A hole site parameter optimization analysis method based on open deep hole blasting

Abstract

The present application belongs to the technical field of deep hole blasting parameter optimization, and discloses a hole site parameter optimization analysis method based on open pit deep hole blasting. The present application determines rock types through multi-index coordination, improves the accuracy of rock classification by combining a dynamic threshold adjustment mechanism, and ensures the accurate matching of hole site parameters and geological conditions. The present application determines through three-level correlation of geological parameters-rock types-mapping relationship, avoids the limitations of empirical formulas, and makes the parameter range more in line with actual geological characteristics. At the same time, historical blasting data sets are introduced for similar case matching, potential risk parameter sets are removed through experience screening, the deviation between theoretical simulation and actual engineering is reduced, and the parameter reliability is improved. The present application adopts a multi-objective optimization model, simultaneously considers fragmentation quality, safety control and economy, outputs a parameter set balancing each target through a Pareto optimal algorithm, and realizes the coordinated optimization of high quality, low risk and low cost.

Description

Technical Field

[0001] This invention belongs to the field of deep hole blasting parameter optimization technology, and relates to a method for optimizing and analyzing hole position parameters based on open-pit deep hole blasting. Background Technology

[0002] In open-pit mining, water conservancy projects, and transportation construction, deep-hole blasting is a commonly used rock breaking technology. Its core lies in achieving efficient, safe, and economical blasting results through the rational setting of borehole position parameters. The rationality of the borehole position parameters directly affects the quality of rock fragmentation after blasting, the intensity of blasting vibration, the distance of flyrock, and the project cost. Therefore, optimizing and analyzing the borehole position parameters has significant engineering implications.

[0003] With the expansion of engineering construction scale and the increasing complexity of geological conditions, traditional methods for setting borehole parameters based on empirical formulas are no longer sufficient to meet the demands of high-precision blasting. The diversity of geological conditions significantly affects blasting results; ignoring these differences can lead to poor blasting performance, increased safety risks, or wasted costs. Therefore, a method is needed that can combine specific geological parameters and achieve precise optimization of borehole parameters through scientific analysis.

[0004] Traditional technical solutions rely heavily on empirical formulas when setting borehole parameters, without fully considering the specific geological parameters of the blasting area, such as the differences in uniaxial compressive strength and joint and fracture density of the rock. This results in insufficient adaptability to different types of rocks, such as soft rock, medium-hard rock, and hard rock.

[0005] Traditional mechanical blasting solutions often focus on a single objective, neglecting the synergistic optimization of safety control and economic efficiency, making it difficult to maximize overall benefits. At the same time, they fail to effectively utilize experience from historical blasting data, determining parameters solely through theoretical simulation or a single calculation model, which may pose a risk of being out of touch with actual engineering scenarios. Summary of the Invention

[0006] In view of this, in order to solve the problems mentioned in the background technology, a method for optimizing and analyzing borehole parameters based on open-pit deep-hole blasting is proposed.

[0007] The objective of this invention can be achieved through the following technical solution: a method for optimizing and analyzing borehole parameters based on open-pit deep-hole blasting, comprising: acquiring basic geological parameters, using geological parameter detection equipment to obtain geological parameters of the blasting area, including basic physical property parameters, integrity parameters and bench characteristic parameters, and constructing a geological parameter dataset.

[0008] Initial borehole location parameters are set by calibrating the rock type of the blasting area based on the geological parameter dataset, including soft rock, medium-hard rock, and hard rock, and determining the range of initial borehole location parameters based on basic empirical formulas combined with rock type.

[0009] The parameter optimization model is constructed by using numerical software to simulate blasting based on the initial borehole parameter range and geological parameter dataset, outputting blasting effect data, inputting the pre-constructed blasting parameter coupling model for multi-objective optimization calculation, and outputting multiple borehole parameter sets and corresponding comprehensive scores.

[0010] The final borehole position parameters are determined by simulation screening based on the blasting effect data and the corresponding comprehensive score. Historical blasting data with similar geological parameters are selected based on the historical blasting dataset. The borehole position parameter set after simulation screening is empirically screened to select the final borehole position parameter set.

[0011] Compared with the prior art, the beneficial effects of the present invention are as follows:

[0012] (1) This invention uses multiple indicators to collaboratively determine rock types and combines a dynamic threshold adjustment mechanism to improve the accuracy of rock classification and ensure the precise matching of pore location parameters with geological conditions.

[0013] (2) This invention determines parameters through a three-level correlation of geological parameters, rock type, and mapping relationship, avoiding the limitations of empirical formulas and making the parameter range more consistent with actual geological characteristics. At the same time, it introduces historical blasting datasets for similar case matching, and eliminates potential risk parameter sets through empirical screening, reducing the deviation between theoretical simulation and actual engineering and improving parameter reliability.

[0014] (3) This invention adopts a multi-objective optimization model, simultaneously considering crushing quality, safety control and economy, and outputs a parameter set that balances each objective through the Pareto optimal algorithm, thereby achieving high-quality, low-risk and low-cost collaborative optimization. Attached Figure Description

[0015] To more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0016] Figure 1 This is a schematic diagram illustrating the implementation steps of the method of the present invention.

[0017] Figure 2 This is a schematic diagram illustrating the implementation steps of setting the boundary threshold according to one embodiment of the present invention.

[0018] Figure 3 This is a schematic diagram of the multi-objective optimization calculation steps corresponding to one embodiment of the present invention. Detailed Implementation

[0019] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0020] Please see Figure 1 As shown, the present invention provides a method for optimizing and analyzing borehole parameters based on open-pit deep-hole blasting, including: acquiring basic geological parameters, using geological parameter detection equipment to obtain geological parameters of the blasting area, including basic physical property parameters, integrity parameters and step characteristic parameters, and constructing a geological parameter dataset.

[0021] It should be noted that the basic physical properties parameters mainly reflect the physical and mechanical characteristics of the rock itself. The core indicators include, but are not limited to, the uniaxial compressive strength, density, and elastic modulus of the rock. These parameters directly affect the rock's resistance to blasting and the difficulty of breaking it.

[0022] Integrity parameters focus on the structural integrity of rocks, with the core indicator being joint and fracture density, and may also include fracture opening and infill material properties. These parameters determine the distribution of weak points within the rock, affecting the transfer of blasting energy and the uniformity of the fragmentation effect.

[0023] The characteristic parameters of the bench, which are relevant to open-pit blasting engineering scenarios, mainly include bench height and bench slope angle. These parameters are directly related to the borehole layout and the delineation of the blasting range, and are important constraints for setting borehole location parameters.

[0024] Initial borehole location parameters are set by calibrating the rock type of the blasting area based on the geological parameter dataset, including soft rock, medium-hard rock, and hard rock, and determining the range of initial borehole location parameters based on basic empirical formulas combined with rock type.

[0025] In a preferred embodiment of the present invention, the specific analysis method for determining the rock type of the blasting area is as follows: the rock type of the blasting area is identified based on the uniaxial compressive strength and joint density of the rock in the geological parameter dataset.

[0026] It's important to explain why uniaxial compressive strength and joint density were chosen as the criteria for rock type classification: Uniaxial compressive strength reflects a rock's ability to resist axial pressure damage and is a key physical property parameter for measuring rock hardness. Generally, higher strength indicates a harder rock. Joint density reflects the degree of development of internal fissures and joints within the rock and is a core parameter for measuring rock integrity. Generally, higher density indicates a more fragmented rock structure and poorer integrity.

[0027] First rock type tendency identification is performed based on pre-constructed uniaxial compressive strength boundary thresholds for soft to medium-hard rocks and medium-hard to hard rocks.

[0028] Preferably, if the uniaxial compressive strength of the rock is less than or equal to the threshold for the uniaxial compressive strength of soft to medium-hard rocks, then the first rock type tends to be soft rock.

[0029] If the uniaxial compressive strength of the rock is greater than the threshold for the uniaxial compressive strength of soft to medium-hard rock, and less than or equal to the threshold for the uniaxial compressive strength of medium-hard to hard rock, then the first rock type tends to be medium-hard rock.

[0030] If the uniaxial compressive strength of the rock is greater than the threshold for the uniaxial compressive strength of medium-hard to hard rock, then the first rock type tends to be hard rock.

[0031] Second rock type tendency identification is performed based on pre-constructed soft-to-medium-hard joint density boundary thresholds and medium-hard-to-hard joint density boundary thresholds.

[0032] Similarly, if the density of rock joints and fissures is greater than or equal to the threshold for the soft-to-medium-hard joint and fissure density, then the second rock type tends to be soft rock.

[0033] If the density of rock joints and fissures is less than the threshold for the soft-to-medium-hard joint and fissure density division, and greater than or equal to the threshold for the medium-hard-to-hard rock joint and fissure density division, then the second rock type tends to be medium-hard rock.

[0034] If the density of rock joints and fissures is less than the threshold for the joint and fissure density distinction between medium-hard and hard rocks, then the second rock type tends to be hard rock.

[0035] When the first rock type and the second rock type are the same, the rock type is taken as the rock type of the blasting area.

[0036] When the first rock type and the second rock type are inconsistent, the relative deviation of the uniaxial compressive strength and joint density of the rock is analyzed with respect to the corresponding thresholds, and the rock type with the smallest relative deviation is taken as the rock type of the blasting area.

[0037] One embodiment of the method for determining the specific rock type when the first rock type and the second rock type are inconsistent:

[0038] D1. If the first type of tendency is A and the second type of tendency is B (A≠B), then extract the boundary threshold between A and B. For example, if the first type of tendency is soft rock and the second type of tendency is medium-hard rock, then the boundary threshold between the uniaxial compressive strength of soft and medium-hard rocks and the boundary threshold between the joint and fracture density of soft and medium-hard rocks need to be considered.

[0039] D2. Calculate the relative deviation between the uniaxial compressive strength of the rock, the joint and fracture density, and the corresponding boundary threshold.

[0040] D3. Compare the relative deviation of the two indicators: If the relative deviation of the uniaxial compressive strength is smaller, it means that the indicator matches the corresponding threshold better, and the first rock type is taken as the final result; if the relative deviation of the joint and fracture density is smaller, the second rock type is taken as the final result.

[0041] For a preferred embodiment of the present invention, please refer to Figure 2 As shown, the specific construction methods of the uniaxial compressive strength boundary thresholds for soft-medium hard rocks, medium-hard-hard rocks, soft-medium hard joint and fracture density boundary thresholds, and medium-hard-hard joint and fracture density boundary thresholds are as follows: A1. Collect historical experimental data, covering rock samples from different geological regions, and extract the uniaxial compressive strength, joint and fracture density, and corresponding blasting effect data for each sample.

[0042] A2. Group the data and divide it into several combination intervals according to the numerical range of uniaxial compressive strength and joint density. Calculate the mean value of the blasting effect in each interval.

[0043] A3. Using the achievement of blasting effect as the core screening criterion, mark the percentage of samples that achieve the effect in each combination interval.

[0044] A4. For uniaxial compressive strength, gradually increase the critical value from low to high, calculate the cumulative proportion of qualified samples under different critical values, and determine the boundary thresholds for uniaxial compressive strength of soft-medium-hard rocks and medium-hard-hard rocks.

[0045] For example, the critical value is gradually increased from low to high, such as starting from 10 MPa and increasing by 5 MPa each time. The cumulative percentage of samples that meet the blasting effect standard is calculated at each critical value. When the critical value increases to a certain value, the cumulative percentage of the standard meets the standard shows a significant inflection point, such as jumping from 60% to 80%. The critical value corresponding to this inflection point is used as the boundary threshold for uniaxial compressive strength of soft-medium-hard rock and the boundary threshold for uniaxial compressive strength of medium-hard rock, respectively. For example, the low inflection point corresponds to the soft-medium-hard threshold, and the high inflection point corresponds to the medium-hard-hard rock threshold.

[0046] A5. For joint and fracture density, set critical values ​​from high to low, and statistically analyze the proportion of qualified samples in the corresponding uniaxial compressive strength range under different critical values ​​to determine the boundary thresholds for soft-medium-hard joint and fracture density and medium-hard-hard joint and fracture density.

[0047] A6. Verify the matching of rock type and blasting parameters using historical engineering cases. If the rock type determination of the vast majority of cases matches the actual blasting effect under a certain threshold, then confirm that threshold. If the deviation is large, readjust the threshold and repeat the above steps until the proportion of qualified samples corresponding to the threshold stabilizes at a high level, and finally determine the specific value of each threshold.

[0048] In a preferred embodiment of the present invention, the specific analysis method for determining the initial pore location parameter range is as follows: based on the determined rock type, extract the uniaxial compressive strength, joint and fracture density, step height and selected pore diameter value from the basic geological parameters.

[0049] Based on the pre-constructed rock type-related parameter mapping relationship, the uniaxial compressive strength, joint and fracture density, step height and selected pore diameter values ​​in the basic geological parameters are matched with the related parameter mapping relationship corresponding to each rock type to obtain the initial pore location parameter range of the blasting area.

[0050] In one specific embodiment, the relevant borehole position parameter range is determined as follows: 1. Determine the borehole depth range: Based on the step height, the over-depth ratio is set according to the rock type. For soft rock, the ratio is 10%-12% of the step height; for medium-hard rock, the ratio is 12%-14%; and for hard rock, the ratio is 14%-16%. The borehole depth range is the sum of the step height and the corresponding over-depth range.

[0051] 2. Determine the range of the chassis resistance line: Calculate the initial value according to the borehole diameter using the empirical coefficient method. For soft rock, take 25-30 times the borehole diameter; for medium-hard rock, take 30-35 times; and for hard rock, take 35-40 times. Then, combine this with the uniaxial compressive strength correction. The higher the strength, the higher the upper limit of the resistance line range will be increased by 5%-10%.

[0052] 3. Determine the range of hole spacing: Based on the range of the chassis resistance line, calculate according to the hole spacing coefficient. For soft rock, take 1.0-1.2 times the resistance line; for medium-hard rock, take 1.2-1.4 times; for hard rock, take 1.4-1.5 times. When the joint and fissure density is high, appropriately reduce the lower limit of the coefficient.

[0053] 4. Determine the spacing range: According to the hole layout method, take 0.8-1.0 times the hole spacing range when the layout is rectangular, and take 0.85-0.95 times the hole spacing range when the layout is quincunx. The lower limit of the soft rock deviation range and the upper limit of the hard rock deviation range.

[0054] It should be noted that the advantage of determining the initial borehole location parameter range lies in its precise extraction of key geological parameters such as uniaxial compressive strength and joint / fracture density based on the identified rock type. These parameters are then matched with a pre-constructed mapping relationship between rock type and related parameters. This process incorporates the engineering wisdom of basic empirical formulas and achieves precise adjustment through the dynamic correlation between geological parameters and mapping relationships. This avoids the limitations of traditional empirical formulas, ensuring that the initial borehole location parameter range is highly adapted to actual geological conditions. This provides a scientific and reasonable input boundary for subsequent parameter optimization models, improving the relevance and reliability of borehole location parameter setting.

[0055] In a preferred embodiment of the present invention, the specific construction method of the rock type-related parameter mapping relationship combination is as follows: collect historical blasting data, filter out complete data corresponding to different rock types, including uniaxial compressive strength, joint and fracture density, step height, selected pore diameter value, and pore position parameters for actual application.

[0056] Data for soft rock, medium-hard rock, and hard rock were grouped separately, and the relationship between basic geological parameters and pore location parameters in each group was analyzed based on rock type.

[0057] For each rock type, the response patterns of pore location parameters to changes in basic geological parameters are analyzed, and the mapping patterns between parameters are extracted.

[0058] It should be noted that, for each rock type, the response patterns of borehole location parameters to changes in basic geological parameters are analyzed in depth. Through statistical analysis and data fitting, the mapping patterns between basic geological parameters and borehole location parameters are extracted. For example, it was found that in hard rock, the higher the uniaxial compressive strength, the smaller the borehole spacing and row spacing tend to be; when the step height increases, the borehole depth increases accordingly, and the bottom resist line will also be adjusted.

[0059] The mapping patterns of basic geological parameters and pore location parameters under each rock type are integrated to form the relevant parameter mapping relationships for soft rock, medium-hard rock, and hard rock, respectively, thus constructing a combination of rock type-related parameter mapping relationships.

[0060] It should be noted that the advantage of constructing a rock type-related parameter mapping relationship combination lies in its foundation based on a large amount of historical blasting data. It categorizes geological parameters and borehole location parameters according to soft rock, medium-hard rock, and hard rock, and extracts targeted mapping patterns. This combination integrates parameter response patterns under different rock types, accurately matching specific geological conditions. It provides a scientific basis for determining the initial borehole location parameter range, avoids the limitations of single empirical formulas, improves the rationality and adaptability of parameter settings, and lays a reliable foundation for subsequent optimization.

[0061] The parameter optimization model is constructed by using numerical software to simulate blasting based on the initial borehole parameter range and geological parameter dataset, outputting blasting effect data, inputting the pre-constructed blasting parameter coupling model for multi-objective optimization calculation, and outputting multiple borehole parameter sets and corresponding comprehensive scores.

[0062] For a preferred embodiment of the present invention, please refer to Figure 3 As shown, the specific method for performing multi-objective optimization calculations is as follows: construct an optimization model with crushing quality, safety control, and economy as objective functions.

[0063] In this invention, the specific indicators corresponding to the crushing quality, safety control, and economy are the large-piece ratio, blasting vibration intensity, and drilling explosive cost. The large-piece ratio directly reflects the crushing effect and affects the efficiency of subsequent operations; blasting vibration intensity is a core safety indicator, related to the safety of surrounding facilities; and drilling explosive cost is a key component of economy, directly affecting project costs. These three indicators cover the core objectives of blasting, are easily quantifiable, and have readily available data, adapting to multi-objective optimization needs and ensuring that the optimization direction aligns with actual engineering conditions.

[0064] Hole spacing, row spacing, and chassis resistance line were selected as optimization variables, and the value range of each variable was set based on the initial hole position parameter range.

[0065] A multi-objective optimization algorithm is adopted, in which the objective function and the range of variable values ​​are input into the computational model, and the Pareto optimal solution set is found through algorithm iteration.

[0066] Multiple sets of pore location parameters are extracted and output from the Pareto optimal solution set.

[0067] It's important to note that multi-objective optimization calculations are performed because open-pit deep-hole blasting requires simultaneous consideration of multiple dimensions, including crushing quality, safety control, and economic efficiency. Optimizing a single objective can easily lead to neglecting some aspects. By using hole spacing and other parameters as variables, combined with the initial parameter range, an algorithm is employed to find the Pareto optimal solution set, outputting a balanced set of hole position parameters for all objectives. This approach avoids the limitations of single-dimensional optimization, ensuring that parameters improve crushing performance while controlling safety risks and reducing costs, maximizing overall benefits and aligning with actual engineering needs.

[0068] In a preferred embodiment of the present invention, the specific calculation method of the corresponding comprehensive score is as follows: the parameters such as crushing quality, safety control and economy corresponding to each hole position parameter set are standardized to eliminate the difference in dimensions.

[0069] It should be noted that standardizing parameters such as the bulk density, blasting vibration intensity, and drilling explosive cost corresponding to each borehole parameter set is primarily aimed at eliminating dimensional differences between parameters, ensuring that all parameters are on the same order of magnitude, thus facilitating subsequent weighted summation for comprehensive scoring. Specifically, this invention uses the formula (maximum value - measured value) / (maximum value - minimum value) for normalization, ensuring a consistent evaluation scale for parameters across different dimensions and preventing the undue amplification or reduction of the weight of a particular parameter in the comprehensive score due to dimensional differences.

[0070] The standardized parameters are weighted and summed according to preset weights to obtain the corresponding comprehensive score.

[0071] It should be noted that the preset weights are based on the priority settings for each objective in the project.

[0072] The final borehole position parameters are determined by simulation screening based on the blasting effect data and the corresponding comprehensive score. Historical blasting data with similar geological parameters are selected based on the historical blasting dataset. The borehole position parameter set after simulation screening is empirically screened to select the final borehole position parameter set.

[0073] In a preferred embodiment of the present invention, the specific analysis method for performing the simulation screening is as follows: the comprehensive score corresponding to each pore position parameter set is compared with a pre-set comprehensive score threshold to determine whether it meets the engineering requirements.

[0074] If the comprehensive score corresponding to a certain set of parameters for a hole position is greater than or equal to the comprehensive score threshold, it is determined to meet the engineering requirements; otherwise, it is determined not to meet the engineering requirements.

[0075] It should be noted that the comprehensive scoring threshold is primarily based on two factors: first, the distribution of comprehensive scores from compliant cases in historical blasting data, statistically analyzing the comprehensive scores of the parameter sets of boreholes that achieved past results, and using the lowest value or the lower limit of the confidence interval as the basic threshold; second, actual engineering needs, combining the project's minimum standards for crushing quality, safety control, and economy, and converting these standards into corresponding comprehensive scoring thresholds. Simultaneously, the rationality of the threshold is verified through trial calculations to ensure that it can both select parameter sets that meet engineering requirements and avoid excessively narrowing the optimization range, ultimately forming a comprehensive scoring threshold that balances historical experience and actual needs.

[0076] It should be noted that the reason for conducting simulation screening is that the candidate parameter set after empirical screening still needs to be verified for its practical application effect. By simulating blasting scenarios, the performance of the parameter set in terms of crushing quality, safety control, etc., can be tested in a virtual environment, and potential problems can be identified in advance. This approach can avoid the risks of directly applying the parameter set to engineering projects, further eliminate parameters that are theoretically feasible but have poor practical adaptability, and ensure that the final parameter set meets multiple objectives while conforming to the complex geological conditions on site, thereby improving the reliability of the blasting scheme.

[0077] In a preferred embodiment of the present invention, the specific analysis process for selecting historical blasting data corresponding to similar geological parameters is as follows: extract the historical blasting dataset and obtain the geological parameters and rock type data corresponding to each historical blasting data.

[0078] The geological parameters and rock type data are compared with the geological parameters and rock type data of the current blasting area. If all of the following conditions are met, the historical blasting data is determined to be similar to the actual geological conditions of the current blasting area:

[0079] Condition 1: The rock type in the historical blasting data is consistent with the rock type in the current blasting area.

[0080] Condition 2: The relative deviations between the geological parameters corresponding to historical blasting data and the geological parameters of the current blasting area are all less than the preset threshold.

[0081] It should be noted that the historical data is completely consistent with the rock type of the current area, ensuring that both belong to the same category in terms of overall hardness and structural characteristics. Geological parameter deviation thresholds: The numerical deviations of all key geological parameters are within the preset threshold range, ensuring that the quantitative differences of specific parameters are within the acceptable similarity range for engineering purposes. By simultaneously satisfying type consistency and controllable parameter deviations, it can be ensured that the geological conditions reflected by the historical blasting data have a high degree of similarity to the current area. The corresponding borehole location parameters and blasting effects are valuable for reference in the current project, providing a reliable basis for the empirical selection of subsequent borehole location parameters.

[0082] In a preferred embodiment of the present invention, the specific process of performing the experience screening is as follows: screening historical blasting data with abnormal blasting effects from historical blasting data corresponding to similar geological parameters.

[0083] A similarity analysis was performed between the set of hole position parameters after simulation screening and the set of hole position parameters corresponding to historical blasting data with abnormal blasting effects.

[0084] It should be noted that this process involves comparing the hole location parameter set retained after simulation screening with hole location parameter sets from historical blasting operations that exhibited abnormal results, such as excessive block ratios or excessive vibration. The similarity between the two sets is analyzed. Specifically, the numerical deviations of key indicators in the parameter set, such as hole depth, hole diameter, hole spacing, and charge amount, are calculated and combined with geological parameter matching to determine the similarity of the parameter sets. If the parameter set after simulation screening is highly similar to the abnormal historical parameter set, it indicates a potential risk and requires further evaluation or removal to avoid repeating historical errors and improve the safety and reliability of the final hole location parameter set.

[0085] If the similarity analysis result is greater than a preset threshold, the pore position parameter set is removed, and the pore position parameter sets that meet the conditions for comparison with the similarity analysis result are retained.

[0086] The similarity analysis results are compared, and the pore location parameter sets that meet the conditions are sorted according to the comprehensive score. The pore location parameter set corresponding to the highest comprehensive score is selected as the final pore location parameter set.

[0087] It should be noted that the reason for conducting empirical screening is that while multi-objective optimization calculations can provide theoretically optimal solutions, they may lack sufficient adaptability to actual engineering scenarios. Hole location parameters in historical blasting data that are similar to the current regional geological conditions have been verified to better fit the field conditions. By selecting a high-scoring empirical parameter set and combining it with the optimized solution set, we can integrate historical engineering practice experience with algorithm optimization results, overcome the limitations of purely theoretical optimization, further improve the reliability and applicability of the candidate parameter set, and ensure that the final hole location parameters better meet the actual needs of open-pit deep-hole blasting.

[0088] The above content is merely an example and illustration of the concept of the present invention. Those skilled in the art can make various modifications or additions to the specific embodiments described, or use similar methods to replace them, as long as they do not deviate from the concept of the invention or exceed the scope defined by the present invention, and all such modifications and additions should fall within the protection scope of the present invention.

Claims

1. A method for optimizing and analyzing borehole position parameters based on open-pit deep-hole blasting, characterized in that, include: Basic geological parameter acquisition involves using geological parameter detection equipment to obtain geological parameters of the blasting area, including basic physical property parameters, integrity parameters, and bench characteristic parameters, and constructing a geological parameter dataset. Initial borehole position parameters are set by calibrating the rock type of the blasting area based on the geological parameter dataset, including soft rock, medium-hard rock and hard rock, and determining the range of initial borehole position parameters based on basic empirical formulas combined with rock type. The parameter optimization model is constructed by using numerical software to simulate blasting based on the initial borehole parameter range and geological parameter dataset, outputting blasting effect data, inputting the pre-constructed blasting parameter coupling model for multi-objective optimization calculation, and outputting multiple borehole parameter sets and corresponding comprehensive scores. The final borehole position parameters are determined by simulation screening based on the blasting effect data and the corresponding comprehensive score. Based on the historical blasting dataset, historical blasting data with similar geological parameters are selected. The borehole position parameter set after simulation screening is empirically screened to select the final borehole position parameter set. The specific analysis method for the rock type of the designated blasting area is as follows: Rock type identification in blasting areas based on uniaxial compressive strength and joint density of rocks in geological parameter datasets; First rock type tendency identification is performed based on pre-constructed uniaxial compressive strength boundary thresholds for soft to medium-hard rocks and medium-hard to hard rocks; Second rock type tendency identification is performed based on pre-constructed soft-to-medium-hard joint fracture density boundary thresholds and medium-hard-to-hard joint fracture density boundary thresholds; When the first rock type and the second rock type are the same, the rock type of the blasting area shall be used; When the first rock type and the second rock type are inconsistent, the relative deviation of the uniaxial compressive strength and joint density of the rock is analyzed with each corresponding threshold, and the rock type with the smallest relative deviation is taken as the rock type of the blasting area. The specific construction methods for the uniaxial compressive strength boundary thresholds for soft-to-medium-hard rocks, medium-hard-to-hard rocks, soft-to-medium-hard joint and fracture density boundary thresholds, and medium-hard-to-hard joint and fracture density boundary thresholds are as follows: A1. Collect historical experimental data, covering rock samples from different geological regions, and extract the uniaxial compressive strength, joint and fracture density, and corresponding blasting effect data for each sample; A2. Group the data and divide it into several combination intervals according to the numerical range of uniaxial compressive strength and joint fracture density. Calculate the mean value of the blasting effect in each interval. A3. Using the achievement of blasting effect as the core screening criterion, mark the percentage of samples that achieve the effect in each combination interval; A4. For uniaxial compressive strength, gradually increase the critical value from low to high, calculate the cumulative proportion of samples meeting the standard under different critical values, and determine the boundary thresholds for uniaxial compressive strength of soft-medium-hard rocks and uniaxial compressive strength of medium-hard-hard rocks. A5. For joint fracture density, set critical values ​​from high to low, and statistically analyze the proportion of qualified samples in the corresponding uniaxial compressive strength range under different critical values ​​to determine the soft-medium hard joint fracture density boundary threshold and the medium hard-hard joint fracture density boundary threshold. A6. Verify by introducing matching cases of rock type and blasting parameters from historical engineering projects. If the rock type determination of a case under a certain threshold matches the actual blasting effect, then confirm the boundary threshold. If the deviation is large, readjust the boundary threshold and repeat the steps A1 to A5 above until the proportion of qualified samples corresponding to the boundary threshold stabilizes at a high level, and finally determine the specific value of each boundary threshold.

2. The method for optimizing and analyzing borehole parameters based on open-pit deep-hole blasting as described in claim 1, characterized in that: The specific analysis method for determining the initial borehole position parameter range is as follows: Based on the identified rock type, extract the uniaxial compressive strength, joint and fracture density, step height, and selected pore size from the basic geological parameters; Based on the pre-constructed rock type-related parameter mapping relationship, the uniaxial compressive strength, joint and fracture density, step height and selected pore diameter values ​​in the basic geological parameters are matched with the related parameter mapping relationship corresponding to each rock type to obtain the initial pore location parameter range of the blasting area.

3. The method for optimizing and analyzing borehole parameters based on open-pit deep-hole blasting as described in claim 2, characterized in that: The specific construction method of the rock type-related parameter mapping relationship combination: Collect historical blasting data, filter out complete data corresponding to different rock types, including uniaxial compressive strength, joint and fracture density, step height, selected hole diameter value, and hole position parameters for actual application; The data for soft rock, medium-hard rock, and hard rock were grouped separately, and the relationship between basic geological parameters and pore location parameters in each group was analyzed based on rock type. For each rock type, the response law of pore location parameters to changes in basic geological parameters is analyzed, and the mapping pattern between parameters is extracted. The mapping patterns of basic geological parameters and pore location parameters under each rock type are integrated to form the relevant parameter mapping relationships for soft rock, medium-hard rock, and hard rock, respectively, thus constructing a combination of rock type-related parameter mapping relationships.

4. The method for optimizing and analyzing borehole parameters based on open-pit deep-hole blasting as described in claim 1, characterized in that: The specific method for performing multi-objective optimization calculations is as follows: An optimization model is constructed with crushing quality, safety control, and economy as objective functions; Hole spacing, row spacing, and chassis resistance line are selected as optimization variables, and the value range of each variable is set based on the initial hole position parameter range. A multi-objective optimization algorithm is adopted, in which the objective function and the range of variable values ​​are input into the computational model, and the Pareto optimal solution set is found through algorithm iteration; Multiple sets of pore location parameters are extracted and output from the Pareto optimal solution set.

5. The method for optimizing and analyzing borehole parameters based on open-pit deep-hole blasting as described in claim 4, characterized in that: The specific calculation method for the corresponding comprehensive score is as follows: The crushing quality, safety control, and economic parameters corresponding to each orifice parameter set are standardized to eliminate dimensional differences. The standardized parameters are weighted and summed according to preset weights to obtain the corresponding comprehensive score.

6. The method for optimizing and analyzing borehole parameters based on open-pit deep-hole blasting as described in claim 5, characterized in that: The specific analysis method for conducting simulated screening is as follows: The comprehensive score corresponding to each hole position parameter set is compared with the pre-set comprehensive score threshold to determine whether it meets the engineering requirements. If the comprehensive score corresponding to a certain set of parameters for a hole position is greater than or equal to the comprehensive score threshold, it is determined to meet the engineering requirements; otherwise, it is determined not to meet the engineering requirements.

7. The method for optimizing and analyzing borehole parameters based on open-pit deep-hole blasting as described in claim 1, characterized in that: The specific analysis process for selecting historical blasting data corresponding to similar geological parameters is as follows: Extract historical blasting datasets to obtain the geological parameters and rock type data for each historical blasting data; The geological parameters and rock type data are compared with the geological parameters and rock type data of the current blasting area. If all of the following conditions are met, the historical blasting data is determined to be similar to the actual geological conditions of the current blasting area: Condition 1: The rock type in the historical blasting data is consistent with the rock type in the current blasting area; Condition 2: The relative deviations between the geological parameters corresponding to historical blasting data and the geological parameters of the current blasting area are all less than the preset threshold.

8. The method for optimizing and analyzing borehole parameters based on open-pit deep-hole blasting as described in claim 7, characterized in that: The specific process of conducting experience-based screening is as follows: Select historical blasting data with abnormal blasting effects from historical blasting data corresponding to similar geological parameters; A similarity analysis was performed between the set of hole position parameters after simulation screening and the set of hole position parameters corresponding to historical blasting data with abnormal blasting effects. If the similarity analysis result is greater than the preset threshold, the pore position parameter set is removed, and the pore position parameter set that meets the conditions is retained for comparison with the similarity analysis result. The similarity analysis results are compared, and the pore location parameter sets that meet the conditions are sorted according to the comprehensive score. The pore location parameter set corresponding to the highest comprehensive score is selected as the final pore location parameter set.

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