Optimization method of charging parameters for large boulder blasting

Abstract

This invention discloses a method for optimizing charge parameters in the blasting of large boulders, relating to the fields of blasting engineering and data optimization processing. The method includes: constructing a three-dimensional model of the boulder by fusing three-dimensional laser scanning and ground-penetrating radar data and performing finite element mesh generation; determining the dynamic mechanical parameters of the rock using Hopkinson bar experiments and calibrating the RHT constitutive model; calculating the basic charge amount based on the principle of energy conservation and optimizing it by combining morphological sensitivity coefficients and local defect correction coefficients; conducting numerical simulation of the entire blasting process using borehole parameters and single-hole charge amount as decision variables; constructing a multi-objective function including predicted fragmentation size, specific charge amount, and safety risk index, and iteratively solving it using a multi-objective optimization algorithm. This invention uses an experimentally calibrated constitutive model as its physical basis, predicts blasting effects through numerical simulation, and uses optimization algorithms to automatically optimize charge parameters, effectively balancing fragmentation effects and safety.

Description

Technical Field

[0001] This invention relates to the fields of blasting engineering and data optimization processing technology, specifically to a method for optimizing charge parameters for blasting large boulders. Background Technology

[0002] The complex geological conditions and frequent tectonic activity in the western mountainous areas, coupled with the impact of large-scale engineering projects such as the Sichuan-Tibet Railway, make them prone to geological disasters such as landslides, mudslides, and collapses. Timely and effective emergency rescue operations can significantly reduce the threat of disasters to people's lives and property, and enhance social security and stability. However, natural disasters in mountainous areas can easily trigger boulders to roll down, often blocking rescue channels. How to quickly handle large boulders blocking rescue channels has become an urgent problem to be solved. The core challenge lies in striking a balance between effective fragmentation and safety control: that is, to achieve complete disintegration of the boulder with the minimum amount of explosives, avoiding costly secondary blasting, while strictly suppressing harmful effects such as flying rocks, shock waves, and vibrations from blasting, ensuring the safety of surrounding personnel, buildings, and equipment.

[0003] Traditional methods struggle to fully account for the unique geometry, internal structure, and individual differences in rock mechanics properties of each boulder, making it impossible to achieve a refined "one boulder, one plan" design. Furthermore, parameters such as charge quantity, perforation parameters, and detonation timing are interdependent and have complex influences, while existing methods lack effective collaborative optimization tools and cannot accurately predict blasting effects and safety hazards before construction. Therefore, there is an urgent need for a systematic solution that integrates precise detection, scientific calculation, simulation prediction, and intelligent optimization to achieve intelligent design for large-scale boulder blasting.

[0004] For example, Chinese Patent Publication No. CN120558037A discloses a method for optimizing blasting parameters based on surrounding rock parameters, including: S1: obtaining surrounding rock parameters and classifying surrounding rock levels according to the surrounding rock parameters; S2: applying the surrounding rock level obtained in step S1 to determine the excavation method and surrounding rock strength coefficient; S3: applying the excavation method obtained in step S2 to determine the excavation outline and cross-sectional area; S4: calculating the charge amount using the cross-sectional area obtained in step S3 and the surrounding rock strength coefficient obtained in step S2; S5: determining the number of boreholes and arranging the boreholes using the charge amount obtained in step S4 and the cross-sectional area obtained in step S3. This invention improves the targeting and accuracy of excavation, increases excavation efficiency, ensures the rationality of the charge amount and the uniformity of the borehole arrangement, avoids surrounding rock damage and safety hazards caused by over-charge, improves the smoothness of the excavation outline, reduces over-excavation and under-excavation, and lowers construction costs.

[0005] For example, Chinese patent CN117537676A discloses a method for blasting isolated boulders in marine shield tunnels that considers the impact on the ecological environment. The method includes: preliminary design of the blasting borehole network parameters; determination of the explosive consumption per unit volume for blasting isolated boulders; calculation of the single-hole charge amount for each borehole; calculation of the average size of the blasted blocks corresponding to the maximum single-hole charge amount; determination of the blasted block size distribution model; prediction of the block size distribution effect of blasting isolated boulders; preliminary design of the blasting charge structure and blasting network for isolated boulders; and further optimization of the isolated boulder blasting scheme considering the impact on the ecological environment. This method overcomes the shortcomings and deficiencies of existing technologies, such as the lack of specific theoretical basis for determining the parameters of blasting isolated boulders in marine areas, unsatisfactory control of block size effects, and failure to consider the impact of blasting operations on the marine ecological environment. It achieves refined, ecological, efficient, and safe blasting construction for isolated boulders in marine shield tunnels. Summary of the Invention

[0006] The technical problem to be solved by the present invention is to address the shortcomings of the existing technology by providing a method for optimizing charge parameters for blasting large boulders.

[0007] To achieve the above objectives, the technical solution adopted by the present invention is as follows:

[0008] A method for optimizing charge parameters for large boulder blasting includes the following steps:

[0009] Step S1: By fusing three-dimensional laser scanning data and ground-penetrating radar detection data, a three-dimensional model of the isolated rock is constructed, and the three-dimensional model of the isolated rock is divided into finite element meshes.

[0010] Step S2: Determine the dynamic mechanical parameters of the isolated rock through the Hopkinson bar test, and calibrate the RHT constitutive model for numerical simulation of the blasting process based on the dynamic mechanical parameters;

[0011] Step S3: Calculate the basic charge amount based on the three-dimensional model of the boulder, and perform morphological correction and local defect correction on the basic charge amount in sequence to obtain the final optimized charge amount;

[0012] Step S4: Using the drilling parameters and the final optimized charge amount as decision variables, perform numerical simulation of the blasting process to simulate the entire process of explosive detonation, stress wave propagation, rock fragmentation, and flyrock generation.

[0013] Step S5: Construct a multi-objective optimization function that includes the predicted fragmentation index, the specific charge index, and the safety risk index. Use a multi-objective optimization algorithm to iteratively optimize the decision variables and output the Pareto optimal solution set.

[0014] Furthermore, step S1 specifically includes the following steps:

[0015] Step S1.1: Use three-dimensional laser scanning technology to obtain high-precision point cloud data of the surface of the boulder, and use ground-penetrating radar to detect the spatial distribution of macroscopic cracks and defects inside the boulder.

[0016] Step S1.2: Import the point cloud data into 3D modeling software to generate a non-uniform rational B-spline surface model of the isolated rock;

[0017] Step S1.3: Import the non-uniform rational B-spline surface model into the finite element analysis software and perform unstructured tetrahedral mesh generation;

[0018] Step S1.4: Based on the detection results of the ground-penetrating radar, locate and mark the grid cells corresponding to the internal defects in the model that has been gridded.

[0019] Step S1.5: Based on the mechanical properties of the defect, assign differentiated material property parameters to the defect unit and the complete rock matrix unit respectively.

[0020] Furthermore, step S2 specifically includes the following steps:

[0021] Step S2.1: Obtain dynamic compressive strength data of rock specimens under different strain rates through Hopkinson bar compression tests;

[0022] Step S2.2: Perform regression analysis on the dynamic compressive strength data, fit the constitutive formula characterizing the relationship between dynamic strength and strain rate, and define the slope obtained by fitting as the strain rate sensitivity coefficient of the rock.

[0023] Step S2.3: The strain rate sensitivity coefficient, together with the rock static compressive strength, density, elastic modulus and Poisson's ratio parameters obtained through standard rock mechanics tests, are used as input conditions to systematically calibrate the strength surface parameters and damage accumulation parameters of the RHT constitutive model.

[0024] Furthermore, step S3 specifically includes the following steps:

[0025] Step S3.1: Calculate the basic charge quantity based on the principle of energy conservation;

[0026] Step S3.2: Introduce a morphology sensitivity coefficient to correct the morphology of the basic charge amount;

[0027] Step S3.3: Determine the fracture correction factor based on the orientation and density of the fractures around the borehole, and determine the anisotropy correction factor based on the rock anisotropy.

[0028] Step S3.4: Based on the crack correction coefficient and the anisotropy correction coefficient, perform local defect correction to obtain the final optimized charge amount.

[0029] Further, in step S3.1, the basic charge amount is specifically: the basic charge amount is equal to the product of the energy effective utilization coefficient, the volume of the boulder, and the static compressive strength of the rock, multiplied by one and the sum of the fragmentation coefficient, and the result is divided by the product of the explosive energy conversion efficiency and the explosive heat of explosion.

[0030] Furthermore, in step S3.2, the specific method for determining the morphological sensitivity coefficient is as follows:

[0031] Construct a series of numerical models of isolated boulders with the same volume but different geometric shapes;

[0032] Using the calibrated RHT constitutive model, the blasting process was simulated for the numerical model of each shape.

[0033] The optimal charge amount required to achieve the predetermined fracturing effect for each type of boulder model is calculated through inversion.

[0034] A linear regression analysis was performed on the optimal charge amount and the specific surface area of ​​the corresponding boulder model, and the slope of the resulting regression line was defined as the morphology sensitivity coefficient.

[0035] Further, in step S5, the predicted fragmentation size index in the multi-objective optimization function includes: the ratio of the average rock fragmentation size obtained from numerical simulation to the target fragmentation size, and the ratio of unqualified large fragments; the specific charge quantity index is the ratio of the total charge quantity designed for blasting to the volume of the isolated rock; the safety risk index includes: the ratio of the predicted farthest throwing distance of flying rocks to the safe permissible distance, and the ratio of the predicted peak value of shock wave overpressure to the safe overpressure threshold.

[0036] Furthermore, in step S5, the multi-objective optimization algorithm is a fast non-dominated sorting genetic algorithm.

[0037] A storage medium, characterized in that the storage medium stores instructions, which, when read by a computer, cause the computer to execute a method for optimizing charge parameters for large boulder blasting.

[0038] An electronic device, characterized in that it includes a processor and a storage medium, wherein the processor executes instructions in the storage medium.

[0039] Compared with the prior art, the beneficial effects of the present invention are as follows:

[0040] 1. This invention accurately characterizes the geometric and geological features of isolated boulders using three-dimensional digital means, and establishes a quantitative calculation model based on the principle of energy conservation and dynamic theory, thereby improving the scientific nature and reliability of the design.

[0041] 2. By embedding the calibrated RHT constitutive model into numerical simulation, this invention can accurately predict the initial velocity of the flying stone, the projection distance, and the shock wave overpressure before the implementation of the scheme, and use this as a hard constraint for optimizing the objective function, thereby reducing safety risks.

[0042] 3. This invention improves the accuracy and adaptability of charge calculation by introducing a morphology sensitivity coefficient for morphology correction and by correcting local defects based on crack correction coefficient and anisotropy correction coefficient, thereby enhancing the relevance and reliability of the scheme for complex actual working conditions.

[0043] 4. This invention uses a multi-objective optimization algorithm to automatically search for the Pareto optimal solution set that minimizes the total charge while meeting the requirements for fragmentation size. This achieves a balance between fragmentation effect and economic cost, effectively avoiding insufficient or excessive charge and saving blasting costs. Attached Figure Description

[0044] Other features, objects, and advantages of the invention will become more apparent from the following detailed description of non-limiting embodiments with reference to the accompanying drawings:

[0045] Figure 1 This is a flowchart illustrating an embodiment of the present invention;

[0046] Figure 2 This is a schematic diagram of the charge parameter optimization module in an embodiment of the present invention. Detailed Implementation

[0047] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be described in detail below with reference to the accompanying drawings and specific embodiments.

[0048] like Figure 1 As shown, the method for optimizing charge parameters for blasting large boulders includes the following steps:

[0049] Step S1: By fusing three-dimensional laser scanning data and ground-penetrating radar detection data, a three-dimensional model of the isolated rock is constructed, and the three-dimensional model of the isolated rock is divided into finite element meshes.

[0050] Step S2: Determine the dynamic mechanical parameters of the isolated rock through the Hopkinson bar test, and calibrate the RHT constitutive model for numerical simulation of the blasting process based on the dynamic mechanical parameters;

[0051] Step S3: Calculate the basic charge amount based on the three-dimensional model of the boulder, and perform morphological correction and local defect correction on the basic charge amount in sequence to obtain the final optimized charge amount;

[0052] Step S4: Using the drilling parameters and the final optimized charge amount as decision variables, perform numerical simulation of the blasting process to simulate the entire process of explosive detonation, stress wave propagation, rock fragmentation, and flyrock generation.

[0053] Step S5: Construct a multi-objective optimization function that includes the predicted fragmentation index, the specific charge index, and the safety risk index. Use a multi-objective optimization algorithm to iteratively optimize the decision variables and output the Pareto optimal solution set.

[0054] Step S1 specifically includes the following steps:

[0055] Step S1.1: Use three-dimensional laser scanning technology to obtain high-precision point cloud data of the surface of the boulder, and use ground-penetrating radar to detect the spatial distribution of macroscopic cracks and defects inside the boulder.

[0056] Step S1.2: Import the point cloud data into 3D modeling software to generate a non-uniform rational B-spline surface model of the isolated rock;

[0057] Step S1.3: Import the non-uniform rational B-spline surface model into the finite element analysis software and perform unstructured tetrahedral mesh generation;

[0058] Step S1.4: Based on the detection results of the ground-penetrating radar, locate and mark the grid cells corresponding to the internal defects in the model that has been gridded.

[0059] Step S1.5: Based on the mechanical properties of the defect, assign differentiated material property parameters to the defect unit and the complete rock matrix unit respectively.

[0060] Step S2 specifically includes the following steps:

[0061] Step S2.1: Obtain dynamic compressive strength data of rock specimens under different strain rates through Hopkinson bar compression tests;

[0062] Step S2.2: Perform regression analysis on the dynamic compressive strength data, fit the constitutive formula characterizing the relationship between dynamic strength and strain rate, and define the slope obtained by fitting as the strain rate sensitivity coefficient of the rock.

[0063] The specific formula of the constitutive formula is as follows:

[0064]

[0065] in, Represents dynamic compressive strength, indicating the peak compressive strength of a rock specimen measured under high strain rate conditions. Represents static compressive strength, indicating the peak compressive strength of the rock specimen measured under quasi-static loading conditions. Indicates strain rate. This represents the reference strain rate, typically on the order of strain rate from a quasi-static test. The coefficient of fit is a dimensionless constant obtained through regression analysis of experimental data. The strain rate sensitivity coefficient is obtained by performing linear regression analysis on experimental data in a double logarithmic coordinate system, and its value is equal to the slope of the fitted line.

[0066] Taking the logarithm of both sides of the above formula and transforming it into a linear relationship, the specific formula is as follows:

[0067]

[0068] A series of data points obtained from the experiment are subjected to linear regression analysis. The slope obtained from the regression is the strain rate sensitivity coefficient.

[0069] Step S2.3: The strain rate sensitivity coefficient, together with the rock static compressive strength, density, elastic modulus and Poisson's ratio parameters obtained through standard rock mechanics tests, are used as input conditions to systematically calibrate the strength surface parameters and damage accumulation parameters of the RHT constitutive model.

[0070] Step S3 specifically includes the following steps:

[0071] Step S3.1: Calculate the basic charge amount based on the principle of energy conservation. The specific formula is as follows:

[0072]

[0073] in, Indicates the basic charge quantity. This represents the energy efficiency coefficient, which is related to the free surface conditions and ranges from 0.05 to 0.2. This indicates the current volume of the isolated rock. This represents the fragmentation coefficient, which is determined based on the desired fragment size. This represents the energy conversion efficiency of explosives, which is related to the type of explosive and the coupling coefficient, and is typically between 0.15 and 0.3. This indicates that the explosive is overheating;

[0074] The formula for calculating the breakage coefficient is as follows:

[0075]

[0076] in, This represents the largest characteristic dimension of the boulder, specifically the length of the major axis of a roughly ellipsoidal boulder. This represents the desired maximum fragmentation size, which is the largest size of any fragment produced after blasting.

[0077] Step S3.2: Introduce a morphology sensitivity coefficient to correct the morphology of the base charge. The specific formula is as follows:

[0078]

[0079] in, This indicates the dosage after morphological correction. Represents the morphological sensitivity coefficient. This represents the surface area of ​​the current isolated rock. This indicates the current volume of the isolated rock. The volume is The surface area of ​​a sphere, This indicates the estimated average strain rate;

[0080] The formula for calculating the estimated average strain rate is as follows:

[0081]

[0082] in, Indicates the longitudinal wave velocity in the rock;

[0083] Step S3.3: Determine the fracture correction factor based on the orientation and density of the fractures around the borehole, and determine the anisotropy correction factor based on the rock anisotropy.

[0084] For each borehole, the distribution of fractures within a certain range around it is analyzed. If the fractures are connected to the borehole, the fracture correction factor is 0.8 to 1.0; if the fractures are perpendicular to the borehole, the fracture correction factor is 1.0 to 1.2; if the rock has obvious anisotropy (such as bedding), the anisotropy correction factor is 0.8 to 0.9 when blasting along the weak plane direction, and 1.1 to 1.3 when blasting perpendicular to it.

[0085] Step S3.4: Based on the crack correction coefficient and the anisotropy correction coefficient, perform local defect correction to obtain the final optimized charge amount.

[0086] The final optimized charge amount is calculated using the following formula:

[0087]

[0088] in, This indicates the final optimized charge amount. Indicates the crack correction factor. This represents the anisotropy correction coefficient.

[0089] The final charge per hole is determined by dividing the final optimized charge by the number of holes obtained through optimization.

[0090] In step S3.1, the basic charge amount is specifically: the basic charge amount is equal to the product of the energy effective utilization coefficient, the volume of the boulder, and the static compressive strength of the rock, multiplied by one and the sum of the fragmentation coefficient, and the result is divided by the product of the explosive energy conversion efficiency and the explosive heat of explosion.

[0091] In step S3.2, the specific method for determining the morphological sensitivity coefficient is as follows:

[0092] Construct a series of numerical models of isolated boulders with the same volume but different geometric shapes, i.e., virtual isolated boulder models with different specific surface areas, such as spheres, cubes, cuboids, and sheet-like bodies;

[0093] Using the calibrated RHT constitutive model, blasting simulations were performed on each boulder model with an initial, baseline charge, and the fragmentation effect of each model was recorded, such as the pass rate of fragment size or the average fragment size.

[0094] For each shape, by adjusting the charge amount multiple times and repeating the simulation, the charge amount that can achieve the same expected crushing effect is found and recorded as the optimal charge amount for that shape.

[0095] A linear regression analysis was performed on the optimal charge amount and the specific surface area of ​​the corresponding boulder model. The slope of the resulting regression line was defined as the morphological sensitivity coefficient, which represents the change in relative charge amount caused by a change in unit specific surface area.

[0096] Step S4 specifically includes:

[0097] 1. Drilling Parameter Setting Based on Numerical Simulation: The initial drilling parameters are set first. The borehole depth is determined by the product of the path coefficient and the shortest fracturing path length from the borehole opening to the nearest free face. This path length is calculated using a 3D model. The path coefficient is typically set within a specific range to ensure the bottom of the borehole exceeds the predetermined fracturing interface. The initial values ​​for borehole spacing and row spacing are calculated based on the optimized single-hole charge, rock density, and rock wave impedance.

[0098] 2. Numerical Simulation Setting of the Initiation Network: In the numerical model, different initiation delay times are set for different boreholes, and continuous micro-delay initiation technology is adopted. The superposition effect of stress waves in the rock under different initiation sequences is calculated through simulation. The optimization objective is to enable the stress waves generated by adjacent boreholes to form an effective tensile stress superposition zone within the rock, thereby improving the fracturing effect. The optimal initiation delay time can be initially estimated based on empirical formulas, and finally fine-tuned through simulation to obtain the best effect.

[0099] 3. For the quantitative prediction and constraint of safety hazards, a flyrock initial velocity prediction model was established. This model, based on numerical simulation, predicts the flyrock initial velocity by extracting the maximum velocity reached by rock elements above the borehole plug under blasting load. The calculation of the flyrock initial velocity is related to the initial peak pressure of the borehole wall, the tensile strength of the rock, the cross-sectional area and mass of the rock above the plug, and a velocity coefficient is introduced. The flight distance of the flyrock is calculated using the flyrock initial velocity, projection angle, and gravitational acceleration. By statistically analyzing the number of flyrock particles with different initial velocities, the possible distribution range of the flyrock can be predicted. The prediction of the air shock wave overpressure peak value adopts an empirical formula method.

[0100] In step S5, the multi-objective optimization function includes the following: the predicted fragmentation size index includes the ratio of the average rock fragmentation size obtained from numerical simulation to the target fragmentation size, and the proportion of unqualified large fragments; the specific charge index is the ratio of the total charge amount in the blasting design to the volume of the isolated rock; the safety risk index includes the ratio of the predicted farthest throwing distance of the flyrock to the safe permissible distance, and the ratio of the predicted peak value of the shock wave overpressure to the safe overpressure threshold.

[0101] In step S5, the multi-objective optimization algorithm is a fast non-dominated sorting genetic algorithm.

[0102] The formula for calculating the multi-objective optimization function is as follows:

[0103]

[0104]

[0105]

[0106] in, This represents the objective function for predicting the fragmentation degree index. This represents the objective function for the specific charge quantity index. This represents the objective function of the safety risk index. This represents the average block size of the rock after blasting, calculated through numerical simulation. This represents the target average block size required by the engineering design. This indicates the percentage of unqualified large blocks (those exceeding the maximum allowable block size) in the simulation results. and These represent the weighting coefficients for average block size and the percentage of large, defective blocks, respectively. This indicates the total amount of explosives required for the blasting design. This represents the farthest projectile distance predicted by numerical simulation. This indicates the safe distance determined based on the site conditions. This represents the peak overpressure of the air shock wave predicted through numerical simulation. This indicates the safe overpressure threshold for people or buildings. and These represent the weighting coefficients for the risk of flying rocks and the risk of shock waves, respectively.

[0107] Multi-objective optimization problems can be uniformly formulated as:

[0108]

[0109] in, This represents the set of decision variables, including the number of boreholes, the amount of explosive per hole, the hole spacing, the row spacing, the detonation sequence, and the plugging length. This represents the objective function of a vector value.

[0110] For multi-objective optimization using the fast non-dominated sorting genetic algorithm NSGA-II with an elitist strategy, the algorithm generates a set of parameter combinations → calls simulation to calculate F1, F2, and F3 → selects, crosses over, and mutates based on fitness to generate a new generation of population → until convergence; the final output is a set of Pareto optimal solutions, from which engineers can choose the final solution according to their field preferences, such as whether they value performance or safety more.

[0111] like Figure 2 The diagram shows the module structure of the boulder blasting parameter optimization system described in this invention. The system input receives raw data from 3D scanning equipment, ground-penetrating radar, and SHPB experiments. Data integration fuses multi-source data and sends it to 3D geological modeling and mesh generation to generate a computational model for simulation. The rock dynamic constitutive model calibration uses experimental data to assign accurate physical properties to the model. The charge calculation optimization is responsible for performing basic charge, dynamic correction, and local correction calculations. The core parameterized numerical simulation engine for the blasting process receives charge parameters and runs the simulation; the results are sent to a multi-objective optimizer (using the NSGA-II algorithm). The optimization decision maker determines convergence based on the objective function; if convergence fails, it drives the optimizer to generate new parameters and re-simulate, forming an inner loop; if convergence occurs, the optimal solution output module provides a parameter table.

[0112] Any combination of one or more computer-readable media may be used. A computer-readable medium can be a computer-readable signal medium or a computer-readable storage medium. A computer-readable storage medium can be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination thereof. More specific examples (a non-exhaustive list) of computer-readable storage media include: an electrical connection having one or more wires, a portable computer disk, a hard disk, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage device, magnetic storage device, or any suitable combination thereof. In this document, a computer-readable storage medium can be any tangible medium that contains or stores a program that can be used by or in conjunction with an instruction execution system, apparatus, or device.

[0113] The examples described herein are merely preferred embodiments of the invention and are not intended to limit the concept and scope of the invention. Any modifications and improvements made by those skilled in the art to the technical solutions of the invention without departing from the design concept of the invention should fall within the protection scope of the invention.

Claims

1. A method for optimizing charge parameters for large boulder blasting, characterized in that, Includes the following steps: Step S1: By fusing three-dimensional laser scanning data and ground-penetrating radar detection data, a three-dimensional model of the isolated rock is constructed, and the three-dimensional model of the isolated rock is divided into finite element meshes. Step S2: Determine the dynamic mechanical parameters of the isolated rock through the Hopkinson bar test, and calibrate the RHT constitutive model for numerical simulation of the blasting process based on the dynamic mechanical parameters. Step S3: Calculate the basic charge amount based on the three-dimensional model of the boulder, and perform morphological correction and local defect correction on the basic charge amount in sequence to obtain the final optimized charge amount; Step S4: Using the drilling parameters and the final optimized charge amount as decision variables, perform numerical simulation of the blasting process to simulate the entire process of explosive detonation, stress wave propagation, rock fragmentation, and flyrock generation. Step S5: Construct a multi-objective optimization function that includes the predicted fragmentation index, the specific charge index, and the safety risk index. Use a multi-objective optimization algorithm to iteratively optimize the decision variables and output the Pareto optimal solution set.

2. The method of claim 1, wherein, Step S1 specifically includes the following steps: Step S1.1: Use three-dimensional laser scanning technology to obtain high-precision point cloud data of the surface of the boulder, and use ground-penetrating radar to detect the spatial distribution of macroscopic cracks and defects inside the boulder. Step S1.2: Import the point cloud data into 3D modeling software to generate a non-uniform rational B-spline surface model of the isolated rock; Step S1.3: Import the non-uniform rational B-spline surface model into the finite element analysis software and perform unstructured tetrahedral mesh generation; Step S1.4: Based on the detection results of the ground-penetrating radar, locate and mark the grid cells corresponding to the internal defects in the model that has been gridded. Step S1.5: Based on the mechanical properties of the defect, assign differentiated material property parameters to the defect unit and the complete rock matrix unit respectively.

3. The method of claim 2, wherein, Step S2 specifically includes the following steps: Step S2.1: Obtain dynamic compressive strength data of rock specimens under different strain rates through Hopkinson bar compression tests; Step S2.2: Perform regression analysis on the dynamic compressive strength data, fit the constitutive formula characterizing the relationship between dynamic strength and strain rate, and define the slope obtained by fitting as the strain rate sensitivity coefficient of the rock. Step S2.3: The strain rate sensitivity coefficient, together with the rock static compressive strength, density, elastic modulus and Poisson's ratio parameters obtained through standard rock mechanics tests, are used as input conditions to systematically calibrate the strength surface parameters and damage accumulation parameters of the RHT constitutive model.

4. The method of claim 3, wherein, Step S3 specifically includes the following steps: Step S3.1: Calculate the basic charge quantity based on the principle of energy conservation; Step S3.2: Introduce a morphology sensitivity coefficient to correct the morphology of the basic charge amount; Step S3.3: Determine the fracture correction factor based on the orientation and density of the fractures around the borehole, and determine the anisotropy correction factor based on the rock anisotropy. Step S3.4: Based on the crack correction coefficient and the anisotropy correction coefficient, perform local defect correction to obtain the final optimized charge amount.

5. The method of claim 4, wherein, In step S3.1, the basic charge amount is specifically: the basic charge amount is equal to the product of the energy effective utilization coefficient, the volume of the boulder, and the static compressive strength of the rock, multiplied by one and the sum of the fragmentation coefficient, and the result is divided by the product of the explosive energy conversion efficiency and the explosive heat of explosion.

6. The method of claim 5, wherein, In step S3.2, the specific method for determining the morphological sensitivity coefficient is as follows: Construct a series of numerical models of isolated boulders with the same volume but different geometric shapes; Using the calibrated RHT constitutive model, the blasting process was simulated for the numerical model of each shape. The optimal charge amount required to achieve the predetermined fracturing effect for each type of boulder model is calculated through inversion. A linear regression analysis was performed on the optimal charge amount and the specific surface area of ​​the corresponding boulder model, and the slope of the resulting regression line was defined as the morphological sensitivity coefficient.

7. The method of claim 6, wherein, In step S5, the multi-objective optimization function includes the following: the predicted fragmentation size index includes the ratio of the average rock fragmentation size obtained from numerical simulation to the target fragmentation size, and the proportion of unqualified large fragments; the specific charge index is the ratio of the total charge amount in the blasting design to the volume of the isolated rock; the safety risk index includes the ratio of the predicted farthest throwing distance of the flyrock to the safe permissible distance, and the ratio of the predicted peak value of the shock wave overpressure to the safe overpressure threshold.

8. The method of claim 7, wherein, In step S5, the multi-objective optimization algorithm is a fast non-dominated sorting genetic algorithm.

9. A storage medium, characterized by The storage medium stores instructions that, when read by a computer, cause the computer to execute the charge parameter optimization method for large boulder blasting as described in any one of claims 1-7.

10. An electronic device, comprising: It includes a processor and the storage medium of claim 9, wherein the processor executes instructions in the storage medium.

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