BIM-based open bench blasting parameter adaptive calculation system

Abstract

The invention provides a BIM-based open bench blasting parameter adaptive calculation system, and relates to the technical field of computer simulation assistance, and the system comprises the following modules: a warning range processing module which collects warning safety reference parameters; the energy parameter acquisition module is used for acquiring blasting energy parameters directly related to blasting operation; the splash simulation generation module is used for carrying out integrated processing on the collected multiple groups of blasting energy parameters and calculating and outputting a blasting splash range; and the risk decision regulation and control module is used for calculating the single-hole explosive quantity and the minimum blast hole number under the condition that the blast splash range does not exceed the maximum alertable range. According to the method, the overlapping degree of the maximum warning range and the blasting splash range is calculated in real time, the constraint optimization algorithm is triggered when the overlapping degree exceeds the preset threshold value through spatial overlay analysis, the gradient distribution intensity, the nearest protection area distance and the facility shock resistance index can be comprehensively considered, and therefore energy parameters during blasting are reversely regulated and controlled. The safety is improved; and the workload is reduced.

Description

Technical Field

[0001] This invention relates to the field of computer simulation-aided technology, specifically to an adaptive calculation system for open-air bench blasting parameters based on BIM. Background Technology

[0002] In open-pit bench blasting operations, accurately calculating the blast spatter range is a core technical aspect for ensuring project safety and efficiency. The spatter range directly determines the maximum distance that rock fragments produced by the blast may be ejected, and is the only scientific basis for setting warning zones and avoiding the risks of personnel casualties and facility damage. Traditional empirical estimations or static model calculations are often significantly biased due to the heterogeneity of the rock mass, fluctuations in explosive properties, and the complexity of geological conditions—underestimating the range may lead to safety accidents, while overestimating the range leads to excessive vigilance and idle working faces, causing project delays and soaring costs. Therefore, constructing a calculation model that can dynamically integrate borehole parameters, reflect the influence of rock mass hardness in real time, and continuously optimize the empirical constant of spatter through a feedback mechanism has irreplaceable engineering value for achieving precise control of blasting energy, reliable delineation of safety boundaries, and optimal allocation of operational resources.

[0003] In the prior art, CN118627376A discloses a refined intelligent design method for open-pit bench blasting. This technology includes: collecting information on bench blasting schemes and blasting effects in open-pit mines both domestically and internationally; establishing a historical database of bench blasting schemes; establishing and training a bench blasting hole network parameter prediction model based on machine learning algorithms and the historical database; inputting the blastability of the working bench into the prediction model to recommend suitable blasting hole network parameters; monitoring and acquiring drilling parameters during the drilling process; inverting the rock mechanics parameters of the working area in real time; determining the charge parameters in combination with the hole network parameters; then performing numerical simulation of the scheme; carrying out on-site blasting of the determined scheme; conducting on-site multi-source monitoring; evaluating the rationality of the blasting scheme; and dynamically updating the database.

[0004] However, in the aforementioned existing technologies, the blasting calculation system uses a static model, and the calculation of the warning range depends on fixed parameters, which cannot be dynamically updated to adapt to changes in terrain undulations and facility vulnerability, resulting in a large deviation in the maximum warning range; the energy parameter acquisition lacks a multi-round verification mechanism, and input errors occur frequently; the spatter simulation uses rigid empirical constants, resulting in low accuracy in predicting the blast spatter range; and for areas around the blasting point that need protection, it is impossible to automatically adjust the various parameters required for blasting energy based on these areas. Therefore, in order to avoid affecting surrounding equipment or protected areas, the entire blasting process tends to reduce the blasting energy, thereby reducing the overall blasting efficiency.

[0005] The information disclosed in the background section is only intended to enhance the understanding of the background of this disclosure, and therefore may include information that does not constitute prior art known to those skilled in the art. Summary of the Invention

[0006] The purpose of this invention is to provide a BIM-based adaptive calculation system for open-pit bench blasting parameters to address the problems mentioned in the background section. This invention calculates the overlap between the maximum warning range and the blast spatter range in real time. Through spatial overlay analysis, a constraint optimization algorithm is triggered when the overlap exceeds a preset threshold. This algorithm optimizes key parameters such as the charge per hole and the number of holes with the goal of minimizing total blasting energy. It comprehensively considers slope distribution density, the distance between the nearest protected areas, and the impact resistance index of the facilities, thereby controlling the energy parameters during blasting. This improves safety and reduces workload.

[0007] To achieve the above objectives, the present invention provides the following technical solution:

[0008] The BIM-based adaptive calculation system for open-pit bench blasting parameters includes the following modules:

[0009] Warning range processing module: Collects warning safety benchmark parameters, including slope distribution density, distance between nearest protection areas and facility impact resistance index. Preprocesses the warning safety benchmark parameters, including cleaning outliers and eliminating dimensional differences. Establishes warning range formula, calculates the maximum warning range, and dynamically collects terrain undulation and facility vulnerability to adapt the calculation results to changes on site.

[0010] Energy Parameter Acquisition Module: Acquires blasting energy parameters directly related to blasting operations, including single-hole charge, number of blasting holes, blasting hole depth, and rock hardness. Inputs the blasting energy parameters into the adaptive calculation system through a unified data interface, controls the standardized format, and uses data templates to filter invalid inputs and perform integrity verification.

[0011] The splash simulation generation module integrates and processes multiple sets of collected blast energy parameters, calculates and outputs the blast splash range, and generates a polygonal boundary map based on the output results, presenting it in the form of spatial range to provide a visual output.

[0012] Risk Decision Control Module: Based on the obtained maximum warning range and blasting splash range, it performs dynamic comparison and decision optimization, detects the overlap between the maximum warning range and the blasting splash range, sets an allowable overlap threshold, and if the overlap exceeds the overlap threshold, it activates the blasting energy parameter control mechanism to calculate the minimum single-hole charge and number of blasting holes under the condition that the blasting splash range does not exceed the maximum warning range.

[0013] Furthermore, in the warning range processing module, warning safety benchmark parameters are collected. The collection process is carried out by obtaining data from on-site monitoring data sources and historical records through a standardized data interface. Then, preprocessing is performed, in which outlier cleaning is carried out by using an outlier detection algorithm to identify and remove invalid or extreme data points, and normalization is used to eliminate dimensional differences and unify parameter scales. The warning range formula is established based on the preprocessed parameters.

[0014] Simultaneously, terrain undulation data and facility vulnerability data are dynamically collected, and the calculation model is continuously adjusted using a real-time data stream update mechanism. Based on the calculation results, a range boundary image is generated and visualized as a spatial polygon.

[0015] Furthermore, the formula for the warning range is:

[0016]

[0017] in:

[0018] This is the maximum vigilance range;

[0019] It is a proportionality constant;

[0020] This refers to the density of slope distribution. The attenuation coefficient is used to control the attenuation rate of the effect of slope distribution density. It is a natural constant; by introducing an exponential function Slope distribution density The impact on the final result decays exponentially, with a decay coefficient of... Controlling the decay rate, used to increase the marginal benefit of the warning range, results in diminishing returns. At that time, the warning range formula is converted into its original linear form:

[0021]

[0022] The distance between the nearest protected areas; For the impact resistance index of the facility;

[0023] When the density of slope distribution As it increases, the exponential function The value decreases, reducing the maximum alert range. . contributions.

[0024] Furthermore, the proportionality constant Set the proportional constant through the following process: The initial value is 1. Then, based on historical blasting data and real-time monitoring results, the deviation data between the actual splash range and the calculated value of the warning range are collected, the adjustment factor is calculated, and the proportional constant is dynamically updated. To minimize prediction errors, and through continuous verification and fine-tuning in subsequent blasting operations, the proportionality constant was ultimately achieved. Stabilizing at the optimal value ensures that the calculation of the maximum alertable range is accurately adapted to specific on-site conditions.

[0025] Furthermore, the calculation process of the adjustment factor is as follows: the actual observed splash range recorded in historical blasting operations is compared with the initial theoretical range calculated by the formula to generate a continuous deviation sequence. Then, weights are assigned to these deviation data. When assigning weights, the principle of the more recent the time is followed to reflect the changing trend on site. The weighted least squares method is used to fit the deviation sequence and a loss function with the goal of minimizing the sum of squared prediction errors is constructed and iteratively optimized to find the optimal solution.

[0026] An adaptive learning rate is introduced to avoid the optimization process getting stuck in local optima and to improve the convergence speed. The warning range processing module also performs a comprehensive analysis of the statistical characteristics of the bias, including the mean and standard deviation, to assess the overall bias and stability of the model's predictions. The statistical characteristics are then converted into a proportionality constant through a predefined mapping function. The initial adjustment suggestions are compared and tailored with the prior threshold range set by domain experts. The adjustment range is kept within reasonable engineering limits. Finally, the validated adjustment factor is compared with the current scaling constant. In the synthetic calculation example, the proportional constant is updated using an exponentially weighted moving average. The values ​​form an adaptive closed-loop control process that can autonomously evolve and continuously optimize based on actual feedback data.

[0027] Furthermore, in the energy parameter acquisition module, after receiving the initial input blasting energy parameters through a unified data interface, the energy parameter acquisition module initiates a multi-round data verification process, applies preset data validity rules to perform threshold verification on single-hole charge, number of blasting holes, blasting hole depth and rock hardness, and automatically performs boundary correction and marking on values ​​that exceed the physically reasonable range.

[0028] Subsequently, a data integrity check was initiated. For fields with missing data, spatial interpolation was performed using historical data from the same blasting area, or the missing data was filled in based on the correlation of rock mass hardness. Then, the preprocessed parameter set was subjected to unit unification conversion and standardization to eliminate calculation deviations caused by differences in measurement units. Logical consistency checks between parameters were then introduced.

[0029] Furthermore, in the splash simulation generation module, the range of the blast splash is calculated using the following formula:

[0030]

[0031] in:

[0032] The range of the blast fragments; This is the splash empirical constant; This is the dosage per orifice; This refers to the number of blasting holes; For blasting hole depth; single hole charge , number of blast holes and blast hole depth The product of these represents the total energy input of the blast;

[0033] This refers to the rock mass hardness coefficient. The benchmark rock mass hardness is determined by a correction factor. Adjustments were made to the rock mass hardness coefficient. Increasing the value of the blasting parameters will increase the spatter range. The growth rate of the blasting spatter range with increasing blasting parameters is controlled by the cube root relationship, and the growth rate shows a slowing trend. This spatter empirical constant... Influencing factors include the type of explosive and the quality of the plugging.

[0034] Furthermore, an initial empirical value database is established, and initial values ​​for the spatter empirical constant are generated from this database. During each blasting operation, the measured spatter range is compared with the predicted value from the blasting spatter range formula to calculate the relative error. Then, a recursive least squares method is used to automatically adjust and optimize the initial value of the spatter empirical constant using the relative error data, so that the model's predicted value continuously approaches the measured value. Finally, through iterative learning during blasting operations, the initial value of the spatter empirical constant converges and stabilizes at an optimal value reflecting the current situation. This optimal value is then used as the spatter empirical constant. .

[0035] Furthermore, the risk decision-making and control module will calculate the blast spatter range in real time. The polygon is spatially superimposed on the boundary of the maximum alertable range. The overlap is precisely quantified by calculating the ratio of the intersection area to the splash area. When the calculation system detects that the overlap exceeds the preset allowable threshold, the parameter adjustment mechanism is triggered.

[0036] Based on the constraint optimization algorithm, while satisfying the blast spatter range... Not greater than the maximum warning range Under this rigid constraint, the single-hole charge and the number of blasting holes are used as key decision variables to construct an optimization model with the goal of minimizing the total blasting energy. During the optimization process, the computing system integrates real-time data of related parameters such as rock mass hardness and hole depth, and quickly finds the optimal solution through multiple rounds of iterative calculations. Finally, it outputs a set of optimal blasting energy parameters that simultaneously meet safety constraints and engineering requirements, and automatically updates them to the operation instruction set.

[0037] Furthermore, the overlap threshold is set as follows: The formula for calculating the overlap between the maximum warning range and the blast splash range is:

[0038]

[0039] in:

[0040] Maximum alert range and blast splash range The degree of overlap; Maximum alert range and blast splash range The area of ​​the overlapping region; Explosion splash range The total area;

[0041] like If the system is in a state of alert, the parameter control mechanism will be activated immediately.

[0042] like If the conditions are met, the approved blasting plan will be output.

[0043] Compared with the prior art, the beneficial effects of the present invention are:

[0044] This invention calculates the overlap between the maximum warning range and the blast spatter range in real time. Through spatial overlay analysis, it triggers a constraint optimization algorithm when the overlap exceeds a preset threshold. This algorithm optimizes key parameters such as the charge per hole and the number of holes with the goal of minimizing total blast energy. It comprehensively considers factors such as slope density, the distance between the nearest protected areas, and the impact resistance of the facilities, thereby controlling the energy parameters during blasting. This improves safety and reduces workload. Through the dynamic parameter acquisition and adaptive adjustment mechanism of the warning range processing module, the multi-round data verification of the energy parameter acquisition module, the recursive optimization of the spatter simulation generation module, and the intelligent decision-making of the risk decision control module, it has significant advantages over conventional static blasting calculation systems, including higher calculation accuracy and stronger real-time response. Attached Figure Description

[0045] Figure 1This is a block diagram of the BIM-based adaptive calculation system for open-air bench blasting parameters according to the present invention.

[0046] Figure 2 This is a flowchart illustrating the operation of the BIM-based adaptive calculation system for open-air bench blasting parameters according to the present invention. Detailed Implementation

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

[0048] It should be noted that, unless otherwise defined, the technical or scientific terms used in this invention should have the ordinary meaning understood by one of ordinary skill in the art to which this invention pertains. The terms "first," "second," and similar terms used in this invention do not indicate any order, quantity, or importance, but are merely used to distinguish different components. Terms such as "comprising" or "including" mean that the element or object preceding the word encompasses the elements or objects listed following the word and their equivalents, without excluding other elements or objects. Terms such as "connected" or "linked" are not limited to physical or mechanical connections, but can include electrical connections, whether direct or indirect. Terms such as "upper," "lower," "left," and "right" are used only to indicate relative positional relationships; when the absolute position of the described object changes, the relative positional relationship may also change accordingly.

[0049] Example:

[0050] Please see Figures 1-2 The present invention provides the following technical solutions:

[0051] The BIM-based adaptive calculation system for open-pit bench blasting parameters includes the following modules:

[0052] Warning Range Processing Module: The warning safety benchmark parameters mainly include key information such as slope distribution density, distance between the nearest protected areas, and facility impact resistance indicators. After parameter acquisition is completed, the system immediately performs comprehensive and detailed preprocessing operations on the warning safety benchmark parameters. This preprocessing stage focuses on cleaning up abnormal data points and completely eliminating dimensional differences between parameters to ensure that all parameters are within a uniform and comparable numerical scale. Based on this, the system further constructs and applies the warning range formula to calculate the maximum warning range under the current conditions.

[0053] To enhance the model's adaptability to complex on-site environments, the system continuously and dynamically collects real-time information, including terrain undulations and facility vulnerability, enabling the calculation results to flexibly respond to dynamic changes in actual on-site conditions. Finally, the system automatically generates corresponding boundary images based on the above processing flow, displaying the spatial extent of the safety warning area.

[0054] Collecting baseline safety parameters is the first step in achieving accurate safety assessments. This process utilizes standardized data interfaces to efficiently and automatically acquire parameters from real-time data streams transmitted from various on-site monitoring devices and accumulated historical blasting records. After successfully acquiring the raw parameters, the system initiates a rigorous preprocessing procedure. In the outlier removal phase, the system employs a mature outlier detection algorithm to intelligently identify and remove invalid or extreme data points that significantly deviate from the normal range, thereby effectively improving the overall quality and reliability of the dataset. Next, through normalization techniques, the system unifies the parameters, which originally had different units and dimensions, onto the same numerical scale, laying a consistent data foundation for subsequent modeling and calculations. Finally, based on these thoroughly cleaned and standardized reliable parameters, the system establishes a formula for the warning range used in safety assessments.

[0055] The system continuously and dynamically collects high-precision terrain undulation data and detailed facility vulnerability data. This data is acquired in real time through a sensor network and monitoring equipment deployed on-site, ensuring the accuracy and timeliness of the information source. Utilizing an efficient real-time data stream update mechanism, the system seamlessly injects the latest acquired terrain changes and facility status information into the computational model, thereby continuously fine-tuning and optimizing the model parameters to ensure that the calculation process is always synchronized with the actual on-site conditions. Based on these dynamically adjusted calculation results, the system automatically generates the corresponding safety range boundary image. This image is visualized in a clear and intuitive spatial polygon format.

[0056] The formula for the warning range is:

[0057]

[0058] in:

[0059] This is the maximum vigilance range; It is a proportionality constant. This represents the overall environmental impact, encompassing all factors that are not explicitly captured by other variables in the formula but have a systematic influence on the warning range. These include: atmospheric conditions, geological background, blasting techniques, and the conservatism coefficient.

[0060] This refers to the density of slope distribution. The attenuation coefficient is used to control the attenuation rate of the effect of slope distribution density. It is a natural constant; by introducing an exponential function Slope distribution density The impact on the final result decays exponentially, with a decay coefficient of... Controlling the decay rate, used to increase the marginal benefit of the warning range, results in diminishing returns. At that time, the warning range formula is converted into its original linear form:

[0061]

[0062] The distance between the nearest protected areas; For the impact resistance index of the facility, when switching from a nonlinear formula (considering the slope S) to a linear formula The value will be increased accordingly to compensate for the risk that may be underestimated due to ignoring the slope decay effect;

[0063] When the density of slope distribution As it increases, the exponential function The value decreases, reducing the maximum alert range. . contributions.

[0064] proportionality constant The system is set up through the following process: The proportionality constant k is assigned a reasonable initial value, typically a standardized starting value, to initiate the initial calculation process. Subsequently, the system continuously collects deviation data between the actual observed spatter range and the theoretical value calculated using the warning range formula, utilizing extensive historical blasting data and the latest real-time monitoring results. Based on this accumulated deviation information, the system activates an internal algorithm to calculate a precise adjustment factor and dynamically updates and calibrates the proportionality constant k accordingly, aiming to progressively minimize the model's prediction error.

[0065] This optimization process is not completed in one go. The system will continuously verify and fine-tune the k value in a series of subsequent blasting operations. Through continuous feedback and learning, the proportionality constant k will eventually converge and stabilize at an optimal value that closely matches the current site conditions. This fully optimized k value will ensure that the calculation results of the maximum warning range can accurately adapt to the specific geological and environmental conditions of the construction site.

[0066] The calculation of the adjustment factor is a systematic and rigorous data processing procedure. In this embodiment, the system comprehensively collects detailed records of actual observed splash range data from historical blasting operations and precisely compares them with the initial theoretical range calculated using the warning range formula. Through this comparison, the system generates a temporally continuous deviation data sequence, which clearly reflects the difference between theoretical predictions and actual results. Subsequently, the system assigns different importance weights to these deviation data, strictly adhering to the core principle that the more recent the data, the higher the weight, to ensure that the calculation process can accurately capture and reflect the latest dynamics and trends at the construction site.

[0067] The weighted least squares method, a classic statistical approach, is used to fit curves to the weighted bias sequence. To find the solution that best matches the data pattern, the system constructs a loss function with the core objective of minimizing the sum of squared prediction errors. This function is then iteratively optimized using an algorithm to gradually approach and ultimately determine the optimal adjustment factor that enables the model to make the most accurate predictions.

[0068] An adaptive learning rate is introduced to avoid the optimization process getting stuck in local optima and to improve the convergence speed. The warning range processing module also performs a comprehensive analysis of the statistical characteristics of the bias, including the mean and standard deviation, to assess the overall bias and stability of the model's predictions. The statistical characteristics are then converted into a proportionality constant through a predefined mapping function. The initial adjustment suggestions are compared and tailored with the prior threshold range set by domain experts. The adjustment range is kept within reasonable engineering limits. Finally, the validated adjustment factor is compared with the current scaling constant. In the synthetic calculation example, the proportional constant is updated using an exponentially weighted moving average. The values ​​form an adaptive closed-loop control process that can autonomously evolve and continuously optimize based on actual feedback data.

[0069] Energy Parameter Acquisition Module: Acquires blasting energy parameters directly related to blasting operations, including single-hole charge, number of blasting holes, blasting hole depth, and rock hardness. Inputs the blasting energy parameters into the adaptive calculation system through a unified data interface, controls the standardized format, and uses data templates to filter invalid inputs and perform integrity verification.

[0070] After receiving the initial blasting energy parameters through a unified data interface, the energy parameter acquisition module immediately initiates a complete multi-round data verification process. The system first calls upon preset data validity rules from its built-in engineering experience database and physical law database to rigorously verify the threshold compliance of key parameters such as single-hole charge, number of blasting holes, blasting hole depth, and rock hardness.

[0071] The verification process compares the value of each parameter one by one to ensure it falls within its physically reasonable range. For abnormal values ​​identified as significantly exceeding reasonable boundaries during verification, the system does not simply reject them. Instead, it initiates an automated boundary correction procedure, intelligently adjusting the values ​​based on preset safety upper or lower limits to ensure they return to the valid range. Simultaneously, the system specially marks all corrected or originally abnormal values ​​and generates a detailed data quality report.

[0072] Subsequently, a data integrity check is initiated. For fields identified as having missing data, the system intelligently calls up historical engineering data accumulated in the same blasting area and uses an interpolation algorithm based on the principle of spatial proximity to fill in the missing data. For parameters closely related to rock mass characteristics, an intelligent reasoning method based on the correlation of rock mass hardness is used to complete the data, ensuring the integrity of the dataset.

[0073] The system performs meticulous unit unification and standardization on all preprocessed parameter sets, converting all parameter values ​​to a measurement unit system conforming to international standards. A standardization algorithm is then used to eliminate potential calculation biases caused by inconsistencies in measurement units. Building upon this foundation, the system further introduces a logical consistency verification mechanism between parameters, thoroughly examining the rationality of the inherent logical relationships between each parameter and identifying and eliminating parameter combinations that are impossible in practical engineering scenarios.

[0074] The splash simulation generation module systematically integrates and fuses multiple sets of blast energy parameters collected on-site. Through a built-in professional calculation model, it comprehensively analyzes these key parameters to accurately calculate the potential splash impact range of the blasting operation. After obtaining precise splash range data, the module immediately initiates a spatial processing flow, converting the abstract calculation results into a concrete graphical representation and automatically generating a corresponding polygonal boundary map. This boundary map presents the splash area in a clear and intuitive spatial form and provides visual output through the system interface.

[0075] The blast radius is calculated using the following formula:

[0076]

[0077] in:

[0078] The range of the blast fragments; Let be the splash empirical constant, the splash empirical constant that is stably convergent. The value represents that the working face has reached a state of technological stability and controllable risk, and quantifies the efficiency of converting blasting energy into splash kinetic energy;

[0079] To determine the dosage per orifice, the dosage per orifice is reduced during parameter adjustment in the risk decision module. Used to reduce energy intensity;

[0080] The number of blasting holes is determined by the interactions between the holes, namely stress superposition and rock clamping. The actual contribution to the blast spatter range F is sublinear, and the number of blast holes is adjusted accordingly. This is to avoid the cumulative risks that may be caused by detonating too many blast holes at the same time;

[0081] The depth of the blasting hole determines the path and constraints of the propagation and action of the explosion energy in the rock mass;

[0082] Single-hole dosage , number of blast holes and blast hole depth The product represents the total energy input of the blast, and the number of blast holes. and blast hole depth They jointly defined the resistance characteristics of rock masses in converting blasting energy into splash kinetic energy;

[0083] This refers to the rock mass hardness coefficient. The benchmark rock mass hardness is determined by a correction factor. Adjustments were made to the rock mass hardness coefficient. Increasing the value of the blasting parameters will increase the spatter range. The growth rate of the blasting spatter range with increasing blasting parameters is controlled by the cube root relationship, and the growth rate shows a slowing trend. This spatter empirical constant... Influencing factors include the type of explosive and the quality of the plugging.

[0084] By integrating industry standard data and historical blasting cases, a comprehensive database of initial empirical values ​​is constructed. Based on this database, the system can intelligently generate initial values ​​for spatter empirical constants applicable to the current geological and blasting conditions. During each subsequent blasting operation, the system synchronously collects actual measured spatter range data and precisely compares it with the predicted value derived from the blasting spatter range formula. Through this comparison, the system automatically calculates the relative error between the predicted and measured values.

[0085] Subsequently, the system employs the efficient online parameter estimation algorithm, recursive least squares, to automatically and continuously adjust and dynamically optimize the initial value of the splash empirical constant using real-time acquired relative error data. This process allows the model's predicted output to continuously approach the actual splash range as practical data accumulates. Through iterative learning and feedback from multiple blasting operations, the value of the splash empirical constant gradually converges and eventually stabilizes at an optimal value that accurately reflects the complex working conditions at the current site. The system then formally adopts this optimal value, verified through practice, as the splash empirical constant. .

[0086] The risk decision-making and control module performs precise spatial overlay analysis between the polygonal region of the blasting spatter range F, calculated in real time, and the maximum warning range boundary determined by the model. This analysis utilizes professional geographic information system (GIS) technology, precisely quantifying the degree of spatial overlap by calculating the ratio of the area of ​​the intersection of the two polygonal regions to the total area of ​​the blasting spatter range F itself. When the calculation system detects that the calculated overlap value exceeds the preset engineering safety threshold through this rigorous analysis process, it immediately identifies this as a potential risk exceeding the limit and automatically triggers the built-in parameter control mechanism, initiating an optimization and adjustment process for key blasting operation parameters to proactively bring the safety risk back under control within an acceptable range.

[0087] The real-time calculated blast spatter range The polygon is spatially superimposed on the boundary of the maximum alertable range. The overlap is precisely quantified by calculating the ratio of the intersection area to the splash area. When the calculation system detects that the overlap exceeds the preset allowable threshold, the parameter adjustment mechanism is triggered.

[0088] After the parameter control mechanism is triggered, the system will make intelligent decisions based on a mature constraint optimization algorithm. This algorithm uses the inviolable safety requirement that the blast spatter range F does not exceed the maximum warning range A as its core constraint, and identifies the single-hole charge and the number of blasting holes—the two factors most significantly affecting blast energy—as core decision variables. The system then constructs a mathematical programming model with the explicit objective of minimizing the total blast energy for precise solution.

[0089] Throughout the optimization process, the computing system integrates and fuses the latest field data on key related parameters such as rock mass hardness and blasting hole depth in real time, continuously approximating the optimal solution through efficient multi-round iterative calculations. Ultimately, the system outputs a set of precisely calculated optimal blasting energy parameters. This system automatically updates the current blasting operation's instruction set with these determined optimal parameters, enabling intelligent dynamic adjustment of the operation plan.

[0090] Set the overlap threshold to The formula for calculating the overlap between the maximum warning range and the blast splash range is:

[0091]

[0092] in:

[0093] Maximum alert range and blast splash range The degree of overlap; Maximum alert range and blast splash range The area of ​​the overlapping region; Explosion splash range The total area represents the overall risk level of this blasting operation.

[0094] when This means the splash area falls entirely within the safe zone, with an extremely low probability of risk; when This means that the splash area is completely covered by the danger zone. Once a splash occurs, it will inevitably pose a threat to the protected target, and the probability of an accident is extremely high.

[0095] like If the system is in a state of alert, the parameter control mechanism will be activated immediately.

[0096] like If the conditions are met, the approved blasting plan will be output.

[0097] The above formulas are all dimensionless calculations. The formulas are derived from software simulations based on a large amount of collected data to obtain the most recent real-world results. The preset parameters in the formulas are set by those skilled in the art according to the actual situation.

[0098] The above embodiments can be implemented, in whole or in part, by software, hardware, firmware, or any other combination thereof. When implemented in software, the above embodiments can be implemented, in whole or in part, as a computer program product. Those skilled in the art will recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented by electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution.

[0099] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment, depending on actual needs.

[0100] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any changes or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application.

Claims

1. A BIM-based adaptive calculation system for open-pit bench blasting parameters, characterized in that, Includes the following modules: Warning range processing module: Collects warning safety benchmark parameters, including slope distribution density, distance between nearest protection areas and facility impact resistance index. Preprocesses the warning safety benchmark parameters, including cleaning outliers and eliminating dimensional differences. Establishes warning range formula, calculates the maximum warning range, and dynamically collects terrain undulation and facility vulnerability to adapt the calculation results to changes on site. Energy parameter acquisition module: Acquires blasting energy parameters directly related to blasting operations, including single-hole charge, number of blasting holes, blasting hole depth, and rock hardness. Inputs the blasting energy parameters into the adaptive calculation system through a unified data interface, controls the standardized format, and uses data templates to filter invalid inputs and perform integrity verification. The splash simulation generation module integrates and processes multiple sets of collected blast energy parameters, calculates and outputs the blast splash range, and generates a polygonal boundary map based on the output results, presenting it in the form of spatial range for visualization. Risk Decision Control Module: Based on the obtained maximum warning range and blasting splash range, it performs dynamic comparison and decision optimization, detects the overlap between the maximum warning range and the blasting splash range, sets an allowable overlap threshold, and if the overlap exceeds the overlap threshold, it activates the blasting energy parameter control mechanism to calculate the minimum single-hole charge and number of blasting holes under the condition that the blasting splash range does not exceed the maximum warning range.

2. The BIM-based adaptive calculation system for open-pit bench blasting parameters according to claim 1, characterized in that: In the warning range processing module, warning safety benchmark parameters are collected. The collection process is carried out by obtaining data from on-site monitoring data sources and historical records through a standardized data interface. Then, preprocessing is performed. Among them, outlier cleaning uses an outlier detection algorithm to identify and remove invalid or extreme data points. Normalization is used to eliminate dimensional differences and unify parameter scales. The warning range formula is established based on the preprocessed parameters. Simultaneously, terrain undulation data and facility vulnerability data are dynamically collected, and the calculation model is continuously adjusted using a real-time data stream update mechanism. Based on the calculation results, a range boundary image is generated and visualized as a spatial polygon.

3. The BIM-based adaptive calculation system for open-pit bench blasting parameters according to claim 2, characterized in that, The formula for the warning range is: ， in: This is the maximum vigilance range; It is a proportionality constant; This refers to the density of slope distribution. The attenuation coefficient is used to control the attenuation rate of the effect of slope distribution density. It is a natural constant; by introducing an exponential function Slope distribution density The impact on the final result decays exponentially, with a decay coefficient of... Controlling the decay rate, used to increase the marginal benefit of the warning range, results in diminishing returns. At that time, the warning range formula is converted into its original linear form: ， This refers to the distance between the nearest protected areas; For the impact resistance index of the facility; When the density of slope distribution As the exponential function increases, The value decreases, reducing the maximum alert range. . contributions.

4. The BIM-based adaptive calculation system for open-pit bench blasting parameters according to claim 3, characterized in that, The proportionality constant Set the proportional constant through the following process: The initial value is 1. Then, based on historical blasting data and real-time monitoring results, the deviation data between the actual splash range and the calculated value of the warning range are collected, the adjustment factor is calculated, and the proportional constant is dynamically updated. To minimize prediction errors, and through continuous verification and fine-tuning in subsequent blasting operations, the proportionality constant was ultimately achieved. Stabilizing at the optimal value ensures that the calculation of the maximum alertable range is accurately adapted to specific on-site conditions.

5. The BIM-based adaptive calculation system for open-pit bench blasting parameters according to claim 4, characterized in that, The calculation process of the adjustment factor is as follows: the actual observed splash range recorded in historical blasting operations is compared with the initial theoretical range calculated by the formula to generate a continuous deviation sequence. Then, weights are assigned to these deviation data. When assigning weights, the principle of the more recent the time is followed to reflect the changing trend on site. The weighted least squares method is used to fit the deviation sequence and a loss function with the goal of minimizing the sum of squared prediction errors is constructed for iterative optimization to find the optimal solution. An adaptive learning rate is introduced to avoid the optimization process getting stuck in local optima and to improve the convergence speed. The warning range processing module also performs a comprehensive analysis of the statistical characteristics of the bias, including the mean and standard deviation, to assess the overall bias and stability of the model's predictions. The statistical characteristics are then converted into a proportionality constant through a predefined mapping function. The initial adjustment suggestions are compared and tailored with the prior threshold range set by domain experts. The adjustment range is kept within reasonable engineering limits. Finally, the validated adjustment factor is compared with the current scaling constant. In the synthetic calculation example, the proportional constant is updated using an exponentially weighted moving average. The values ​​form an adaptive closed-loop control process that can autonomously evolve and continuously optimize based on actual feedback data.

6. The BIM-based adaptive calculation system for open-pit bench blasting parameters according to claim 1, characterized in that: In the energy parameter acquisition module, after receiving the initial input blasting energy parameters through a unified data interface, the energy parameter acquisition module starts a multi-round data verification process, applies preset data validity rules to perform threshold verification on single hole charge, number of blasting holes, blasting hole depth and rock hardness, and automatically performs boundary correction and marking on values ​​that exceed the physical reasonable range. Subsequently, a data integrity check was initiated. For fields with missing data, spatial interpolation was performed using historical data from the same blasting area, or the missing data was filled in based on the correlation of rock mass hardness. Then, the preprocessed parameter set was subjected to unit unification conversion and standardization to eliminate calculation deviations caused by differences in measurement units. Logical consistency checks between parameters were then introduced.

7. The BIM-based adaptive calculation system for open-pit bench blasting parameters according to claim 1, characterized in that: In the splash simulation generation module, the range of the blast splash is calculated using the following formula: ， in: The range of the blast fragments; This is the splash empirical constant; This is the dosage per orifice; This refers to the number of blasting holes; For blasting hole depth; single hole charge , number of blast holes and blast hole depth The product of these represents the total energy input of the blast; This refers to the rock mass hardness coefficient. The benchmark rock mass hardness is determined by a correction factor. Adjustments were made to the rock mass hardness coefficient. Increasing the value of the blasting parameters will increase the spatter range. The growth rate of the blasting spatter range with increasing blasting parameters is controlled by the cube root relationship, and the growth rate shows a slowing trend. This spatter empirical constant... Influencing factors include the type of explosive and the quality of the plugging.

8. The BIM-based adaptive calculation system for open-pit bench blasting parameters according to claim 7, characterized in that: An initial empirical value database is established, and initial values ​​for the spatter empirical constant are generated from this database. During each blasting operation, the measured spatter range is compared with the predicted value using the blast spatter range formula to calculate the relative error. Then, a recursive least squares method is used to automatically adjust and optimize the initial values ​​of the spatter empirical constant using the relative error data, so that the model's predicted value continuously approaches the measured value. Finally, through iterative learning during blasting operations, the initial values ​​of the spatter empirical constant converge and stabilize at an optimal value reflecting the current situation. This optimal value is then used as the spatter empirical constant. .

9. The BIM-based adaptive calculation system for open-pit bench blasting parameters according to claim 1, characterized in that: The risk decision-making and control module will calculate the blast spatter range in real time. Spatial overlay analysis is performed between the polygon and the boundary of the maximum alertable range, and the degree of overlap is accurately quantified by calculating the ratio of the intersection area to the splash area. When the computing system detects that the overlap exceeds the preset allowable threshold, it triggers the parameter adjustment mechanism; Based on the constraint optimization algorithm, while satisfying the blast spatter range... Not greater than the maximum warning range Under this rigid constraint, the single-hole charge and the number of blasting holes are used as key decision variables to construct an optimization model with the goal of minimizing the total blasting energy. During the optimization process, the computing system integrates real-time data of related parameters such as rock mass hardness and hole depth, and quickly finds the optimal solution through multiple rounds of iterative calculations. Finally, it outputs a set of optimal blasting energy parameters that simultaneously meet safety constraints and engineering requirements, and automatically updates them to the operation instruction set.

10. The BIM-based adaptive calculation system for open-pit bench blasting parameters according to claim 9, characterized in that: Set the overlap threshold to The formula for calculating the overlap between the maximum warning range and the blast splash range is: ， in: Maximum alert range and blast splash range The degree of overlap; Maximum alert range and the range of blast fragments The area of ​​the overlapping region; Explosion splash range The total area; like If the system is in a state of alert, the parameter control mechanism will be activated immediately. like If the conditions are met, the approved blasting plan will be output.

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