Intelligent bench blasting method and system for rock-mud interbedded open-pit mine

Abstract

The application discloses an intelligent bench blasting method and system for rock and mud staggered open-pit mines, and relates to the technical field of blasting. The method comprises the following steps: obtaining point cloud data of a bench blasting area of an open-pit mine through three-dimensional laser scanning; constructing a three-dimensional geological model reflecting the bench shape and rock and mud distribution characteristics; establishing a comprehensive blasting prediction model based on the characteristics of bench blasting; constructing a multi-objective optimization function including fly rock control, vibration control and block uniformity; determining the benchmark hole network parameters, single-hole charge and millisecond delay of bench blasting by processing the multi-objective optimization function through a multi-objective optimization algorithm; and adaptively adjusting the benchmark hole network parameters, single-hole charge and millisecond delay according to local rock and mud distribution. The application solves the blasting problem under the rock and mud staggered geological condition through three-dimensional geological modeling, multi-objective parameter optimization and adaptive adjustment of blasting parameters.

Description

Technical Field

[0001] This invention relates to the field of blasting technology, specifically to an intelligent bench blasting method and system for open-pit mines with alternating rock and mud. Background Technology

[0002] Open-pit bench blasting is a rock blasting operation that involves excavating rocks in benches on the ground. As a major method of ore and rock stripping in open-pit mines, open-pit bench blasting has achieved significant technological breakthroughs with the rapid development of blasting materials and rock drilling equipment. The scale of mining operations has continued to expand, creating significant economic and social benefits.

[0003] However, due to the ever-changing geological conditions in mining engineering, such as the complex interface formed by the intermingling of rocks and mud, the blasting stress waves will be reflected, refracted and attenuated when they encounter the mudstone interface during propagation, resulting in uncontrollable blasting energy distribution and uneven block size after blasting. At the same time, the blasting point is close to residential buildings, making blasting vibration control a key constraint, which is difficult to cope with using traditional experience methods.

[0004] Chinese patent CN117781796A discloses a method for precise blasting construction of tunnels with muddy interlayer surrounding rock. The method includes: designing blasting holes according to the tunnel excavation cross-section, wherein the blasting holes include cut holes, auxiliary holes, and peripheral holes arranged sequentially from the center of the excavation cross-section to the excavation outline; the cut holes are equipped with a shaped charge cavity structure; and the peripheral holes include spaced-apart charge holes and empty holes; measuring and setting out the excavation cross-section according to the designed blasting holes, laying out the hole positions, and drilling; determining the charge parameters and charge structure of the blasting holes; connecting the charge of the blasting holes and linking the detonation network; checking and analyzing the blasting results after detonation, and adjusting the blasting parameters as needed. This method, by introducing a shaped charge cavity structure through cut holes and spaced-apart charge holes and empty holes in the peripheral holes, and adjusting the blasting parameters of the peripheral holes based on the different positions of the muddy interlayer relative to the tunnel excavation outline, can reduce the over-excavation and under-excavation rate and safety risks caused by the muddy interlayer, improve the efficiency of blasting construction, and has broad application significance.

[0005] For example, Chinese Patent Publication No. CN120627820A discloses a method for predicting tunnel blasting vibration in interbedded sandstone and mudstone areas, mainly involving the field of tunnel blasting technology. The method includes the following steps: S1, determining the key factors affecting vibration velocity; S2, establishing a blasting vibration prediction model for interbedded sandstone and mudstone areas; S3, performing data normalization; S4, introducing a BP neural network to train the prediction model; S5, introducing a particle swarm optimization algorithm and initializing relevant particle swarm parameters; S6, improving the particle swarm optimization algorithm; S7, deriving an improved particle swarm optimized BP neural network-based blasting vibration prediction model for interbedded sandstone and mudstone tunnels, and using this model in conjunction with the blasting characteristic parameters of specific interbedded sandstone and mudstone tunnel projects to make predictions and obtain blasting effect prediction results. This invention can provide important theoretical guidance for predicting blasting vibration in layered rock structures with varying strata and provides a practical safety assessment tool for surface vibration velocity control in similar projects. Summary of the Invention

[0006] The technical problem to be solved by the present invention is to address the shortcomings of the prior art by providing an intelligent bench blasting method and system for open-pit mines with alternating rock and mud.

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

[0008] The intelligent bench blasting method for open-pit mines with alternating rock and mud includes the following steps:

[0009] Step S1: Obtain point cloud data of the bench blasting area in the open-pit mine through 3D laser scanning;

[0010] Step S2: Construct a three-dimensional geological model that reflects the morphology of the steps and the distribution characteristics of rock and mud;

[0011] Step S3: Based on the characteristics of bench blasting, construct a comprehensive blasting prediction model;

[0012] Step S4: Construct a multi-objective optimization function that includes flystone control, vibration control, and block size uniformity;

[0013] Step S5: The multi-objective optimization function is processed by a multi-objective optimization algorithm to determine the reference hole network parameters, single hole charge, and differential delay for bench blasting. The hole network parameters include hole spacing, row spacing, and over-depth.

[0014] Step S6: Adaptively adjust the reference hole network parameters, single hole charge, and differential delay based on the local rock and mud distribution.

[0015] Furthermore, step S2 specifically includes the following steps:

[0016] Step S2.1: Perform point cloud registration preprocessing on the acquired point cloud data and use a statistical filtering algorithm to remove noise;

[0017] Step S2.2: Extract the geometric features of the steps based on the denoised point cloud data, including step height, slope angle and platform width;

[0018] Step S2.3: Identify rock-mud boundaries based on color features, curvature features, and spatial distribution features;

[0019] Step S2.4: Generate a three-dimensional geological model using the Delaunay triangulation algorithm, integrating rock and mud classification results and residential building location information.

[0020] Furthermore, step S2.3 specifically includes the following steps:

[0021] Construct the color feature components, curvature feature components, and spatial distribution feature components for each point in the point cloud;

[0022] The comprehensive feature value of rock and mud classification for each point is obtained by combining the feature components of each point, that is, the probability that the point belongs to rock or mud.

[0023] The formula for calculating the color feature components is as follows:

[0024]

[0025] in, Point Color feature components, Represents a point index. Representing points respectively The intensity values ​​of the red channel, green channel, and blue channel. This represents the set of rock color ranges, with RGB values ​​from (120, 120, 120) to (200, 200, 200). This represents the set of mud color ranges, with RGB values ​​from (80, 60, 40) to (150, 120, 80);

[0026] The formula for calculating the curvature characteristic component is:

[0027]

[0028] in, Point The curvature characteristic components, Represents the gradient operator, Point The unit normal vector at that location, This represents the absolute value operator;

[0029] The formula for calculating the spatial distribution characteristic components is as follows:

[0030]

[0031] in, Point Spatial distribution characteristic components, Point The total number of point clouds in the neighborhood, Point The j-th point in the neighborhood, This indicates an indicator function that returns 1 if the condition is true and 0 otherwise. Representation and point A set of points belonging to the same material region.

[0032] Furthermore, in step S3, the comprehensive blasting prediction model includes three models: a blasting vibration prediction model considering elevation effects and rock mass damage; a flyrock distance prediction model that integrates surface flyrock, blockage flyrock, and geological defect flyrock; and a Kuz-Ram model oriented towards block size prediction.

[0033] Furthermore, in step S4, the specific formula for the multi-objective optimization function is as follows:

[0034]

[0035] in, This represents a multi-objective optimization function vector composed of the values ​​of the three objective functions. This represents the objective function for vibration control. Let represent the objective function for controlling the flying stone. The objective function represents the uniformity of block size. This represents the blasting parameter vector.

[0036] Furthermore, in step S5, the multi-objective optimization algorithm employs a non-dominated sorting genetic algorithm, which includes the following specific steps:

[0037] Step S5.1: Randomly generate an initial population. Each individual in the population is a solution vector, which includes the mesh parameters, the amount of drug per hole, and the differential delay.

[0038] Step S5.2: For each individual in the population, calculate the values ​​of the three objective functions in the multi-objective optimization function;

[0039] Step S5.3: Perform non-dominated sorting on the population based on the objective function value;

[0040] Step S5.4: Calculate the crowding degree of each solution;

[0041] Step S5.5: Use a binary tournament to select parent individuals, and then perform crossover and mutation operations on the selected parent individuals to generate the offspring population;

[0042] Step S5.6: Merge the parent population and the offspring population into a new population;

[0043] Step S5.7: Perform non-dominated sorting and crowding calculation on the new population again, and finally select the next generation population;

[0044] Step S5.8: Stop outputting the Pareto optimal solution set after reaching the maximum number of iterations of 200;

[0045] Step S5.9: Select the optimal solution from the Pareto optimal solution set, namely the reference hole mesh parameters, single hole charge, and differential delay.

[0046] Furthermore, in step S6, the adaptive adjustment of the mesh parameters specifically includes adaptive adjustment of the hole spacing, adaptive adjustment of the row spacing, and adaptive adjustment of the ultra-deep mesh.

[0047] The adaptive hole spacing adjustment comprehensively considers the reference hole spacing, the local rock and mud distribution ratio, and the lithology adjustment coefficient, and scales the reference hole spacing according to the local rock ratio and mud ratio.

[0048] The adaptive adjustment of the row spacing is based on the adjusted hole spacing and step height factors. The relationship between the row spacing and the hole spacing is established through the row spacing coefficient, and the correction is considered when the step height deviates from the standard value.

[0049] The ultra-deep adaptive adjustment comprehensively considers the influence of the benchmark ultra-deepness, local lithological differences, and step height. It adjusts the ultra-deep value according to the difference in the ratio of rock to mud and compensates for non-standard step heights.

[0050] Furthermore, in step S6, the adaptive adjustment of the single-hole charge is dynamically adjusted based on the optimized baseline single-hole charge, combined with local lithological characteristics, the relative distance between the borehole and the houses, and vibration control requirements.

[0051] Furthermore, in step S6, the micro-delay adaptive adjustment optimizes the micro-delay within the borehole. Based on the distribution characteristics of the rock-mud interface and the stress wave propagation characteristics, the optimized benchmark micro-delay is corrected.

[0052] An intelligent bench blasting system for open-pit mines with alternating rock and mud, implemented based on the aforementioned intelligent bench blasting method for such mines, includes:

[0053] The 3D laser scanning module is used to acquire point cloud data of the blasting area of ​​the open-pit mine bench, including bench geometric parameters, rock and mud distribution characteristics, and the location of houses;

[0054] The geological modeling and analysis module is used to process point cloud data and construct a three-dimensional geological model that reflects the morphology of steps and the intermingling distribution of rock and mud.

[0055] The blasting effect prediction module integrates a blasting vibration prediction model, a flyrock distance prediction model, and a block size distribution prediction model, which are used to predict the vibration response, flyrock risk, and block size uniformity under different combinations of blasting parameters.

[0056] The multi-objective optimization module is used to execute the non-dominated sorting genetic algorithm to process the three-objective optimization function with the objectives of flyrock control, vibration control and block size uniformity, and determine the global optimal benchmark values ​​of blasting parameters including hole mesh parameters, single hole charge and micro-delay time.

[0057] The parameter adaptive adjustment module is used to adaptively correct the benchmark values ​​of blasting parameters output by the multi-objective optimization module, including the hole mesh parameters, single hole charge, and micro-delay time.

[0058] The blasting scheme generation and output module is used to integrate adaptively adjusted blasting parameters, generate a complete blasting design scheme, and output a visualized construction guidance document.

[0059] Compared with the prior art, the beneficial effects of the present invention are as follows:

[0060] 1. This invention achieves accurate perception of complex geological conditions through three-dimensional laser scanning and rock and mud identification, providing a reliable basis for differentiated blasting design.

[0061] 2. This invention establishes a comprehensive blasting prediction model, including a blasting vibration prediction model, a flyrock distance prediction model, and a Kuz-Ram model, which can comprehensively evaluate blasting effects and safety, and is suitable for the protection of nearby residential buildings.

[0062] 3. The multi-objective optimization function constructed in this invention can find a balance between the vibration control objective, the flyrock control objective function, and the block uniformity objective. It provides the Pareto optimal solution set through the optimization algorithm, thereby achieving global optimization of the blasting parameters.

[0063] 4. The adaptive adjustment mechanism constructed in this invention adjusts and corrects the blasting parameters according to the local rock and mud distribution, realizing the combination of global optimization and local optimization, and improving the uniformity of blasting effect. Attached Figure Description

[0064] 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:

[0065] Figure 1 This is a flowchart illustrating an embodiment of the present invention;

[0066] Figure 2 This is a schematic diagram of the system structure according to an embodiment of the present invention;

[0067] Figure 3 This is a site view of an open-pit mine with alternating rock and mud according to an embodiment of the present invention. Detailed Implementation

[0068] 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.

[0069] like Figure 1 As shown, the intelligent bench blasting method for open-pit mines with alternating rock and mud includes the following steps:

[0070] Step S1: Obtain point cloud data of the bench blasting area in the open-pit mine through 3D laser scanning;

[0071] Step S2: Construct a three-dimensional geological model that reflects the morphology of the steps and the distribution characteristics of rock and mud;

[0072] Step S3: Based on the characteristics of bench blasting, construct a comprehensive blasting prediction model;

[0073] Step S4: Construct a multi-objective optimization function that includes flystone control, vibration control, and block size uniformity;

[0074] Step S5: The multi-objective optimization function is processed by a multi-objective optimization algorithm to determine the reference hole network parameters, single hole charge, and differential delay for bench blasting. The hole network parameters include hole spacing, row spacing, and over-depth.

[0075] Step S6: Adaptively adjust the reference hole network parameters, single hole charge, and differential delay based on the local rock and mud distribution.

[0076] Step S2 specifically includes the following steps:

[0077] Step S2.1: Perform point cloud registration preprocessing on the acquired point cloud data and use a statistical filtering algorithm to remove noise;

[0078] The filtering function of the statistical filtering algorithm is:

[0079]

[0080] in, This represents the denoised point cloud data. This represents the raw point cloud data. This represents the i-th point in the point cloud. and This represents the mean and standard deviation of a local neighborhood, i.e., removing points in the original point cloud data whose difference from the mean is greater than or equal to three times the standard deviation;

[0081] The point cloud registration preprocessing involves using the iterative nearest point algorithm to accurately register multi-station scan data to a unified coordinate system.

[0082] Step S2.2: Extract the geometric features of the steps based on the denoised point cloud data, including step height, slope angle and platform width;

[0083] The step height is calculated by the elevation difference between the top and bottom lines of the step in the point cloud data, i.e., the average elevation of the top line of the step minus the average elevation of the bottom line of the step.

[0084] The slope angle is calculated by the angle between the normal vector of the step slope in the point cloud data and the vertical direction, or by the arctangent of the height difference and horizontal distance of the slope.

[0085] The platform width is calculated by the horizontal distance between the top and bottom lines of the steps in the point cloud data, which is the projection distance between the top and bottom lines on the horizontal plane.

[0086] Step S2.3: Identify rock-mud boundaries based on color features, curvature features, and spatial distribution features;

[0087] Step S2.4: Generate a three-dimensional geological model using the Delaunay triangulation algorithm, integrating rock and mud classification results and residential building location information.

[0088] Step S2.4 specifically includes: first, projecting the optimized point cloud data onto a two-dimensional horizontal plane to generate a basic triangular mesh; then, introducing terrain feature lines as constraint edges to ensure that these important features are accurately represented in the triangular mesh; finally, mapping the two-dimensional triangular mesh back to three-dimensional space and recovering the three-dimensional terrain surface through the elevation information of the point cloud; integrating the classification results of the rock and mud identification algorithm into the three-dimensional geological model; assigning rock and mud classification attributes to each vertex in the triangular mesh, with vertices in rock areas marked as 1, vertices in mud areas marked as 0, and vertices in transition areas obtaining continuous values ​​between 0 and 1 based on interpolation of neighboring vertices; accurately integrating the location and structural information of houses into the geological model, obtaining the three-dimensional coordinates of the corner points of houses through high-precision GPS measurement, and adding these points as fixed points to the point cloud dataset during the triangulation process; then inserting constraint edges at the boundary of the house outline to ensure that the boundary of the triangular mesh in the house area is consistent with the actual situation; finally, assigning special attribute labels to the triangular facets of the house area to highlight the location of the houses in the model.

[0089] Step S2.3 specifically includes the following steps:

[0090] Construct the color feature components, curvature feature components, and spatial distribution feature components for each point in the point cloud;

[0091] The comprehensive feature value of rock and mud classification for each point is obtained by combining the feature components of each point, that is, the probability that the point belongs to rock or mud.

[0092] The formula for calculating the color feature components is as follows:

[0093]

[0094] in, Point Color feature components, Represents a point index. Representing points respectively The intensity values ​​of the red channel, green channel, and blue channel. This represents the set of rock color ranges, with RGB values ​​from (120, 120, 120) to (200, 200, 200). This represents the set of mud color ranges, with RGB values ​​from (80, 60, 40) to (150, 120, 80);

[0095] The formula for calculating the curvature characteristic component is:

[0096]

[0097] in, Point The curvature characteristic components, Represents the gradient operator, Point The unit normal vector at that location, This represents the absolute value operator;

[0098] The formula for calculating the spatial distribution characteristic components is as follows:

[0099]

[0100] in, Point Spatial distribution characteristic components, Point The total number of point clouds in the neighborhood, Point The j-th point in the neighborhood, This indicates an indicator function that returns 1 if the condition is true and 0 otherwise. Representation and point A set of points belonging to the same material region.

[0101] The formula for calculating the comprehensive characteristic value of rock and mud classification is as follows:

[0102]

[0103] in, Point The comprehensive characteristic values ​​for rock and mud classification, , and These represent the weights of the color feature component, curvature feature component, and spatial distribution feature component, respectively.

[0104] For areas where rock and mud intermingle, the weights are set as follows: =0.33, =0.33, =0.34, when the color contrast between the rock and mud is strong, it increases. The value increases when the surface of the rock and mud has obvious unevenness. The value increases when the mudstone is distributed in continuous layers. value.

[0105] In step S3, the comprehensive blasting prediction model includes three models: a blasting vibration prediction model that considers elevation effects and rock mass damage; a fly rock distance prediction model that integrates surface fly rocks, fly rocks in the blocking section, and fly rocks from geological defects; and a Kuz-Ram model oriented towards block size prediction.

[0106] The calculation formula for the blasting vibration prediction model is as follows:

[0107]

[0108] in, Indicates the peak vibration velocity of a particle. Indicates the dosage per orifice. Indicates the distance between the centers of the explosion. This indicates the elevation difference between the explosion center and the measuring point. Indicates the relative height of the measuring points inside the steps. Indicates the height of the step. Indicates rock mass damage factor. This represents the site foundation coefficient, reflecting the comprehensive impact of site geological conditions on vibration propagation, with a value range of 50-300. This represents the elevation influence coefficient, reflecting the amplification effect of elevation difference on vibration, with a value range of 0.02-0.08. This represents the damage attenuation coefficient, reflecting the degree to which rock mass damage promotes vibration attenuation, and its value ranges from 0.1 to 0.3. This represents the influence coefficient of step height, reflecting the amplification effect of the height of the measuring point inside the step on vibration. Its value ranges from 0.1 to 0.3. This represents the dosage index, reflecting the degree of nonlinear influence of dosage on vibration intensity, with a value ranging from 0.5 to 0.8. This represents the distance index, reflecting the degree of influence of distance on vibration attenuation, with a value range of 1.2-1.8.

[0109] Rock mass damage factor It is obtained by subtracting the difference in rock quality index RQD from 100 and then dividing by 100;

[0110] The specific formula for the flying stone distance prediction model is as follows:

[0111]

[0112] in, This represents a model for predicting the distance to flying rocks. The distance of the flyrock on the surface is calculated based on the borehole diameter and explosive parameters. This indicates the distance of the flying rocks from the blocked section, calculated based on the length and mass of the blockage. The distance of flyrock from geological defects is calculated based on joint density and width.

[0113] The specific calculation formulas for surface fly distance, clogged section fly distance, and geological defect fly distance are as follows:

[0114]

[0115]

[0116]

[0117] in, Indicates the diameter of the borehole. Indicates the density of the explosive. Indicates the actual length of the blockage. This indicates the required blockage length. Indicates joint density, Indicates the joint width;

[0118] The Kuz-Ram model for block size prediction consists of the Kuznetsov equation, the R-R distribution function, and the uniformity index, and the calculation formula is as follows:

[0119]

[0120]

[0121]

[0122] in, The average size of the broken pieces; The rock coefficient is defined by Kuznetsov, who estimates its range to be 7-13. This refers to the amount of explosives consumed per unit. This is the dosage per orifice; For relative mass power of explosives, 100 is for ammonium nitrate oil explosives and 110 is for emulsion explosives; It is the uniformity index; The line of least resistance; The diameter of the borehole; Hole spacing; The standard deviation of drilling accuracy is typically taken as 0.05H; The height of the step; The length of the medicine pack above the base plate elevation; This represents the percentage of rock fragments smaller than a certain grain size by mass. This refers to the sieve aperture size. The characteristic size of the rock block is the block size when the cumulative undersize percentage is approximately 63.21%.

[0123] In step S4, the specific formula for the multi-objective optimization function is as follows:

[0124]

[0125] in, This represents a multi-objective optimization function vector composed of the values ​​of the three objective functions. This represents the objective function for vibration control. Let represent the objective function for controlling the flying stone. The objective function represents the uniformity of block size. This represents the blasting parameter vector.

[0126] The specific formulas for the vibration control objective function, the flystone control objective function, and the block size uniformity objective function are as follows:

[0127]

[0128]

[0129]

[0130] in, Indicates the blasting parameter vector The maximum vibration velocity was calculated using the blasting vibration prediction model. This represents the maximum permissible vibration velocity, taken as 2.5 cm / s. Indicates the blasting parameter vector The original fly-rock distance is calculated using the fly-rock distance prediction model. To indicate a safe distance, take 50m. Indicates the blasting parameter vector The standard deviation of the block size distribution is calculated using the Kuz-Ram model. Indicates the blasting parameter vector The average fragmentation size was calculated using the Kuz-Ram model.

[0131] The constraint settings for a multi-objective optimization function include three levels: safety constraints, technical feasibility constraints, and performance guarantee constraints.

[0132] Safety constraints mainly include blasting vibration safety constraints and flyrock safety constraints. Blasting vibration safety constraints require that the predicted peak particle velocity must not exceed the allowable safety threshold to ensure the structural safety of residential buildings. Flyrock safety constraints require that the predicted maximum flight distance of flyrock must be less than the safe distance and retain sufficient safety margin to prevent flyrock from damaging residential buildings.

[0133] Technical feasibility constraints involve limitations on the range of blasting parameters, including upper and lower limits on hole spacing, row spacing, over-depth, single-hole charge, and micro-delay. These constraints ensure that the blasting parameters are within the range feasible in engineering practice, while the plugging length must meet minimum requirements to guarantee blasting safety and energy utilization.

[0134] The performance guarantee constraint mainly targets the quality of blasted blocks, requiring that the uniformity of block size distribution must not exceed the allowable limit to ensure uniform block size after blasting, which facilitates subsequent mining and loading operations and reduces secondary crushing costs.

[0135] All constraints are treated as hard constraints during the optimization process, and any solution that violates the constraints will be directly eliminated.

[0136] In step S5, the multi-objective optimization algorithm employs a non-dominated sorting genetic algorithm, which includes the following specific steps:

[0137] Step S5.1: Randomly generate an initial population. Each individual in the population is a solution vector, which includes the mesh parameters, the amount of drug per hole, and the differential delay.

[0138] Step S5.2: For each individual in the population, calculate the values ​​of the three objective functions in the multi-objective optimization function;

[0139] Step S5.3: Perform non-dominated sorting on the population based on the objective function value;

[0140] Step S5.4: Calculate the crowding degree of each solution;

[0141] Step S5.5: Use a binary tournament to select parent individuals, and then perform crossover and mutation operations on the selected parent individuals to generate the offspring population;

[0142] Step S5.6: Merge the parent population and the offspring population into a new population;

[0143] Step S5.7: Perform non-dominated sorting and crowding calculation on the new population again, and finally select the next generation population;

[0144] Step S5.8: Stop outputting the Pareto optimal solution set after reaching the maximum number of iterations of 200;

[0145] Step S5.9: Select the optimal solution from the Pareto optimal solution set, namely the reference hole mesh parameters, single hole charge, and differential delay.

[0146] The parameter initialization range is as follows: hole spacing 3.0-5.5m, row spacing 2.5-4.2m, ultra-deep 0.2-1.8m, single hole charge 30-180kg, and micro-delay 15-150ms.

[0147] In step S6, the adaptive adjustment of the mesh parameters specifically includes adaptive adjustment of the hole spacing, adaptive adjustment of the row spacing, and adaptive adjustment of the ultra-deep mesh.

[0148] The adaptive hole spacing adjustment comprehensively considers the reference hole spacing, the local rock and mud distribution ratio, and the lithology adjustment coefficient, and scales the reference hole spacing according to the local rock ratio and mud ratio.

[0149] The adaptive adjustment of the row spacing is based on the adjusted hole spacing and step height factors. The relationship between the row spacing and the hole spacing is established through the row spacing coefficient, and the correction is considered when the step height deviates from the standard value.

[0150] The ultra-deep adaptive adjustment comprehensively considers the influence of the benchmark ultra-deepness, local lithological differences, and step height. It adjusts the ultra-deep value according to the difference in the ratio of rock to mud and compensates for non-standard step heights.

[0151] The formula for adaptive adjustment of the mesh parameters is:

[0152]

[0153]

[0154]

[0155] in, This indicates the adaptively adjusted hole spacing. This represents the optimized reference hole spacing. This indicates the local rock ratio, which is the proportion of rock within a 5m radius around the current borehole. This indicates the local mud ratio, which is the proportion of mud within a 5m radius around the current borehole. This indicates the adaptively adjusted row spacing. This indicates the adaptively adjusted ultradepth. This indicates the baseline ultradepth obtained through optimization.

[0156] In step S6, the adaptive adjustment of the single-hole charge is based on the optimized baseline single-hole charge, and is dynamically adjusted in combination with local lithological characteristics, the relative distance between the borehole and the houses, and vibration control requirements.

[0157] The formula for adaptive adjustment of the single-hole drug dosage is:

[0158]

[0159] in, This indicates the adaptively adjusted dosage per orifice. This represents the baseline single-orifice dosage obtained through optimization. This represents the distance attenuation factor, with a value between 0.7 and 0.8. This represents the vibration control factor, with a value range of 0.7-1.0.

[0160] In step S6, the micro-delay adaptive adjustment optimizes the micro-delay within the borehole. Based on the distribution characteristics of the rock-mud interface and the stress wave propagation characteristics, the optimized benchmark micro-delay is corrected.

[0161] The formula for the adaptive adjustment of the differential delay is:

[0162]

[0163] in, This indicates the adaptively adjusted micro-delay. This represents the baseline differential delay obtained through optimization. This indicates an additional delay at the rock-mud interface, with a value ranging from 10 to 25 ms.

[0164] like Figure 2 As shown, the intelligent bench blasting system for open-pit mines with alternating rock and mud includes:

[0165] The 3D laser scanning module is used to acquire point cloud data of the blasting area of ​​the open-pit mine bench, including bench geometric parameters, rock and mud distribution characteristics, and the location of houses;

[0166] The geological modeling and analysis module is used to process point cloud data and construct a three-dimensional geological model that reflects the morphology of steps and the intermingling distribution of rock and mud.

[0167] The blasting effect prediction module integrates a blasting vibration prediction model, a flyrock distance prediction model, and a block size distribution prediction model, which are used to predict the vibration response, flyrock risk, and block size uniformity under different combinations of blasting parameters.

[0168] The multi-objective optimization module is used to execute the non-dominated sorting genetic algorithm to process the three-objective optimization function with the objectives of flyrock control, vibration control and block size uniformity, and determine the global optimal benchmark values ​​of blasting parameters including hole mesh parameters, single hole charge and micro-delay time.

[0169] The parameter adaptive adjustment module is used to adaptively correct the benchmark values ​​of blasting parameters output by the multi-objective optimization module, including the hole mesh parameters, single hole charge, and micro-delay time.

[0170] The blasting scheme generation and output module is used to integrate adaptively adjusted blasting parameters, generate a complete blasting design scheme, and output a visualized construction guidance document.

[0171] like Figure 3 The image shows the construction site after the blasting of the open-pit bauxite bench. The soil and rocks are interspersed. The main difficulty of blasting here is that the rocks are divided by mud and it is very close to the houses, about 50 meters away. It is necessary to ensure the blasting effect, reduce blasting vibration, and ensure safety.

[0172] 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. An intelligent bench blasting method for open-pit mines with alternating rock and mud, characterized in that, Includes the following steps: Step S1: Obtain point cloud data of the bench blasting area in the open-pit mine through 3D laser scanning; Step S2: Construct a three-dimensional geological model that reflects the morphology of the steps and the distribution characteristics of rock and mud; Step S3: Based on the characteristics of bench blasting, construct a comprehensive blasting prediction model; Step S4: Construct a multi-objective optimization function that includes flystone control, vibration control, and block size uniformity; Step S5: The multi-objective optimization function is processed by a multi-objective optimization algorithm to determine the reference hole network parameters, single hole charge, and differential delay for bench blasting. The hole network parameters include hole spacing, row spacing, and over-depth. Step S6: Adaptively adjust the reference borehole parameters, single-hole charge, and differential delay based on the local rock and mud distribution; In step S6, the adaptive adjustment of the mesh parameters specifically includes adaptive adjustment of the hole spacing, adaptive adjustment of the row spacing, and adaptive adjustment of the ultra-deep mesh. The adaptive hole spacing adjustment comprehensively considers the reference hole spacing, the local rock and mud distribution ratio, and the lithology adjustment coefficient, and scales the reference hole spacing according to the local rock ratio and mud ratio. The adaptive adjustment of the row spacing is based on the adjusted hole spacing and step height factors. The relationship between the row spacing and the hole spacing is established through the row spacing coefficient, and the correction is considered when the step height deviates from the standard value. The ultra-deep adaptive adjustment comprehensively considers the influence of the benchmark ultra-deepness, local lithological differences, and step height. It adjusts the ultra-deep value according to the difference in the ratio of rock to mud and compensates for non-standard step heights. In step S6, the adaptive adjustment of the single-hole charge is based on the optimized baseline single-hole charge, and is dynamically adjusted in combination with local lithological characteristics, the relative distance between the borehole and the houses, and vibration control requirements. In step S6, the micro-delay adaptive adjustment optimizes the micro-delay within the borehole. Based on the distribution characteristics of the rock-mud interface and the stress wave propagation characteristics, the optimized benchmark micro-delay is corrected.

2. The method according to claim 1, characterized in that, Step S2 specifically includes the following steps: Step S2.1: Perform point cloud registration preprocessing on the acquired point cloud data and use a statistical filtering algorithm to remove noise; Step S2.2: Extract the geometric features of the steps based on the denoised point cloud data, including step height, slope angle and platform width; Step S2.3: Identify rock-mud boundaries based on color features, curvature features, and spatial distribution features; Step S2.4: Generate a three-dimensional geological model using the Delaunay triangulation algorithm, integrating rock and mud classification results and residential building location information.

3. The method according to claim 2, characterized in that, Step S2.3 specifically includes the following steps: Construct the color feature components, curvature feature components, and spatial distribution feature components for each point in the point cloud; The comprehensive feature value of rock and mud classification for each point is obtained by combining the feature components of each point, that is, the probability that the point belongs to rock or mud. The formula for calculating the color feature components is as follows: in, Point Color feature components, Represents a point index. Representing points respectively The intensity values ​​of the red channel, green channel, and blue channel. This represents the set of rock color ranges, with RGB values ​​from (120, 120, 120) to (200, 200, 200). This represents the set of mud color ranges, with RGB values ​​from (80, 60, 40) to (150, 120, 80); The formula for calculating the curvature characteristic component is: in, Point The curvature characteristic components, Represents the gradient operator, Point The unit normal vector at that location, This represents the absolute value operator; The formula for calculating the spatial distribution characteristic components is as follows: in, Point Spatial distribution characteristic components, Point The total number of point clouds in the neighborhood, Point The j-th point in the neighborhood, This indicates an indicator function that returns 1 if the condition is true and 0 otherwise. Representation and point A set of points belonging to the same material region.

4. The method according to claim 3, characterized in that, In step S3, the comprehensive blasting prediction model includes three models: a blasting vibration prediction model that considers elevation effects and rock mass damage; a fly rock distance prediction model that integrates surface fly rocks, fly rocks in the blocking section, and fly rocks from geological defects; and a Kuz-Ram model oriented towards block size prediction.

5. The method according to claim 4, characterized in that, In step S4, the specific formula for the multi-objective optimization function is as follows: in, This represents a multi-objective optimization function vector composed of the values ​​of the three objective functions. This represents the objective function for vibration control. Let represent the objective function for controlling the flying stone. The objective function represents the uniformity of block size. This represents the blasting parameter vector.

6. The method according to claim 5, characterized in that, In step S5, the multi-objective optimization algorithm employs a non-dominated sorting genetic algorithm, which includes the following specific steps: Step S5.1: Randomly generate an initial population. Each individual in the population is a solution vector, which includes the mesh parameters, the amount of drug per hole, and the differential delay. Step S5.2: For each individual in the population, calculate the values ​​of the three objective functions in the multi-objective optimization function; Step S5.3: Perform non-dominated sorting on the population based on the objective function value; Step S5.4: Calculate the crowding degree of each solution; Step S5.5: Use a binary tournament to select parent individuals, and then perform crossover and mutation operations on the selected parent individuals to generate the offspring population; Step S5.6: Merge the parent population and the offspring population into a new population; Step S5.7: Perform non-dominated sorting and crowding calculation on the new population again, and finally select the next generation population; Step S5.8: Stop outputting the Pareto optimal solution set after reaching the maximum number of iterations of 200; Step S5.9: Select the optimal solution from the Pareto optimal solution set, namely the reference hole mesh parameters, single hole charge, and differential delay.

7. An intelligent bench blasting system for open-pit mines with alternating rock and mud, implemented based on the intelligent bench blasting method for open-pit mines with alternating rock and mud as described in any one of claims 1-6, characterized in that, include: The 3D laser scanning module is used to acquire point cloud data of the blasting area of ​​the open-pit mine bench, including bench geometric parameters, rock and mud distribution characteristics, and the location of houses; The geological modeling and analysis module is used to process point cloud data and construct a three-dimensional geological model that reflects the morphology of steps and the intermingling distribution of rock and mud. The blasting effect prediction module integrates a blasting vibration prediction model, a flyrock distance prediction model, and a block size distribution prediction model, which are used to predict the vibration response, flyrock risk, and block size uniformity under different combinations of blasting parameters. The multi-objective optimization module is used to execute the non-dominated sorting genetic algorithm to process the three-objective optimization function with the objectives of flyrock control, vibration control and block size uniformity, and determine the global optimal benchmark values ​​of blasting parameters including hole mesh parameters, single hole charge and micro-delay time. The parameter adaptive adjustment module is used to adaptively correct the benchmark values ​​of blasting parameters output by the multi-objective optimization module, including the hole mesh parameters, single hole charge, and micro-delay time. The blasting scheme generation and output module is used to integrate adaptively adjusted blasting parameters, generate a complete blasting design scheme, and output a visualized construction guidance document.

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