Integrated intelligent operation system for coal mine underground drilling extraction monitoring and control method

Abstract

The application discloses a coal mine underground drilling extraction monitoring integrated intelligent operation system and a control method, and relates to the technical field of intelligent monitoring in coal mines.The system comprises a mine mobile chassis unit, a drilling execution unit, a gas extraction unit, an intelligent monitoring unit, a central control unit and a communication computing unit.The system realizes closed-loop control of the drilling trajectory through a dynamic path planning algorithm driven by digital twinning, effectively solving the problem of large deviation of the traditional drilling trajectory.Meanwhile, the drilling parameters are adjusted in real time according to the drilling resistance, the drilling head is accurately matched with the lithology, the abnormal wear of the drilling head is effectively reduced, the equipment maintenance cost is reduced, the gas extraction negative pressure is self-adaptively adjusted through a fuzzy PID algorithm, and the extraction parameters are dynamically adjusted according to the gas concentration and flow.

Description

Technical Field

[0001] This invention relates to the field of intelligent monitoring technology in underground coal mines, specifically to an integrated intelligent operation system and control method for monitoring and controlling underground drilling and extraction in coal mines. Background Technology

[0002] Underground drilling, gas extraction, and disaster monitoring in coal mines are core and crucial links in ensuring safe production, directly impacting the lives of underground workers and the efficient extraction of coal resources. However, current traditional underground mining operations still face numerous technical bottlenecks, specifically in the following aspects: 1. Low drilling accuracy and high equipment wear: Existing directional drilling rigs mainly rely on manual experience to adjust the drilling trajectory. Under complex geological conditions such as fractured coal seams and high-stress areas, due to the lack of real-time geological feedback and dynamic path correction, the drilling trajectory deviation is generally large, and the accuracy error often exceeds 1°. At the same time, the low matching degree between drilling parameters and lithology can easily lead to a sudden increase in drilling resistance, causing abnormal wear of diamond composite drill bits and deformation of drill rods, which not only reduces drilling efficiency but also increases equipment maintenance costs.

[0003] 2. Low gas extraction efficiency, high energy consumption, and safety hazards: Traditional gas extraction systems use fixed negative pressure pump stations for extraction, which cannot adjust the extraction negative pressure according to dynamic parameters such as the concentration and flow rate of gas emanating from the borehole. For low-concentration gas extraction, there is a problem of high energy consumption, while for high-concentration gas emanating, insufficient negative pressure leads to incomplete extraction and gas accumulation. In addition, the extraction pipeline lacks effective blowout prevention, anti-clogging, and gas-liquid separation measures. Coal slag, rock cuttings, and condensate can easily cause pipeline blockage and even cause wellhead blowout accidents, seriously threatening the safety of underground operations.

[0004] 3. Delayed disaster monitoring and early warning, low accuracy, and information silos: Existing downhole monitoring systems mainly rely on fixed-point monitoring, which cannot update monitoring data of risk areas in real time as drilling progresses. Moreover, alarm thresholds depend on static manual experience values, making it difficult to adapt to dynamically changing geological conditions. At the same time, the monitoring data from drilling, extraction, and monitoring are heterogeneous, lacking an effective data fusion and sharing mechanism, forming information silos. This makes it impossible to achieve comprehensive assessment and coordinated prevention and control of disaster risks, resulting in delayed early warnings and high false alarm and missed alarm rates for disasters such as gas over-limit, water inrush, and surrounding rock instability.

[0005] 4. High safety risks and low efficiency of manual operation: In the traditional operation mode, drilling, extraction and monitoring all require a lot of manual on-site operation. The operators are exposed to the dangerous underground environment of high gas, high dust and high stress for a long time, which poses extremely high safety risks. At the same time, manual operation is highly subjective and has low efficiency, making it difficult to achieve coordination between various links, resulting in a complicated overall operation process and long construction period.

[0006] Currently, although some intelligent drilling or extraction equipment has emerged in the industry, these are all single-function devices and have not achieved integrated and coordinated control of drilling, extraction, and monitoring, thus failing to fundamentally solve the aforementioned technical problems. Therefore, there is an urgent need to develop a highly integrated and intelligent intelligent operation system and control method for underground drilling, extraction, and monitoring in coal mines. This system should achieve fully unmanned operation, including precise borehole positioning, dynamic optimization of extraction, and real-time disaster early warning, through multi-unit collaborative control, deep fusion of multi-source data, and intelligent algorithm-driven operation, filling a technological gap in the industry. Summary of the Invention

[0007] (a) Technical problems to be solved To address the shortcomings of existing technologies, this invention provides an integrated intelligent operation system and control method for underground drilling, extraction, and monitoring in coal mines. This system enables intelligent, integrated, and unmanned operation throughout the entire process of drilling, extraction, and monitoring, improving drilling trajectory accuracy, gas extraction efficiency, and disaster early warning accuracy. It also breaks down information silos between different stages, reduces the safety risks of manual underground operations, and enhances the level of safe production and resource recovery rate in coal mines.

[0008] (II) Technical Solution To achieve the above objectives, the present invention provides the following technical solution: an integrated intelligent operation system for monitoring and drilling in underground coal mines, comprising: The mining mobile chassis unit adopts a mining explosion-proof and anti-slip design, which is suitable for complex road conditions and explosive environments in coal mines. It is used to support various functional units and realize the movement and positioning of the system. The drilling execution unit, which is fixedly connected to the mining mobile chassis unit, includes a 6-8 degree-of-freedom mining explosion-proof multi-degree-of-freedom robotic arm, a measurement while drilling (MWD) system, a diamond composite drill bit, a drilling power unit, and a drill rod pushing unit. It is used to perform drilling operations and collect drilling inclination angle, azimuth angle, drilling resistance, drilling speed, and in-hole pressure parameters in real time. The gas extraction unit, which is connected to the drilling end of the drilling execution unit, includes a negative pressure adaptive adjustment device, a multi-stage filtration system, a three-stage blowout prevention device, a gas-liquid separation unit, and a pipeline anti-clogging unit, and is used to extract, purify, prevent blowouts, and prevent clogging of the gas gushing from the borehole. The intelligent monitoring unit, deployed around the mining mobile chassis unit and the work area, includes an inertial navigation unit, a gas concentration sensor, a dust laser scattering instrument, a micro-vibration monitoring array, a temperature and humidity sensor, a pressure sensor, and a mining explosion-proof video monitoring unit. It is used to collect real-time data on gas concentration, dust concentration, surrounding rock micro-vibration signals, ambient temperature and humidity, work area pressure, and video image data of the work environment. The central control unit is electrically connected to the drilling execution unit, the gas extraction unit, and the intelligent monitoring unit, and includes a data processing module, an algorithm calculation module, and an instruction issuing module. It is used to receive and process the collected data from each unit to realize closed-loop control of the drilling trajectory, adaptive adjustment of gas extraction parameters, and disaster risk classification and early warning. The communication computing unit is connected to the central control unit and includes a 5G hybrid private network module, a mine-use wired communication module, and an edge computing node. The 5G hybrid private network module adopts a mine-use explosion-proof base station and antenna design to achieve low-latency data interaction between the underground and ground monitoring systems. The mine-use wired communication module serves as a backup communication link, and the edge computing node is used to achieve local rapid processing of multi-source data.

[0009] Preferably, the algorithm operation module of the central control unit pre-stores a digital twin-driven dynamic path planning algorithm, a fuzzy PID algorithm, and a multi-source data fusion algorithm that integrates DS evidence theory with neural networks; The parameters of the fuzzy PID algorithm are tuned online using an online self-tuning method. The gas concentration threshold is set to 0.5%-1.0%, and the extraction flow rate threshold is set to 5-20 m³ / min. When the detected value exceeds the threshold, the algorithm automatically adjusts the proportional coefficient, integral coefficient, and derivative coefficient to achieve rapid and accurate adjustment of the negative pressure.

[0010] Preferably, the multi-stage filtration system includes a coarse filter layer, a fine filter layer, and an activated carbon adsorption layer. The coarse filter layer filters out coal slag and rock debris solid impurities in the gas, the fine filter layer filters out fine particulate matter, and the activated carbon adsorption layer adsorbs harmful gases in the gas. The dust content of the filtered gas is less than 10 mg / m³.

[0011] Preferably, the microseismic monitoring array includes at least four mine-use explosion-proof microseismic sensors, which are deployed within a 5-10m radius around the borehole. The monitoring frequency range is 0.1-100Hz, which can capture the amplitude, frequency, and propagation speed of the surrounding rock microseismic signal, thereby enabling early identification of surrounding rock instability.

[0012] Preferably, the 5G hybrid private network module adopts network slicing technology to allocate dedicated slices for drilling, extraction, and monitoring services, with end-to-end transmission latency ≤20ms, uplink peak rate ≥100Mbps, downlink peak rate ≥1Gbps, and data only flows within the mining area private network. VxLAN tunneling technology is used to achieve transmission layer encryption.

[0013] Preferably, it also includes a remote upgrade and fault diagnosis module. The central control unit collects the operating status data of each unit in real time, and uploads the fault data to the ground monitoring system through the communication computing unit. Ground technicians can update the algorithm and program through the remote upgrade module and remotely locate and troubleshoot equipment faults through the fault diagnosis module.

[0014] This invention also provides a control method for an integrated intelligent operation system for monitoring and draining underground coal mine boreholes, specifically including the following steps: S1. System initialization and positioning: The positioning module of the mining mobile chassis unit, combined with the underground three-dimensional geological digital twin model, completes the precise positioning of the work area. The intelligent monitoring unit starts full parameter acquisition, the communication computing unit establishes a two-way communication link between the underground and the ground, and each unit completes self-check and enters the standby state. S2. Closed-loop control of borehole trajectory driven by digital twin: The central control unit pre-plans the borehole path based on the downhole three-dimensional geological digital twin model. The borehole execution unit starts the drilling operation. The measurement while drilling system collects the dynamic parameters of the borehole in real time and transmits them to the central control unit. The algorithm calculation module compares and analyzes the actual parameters with the planned parameters. Through the dynamic path planning algorithm, the attitude of the multi-degree-of-freedom robotic arm and the parameters of the drilling power unit are adjusted in real time to achieve closed-loop correction of the borehole trajectory. S3. Adaptive adjustment of negative pressure in gas extraction driven by fuzzy PID algorithm: After drilling, the gas extraction unit connects to the borehole end and starts extraction. Gas concentration sensor and flow sensor collect gas concentration and extraction flow data in real time. The fuzzy PID algorithm of the central control unit takes gas concentration and extraction flow as input, and outputs negative pressure adjustment value after fuzzification, fuzzy inference and defuzzification. The negative pressure adaptive adjustment device adjusts the extraction negative pressure in real time according to the adjustment value. At the same time, the multi-stage filtration system, gas-liquid separation unit and pipeline anti-blocking unit work synchronously to achieve efficient purification and extraction of gas. S4. Disaster Risk Grading and Early Warning Based on Multi-Source Data Fusion: The intelligent monitoring unit transmits the collected gas concentration, dust concentration, surrounding rock microseismic signals, and ambient temperature and humidity data to the edge computing node for local preliminary processing. The processed data is then transmitted to the central control unit via the communication computing unit. The algorithm operation module performs fusion analysis on the multi-source data using an algorithm that combines DS evidence theory and neural networks. Based on the analysis results, it provides level one, level two, and level three early warnings for risks such as gas exceeding limits, water inrush signs, surrounding rock instability, and dust exceeding standards, and triggers corresponding linkage and response measures. S5. Full-process collaborative scheduling and linkage control: The central control unit realizes the collaborative scheduling of each unit based on real-time data from drilling, extraction, and monitoring. When a risk warning is detected, the corresponding linkage control is automatically triggered. At the same time, all operation data is uploaded to the ground monitoring system in real time through the communication computing unit, realizing unmanned underground operation and remote ground monitoring.

[0015] Preferably, in step S1, the downhole three-dimensional geological digital twin model is constructed using the IMMC-Geo modeling framework, which integrates UAV aerial photography of surface rock strata, downhole measured geological data, and real-time drilling data from the measurement while drilling system to achieve dynamic updates of the model, with an interface recognition accuracy of no less than 80%.

[0016] Preferably, the graded early warning in step S4 is as follows: the first-level early warning is a potential risk state, where the monitoring data is close to the threshold, the system issues an early warning and increases the data collection frequency; Level 2 warning is a risk warning state. The monitored data exceeds the threshold but does not reach an emergency state. The system issues an alarm message and automatically adjusts the extraction and drilling parameters. Level 3 early warning indicates a risk emergency state. When monitoring data significantly exceeds the threshold, the system issues an emergency alarm and triggers emergency response measures such as shutdown, drilling failure, and increased extraction. At the same time, it sends an emergency rescue signal to the ground monitoring system.

[0017] Preferably, the dynamic path planning algorithm in step S2 specifically includes the following steps: T1. Digital Twin Model Initialization and Geological Attribute Mapping: Load the downhole 3D geological digital twin model, complete the mesh generation and attribute labeling of rock interface, stress field, fracture zone, and fractured zone, and map geological risk to geological cost weight ω. g High-risk area ω g Take the largest value, force path avoidance, and establish the path from the orifice P0 to the target point P. t The global coordinate system is used to complete coordinate normalization; T2. Initial optimal path pre-planning: Starting from P0, P... t As the endpoint, a set of feasible path nodes is generated in the geological model. A hybrid evaluation function considering geological cost, distance cost, and technological cost is used to select the optimal initial path, outputting the initial planned trajectory sequence: P plan =[P0,P1,P2,...,P n ], and the corresponding design tilt and azimuth sequence [α0, α1, ..., α n ]、[β0,β1,...,β n ] T3. Real-time data acquisition and spatiotemporal synchronization while drilling: The MWD system acquires real-time trajectory parameters α(t), β(t), and L(t) at a high frequency of 10Hz, and simultaneously acquires drilling resistance F(t) and lithology R(t). The real-time data is mapped to three-dimensional coordinates according to the hole depth L(t) to obtain the actual trajectory point Pact(t), and the spatiotemporal stamp alignment between the planned trajectory and the actual trajectory is completed to eliminate sampling delay error. T4. Track Deviation Quantification Calculation: Calculate the tilt angle deviation Δα(t) = α(t) − α plan(t), azimuth deviation Δβ(t)=β(t)−β plan (t), calculate the three-dimensional spatial position deviation ΔS(t), i.e., P act (t) and P plan The Euclidean distance between corresponding nodes is used to determine whether the deviation exceeds the limit: threshold Δα max =±0.5∘、Δβ max =±0.5∘、ΔS max =0.1m; T5. Dynamic Cost Update and Correction Path Generation: The geological model is updated while drilling, incorporating real-time lithology, resistance, and microseismic signals into the cost function. Starting from the current actual borehole depth L(t), the optimal correction path for the remaining borehole depth is replanned, generating a correction trajectory sequence P. adjust Output target correction tilt angle α adj Azimuth β adj ; T6. Closed-loop correction of robotic arm posture and drilling parameters: Converting the correction angles into multi-degree-of-freedom robotic arm joint angle increments Δθ1, Δθ2, ..., Δθ n Combined with the drilling resistance F(t), the drilling speed v and torque T are dynamically adjusted. When the resistance exceeds the threshold, the speed is reduced to maintain drilling. The command is sent to the actuator to complete the real-time trajectory correction. T7. Trajectory Tracking and Termination Determination: Repeat steps 3-6 to continuously track the trajectory. When L(t) ≥ L... total And P act If the deviation between (t) and Pt is less than or equal to the allowable value, drilling will stop and the operation will be completed.

[0018] (III) Beneficial Effects This invention provides an integrated intelligent operation system and control method for monitoring and controlling underground drilling extraction in coal mines. Compared with existing technologies, it has the following advantages: (1) The integrated intelligent operation system and control method for monitoring and controlling underground drilling in coal mines achieves closed-loop control of drilling trajectory through a dynamic path planning algorithm driven by digital twins, which effectively solves the problem of large deviation in traditional drilling trajectory; at the same time, the drilling parameters are adjusted in real time according to the drilling resistance to achieve precise matching between the drill bit and the rock type, effectively reducing abnormal wear of the drill bit and reducing equipment maintenance costs; the adaptive adjustment of negative pressure for gas extraction is achieved through a fuzzy PID algorithm, and the extraction parameters are dynamically adjusted according to the gas concentration and flow rate; at the same time, it integrates three-level blowout prevention, multi-level filtration, gas-liquid separation and pipeline anti-blockage units to effectively prevent accidents such as blowouts and pipeline blockages, and improve the safety of gas extraction.

[0019] (2) The integrated intelligent operation system and control method for drilling, extraction and monitoring in coal mines adopts a multi-source data fusion algorithm that combines DS evidence theory and neural network to comprehensively analyze multi-source monitoring data; at the same time, it relies on edge computing nodes to realize local rapid data processing, realize early identification and graded disposal of disaster risks, realize unmanned operation throughout the process, reduce safety risks. All units of the system are designed to be intelligent and automated, and no manual on-site operation is required. It realizes unmanned operation of drilling, extraction and monitoring throughout the entire process, completely solves the problem of human exposure to dangerous environments in traditional operations, and greatly reduces the safety risks of underground operations.

[0020] (3) The integrated intelligent operation system and control method for drilling, extraction and monitoring in coal mines breaks down information silos and achieves synergistic integration of all links. Through the central control unit and the communication computing unit, data communication and sharing between drilling, extraction and monitoring links are realized, breaking down the information silos of traditional operations. At the same time, the coordinated scheduling and linkage control of each unit are realized, so that drilling, extraction and monitoring links are deeply integrated. Each unit of the system adopts the explosion-proof design for mining, which is suitable for the complex working environment of high gas, high dust and explosiveness in coal mines. At the same time, the dual communication links of 5G + mining wired are adopted to realize redundant backup of communication and high reliability.

[0021] (4) The integrated intelligent operation system and control method for monitoring and drilling in underground coal mines realizes real-time data interaction between underground and surface through a 5G hybrid private network. Surface technicians can remotely monitor and issue remote commands. At the same time, it has a built-in remote upgrade and fault diagnosis module to realize remote location and troubleshooting of equipment faults and remote updating of algorithm programs, which greatly improves the management efficiency of underground coal mine operations. Attached Figure Description

[0022] Figure 1 This is a block diagram illustrating the structural principle of the control system of the present invention; Figure 2 This is a flowchart of the control method of the present invention. Detailed Implementation

[0023] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0024] Please see Figure 1-2 This invention provides two technical solutions: an integrated intelligent operation system and control method for monitoring and controlling underground coal mine drilling extraction, specifically including the following embodiments: Example 1: An integrated intelligent operation system for monitoring and drilling in underground coal mines, comprising: The mining mobile chassis unit adopts a mining explosion-proof and anti-slip design, which is suitable for complex road conditions and explosive environments in coal mines. It is used to support various functional units and realize the movement and positioning of the system. The drilling execution unit, which is fixedly connected to the mining mobile chassis unit, includes a 6-8 degree-of-freedom mining explosion-proof multi-degree-of-freedom robotic arm, a drilling measurement system, a diamond composite drill bit, a drilling power unit, and a drill rod pushing unit. It is used to perform drilling operations and collect drilling inclination angle, azimuth angle, drilling resistance, drilling speed, and in-hole pressure parameters in real time. The gas extraction unit, which is connected to the drilling end of the drilling execution unit, includes a negative pressure adaptive adjustment device, a multi-stage filtration system, a three-stage blowout prevention device, a gas-liquid separation unit, and a pipeline anti-clogging unit, and is used to extract, purify, prevent blowouts, and prevent clogging of the gas gushing from the borehole. The intelligent monitoring unit is deployed around the mining mobile chassis unit and the work area. It includes an inertial navigation unit, a gas concentration sensor, a dust laser scattering instrument, a micro-vibration monitoring array, a temperature and humidity sensor, a pressure sensor, and a mining explosion-proof video monitoring unit. It is used to collect real-time data on gas concentration, dust concentration, surrounding rock micro-vibration signals, ambient temperature and humidity, work area pressure, and video image data of the work environment. The central control unit is electrically connected to the drilling execution unit, the gas extraction unit, and the intelligent monitoring unit, and includes a data processing module, an algorithm calculation module, and an instruction issuing module. It is used to receive and process the collected data from each unit to realize closed-loop control of the drilling trajectory, adaptive adjustment of gas extraction parameters, and disaster risk classification and early warning. The communication computing unit, which communicates with the central control unit, includes a 5G hybrid private network module, a mine-use wired communication module, and an edge computing node. The 5G hybrid private network module adopts a mine-use explosion-proof base station and antenna design to achieve low-latency data interaction between the underground and ground monitoring systems. The mine-use wired communication module serves as a backup communication link, and the edge computing node is used to achieve local rapid processing of multi-source data.

[0025] In this embodiment of the invention, the algorithm operation module of the central control unit pre-stores a dynamic path planning algorithm driven by digital twins, a fuzzy PID algorithm, and a multi-source data fusion algorithm that integrates DS evidence theory with neural networks; The parameters of the fuzzy PID algorithm are tuned online using an online self-tuning method. The gas concentration threshold is set to 0.5%-1.0%, and the extraction flow rate threshold is set to 5-20 m³ / min. When the detected value exceeds the threshold, the algorithm automatically adjusts the proportional coefficient, integral coefficient, and derivative coefficient to achieve rapid and accurate adjustment of negative pressure.

[0026] In this embodiment of the invention, the multi-stage filtration system includes a coarse filter layer, a fine filter layer, and an activated carbon adsorption layer. The coarse filter layer filters out coal slag and rock debris solid impurities in the gas, the fine filter layer filters out fine particulate matter, and the activated carbon adsorption layer adsorbs harmful gases in the gas. The dust content of the filtered gas is less than 10 mg / m³.

[0027] In this embodiment of the invention, the microseismic monitoring array includes at least four mine-use explosion-proof microseismic sensors, which are deployed within a 5-10m radius around the borehole. The monitoring frequency range is 0.1-100Hz, which can capture the amplitude, frequency and propagation speed of the surrounding rock microseismic signal, and realize the early identification of surrounding rock instability.

[0028] In this embodiment of the invention, the 5G hybrid private network module adopts network slicing technology to allocate dedicated slices for drilling, extraction, and monitoring services. The end-to-end transmission latency is ≤20ms, the uplink peak rate is ≥100Mbps, the downlink peak rate is ≥1Gbps, and the data only flows within the mining area private network. VxLAN tunneling technology is used to achieve transmission layer encryption.

[0029] In this embodiment of the invention, a remote upgrade and fault diagnosis module is also included. The central control unit collects the operating status data of each unit in real time, and uploads the fault data to the ground monitoring system through the communication computing unit. Ground technicians update the algorithm and program through the remote upgrade module, and realize the remote location and troubleshooting of equipment faults through the fault diagnosis module.

[0030] Example 2: The technical solution of this embodiment of the invention differs from that of Example 1 in that: the control method of the integrated intelligent operation system for monitoring and extraction of underground coal mine boreholes specifically includes the following steps: S1. System initialization and positioning: The positioning module of the mining mobile chassis unit, combined with the underground three-dimensional geological digital twin model, completes the precise positioning of the work area. The intelligent monitoring unit starts full parameter acquisition, the communication computing unit establishes a two-way communication link between the underground and the ground, and each unit completes self-check and enters the standby state. S2. Closed-loop control of borehole trajectory driven by digital twin: The central control unit pre-plans the borehole path based on the downhole three-dimensional geological digital twin model. The borehole execution unit starts the drilling operation. The measurement while drilling system collects the dynamic parameters of the borehole in real time and transmits them to the central control unit. The algorithm calculation module compares and analyzes the actual parameters with the planned parameters. Through the dynamic path planning algorithm, the attitude of the multi-degree-of-freedom robotic arm and the parameters of the drilling power unit are adjusted in real time to achieve closed-loop correction of the borehole trajectory. S3. Adaptive adjustment of negative pressure in gas extraction driven by fuzzy PID algorithm: After drilling, the gas extraction unit connects to the borehole end and starts extraction. Gas concentration sensor and flow sensor collect gas concentration and extraction flow data in real time. The fuzzy PID algorithm of the central control unit takes gas concentration and extraction flow as input, and outputs negative pressure adjustment value after fuzzification, fuzzy inference and defuzzification. The negative pressure adaptive adjustment device adjusts the extraction negative pressure in real time according to the adjustment value. At the same time, the multi-stage filtration system, gas-liquid separation unit and pipeline anti-blocking unit work synchronously to achieve efficient purification and extraction of gas. S4. Disaster Risk Grading and Early Warning Based on Multi-Source Data Fusion: The intelligent monitoring unit transmits the collected gas concentration, dust concentration, surrounding rock microseismic signals, and ambient temperature and humidity data to the edge computing node for local preliminary processing. The processed data is then transmitted to the central control unit via the communication computing unit. The algorithm operation module performs fusion analysis on the multi-source data using an algorithm that combines DS evidence theory and neural networks. Based on the analysis results, it provides level one, level two, and level three early warnings for risks such as gas exceeding limits, water inrush signs, surrounding rock instability, and dust exceeding standards, and triggers corresponding linkage and response measures. S5. Full-process collaborative scheduling and linkage control: The central control unit realizes the collaborative scheduling of each unit based on real-time data from drilling, extraction, and monitoring. When a risk warning is detected, the corresponding linkage control is automatically triggered. At the same time, all operation data is uploaded to the ground monitoring system in real time through the communication computing unit, realizing unmanned underground operation and remote ground monitoring.

[0031] In this embodiment of the invention, the downhole three-dimensional geological digital twin model in step S1 is constructed using the IMMC-Geo modeling framework, which integrates UAV aerial photography of surface rock strata information, downhole measured geological data, and real-time drilling data from the measurement while drilling system to achieve dynamic updates of the model. The interface recognition accuracy of the model is not less than 80%.

[0032] In this embodiment of the invention, the graded early warning in step S4 is specifically as follows: the first-level early warning is a potential risk state, where the monitoring data is close to the threshold, the system issues an early warning prompt and increases the data collection frequency; Level 2 warning is a risk warning state. The monitored data exceeds the threshold but does not reach an emergency state. The system issues an alarm message and automatically adjusts the extraction and drilling parameters. Level 3 early warning indicates a risk emergency state. When monitoring data significantly exceeds the threshold, the system issues an emergency alarm and triggers emergency response measures such as shutdown, drilling failure, and increased extraction. At the same time, it sends an emergency rescue signal to the ground monitoring system.

[0033] In this embodiment of the invention, the accuracy error of the closed-loop control of the drilling trajectory in step S2 is ≤ ±0.5°. When the drilling resistance exceeds the preset threshold, the central control unit automatically reduces the drilling speed and starts the drill bit protection program to prevent abnormal wear of the drill bit.

[0034] In this embodiment of the invention, the dynamic path planning algorithm in step S2 specifically includes the following steps: T1. Digital Twin Model Initialization and Geological Attribute Mapping: Load the downhole 3D geological digital twin model, complete the mesh generation and attribute labeling of rock interface, stress field, fracture zone, and fractured zone, and map geological risks (fracture, high stress, water-conducting fractures) into geological cost weights ω. g High-risk area ω g Take the largest value, force path avoidance, and establish the path from the orifice P0 to the target point P. t The global coordinate system is used to complete coordinate normalization; T2. Initial optimal path pre-planning: Starting from P0, P... t As the endpoint, a set of feasible path nodes is generated in the geological model. A hybrid evaluation function considering geological cost, distance cost, and technological cost is used to select the optimal initial path, outputting the initial planned trajectory sequence: P plan =[P0,P1,P2,...,P n ], and the corresponding design tilt and azimuth sequence [α0, α1, ..., α n ]、[β0,β1,...,β n ] T3. Real-time data acquisition and spatiotemporal synchronization while drilling: The MWD system acquires real-time trajectory parameters α(t), β(t), and L(t) at a high frequency of 10Hz, and simultaneously acquires drilling resistance F(t) and lithology R(t). The real-time data is mapped to three-dimensional coordinates according to the hole depth L(t) to obtain the actual trajectory point Pact(t), and the spatiotemporal stamp alignment between the planned trajectory and the actual trajectory is completed to eliminate sampling delay error. T4. Track Deviation Quantification Calculation: Calculate the tilt angle deviation Δα(t) = α(t) − α plan (t), azimuth deviation Δβ(t)=β(t)−β plan (t), calculate the three-dimensional spatial position deviation ΔS(t), i.e., P act (t) and P plan The Euclidean distance between corresponding nodes is used to determine whether the deviation exceeds the limit: threshold Δα max =±0.5∘、Δβ max =±0.5∘、ΔS max =0.1m; T5. Dynamic Cost Update and Correction Path Generation: The geological model is updated while drilling, incorporating real-time lithology, resistance, and microseismic signals into the cost function. Starting from the current actual borehole depth L(t), the optimal correction path for the remaining borehole depth is replanned, generating a correction trajectory sequence P. adjust Output target correction tilt angle α adj Azimuth β adj ; T6. Closed-loop correction of robotic arm posture and drilling parameters: Converting the correction angles into multi-degree-of-freedom robotic arm joint angle increments Δθ1, Δθ2, ..., Δθ n Combined with the drilling resistance F(t), the drilling speed v and torque T are dynamically adjusted. When the resistance exceeds the threshold, the speed is reduced to maintain drilling. The command is sent to the actuator to complete the real-time trajectory correction. T7. Trajectory Tracking and Termination Determination: Repeat steps 3-6 to continuously track the trajectory. When L(t) ≥ L... total And P act If the deviation between (t) and Pt is less than or equal to the allowable value, drilling will stop and the operation will be completed.

[0035] The fuzzy PID algorithm is used for adaptive regulation of negative pressure in gas extraction, with the gas concentration deviation e(t) and the rate of change of deviation ec(t) as input variables, and the PID parameter correction amount ΔK. p ΔK i ΔK d As the output variable, precise negative pressure control is achieved through fuzzification, fuzzy inference, defuzzification, and parameter self-tuning, as detailed below: 1. Deviation calculation: e(t) = r(t) − y(t), , where r(t) is the set gas concentration and y(t) is the real-time gas concentration; 2. Fuzzification: e(t) and ec(t) are quantized to the universe of discourse [-6,6] and converted into fuzzy subsets {NB,NM,NS,ZO,PS,PM,PB} using a triangular membership function; 3. Fuzzy Inference: Based on the Mamdani inference method, the output fuzzy quantity is obtained according to the preset fuzzy rule base; 4. Defuzzification: The centroid method is used to convert the fuzzy quantity into a precise correction quantity ΔK. p ΔK i ΔK d ; 5. Parameter tuning: K p =K p0 +ΔK p K i =K i0 +ΔK i K d =K d0 +ΔK d Complete online parameter self-calibration; 6. Control output: The function converts u(t) into a negative pressure command of 0-0.1MPa, which drives the negative pressure adaptive adjustment device to achieve dynamic adjustment.

[0036] In summary, this invention achieves closed-loop control of borehole trajectory through a digital twin-driven dynamic path planning algorithm, effectively solving the problem of large deviations in traditional borehole trajectories. Simultaneously, it adjusts drilling parameters in real time based on drilling resistance, achieving precise matching between the drill bit and rock type, effectively reducing abnormal drill bit wear and equipment maintenance costs. It also achieves adaptive adjustment of gas extraction negative pressure through a fuzzy PID algorithm, dynamically adjusting extraction parameters based on gas concentration and flow rate. Furthermore, it integrates three-stage blowout prevention, multi-stage filtration, gas-liquid separation, and pipeline anti-blockage units, effectively preventing accidents such as blowouts and pipeline blockages, improving the safety of gas extraction. By employing a multi-source data fusion algorithm that combines DS evidence theory with neural networks, it comprehensively analyzes multi-source monitoring data. Simultaneously, it relies on edge computing nodes to achieve rapid local data processing, enabling early identification and graded handling of disaster risks, achieving fully unmanned operation, and reducing safety risks. All units of the system are intelligently and automatically designed, requiring no manual on-site operation, realizing unmanned downhole operation throughout the entire process of drilling, extraction, and monitoring. This completely solves the problem of human exposure to hazardous environments in traditional operations, significantly reducing downhole safety risks.

[0037] Breaking down information silos and achieving collaborative integration across all stages, the system enables data exchange and sharing among drilling, extraction, and monitoring stages through a central control unit and communication computing units, thus breaking down the information silos of traditional operations. Simultaneously, it achieves collaborative scheduling and linkage control among units, deeply integrating drilling, extraction, and monitoring. All units of the system adopt a mine-grade explosion-proof design, adapting to the complex working environment of high gas, high dust, and explosiveness in underground coal mines. Furthermore, it employs dual communication links of 5G and mine-grade wired connections, achieving redundant backup and high reliability. Real-time data interaction between underground and surface operations is achieved through a 5G hybrid private network, allowing surface technicians to remotely monitor and issue commands. Additionally, it incorporates remote upgrade and fault diagnosis modules, enabling remote location and troubleshooting of equipment faults and remote updates of algorithm programs, significantly improving the management efficiency of underground coal mine operations.

[0038] Furthermore, any content not described in detail in this specification is existing technology known to those skilled in the art.

[0039] It should be noted that, in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, article, or apparatus.

[0040] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. An integrated intelligent operation system for monitoring and drilling in underground coal mines, characterized in that: include: The mining mobile chassis unit adopts a mining explosion-proof and anti-slip design, which is suitable for complex road conditions and explosive environments in coal mines. It is used to support various functional units and realize the movement and positioning of the system. The drilling execution unit, which is fixedly connected to the mining mobile chassis unit, includes a 6-8 degree-of-freedom mining explosion-proof multi-degree-of-freedom robotic arm, a drilling measurement system, a diamond composite drill bit, a drilling power unit, and a drill rod pushing unit. It is used to perform drilling operations and collect drilling inclination angle, azimuth angle, drilling resistance, drilling speed, and in-hole pressure parameters in real time. The gas extraction unit, which is connected to the drilling end of the drilling execution unit, includes a negative pressure adaptive adjustment device, a multi-stage filtration system, a three-stage blowout prevention device, a gas-liquid separation unit, and a pipeline anti-clogging unit, and is used to extract, purify, prevent blowouts, and prevent clogging of the gas gushing from the borehole. The intelligent monitoring unit, deployed around the mining mobile chassis unit and the work area, includes an inertial navigation unit, a gas concentration sensor, a dust laser scattering instrument, a micro-vibration monitoring array, a temperature and humidity sensor, a pressure sensor, and a mining explosion-proof video monitoring unit. It is used to collect real-time data on gas concentration, dust concentration, surrounding rock micro-vibration signals, ambient temperature and humidity, work area pressure, and video image data of the work environment. The central control unit is electrically connected to the drilling execution unit, the gas extraction unit, and the intelligent monitoring unit, and includes a data processing module, an algorithm calculation module, and an instruction issuing module. It is used to receive and process the collected data from each unit to realize closed-loop control of the drilling trajectory, adaptive adjustment of gas extraction parameters, and disaster risk classification and early warning. The communication computing unit is connected to the central control unit and includes a 5G hybrid private network module, a mine-use wired communication module, and an edge computing node. The 5G hybrid private network module adopts a mine-use explosion-proof base station and antenna design to achieve low-latency data interaction between the underground and ground monitoring systems. The mine-use wired communication module serves as a backup communication link, and the edge computing node is used to achieve local rapid processing of multi-source data.

2. The integrated intelligent operation system for monitoring and extraction in underground coal mine drilling as described in claim 1, characterized in that: The algorithm processing module of the central control unit pre-stores a dynamic path planning algorithm driven by digital twins, a fuzzy PID algorithm, and a multi-source data fusion algorithm that integrates DS evidence theory with neural networks. The parameters of the fuzzy PID algorithm are tuned online using an online self-tuning method. The gas concentration threshold is set to 0.5%-1.0%, and the extraction flow rate threshold is set to 5-20 m³ / min. When the detected value exceeds the threshold, the algorithm automatically adjusts the proportional coefficient, integral coefficient, and derivative coefficient to achieve rapid and accurate adjustment of the negative pressure.

3. The integrated intelligent operation system for monitoring and extraction in underground coal mine drilling as described in claim 1, characterized in that: The multi-stage filtration system includes a coarse filter layer, a fine filter layer, and an activated carbon adsorption layer. The coarse filter layer filters out coal slag and rock debris solid impurities from the gas, the fine filter layer filters out fine particulate matter, and the activated carbon adsorption layer adsorbs harmful gases from the gas. The dust content of the filtered gas is less than 10 mg / m³.

4. The integrated intelligent operation system for monitoring and extraction in underground coal mine drilling as described in claim 1, characterized in that: The microseismic monitoring array includes at least four mine-use explosion-proof microseismic sensors, which are deployed within a 5-10m radius around the borehole. The monitoring frequency range is 0.1-100Hz, and the array is capable of capturing the amplitude, frequency, and propagation speed of the surrounding rock microseismic signals, thereby enabling early identification of surrounding rock instability.

5. The integrated intelligent operation system for monitoring and extraction in underground coal mine drilling as described in claim 1, characterized in that: The 5G hybrid private network module adopts network slicing technology to allocate dedicated slices for drilling, extraction, and monitoring services. The end-to-end transmission latency is ≤20ms, the uplink peak rate is ≥100Mbps, the downlink peak rate is ≥1Gbps, and the data only flows within the mining area private network. VxLAN tunneling technology is used to achieve transmission layer encryption.

6. The integrated intelligent operation system for monitoring and extraction in underground coal mine drilling as described in claim 1, characterized in that: It also includes a remote upgrade and fault diagnosis module. The central control unit collects the operating status data of each unit in real time, and uploads the fault data to the ground monitoring system through the communication computing unit. Ground technicians can update the algorithm and program through the remote upgrade module and remotely locate and troubleshoot equipment faults through the fault diagnosis module.

7. A control method for the integrated intelligent operation system for monitoring and extraction of underground coal mine boreholes as described in any one of claims 1-6, characterized in that: Specifically, the following steps are included: S1. System initialization and positioning: The positioning module of the mining mobile chassis unit, combined with the underground three-dimensional geological digital twin model, completes the precise positioning of the work area. The intelligent monitoring unit starts full parameter acquisition, the communication computing unit establishes a two-way communication link between the underground and the ground, and each unit completes self-check and enters the standby state. S2. Closed-loop control of borehole trajectory driven by digital twin: The central control unit pre-plans the borehole path based on the downhole three-dimensional geological digital twin model. The borehole execution unit starts the drilling operation. The measurement while drilling system collects the dynamic parameters of the borehole in real time and transmits them to the central control unit. The algorithm calculation module compares and analyzes the actual parameters with the planned parameters. Through the dynamic path planning algorithm, the attitude of the multi-degree-of-freedom robotic arm and the parameters of the drilling power unit are adjusted in real time to achieve closed-loop correction of the borehole trajectory. S3. Adaptive adjustment of negative pressure in gas extraction driven by fuzzy PID algorithm: After drilling, the gas extraction unit connects to the borehole end and starts extraction. Gas concentration sensor and flow sensor collect gas concentration and extraction flow data in real time. The fuzzy PID algorithm of the central control unit takes gas concentration and extraction flow as input, and outputs negative pressure adjustment value after fuzzification, fuzzy inference and defuzzification. The negative pressure adaptive adjustment device adjusts the extraction negative pressure in real time according to the adjustment value. At the same time, the multi-stage filtration system, gas-liquid separation unit and pipeline anti-blocking unit work synchronously to achieve efficient purification and extraction of gas. S4. Disaster Risk Grading and Early Warning Based on Multi-Source Data Fusion: The intelligent monitoring unit transmits the collected gas concentration, dust concentration, surrounding rock microseismic signals, and ambient temperature and humidity data to the edge computing node for local preliminary processing. The processed data is then transmitted to the central control unit via the communication computing unit. The algorithm operation module performs fusion analysis on the multi-source data using an algorithm that combines DS evidence theory and neural networks. Based on the analysis results, it provides level one, level two, and level three early warnings for risks such as gas exceeding limits, water inrush signs, surrounding rock instability, and dust exceeding standards, and triggers corresponding linkage and response measures. S5. Full-process collaborative scheduling and linkage control: The central control unit realizes the collaborative scheduling of each unit based on real-time data from drilling, extraction, and monitoring. When a risk warning is detected, the corresponding linkage control is automatically triggered. At the same time, all operation data is uploaded to the ground monitoring system in real time through the communication computing unit, realizing unmanned underground operation and remote ground monitoring.

8. The control method of the integrated intelligent operation system for monitoring and extraction in underground coal mine drilling as described in claim 7, characterized in that: In step S1, the downhole three-dimensional geological digital twin model is constructed using the IMMC-Geo modeling framework, which integrates UAV aerial photography of surface rock strata, downhole measured geological data, and real-time drilling data from the measurement while drilling system to achieve dynamic updates of the model. The model's interface recognition accuracy is no less than 80%.

9. The control method of the integrated intelligent operation system for monitoring and extraction in underground coal mine drilling as described in claim 7, characterized in that: The graded early warning in step S4 is as follows: Level 1 early warning is a potential risk state, where the monitoring data is close to the threshold, the system issues an early warning prompt and increases the data collection frequency; Level 2 warning is a risk warning state. The monitored data exceeds the threshold but does not reach an emergency state. The system issues an alarm message and automatically adjusts the extraction and drilling parameters. Level 3 early warning indicates a risk emergency state. When monitoring data significantly exceeds the threshold, the system issues an emergency alarm and triggers emergency response measures such as shutdown, drilling failure, and increased extraction. At the same time, it sends an emergency rescue signal to the ground monitoring system.

10. The control method of the integrated intelligent operation system for monitoring and extraction in underground coal mine drilling as described in claim 7, characterized in that: The dynamic path planning algorithm in step S2 specifically includes the following steps: T1. Digital Twin Model Initialization and Geological Attribute Mapping: Load the downhole 3D geological digital twin model, complete the mesh generation and attribute labeling of rock interface, stress field, fracture zone, and fractured zone, and map geological risk to geological cost weight ω. g High-risk area ω g Take the largest value, force path avoidance, and establish the path from the orifice P0 to the target point P. t The global coordinate system is used to complete coordinate normalization; T2. Initial optimal path pre-planning: Starting from P0, P... t As the endpoint, a set of feasible path nodes is generated in the geological model. A hybrid evaluation function considering geological cost, distance cost, and technological cost is used to select the optimal initial path, outputting the initial planned trajectory sequence: P plan =[P0,P1,P2,...,P n ], and the corresponding design tilt and azimuth sequence [α0, α1, ..., α n ]、[β0,β1,...,β n ] T3. Real-time data acquisition and spatiotemporal synchronization while drilling: The MWD system acquires real-time trajectory parameters α(t), β(t), and L(t) at a high frequency of 10Hz, and simultaneously acquires drilling resistance F(t) and lithology R(t). The real-time data is mapped to three-dimensional coordinates according to the hole depth L(t) to obtain the actual trajectory point Pact(t), and the spatiotemporal stamp alignment between the planned trajectory and the actual trajectory is completed to eliminate sampling delay error. T4. Track Deviation Quantification Calculation: Calculate the tilt angle deviation Δα(t) = α(t) − α plan (t), azimuth deviation Δβ(t)=β(t)−β plan (t), calculate the three-dimensional spatial position deviation ΔS(t), i.e., P act (t) and P plan The Euclidean distance between corresponding nodes is used to determine whether the deviation exceeds the limit: threshold Δα max =±0.5∘、Δβ max =±0.5∘、ΔS max =0.1m; T5. Dynamic Cost Update and Correction Path Generation: The geological model is updated while drilling, incorporating real-time lithology, resistance, and microseismic signals into the cost function. Starting from the current actual borehole depth L(t), the optimal correction path for the remaining borehole depth is replanned, generating a correction trajectory sequence P. adjust Output target correction tilt angle α adj Azimuth β adj ; T6. Closed-loop correction of robotic arm posture and drilling parameters: Converting the correction angles into multi-degree-of-freedom robotic arm joint angle increments Δθ1, Δθ2, ..., Δθ n Combined with the drilling resistance F(t), the drilling speed v and torque T are dynamically adjusted. When the resistance exceeds the threshold, the speed is reduced to maintain drilling. The command is sent to the actuator to complete the real-time trajectory correction. T7. Trajectory Tracking and Termination Determination: Repeat steps 3-6 to continuously track the trajectory. When L(t) ≥ L... total And P act If the deviation between (t) and Pt is less than or equal to the allowable value, drilling will stop and the operation will be completed.

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