Numerical simulation and iterative control based method and device for determining blast hole pattern parameters, electronic equipment and detection system

Abstract

The present application provides a kind of open-air blasting hole net parameter determination method, device, electronic equipment and detection system, belongs to blasting engineering technical field.The method comprises: S1, obtains the rock mass explosibility parameter of target blast area, initial hole net parameter;S2, the parameters obtained and blasting energy parameters are input into numerical simulation model, and output prediction result;S3, obtains the measured result according to the current hole net parameter execution blasting;S4, according to the measured result and prediction result, corrects the parameters of numerical simulation model;S5, optimizes hole net parameter;S6, based on the iteration control of multi-objective optimization repeatedly executes S2-S5, and the first preset number of iterations control executes S2-S5, subsequent iteration control only executes S2 and S5;Until the prediction boulder rate exceeds the first threshold value, stop iteration, and the optimal hole net parameter with the prediction boulder rate less than the first threshold value is determined as target hole net parameter.The method is used to solve the problem of high explosive specific consumption in related art.

Description

Technical Field

[0001] This invention belongs to the field of mining blasting engineering technology, specifically relating to a method, apparatus, electronic equipment and detection system for determining open-pit blasting hole network parameters based on numerical simulation and iterative control. Background Technology

[0002] With the large-scale promotion and application of mixed explosives in open-pit mines, the technical bottlenecks in their application process are becoming increasingly prominent. The main issue is the high unit consumption of mixed explosives, which directly affects mining costs and resource utilization.

[0003] The reason for this problem is that existing blasting design and optimization methods have the following limitations:

[0004] The first category is empirical formulas and engineering analogy methods (such as the Kuz-Ram model and blasting design manuals). Although these methods are simple to calculate, the selection of parameters is highly dependent on engineering experience, making it difficult to objectively reflect the anisotropy of the rock mass and the differences in the energy characteristics of mixed explosives. They also lack dynamic correction capabilities, which can easily lead to conservative hole mesh parameters or blind hole enlargement.

[0005] The second category is numerical simulation methods (such as finite element method and discrete element method) and image analysis technology, which can predict the blasting and breaking process or quantify the post-blast effects, but they mostly remain at the simulation stage and lack effective connection with the on-site construction process.

[0006] The third category is intelligent optimization algorithms (such as genetic algorithms, NSGA-II, etc.), which are mostly in the offline optimization stage. It is difficult to obtain model parameters and therefore difficult to directly guide engineering implementation.

[0007] Therefore, existing technologies cannot continuously reduce the consumption of explosives while controlling the bulk ratio, and still cannot meet the engineering requirements for large-scale application and efficient utilization of mixed explosives. Summary of the Invention

[0008] The technical problem to be solved by the present invention is to address the above-mentioned shortcomings of the prior art by providing a method, device, electronic equipment and detection system for determining open-pit blasting hole network parameters based on numerical simulation and iterative control, which can continuously reduce the unit consumption of explosives while controlling the bulk ratio and realize closed-loop feedback between numerical simulation and field measurement.

[0009] In a first aspect, the present invention provides a method for determining the parameters of an open-pit blasting borehole network based on numerical simulation and iterative control, comprising:

[0010] S1, obtain the rock mass blastability parameters and initial hole network parameters of the target blasting area;

[0011] S2 inputs the rock mass blastability parameters, current hole network parameters and blasting energy parameters into the numerical simulation model for calculation, and outputs the prediction results, including the predicted block rate.

[0012] S3, obtain the measured results of blasting performed based on the current hole mesh parameters;

[0013] S4. Based on the measured and predicted results, correct the parameters of the numerical simulation model;

[0014] S5, optimize the perforation parameters by gradually increasing the hole spacing or row spacing to obtain the updated perforation parameters;

[0015] S6, based on the multi-objective optimization of reducing explosive consumption and controlling the block ratio to not exceed the first threshold, iterative control of S2-S5 is repeatedly executed. The first preset number of iterations of control execute S2-S5 until the prediction deviation of the numerical simulation model is less than the second threshold. Subsequent iterations of control only execute S2 and S5. Iterative control stops when the predicted block ratio exceeds the first threshold, and the optimal aperture parameters with the predicted block ratio less than the first threshold are determined as the target aperture parameters.

[0016] In some embodiments, obtaining the rock mass blastability parameters of the target blasting area specifically includes: obtaining the rock mass parameters, engineering parameters, and historical blasting data of the target blasting area; and correcting the empirical parameters of blastability based on data fitting and the obtained rock mass parameters, engineering parameters, and historical blasting data to obtain the rock mass blastability parameters of the target blasting area.

[0017] In some embodiments, the numerical simulation model is implemented using the Blo-Up software of the Hybrid Stress Blast Model (HSBM); Blo-Up uses a coupling method of a continuous medium model, a brittle discrete element model, and a gaseous product model for numerical simulation calculation.

[0018] In some embodiments, obtaining the measured results of blasting performed according to the current hole mesh parameters specifically includes: obtaining an image of the blast pile after blasting performed according to the current hole mesh parameters or point cloud data obtained through three-dimensional measurement; calling the crushing analysis software to analyze the image or point cloud data and generate the measured results.

[0019] In some embodiments, the prediction results also include a pre-blast fragmentation distribution curve, and the measured results include a measured fragmentation distribution curve. Based on the measured results and the prediction results, the parameters of the numerical simulation model are corrected, specifically including: integrating the pre-blast predicted fragmentation distribution curve with the measured fragmentation distribution curve, and calculating the prediction deviation for each particle size range; using a nonlinear fitting algorithm to minimize the prediction deviation, the parameters of the numerical simulation model are corrected, and the parameters of the numerical simulation model include at least one of the following: rock mass influence coefficient, damping coefficient, and explosive-rock mass interaction parameters.

[0020] In some embodiments, the preset number of times n satisfies: 1≤n≤3, where n is a positive integer.

[0021] In some embodiments, after determining the optimal hole mesh parameters with a predicted large block rate less than a first threshold as the target hole mesh parameters, the open-pit blasting hole mesh parameter determination method based on numerical simulation and iterative control further includes: constructing a rock mass blastability database for reuse in blasting areas with the same geological conditions; the database includes rock mass blastability parameters, target hole mesh parameters and their mapping relationships.

[0022] Secondly, the present invention also provides a device for determining open-pit blasting hole network parameters based on numerical simulation and iterative control, comprising:

[0023] The acquisition module is used to acquire the rock mass blastability parameters and initial hole network parameters of the target blasting area.

[0024] The simulation module, connected to the acquisition module, is used to input rock mass blastability parameters, current borehole network parameters, and blasting energy parameters into the numerical simulation model for calculation and output prediction results, including the predicted block rate.

[0025] The measured data acquisition module is used to acquire the measured results of blasting performed based on the current hole mesh parameters.

[0026] The calibration module, connected to the simulation module and the measured acquisition module, is used to calibrate the parameters of the numerical simulation model based on the measured and predicted results.

[0027] The optimization module is used to optimize the perforation parameters by gradually increasing the hole spacing or row spacing to obtain updated perforation parameters.

[0028] The iterative control module, connected to the simulation module, the measured acquisition module, the calibration module, and the optimization module, is used for: repeatedly calling the above modules in iterative control based on multi-objective optimization of reducing explosive consumption and controlling the block ratio to not exceed a first threshold; wherein, the simulation module, the measured acquisition module, the calibration module, and the optimization module are repeatedly called in the first preset number of iterations until the prediction deviation of the numerical simulation model is less than a second threshold; subsequent iterations only call the simulation module and the optimization module; the iterative control stops when the predicted block ratio exceeds the first threshold, and the optimal aperture parameters with the predicted block ratio less than the first threshold are determined as the target aperture parameters.

[0029] Thirdly, the present invention also provides an electronic device, including a memory and a processor, wherein the memory stores a computer program and the processor is configured to run the computer program to implement a method for determining open-pit blasting hole network parameters based on numerical simulation and iterative control as described in the first aspect.

[0030] Fourthly, the present invention also provides an open-pit blasting detection system, comprising: a mining industrial control computer configured to execute the open-pit blasting hole network parameter determination method based on numerical simulation and iterative control as described in the first aspect; on-site blasting detection equipment communicatively connected to the mining industrial control computer; and a three-dimensional scanning device communicatively connected to the mining industrial control computer.

[0031] This invention provides a method, apparatus, electronic equipment, and detection system for determining open-pit blasting borehole network parameters based on numerical simulation and iterative control. By inputting rock mass blastability parameters reflecting the actual rock mass characteristics of the target blasting area into a numerical simulation model, and through quantitative analysis of measured data and the numerical model, the borehole network parameter design can objectively reflect the rock mass conditions and explosive energy characteristics, avoiding reliance on empirical formulas. Subsequently, the measured data is fed back to the model calibration stage, enabling the numerical simulation model to be dynamically updated according to the actual field conditions, achieving effective integration of simulation and construction. Furthermore, the optimization process is embedded in the iterative control of actual blasting; model parameters are automatically acquired and corrected through on-site measurements, allowing the optimization results to directly guide engineering implementation. In summary, this invention, through a closed-loop mechanism of "pre-blast prediction → on-site measurement → measured data to correct the model → iterative control to optimize borehole enlargement → large block ratio threshold constraint," transforms experience-based dependence into data-driven approaches, simulation disconnect into closed-loop feedback, and offline optimization into online iteration, thereby continuously reducing explosive consumption while controlling the large block ratio.

[0032] It should be understood that the description in this section is not intended to identify key or essential features of the embodiments of the present invention, nor is it intended to limit the scope of the invention. Other features of the invention will become readily apparent from the following description. Attached Figure Description

[0033] The accompanying drawings are provided to further illustrate the invention and form part of the specification. They are used together with the embodiments of the invention to explain the invention and do not constitute a limitation thereof. The above and other features and advantages will become more apparent to those skilled in the art from the detailed description of exemplary embodiments with reference to the accompanying drawings, in which:

[0034] Figure 1 A flowchart illustrating a method for determining open-pit blasting hole network parameters based on numerical simulation and iterative control, provided for an embodiment of the present invention;

[0035] Figure 2 This is a schematic diagram of a device for determining the parameters of an open-pit blasting borehole network based on numerical simulation and iterative control, provided in an embodiment of the present invention. Detailed Implementation

[0036] To enable those skilled in the art to better understand the technical solutions of the present invention, exemplary embodiments of the present invention are described below in conjunction with the accompanying drawings, including various details of the embodiments of the present invention to aid understanding. These should be considered merely exemplary. Therefore, those skilled in the art should recognize that various changes and modifications can be made to the embodiments described herein without departing from the scope and spirit of the present invention. Similarly, for clarity and brevity, descriptions of well-known functions and structures are omitted in the following description.

[0037] Where there is no conflict, the various embodiments of the present invention and the features thereof may be combined with each other.

[0038] As used herein, the term “and / or” includes any and all combinations of one or more related enumerated entries.

[0039] The terminology used herein is for the purpose of describing particular embodiments only and is not intended to limit the invention. As used herein, the singular forms “a” and “the” are intended to include the plural forms as well, unless the context clearly indicates otherwise. It will also be understood that when the terms “comprising” and / or “made of” are used in this specification, the presence of the stated feature, integral, step, operation, element, and / or component is specified, but the presence or addition of one or more other features, integrals, steps, operations, elements, components, and / or groups thereof is not excluded. Terms such as “connected” or “linked” are not limited to physical or mechanical connections but can include electrical connections, whether direct or indirect.

[0040] Unless otherwise specified, all terms used herein (including technical and scientific terms) have the same meaning as commonly understood by one of ordinary skill in the art. It will also be understood that terms such as those defined in commonly used dictionaries should be interpreted as having the meaning consistent with their meaning in the context of the relevant art and the invention, and will not be interpreted as having an idealized or overly formal meaning unless expressly so defined herein.

[0041] Firstly, such as Figure 1 As shown in the figure, this embodiment provides a method for determining the parameters of an open-pit blasting borehole network based on numerical simulation and iterative control, including:

[0042] S1, obtain the rock mass blastability parameters and initial hole network parameters of the target blasting area;

[0043] S2 inputs the rock mass blastability parameters, current hole network parameters and blasting energy parameters into the numerical simulation model for calculation, and outputs the prediction results, including the predicted block rate.

[0044] S3, obtain the measured results of blasting performed based on the current hole mesh parameters;

[0045] S4. Based on the measured and predicted results, correct the parameters of the numerical simulation model;

[0046] S5, optimize the perforation parameters by gradually increasing the hole spacing or row spacing to obtain the updated perforation parameters;

[0047] S6, based on the multi-objective optimization of reducing explosive consumption and controlling the block ratio to not exceed the first threshold, iterative control repeatedly executes S2-S5. The first preset number of iterations executes S2-S5 until the prediction deviation of the numerical simulation model is less than the second threshold. Subsequent iterations execute only S2 and S5. Iterative control stops when the predicted block ratio exceeds the first threshold, and the optimal aperture parameters with the predicted block ratio less than the first threshold are determined as the target aperture parameters.

[0048] The blastability parameters of the rock mass in the target blasting zone can be obtained by looking up tables, calculating using empirical formulas, or through data fitting and correction methods. The numerical simulation model can be implemented using any of the following: ANSYS software based on the finite element method, a meshless numerical model based on smoothed particle hydrodynamics (SPH), or Blo-Up software based on the hybrid stress blasting model (HSBM). Correcting the parameters of the numerical simulation model specifically includes: correcting at least one of the following parameters: rock mass influence coefficients (e.g., dynamic compressive strength, dynamic tensile strength, cohesion of the rock mass), damping coefficients (e.g., mass damping coefficient, stiffness damping coefficient), and rock-explosive interaction parameters (e.g., energy coupling coefficient between explosive and rock mass, expansion efficiency coefficient of explosive gas). Optimizing the borehole mesh parameters specifically includes at least one of the following methods: gradually increasing the hole spacing or row spacing with a fixed step size; gradually increasing the hole spacing and / or row spacing with a variable step size; and determining the optimized borehole mesh parameters through numerical simulation comparative analysis. Multi-objective optimization includes optimization aimed at reducing explosive consumption per unit and controlling the rate of large blocks, or optimization aimed at reducing explosive consumption per unit and controlling the rate of large blocks and reducing the number of boreholes, or other multi-objective optimizations. Generally, the preset number of iterations in S6 is less than the total number of iterations in the control loop. The first threshold ranges from 5% to 10%, and the second threshold is 10% in this example.

[0049] In this embodiment, by inputting the rock mass blastability parameters, which reflect the actual rock mass characteristics of the target blasting area, into the numerical simulation model, and through quantitative analysis based on measured data and the numerical model, the borehole mesh parameter design can objectively reflect the rock mass conditions and explosive energy characteristics, avoiding reliance on empirical formulas. Subsequently, the measured data is fed back to the model calibration stage, enabling the numerical simulation model to be dynamically updated according to the actual field conditions, achieving effective integration of simulation and construction. Furthermore, the optimization process is embedded in the iterative control of actual blasting, with model parameters automatically acquired and calibrated through on-site measurements, allowing the optimization results to directly guide engineering implementation. In summary, through a closed-loop mechanism of "pre-blast prediction → on-site measurement → measured data calibration of the model → iterative control for borehole enlargement optimization → large block ratio threshold constraint," empirical dependence is transformed into data-driven operation, simulation disconnect is transformed into closed-loop feedback, and offline optimization is transformed into online iteration, thereby continuously reducing explosive consumption while controlling the large block ratio.

[0050] In some embodiments, obtaining the rock mass blastability parameters of the target blasting area specifically includes: obtaining the rock mass parameters, engineering parameters, and historical blasting data of the target blasting area; and correcting the empirical parameters of blastability based on data fitting and the obtained rock mass parameters, engineering parameters, and historical blasting data to obtain the rock mass blastability parameters of the target blasting area.

[0051] The rock mass parameters include at least one of the following: step height, slope angle, blast zone coordinates, rock structure, joint and fracture distribution, rock compressive strength, and weathering degree. Engineering parameters include at least one of the following: drilling equipment model, borehole diameter, borehole depth, type of mixed explosives, charging efficiency, and loading equipment capacity. Historical blasting data includes at least one of the following: borehole network parameters, charge quantity, explosive consumption per unit area, large block ratio, and fragmentation effect. This can be historical blasting data within a preset time period, such as 3 months or 5 months. Explosiveness empirical parameters refer to initial parameter values ​​predetermined based on historical engineering experience, theoretical formulas, or design manuals, used to characterize the ease with which rock fragments under blasting.

[0052] In this embodiment, due to the differences in rock mass characteristics across different mining areas (such as joint development, weathering degree, and water content), directly using empirical parameters of explosiveness can lead to problems such as: overestimating the predicted explosive consumption (wasting explosives) or underestimating it (high proportion of large blocks), large deviations between model predictions and actual field conditions, and the inability to achieve refined blasting design. This embodiment corrects the empirical parameters of explosiveness based on data fitting using rock mass parameters, engineering parameters, and historical blasting data collected on-site, obtaining the explosiveness parameters of the rock mass in the target blasting area. This allows the prediction results of the numerical simulation model to better match the actual rock mass conditions of the target blasting area, thus improving the prediction accuracy of the numerical simulation model. This reduces the number of field measurements required for subsequent iterative corrections and lowers the overall optimization cost. Furthermore, the corrected explosiveness parameters can be mapped to the rock mass characteristics of the blasting area and stored in a database. When encountering a new blasting area with similar geological conditions, these parameters can be directly retrieved and used without repeated corrections, significantly improving optimization efficiency.

[0053] In some embodiments, obtaining the initial hole network parameters of the target blasting area includes: obtaining the initial hole network parameters of the target blasting area based on production constraints. Specifically, based on the coordinates and range of the target blasting area, the hole network parameters are designed by comprehensively considering production requirements, drilling conditions, mixed explosive loading capacity, and shoveling efficiency, determining the initial hole spacing, row spacing, detonation sequence, over-depth, filling length, and single-hole charge amount, so as to achieve "full utilization" within the target blasting area and ensure that the blasting volume matches the subsequent shoveling operations.

[0054] In some embodiments, the numerical simulation model is implemented using the Blo-Up software of the Hybrid Stress Blast Model (HSBM); Blo-Up uses a coupling method of a continuous medium model, a brittle discrete element model, and a gaseous product model for numerical simulation calculation.

[0055] In this embodiment, rock mass blastability parameters, current borehole parameters (initial borehole parameters in the first round of iteration control), and blasting energy parameters (such as mixed explosive energy parameters, charge structure, and detonation sequence) are input into the Blo-Up software of the HSBM (Hybrid Stress Blasting Model) for numerical simulation. A three-dimensional numerical simulation is performed using a coupling method of a continuous medium model, a brittle discrete element model, and a gaseous product model, outputting the prediction results. This numerical simulation model is an HSBM / Blo-Up prediction model. This embodiment, based on the HSBM Blo-Up software and using a three-dimensional numerical simulation through a coupled model, can completely simulate the entire blasting process, covering all key stages from explosive detonation and rock fragmentation to the formation of the blast pile. It overcomes the limitations of single numerical methods in fully describing the blasting physical process, making the blasting process more intuitive and the blasting effect closer to reality. Because the entire process is visualized, it is convenient to adjust model parameters in a targeted manner, providing a high-precision prediction basis for borehole parameter optimization.

[0056] In some embodiments, obtaining the measured results of blasting performed according to the current hole mesh parameters specifically includes: obtaining an image of the blast pile after blasting performed according to the current hole mesh parameters or point cloud data obtained through three-dimensional measurement; calling the crushing analysis software to analyze the image or point cloud data and generate the measured results.

[0057] In this embodiment, actual blasting is performed in the target blasting area, and the measured data are fully recorded. The measured data includes hole mesh parameters, charge amount, and number of boreholes. Images of the blasted pile are captured using mobile phones, tablets, or computers, or point cloud data is obtained through 3D scanning using drones. Fracture analysis software (such as Split-Desktop, WipFrag, 3DPCFM) is called to import the blasted pile images or point cloud data, automatically analyze the block size distribution, and generate measured results. 3D measurement includes any of the following: 3D laser scanning, drone photogrammetry, structured light scanning, close-range photogrammetry, etc.

[0058] In some embodiments, the prediction results also include a pre-blast fragmentation distribution curve, and the measured results include a measured fragmentation distribution curve. Based on the measured and prediction results, the parameters of the numerical simulation model are corrected, specifically including: integrating the pre-blast predicted fragmentation distribution curve with the measured fragmentation distribution curve, calculating the prediction deviation for each particle size range; and using a nonlinear fitting algorithm to minimize the prediction deviation, correcting the parameters of the numerical simulation model. The parameters of the numerical simulation model include at least one of the following: rock mass influence coefficient, damping coefficient, and explosive-rock mass interaction parameters.

[0059] In this embodiment, the predicted fragmentation distribution curve before blasting and the measured fragmentation distribution curve after blasting are integrated. A nonlinear fitting algorithm is used to fuse and correct the numerical simulation model (such as the HSBM / Blo-Up prediction model), continuously correcting the rock mass influence coefficient, damping coefficient, and rock-explosive interaction parameters, thereby continuously improving the model fitting accuracy and achieving a high degree of consistency and matching between the predicted results and the measured results on site. It should be noted that when the prediction deviation between the predicted results and the measured results is less than a second threshold, it indicates that the prediction accuracy of the model is high enough, and the parameter correction of the numerical simulation model is stopped. Specifically, the preset number of parameter corrections is 1 to 3. For example, after correcting the parameters of the numerical simulation model once, the prediction deviation is already less than the second threshold, and blasting measurements are not required for subsequent iterative control. Therefore, it is not necessary to correct the parameters of the numerical simulation model again; only the numerical simulation model is used for hole mesh parameter optimization and blasting prediction. The prediction results also include at least one of the following: P80 block size index, blast pile morphology, microcrack distribution, and throwing range. The numerical simulation model in this embodiment can be corrected in real time according to the fluctuation of rock mass parameters, achieving a result that closely matches the actual situation and making the prediction results more accurate. A threshold for determining large blocks > 1000mm can be set, and the target for controlling the large block rate ≤ the first threshold (e.g., 5%), ensuring that the prediction results closely match the actual values ​​measured on-site.

[0060] The perforated mesh parameters include the hole spacing and row spacing. Optimizing the perforated mesh parameters specifically involves adjusting only the hole spacing or row spacing at a time, gradually increasing the perforated mesh parameters in preset step sizes, with the preset step size ranging from 0.1m to 0.2m.

[0061] In this embodiment, the preset step size is a fixed step size. Specifically, the optimization function is to reduce the unit consumption of explosives, reduce the amount of drilling work, and control the size of broken pieces: (1) Under the condition of having an intelligent model, the system automatically outputs the optimal hole spacing, row spacing, filling length, ultra-deep and linear charge density; (2) Under the condition of not having an intelligent model, the hole is enlarged step by step according to the principle of the blasting manual. Each hole enlargement range (i.e., the preset step size) is 0.1m, 0.2m, etc., combined with the numerical simulation model to predict the large piece rate, so as to avoid the large piece rate being too high; the parameter adjustment follows the principle of "single-time single variable, small-amplitude gradual", and only one parameter of hole spacing or row spacing is adjusted each time, and the breaking effect, large piece rate and unit consumption data are recorded in real time; when the large piece rate is close to the first threshold (e.g. 5%) critical value, or when the large piece rate exceeds the first threshold, the hole mesh parameters are stopped and the optimal hole mesh parameters are locked.

[0062] In some embodiments, the preset number of times n satisfies: 1≤n≤3, where n is a positive integer.

[0063] In this embodiment, a closed-loop control mechanism of "pre-detonation prediction - post-detonation measurement - model correction - parameter optimization" is realized, which effectively integrates the numerical simulation model, field measurement results and hole mesh parameter adjustment.

[0064] In some embodiments, the optimal aperture mesh parameter that predicts a bulk rate less than a first threshold is determined as the target aperture mesh parameter. Specifically, this includes: determining the aperture mesh parameter that satisfies the prediction bulk rate being less than the first threshold in the last round before iterative control stops as the target aperture mesh parameter; or, determining the aperture mesh parameter that satisfies the prediction bulk rate being less than the first threshold in the penultimate round before iterative control stops as the target aperture mesh parameter.

[0065] In some embodiments, after determining the optimal hole mesh parameters with a predicted large block rate less than a first threshold as the target hole mesh parameters, the open-pit blasting hole mesh parameter determination method based on numerical simulation and iterative control further includes: constructing a rock mass blastability database for reuse in blasting areas with the same geological conditions; the database includes rock mass blastability parameters, target hole mesh parameters and their mapping relationships.

[0066] In this embodiment, the number of boreholes, explosive consumption per unit, large block rate, and drilling and blasting costs before and after optimization are statistically analyzed to form an effect analysis report; the target borehole network parameters and the rock mass blastability parameters of the target blasting area are stored in the rock mass blastability database for rapid reuse in blasting areas with the same geological conditions; and the borehole network parameters are constrained by field experience to avoid excessively aggressive adjustments, ensuring blasting safety and production continuity.

[0067] The method for determining the hole network parameters of open-pit blasting based on numerical simulation and iterative control in this embodiment solves the technical problems of high explosive consumption per unit in existing open-pit blasting, lack of quantitative basis for hole network parameter design, and disconnect between numerical simulation and on-site construction. It can continuously reduce explosive consumption per unit while controlling the bulk ratio and realize closed-loop feedback between numerical simulation and on-site measurement. Specifically, the HSBM / Blo-Up three-coupled blasting model is deeply integrated with the optimization of borehole mesh parameters for mixed explosives in the field, achieving high-precision fracture prediction. A standardized, quantifiable, and replicable closed-loop process is constructed, encompassing lithology acquisition, pre-blast prediction, fracture measurement, model correction, step-by-step borehole enlargement, optimal locking, cost accounting, and database reuse. A safe optimization strategy is proposed, using 0.1m graded step-by-step borehole enlargement, single-volume single-variable enlargement, and large-block rate ≤ first threshold control, to solve the problem of excessive large-block rate caused by blind borehole enlargement. Specifically targeting mixed explosives in the field, with the core objectives of reducing unit consumption, improving utilization rate, and controlling cost, this fills the technical gap in intelligent borehole mesh optimization for large-scale applications of mixed explosives. Furthermore, it achieves bi-curve fusion correction of pre-blast prediction curves and post-blast measured curves, continuously improving model accuracy and adapting to complex geological conditions.

[0068] Example:

[0069] The following section provides a detailed explanation of a method for determining open-pit blasting hole network parameters based on numerical simulation and iterative control, using a real-world engineering project of a large open-pit coal mine as an example.

[0070] 1. Project Overview

[0071] In a certain open-pit coal mine, the bench height is 15m. The rock mass is mainly medium-hard sandstone with local mudstone interlayers and moderately developed joints and fissures. Deep-hole blasting of the bench is carried out using on-site mixed emulsion explosives. The mining scale is over 80 million cubic meters per year. The original borehole network parameter design was unreasonable, the explosive consumption per unit was too high, and the drilling workload was large. This embodiment optimizes the borehole network parameters to reduce explosive consumption per unit, reduce costs, and control large blocks.

[0072] 2. Specific Implementation Steps

[0073] S21, obtain the rock mass blastability parameters and initial hole network parameters of the target blasting area.

[0074] (1) On-site information collection and calibration of rock mass blastability parameters.

[0075] Specifically, comprehensive information was collected for the target blasting area: step height 15m; slope angle 60°; uniaxial compressive strength of rock mass 60-80MPa; gentle joint orientation, indicating good overall rock mass stability; drilling equipment was a down-the-hole drill with a diameter of φ115mm and a designed depth of 16.5m; explosive type was on-site mixed emulsion explosive; loading equipment was a hydraulic excavator with a rated bucket capacity of 5.0m³; historical blasting data from the past three months was also collected, including borehole parameters, charge quantity, explosive consumption per unit area, large block rate, and fragmentation effect. By fitting the collected rock mass parameters and engineering parameters with historical blasting data, the rock mass blastability parameters were corrected, establishing a dedicated rock mass blastability database for the target blasting area, providing fundamental parameters for subsequent fragmentation prediction.

[0076] (2) Initial mesh parameter design based on production constraints.

[0077] Specifically, based on the coordinates and boundary range of the target blasting area given in the mining plan, the initial hole network parameters are designed by comprehensively considering drilling efficiency, mixed explosive loading capacity, and shoveling efficiency: initial hole spacing a = 5.0m, initial row spacing b = 4.5m; over-depth 1.0m; filling length not less than 3.5m; blasting energy parameters (single hole charge, detonation network, delay time) are designed according to conventional bench blasting to ensure that the blasting volume matches the shoveling efficiency and achieve "full utilization" within the blasting area.

[0078] S22 inputs the rock mass blastability parameters, current borehole parameters (initial borehole parameters in the first round of iteration control) and blasting energy parameters into the numerical simulation model for calculation, and outputs the prediction results, including the predicted block rate.

[0079] Specifically, rock mass blastability parameters, current borehole parameters, and blasting energy parameters (type of mixed explosives, charge structure, detonation sequence, and delay time) are input into Blo-Up software based on the Hybrid Stress Blasting Model (HSBM). The criteria for large blocks are set as particles > 1000 mm in diameter, and the target for the large block rate is ≤ a first threshold (e.g., 5%). Blo-Up employs a three-coupling approach—continuous medium model + brittle discrete element model + gaseous product model—to perform three-dimensional numerical simulations. The predicted results include the pre-blast predicted fragmentation distribution curve, block size index P80, predicted large block rate, blast pile morphology, rock mass damage range, and throwing trend. These results guide the subsequent borehole widening range and pace.

[0080] S23, obtain the measured results of blasting performed based on the current hole mesh parameters.

[0081] (1) Benchmark blasting and condition record

[0082] A representative area was selected within the target blasting zone to conduct benchmark blasting, and benchmark state data was fully recorded: benchmark hole network parameters: hole spacing 5.0m, row spacing 4.5m; single hole charge amount, explosive consumption q0; total number of holes, total length of holes drilled; measured large block rate, fragmentation curve, and loading efficiency. The benchmark data serves as the basis for subsequent parameter iteration and effect comparison.

[0083] (2) Quantitative collection and analysis of the crushing effect of the blast pile

[0084] After blasting, use a mobile terminal such as a mobile phone or tablet to take high-definition photos of the blast pile, or use a drone to perform three-dimensional laser scanning of the blast pile to obtain point cloud data. Import the data into Wipware / WipFrag fragmentation analysis software to automatically calculate the block size distribution and output the measured results, including the measured fragmentation distribution curve and the measured large block rate.

[0085] S24. Based on the measured and predicted results, correct the parameters of the numerical simulation model.

[0086] By integrating the pre-blast predicted fragmentation distribution curves with the post-blast measured fragmentation distribution curves, nonlinear fitting analysis was used to correct the parameters of the HSBM / Blo-Up prediction model. This corrected parameters such as the rock mass influence coefficient, damping coefficient, explosive-rock mass interaction parameters, and energy utilization rate coefficient, ensuring a high degree of fit between the model's predictions and field measurements, thus improving the model's subsequent prediction accuracy. After a preset number of iterations (1-3 times), the model can be stably used for pre-blast prediction in areas with similar geological conditions.

[0087] S25, optimize the perforation parameters to obtain the updated perforation parameters.

[0088] S26, iterative control based on multi-objective optimization repeatedly executes S22-S25, wherein the first preset number of iterations execute S22-S25, and subsequent iterations execute only S22 and S25; the iterative control stops when the predicted bulk rate exceeds the first threshold, and the optimal aperture parameters with the predicted bulk rate less than the first threshold are determined as the target aperture parameters.

[0089] Specifically, S25 and S26 include: multi-objective optimization with the goals of reducing explosive consumption, reducing drilling volume, and controlling the large block rate to ≤5%, implementing graded step-by-step hole enlargement: each time, only the hole spacing or row spacing is widened by a small increment of 0.1m, following the principle of single-time single-variable and gradual adjustment; after each parameter adjustment, a blast is performed and the fragmentation effect is measured; hole network parameters, explosive consumption, large block rate, and number of drill holes are recorded in real time; when the large block rate approaches the 5% critical control value, hole enlargement is immediately stopped, and the parameters of the previous round are used as the optimal hole network parameters. In this example, after 5 rounds of step-by-step hole enlargement, the large block rate increased and approached the threshold in the 5th round of hole enlargement, so the parameters of the 4th round were locked as optimal, with the optimal hole spacing a_opt=5.4m, the optimal row spacing b_opt=4.6m, and the ultra-deep and filling length were simultaneously optimized and matched.

[0090] S27, Explosion Effect Analysis.

[0091] Specifically, a comparative statistical analysis of the borehole network parameters before and after optimization revealed that, under the same blasting volume, the total number of boreholes was significantly reduced, the explosive consumption per unit volume decreased markedly, the large block rate was consistently controlled below 5%, the workload of secondary crushing was reduced, and the drilling cost, explosive cost, and overall drilling and blasting cost were significantly decreased. This example stores the optimal borehole network parameters, rock mass blastability parameters, and blasting effects in a database to form a standardized design template, which can be directly reused in blasting areas with the same lithology and bench height. Simultaneously, by incorporating field engineering experience, excessively aggressive parameter adjustments are avoided, ensuring blasting safety and continuous, stable production.

[0092] The beneficial effects of this example:

[0093] (1) Significantly reduce drilling and blasting costs: By gradually widening the hole network parameters, the number of boreholes and borehole length are greatly reduced, and the unit consumption of explosives is reduced. In large-scale mining areas, cost savings of several million yuan can be achieved.

[0094] (2) Precise and controllable crushing effect: Using >1000mm as the standard for judging large pieces, the large piece rate is stably controlled below the first threshold (e.g., 5%), reducing secondary crushing and improving loading and transportation efficiency;

[0095] (3) The blasting design is scientific and reliable: the HSBM and Blo-Up coupled numerical simulation are integrated to achieve accurate prediction of the pre-blast fragmentation degree. The prediction error is small, the design basis is sufficient, and the experience-based blind adjustment is avoided.

[0096] (4) The adjustment process is safe and stable: the hole is expanded by small-amplitude stepping of 0.1m, following the principle of single-variable and gradual adjustment. The fault tolerance rate is high, which can effectively avoid safety risks such as flying stones, vibration and large block loss of control caused by overly aggressive parameters.

[0097] (5) Model and field closed-loop iteration: By comparing the pre-blast prediction curve and the post-blast measured curve, the rock mass parameters and model coefficients are continuously corrected, so that the prediction accuracy is continuously improved and adapted to complex geological conditions and rock mass fluctuations.

[0098] (6) Improve the utilization rate of mixed explosives: Under the premise of ensuring crushing quality and production safety, significantly increase the proportion of mixed explosives and energy utilization rate.

[0099] Secondly, such as Figure 2 As shown, this embodiment provides a device for determining open-pit blasting hole network parameters based on numerical simulation and iterative control, including:

[0100] The acquisition module 21 is used to acquire the rock mass blastability parameters and initial hole network parameters of the target blasting area.

[0101] The simulation module 22, connected to the acquisition module 21, is used to input the rock mass blastability parameters, current hole network parameters and blasting energy parameters into the numerical simulation model for calculation, and output the prediction results, including the predicted block rate.

[0102] The measured acquisition module 23 is used to acquire the measured results of blasting performed based on the current hole mesh parameters.

[0103] The calibration module 24, connected to the simulation module 22 and the measured acquisition module 23, is used to calibrate the parameters of the numerical simulation model based on the measured results and the predicted results.

[0104] The optimization module 25 is used to optimize the perforation parameters by gradually increasing the hole spacing or row spacing to obtain updated perforation parameters.

[0105] The iterative control module 26, connected to the simulation module 22, the measured acquisition module 23, the calibration module 24, and the optimization module 25, is used for: repeatedly calling the above modules in the iterative control based on multi-objective optimization of reducing explosive consumption and controlling the block ratio to not exceed the first threshold; wherein, the simulation module 22, the measured acquisition module 23, the calibration module 24, and the optimization module 25 are repeatedly called in the first preset number of iterations until the prediction deviation of the numerical simulation model is less than the second threshold; the subsequent iterative control only calls the simulation module 22 and the optimization module 25; the iterative control stops when the predicted block ratio exceeds the first threshold, and the optimal aperture parameters with the predicted block ratio less than the first threshold are determined as the target aperture parameters.

[0106] In some embodiments, the acquisition module 21 is used to acquire rock mass parameters, engineering parameters and historical blasting data of the target blasting area, and to correct the explosiveness empirical parameters based on data fitting and the acquired rock mass parameters, engineering parameters and historical blasting data, so as to obtain the rock mass explosiveness parameters of the target blasting area.

[0107] In some embodiments, the numerical simulation model in simulation module 22 is implemented using the Blo-Up software of the Hybrid Stress Blast Model (HSBM). Blo-Up uses a coupling method of a continuous medium model, a brittle discrete element model, and a gaseous product model for numerical simulation calculation.

[0108] In some embodiments, the measured acquisition module 23 is used to acquire images of the blast pile after blasting based on the current hole mesh parameters or point cloud data acquired through three-dimensional measurement, and call the crushing analysis software to analyze the images or point cloud data to generate measured results.

[0109] In some embodiments, the prediction results also include the pre-explosion fragmentation distribution curve, and the measured results include the measured fragmentation distribution curve.

[0110] The calibration module 24 is used to integrate the pre-blast predicted fragmentation distribution curve with the measured fragmentation distribution curve, calculate the prediction deviation for each particle size range, and use a nonlinear fitting algorithm to calibrate the parameters of the numerical simulation model with the goal of minimizing the prediction deviation. The parameters of the numerical simulation model include at least one of the following: rock mass influence coefficient, damping coefficient, and explosive-rock mass interaction parameters.

[0111] In some embodiments, the perforation parameters include the perforation spacing and the row spacing.

[0112] The optimization module 25 is used to adjust the hole spacing or row spacing only each time, and gradually expand the hole mesh parameters with a preset step size.

[0113] In some embodiments, the preset number of times n satisfies: 1≤n≤3, where n is a positive integer.

[0114] In some embodiments, the open-pit blasting borehole network parameter determination device based on numerical simulation and iterative control further includes a construction module. The construction module is used to construct a rock mass blastability database for reuse in blasting areas with the same geological conditions; the database includes rock mass blastability parameters, target borehole network parameters, and their mapping relationships.

[0115] Thirdly, this embodiment provides an electronic device, including a memory and a processor. The memory stores a computer program, and the processor is configured to run the computer program to implement a method for determining open-pit blasting hole network parameters based on numerical simulation and iterative control, as described in the first aspect.

[0116] Fourthly, this embodiment provides an open-pit blasting detection system, comprising: a mining industrial control computer configured to execute the open-pit blasting hole network parameter determination method based on numerical simulation and iterative control as described in the first aspect; on-site blasting detection equipment communicatively connected to the mining industrial control computer; and a three-dimensional scanning device communicatively connected to the mining industrial control computer.

[0117] The second aspect, the device for determining open-pit blasting hole network parameters based on numerical simulation and iterative control, the third aspect, the electronic equipment, and the fourth aspect, the open-pit blasting detection system, all achieve high-precision numerical simulation and prediction of pre-blast fragmentation, solving the problem of insufficient basis for hole network parameter design. They abandon the limitations of traditional empirical formula estimation and utilize the HSBM hybrid stress model and Blo-Up three-dimensional numerical simulation technology to construct a rock-explosive coupled calculation model. Through accurate numerical simulation and prediction of pre-blast fragmentation distribution, P80 block size index, and large block ratio, they provide a scientific and quantitative theoretical basis for hole network parameter adjustment, avoiding the quality risks caused by blind test blasts from the source. A safe, graded step-by-step (e.g., 0.1mm step size) iterative control mechanism is established to solve the problem of uncontrollable risks in hole network parameter adjustment. Changing the traditional "one-time solution" or "large jump" hole network parameter adjustment mode, an innovative graded step-by-step iterative control strategy is proposed. By setting a tiny step size of 0.1m and adhering to the "single-time, single-variable" principle, combined with a closed-loop feedback mechanism of "pre-blast prediction → on-site measurement → model correction," the borehole network parameters are dynamically and progressively iteratively optimized. This ensures that the bulk ratio remains within a controllable threshold during each parameter expansion process, achieving safety, stability, and traceability in blasting parameter optimization. This significantly reduces the unit consumption of explosives in open-pit blasting, addressing the issues of low utilization and high cost of mixed explosives. With reducing unit consumption as the core guiding principle, and through the synergistic effect of the aforementioned precise prediction and safety control, the borehole network parameters (hole spacing, row spacing) are expanded to the maximum extent within the safety boundary. By reducing drilling workload and lowering explosive unit consumption, the overall drilling and blasting costs are directly and significantly reduced; simultaneously, the energy utilization rate and large-scale application ratio of mixed explosives on-site are improved, helping mining enterprises overcome production capacity bottlenecks and achieve a dual improvement in economic benefits and production efficiency.

[0118] Those skilled in the art will understand that all or some of the steps, systems, and apparatuses disclosed above, and their functional modules / units, can be implemented as software, firmware, hardware, or suitable combinations thereof. In hardware implementations, the division between functional modules / units mentioned above does not necessarily correspond to the division of physical components; for example, a physical component may have multiple functions, or a function or step may be performed collaboratively by several physical components. Some or all physical components can be implemented as software executed by a processor, such as a central processing unit, digital signal processor, or microprocessor, or as hardware, or as integrated circuits, such as application-specific integrated circuits (ASICs).

[0119] Example embodiments have been disclosed herein, and while specific terminology has been used, it is for illustrative purposes only and should be construed as such, and is not intended to be limiting. In some instances, it will be apparent to those skilled in the art that features, characteristics, and / or elements described in conjunction with particular embodiments may be used alone, or in combination with features, characteristics, and / or elements described in conjunction with other embodiments, unless otherwise expressly indicated. Therefore, those skilled in the art will understand that various changes in form and detail may be made without departing from the scope of the invention as set forth in the appended claims.

Claims

1. A method for determining the parameters of an open-pit blasting borehole network based on numerical simulation and iterative control, characterized in that, include: S1, obtain the rock mass blastability parameters and initial hole network parameters of the target blasting area; S2, input the rock mass blastability parameters, current hole network parameters and blasting energy parameters into the numerical simulation model for calculation, and output the prediction results, including the predicted block rate; S3, obtain the measured results of blasting performed based on the current hole mesh parameters; S4. Based on the measured results and the predicted results, correct the parameters of the numerical simulation model; S5, optimize the perforation parameters by gradually increasing the hole spacing or row spacing to obtain the updated perforation parameters; S6, based on the multi-objective optimization of reducing explosive consumption and controlling the block ratio to not exceed the first threshold, iterative control of S2-S5 is repeatedly executed. The first preset number of iterations of control execute S2-S5 until the prediction deviation of the numerical simulation model is less than the second threshold. Subsequent iterations of control only execute S2 and S5. Iterative control stops when the predicted block ratio exceeds the first threshold, and the optimal aperture parameters with the predicted block ratio less than the first threshold are determined as the target aperture parameters.

2. The method for determining open-pit blasting hole network parameters based on numerical simulation and iterative control according to claim 1, characterized in that, The acquisition of rock mass blastability parameters of the target blasting area specifically includes: Obtain rock mass parameters, engineering parameters, and historical blasting data for the target blasting area; Based on data fitting and the obtained rock mass parameters, engineering parameters and historical blasting data, the explosiveness empirical parameters are corrected to obtain the explosiveness parameters of the rock mass in the target blasting area.

3. The method for determining open-pit blasting hole network parameters based on numerical simulation and iterative control according to claim 1, characterized in that, The numerical simulation model was implemented using the Blo-Up software of the Hybrid Stress Blasting Model (HSBM). The Blo-Up model employs a coupling method combining a continuous medium model, a brittle discrete element model, and a gaseous product model for numerical simulation calculations.

4. The method for determining open-pit blasting hole network parameters based on numerical simulation and iterative control according to claim 1, characterized in that, The acquisition of the measured results of blasting performed based on the current hole mesh parameters specifically includes: Obtain images of the blast pile after blasting based on the current hole mesh parameters, or point cloud data obtained through 3D measurement; The image or point cloud data is analyzed using fragmentation analysis software to generate measurement results.

5. The method for determining open-pit blasting hole network parameters based on numerical simulation and iterative control according to claim 1, characterized in that, The prediction results also include the pre-explosion fragmentation distribution curve, and the measured results include the measured fragmentation distribution curve. Based on the measured results and the predicted results, the parameters of the numerical simulation model are corrected, specifically including: The predicted fragmentation distribution curve before detonation is integrated with the measured fragmentation distribution curve, and the prediction deviation for each particle size range is calculated. A nonlinear fitting algorithm is used to correct the parameters of the numerical simulation model with the goal of minimizing the prediction deviation. The parameters of the numerical simulation model include at least one of the following: rock mass influence coefficient, damping coefficient, and explosive-rock mass interaction parameters.

6. The method for determining open-pit blasting hole network parameters based on numerical simulation and iterative control according to claim 1, characterized in that, The preset number of times n satisfies: 1≤n≤3, where n is a positive integer.

7. The method for determining open-pit blasting hole network parameters based on numerical simulation and iterative control according to claim 1, characterized in that, After determining the optimal aperture parameters with a predicted bulk ratio less than a first threshold as the target aperture parameters, the method further includes: A rock mass blastability database is constructed for reuse in blasting areas with the same geological conditions; the database includes rock mass blastability parameters, target borehole network parameters and their mapping relationships.

8. A device for determining the parameters of an open-pit blasting borehole network based on numerical simulation and iterative control, characterized in that, include: The acquisition module is used to acquire rock mass blastability parameters and initial hole network parameters of the target blasting area; The simulation module, connected to the acquisition module, is used to input rock mass blastability parameters, current hole network parameters, and blasting energy parameters into the numerical simulation model for calculation and output prediction results, including the predicted block rate. The measured data acquisition module is used to acquire the measured results of blasting performed based on the current hole mesh parameters; A calibration module, connected to the simulation module and the measured acquisition module, is used to calibrate the parameters of the numerical simulation model based on the measured results and the predicted results. The optimization module is used to optimize the perforation parameters by gradually increasing the hole spacing or row spacing to obtain updated perforation parameters. The iterative control module, connected to the simulation module, the measured acquisition module, the calibration module, and the optimization module, is used for: repeatedly calling the above modules in iterative control based on multi-objective optimization of reducing explosive consumption and controlling the block ratio to not exceed a first threshold; wherein, the simulation module, the measured acquisition module, the calibration module, and the optimization module are repeatedly called in the first preset number of iterations until the prediction deviation of the numerical simulation model is less than a second threshold; subsequent iterations only call the simulation module and the optimization module; the iterative control stops when the predicted block ratio exceeds the first threshold, and the optimal aperture parameters with the predicted block ratio less than the first threshold are determined as the target aperture parameters.

9. An electronic device, characterized in that, The device includes a memory and a processor, wherein the memory stores a computer program and the processor is configured to run the computer program to implement a method for determining open-pit blasting hole network parameters based on numerical simulation and iterative control as described in any one of claims 1 to 7.

10. An open-pit blasting detection system, characterized in that, include: A mining industrial control computer is configured to execute the method for determining open-pit blasting hole network parameters based on numerical simulation and iterative control as described in any one of claims 1 to 7. The on-site blasting detection equipment is communicatively connected to the mine's industrial control computer. The 3D scanning equipment is communicatively connected to the mine's industrial control computer.

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