Blast hole blocking method and system based on intelligent control

Abstract

The invention discloses a blast hole blocking method and system based on intelligent control, and the method achieves the closed-loop adaptive control of the whole process of blast hole blocking through the cooperation of a plurality of technologies, such as three-dimensional recognition modeling, continuous scanning reconstruction, segmented integral prediction, double-bin metering control and real-time error compensation. Compared with the prior art which only depends on identification or automatic filling, the method has higher blocking precision, construction efficiency and stability, and is suitable for complex blasting environments such as mines and tunnels. According to the method, a closed-loop control system of recognition, measurement, prediction, metering and feedback correction is constructed, so that the supply quantity of the blocking material can be dynamically corrected according to the actual geometric parameters of the blast hole and the real-time error in the blanking process, and the blocking precision and the construction consistency are remarkably improved.

Description

Technical Field

[0001] This invention relates to the fields of geotechnical engineering and blasting engineering technology, specifically to a method and system for plugging blast holes based on intelligent control. Background Technology

[0002] In mining, tunnel construction, and other blasting projects, borehole plugging is a crucial step affecting blasting effectiveness, safety, and energy utilization. Current borehole plugging operations typically rely on manual experience or fixed parameters, resulting in low efficiency, inaccurate plugging precision, and high labor intensity.

[0003] Meanwhile, due to factors such as geological conditions and drilling deviations during the drilling process, different blast holes vary greatly in terms of hole diameter, hole depth, and hole wall condition. Using uniform plugging parameters or fixed feeding methods is difficult to adapt to the actual conditions of different blast holes, which can easily lead to insufficient or excessive plugging, thereby affecting the blasting effect and even causing safety hazards.

[0004] In summary, existing borehole plugging operations rely on manual experience or fixed parameter control, which makes it difficult to adapt to complex working conditions such as random fluctuations in borehole geometry, borehole wall collapse, and irregular changes in borehole diameter. Although existing technologies can achieve borehole identification or automatic loading, they still cannot achieve real-time adaptive and precise control and continuous closed-loop adjustment of the plugging amount, resulting in insufficient plugging accuracy, material waste, and low utilization of blasting energy.

[0005] Therefore, there is an urgent need for an intelligent borehole plugging method that can adaptively adjust according to the actual parameters of the borehole, so as to improve the accuracy, efficiency and safety of borehole plugging operations. Summary of the Invention

[0006] To address the shortcomings of existing technologies, this invention aims to provide a method and system for plugging blast holes based on intelligent control.

[0007] To achieve the above objectives, the present invention adopts the following technical solution: A method for plugging blast holes based on intelligent control includes the following steps: S1. Intelligent identification and spatial coordinate modeling of blast holes: Three-dimensional image data of the blasting operation face are acquired by drones or mobile vision terminals. Deep learning recognition algorithms are used to automatically extract the edge features and center feature points of the blast holes in the three-dimensional image data, and a three-dimensional spatial coordinate model of each blast hole is established to output the center coordinates and surface shape information of the blast holes. S2. Adaptive scanning measurement and cross-section reconstruction: Guide the laser ranging unit and the ring scanning unit to perform multi-point continuous scanning along the axis of the borehole, obtain borehole diameter data at different depth positions of the borehole, and generate a real three-dimensional contour model of the borehole through the cross-section reconstruction algorithm to obtain the borehole diameter variation curve along the depth direction. S3. Segmented integral blockage volume prediction calculation: Based on the actual three-dimensional contour model of the borehole, the blockage depth of the borehole is divided into multiple micro segments. The cross-sectional area of ​​each micro segment is numerically integrated to obtain the theoretical blockage volume of the borehole. At the same time, the compaction coefficient model and material loosening coefficient model established by combining the historical construction database are used to compensate and correct the theoretical blockage volume to obtain the target material supply of the borehole. S4. Dual-compartment metering quantitative feeding control: The dual-compartment metering feeding control module includes a main compartment and an auxiliary metering compartment. The main compartment is used for continuous feeding of the blockage material, while the auxiliary metering compartment is set up to temporarily store the blockage material required for a single borehole. The auxiliary metering compartment is equipped with a metering sensor unit, which feeds back real-time metering data to the controller. The controller then controls the start and stop of the main compartment based on this data. When the blockage material in the auxiliary metering compartment reaches the target feeding amount, the controller automatically controls the main compartment to stop feeding and controls the auxiliary metering compartment to feed material into the borehole, thus forming precise metering control. S5. Real-time feedback compensation control for feeding error: During the feeding process, the feeding amount, orifice height change and blockage surface settlement status of the blast hole are monitored in real time. The controller compares and analyzes the monitoring results with the target feeding amount. When insufficient blockage is detected, the feeding control is automatically triggered, and the main silo and auxiliary metering silo are started to feed the blast hole. S6. Multi-hole path planning continuous operation: Based on the three-dimensional spatial coordinate model of the borehole, the optimal moving path is automatically planned, and the machine automatically navigates to the next borehole. Steps S2-S5 are repeated to achieve continuous intelligent blocking.

[0008] Furthermore, in step S1, after acquiring the three-dimensional image data of the blasting operation face through a drone or mobile vision terminal, the acquired three-dimensional image data is subjected to noise reduction enhancement, brightness equalization, geometric distortion correction and scale normalization processing to improve the clarity of the blast hole edges and orifice features in the image.

[0009] Furthermore, in step S4, the metering sensing unit is a weighing sensor or a volume sensor, used to measure the mass or volume of the blockage material in the auxiliary metering hopper.

[0010] Furthermore, in step S1, the mobile vision terminal is a multi-view camera or a depth vision sensor.

[0011] The present invention also provides a system for implementing the above method, including a three-dimensional image acquisition unit, an image preprocessing unit, a borehole identification unit, a spatial coordinate modeling unit, an adaptive scanning measurement and cross-section reconstruction module, a segmented integral volume prediction calculation module, a dual-compartment metering feeding control module, a real-time feedback compensation module for feeding error, and a path planning and movement control module. The three-dimensional image acquisition unit acquires three-dimensional image data of the blasting operation face through a drone or mobile vision terminal; The image preprocessing unit is used to perform noise reduction, brightness equalization, geometric distortion correction, and scale normalization on the acquired three-dimensional image data to improve the clarity of the borehole edges and borehole features. The borehole recognition unit is used to automatically identify borehole regions in three-dimensional image data based on a deep learning target detection network, and extract borehole contour edge information and center feature points; The spatial coordinate modeling unit is used to establish a three-dimensional spatial coordinate model for each borehole based on the recognition results of the borehole recognition unit and combined with visual ranging or point cloud data, and output the borehole center coordinates and surface shape information. The adaptive scanning measurement and cross-section reconstruction module includes a laser ranging unit, a ring scanning unit, and a cross-section reconstruction unit. The laser ranging unit is used to continuously measure along the borehole axis to obtain the actual clogging depth of the borehole. The ring scanning unit is used to perform 360° scanning of the inner wall of the borehole at different depth positions to obtain multiple sets of borehole diameter data. The cross-section reconstruction unit is used to fit and reconstruct the cross-sectional profile of the borehole based on the scanning data obtained by the ring scanning unit, forming a cross-sectional area distribution curve corresponding to different depths, and establishing a real three-dimensional profile model of the borehole. The segmented integral volume prediction and calculation module is used to divide the clogging depth range of the borehole into multiple micro segments after obtaining the real three-dimensional contour model of the borehole. The cross-sectional area of ​​each segment is numerically integrated to obtain the theoretical clogging volume of the borehole. At the same time, the module is combined with the preset material compaction coefficient model and loosening coefficient model to compensate and correct the theoretical clogging volume, so as to eliminate the error caused by the change in the bulk density of the clogging material, thereby obtaining the target feed rate. The dual-compartment metering feeding control module includes a main compartment, an auxiliary metering compartment, a metering sensor unit, and a controller. The main compartment is used for continuous feeding of blockage material, while the auxiliary metering compartment is used to temporarily store the blockage material required for a single borehole and discharge the blockage material into the borehole. The metering sensor unit is located at the bottom of the auxiliary metering compartment and is used to detect the mass or volume of the blockage material in the auxiliary metering compartment in real time. The controller is used to control the start and stop of feeding from the main compartment based on the real-time feedback signal from the metering sensor unit. When the mass or volume of the blockage material in the auxiliary metering compartment reaches the target feeding amount, the controller automatically controls the main compartment to stop feeding and controls the auxiliary metering compartment to start discharging into the borehole, thereby achieving precise quantitative metering control. The real-time feedback compensation module for material feeding error is used to monitor the amount of blockage material falling into the borehole, the change in borehole height, and the settlement state of the blockage surface in real time during the process of feeding material from the auxiliary metering hopper into the borehole. The monitoring results are transmitted to the controller of the dual-hopper metering feeding control module. The controller compares and analyzes the detection results of the real-time feedback compensation module for material feeding error with the target feeding amount. When insufficient blockage is detected, the feeding control is automatically triggered to control the main hopper and the auxiliary metering hopper to feed material into the borehole. The path planning and movement control module is used to automatically plan the optimal movement path based on the three-dimensional spatial coordinate model of the borehole, guiding the entire system to move sequentially to the position of each borehole, thereby realizing continuous intelligent plugging operation of multiple boreholes.

[0012] The beneficial effects of this invention are as follows: This invention achieves closed-loop adaptive control of the entire process of blast hole plugging through the synergy of multiple technologies, including 3D recognition modeling, continuous scanning reconstruction, segmented integral prediction, dual-compartment metering control, and real-time error compensation. Compared with existing technologies that rely solely on recognition or automatic loading, it has higher plugging accuracy, construction efficiency, and stability, and is suitable for complex blasting environments such as mines and tunnels.

[0013] This invention constructs a closed-loop control system of "identification-measurement-prediction-metering-feedback correction," which enables the supply of plugging material to be dynamically corrected based on the actual geometric parameters of the borehole and the real-time error during the feeding process, thereby significantly improving the plugging accuracy and construction consistency. Detailed Implementation

[0014] The present invention will be further described below. It should be noted that this embodiment is based on the present technical solution and provides detailed implementation methods and specific operation processes, but the protection scope of the present invention is not limited to this embodiment.

[0015] Example 1 This embodiment provides a borehole plugging method based on intelligent control, including the following steps: S1. Intelligent identification and spatial coordinate modeling of blast holes: Three-dimensional image data of the blasting operation face are acquired by drones or mobile vision terminals. Deep learning recognition algorithms are used to automatically extract the edge features and center feature points of the blast holes in the three-dimensional image data, and a three-dimensional spatial coordinate model of each blast hole is established to output the center coordinates and surface shape information of the blast holes. S2. Adaptive scanning measurement and cross-section reconstruction: Guide the laser ranging unit and the ring scanning unit to perform multi-point continuous scanning along the axis of the borehole, obtain borehole diameter data at different depth positions of the borehole, and generate a real three-dimensional contour model of the borehole through the cross-section reconstruction algorithm to obtain the borehole diameter variation curve along the depth direction. By using the aforementioned continuous scanning and three-dimensional reconstruction methods, the irregular shape of the borehole caused by hole collapse, enlargement, or off-center drilling can be accurately reflected, significantly improving the accuracy of geometric parameter measurement.

[0016] S3. Segmented integral blockage volume prediction calculation: Based on the actual three-dimensional contour model of the borehole, the blockage depth of the borehole is divided into multiple micro segments. The cross-sectional area of ​​each micro segment is numerically integrated to obtain the theoretical blockage volume of the borehole. At the same time, the compaction coefficient model and material loosening coefficient model established by combining the historical construction database are used to compensate and correct the theoretical blockage volume to obtain the target material supply of the borehole. The above-mentioned method of combining piecewise integration with compensation prediction has higher volume estimation accuracy compared to the traditional equivalent cylinder calculation method.

[0017] S4. Dual-compartment metering quantitative feeding control: The dual-compartment metering feeding control module includes a main compartment and an auxiliary metering compartment. The main compartment is used for continuous feeding of the blockage material, while the auxiliary metering compartment is set up to temporarily store the blockage material required for a single borehole. The auxiliary metering compartment is equipped with a metering sensor unit, which feeds back real-time metering data to the controller. The controller then controls the start and stop of the main compartment based on this data. When the blockage material in the auxiliary metering compartment reaches the target feeding amount, the controller automatically controls the main compartment to stop feeding and controls the auxiliary metering compartment to feed material into the borehole, thus forming precise metering control. S5. Real-time feedback compensation control for feeding error: During the feeding process, the feeding amount, orifice height change and blockage surface settlement status of the blast hole are monitored in real time. The controller compares and analyzes the monitoring results with the target feeding amount. When insufficient blockage is detected, the feeding control is automatically triggered, and the main silo and auxiliary metering silo are started to feed the blast hole. S6. Multi-hole path planning continuous operation: Based on the three-dimensional spatial coordinate model of the borehole, the optimal moving path is automatically planned, and the machine automatically navigates to the next borehole. Steps S2-S5 are repeated to achieve continuous intelligent blocking.

[0018] Furthermore, in step S1, after acquiring the three-dimensional image data of the blasting operation face through a drone or mobile vision terminal, the acquired three-dimensional image data is subjected to noise reduction enhancement, brightness equalization, geometric distortion correction and scale normalization processing to improve the clarity of the blast hole edges and orifice features in the image.

[0019] Furthermore, in step S4, the metering sensing unit is a weighing sensor or a volume sensor, used to measure the mass or volume of the blockage material in the auxiliary metering hopper.

[0020] Furthermore, in step S1, the mobile vision terminal is a multi-view camera or a depth vision sensor.

[0021] Example 2 This embodiment provides a system for implementing the above method, including a three-dimensional image acquisition unit, an image preprocessing unit, a borehole identification unit, a spatial coordinate modeling unit, an adaptive scanning measurement and cross-section reconstruction module, a segmented integral volume prediction calculation module, a dual-compartment metering feeding control module, a real-time feedback compensation module for feeding error, and a path planning and movement control module. The three-dimensional image acquisition unit acquires three-dimensional image data of the blasting operation face through a drone or mobile vision terminal; The image preprocessing unit is used to perform noise reduction, brightness equalization, geometric distortion correction, and scale normalization on the acquired three-dimensional image data to improve the clarity of the borehole edges and borehole features. The borehole recognition unit is used to automatically identify borehole regions in three-dimensional image data based on a deep learning target detection network, and extract borehole contour edge information and center feature points; The spatial coordinate modeling unit is used to establish a three-dimensional spatial coordinate model for each borehole based on the recognition results of the borehole recognition unit and combined with visual ranging or point cloud data, and output the borehole center coordinates and surface shape information. The adaptive scanning measurement and cross-section reconstruction module includes a laser ranging unit, a ring scanning unit, and a cross-section reconstruction unit. The laser ranging unit is used to continuously measure along the borehole axis to obtain the actual clogging depth of the borehole. The ring scanning unit is used to perform 360° scanning of the inner wall of the borehole at different depth positions to obtain multiple sets of borehole diameter data. The cross-section reconstruction unit is used to fit and reconstruct the cross-sectional profile of the borehole based on the scanning data obtained by the ring scanning unit, forming a cross-sectional area distribution curve corresponding to different depths, and establishing a real three-dimensional profile model of the borehole. The segmented integral volume prediction and calculation module is used to divide the clogging depth range of the borehole into multiple micro segments after obtaining the real three-dimensional contour model of the borehole. The cross-sectional area of ​​each segment is numerically integrated to obtain the theoretical clogging volume of the borehole. At the same time, the module is combined with the preset material compaction coefficient model and loosening coefficient model to compensate and correct the theoretical clogging volume, so as to eliminate the error caused by the change in the bulk density of the clogging material, thereby obtaining the target feed rate. The dual-compartment metering feeding control module includes a main compartment, an auxiliary metering compartment, a metering sensor unit, and a controller. The main compartment is used for continuous feeding of blockage material, while the auxiliary metering compartment is used to temporarily store the blockage material required for a single borehole and discharge the blockage material into the borehole. The metering sensor unit is located at the bottom of the auxiliary metering compartment and is used to detect the mass or volume of the blockage material in the auxiliary metering compartment in real time. The controller is used to control the start and stop of feeding from the main compartment based on the real-time feedback signal from the metering sensor unit. When the mass or volume of the blockage material in the auxiliary metering compartment reaches the target feeding amount, the controller automatically controls the main compartment to stop feeding and controls the auxiliary metering compartment to start discharging into the borehole, thereby achieving precise quantitative metering control. The real-time feedback compensation module for material feeding error is used to monitor the amount of blockage material falling into the borehole, the change in borehole height, and the settlement state of the blockage surface in real time during the process of feeding material from the auxiliary metering hopper into the borehole. The monitoring results are transmitted to the controller of the dual-hopper metering feeding control module. The controller compares and analyzes the detection results of the real-time feedback compensation module for material feeding error with the target feeding amount. When insufficient blockage is detected, the feeding control is automatically triggered to control the main hopper and the auxiliary metering hopper to feed material into the borehole. The path planning and movement control module is used to automatically plan the optimal movement path based on the three-dimensional spatial coordinate model of the borehole, guiding the entire system to move sequentially to the position of each borehole, thereby realizing continuous intelligent plugging operation of multiple boreholes.

[0022] For those skilled in the art, various corresponding changes and modifications can be made based on the above technical solutions and concepts, and all such changes and modifications should be included within the protection scope of the claims of this invention.

Claims

1. A method for plugging boreholes based on intelligent control, characterized in that, Includes the following steps: S1. Intelligent identification and spatial coordinate modeling of blast holes: Three-dimensional image data of the blasting operation face are acquired by drones or mobile vision terminals. Deep learning recognition algorithms are used to automatically extract the edge features and center feature points of the blast holes in the three-dimensional image data, and a three-dimensional spatial coordinate model of each blast hole is established to output the center coordinates and surface shape information of the blast holes. S2. Adaptive scanning measurement and cross-section reconstruction: Guide the laser ranging unit and the ring scanning unit to perform multi-point continuous scanning along the axis of the borehole, obtain borehole diameter data at different depth positions of the borehole, and generate a real three-dimensional contour model of the borehole through the cross-section reconstruction algorithm to obtain the borehole diameter variation curve along the depth direction. S3. Segmented integral blockage volume prediction calculation: Based on the actual three-dimensional contour model of the borehole, the blockage depth of the borehole is divided into multiple micro segments. The cross-sectional area of ​​each micro segment is numerically integrated to obtain the theoretical blockage volume of the borehole. At the same time, the compaction coefficient model and material loosening coefficient model established by combining the historical construction database are used to compensate and correct the theoretical blockage volume to obtain the target material supply of the borehole. S4. Dual-compartment metering quantitative feeding control: The dual-compartment metering feeding control module includes a main compartment and an auxiliary metering compartment. The main compartment is used for continuous feeding of the blockage material, while the auxiliary metering compartment is set up to temporarily store the blockage material required for a single borehole. The auxiliary metering compartment is equipped with a metering sensor unit, which feeds back real-time metering data to the controller. The controller then controls the start and stop of the main compartment based on this data. When the blockage material in the auxiliary metering compartment reaches the target feeding amount, the controller automatically controls the main compartment to stop feeding and controls the auxiliary metering compartment to feed material into the borehole, thus forming precise metering control. S5. Real-time feedback compensation control for feeding error: During the feeding process, the feeding amount, orifice height change and blockage surface settlement status of the blast hole are monitored in real time. The controller compares and analyzes the monitoring results with the target feeding amount. When insufficient blockage is detected, the feeding control is automatically triggered, and the main silo and auxiliary metering silo are started to feed the blast hole. S6. Multi-hole path planning continuous operation: Based on the three-dimensional spatial coordinate model of the borehole, the optimal moving path is automatically planned, and the machine automatically navigates to the next borehole. Steps S2-S5 are repeated to achieve continuous intelligent blocking.

2. The method according to claim 1, characterized in that, In step S1, after acquiring three-dimensional image data of the blasting operation face through a drone or mobile vision terminal, the acquired three-dimensional image data is subjected to noise reduction enhancement, brightness equalization, geometric distortion correction and scale normalization processing to improve the clarity of the blast hole edges and orifice features in the image.

3. The method according to claim 1, characterized in that, In step S4, the metering sensing unit is a weighing sensor or a volume sensor, used to measure the mass or volume of the blockage material in the auxiliary metering hopper.

4. The method according to claim 1, characterized in that, In step S1, the mobile vision terminal is a multi-view camera or a depth vision sensor.

5. A system for implementing the method of any one of claims 1-4, characterized in that, It includes a 3D image acquisition unit, an image preprocessing unit, a borehole identification unit, a spatial coordinate modeling unit, an adaptive scanning measurement and cross-section reconstruction module, a segmented integral volume prediction calculation module, a dual-compartment metering feeding control module, a real-time feedback compensation module for feeding errors, and a path planning and movement control module. The three-dimensional image acquisition unit acquires three-dimensional image data of the blasting operation face through a drone or mobile vision terminal; The image preprocessing unit is used to perform noise reduction, brightness equalization, geometric distortion correction, and scale normalization on the acquired three-dimensional image data to improve the clarity of the borehole edges and borehole features. The borehole recognition unit is used to automatically identify borehole regions in three-dimensional image data based on a deep learning target detection network, and extract borehole contour edge information and center feature points; The spatial coordinate modeling unit is used to establish a three-dimensional spatial coordinate model for each borehole based on the recognition results of the borehole recognition unit and combined with visual ranging or point cloud data, and output the borehole center coordinates and surface shape information. The adaptive scanning measurement and cross-section reconstruction module includes a laser ranging unit, a ring scanning unit, and a cross-section reconstruction unit. The laser ranging unit is used to continuously measure along the borehole axis to obtain the actual clogging depth of the borehole. The ring scanning unit is used to perform 360° scanning of the inner wall of the borehole at different depth positions to obtain multiple sets of borehole diameter data. The cross-section reconstruction unit is used to fit and reconstruct the cross-sectional profile of the borehole based on the scanning data obtained by the ring scanning unit, forming a cross-sectional area distribution curve corresponding to different depths, and establishing a real three-dimensional profile model of the borehole. The segmented integral volume prediction and calculation module is used to divide the clogging depth range of the borehole into multiple micro segments after obtaining the real three-dimensional contour model of the borehole. The cross-sectional area of ​​each segment is numerically integrated to obtain the theoretical clogging volume of the borehole. At the same time, the module is combined with the preset material compaction coefficient model and loosening coefficient model to compensate and correct the theoretical clogging volume, so as to eliminate the error caused by the change in the bulk density of the clogging material, thereby obtaining the target feed rate. The dual-compartment metering feeding control module includes a main compartment, an auxiliary metering compartment, a metering sensor unit, and a controller. The main compartment is used for continuous feeding of blockage material, while the auxiliary metering compartment is used to temporarily store the blockage material required for a single borehole and discharge the blockage material into the borehole. The metering sensor unit is located at the bottom of the auxiliary metering compartment and is used to detect the mass or volume of the blockage material in the auxiliary metering compartment in real time. The controller is used to control the start and stop of feeding from the main compartment based on the real-time feedback signal from the metering sensor unit. When the mass or volume of the blockage material in the auxiliary metering compartment reaches the target feeding amount, the controller automatically controls the main compartment to stop feeding and controls the auxiliary metering compartment to start discharging into the borehole, thereby achieving precise quantitative metering control. The real-time feedback compensation module for material feeding error is used to monitor the amount of blockage material falling into the borehole, the change in borehole height, and the settlement state of the blockage surface in real time during the process of feeding material from the auxiliary metering hopper into the borehole. The monitoring results are transmitted to the controller of the dual-hopper metering feeding control module. The controller compares and analyzes the detection results of the real-time feedback compensation module for material feeding error with the target feeding amount. When insufficient blockage is detected, the feeding control is automatically triggered to control the main hopper and the auxiliary metering hopper to feed material into the borehole. The path planning and movement control module is used to automatically plan the optimal movement path based on the three-dimensional spatial coordinate model of the borehole, guiding the entire system to move sequentially to the position of each borehole, thereby realizing continuous intelligent plugging operation of multiple boreholes.