Method for rock blastability classification of open-pit mine in alpine region

Abstract

The application provides a high-cold-region open-pit mine rock blastability grading method, and relates to the technical field of rock blastability grading. The method combines field experiments, numerical simulation and intelligent algorithms, considers the influence of high-cold-region climate and geological conditions on rock mass blastability, establishes a blastability index system containing cold-region climate and geological conditions, rock mass physical and mechanical properties, explosive blasting parameters and blasting technology, uses a fuzzy evaluation method, adopts a trapezoidal membership function, determines the fuzzy membership degree, relative weight and comprehensive weight of each evaluation index, and proposes a blastability grading method considering the influence of cold regions.

Description

Technical Field

[0001] This invention relates to the field of explosiveness classification technology for minerals and rocks, and more particularly to a method for explosiveness classification of open-pit minerals and rocks in high-altitude and cold regions. Background Technology

[0002] There are hundreds of large and medium-sized open-pit mines in high-altitude and cold regions of my country. In these mines, the rock mass at a certain depth below the surface freezes under the influence of winter snowfall and groundwater, altering its physical and mechanical properties and wave impedance characteristics. Currently, scholars both domestically and internationally have conducted research on the mechanical properties of rocks under freeze-thaw cycles, blasting funnel experiments, and blasting vibration effects. However, the impact of the unique freezing environment in high-altitude and cold regions on rock mass blastability remains unclear, and a suitable method for classifying rock mass blastability for these regions is lacking. Since rock mass blastability classification is fundamental to blasting design, the inability to quantitatively characterize rock mass blastability in high-altitude and cold regions leads to problems such as a high proportion of large blasted blocks, high explosive consumption per unit, and small blasting hole parameters, posing challenges to reducing energy consumption in open-pit blasting in cold regions.

[0003] Domestic and international scholars have conducted extensive research on the blastability of rock masses at normal temperatures. Currently, there are two main categories of methods for classifying rock mass blastability. The first category uses single comprehensive indicators such as rock firmness coefficient, breaking work index, and rock blastability index for blastability classification. The second category uses evaluation methods such as set pair analysis, fuzzy comprehensive evaluation, neural networks, cluster analysis, matter-element extension method, attribute recognition, grey relational analysis, and fuzzy recognition for blastability classification. However, the above methods are mainly for rock masses at normal temperatures and have not yet been studied in depth for rock masses in cold regions. Furthermore, they have not organically combined field experiments, numerical simulations, and intelligent algorithms. Summary of the Invention

[0004] To address the shortcomings of existing technologies, this invention provides a method for classifying the blastability of open-pit rocks in high-altitude and cold regions. It combines field experiments, numerical simulations, and intelligent algorithms, considering the influence of climate and geological conditions in high-altitude and cold regions on rock blastability. A blastability index system is established, incorporating cold-region climate and geological conditions, rock physical and mechanical properties, explosive blasting parameters, and blasting techniques. A fuzzy evaluation method is used, employing a trapezoidal membership function to determine the fuzzy membership degree, relative weight, and comprehensive weight of each evaluation index, thus proposing a blastability classification method that considers the influence of cold regions.

[0005] A method for classifying the explosiveness of open-pit rocks in high-altitude and cold regions includes the following steps:

[0006] Step 1: Collect rock samples from the slope of an open-pit mine in a cold region, conduct basic physical and mechanical tests to test the basic physical and mechanical parameters of the rock samples; conduct freeze-thaw cycle tests on the rocks to establish the relationship between the freeze-thaw temperature and number of cycles and the basic mechanical parameters of the rock samples.

[0007] The basic physical and mechanical parameters include rock compressive strength, elastic modulus, wave velocity, density, porosity, and tensile strength.

[0008] The basic mechanical parameters include compressive strength, elastic modulus, density, and tensile strength;

[0009] Step 2: Using basic physical and mechanical parameters, combined with the blasting process at the open-pit mine, a blasting funnel model is established using the finite element analysis software ANSYS, and the RHT constitutive relation is used to determine the initial parameters of the RHT constitutive model.

[0010] Step 3: Conduct blasting funnel experiments at the mine site to verify the initial parameters of the RHT constitutive model, optimize and determine the final parameters of the RHT constitutive model;

[0011] Step 3.1: Conduct the blasting funnel experiment;

[0012] Blasting funnel experiments were conducted in the mining area, specifically single-hole and double-hole blasting funnel experiments. The single-hole blasting funnel experiments yielded characteristic parameter variation curves of the blasting funnel at different borehole depths, thus determining the optimal burial depth, optimal funnel radius, and optimal funnel volume. The double-hole blasting funnel experiments established the relationship between the borehole spacing and the optimal blasting funnel radius, thereby determining the parameters for bench deep-hole blasting.

[0013] Step 3.2: Determination of the radius and volume of the blasting funnel;

[0014] The radius of the blasting funnel is determined by taking the average value of the funnel radius at eight different locations with the blast hole as the center and at 45° intervals, and constructing concentric circles to determine the radius of the blasting funnel.

[0015] The volume of the blasting funnel needs to be determined first by measuring the depth of the blasting funnel. The funnel depth is measured sequentially at all grid nodes, and then the volume of the blasting funnel is calculated using the Simpson method. The calculation method is as follows:

[0016]

[0017] In the formula: S i Let B be the cross-sectional area of ​​a funnel, B be the distance between measuring points, and Y be the distance between measuring points. i Let Y be the blasting depth at point i, and Y0 be the blasting depth at the measurement origin. n The blasting depth is the last point; after calculating the area of ​​each cross-section, the funnel volume V is obtained by using the frustum shape:

[0018]

[0019] Step 3.3: Parameter optimization of RHT constitutive model;

[0020] Using the initial parameters of the RHT constitutive model determined in step 2, numerical simulation of the blasting funnel experiment was carried out using finite element analysis software. Based on the funnel radius and funnel volume measured in the blasting funnel experiment in step 3.2, the parameters of the RHT constitutive model were optimized to determine the final parameters of the RHT constitutive model.

[0021] Step 4: Based on the mine engineering geology, basic physical and mechanical parameters, on-site explosive parameters and blasting process parameters, establish an explosiveness classification index system that takes into account the influence of cold region conditions;

[0022] The explosiveness evaluation index system includes evaluation indexes for rock explosiveness. The evaluation indexes specifically include a set of evaluation factors and a set of factors. The set of evaluation factors includes the geological conditions, physical and mechanical properties, charge parameters and structure, and cold region conditions of the rock. The set of factors includes the rock's unit weight, fracture conditions, compressive strength, tensile strength, elastic modulus, explosive detonation velocity, charge structure, initiation method, freezing depth, freeze-thaw cycle, and ice thickness inside the borehole.

[0023] Step 5: Using the final parameters of the RHT constitutive model, perform blasting numerical simulation using finite element analysis software to analyze the influence of different evaluation indicators on the stress, displacement, and velocity of the monitoring points, thereby determining the grading indicators and analyzing the relative weights of each grading indicator.

[0024] The grading indicators include rock bulk density, fracture conditions, compressive strength, elastic modulus, explosive detonation velocity, freezing depth, freeze-thaw cycles, and ice thickness inside the borehole.

[0025] Step 6: Based on the on-site geological conditions, the explosiveness classification index system and relative weights, the explosiveness level of rock is divided into Level I, Level II, Level III and Level IV, with the explosiveness of Level I, Level II, Level III and Level IV decreasing in that order.

[0026] Step 7: Based on the explosiveness classification standards and the field measured values ​​of the classification indicators, calculate the membership degree of the classification indicators using membership functions and establish the membership degree matrix R.

[0027] The membership function is specifically as follows: for quantitative indicators of rock blastability with better grading index, an ascending semi-trapezoidal distribution membership function curve is used; for quantitative indicators of rock blastability with smaller grading index, a descending semi-trapezoidal distribution membership function curve is used. The membership degree of the index is determined through the membership function curve.

[0028] Step 8: Use the analytic hierarchy process (AHP) to determine the comprehensive weights of the graded indicators. Scale the importance of the evaluation indicators according to the Saaty proportional scale to obtain the corresponding weight judgment matrix. Perform a consistency test to determine whether the matrix is ​​consistent. Otherwise, the evaluation indicators need to be rescaled. After the consistency test is passed, calculate the eigenvector of the weight judgment matrix and normalize it to obtain the weight vector A.

[0029] Step 9: Calculate the evaluation results A·R for each level based on the weight vector A and the membership matrix R. T The rock blastability level is determined based on the principle of maximum membership.

[0030] The beneficial effects of adopting the above technical solution are as follows:

[0031] This invention provides a method for classifying the explosiveness of open-pit rocks in high-altitude and cold regions. Compared with existing rock explosiveness classification methods, this method fully considers the geological and climatic conditions of high-altitude and cold regions, and quantifies the influence of classification indicators on rock explosiveness through numerical simulation. Attached Figure Description

[0032] Figure 1 This is an overall flowchart of the method for classifying the explosiveness of open-pit rocks in high-altitude and cold regions according to the present invention;

[0033] Figure 2 This is a schematic diagram illustrating the calculation of the volume of the blasting funnel in this invention;

[0034] Wherein, (a) is a schematic diagram of the radius of the blasting funnel, and (b) is a schematic diagram of the depth of the blasting funnel. Detailed Implementation

[0035] The specific embodiments of the present invention will be described in further detail below with reference to the accompanying drawings and examples. The following examples are for illustrative purposes only and are not intended to limit the scope of the invention.

[0036] A method for classifying the explosiveness of open-pit rocks in high-altitude and cold regions, such as Figure 1 As shown, Figure 1 This is the main flowchart of the technical solution of the present invention, which includes the following steps:

[0037] Step 1: Collect rock samples from the slope of an open-pit mine in a cold region, conduct basic physical and mechanical tests in the laboratory to test the basic physical and mechanical parameters of the rock samples; conduct freeze-thaw cycle tests on the rocks to establish the relationship between the freeze-thaw temperature and number of cycles and the basic mechanical parameters of the rock samples.

[0038] The basic physical and mechanical parameters include rock compressive strength, elastic modulus, wave velocity, density, porosity, and tensile strength; the basic mechanical parameters include compressive strength, elastic modulus, density, and tensile strength.

[0039] In this embodiment, in accordance with the standard specimen requirements in 3.1.1 of the International Code for Testing Rock Mechanics, after on-site sampling, the specimens were made into standard cylindrical specimens and indoor experimental tests were carried out to test the specimen dimensions, mass, density, wave velocity, shear strength, tensile strength, uniaxial compressive strength, etc. Based on the seasonal freeze-thaw temperature changes on-site, freeze-thaw temperature, time, and number of freeze-thaw cycles were set to determine the specimen's freeze resistance, and the relationship between specimen strength, elastic modulus, etc. and the number of freeze-thaw cycles and temperature changes was obtained.

[0040] Step 2: Using the basic physical and mechanical parameters tested in the laboratory, and combining them with the blasting process in the open mine, a blasting funnel model is established using the finite element analysis software ANSYS. The RHT constitutive relation is then used to determine the initial parameters of the RHT constitutive relation.

[0041] In this embodiment, the RHT constitutive model is used to model an open-air platform in a cold region. As shown in Table 1, the model includes 34 parameters. The first part consists of the basic parameters of the specimen, obtained through indoor experimental tests and field geological data, such as uniaxial compressive strength, shear modulus, and material density. The second part consists of the model's inherent parameters, such as the Rhodes angle correlation coefficient and the tensile-compressive meridian ratio, using the model's default parameters. The third part consists of initial parameters obtained by consulting literature, which are then fine-tuned according to simulation needs, such as the minimum failure strain. The initial parameters of the RHT constitutive model are determined by conducting basic mechanical tests through field sampling in the first step, combined with consulting field data and relevant literature.

[0042] Table 1. Schematic diagram of RHT constitutive model parameters

[0043]

[0044] Step 3: Conduct blasting funnel experiments at the mine site to verify the initial parameters of the RHT constitutive model, optimize and determine the final parameters of the RHT constitutive model;

[0045] Step 3.1: Conduct the blasting funnel experiment;

[0046] Based on on-site investigations and indoor testing, suitable areas were selected in the mining area to conduct blasting funnel experiments, specifically single-hole and double-hole blasting funnel experiments. The single-hole blasting funnel experiments yielded characteristic parameter variation curves for blasting funnels at different borehole depths, identifying the optimal burial depth, optimal funnel radius, and optimal funnel volume. The double-hole blasting funnel experiments established the relationship between borehole spacing and the optimal blasting funnel radius, thus determining the parameters for bench deep-hole blasting.

[0047] Step 3.2: Determination of the radius and volume of the blasting funnel;

[0048] The radius of the blasting funnel is the average of the radii of eight different locations measured at 45° intervals, centered on the blast hole. Figure 2 As shown in (a), from Figure 2 As can be seen from (a), the radius of the blasting funnel can be roughly determined by constructing multiple concentric circles.

[0049] The volume of the blasting funnel needs to be determined first by measuring the depth of the blasting funnel. The measurement method is as follows: Figure 2 As shown in (b) Figure 2 The specific method in (b) involves measuring the depth at each point within the defined area and then calculating the volume using a formula. The funnel depth is measured sequentially at all grid nodes, and then the Simpson method is used to calculate the volume of the blasting funnel. The calculation method is as follows:

[0050]

[0051] In the formula: S i Let Y be the cross-sectional area of ​​a funnel, B be the distance between measuring points (0.2m), and Y be the distance between measuring points. i Let Y be the blasting depth at point i, and Y0 be the blasting depth at the measurement origin. n The blasting depth is the last point; after calculating the area of ​​each cross-section, the funnel volume V is obtained by using the frustum shape:

[0052]

[0053] Step 3.3: Parameter optimization of RHT constitutive model;

[0054] Using the initial parameters of the RHT constitutive model determined in step 2, numerical simulation of the blasting funnel experiment was carried out using the finite element analysis software ANSYS / LSDYNA. Based on the funnel radius and funnel volume measured in the blasting funnel experiment in step 3.2, the parameters of the RHT constitutive model were optimized so that the simulation results were closer to the experimental results, and the final parameters of the RHT constitutive model were determined.

[0055] Step 4: Based on mine engineering geology, basic physical and mechanical parameters in the laboratory, on-site explosive parameters and blasting process parameters, establish an explosiveness classification index system that takes into account the influence of cold region conditions;

[0056] The explosiveness evaluation index system includes evaluation indexes for rock explosiveness. The evaluation indexes specifically include a set of evaluation factors and a set of factors. The set of evaluation factors includes the geological conditions, physical and mechanical properties, charge parameters and structure, and cold region conditions of the rock. The set of factors includes the rock's unit weight, fracture conditions, compressive strength, tensile strength, elastic modulus, explosive detonation velocity, charge structure, initiation method, freezing depth, freeze-thaw cycle, and ice thickness inside the borehole.

[0057] Step 5: Using the final parameters of the RHT constitutive model, perform blasting numerical simulation using the finite element analysis software ANSYS / LSDYNA to analyze the influence of different evaluation indicators on the stress, displacement, and velocity of the monitoring points, thereby determining the grading indicators and analyzing the relative weights of each grading indicator.

[0058] The grading indicators include rock bulk density, fracture conditions, compressive strength, elastic modulus, explosive detonation velocity, freezing depth, freeze-thaw cycles, and ice thickness inside the borehole.

[0059] Using the established numerical model, the impact of changes in individual explosiveness indicators, such as compressive strength, tensile strength, rock bulk density, rock density, explosive detonation velocity, and freezing depth, on blasting effects was analyzed, and the influence range of each indicator was determined. The values ​​of the indicators in the explosiveness classification system were changed pairwise to determine the relative weights of different indicators, providing a reference for weight allocation using the analytic hierarchy process (AHP). The influence trend of the numerical magnitude of each evaluation indicator on the magnitude of rock explosiveness was confirmed through numerical simulation results, providing a reference for selecting appropriate membership functions to calculate membership degrees. The influence range of each indicator was mainly analyzed and determined by obtaining parameters such as peak stress, borehole wall displacement, and vibration velocity at monitoring points through numerical simulation. If the influence of an indicator is small, it can be excluded from classification conditions.

[0060] Step 6: Based on the on-site geological conditions, the explosiveness classification index system and relative weights, the explosiveness level of rock is divided into Level I, Level II, Level III and Level IV, with the explosiveness of Level I, Level II, Level III and Level IV decreasing in that order.

[0061] Class I indicates that the ore and rock have good blastability and the rock mass is easily broken under the action of explosives; Class II indicates that the blastability is moderate and the rock mass is of moderate difficulty to break under the action of explosives; Class III indicates that the ore and rock are difficult to blast and the rock mass has a high ability to resist the breaking of explosives; Class IV indicates that the ore and rock are extremely difficult to blast and the rock mass is extremely difficult to break under the action of explosives.

[0062] Step 7: Based on the explosiveness classification standards and the field measured values ​​of the classification indicators, calculate the membership degree of the classification indicators using membership functions and establish the membership degree matrix R.

[0063] The membership function is specifically as follows: for quantitative indicators of rock blastability with better grading index, an ascending semi-trapezoidal distribution membership function curve is used; for quantitative indicators of rock blastability with smaller grading index, a descending semi-trapezoidal distribution membership function curve is used.

[0064] Based on a review of relevant literature, field data from high-altitude and cold regions, and numerical simulations, the rock blastability classification standards and measured values ​​of each evaluation index were obtained in Table 2. Membership function curves were used to determine the membership degree of the indicators. A semi-trapezoidal distribution was used to determine the membership function; for quantitative indicators where a larger classification index is better, an ascending semi-trapezoidal distribution membership function curve was used; while for quantitative indicators where a smaller classification index is better, a descending semi-trapezoidal distribution membership function curve was used. Table 3 shows the relationship between evaluation indicators and evaluation levels.

[0065]

[0066]

[0067] Table 2. Criteria for classifying rock explosiveness levels and field measured values.

[0068] Table 3 Relationship between evaluation indicators and evaluation levels

[0069]

[0070] Where, α k,1 α k,2 α k,3 This is the boundary value of the k-th evaluation factor indicator. Based on the relationship between evaluation indicators and evaluation levels, a semi-trapezoidal distribution is used to determine the membership function, with the general formula:

[0071]

[0072] The membership degree R of each classification index in the rock was calculated using equations (1)-(4), as shown in Table 4.

[0073] Table 4 Membership Degree of Hierarchical Indicators

[0074]

[0075]

[0076] The membership degrees calculated from Table 4 show the influence of each individual index factor on the rock blastability classification, which can provide further reference for the classification. However, the final classification still needs to be further divided by the comprehensive weight of each factor.

[0077] Step 8: Use the analytic hierarchy process (AHP) to determine the comprehensive weights of the graded indicators. Scale the importance of the evaluation indicators according to the Saaty proportional scale to obtain the corresponding weight judgment matrix. Perform a consistency test to determine whether the matrix is ​​consistent. Otherwise, the evaluation indicators need to be rescaled. After the consistency test is passed, calculate the eigenvector of the weight judgment matrix and normalize it to obtain the weight vector A.

[0078] The impact of a single indicator on rock blastability is difficult to evaluate and quantify directly. However, the relative importance of indicators can be determined through comparison. The analytic hierarchy process (AHP) is used to determine the comprehensive weight of the evaluation and grading indicators, which has good operability. The importance of different indicators is represented by a judgment matrix. The importance of each indicator is quantified according to the Saaty proportional scaling method, and the quantification criteria are shown in Table 5.

[0079] Table 5 Quantization Criteria for Judging the Matrix

[0080]

[0081] To improve the reliability and accuracy of the judgment matrix ranking and avoid interference from other factors, the consistency of the judgment matrix should be checked: CI is introduced as a consistency index, and its calculation formula is as follows: Where λmax is the largest eigenvalue of the judgment matrix, and n is the total number of grading indicators. The average random consistency index RI is determined by querying the consistency index parameters shown in Table 6, and then using the formula... Determine the consistency ratio CR, and the acceptable condition for the consistency test of the judgment matrix is ​​that the consistency ratio CR < 0.1.

[0082] Table 6 shows the index values ​​for judging the random consistency of a matrix.

[0083]

[0084] Based on the Saaty proportional scaling method introduced in Table 5, the judgment matrix of each graded index is constructed. The construction method is to find through numerical simulation comparison that if the importance of a to b is 3, then the importance of b to a is 1 / 3. Thus, the corresponding weight judgment matrix is ​​obtained, as shown in Table 7. Using the consistency test formulas (5) and (6) and Table 5, CR = 0.00857 < 0.1 is calculated, that is, the judgment matrix has good consistency. Otherwise, the matrix needs to be reconstructed. Then the eigenvector of the judgment matrix is ​​calculated. After normalization, the weight vector of the system A = [0.1799 0.1310 0.0291 0.0705 0.2656 0.1586 0.0356 0.1297].

[0085] Table 7 Weight Judgment Table

[0086]

[0087] Step 9: Calculate the evaluation results A·R for each level based on the weight vector A and the membership matrix R. T The rock blastability level is determined based on the principle of maximum membership.

[0088] In summary, the final evaluation result can be calculated using the weight vector A and the membership matrix R: A·R T = [0.0687 0.2521 0.4796 0.4679]. According to the principle of maximum membership, the maximum membership of this explosiveness classification is 0.4796. The membership of Class III and Class IV is similar, while the membership of Class III is slightly larger. Therefore, it belongs to the range between difficult to explode and extremely difficult to explode.

[0089] The above description is merely a preferred embodiment of this disclosure and an explanation of the technical principles employed. Those skilled in the art should understand that the scope of the invention involved in the embodiments of this disclosure is not limited to technical solutions formed by specific combinations of the above-described technical features, but should also cover other technical solutions formed by arbitrary combinations of the above-described technical features or their equivalents without departing from the above-described inventive concept. For example, technical solutions formed by substituting the above-described features with (but not limited to) technical features with similar functions disclosed in the embodiments of this disclosure.

Claims

1. A method for classifying the explosiveness of open-pit rocks in high-altitude and cold regions, characterized in that, Includes the following steps: Step 1: Collect rock samples from the slope of an open-pit mine in a cold region, conduct basic physical and mechanical tests to test the basic physical and mechanical parameters of the rock samples; conduct freeze-thaw cycle tests on the rocks to establish the relationship between the freeze-thaw temperature and number of cycles and the basic mechanical parameters of the rock samples. Step 2: Using basic physical and mechanical parameters, combined with the blasting process at the open-pit mine, a blasting funnel model is established using the finite element analysis software ANSYS, and the RHT constitutive relation is used to determine the initial parameters of the RHT constitutive model. Step 3: Conduct blasting funnel experiments at the mine site to verify the initial parameters of the RHT constitutive model, optimize and determine the final parameters of the RHT constitutive model; Step 4: Based on the mine engineering geology, basic physical and mechanical parameters, on-site explosive parameters and blasting process parameters, establish an explosiveness classification index system that takes into account the influence of cold region conditions; Step 5: Using the final parameters of the RHT constitutive model, perform blasting numerical simulation using finite element analysis software to analyze the influence of different evaluation indicators on the stress, displacement, and velocity of the monitoring points, thereby determining the grading indicators and analyzing the relative weights of each grading indicator. Step 6: Based on the on-site geological conditions, the explosiveness classification index system and relative weights, the explosiveness level of the rock is divided into Level I, Level II, Level III and Level IV; Step 7: Based on the explosiveness classification standards and the field measured values ​​of the classification indicators, calculate the membership degree of the classification indicators using membership functions and establish the membership degree matrix R. Step 8: Use the analytic hierarchy process (AHP) to determine the comprehensive weights of the graded indicators. Scale the importance of the evaluation indicators according to the Saaty proportional scale to obtain the corresponding weight judgment matrix. Perform a consistency test to determine whether the matrix is ​​consistent. Otherwise, the evaluation indicators need to be rescaled. After the consistency test is passed, calculate the eigenvector of the weight judgment matrix and normalize it to obtain the weight vector A. Step 9: Calculate the evaluation results A·R for each level based on the weight vector A and the membership matrix R. T The rock blastability level is determined based on the principle of maximum membership.

2. The method for classifying the explosiveness of open-pit rocks in high-altitude and cold regions according to claim 1, characterized in that, The basic physical and mechanical parameters mentioned in step 1 include rock compressive strength, elastic modulus, wave velocity, density, porosity, and tensile strength; The basic mechanical parameters include compressive strength, elastic modulus, density, and tensile strength.

3. The method for classifying the explosiveness of open-pit rocks in high-altitude and cold regions according to claim 1, characterized in that, Step 3 includes the following steps: Step 3.1: Conduct the blasting funnel experiment; Blasting funnel experiments were conducted in the mining area, specifically single-hole and double-hole blasting funnel experiments. The single-hole blasting funnel experiments yielded characteristic parameter variation curves of the blasting funnel at different borehole depths, thus determining the optimal burial depth, optimal funnel radius, and optimal funnel volume. The double-hole blasting funnel experiments established the relationship between the borehole spacing and the optimal blasting funnel radius, thereby determining the parameters for bench deep-hole blasting. Step 3.2: Determination of the radius and volume of the blasting funnel; Step 3.3: Parameter optimization of RHT constitutive model; Using the initial parameters of the RHT constitutive model determined in step 2, numerical simulations were performed using finite element analysis software to conduct a blasting funnel experiment. Based on the funnel radius and volume measured in the blasting funnel experiment in step 3.2, the parameters of the RHT constitutive model were optimized to determine the final parameters of the RHT constitutive model.

4. The method for classifying the explosiveness of open-pit rocks in high-altitude and cold regions according to claim 3, characterized in that, The radius of the blasting funnel mentioned in step 3.2 is determined by taking the average value of the funnel radius at eight different positions with the blast hole as the center and at 45° intervals, and constructing concentric circles to determine the radius of the blasting funnel. The volume of the blasting funnel needs to be determined first by measuring the depth of the blasting funnel. The funnel depth is measured sequentially at all grid nodes, and then the volume of the blasting funnel is calculated using the Simpson method. The calculation method is as follows: In the formula: S i Let B be the cross-sectional area of ​​a funnel, B be the distance between measuring points, and Y be the distance between measuring points. i Let Y be the blasting depth at point i, and Y0 be the blasting depth at the measurement origin. n The blasting depth is the last point; after calculating the area of ​​each cross-section, the funnel volume V is obtained by using the frustum shape:

5. The method for classifying the explosiveness of open-pit rocks in high-altitude and cold regions according to claim 1, characterized in that, The explosiveness evaluation index system mentioned in step 4 includes evaluation indexes for rock explosiveness. The evaluation indexes specifically include a set of evaluation factors and a set of factors. The set of evaluation factors includes the geological conditions, physical and mechanical properties, charge parameters and structure, and cold region conditions of the rock. The set of factors includes the rock's unit weight, fracture conditions, compressive strength, tensile strength, elastic modulus, explosive detonation velocity, charge structure, initiation method, freezing depth, freeze-thaw cycle, and ice thickness inside the borehole.

6. The method for classifying the explosiveness of open-pit rocks in high-altitude and cold regions according to claim 1, characterized in that, The grading indicators mentioned in step 5 include rock bulk density, fracture conditions, compressive strength, elastic modulus, explosive detonation velocity, freezing depth, freeze-thaw cycles, and ice thickness inside the borehole.

7. The method for classifying the explosiveness of open-pit rocks in high-altitude and cold regions according to claim 1, characterized in that, The explosiveness of Level I, Level II, Level III, and Level IV, as described in step 6, decreases sequentially.

8. The method for classifying the explosiveness of open-pit rocks in high-altitude and cold regions according to claim 1, characterized in that, The membership function mentioned in step 7 is as follows: For quantitative indicators of rock blastability with better classification index, a semi-trapezoidal membership distribution is adopted. For quantitative indicators where a smaller classification index indicates better rock blastability, a descending semi-trapezoidal membership function curve is used. The membership degree of an index is determined by the membership function curve.

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