Intelligent collaborative support and recovery method for residual mining of special-shaped pillars in limited space

Abstract

The invention discloses an intelligent collaborative supporting and recycling method for residual mining of special-shaped pillars in a limited space, and belongs to the technical field of mining engineering. According to the method, an intelligent closed-loop system of sensing, decision-making, execution and feedback is constructed aiming at the problems of narrow space, complicated stress, high safety risk and the like faced by special-shaped ore pillar recovery in a limited space. The method comprises the following steps: firstly, constructing a dynamic digital twinborn model by using LiDAR and GPR fusion detection; secondly, performing stoping-supporting collaborative optimization design based on numerical simulation and an AI algorithm; then, low-disturbance blasting and micro intelligent equipment are adopted for precise subsection stoping; the mining-supporting-filling cycle operation is synchronously implemented, and an artificial key layer is quickly constructed by using a quick-setting expansion material; and finally, multi-stage intelligent early warning and dynamic regulation and control are realized through a multi-element sensor network. According to the method, the recovery rate of residual ore resources can be remarkably increased to 90% or above, ground pressure disasters are effectively controlled, and green, safe and efficient recovery of mine resources is achieved.

Description

Technical Field

[0001] This invention belongs to the field of mining engineering technology, specifically relating to an intelligent collaborative support and efficient recovery method for irregularly shaped residual ore pillars in confined spaces under complex geological and engineering environments. Background Technology

[0002] In the process of mineral resource development and utilization, pillars, as important structural units for maintaining the stability of underground mining areas, are often left between the mining area and the goaf. However, after long-term mining, a large number of pillars still contain high-grade ore. Especially in the confined spaces formed under the room-and-pillar subsequent backfill mining method, there are a large number of irregularly shaped pillars (such as peach-shaped, saddle-shaped, etc.), which are the focus of deep resource utilization.

[0003] However, the recovery of residual ore in confined spaces faces severe challenges:

[0004] 1. Limited working environment: The space is small and poorly ventilated, making it difficult for traditional large machinery to enter. Furthermore, the boundary of the filling body is prone to deformation due to disturbance, resulting in a high channel shrinkage rate and extremely high safety risks.

[0005] 2. Complex mechanical mechanism: Irregularly shaped pillars have complex geometry, high stress concentration factor (up to 2.0 to 3.5), and the interaction mechanism between the filling body and the pillar is unclear, making them prone to asymmetric lateral shear slip failure or chain collapse.

[0006] 3. Limited technical means: Existing technologies lack the means to quickly obtain accurate geometric information and mechanical parameters in an environment surrounded by filling bodies; the mining and support processes are disconnected and cannot achieve synchronous coordination; and single monitoring methods are insufficient to cope with the risks of multiple disasters coupled together.

[0007] Therefore, there is an urgent need for a systematic approach that integrates precise detection, digital modeling, collaborative support, and intelligent monitoring to achieve the safe and efficient recovery of irregularly shaped ore pillars in confined spaces. Summary of the Invention

[0008] To address the aforementioned problems, this invention proposes an intelligent collaborative support and recovery method for residual mining of irregularly shaped pillars in confined spaces.

[0009] To achieve the above objectives, the present invention adopts the following technical solution:

[0010] A method for intelligent collaborative support and recovery of residual mining of irregularly shaped pillars in confined spaces, characterized in that the method includes the following steps:

[0011] Step S1: Multi-source data fusion and dynamic digital twin model construction

[0012] Using mobile detection equipment equipped with a 3D laser scanner and ground-penetrating radar, geometric point cloud data of the exterior of the pillar and internal structural data of the filling body are acquired in a confined space. The data is then fused and mapped to a mine geological database to construct a true 3D geomechanical model that includes geological bodies, pillars, goafs, and engineering structures, forming a digital twin that can be updated in real time during mining operations.

[0013] Step S2: Dynamic Programming for Collaborative Mining Based on Intelligent Algorithms

[0014] In the digital twin, numerical simulation methods are used to simulate the stress distribution and potential failure modes of irregularly shaped pillars under mining and blasting disturbances; intelligent optimization algorithms are applied to generate a mining-support coordinated optimization scheme with the goal of maximizing recovery rate and minimizing comprehensive risk. The scheme includes mining sequence, segment height and support timing.

[0015] Step S3: Low-disturbance intelligent precision mining

[0016] According to the aforementioned collaborative optimization scheme, the mining operation is carried out using the segmented control principle; non-blasting pre-splitting is given priority, and low-disturbance controlled blasting technology is used for areas that must be blasted to keep the peak velocity of blasting vibration below a preset threshold.

[0017] Step S4: Collaborative Operations During Sampling, Support, and Replenishment

[0018] The operation mode of "one section of mining, one unit of support, and one area of ​​backfilling" is implemented. After each section of mining is completed, quick-setting backfilling material is used immediately for precise backfilling and support, and artificial key layers are constructed in key areas.

[0019] Step S5: Full-cycle intelligent monitoring and multi-level early warning feedback

[0020] Real-time monitoring data is collected through a multi-sensor network deployed throughout the mining area, and the data is compared with the model predictions by connecting it to a digital twin. A multi-level early warning mechanism is established based on multi-parameter fusion, and mining parameters are dynamically adjusted or emergency responses are triggered according to the early warning level.

[0021] Furthermore, in step S1, the mobile detection device is an explosion-proof robot equipped with a high-precision inertial measurement unit, used for mobile scanning in extremely confined spaces where personnel cannot enter; the data fusion includes spatial registration of geometric point cloud data acquired by a 3D laser scanner with data on infill body fissures and cavities detected by ground-penetrating radar.

[0022] Furthermore, in step S2, the numerical simulation method employs a finite element-discrete element coupling method or a granular flow method; the intelligent optimization algorithm includes a genetic algorithm or a reinforcement learning algorithm.

[0023] Furthermore, in step S3, the segmented control is "outer first, then inner; lower first, then higher; mining and support synchronized"; the low-disturbance controlled blasting technology includes optimizing the micro-delay initiation sequence and charge structure, and the preset threshold blasting vibration peak velocity is 5 cm / s; the non-blasting method includes using a hydraulic splitter or a static blasting agent.

[0024] Furthermore, in step S4, the fast-setting filling material has micro-expansion characteristics, with an initial setting time of less than 10 minutes and a strength of greater than or equal to 1 MPa after 1 hour; the construction of the artificial critical layer refers to the priority of high-strength filling in the top plate or stress concentration area to form a temporary load-bearing structure.

[0025] Furthermore, in step S5, the multi-sensor network includes a fiber optic grating sensor, a micro-vibration monitoring system, a wireless displacement sensor, a wireless stress sensor, and an ambient gas sensor.

[0026] Furthermore, in step S5, the multi-level early warning mechanism includes:

[0027] Blue alert: Monitoring values ​​show a trend change, suggesting increased monitoring;

[0028] Yellow alert: When key indicators exceed the first preset threshold, the system will automatically adjust the mining or support parameters.

[0029] Red alert: If key indicators exceed the second preset threshold or micro-vibration energy increases suddenly or gas concentration exceeds the standard, the system will automatically trigger a stop operation command and start the emergency procedure.

[0030] Furthermore, the key indicators include top plate displacement, with the first preset threshold being 5 mm and the second preset threshold being 10 mm.

[0031] Furthermore, in step S4, a jetting robot or grouting robot is used to perform differentiated and customized filling based on the void morphology indicated by the digital twin and the stress state fed back by monitoring.

[0032] Furthermore, the confined space refers to the working environment that is small, poorly ventilated, and enclosed by the filling body due to the room-and-pillar subsequent backfilling mining method; the irregular pillars include peach-shaped or saddle-shaped pillars.

[0033] The beneficial effects of this invention are as follows:

[0034] 1. Significantly improved resource recovery rate: Through precise detection and intelligent planning, combined with segmented cutting process, the recovery rate of residual ore can be increased to over 90%.

[0035] 2. Significantly enhanced safety: Through the simultaneous "mining-support" process and fast-setting materials, the void area is quickly sealed to prevent roof collapse; the intelligent early warning mechanism based on multi-source information fusion enables full-process safety control.

[0036] 3. Intelligent and low-disturbance operation: Digital twins and AI algorithms have enabled the transformation from experience-driven to data-driven operations; low-disturbance blasting and micro intelligent equipment have adapted to the needs of confined space operations and reduced damage to the filling material and surrounding rock.

[0037] 4. Green and environmentally friendly: Fully utilize solid waste for backfilling, reduce waste emissions, and lower the environmental impact of mining. Attached Figure Description

[0038] Figure 1 This is a flowchart of the method of the present invention. Detailed Implementation

[0039] The present invention will now be described in further detail with reference to the accompanying drawings and specific embodiments.

[0040] This invention proposes an intelligent collaborative support and recovery method for residual mining of irregularly shaped pillars in confined spaces. From a systems engineering perspective, this method constructs a closed-loop operation system of "perception-decision-execution-feedback". At the perception level, multi-source sensors such as LiDAR and GPR are used to acquire precise three-dimensional information of the pillars and backfill within the confined space, constructing a dynamically updated digital twin model to solve the problem of "unclear visibility". At the decision-making level, based on numerical simulation methods such as FDEM and AI optimization algorithms such as genetic algorithms, multi-objective collaborative optimization is performed on the mining sequence, segmentation parameters, and support timing, realizing a shift from experience-based decision-making to data-driven decision-making. At the execution level, miniaturized intelligent equipment and low-disturbance blasting technology are used for segmented and precise mining, and an integrated "mining-support-backfilling" collaborative operation mode is introduced. Rapidly solidifying micro-expansion materials are used to quickly construct artificial key layers, solving the problems of "unable to mine or support". At the feedback level, a multi-sensor network covering the entire area is used for real-time monitoring, establishing a hierarchical early warning mechanism to achieve closed-loop safety management throughout the entire process.

[0041] To better illustrate the implementation process of this invention, the following detailed description is based on an actual engineering example from the Yongping Copper Mine of Jiangxi Copper Corporation.

[0042] Example

[0043] This mine is a typical open-pit to underground mine, with numerous peach-shaped and saddle-shaped irregular pillars remaining at depth. These pillars are situated in extremely confined spaces, with an average working height of less than 2.5m and a width of less than 3m, and are surrounded by unstable backfill, making direct mining extremely risky. The method described in this invention is used for recovery, and the specific implementation steps are as follows:

[0044] Step 1: High-Precision Spatial Perception and Dynamic Digital Twin Model Construction. First, an explosion-proof robot equipped with a 3D laser scanner (LiDAR), ground-penetrating radar (GPR), and a high-precision inertial measurement unit (IMU) is remotely driven into the confined space. The LiDAR system performs a full-coverage scan of the pillar surface, acquiring geometric point cloud data with centimeter-level precision; the GPR system simultaneously detects the development of fractures and the distribution of cavities within the filling body. The acquired point cloud data and GPR data are spatially registered and fused, and mapped onto the mine's existing geological database to construct a true 3D geomechanical model including geological bodies, irregularly shaped pillars, goaf areas, and engineering structures. This model, as a digital twin, can be dynamically updated in real time based on the latest scan data as subsequent mining operations proceed. Simultaneously, machine learning algorithms (such as point cloud segmentation networks) are used to automatically identify ore-rock boundaries and accurately calculate the residual ore volume and grade distribution.

[0045] Step 2: Dynamic Programming for Collaborative Mining Based on Intelligent Algorithms. In the constructed digital twin platform, the stress evolution of the target peach-shaped pillar under different mining paths was numerically simulated using the finite element-discrete element (FDEM) method. Simulation results showed significant stress concentration in the curvature abrupt change zone at the top of the pillar, with a coefficient as high as 2.8. If integral mining is adopted, lateral shear failure is highly likely. Based on the simulation results, a genetic algorithm was applied to optimize the process with the objective functions of "maximizing recovery rate, minimizing production cycle, and minimizing overall risk." The final optimized scheme was determined to be a mining sequence of "bottom-up, outside-in, segment-by-segment advancement," with the height of each segment strictly controlled within 2.0m.

[0046] Step 3: Low-Disturbance Intelligent Precision Mining. Based on the optimized plan, two miniature intelligent mining robots were deployed for operation. The robots have integrated functions of drilling, cutting, crushing, and loading, and are operated remotely.

[0047] Guide groove cutting: The robot first drills a directional guide groove on the surface of the pillar to control the subsequent crushing range and reduce over-excavation.

[0048] Low-disturbance blasting: Segmented charges are arranged in the guide channel, using micro-delay initiation technology with an initiation interval set at 25ms and the charge per hole controlled to within 1.2kg. Actual measurements show that the peak power velocity (PPV) of blasting is consistently controlled below 5cm / s, effectively preventing instability of the surrounding filling material.

[0049] Ore crushing and transportation: The ore crushed by blasting is transported to the segmented ore pass by the robot's own shovel and transport device, and then transported out by the circular transport system.

[0050] Step 4: Strictly adhere to the coordinated operation of mining-supporting-filling, following a cyclical work pattern of "mining one section, supporting one unit, and filling one area." After a 2m high section is mined, immediately inject fast-setting micro-expansion cemented filling material using a jetting robot. This material uses an early-strength formula, with an initial setting time of only 8 minutes and a strength reaching 1.2MPa within 1 hour. In critical areas such as the roof and stress concentration zones, prioritize the construction of "artificial key layers" through precise grouting. These layers serve as temporary roof supports for the overlying strata, ensuring subsequent mining operations are carried out under the protection of the support structure.

[0051] Step 5: Full-cycle intelligent monitoring and multi-level early warning feedback. Before the start of the mining operation, six wireless displacement gauges and four stress gauges were deployed on the roof of the experimental stope, acoustic emission sensors were deployed at the boundary of the backfill, and wireless toxic and harmful gas and dust monitors were deployed in the working space. All sensor data were connected to the digital twin platform in real time and compared with the model predictions. The system was set with multi-level early warning thresholds:

[0052] Yellow alert: Triggered when the roof displacement exceeds 5mm, the system prompts operators to strengthen local support or reduce the mining advance.

[0053] Red Alert: Triggered when roof displacement exceeds 10mm, or when a sudden increase in micro-vibration energy or excessive concentration of toxic or harmful gases is detected. The system automatically issues a command to suspend operations and initiates emergency ventilation and personnel evacuation procedures. In actual implementation, through real-time system control, the maximum roof displacement was consistently kept within 7mm, no red alert events occurred, and there were no roof collapses or sidewall spalling accidents throughout the entire operation.

[0054] Through the above implementation, the residual ore recovery rate in the area has been significantly increased from 70% by traditional methods to 92%, the efficiency of single-stage operations has been increased by about 20%, and inherent safety and green mining have been achieved, verifying the advanced nature and practicality of the technical solution of this invention.

[0055] Those skilled in the art will understand that the above embodiments are only for illustrating the present invention and are not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A method for intelligent collaborative support and recovery of residual mining of irregularly shaped pillars in confined spaces, characterized in that, The method includes the following steps: Step S1: Multi-source data fusion and dynamic digital twin model construction Using mobile detection equipment equipped with a 3D laser scanner and ground-penetrating radar, geometric point cloud data of the exterior of the pillar and internal structural data of the filling body are acquired in a confined space. The data is then fused and mapped to a mine geological database to construct a true 3D geomechanical model that includes geological bodies, pillars, goafs, and engineering structures, forming a digital twin that can be updated in real time during mining operations. Step S2: Dynamic Programming for Collaborative Mining Based on Intelligent Algorithms In the digital twin, numerical simulation methods are used to simulate the stress distribution and potential failure modes of irregularly shaped pillars under mining and blasting disturbances; intelligent optimization algorithms are applied to generate a mining-support coordinated optimization scheme with the goal of maximizing recovery rate and minimizing comprehensive risk. The scheme includes mining sequence, segment height and support timing. Step S3: Low-disturbance intelligent precision mining According to the aforementioned collaborative optimization scheme, the mining operation is carried out using the segmented control principle; non-blasting pre-splitting is given priority, and low-disturbance controlled blasting technology is used for areas that must be blasted to keep the peak velocity of blasting vibration below a preset threshold. Step S4: Collaborative Operations During Sampling, Support, and Replenishment The operation mode of "one section of mining, one unit of support, and one area of ​​backfilling" is implemented. After each section of mining is completed, quick-setting backfilling material is used immediately for precise backfilling and support, and artificial key layers are constructed in key areas. Step S5: Full-cycle intelligent monitoring and multi-level early warning feedback Real-time monitoring data is collected through a multi-sensor network deployed throughout the mining area, and the data is compared with the model predictions by connecting it to a digital twin. A multi-level early warning mechanism is established based on multi-parameter fusion, and mining parameters are dynamically adjusted or emergency responses are triggered according to the early warning level.

2. The method according to claim 1, characterized in that, In step S1, the mobile detection device is an explosion-proof robot equipped with a high-precision inertial measurement unit, used for mobile scanning in extremely confined spaces where personnel cannot enter; the data fusion includes spatial registration of geometric point cloud data acquired by a 3D laser scanner with data on infill cracks and cavities detected by ground-penetrating radar.

3. The method according to claim 1, characterized in that, In step S2, the numerical simulation method employs finite element-discrete element coupling or granular flow methods; the intelligent optimization algorithm includes genetic algorithms or reinforcement learning algorithms.

4. The method according to claim 1, characterized in that, In step S3, the segmented control is "outer first, then inner; lower first, then higher; mining and support synchronized"; the low-disturbance controlled blasting technology includes optimizing the micro-delay initiation sequence and charge structure, and the preset threshold blasting vibration peak velocity is 5 cm / s; the non-blasting method includes using a hydraulic splitter or a static blasting agent.

5. The method according to claim 1, characterized in that, In step S4, the fast-setting filling material has micro-expansion characteristics, with an initial setting time of less than 10 minutes and a strength of greater than or equal to 1 MPa after 1 hour; the construction of the artificial critical layer refers to the priority of high-strength filling in the top plate or stress concentration area to form a temporary load-bearing structure.

6. The method according to claim 1, characterized in that, In step S5, the multi-sensor network includes a fiber optic grating sensor, a micro-vibration monitoring system, a wireless displacement sensor, a wireless stress sensor, and an ambient gas sensor.

7. The method according to claim 1, characterized in that, In step S5, the multi-level early warning mechanism includes: blue warning: the monitoring value shows a trend change, prompting increased observation; yellow warning: the key indicator exceeds the first preset threshold, and the system automatically adjusts the mining or support parameters; red warning: the key indicator exceeds the second preset threshold or the micro-seismic energy increases suddenly or the gas concentration exceeds the standard, and the system automatically triggers a stop operation command and starts the emergency procedure.

8. The method according to claim 7, characterized in that, The key indicators include top plate displacement, with the first preset threshold being 5mm and the second preset threshold being 10mm.

9. The method according to claim 1, characterized in that, In step S4, a jetting robot or grouting robot is used to perform differentiated and customized filling based on the void shape indicated by the digital twin and the stress state fed back by monitoring.

10. The method according to claim 1, characterized in that, The confined space refers to the narrow, poorly ventilated working environment formed by the room-and-pillar subsequent backfilling mining method, which is enclosed by the backfill body; the irregular pillars include peach-shaped or saddle-shaped pillars.

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