in Example 3 of the present invention;

[0068] Fig.11 The spatial distribution model of rock explosive unit consumption provided by Example 3 of the present invention;

[0069] Fig.12 The spatial distribution model of blasting cost provided in Example 3 of the present invention;

[0070] Fig.13 The spatial distribution model of mining costs provided in Example 3 of the present invention;

[0071] Fig.14 The spatial distribution model of mining value provided by Example 3 of the present invention;

[0072] Fig.15 The optimized housing of Solution 1 and Solution 2 provided in Example 3 of the present invention;

[0073] Fig.16 The difference in total blasting costs between Scheme 1 and Scheme 2 provided in Example 3 of the present invention. DETAILED DESCRIPTION

[0074] 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.

[0075] Example 1: This example provides an open-pit mine boundary optimization method based on rock mass quality mining cost characterization, such as Figure 1 As shown, the following steps are included:

[0076] S1. Collect sample point data of multiple spatial position coordinates in open-pit mines.

[0077] S2. Determine the optimal power N required for calculating spatial interpolation using the inverse distance power method using the sample point data collected in step S1, construct a rock mass RMR calculation model based on the optimal power N and the sample point data, and obtain a spatial distribution model of the RMR.

[0078] S3. Constructing a calculation model for explosive unit consumption, and using the spatial distribution model of the RMR obtained in step S2 to obtain a spatial model of explosive unit consumption and a spatial model of blasting cost.

[0079] S4. Construct a value mining calculation model, use the spatial model of the explosive unit consumption and the spatial model of the blasting cost obtained in step S3 to obtain a spatial distribution model of the mining cost, and further obtain a spatial distribution model of the mining value for open-pit mine boundary estimation.

[0080] The explosive unit consumption prediction model based on rock mass quality and the open-pit mine boundary optimization provided in this embodiment are based on the analysis of a large amount of geological data and the geostatistical method 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, the mapping relationship between rock mass quality and explosive unit consumption is obtained by combining multiple sets of on-site blasting data, and the explosive unit consumption and blasting cost distribution model of the open-pit mine is constructed. Finally, the final boundary optimization is carried out to realize the refined mining of low-grade open-pit mines, improve production efficiency, and reduce mining costs.

[0081] Embodiment 2: This embodiment provides an open-pit mine boundary optimization method based on rock mass quality mining cost characterization, comprising the following steps:

[0082] S1. Collect sample point data of multiple spatial position coordinates in open-pit mines.

[0083] Preferably, the sample point data in step S1 in this embodiment is used to calculate the rock mass RMR, and the RMR is calculated by formula (1):

[0084] RMR=R1+R2+R3+R4+R5+R6(1)

[0085] Where R1 is the uniaxial compressive strength score of the rock block, R2 is the RQD value score, R3 is the joint spacing score, R4 is the joint condition, R5 is the groundwater condition, and R6 is the joint occurrence.

[0086] S2. Determine the optimal power N required for calculating spatial interpolation using the inverse distance power method using the sample point data collected in step S1, construct a rock mass RMR calculation model based on the optimal power N and the sample point data, and obtain a spatial distribution model of the RMR.

[0087] Preferably, in this embodiment, constructing the rock mass model includes:

[0088] S21. Construct a spatial interpolation calculation model as shown in formula (2) and formula (3) and write a spatial interpolation calculation program.

[0089]

[0090] x v =∑b i x i (3)

[0091] Where b i is the weight between the i-th sample point involved in the valuation and the valued point, 1≤i≤n, n>1, is the number of sample points collected in step S1, h i is the distance between the i-th sample point involved in the estimation and the estimated point, x v is the grade of the calculation module, x i is the grade of the i-th sample point falling into the influence range, and N is the optimal power in the inverse distance power method.

[0092] Preferably, the optimal power N in this embodiment is obtained by inputting the sample point data collected in step S1 into the rock mass quality RMR calculation model and cross-validating, and the steps include:

[0093] S211. Given a power N k , 1≤k≤m; m is the total number of powers.

[0094] S212. For the j-th sample point p j , 1≤j≤n, and remove the jth sample point p in turn j , the jth sample point is estimated

[0095] S213. Calculate the j-th sample point p j and the j-th sample point estimate The error is calculated by using formula (4) to obtain the given N k The root mean square error Re k ,

[0096]

[0097] In the formula, represents the estimate of the jth sample point, p j Represents the measurement value of the jth sample point.

[0098] S214.k=k+1, repeat steps S211-S213 until k=m+1.

[0099] S215. Select the smallest root mean square error Rek The corresponding N k is the optimal power N.

[0100] 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. Preferably, each block of the block model in this embodiment has independent spatial coordinates, and multiple attributes can be added to it. The subsequent rock mass quality RMR calculation model is constructed based on the block model for spatial difference.

[0101] S23. Based on the block model constructed in step S22, a rock block uniaxial compressive strength attribute database S1, a RQD attribute database S2 and a joint spacing attribute database S3 are constructed.

[0102] S24. Input the sample points and spatial position coordinates of the uniaxial compressive strength of the rock block into the uniaxial compressive strength attribute database S1 of the rock block, call the spatial interpolation calculation program constructed in step S21, and input the corresponding optimal power N to obtain the spatial distribution block model of the uniaxial compressive strength of the rock block.

[0103] S25. Input the sample points and spatial position coordinates of the RQD into the RQD attribute database S2, call the spatial interpolation calculation program constructed in step S21, and input its corresponding optimal power N to obtain the spatial distribution block model of the RQD.

[0104] S26. Input the sample points and spatial position coordinates of the joint spacing into the joint spacing attribute database S3, call the spatial interpolation calculation program constructed in step S21, and input its corresponding optimal power N to obtain the spatial distribution block model of the joint spacing. Preferably, the rock block uniaxial compressive strength score R1, the RQD value score R2 and the joint spacing score R3 in this embodiment are all obtained by consulting the rock mass quality parameter table, and the score distribution model corresponding to each block is obtained by a segmented expression.

[0105] S27. Based on the block model constructed in step S22, a rock block uniaxial compressive strength scoring attribute database SR1 is constructed to store the rock block uniaxial compressive strength score R1, an RQD scoring attribute database SR2 is constructed to store the RQD value score R2, and a joint spacing scoring attribute database SR3 is constructed to store the joint spacing score R3, and a scoring distribution model corresponding to each block is obtained through a segmented expression.

[0106] S28. Based on the block model constructed in step S22, a joint condition attribute database SR4 is constructed to store the joint condition R4, a groundwater condition attribute database SR5 is constructed to store the groundwater condition R5, and a joint attitude attribute database SR6 is constructed to store the joint attitude R6.

[0107] S29. Based on the block model constructed in step S22, an attribute database SR7 of RMR is constructed to store the RMR, and the RMR of each block is calculated using formula (1) to obtain a spatial distribution model of the RMR.

[0108] S31. Construct an explosive consumption attribute database SR8 and a blasting cost attribute database SR9 based on the block model constructed in step S22.

[0109] S32. Calculate the explosive consumption of each block using formula (5) and store it in the explosive consumption attribute database SR8 to obtain the spatial distribution model of the explosive consumption.

[0110] y=0.23+0.02e ((RMR-25) / 17.7) (5)

[0111] Where y represents the unit consumption of explosives, in kilograms per ton.

[0112] S33. Calculate the blasting cost of each block using cost formula (6) and store it in the blasting cost attribute database SR9 to obtain the spatial distribution model of the blasting cost.

[0113] C B =P B y(6)

[0114] In the formula, C B is the blasting cost, in yuan / ton; y is the explosive consumption, in kilograms / ton; P B is the price corresponding to the unit consumption of explosives, Yuan / kg.

[0115] S41. Constructing a mining cost attribute database SR based on the block model constructed in step S22 10 and mining value attribute database SR 11 .

[0116] S42. Calculate the mining cost of each block using formula (7) and store it in the mining cost attribute database SR 10 , and obtain the spatial distribution model of the mining cost,

[0117] C M =C B +C O (7)

[0118] In the formula, CM is the total mining cost of ore or waste rock, in yuan / ton, C B is the blasting cost, in RMB / ton, C O is other production costs, in Yuan / ton.

[0119] S43. Calculate the mining value of each block using formula (8) and formula (9) and store it in the mining value attribute database SR 10 , and obtain the spatial distribution model of the mining value,

[0120] V ore =g Au p Au +g Cu p Cu -C ore (8)

[0121] Where V ore is the mining value of the ore, in yuan / ton, g Au is the grade of Au, in grams per ton, g Cu is the grade of Cu, in grams per ton, p Au is the price of Au, in yuan / g, p Cu is the price of Cu, in yuan / gram, C ore is the mining cost of the ore, in yuan / ton, calculated by formula (7),

[0122] V faste =-C faste (9)

[0123] Where V faste is the mining value of waste rock, in yuan / ton, C faste is the mining cost of waste rock, in Yuan / ton, calculated by formula (7).

[0124] The spatial distribution model of mining cost is obtained by utilizing the spatial model of unit consumption of explosives and the spatial model of blasting cost, and the spatial distribution model of mining value is further obtained for open-pit mine boundary estimation.

[0125] Preferably, the open-pit mine boundary optimization method based on rock mass quality mining cost characterization of this embodiment further includes after step S4:

[0126] S51. Use 3DMine software to construct a spatial distribution model of the mining cost of rock mass quality.

[0127] S52. Use 3DMine software to construct a spatial distribution model of the mining value of rock mass quality.

[0128] S53. Compare the mining cost and the mining value under different RMR values ​​to determine the optimal RMR value.

[0129] Embodiment 3, this embodiment provides an open-pit mine boundary optimization method based on rock mass quality mining cost characterization, comprising the following steps:

[0130] S1. Collect sample point data of multiple spatial position coordinates in open-pit mines.

[0131] S2. Determine the optimal power N required for calculating spatial interpolation using the inverse distance power method using the sample point data collected in step S1, construct a rock mass RMR calculation model based on the optimal power N and the sample point data, and obtain a spatial distribution model of the RMR.

[0132] Preferably, in this embodiment, constructing the rock mass model includes constructing a spatial interpolation calculation model and writing a spatial interpolation calculation program.

[0133] Preferably, the optimal power N in this embodiment is obtained by inputting the sample point data collected in step S1 into the rock mass quality RMR calculation model and cross-validating, and the steps include:

[0134] S211. Given a power N k , 1≤k≤m; m is the total number of powers.

[0135] S212. For the j-th sample point p j , 1≤j≤n, and remove the jth sample point p in turn j , the jth sample point is estimated

[0136] S213. Calculate the j-th sample point p j and the j-th sample point estimate The error is calculated and given N k The root mean square error Re k ,in, represents the estimate of the jth sample point, p j Represents the measurement value of the jth sample point.

[0137] S214.k=k+1, repeat steps S211-S213 until k=m+1.

[0138] S215. Select the smallest root mean square error Re k The corresponding N k is the optimal power N.

[0139] In this embodiment, a total of 382 specimen strengths, 78 joint parameters and 4642 RQD values ​​were collected to test the optimal powers of these sample points. Figure 2 is the root mean square error Re under different given optimal power values k , root mean square error Re k With the increase of power, there is a trend of decreasing first and then increasing. The optimal power N of RQD and joint spacing obtained by cross-validation procedure is 3.5 and 1.5 respectively. The uniaxial compressive strength power N of granite, diorite, fracture zone, weathering layer and fault are 3.5, 3.5, 2.5, 1 and 2.5 respectively.

[0140] S22. Based on the octree algorithm, the three-dimensional geological entity model of the mine is constructed as a block model composed of blocks of the same size. Preferably, each block of the block model in this embodiment has independent spatial coordinates, and multiple attributes can be added to it. The subsequent rock mass quality RMR calculation model is constructed based on the block model for spatial difference. In this embodiment, the three-dimensional geological entity model of the mine is converted into 456782 block models with a single block size of 12×12×12 based on the octree algorithm program.

[0141] S23. Based on the block model constructed in step S22, a rock block uniaxial compressive strength attribute database S1, a RQD attribute database S2 and a joint spacing attribute database S3 are constructed.

[0142] S24. Input the sample points and spatial position coordinates of the uniaxial compressive strength of the rock block into the uniaxial compressive strength attribute database S1 of the rock block, call the spatial interpolation calculation program constructed in step S21, and input the corresponding optimal power N to obtain the spatial distribution block model of the uniaxial compressive strength of the rock block.

[0143] S25. Input the sample points and spatial position coordinates of the RQD into the RQD attribute database S2, call the spatial interpolation calculation program constructed in step S21, and input its corresponding optimal power N to obtain the spatial distribution block model of the RQD.

[0144] S26. Input the sample points and spatial position coordinates of the joint spacing into the joint spacing attribute database S3, call the spatial interpolation calculation program constructed in step S21, and input its corresponding optimal power N to obtain the spatial distribution block model of the joint spacing. Preferably, the rock block uniaxial compressive strength score R1, the RQD value score R2 and the joint spacing score R3 in this embodiment are all obtained by consulting the rock mass quality parameter table, and the score distribution model corresponding to each block is obtained by a segmented expression.

[0145] S27. Based on the block model constructed in step S22, a rock block uniaxial compressive strength scoring attribute database SR1 is constructed to store the rock block uniaxial compressive strength score R1, an RQD scoring attribute database SR2 is constructed to store the RQD value score R2, and a joint spacing scoring attribute database SR3 is constructed to store the joint spacing score R3, and a scoring distribution model corresponding to each block is obtained through a segmented expression.

[0146] S28. Based on the block model constructed in step S22, a joint condition attribute database SR4 is constructed to store the joint condition R4, a groundwater condition attribute database SR5 is constructed to store the groundwater condition R5, and a joint attitude attribute database SR6 is constructed to store the joint attitude R6.

[0147] S29. Based on the block model constructed in step S22, construct an attribute database SR7 of RMR for storing the RMR, calculate the RMR of each block using the formula RMR=R1+R2+R3+R4+R5+R6, and obtain the spatial distribution model of the RMR.

[0148] Preferably, this embodiment provides Figure 3 The spatial distribution model of the uniaxial compressive strength of the rock block shown in Figure 4 The spatial distribution model of the rock block uniaxial compressive strength score R1 is shown in FIG. Figure 5 The spatial distribution model of RQD shown in Figure 6 The RQD value score R2 spatial distribution model shown in the figure is then obtained as follows Figure 7 The spatial distribution model of the RMR is shown.

[0149] S31. Construct an explosive consumption attribute database SR8 and a blasting cost attribute database SR9 based on the block model constructed in step S22.

[0150] Explosive consumption per unit is an important parameter to characterize the blasting effect. Reasonable selection of explosive consumption per unit plays a very important role in slope safety production and economic cost.

[0151] S32. Substitute the data of the RMR attribute database SR7 into the data of rock strength and explosive consumption by interpolation fitting y=0.23+0.02e ((RMR-25) / 17.7) The explosive consumption per block is calculated and stored in the explosive consumption attribute database SR8 to obtain the spatial distribution model of the explosive consumption. Figure 8The Pearson correlation coefficients between different geological parameters and explosive consumption are shown. The correlation coefficient between RMR and explosive consumption is the highest, which is 0.877, indicating that RMR is significantly positively correlated with explosive consumption. Secondly, UCS, RQD and spacing are also significantly positively correlated with explosive consumption. The correlation coefficient between rock density and explosive consumption is the lowest, which is 0.25, indicating that the correlation between rock density and explosive consumption is poor.

[0152] Fig. 9 The corresponding relationship between rock strength and explosive consumption in this embodiment shows that as rock strength increases, explosive consumption increases. However, the fitting degree between rock strength and explosive consumption is 0.44, and the fitting effect is not good. If only rock strength is used as the selection parameter of explosive consumption in a mine, a less than ideal value will be obtained, and even the opposite result will occur.

[0153] The object of slope blasting technology is rock mass rather than rock. Therefore, after the blasting block size is determined, the blasting effect is not only related to the rock strength, but also greatly affected by the original geological parameters of the rock mass (joint distribution characteristics, water occurrence state). Although these original geological parameters of the rock mass (joints, water) reduce the quality of the rock mass, they improve the blasting effect to a certain extent. Fig.10 is the corresponding relationship between rock mass quality and explosive unit consumption in this embodiment. It can be seen that the fitting degree between rock mass quality and explosive unit consumption is 0.88, and the fitting effect is very good. Fig.11 The spatial distribution model of explosive consumption in rock mass is shown.

[0154] S33. Substitute the explosive unit consumption attribute database SR8 into the cost formula C B =P B y calculates the blasting cost of each block and stores it in the blasting cost attribute database SR9, and obtains Fig.12 The spatial distribution model of blasting costs is shown.

[0155] S41. Constructing a mining cost attribute database SR based on the block model constructed in step S22 10 and mining value attribute database SR 11 .

[0156] S42. Calculate the mining cost of each block and store it in the mining cost attribute database SR 10 , and get Fig.13 The spatial distribution model of the mining cost of this embodiment is shown.

[0157] S43. Calculate the mining value of each block and store it in the mining value attribute database SR 10 , and get Fig.14 The spatial distribution model of the mining value of this embodiment is shown.

[0158] The spatial distribution model of mining cost is obtained by utilizing the spatial model of unit consumption of explosives and the spatial model of blasting cost, and the spatial distribution model of mining value is further obtained for open-pit mine boundary estimation.

[0159] Preferably, the open-pit mine boundary optimization method based on rock mass quality mining cost characterization of this embodiment also includes open-pit mine boundary estimation and optimization. This embodiment provides two comparison schemes and uses 3DMine software for visual display of boundary optimization.

[0160] Solution 1: The mining cost of rock mass quality and its value block model obtained by the open-pit mine boundary optimization method based on the characterization of rock mass quality mining cost provided in this embodiment.

[0161] Option 2: Use existing technology to obtain mining costs and their corresponding value block model.

[0162] Fig.15 The optimized shells obtained by Scheme 1 and Scheme 2 in this embodiment are shown in Table 1. Table 1 is a comparison of the optimized data of Scheme 1 and Scheme 2. It can be seen that compared with Scheme 2, Scheme 1 in this embodiment has an additional mining of 0.28Mt, a reduction of 0.41Mt in rock stripping, and a reduction of 21.43 million in blasting costs. Taking all factors into consideration, the final total revenue is 43.36 million more than before. Fig.16 It is the value difference between Option 1 and Option 2 in the blasting process. When RMR is less than 72, the cumulative blasting value difference increases with the increase of RMR, and reaches the maximum of 300.7 million when RMR is 68. Since the total amount of ore and rock with RMR between 60 and 70 is the largest, the blasting value difference accounts for the largest proportion, about 99%. When RMR is greater than or equal to 72, since the blasting value difference is a negative value, the cumulative blasting value difference begins to decrease, and when RMR is 80, it drops to the minimum. Therefore, the method studied in this patent can achieve the purpose of reducing blasting costs.

[0163] Table 1 Comparison of optimization data between Scheme 1 and Scheme 2

[0164] plan Ore volume (Mt) Rock volume (Mt) Stripping ratio Total value (million yuan) Solution 1 115.49 65.80 0.57 7381.91 Solution 2 115.21 66.21 0.57 7338.55

[0165] This example collects 382 specimen strengths, 78 joint parameters, and 4642 RQD values ​​as sample point data. Based on the analysis of a large amount of geological data and based on geostatistical methods, a three-dimensional block inhomogeneous model of rock mass quality considering rock strength, structural surface characteristics, and water occurrence is established. Then, a mapping relationship between rock mass quality and explosive unit consumption is obtained by combining multiple sets of on-site blasting data, and a spatial distribution model of explosive unit consumption and blasting cost of open-pit mines is constructed. Finally, the final open-pit mine boundary optimization plan is carried out to achieve refined mining of low-grade open-pit mines, improve production efficiency, and reduce mining costs.

[0166] 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 method for optimizing the open-pit mine boundary based on the characterization of rock mass quality mining cost, characterized in that: The steps include: S1. Collect sample point data of multiple spatial position coordinates of open-pit mines; S2. Determine the optimal power N required for calculating spatial interpolation by the inverse distance power method using the sample point data collected in step S1, construct a rock mass quality RMR calculation model based on the optimal power N and the sample point data, and obtain the spatial distribution model of the RMR; S3. Constructing a calculation model for explosive unit consumption, using the spatial distribution model of the RMR obtained in step S2 to obtain a spatial model of explosive unit consumption and a spatial model of blasting cost; S4. Construct a value mining calculation model, use the spatial model of the explosive unit consumption and the spatial model of the blasting cost obtained in step S3 to obtain a spatial distribution model of the mining cost, and further obtain a spatial distribution model of the mining value for open-pit mine boundary estimation.

2. The open-pit mine boundary optimization method based on rock mass quality mining cost characterization according to claim 1 is characterized in that: The sample point data in step S1 is used to calculate the rock mass RMR, which is calculated by formula (1): RMR=R1+R2+R3+R4+R5+R6 (1) Where R1 is the uniaxial compressive strength score of the rock block, R2 is the RQD value score, R3 is the joint spacing score, R4 is the joint condition, R5 is the groundwater condition, and R6 is the joint occurrence.

3. The method for optimizing the open-pit mine boundary based on the characterization of rock mass quality mining cost according to claim 2 is characterized in that: The step S2 of constructing the rock mass quality RMR calculation model includes: S21. Construct a spatial interpolation calculation model as shown in formula (2) and formula (3) and write a spatial interpolation calculation program. x v =∑b i x i (3) Where b i is the weight between the i-th sample point involved in the valuation and the valued point, 1≤i≤n, n>1, is the number of sample points collected in step S1, h i is the distance between the i-th sample point involved in the estimation and the estimated point, x v is the grade of the calculation module, x i is the grade of the i-th sample point falling into the influence range, N is the best power in the inverse distance power method; 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; S23. Based on the block model constructed in step S22, a rock block uniaxial compressive strength attribute database S1, an RQD attribute database S2 and a joint spacing attribute database S3 are constructed; S24. Input the sample points and spatial position coordinates of the uniaxial compressive strength of the rock block into the uniaxial compressive strength attribute database S1 of the rock block, call the spatial interpolation calculation program constructed in step S21, and input the corresponding optimal power N to obtain the spatial distribution block model of the uniaxial compressive strength of the rock block; S25. Input the sample points and spatial position coordinates of the RQD into the RQD attribute database S2, call the spatial interpolation calculation program constructed in step S21, and input its corresponding optimal power N to obtain the spatial distribution block model of the RQD; S26. Input the sample points and spatial position coordinates of the joint spacing into the joint spacing attribute database S3, call the spatial interpolation calculation program constructed in step S21, and input its corresponding optimal power N to obtain the spatial distribution block model of the joint spacing; S27. Based on the block model constructed in step S22, a rock block uniaxial compressive strength score attribute database SR1 is constructed to store the rock block uniaxial compressive strength score R1, an RQD score attribute database SR2 is constructed to store the RQD value score R2, and a joint spacing score attribute database SR3 is constructed to store the joint spacing score R3, and a score distribution model corresponding to each block is obtained through a segmented expression; S28. Based on the block model constructed in step S22, a joint condition attribute database SR4 is constructed to store the joint condition R4, a groundwater condition attribute database SR5 is constructed to store the groundwater condition R5, and a joint occurrence attribute database SR6 is constructed to store the joint occurrence R6; S29. Based on the block model constructed in step S22, an attribute database SR7 of RMR is constructed to store the RMR, and the RMR of each block is calculated using formula (1) to obtain a spatial distribution model of the RMR.

4. The method for optimizing the open-pit mine boundary based on the characterization of rock mass quality mining cost according to claim 3 is characterized in that: The optimal power N is obtained by inputting the sample point data collected in step S1 into the rock mass quality RMR calculation model and cross-validating, 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≤n, and remove the jth sample point p in turn j , the jth sample point is estimated S213. Calculate the j-th sample point p j and the j-th sample point estimate The error is calculated by using formula (4) to obtain the given N k The root mean square error Re k , In the formula, 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 the open-pit mine boundary based on the characterization of rock mass quality mining cost according to claim 3 is characterized in that: The step S3 comprises: S31. Constructing explosive consumption attribute database SR8 and blasting cost attribute database SR9 based on the block model constructed in step S22; S32. Calculate the explosive consumption of each block using formula (5) and store it in the explosive consumption attribute database SR8 to obtain the spatial distribution model of the explosive consumption. y=0.23+0.02e ((RMR-25) / 17.7) (5)In the formula, y represents the unit consumption of explosives, in kilograms per ton; S33. Calculate the blasting cost of each block using cost formula (6) and store it in the blasting cost attribute database SR9 to obtain the spatial distribution model of the blasting cost. C B =P B y (6) In the formula, C B is the blasting cost, in yuan / ton; y is the explosive consumption, in kilograms / ton; P B is the price corresponding to the unit consumption of explosives, Yuan / kg.

6. The method for optimizing the open-pit mine boundary based on the characterization of rock mass quality mining cost according to claim 5, characterized in that: The step S4 of constructing a value mining model includes: S41. Constructing a mining cost attribute database SR based on the block model constructed in step S22 10 and mining value attribute database SR 11 ; S42. Calculate the mining cost of each block using formula (7) and store it in the mining cost attribute database SR 10 , and obtain the spatial distribution model of the mining cost, C M =C B +C O (7) In the formula, C M is the total mining cost of ore or waste rock, in yuan / ton, C B is the blasting cost, in RMB / ton, C O is other production costs, in Yuan / ton; S42. Calculate the mining value of each block using formula (8) and formula (9) and store it in the mining value attribute database SR 10 , and obtain the spatial distribution model of the mining value, V ore =g Au p Au +g Cu p Cu -C ore (8) Where V ore is the mining value of the ore, in yuan / ton, g Au is the grade of Au, in grams per ton, g Cu is the grade of Cu, in grams per ton, p Au is the price of Au, in yuan / gram, p Cu is the price of Cu, in yuan / gram, C ore is the mining cost of the ore, in yuan / ton, calculated by formula (7), In faste =-C faste (9) Where V faste is the mining value of waste rock, in yuan / ton, C faste is the mining cost of waste rock, in Yuan / ton, calculated by formula (7).

7. The method for optimizing the open-pit mine boundary based on the characterization of rock mass quality mining cost according to claim 6, characterized in that: The step S4 further includes: S51. Using 3DMine software to construct a spatial distribution model of the mining cost of rock mass quality; S52. Using 3DMine software to construct a spatial distribution model of the mining value of rock mass quality; S53. Compare the mining cost and the mining value under different RMR values ​​to determine the optimal RMR value.

8. The method for optimizing the open-pit mine boundary based on the characterization of rock mass quality mining cost according to claim 3 is characterized in that: The rock block uniaxial compressive strength score R1, the RQD value score R2 and the joint spacing score R3 in step S26 are all obtained by referring to the rock mass quality parameter table, and the score distribution model corresponding to each block is obtained through a piecewise expression.

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