Rock-fill dam-level burdening blasting mining method, system and device and medium

Abstract

The invention discloses a rock-fill dam-level burdening blasting mining method, system and device and a medium. The method comprises the following steps that firstly, blasting parameters are intelligently designed; step 2, dynamically controlling the drilling precision; 3, implementing a coupling charging structure; step 4, accurately detonating the electronic detonator; step 5, grading online detection after explosion; and 6, blasting vibration is monitored and fed back in real time. According to the method, a blasting parameter mathematical model is constructed, multiple parameters and constraint conditions are integrated, accurate parameters are output, and secondary machining of stone is reduced; a distributed optical fiber sensing network is arranged, and fine control over blasting vibration is achieved; an unmanned aerial vehicle is used for scanning a muck pile, a compensation scheme is automatically generated, the loading and transporting efficiency is improved, meanwhile, a multi-parameter coupling model is established, a dynamic adjustment scheme is monitored and fed back in real time, the management fragmentation problem is solved, and multiple measures are adopted to improve the accuracy and safety of blasting mining.

Description

Technical Field

[0001] The present invention relates to the field of water conservancy and hydropower engineering construction, and in particular to a rockfill dam-grade batching blasting mining method, system, device and medium. Background Art

[0002] In the construction of water conservancy and hydropower projects, the demand for rockfill dam-grade materials usually reaches millions of cubic meters. The efficiency and quality control of blasting mining directly affect the project cost and progress. Traditional blasting mining methods have the following technical bottlenecks: 1. Rough gradation control: The design of blasting parameters relies on empirical formulas, which makes it difficult to accurately match the particle size requirements of the main rock pile, secondary rock pile, and transition material. As a result, about 30%-40% of the rock needs to be processed twice, which significantly increases the project cost.

[0003] 2. Vibration and safety risks: The lack of refined control over blasting vibration can easily threaten the stability of surrounding buildings and slopes, requiring frequent adjustments to the construction plan.

[0004] 2. The morphology of the explosive pile is uncontrollable: Traditional methods make it difficult to predict the expansion direction and looseness of the explosive pile, resulting in low loading efficiency and affecting construction continuity.

[0005] Although existing technologies have introduced computer-aided design to optimize blasting parameters, they have the following limitations: 1. Single parameter optimization: Only focusing on single variables such as hole grid parameters or charge amount, without establishing a multi-parameter coupling model, it is difficult to achieve coordinated optimization of gradation distribution, vibration control and explosive pile morphology.

[0006] 2. Static design defects: The lack of a dynamic feedback mechanism makes it impossible to adjust the plan in real time according to changes in geological conditions, resulting in a disconnect between design and implementation.

[0007] 3. Management fragmentation: Construction monitoring data is isolated from the blasting design module, making it difficult to support quality control throughout the entire process. Therefore, a rockfill dam-level batching blasting mining method, system, device, and medium are proposed. Summary of the Invention

[0008] In view of the deficiencies in the prior art, the present invention provides a rockfill dam-grade batching blasting mining method, system, device and medium to solve the problems raised in the above background technology.

[0009] To achieve the above object, the present invention provides the following technical solution: a rockfill dam-grade batching blasting mining method, comprising the following steps: Step 1: Intelligent design of blasting parameters: Based on the target gradation curve, a mathematical model of blasting parameters is established by computer. The rock physical and mechanical parameters, explosive performance parameters and constraints are input, and the hole network parameters, unit consumption, charge structure and detonation sequence are output. The constraints include the maximum allowable vibration velocity, blast pile expansion angle and target gradation range. Step 2: Dynamic control of drilling accuracy: A 3D laser scanner is used to verify the blasthole position in real time. When the hole position deviation exceeds ±5cm, an automatic correction program is triggered and the drilling robot arm adjusts the drill rod angle. Step 3: Implementation of coupled charge structure: A flexible buffer layer is set at the bottom of the blasthole, and an axial interval charging structure is adopted. The interval section is filled with a mixture of water bags and rock powder. The ratio of charging length to hole depth is controlled between 0.65-0.75. Step 4: Precise detonation of electronic detonator: A hole-by-hole micro-difference initiation network was established. The initiation time difference Δt was dynamically calculated according to the hole spacing L as Δt=0.8L-1.2L (ms). The first blast hole was selected as the blast hole located in the geometric center of the blasting area. Step 5: Online detection of gradation after blasting: Use drones equipped with multispectral cameras to scan the blast pile, identify the particle size distribution through image segmentation algorithms, and automatically generate a compensatory blasting plan when the critical sieve residue exceeds the preset range; Step 6: Real-time monitoring and feedback of blasting vibration: A distributed fiber optic sensor network is deployed in the blasting area to monitor the vibration speed, frequency, and propagation path in real time. The monitoring data is input into the intelligent design module through a wireless transmission module for iterative parameter optimization. The improved BP neural network algorithm collects at least 50 sets of historical blasting record data as training sets. Each set of data contains rock uniaxial compressive strength, explosive consumption, hole spacing, row spacing and corresponding sieve residue. After data preprocessing and feature selection, the data is input into the model for iterative training until the model prediction error is less than 5%. Through the intelligent design of blasting parameters, the target gradation curve can be accurately matched, significantly improving the blasting effect and resource utilization; dynamic control of drilling accuracy ensures the accurate position of the blasthole and reduces the safety hazards caused by deviation; the coupled charging structure implements the optimized charging method, improves the blasting energy utilization, and reduces flying rocks and vibration; the precise detonation of electronic detonators realizes micro-difference blasting and reduces the impact on the surrounding environment; the post-blast gradation online detection and compensation mechanism ensures the gradation quality and reduces the subsequent processing costs; the real-time monitoring and feedback of blasting vibration form a closed-loop control, continuously optimizes the blasting parameters, and improves the safety and efficiency of blasting operations as a whole.

[0010] Preferably, the blasting parameter mathematical model adopts an improved BP neural network algorithm, and the training data set includes a mapping relationship between the uniaxial compressive strength of rock, explosive consumption per unit, hole spacing, row spacing and corresponding screen residue in historical blasting records; The specific implementation method for the blasting parameter mathematical model is as follows: first, historical blasting records are collected, including parameters such as rock uniaxial compressive strength, explosive consumption, hole spacing, row spacing, and corresponding screen residue data; then, the data is preprocessed, such as normalization, to meet the input requirements of the BP neural network; then, an improved BP neural network algorithm is used, such as adding momentum terms and adaptive learning rate optimization strategies, to construct a mathematical model; finally, the model is trained using the processed data set, and the network weights are adjusted until the model output meets the target gradation curve requirements; The improved BP neural network algorithm is used to construct a mathematical model of blasting parameters, which can make full use of the complex nonlinear relationships in historical blasting data and achieve accurate prediction of blasting parameters by automatically learning and adjusting network parameters. The model not only takes into account the physical and mechanical parameters of rocks and the performance parameters of explosives, but also incorporates constraints such as the maximum allowable vibration velocity and the blast pile expansion angle, making the generated blasting plan more scientific and reasonable. In addition, the model has dynamic optimization capabilities and can be iteratively optimized based on real-time monitoring data to ensure that the blasting effect continues to meet the target requirements, significantly improving the efficiency and safety of blasting mining.

[0011] Preferably, the flexible buffer layer is made of a composite of polyurethane foam and rubber particles, and has a thickness of 15%-20% of the pore diameter; The thickness of the required flexible buffer layer is calculated based on the diameter of the blasthole (15%-20% of the blasthole diameter). Subsequently, polyurethane foam and rubber particles are evenly mixed in a predetermined ratio. Through mold pressing or on-site pouring, a uniform and elastic flexible buffer layer is formed at the bottom of the blasthole to absorb blasting energy and reduce impact damage to the hole wall. The flexible buffer layer is made of a composite of polyurethane foam and rubber particles, and its thickness is designed to be 15%-20% of the aperture. This design can effectively absorb the impact energy generated during the blasting process, reduce the direct impact on the borehole wall, thereby protecting the stability of the borehole wall structure and reducing the damage to the surrounding rock mass caused by blasting. At the same time, the buffer layer can also improve the blasting effect, make the blast pile shape more uniform, and contribute to the subsequent grading control and mining efficiency improvement. In addition, the use of composite materials improves the durability and adaptability of the buffer layer, and can maintain stable performance under different geological conditions, providing a strong guarantee for the safety and economy of blasting operations.

[0012] Preferably, the hole-by-hole differential initiation network adopts a dual-ring redundancy design. When a circuit breaker fault occurs in the primary network, the backup network automatically switches, and the automatic switching time is controlled within 5ms. The dual-ring redundancy design is achieved by arranging two independent initiation networks in parallel. Each network includes independent electronic detonators, conductors, and control units. When the primary network detects a circuit breaker fault, a high-speed switching device (switching time < 5ms) automatically activates the backup network to ensure uninterrupted initiation signals and guarantee the safety and reliability of blasting operations. The hole-by-hole micro-difference initiation network with a dual-ring redundant design significantly improves the safety and stability of blasting operations. When the primary network fails, the backup network can quickly take over, and the automatic switching time is controlled within an extremely short 5ms, with almost no impact on the initiation sequence, effectively avoiding blasting failures or safety accidents caused by network failures. This design not only enhances the system's fault tolerance, but also improves the continuity and efficiency of blasting operations, providing reliable technical support for blasting projects in complex environments.

[0013] Preferably, the compensation blasting scheme includes local additional drilling parameters and corresponding charge amount, and the additional drilling position is determined by analyzing the deviation between the blast pile three-dimensional point cloud model and the target gradation curve; After the blast pile 3D point cloud model is established, an image processing algorithm is used to compare the target gradation curve and identify areas of particle size distribution deviation. Based on the deviation analysis results, an intelligent algorithm is used to calculate the optimal location, depth, diameter, and corresponding charge of local additional drill holes to ensure that the blast pile gradation after compensation blasting meets the design requirements. Through precise deviation analysis between the blast pile 3D point cloud model and the target gradation curve, dynamic adjustment and optimization of the blasting effect is achieved. This method not only improves the accuracy of blasting operations, but also significantly enhances the controllability of the construction process. It can quickly respond to actual on-site conditions and effectively compensate for the shortcomings of the previous blasting through local additional drilling and precise charging, ensuring that the final blast pile gradation meets the project requirements, thereby improving overall construction efficiency and quality and reducing the risk of rework due to gradation mismatch.

[0014] Preferably, the monitoring data of the distributed optical fiber sensor network is fed back to the intelligent design module in real time through the wireless transmission module, triggering the dynamic optimization algorithm of blasting parameters; The wireless transmission module uses LoRa communication technology, operating at a frequency of 433MHz and a data transmission rate of 10kbps, ensuring stable transmission of monitoring data to the intelligent design module. The dynamic optimization algorithm for blasting parameters is based on a particle swarm optimization algorithm, which iteratively adjusts blasting parameters with the goal of minimizing the deviation between the post-blasting gradation and the target gradation. The distributed fiber optic sensing network can monitor blasting vibrations in real time and quickly feed back data to the intelligent design module through the wireless transmission module, enabling dynamic optimization of blasting parameters. This helps to adjust blasting parameters in a timely manner according to actual vibration conditions, improve blasting effects, and ensure that grading meets requirements. At the same time, the intelligent design module can quickly respond to data changes, improve work efficiency, reduce manual intervention, make blasting operations more accurate and safe, and enhance the overall project quality and benefits.

[0015] Preferably, the multispectral camera has a wavelength range of 400-1000 nm and is equipped with an automatic gain control module to adapt to different lighting conditions; The multispectral camera uses a high-precision optical filter set to achieve 400-1000nm wavelength coverage. The built-in photosensor monitors the ambient light intensity in real time. The microprocessor drives the variable gain amplifier to dynamically adjust the signal amplification factor according to the light intensity to ensure stable image signal output. It is also equipped with an automatic exposure algorithm to optimize image quality. The wide wavelength range ensures that the camera can capture the reflectance spectral characteristics of rocks of different particle sizes, providing rich information for the image segmentation algorithm and effectively distinguishing different particle size distributions. The automatic gain control module enables the camera to operate stably under various lighting conditions, avoiding overexposure or underexposure, ensuring image quality, and thus improving the reliability of grading detection. This design not only enhances the environmental adaptability of the detection system, but also provides a solid foundation for the generation of subsequent compensatory blasting plans by improving data accuracy, which helps to achieve more precise control of blasting effects.

[0016] The rockfill dam-grade batching blasting mining system includes: An intelligent design module, which is used for parameter calculation and solution generation of the rockfill dam-grade batching blasting mining method; Construction management module, integrating drilling machinery navigation subsystem, charge monitoring subsystem and detonation safety verification subsystem; The effect evaluation module includes a blast pile morphology analysis unit, a gradation detection unit, and a vibration monitoring unit. Data exchange between these modules is achieved through industrial Ethernet, forming a closed-loop control link between design, construction, and evaluation. The intelligent design module incorporates an improved BP neural network algorithm, which automatically learns the mapping relationship between rock parameters, explosive properties, and blasting effects by training historical blasting data. Based on the target gradation curve, it dynamically generates blasting parameters and construction plans. The construction management module uses a high-precision positioning system and sensor network to achieve precise control of drilling, charging, and detonation. The effect evaluation module utilizes drones and fiber optic sensing technology to monitor and analyze the blast pile morphology, gradation distribution, and vibration in real time to ensure that the blasting effect meets expectations. The system for rockfill dam-level batching blasting and mining significantly improves the accuracy and efficiency of blasting operations by integrating three modules: intelligent design, construction management, and effect evaluation. The intelligent design module uses advanced algorithms to optimize blasting parameters and reduce trial-and-error costs; the construction management module ensures construction safety and quality through real-time monitoring and precise control; and the effect evaluation module provides timely feedback to guide subsequent operation adjustments. The modules are closely connected through industrial Ethernet to form a closed-loop control link, realizing the intelligent, refined, and efficient blasting operations and providing strong support for rockfill dam construction.

[0017] The equipment for rockfill dam-grade batching blasting mining includes: Multi-degree-of-freedom drilling robot arm equipped with a dual-axis inclination sensor and hydraulic correction mechanism; Intelligent loading vehicle, integrating automatic drug roll conveying, water bag filling and density detection functions; Electronic detonator programmer, supporting RFID tag reading and writing and detonation sequence programming; The rockfill dam-level batching blasting mining device realizes collaborative operation and generates operation data through the vehicle-mounted controller, and the operation data is uploaded to the rockfill dam-level batching blasting mining system in real time; A dual-axis inclination sensor monitors the drilling angle in real time, and a hydraulic correction mechanism automatically adjusts the drill rod direction based on sensor data to ensure drilling accuracy. The intelligent charging vehicle uses a robotic arm to automatically transport explosive rolls, has a built-in water bag filling device, and is equipped with a density detection sensor to monitor charge density in real time. The electronic detonator programmer uses wireless radio frequency identification technology to quickly read and write tag information and accurately program the detonation sequence. All devices are centrally scheduled by the onboard controller, working collaboratively and uploading operation data to the management system in real time. The device for rockfill dam-level batching blasting and mining realizes the automation and intelligence of blasting operations by integrating a multi-degree-of-freedom drilling robot arm, an intelligent charging vehicle and an electronic detonator programmer. The dual-axis inclination sensor and hydraulic correction mechanism equipped with the drilling robot arm ensure the accuracy of drilling, the intelligent charging vehicle improves the charging efficiency and density control capability, and the electronic detonator programmer realizes the precise programming of the detonation sequence through wireless radio frequency identification technology, enhancing the safety and controllability of blasting operations. These devices work together through the on-board controller and upload operation data in real time, providing strong support for the optimization of blasting parameters and significantly improving the quality and efficiency of blasting operations.

[0018] A computer-readable storage medium having a computer program stored thereon, wherein when the computer program is executed by a processor, the steps of the rockfill dam-grade batching blasting mining method are implemented, including: Blasting parameter optimization algorithm: This module is used to establish a mathematical model of blasting parameters based on the target gradation curve. This model uses an improved BP neural network algorithm as input, including rock physical and mechanical parameters, explosive performance parameters, and constraints (including maximum allowable vibration velocity, blast pile expansion angle, and target gradation interval). It outputs hole network parameters, unit consumption, charge structure, and detonation sequence. This module uses the mapping relationship between rock uniaxial compressive strength, explosive unit consumption, hole spacing, row spacing, and corresponding screen residue from historical blasting records as a training data set. Real-time blasthole position verification module: Integrates a 3D laser scanner data interface for real-time verification of blasthole positions. When the hole position deviation exceeds ±5cm, an automatic correction program is triggered to adjust the drill rod angle by controlling the drilling robot arm. Coupled Charge Structure Setup Module: Contains instructions for setting a flexible buffer layer made of a composite of polyurethane foam and rubber particles at the bottom of the blasthole (thickness 15%-20% of the hole diameter), using an axially spaced charge structure, with the spacers filled with a mixture of water bags and rock dust, and controlling the charge length to hole depth ratio between 0.65-0.75; Electronic detonator precision initiation control module: This module is used to establish a hole-by-hole micro-difference initiation network. The initiation time difference Δt is dynamically calculated based on the hole spacing L as Δt = 0.8L-1.2L (ms), and the blasthole located at the geometric center of the blasting area is selected as the first blasting hole. This module supports a dual-ring redundancy design. When a circuit breaker fault occurs on the primary network, the backup network automatically switches within 5ms. Post-blast gradation online detection and compensation module: This module integrates the data interface of a multispectral camera (wavelength range: 400-1000nm, equipped with an automatic gain control module) mounted on a drone to scan the blast pile and identify the particle size distribution using an image segmentation algorithm. When the critical screen residue exceeds the preset range, a compensation blasting plan is automatically generated, including local additional drilling parameters and corresponding charge amount. The additional drilling location is determined by analyzing the deviation between the 3D point cloud model of the blast pile and the target gradation curve. Blasting vibration real-time monitoring and feedback module: connected to the distributed optical fiber sensing network, used for real-time monitoring of vibration speed, frequency and propagation path. The monitoring data is fed back to the blasting parameter optimization algorithm module in real time through the wireless transmission module, triggering the parameter dynamic optimization algorithm; System integration and data interaction module: supports integration with intelligent design module, construction management module (integrated drilling machinery navigation subsystem, charge monitoring subsystem and detonation safety verification subsystem), and effect evaluation module (including explosive pile morphology analysis unit, gradation detection unit and vibration monitoring unit). Data interaction is achieved between modules through industrial Ethernet, forming a design-construction-evaluation closed-loop control link. Explosion pile gradation identification model; Compensation blasting plan generation logic; The computer program supports both offline operation and cloud-based collaborative computing modes, ensuring flexible application and data sharing in different environments; Further clarify the parameter settings of the improved BP neural network algorithm, such as the number of network layers, activation function, and number of training iterations. For example, a three-layer network structure (input layer, hidden layer, output layer) is adopted, the hidden layer uses the ReLU activation function, and the number of training iterations is set to 1000 to ensure model convergence and good generalization ability; The computer program on the computer-readable storage medium realizes the intelligent and precise blasting and mining of rockfill dam-grade materials by integrating modules such as blasting parameter optimization, blasthole position verification, coupled charge structure setting, precise detonation control of electronic detonators, online detection and compensation of post-blasting gradation, and real-time monitoring and feedback of blasting vibration. This solution not only improves blasting efficiency and quality, ensures that the blasting effect meets the target gradation requirements, but also effectively reduces the impact of blasting vibration on the surrounding environment through real-time monitoring and feedback mechanisms, ensuring construction safety. At the same time, the design of system integration and data interaction modules promotes the close connection between design, construction and evaluation links, forms a closed-loop control link, and further improves the overall efficiency and management level of blasting operations.

[0019] In summary, compared with the prior art, the present invention provides a rockfill dam-grade batching blasting mining method, system, device, and medium, which have the following beneficial effects: This invention uses a method to construct a blasting parameter mathematical model based on the target gradation curve. The model comprehensively considers the physical and mechanical parameters of the rock, the performance parameters of the explosive, and the constraints such as the maximum allowable vibration velocity, the blast pile expansion angle, and the target gradation interval, and outputs accurate hole network parameters, unit consumption, charge structure, and detonation sequence. It achieves the purpose of accurately matching the particle size requirements of the main rock pile, secondary rock pile, and transition material, reduces the need for secondary processing of the stone, and reduces engineering costs. A distributed fiber optic sensor network is deployed in the blasting area to monitor the blasting vibration speed, frequency, and propagation path in real time. The monitoring data is then input into the intelligent design module via a wireless transmission module for iterative parameter optimization. This achieves refined control of blasting vibration, effectively reducing threats to surrounding buildings and slope stability, and reducing the frequency of frequent adjustments to construction plans. Using a drone-mounted multispectral camera to scan the blast pile and identify the particle size distribution through an image segmentation algorithm, a compensatory blasting plan is automatically generated when the critical screen residue exceeds the preset range. This can more accurately predict the expansion direction and looseness of the blast pile, improve loading efficiency, and ensure construction continuity. This method breaks through the limitations of single-parameter optimization. By establishing a multi-parameter coupling model, it achieves coordinated optimization of gradation distribution, vibration control, and blast pile morphology. It overcomes static design flaws and, with the help of real-time monitoring and feedback mechanisms, dynamically adjusts the plan based on changing geological conditions, avoiding a disconnect between design and implementation. It also addresses the problem of fragmented management by closely integrating construction monitoring data with the blasting design module, supporting quality control throughout the entire process. Furthermore, by using a 3D laser scanner to verify blasthole positions in real time and trigger an automatic correction program, as well as by installing a flexible buffer layer at the bottom of the blasthole, employing an axially spaced charge structure and controlling the charge length to hole depth ratio, establishing a hole-by-hole micro-difference initiation network, and rationally selecting the first blasthole, the accuracy and safety of blasting mining have been further improved. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] Figure 1 It is a step diagram of the rockfill dam-grade batching blasting mining method of the invention.

[0021] Figure 2 It is a schematic diagram of the system for rockfill dam-grade batching blasting mining of the invention.

[0022] Figure 3 It is a schematic diagram of the device for rockfill dam-grade batching blasting mining of the invention. DETAILED DESCRIPTION

[0023] The present invention provides a technical solution, a rockfill dam-grade batching blasting mining method, please refer to Figure 1 、 Figure 2 and Figure 3 , including the following steps: Step 1: Intelligent design of blasting parameters: Based on the target gradation curve, a mathematical model of blasting parameters is established by computer. The rock physical and mechanical parameters, explosive performance parameters and constraints are input, and the output is the hole network parameters, unit consumption, charge structure and detonation sequence. The constraints include the maximum allowable vibration velocity, blast pile expansion angle and target gradation range. Step 2: Dynamic control of drilling accuracy: A 3D laser scanner is used to verify the blasthole position in real time. When the hole position deviation exceeds ±5cm, an automatic correction program is triggered and the drilling robot arm adjusts the drill rod angle. Step 3: Implementation of coupled charge structure: A flexible buffer layer is set at the bottom of the blasthole, and an axial interval charging structure is adopted. The interval section is filled with a mixture of water bags and rock powder. The ratio of charging length to hole depth is controlled between 0.65-0.75. Step 4: Precise detonation of electronic detonator: A hole-by-hole micro-difference initiation network was established. The initiation time difference Δt was dynamically calculated according to the hole spacing L as Δt=0.8L-1.2L (ms). The first blast hole was selected as the blast hole located in the geometric center of the blasting area. Step 5: Online detection of gradation after blasting: Use drones equipped with multispectral cameras to scan the blast pile, identify the particle size distribution through image segmentation algorithms, and automatically generate a compensatory blasting plan when the critical sieve residue exceeds the preset range; Step 6: Real-time monitoring and feedback of blasting vibration: A distributed fiber optic sensor network is deployed in the blasting area to monitor the vibration speed, frequency, and propagation path in real time. The monitoring data is input into the intelligent design module through a wireless transmission module for iterative parameter optimization. The improved BP neural network algorithm collects at least 50 sets of historical blasting record data as training sets. Each set of data contains rock uniaxial compressive strength, explosive consumption, hole spacing, row spacing, and corresponding sieve residue. After data preprocessing and feature selection, it is input into the model for iterative training until the model prediction error is less than 5%. Through the intelligent design of blasting parameters, the target gradation curve can be accurately matched, significantly improving the blasting effect and resource utilization; dynamic control of drilling accuracy ensures the accurate position of the blasthole and reduces the safety hazards caused by deviation; the coupled charging structure implements the optimized charging method, improves the blasting energy utilization, and reduces flying rocks and vibration; the precise detonation of electronic detonators realizes micro-difference blasting and reduces the impact on the surrounding environment; the post-blast gradation online detection and compensation mechanism ensures the gradation quality and reduces the subsequent processing costs; the real-time monitoring and feedback of blasting vibration form a closed-loop control, continuously optimizes the blasting parameters, and improves the safety and efficiency of blasting operations as a whole.

[0024] See also Figure 1 、 Figure 2 and Figure 3 ,The blasting parameter mathematical model adopts the improved BP neural network algorithm, and the training data set includes the mapping relationship between the uniaxial compressive strength of rock, explosive consumption, hole spacing, row spacing and corresponding screen residue in the historical blasting records; The specific implementation method for the blasting parameter mathematical model is as follows: first, historical blasting records are collected, including parameters such as rock uniaxial compressive strength, explosive consumption, hole spacing, row spacing, and corresponding screen residue data; then, the data is preprocessed, such as normalization, to meet the input requirements of the BP neural network; then, an improved BP neural network algorithm is used, such as adding momentum terms and adaptive learning rate optimization strategies, to construct a mathematical model; finally, the model is trained using the processed data set, and the network weights are adjusted until the model output meets the target gradation curve requirements; The improved BP neural network algorithm is used to construct a mathematical model of blasting parameters, which can make full use of the complex nonlinear relationships in historical blasting data and achieve accurate prediction of blasting parameters by automatically learning and adjusting network parameters. The model not only takes into account the physical and mechanical parameters of rocks and the performance parameters of explosives, but also incorporates constraints such as the maximum allowable vibration velocity and the blast pile expansion angle, making the generated blasting plan more scientific and reasonable. In addition, the model has dynamic optimization capabilities and can be iteratively optimized based on real-time monitoring data to ensure that the blasting effect continues to meet the target requirements, significantly improving the efficiency and safety of blasting mining.

[0025] See also Figure 1 、 Figure 2 and Figure 3 , the flexible buffer layer is made of polyurethane foam and rubber particles, and its thickness is 15%-20% of the pore diameter; The thickness of the required flexible buffer layer is calculated based on the diameter of the blasthole (15%-20% of the blasthole diameter). Subsequently, polyurethane foam and rubber particles are evenly mixed in a predetermined ratio. Through mold pressing or on-site pouring, a uniform and elastic flexible buffer layer is formed at the bottom of the blasthole to absorb blasting energy and reduce impact damage to the hole wall. The flexible buffer layer is made of a composite of polyurethane foam and rubber particles, and its thickness is designed to be 15%-20% of the aperture. This design can effectively absorb the impact energy generated during the blasting process, reduce the direct impact on the borehole wall, thereby protecting the stability of the borehole wall structure and reducing the damage to the surrounding rock mass caused by blasting. At the same time, the buffer layer can also improve the blasting effect, make the blast pile shape more uniform, and contribute to the subsequent grading control and mining efficiency improvement. In addition, the use of composite materials improves the durability and adaptability of the buffer layer, and can maintain stable performance under different geological conditions, providing a strong guarantee for the safety and economy of blasting operations.

[0026] See also Figure 1 、 Figure 2 and Figure 3 The hole-by-hole differential blasting network adopts a dual-ring redundancy design. When a circuit breaker fault occurs on the primary network, the backup network automatically switches, and the automatic switching time is controlled within 5ms. The dual-ring redundancy design is achieved by arranging two independent initiation networks in parallel. Each network includes independent electronic detonators, conductors, and control units. When the primary network detects a circuit breaker fault, a high-speed switching device (switching time < 5ms) automatically activates the backup network to ensure uninterrupted initiation signals and guarantee the safety and reliability of blasting operations. The hole-by-hole micro-difference initiation network with a dual-ring redundant design significantly improves the safety and stability of blasting operations. When the primary network fails, the backup network can quickly take over, and the automatic switching time is controlled within an extremely short 5ms, with almost no impact on the initiation sequence, effectively avoiding blasting failures or safety accidents caused by network failures. This design not only enhances the system's fault tolerance, but also improves the continuity and efficiency of blasting operations, providing reliable technical support for blasting projects in complex environments.

[0027] See also Figure 1 、 Figure 2 and Figure 3 ,The compensation blasting scheme includes local additional drilling parameters and the corresponding charge amount. The additional drilling position is determined by ,the deviation analysis between the three-dimensional point cloud model of the blasting pile and the target gradation curve; After the blast pile 3D point cloud model is established, an image processing algorithm is used to compare the target gradation curve and identify areas of particle size distribution deviation. Based on the deviation analysis results, an intelligent algorithm is used to calculate the optimal location, depth, diameter, and corresponding charge of local additional drill holes to ensure that the blast pile gradation after compensation blasting meets the design requirements. Through precise deviation analysis between the blast pile 3D point cloud model and the target gradation curve, dynamic adjustment and optimization of the blasting effect is achieved. This method not only improves the accuracy of blasting operations, but also significantly enhances the controllability of the construction process. It can quickly respond to actual on-site conditions and effectively compensate for the shortcomings of the previous blasting through local additional drilling and precise charging, ensuring that the final blast pile gradation meets the project requirements, thereby improving overall construction efficiency and quality and reducing the risk of rework due to gradation mismatch.

[0028] See also Figure 1 、 Figure 2 and Figure 3 ,The monitoring data of the distributed optical fiber sensor network is fed back to the intelligent design module in real time through the wireless transmission module, triggering the dynamic optimization algorithm of the blasting parameters; The wireless transmission module uses LoRa communication technology, operating at a frequency of 433MHz and a data transmission rate of 10kbps, ensuring stable transmission of monitoring data to the intelligent design module. The dynamic optimization algorithm for blasting parameters is based on a particle swarm optimization algorithm, which iteratively adjusts blasting parameters with the goal of minimizing the deviation between the post-blasting gradation and the target gradation. The distributed fiber optic sensing network can monitor blasting vibrations in real time and quickly feed back data to the intelligent design module through the wireless transmission module, enabling dynamic optimization of blasting parameters. This helps to adjust blasting parameters in a timely manner according to actual vibration conditions, improve blasting effects, and ensure that grading meets requirements. At the same time, the intelligent design module can quickly respond to data changes, improve work efficiency, reduce manual intervention, make blasting operations more accurate and safe, and enhance the overall project quality and benefits.

[0029] See also Figure 1 、 Figure 2 and Figure 3 ,The multispectral camera covers a wavelength range of 400-1000nm and is equipped with an automatic gain control module to adapt to different lighting conditions; The multispectral camera uses a high-precision optical filter set to achieve 400-1000nm wavelength coverage. The built-in photosensor monitors the ambient light intensity in real time. The microprocessor drives the variable gain amplifier to dynamically adjust the signal amplification factor according to the light intensity to ensure stable image signal output. It is also equipped with an automatic exposure algorithm to optimize image quality. The wide wavelength range ensures that the camera can capture the reflectance spectral characteristics of rocks of different particle sizes, providing rich information for the image segmentation algorithm and effectively distinguishing different particle size distributions. The automatic gain control module enables the camera to operate stably under various lighting conditions, avoiding overexposure or underexposure, ensuring image quality, and thus improving the reliability of grading detection. This design not only enhances the environmental adaptability of the detection system, but also provides a solid foundation for the generation of subsequent compensatory blasting plans by improving data accuracy, which helps to achieve more precise control of blasting effects.

[0030] For rockfill dam-grade batching blasting mining systems, please refer to Figure 1 、 Figure 2 and Figure 3 ,include: Intelligent design module, which is used for parameter calculation and solution generation of the rockfill dam-grade batching blasting mining method; Construction management module, integrating drilling machinery navigation subsystem, charge monitoring subsystem and detonation safety verification subsystem; The effect evaluation module includes a blast pile morphology analysis unit, a gradation detection unit, and a vibration monitoring unit. Data exchange between these modules is achieved through industrial Ethernet, forming a closed-loop control link between design, construction, and evaluation. The intelligent design module incorporates an improved BP neural network algorithm, which automatically learns the mapping relationship between rock parameters, explosive properties, and blasting effects by training historical blasting data. Based on the target gradation curve, it dynamically generates blasting parameters and construction plans. The construction management module uses a high-precision positioning system and sensor network to achieve precise control of drilling, charging, and detonation. The effect evaluation module utilizes drones and fiber optic sensing technology to monitor and analyze the blast pile morphology, gradation distribution, and vibration in real time to ensure that the blasting effect meets expectations. The system for rockfill dam-level batching blasting and mining significantly improves the accuracy and efficiency of blasting operations by integrating three modules: intelligent design, construction management, and effect evaluation. The intelligent design module uses advanced algorithms to optimize blasting parameters and reduce trial-and-error costs; the construction management module ensures construction safety and quality through real-time monitoring and precise control; and the effect evaluation module provides timely feedback to guide subsequent operation adjustments. The modules are closely connected through industrial Ethernet to form a closed-loop control link, realizing the intelligent, refined, and efficient blasting operations and providing strong support for rockfill dam construction.

[0031] For equipment for rockfill dam-grade batching blasting, please refer to Figure 1 、 Figure 2 and Figure 3 ,include: Multi-degree-of-freedom drilling robot arm equipped with a dual-axis inclination sensor and hydraulic correction mechanism; Intelligent loading vehicle, integrating automatic drug roll conveying, water bag filling and density detection functions; Electronic detonator programmer, supporting RFID tag reading and writing and detonation sequence programming; The rockfill dam-level batching blasting mining device realizes collaborative operation and generates operation data through the on-board controller, and the operation data is uploaded to the above-mentioned rockfill dam-level batching blasting mining system in real time; A dual-axis inclination sensor monitors the drilling angle in real time, and a hydraulic correction mechanism automatically adjusts the drill rod direction based on sensor data to ensure drilling accuracy. The intelligent charging vehicle uses a robotic arm to automatically transport explosive rolls, has a built-in water bag filling device, and is equipped with a density detection sensor to monitor charge density in real time. The electronic detonator programmer uses wireless radio frequency identification technology to quickly read and write tag information and accurately program the detonation sequence. All devices are centrally scheduled by the onboard controller, working collaboratively and uploading operation data to the management system in real time. The device for rockfill dam-level batching blasting and mining realizes the automation and intelligence of blasting operations by integrating a multi-degree-of-freedom drilling robot arm, an intelligent charging vehicle and an electronic detonator programmer. The dual-axis inclination sensor and hydraulic correction mechanism equipped with the drilling robot arm ensure the accuracy of drilling, the intelligent charging vehicle improves the charging efficiency and density control capability, and the electronic detonator programmer realizes the precise programming of the detonation sequence through wireless radio frequency identification technology, enhancing the safety and controllability of blasting operations. These devices work together through the on-board controller and upload operation data in real time, providing strong support for the optimization of blasting parameters and significantly improving the quality and efficiency of blasting operations.

[0032] A computer-readable storage medium having a computer program stored thereon, wherein when the computer program is executed by a processor, the steps of the rockfill dam-grade batching blasting mining method are implemented, including: Blasting parameter optimization algorithm: This module is used to establish a mathematical model of blasting parameters based on the target gradation curve. This model uses an improved BP neural network algorithm as input, including rock physical and mechanical parameters, explosive performance parameters, and constraints (including maximum allowable vibration velocity, blast pile expansion angle, and target gradation interval). It outputs hole network parameters, unit consumption, charge structure, and detonation sequence. This module uses the mapping relationship between rock uniaxial compressive strength, explosive unit consumption, hole spacing, row spacing, and corresponding screen residue from historical blasting records as a training data set. Real-time blasthole position verification module: Integrates a 3D laser scanner data interface for real-time verification of blasthole positions. When the hole position deviation exceeds ±5cm, an automatic correction program is triggered to adjust the drill rod angle by controlling the drilling robot arm. Coupled Charge Structure Setup Module: Contains instructions for setting a flexible buffer layer made of a composite of polyurethane foam and rubber particles at the bottom of the blasthole (thickness 15%-20% of the hole diameter), using an axially spaced charge structure, with the spacers filled with a mixture of water bags and rock dust, and controlling the charge length to hole depth ratio between 0.65-0.75; Electronic detonator precision initiation control module: This module is used to establish a hole-by-hole micro-difference initiation network. The initiation time difference Δt is dynamically calculated based on the hole spacing L as Δt = 0.8L-1.2L (ms), and the blasthole located at the geometric center of the blasting area is selected as the first blasting hole. This module supports a dual-ring redundancy design. When a circuit breaker fault occurs on the primary network, the backup network automatically switches within 5ms. Post-blast gradation online detection and compensation module: This module integrates the data interface of a multispectral camera (wavelength range: 400-1000nm, equipped with an automatic gain control module) mounted on a drone to scan the blast pile and identify the particle size distribution using an image segmentation algorithm. When the critical screen residue exceeds the preset range, a compensation blasting plan is automatically generated, including local additional drilling parameters and corresponding charge amount. The additional drilling location is determined by analyzing the deviation between the 3D point cloud model of the blast pile and the target gradation curve. Blasting vibration real-time monitoring and feedback module: connected to the distributed optical fiber sensing network, used for real-time monitoring of vibration speed, frequency and propagation path. The monitoring data is fed back to the blasting parameter optimization algorithm module in real time through the wireless transmission module, triggering the parameter dynamic optimization algorithm; System integration and data interaction module: supports integration with intelligent design module, construction management module (integrated drilling machinery navigation subsystem, charge monitoring subsystem and detonation safety verification subsystem), and effect evaluation module (including explosive pile morphology analysis unit, gradation detection unit and vibration monitoring unit). Data interaction is achieved between modules through industrial Ethernet, forming a design-construction-evaluation closed-loop control link. Explosion pile gradation identification model; Compensation blasting plan generation logic; The computer program supports both offline operation and cloud-based collaborative computing modes, ensuring flexible application and data sharing in different environments; Further clarify the parameter settings of the improved BP neural network algorithm, such as the number of network layers, activation function, and number of training iterations. For example, a three-layer network structure (input layer, hidden layer, output layer) is adopted, the hidden layer uses the ReLU activation function, and the number of training iterations is set to 1000 to ensure model convergence and good generalization ability; The computer program on the computer-readable storage medium realizes the intelligent and precise blasting and mining of rockfill dam-grade materials by integrating modules such as blasting parameter optimization, blasthole position verification, coupled charge structure setting, precise detonation control of electronic detonators, online detection and compensation of post-blasting gradation, and real-time monitoring and feedback of blasting vibration. This solution not only improves blasting efficiency and quality, ensures that the blasting effect meets the target gradation requirements, but also effectively reduces the impact of blasting vibration on the surrounding environment through real-time monitoring and feedback mechanisms, ensuring construction safety. At the same time, the design of system integration and data interaction modules promotes the close connection between design, construction and evaluation links, forms a closed-loop control link, and further improves the overall efficiency and management level of blasting operations.

[0033] It should be noted that, in this document, relational terms such as first and second, etc., are used only to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any actual relationship or order between these entities or operations. Moreover, the terms "comprises," "comprising," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that includes a list of elements includes not only those elements but also other elements not explicitly listed, or elements inherent to such process, method, article, or apparatus.

[0034] While embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions, and variations may be made to these embodiments without departing from the principles and spirit of the invention, and that the scope of the invention is defined by the appended claims and their equivalents.

Claims

1. Rockfill dam-grade batching blasting mining method, characterized in that: The steps include: Step 1: Intelligent design of blasting parameters: Based on the target gradation curve, a mathematical model of blasting parameters is established by computer. The rock physical and mechanical parameters, explosive performance parameters and constraints are input, and the hole network parameters, unit consumption, charge structure and detonation sequence are output. The constraints include the maximum allowable vibration velocity, blast pile expansion angle and target gradation range. Step 2: Dynamic control of drilling accuracy: A 3D laser scanner is used to verify the blasthole position in real time. When the hole position deviation exceeds ±5cm, an automatic correction program is triggered and the drilling robot arm adjusts the drill rod angle. Step 3: Implementation of coupled charge structure: A flexible buffer layer is set at the bottom of the blasthole, and an axial interval charging structure is adopted. The interval section is filled with a mixture of water bags and rock powder. The ratio of charging length to hole depth is controlled between 0.65-0.

75. Step 4: Precise detonation of electronic detonator: Establish a hole-by-hole micro-difference initiation network, and select the first blast hole located at the geometric center of the blasting area; Step 5: Online detection of gradation after blasting: Use drones equipped with multispectral cameras to scan the blast pile, identify the particle size distribution through image segmentation algorithms, and automatically generate a compensatory blasting plan when the critical sieve residue exceeds the preset range; Step 6: Real-time monitoring and feedback of blasting vibration: A distributed fiber optic sensing network is arranged in the blasting area to monitor the vibration speed, frequency and propagation path in real time. The monitoring data is input into the intelligent design module through the wireless transmission module.

2. The rockfill dam-grade batching blasting mining method according to claim 1, characterized in that: The blasting parameter mathematical model adopts an improved BP neural network algorithm, and the training data set includes the mapping relationship between the uniaxial compressive strength of rock, explosive consumption, hole spacing, row spacing and corresponding screen residue in historical blasting records.

3. The rockfill dam-grade batching blasting mining method according to claim 1, characterized in that: The flexible buffer layer is made of polyurethane foam and rubber particles.

4. The rockfill dam-grade batching blasting mining method according to claim 1, characterized in that: The hole-by-hole micro-difference blasting network adopts a dual-ring redundancy design. When a circuit breaker failure occurs in the main network, the backup network automatically switches.

5. The rockfill dam-grade batching blasting mining method according to claim 1, characterized in that: The compensation blasting scheme includes local additional drilling parameters and corresponding charge amount, and the additional drilling position is determined by deviation analysis between the blast pile three-dimensional point cloud model and the target gradation curve.

6. The rockfill dam-grade batching blasting mining method according to claim 1, characterized in that: The monitoring data of the distributed optical fiber sensor network is fed back to the intelligent design module in real time through the wireless transmission module, triggering the dynamic optimization algorithm of blasting parameters.

7. The rockfill dam-grade batching blasting mining method according to claim 1, characterized in that: The multispectral camera covers a wavelength range of 400-1000 nm.

8. Rockfill dam-grade batching blasting mining system, characterized by: include: An intelligent design module for performing parameter calculation and solution generation for the rockfill dam-grade batching blasting mining method described in claims 1-7; Construction management module, integrating drilling machinery navigation subsystem, charge monitoring subsystem and detonation safety verification subsystem; The effect evaluation module includes an explosion pile morphology analysis unit, a gradation detection unit, and a vibration monitoring unit.

9. A device for rockfill dam-grade batching blasting mining, characterized in that: include: Multi-degree-of-freedom drilling robot arm equipped with a dual-axis inclination sensor and hydraulic correction mechanism; Intelligent loading vehicle, integrating automatic drug roll conveying, water bag filling and density detection functions; Electronic detonator programmer, supporting RFID tag reading and writing and detonation sequence programming; The rockfill dam-level batching blasting mining device realizes collaborative operation and generates operation data through a vehicle-mounted controller, and the operation data is uploaded to the rockfill dam-level batching blasting mining system according to claim 8 in real time.

10. A computer-readable storage medium, characterized in that The computer readable storage medium stores a computer program, and when the computer program is executed by a processor, the steps of the rockfill dam grade batching blasting mining method according to any one of claims 1 to 7 are implemented, including: Blasting parameter optimization algorithm; Explosion pile gradation identification model; Compensation blasting plan generation logic; The computer program supports two modes: offline operation and cloud collaborative computing.

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