Explosibility grading method for strip mine rock in alpine region

Abstract

The invention provides a high and cold area strip mine rock explosibility grading method, and relates to the technical field of mine rock explosibility grading. According to the method, field experiments, numerical simulation and an intelligent algorithm are combined, and the influence of climate and geological conditions of a high and cold area on rock mass explosibility is considered; the method comprises the following steps: establishing an explosibility index system comprising cold region climate and geological conditions, rock physical and mechanical properties, explosive blasting parameters and a blasting process, determining a fuzzy membership degree, a relative weight and a comprehensive weight of each evaluation index by using a fuzzy evaluation method and adopting a trapezoidal membership function, and proposing an explosibility grading method considering the cold region influence.

Description

Technical Field

[0001] The invention relates to the technical field of blastability classification of mineral rocks, and in particular to a method for blastability classification of open-pit mineral rocks in high-cold areas. Background Art

[0002] There are hundreds of large and medium-sized open-pit mines in my country's alpine regions. The rock mass at a certain depth below the surface of the open-pit mines in the cold regions forms a frozen zone under the action of snowfall and groundwater in winter, and the physical and mechanical properties and wave impedance characteristics of the rock mass change. At present, domestic and foreign scholars have conducted research on the mechanical properties of rocks under freeze-thaw cycle conditions, blasting funnel experiments, and blasting vibration effects. However, the impact of the special freezing environment in alpine regions on the explosiveness of rock mass is still unclear, and there is a lack of rock explosiveness classification methods suitable for alpine regions. Since rock explosiveness classification is the basis for blasting design, it is impossible to quantitatively characterize the explosiveness of rock mass in alpine regions, resulting in high blasting block rate, high unit consumption of explosives, and small blasting hole network parameters, which brings difficulties to blasting consumption reduction in open-pit mines in cold regions.

[0003] Domestic and foreign scholars have conducted a lot of research on the explosiveness of rock masses at room temperature. At present, there are two major methods for rock explosiveness classification. The first method uses single comprehensive indicators such as rock strength coefficient, crushing work index, rock explosiveness index, etc. to classify explosiveness; the second method uses set pair analysis, fuzzy comprehensive evaluation, neural network, cluster analysis, matter-element extension method, attribute recognition, grey correlation analysis, fuzzy recognition and other evaluation methods to classify explosiveness. However, the above methods are mainly for rock masses at room temperature, and no in-depth research has been conducted on rock masses in cold regions. At the same time, they have failed to organically combine field experiments, numerical simulations and intelligent algorithms. Summary of the invention

[0004] In view of the shortcomings of the prior art, the present invention provides a method for grading the explosiveness of open-pit mine rocks in high-cold areas; it combines field experiments, numerical simulations and intelligent algorithms, considers the influence of climate and address conditions in high-cold areas on the explosiveness of rock masses, establishes an explosiveness index system including cold-region climate and geological conditions, rock mass physical and mechanical properties, explosive blasting parameters and blasting technology, uses a fuzzy evaluation method and a trapezoidal membership function to determine the fuzzy membership, relative weight and comprehensive weight of each evaluation index, and proposes an explosiveness grading method considering the influence of cold regions.

[0005] A method for grading the explosiveness of open-pit mine rocks in alpine areas comprises the following steps:

[0006] Step 1: Take rock samples from the slope of open-pit mines in cold regions, conduct basic physical and mechanical tests, and test the basic physical and mechanical parameters of rock samples; conduct freeze-thaw cycle tests on rocks to establish the relationship between the rock freeze-thaw temperature, number of freeze-thaw cycles, and the basic mechanical parameters of 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 and combining the open-pit mine on-site blasting process, the finite element analysis software ANSYS is used to establish a blasting funnel model, and the RHT constitutive relationship is used to determine the initial parameters of the RHT constitutive relationship;

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

[0011] Step 3.1: Conduct a bursting funnel experiment;

[0012] The blasting funnel experiment was carried out in the mining area, specifically single-hole blasting funnel experiment and double-hole blasting funnel experiment. The blasting funnel characteristic parameter change curve under different blasthole depths was obtained through the single-hole blasting funnel experiment, and the optimal blasting burial depth, optimal funnel radius, and optimal funnel volume were obtained. The relationship between the hole spacing and the optimal blasting funnel radius was obtained through the double-hole blasting funnel experiment, and the step deep hole blasting parameters were determined.

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

[0014] The blasting funnel radius is determined by taking the blasthole as the center and measuring the average value of the funnel radius at eight different directions at intervals of 45 degrees, and constructing concentric circles to determine the radius of the blasting funnel;

[0015] The blasting funnel volume needs to first determine the blasting funnel depth, measure the funnel depth at all grid nodes in turn, and then use the Simpson method to calculate the blasting funnel volume. The calculation method is:

[0016]

[0017] Where: S i is the cross-sectional area of ​​the funnel, B is the distance between measuring points, Y i is the blasting depth of the i-th point, Y 0 To measure the blasting depth at the origin, Y n is the blasting depth of the last point; after the area of ​​each section is calculated, the funnel volume V is calculated according to the prism:

[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, the finite element analysis software is used to perform numerical simulation of the explosion funnel experiment. Based on the funnel radius and funnel volume measured in the explosion funnel experiment in step 3.2, the parameters of the RHT constitutive model are 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 blastability evaluation index system includes evaluation indexes of rock blastability, and the evaluation indexes specifically include evaluation factor sets and factor sets; wherein the evaluation factor sets include geological conditions, physical and mechanical properties, charge parameters and structures, and cold zone conditions of rocks, and the factor sets include rock bulk density, fracture conditions, compressive strength, tensile strength, elastic modulus, explosive detonation velocity, charge structure, detonation method, freezing depth, freeze-thaw cycle, and ice thickness in the hole;

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

[0024] The classification indexes include rock bulk density, fracture conditions, compressive strength, elastic modulus, explosive detonation velocity, freezing depth, freeze-thaw cycle, and ice thickness in the hole;

[0025] Step 6: Based on the on-site geological conditions, the blastability classification index system and the relative weight, the rock blastability level is divided into level I, level II, level III and level IV, wherein the blastability of level I, level II, level III and level IV decreases in sequence;

[0026] Step 7: According to the explosiveness classification standard and the field measured values ​​of the classification index, the membership function is used to calculate the membership of the classification index, and the membership matrix R is established;

[0027] The membership function is specifically: for quantitative indicators with better rock blastability as the grading index is higher, an ascending semi-trapezoidal distribution membership function curve is adopted; for quantitative indicators with better rock blastability as the grading index is lower, a descending semi-trapezoidal distribution membership function curve is adopted, and the index membership is determined by the membership function curve;

[0028] Step 8: Use the hierarchical analysis method to determine the comprehensive weight of the grading index, scale the importance of the evaluation index according to the Saaty scale table, obtain the corresponding weight judgment matrix, and perform a consistency test to determine whether the matrix is ​​consistent. Otherwise, the evaluation index needs to be rescaled. After the consistency test passes, the eigenvector of the weight judgment matrix is ​​calculated and normalized to obtain the weight vector A.

[0029] Step 9: Based on the weight vector A and the membership matrix R, the evaluation results A·R of each grade are obtained. T , the rock blastability level is determined according to the maximum membership principle.

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

[0031] The present invention provides a method for grading the explosiveness of open-pit mine rocks in alpine areas. Compared with the existing rock explosiveness grading method, the method fully considers the geological conditions and climatic conditions in alpine areas, and quantifies the influence of grading indicators on rock explosiveness through numerical simulation. BRIEF DESCRIPTION OF THE DRAWINGS

[0032] Figure 1 This is an overall flow chart of the method for grading rock blastability of open-pit mines in high-cold areas of the present invention;

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

[0034] Among them, (a) - schematic diagram of blasting funnel radius, (b) - schematic diagram of blasting funnel depth. DETAILED DESCRIPTION

[0035] The specific implementation of the present invention is further described in detail below 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.

[0036] A method for grading the explosiveness of open-pit mine rocks in high-cold areas, such as Figure 1 As shown, Figure 1 It is the main flow chart of the technical solution of the present invention, comprising the following steps:

[0037] Step 1: Take rock samples from the slope of open-pit mines in cold regions, conduct basic physical and mechanical tests in the laboratory, and test the basic physical and mechanical parameters of rock samples; conduct freeze-thaw cycle tests on rocks to establish the relationship between the rock freeze-thaw temperature, number of freeze-thaw cycles and the basic mechanical parameters of 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 rock compressive strength, elastic modulus, density, and tensile strength;

[0039] In this embodiment, according to the standard specimen requirements in 3.1.1 of the International Rock Mechanics Test Procedure, the specimens are made into standard cylindrical specimens after on-site sampling, and indoor experimental tests are carried out to test the specimen size, mass, density, wave velocity, shear strength, tensile strength, uniaxial compressive strength, etc.; according to the temperature changes of seasonal freeze-thaw on-site, the freeze-thaw temperature, time and number of freeze-thaw cycles are set to measure the frost resistance of the specimens, and the relationship between the specimen strength, elastic modulus, etc. and the number of freeze-thaw cycles and temperature changes is obtained.

[0040] Step 2: Using the basic physical and mechanical parameters tested in the laboratory, the finite element analysis software ANSYS is used to establish a blasting funnel model in combination with the field blasting process of the open-pit mine, and the RHT constitutive relationship is used to determine the initial parameters of the RHT constitutive relationship;

[0041] In this embodiment, the RHT constitutive model is used to model the open-air steps in cold regions. As shown in Table 1, the model includes 34 parameters. The first part is the basic parameters of the specimen, which are obtained through indoor experimental tests and on-site geological data, such as uniaxial compressive strength, shear modulus, material density, etc. The second part is the inherent parameters of the model, such as the Rhodes angle correlation coefficient, tension-compression meridian ratio parameters, etc., and the model default parameters are selected; the third part is to obtain the initial parameters by consulting the literature, and fine-tune according to the simulation needs, such as the minimum failure strain, etc. The initial parameters of the RHT constitutive model are determined by taking basic mechanical tests on-site by sampling in the first step, combined with consulting on-site data, relevant literature, etc.

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

[0043]

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

[0045] Step 3.1: Conduct a bursting funnel experiment;

[0046] Based on the on-site investigation and indoor testing, suitable areas were selected in the mining area for blasting funnel experiments, specifically single-hole blasting funnel experiments and double-hole blasting funnel experiments. The single-hole blasting funnel experiment was used to obtain the blasting funnel characteristic parameter change curves at different blasthole depths, and the optimal blasting burial depth, optimal funnel radius, and optimal funnel volume were obtained. The double-hole blasting funnel experiment was used to obtain the relationship between the hole spacing and the optimal blasting funnel radius, and determine the step deep hole blasting parameters.

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

[0048] The blasting funnel radius is the average value of the funnel radius at eight different directions measured at 45° intervals, such as Figure 2 As shown in (a), Figure 2 It can be seen in (a) that the radius of the blasting funnel can be roughly determined by constructing multiple concentric circles.

[0049] The blasting funnel volume needs to be determined first by measuring the blasting funnel depth. The determination method is as follows: Figure 2 (b) Figure 2 The specific method in (b) is to measure the depth of each point within the circled range and then calculate it through the formula. The funnel depth is measured at all grid nodes in turn, and then the Simpsen method is used to calculate the blasting funnel volume. The calculation method is:

[0050]

[0051] Where: S i is the cross-sectional area of ​​the funnel, B is the distance between measuring points (0.2m), Y i is the blasting depth of the i-th point, Y 0 To measure the blasting depth at the origin, Y n is the blasting depth of the last point; after the area of ​​each section is calculated, the funnel volume V is calculated according to the prism:

[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, the finite element analysis software ANSYS / LSDYNA is used to perform numerical simulation of the explosion funnel experiment. Based on the funnel radius and funnel volume measured in the explosion funnel experiment in step 3.2, the parameters of the RHT constitutive model are optimized so that the simulation results are constantly close to the experimental results, and the final parameters of the RHT constitutive model are determined;

[0055] Step 4: Based on the mine engineering geology, basic physical and mechanical parameters of 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 blastability evaluation index system includes evaluation indexes of rock blastability, and the evaluation indexes specifically include evaluation factor sets and factor sets; wherein the evaluation factor sets include geological conditions, physical and mechanical properties, charge parameters and structures, and cold zone conditions of rocks, and the factor sets include rock bulk density, fracture conditions, compressive strength, tensile strength, elastic modulus, explosive detonation velocity, charge structure, detonation method, freezing depth, freeze-thaw cycle, and ice thickness in the hole;

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

[0058] The classification indexes include rock bulk density, fracture conditions, compressive strength, elastic modulus, explosive detonation velocity, freezing depth, freeze-thaw cycle, and ice thickness in the hole;

[0059] Using the established numerical model, the influence of changes in individual explosiveness indicators such as compressive strength, tensile strength, rock bulk density, rock density, explosive detonation velocity, and freezing depth on the blasting effect is analyzed to determine the influence range of a single indicator; the indicator values ​​in the explosiveness classification index system are changed in pairs to determine the relative weights of different indicators, providing a reference for weight division by the hierarchical analysis method. The numerical simulation results confirm the influence trend of the numerical size of each evaluation indicator on the explosiveness of the rock, providing a reference for the next step of selecting a suitable membership function to calculate the membership degree; the influence range of each indicator is mainly analyzed and determined by the peak stress, hole wall displacement, vibration velocity and other parameters of the monitoring point obtained by numerical simulation. If the influence of a certain indicator is small, it can be excluded as a classification condition.

[0060] Step 6: Based on the on-site geological conditions, the blastability classification index system and the relative weight, the rock blastability level is divided into level I, level II, level III and level IV, wherein the blastability of level I, level II, level III and level IV decreases in sequence;

[0061] Level I indicates that the blastability of the ore and rock is good, and the rock mass is easily broken by the explosion of explosives; Level II indicates that the blastability is average, and the rock mass is moderately difficult to break by the explosion of explosives; Level III indicates that the ore and rock are difficult to explode, and the rock mass has a high ability to resist the blasting and breaking of explosives; Level IV indicates that the ore and rock are extremely difficult to explode, and the rock mass is extremely difficult to break by the blasting of explosives.

[0062] Step 7: According to the explosiveness classification standard and the field measured values ​​of the classification index, the membership function is used to calculate the membership of the classification index, and the membership matrix R is established;

[0063] The membership function is specifically: for the quantitative index of rock blastability with better grading index, an ascending semi-trapezoidal distribution membership function curve is adopted; for the quantitative index of rock blastability with better grading index, a descending semi-trapezoidal distribution membership function curve is adopted;

[0064] The rock blastability classification standards and measured values ​​of each evaluation index in Table 2 were obtained by consulting relevant literature, field data in high-cold areas, and numerical simulation. The index membership was determined using the membership function curve. The membership function was determined using the semi-trapezoidal distribution. The quantitative index with the larger the classification index, the better it should be, and the ascending semi-trapezoidal distribution membership function curve should be used; while the quantitative index with the smaller the classification index, the better it should be, and the descending semi-trapezoidal distribution membership function curve should be used. Table 3 shows the relationship between the evaluation index and the evaluation grade.

[0065]

[0066]

[0067] Table 2 Classification standards and field measured values ​​of rock blastability

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

[0069]

[0070] Among them, α k,1 , α k,2 , α k,3 is the cutoff value of the kth evaluation factor index. According to the relationship between the evaluation index and the evaluation level, the membership function is determined by using the semi-trapezoidal distribution, and its general formula is:

[0071]

[0072] The membership degree R of each classification index in rock is calculated by formula (1)-(4), as shown in Table 4

[0073] Table 4 Membership of grading indicators

[0074]

[0075]

[0076] The membership degree calculated in Table 4 shows the influence of each single index factor on the rock blastability classification, which can provide further reference for the classification, but the final classification still requires further comprehensive weight division of each factor.

[0077] Step 8: Use the hierarchical analysis method to determine the comprehensive weight of the grading index, scale the importance of the evaluation index according to the Saaty scale table, obtain the corresponding weight judgment matrix, and perform a consistency test to determine whether the matrix is ​​consistent. Otherwise, the evaluation index needs to be rescaled. After the consistency test passes, the eigenvector of the weight judgment matrix is ​​calculated and normalized to obtain the weight vector A.

[0078] The influence of a single indicator on rock blastability is difficult to evaluate and quantify directly, but the relative importance of indicators can be determined by comparison. The analytic hierarchy process is used to determine the comprehensive weight of the evaluation index, 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 ratio scaling method. The quantification criteria are shown in Table 5.

[0079] Table 5 Quantitative criteria of judgment matrix

[0080]

[0081] In order to improve the credibility and accuracy of the judgment matrix sorting and avoid interference caused by other factors, the consistency of the judgment matrix should be tested: CI is introduced as the consistency index, and its calculation formula is Among them: λmax is the maximum eigenvalue of the judgment matrix, and n is the total number of classification indicators. By querying the consistency indicator parameters as shown in Table 6, the average random consistency index RI is determined, and the formula is used 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 Random consistency index values ​​of judgment matrix

[0083]

[0084] According to the Saaty proportional scaling method introduced in Table 5, the judgment matrix of each grading 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, thereby obtaining the corresponding weight judgment matrix, as shown in Table 7. Using the consistency test formula (5), (6) and Table 5, CR is calculated to be 0.00857<0.1, that is, the judgment matrix has good consistency, otherwise the matrix needs to be reconstructed. Then the eigenvector of the judgment matrix is ​​calculated, and after normalization, the weight vector A of the system can be obtained as [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: Based on the weight vector A and the membership matrix R, the evaluation results A·R of each grade are obtained. T , the rock blastability level is determined according to the maximum membership principle.

[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 maximum membership principle, the maximum membership of the explosiveness classification is 0.4796, the membership of level III and level IV is similar, and the membership of level III is slightly larger, so it belongs to the level between difficult to explode and extremely difficult to explode.

[0089] The above description is only a preferred embodiment of the present disclosure and an explanation of the technical principles used. Those skilled in the art should understand that the scope of the invention involved in the embodiments of the present disclosure is not limited to the technical solutions formed by a specific combination of the above-mentioned technical features, but should also cover other technical solutions formed by any combination of the above-mentioned technical features or their equivalent features without departing from the above-mentioned inventive concept. For example, the above-mentioned features are replaced with the technical features with similar functions disclosed in the embodiments of the present disclosure (but not limited to) to form a technical solution.

Claims

1. A method for grading the explosiveness of open-pit mine rocks in high-cold areas, characterized in that: The following steps are involved: Step 1: Take rock samples from the slope of open-pit mines in cold regions, conduct basic physical and mechanical tests, and test the basic physical and mechanical parameters of rock samples; conduct freeze-thaw cycle tests on rocks to establish the relationship between the rock freeze-thaw temperature, number of freeze-thaw cycles, and the basic mechanical parameters of rock samples; Step 2: Using basic physical and mechanical parameters and combining the open-pit mine on-site blasting process, the finite element analysis software ANSYS is used to establish a blasting funnel model, and the RHT constitutive relationship is used to determine the initial parameters of the RHT constitutive relationship; Step 3: Carry out blasting funnel experiment at the mine site to verify the initial parameters of the RHT constitutive model, and 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, finite element analysis software is used to perform blasting numerical simulation, analyze the effects of different evaluation indicators on the stress, displacement, and velocity of the monitoring points, thereby determining the classification indicators and analyzing the relative weights of each classification indicator; Step 6: Based on the on-site geological conditions, the blastability classification index system and the relative weight, the rock blastability level is divided into level I, level II, level III and level IV; Step 7: According to the explosiveness classification standard and the field measured values ​​of the classification index, the membership function is used to calculate the membership of the classification index, and the membership matrix R is established; Step 8: Use the hierarchical analysis method to determine the comprehensive weight of the grading index, scale the importance of the evaluation index according to the Saaty scale table, obtain the corresponding weight judgment matrix, and perform a consistency test to determine whether the matrix is ​​consistent. Otherwise, the evaluation index needs to be rescaled. After the consistency test passes, the eigenvector of the weight judgment matrix is ​​calculated and normalized to obtain the weight vector A. Step 9: Based on the weight vector A and the membership matrix R, the evaluation results A·R of each grade are obtained. T , the rock blastability level is determined according to the maximum membership principle.

2. A method for grading rock blastability in open-pit mines in alpine regions according to claim 1, characterized in that: The basic physical and mechanical parameters 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 grading rock blastability of open-pit mines in alpine regions according to claim 1 is characterized in that: The step 3 comprises the following steps: Step 3.1: Conduct a bursting funnel experiment; The blasting funnel experiment was carried out in the mining area, specifically single-hole blasting funnel experiment and double-hole blasting funnel experiment. The blasting funnel characteristic parameter change curve under different blasthole depths was obtained through the single-hole blasting funnel experiment, and the optimal blasting burial depth, optimal funnel radius, and optimal funnel volume were obtained. The relationship between the hole spacing and the optimal blasting funnel radius was obtained through the double-hole blasting funnel experiment, and the step deep hole blasting parameters were determined. Step 3.2: Determination of the blasting funnel radius and blasting funnel volume; Step 3.3: Parameter optimization of RHT constitutive model; Using the initial parameters of the RHT constitutive model determined in step 2, the finite element analysis software is used to perform numerical simulation of the explosion funnel experiment. Based on the funnel radius and funnel volume measured in the explosion funnel experiment in step 3.2, the parameters of the RHT constitutive model are optimized to determine the final parameters of the RHT constitutive model.

4. A method for grading rock blastability in open-pit mines in alpine regions according to claim 3, characterized in that: The blasting funnel radius in step 3.2 is the average value of the funnel radius at eight different directions measured at intervals of 45° with the blasthole as the center, and the radius of the blasting funnel is determined by constructing concentric circles; The blasting funnel volume needs to first determine the blasting funnel depth, measure the funnel depth at all grid nodes in turn, and then use the Simpson method to calculate the blasting funnel volume. The calculation method is: Where: S i is the cross-sectional area of ​​the funnel, B is the distance between measuring points, Y i is the blasting depth of the i-th point, Y0 is the blasting depth of the measurement origin, Y n is the blasting depth of the last point; after the area of ​​each section is calculated, the funnel volume V is calculated according to the prism:

5. The method for grading rock blastability in open-pit mines in alpine regions according to claim 1 is characterized in that: The explosiveness evaluation index system described in step 4 includes evaluation indicators of rock explosiveness, and the evaluation indicators specifically include an evaluation factor set and a factor set; wherein the evaluation factor set includes geological conditions, physical and mechanical properties, charging parameters and structure, and cold zone conditions of the rock, and the factor set includes rock bulk density, fracture conditions, compressive strength, tensile strength, elastic modulus, explosive detonation velocity, charging structure, detonation method, freezing depth, freeze-thaw cycle, and ice thickness in the hole.

6. The method for grading rock blastability of open-pit mines in alpine regions according to claim 1, characterized in that: The classification indexes described in step 5 include rock bulk density, fracture conditions, compressive strength, elastic modulus, explosive detonation velocity, freezing depth, freeze-thaw cycle, and ice thickness in the hole.

7. The method for grading rock blastability of open-pit mines in alpine regions according to claim 1 is characterized in that: The explosiveness of level I, level II, level III, and level IV in step 6 decreases in sequence.

8. The method for grading rock blastability of open-pit mines in alpine regions according to claim 1, characterized in that: The membership function described in step 7 is specifically: for the quantitative index of rock blastability with better classification index, the ascending semi-trapezoidal distribution membership function is adopted. Function curve, for the quantitative index that the smaller the classification index, the better the rock blastability, the descending semi-trapezoidal distribution membership function curve is adopted. The indicator membership is determined through the membership function curve.

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