Intelligent Control System for Deep Well Mining Equipment

Abstract

The present invention belongs to the technical field of equipment control systems, and discloses an intelligent control system for deep well mining equipment, including a multi-source data perception module, a decision-making module, and an equipment collaborative control module. The multi-source data perception module includes a distributed Internet of Things sensing unit, an InSAR remote sensing detection unit, and a data fusion gateway unit. The decision-making module receives multi-source signals. The decision-making module includes a prediction unit, an optimization unit, and a regulation unit. The prediction unit processes the multi-source signals and outputs a height prediction signal and an aquifer breakthrough risk signal. The optimization unit processes the multi-source signals and the height prediction signal to obtain an optimal grouting signal. The regulation unit defines a water conservation constraint condition according to the multi-source signals and outputs a safe mining threshold. The intelligent control system for deep well mining equipment of the present invention can solve the problems of water resource loss and pollution caused by traditional deep well mining operations.

Description

Technical Field

[0001] The present invention belongs to the technical field of equipment control systems, and specifically relates to an intelligent control system for deep well mining equipment, which is particularly suitable for realizing safe and efficient mineral resource development under the technical requirements of water protection mining. Background Art

[0002] In the field of deep well mineral resource mining, especially in underground mining operations such as coal and metal mines, how to achieve safe and efficient mining and the protection of underground water resources has always been the core problem faced by the industry. The traditional control systems of mining equipment are mainly designed around production efficiency and personnel safety, lacking an active protection mechanism for the hydrogeological environment of the mining area, resulting in long-term problems such as uncontrollable hydrogeological damage, weak data perception and fusion capabilities, extensive and inefficient grouting processes, low level of equipment collaborative intelligence, and sharp contradictions between ecological protection and mining. Existing technologies are difficult to balance the mining intensity and the demand for water resource protection. Conventional control systems aim at fixed production capacity and lack dynamic constraints on ecological indicators such as underground water level and water quality.

[0003] Existing technologies rely on local point monitoring and lack the multi-physical field coupling perception ability of "rock stress - fracture development - hydrogeological change". The data update frequency and spatial resolution cannot meet the requirements of deep mining. At the same time, the control strategy is based on a preset rule library and cannot adapt to changes in geological conditions. Summary of the Invention

[0004] In view of the above disadvantages of the prior art, the purpose of the present invention is to provide an intelligent control system for deep well mining equipment to solve the problems of water resource loss and pollution caused by deep well mining operations. By constructing an intelligent control system for deep well mining equipment that deeply integrates Internet of Things perception, intelligent decision-making, and precise execution, the deficiencies of traditional mining technologies in water resource protection, equipment collaboration, and risk prevention and control are solved.

[0005] The intelligent control system for deep well mining equipment provided by the present invention includes:

[0006] A multi-source data perception module, which includes a data fusion gateway unit that forms multi-source signals;

[0007] A decision-making module that receives multi-source signals. The decision-making module includes a prediction unit, an optimization unit, and a regulation unit. The prediction unit processes the multi-source signals and outputs a height prediction signal and an aquifer penetration risk signal. The optimization unit processes the multi-source signals and the height prediction signal to obtain an optimal grouting signal. The regulation unit defines water protection constraint conditions based on the multi-source signals and outputs a safe mining threshold;

[0008] Equipment collaborative control module, which includes an adaptive grouting execution unit, a mining equipment group control unit, and a human-machine interaction and warning module. The adaptive grouting execution unit adjusts grouting parameters according to the optimal grouting signal. The mining equipment group control unit adjusts mining equipment parameters according to the safe mining threshold and multi-source signals, and collects real-time detection signals. The human-machine interaction and warning module processes the aquifer breakthrough risk signal and the height prediction signal and outputs a comprehensive risk index.

[0009] Furthermore, the multi-source data perception module includes a distributed Internet of Things sensing unit and an InSAR (Interferometric Synthetic Aperture Radar) remote sensing detection unit. The multi-source data perception module is deployed in the underground mining face, roadway, and surface, and collects sensing signals. The InSAR remote sensing detection unit obtains the surface deformation data of the mining area through satellite interferometry technology and forms a surface signal. The data fusion gateway unit receives the sensing signal and the surface signal to achieve heterogeneous data fusion and forms a multi-source signal. The multi-source signal includes a predicted multi-source signal, an optimized multi-source signal, and a regulated multi-source signal.

[0010] Furthermore, the prediction unit includes a time series prediction model based on an LSTM (Long Short-Term Memory Artificial Neural Network) neural network and a D-S evidence theory model. The predicted multi-source signal includes rock stratum stress data, microseismic event energy distribution data, InSAR surface deformation gradient data, historical mining parameters, and historical height data of the water-conducting fissure zone. The rock stratum stress data, historical mining parameters, microseismic event energy distribution data, InSAR surface deformation gradient data, and historical height data of the water-conducting fissure zone are input into the time series prediction model and output a height prediction signal. The aquifer breakthrough risk signal is output by fusing the rock stratum stress data, microseismic event energy distribution data, and InSAR surface deformation gradient data through an improved D-S evidence theory model.

[0011] Furthermore, the time series prediction model includes a time series feature extraction sub-module, a multi-modal fusion sub-module, a risk quantification sub-module, and a prediction result visualization module. The time series feature extraction sub-module performs wavelet packet decomposition on the microseismic event energy distribution data, and extracts the energy mutation feature of the frequency band that satisfies the precursor frequency band range of fissure expansion as the precursor signal of fissure expansion. The multi-modal fusion sub-module uses a bidirectional LSTM network weighted by an attention mechanism to process the rock stratum stress data, microseismic event energy distribution data, and InSAR surface deformation gradient data and outputs the data to the risk quantification sub-module. The risk quantification sub-module defines a risk index algorithm as follows:

[0012] R = ;

[0013] where R is the risk index, is the coefficient, is the maximum principal stress, is the mean value of the deformation gradient, is the peak value of microseismic energy. The prediction result visualization module generates a dynamic risk cloud map of the risk index based on three-color blocks, and uses color blocks of different colors to represent different risk indices at different heights.

[0014] Furthermore, the optimization unit processes the optimized multi-source signal and the height prediction signal through a grouting-fracture nonlinear relationship model, outputs a grouting signal, and processes the grouting diffusion signal using a genetic algorithm to obtain the optimal grouting signal; the optimized multi-source signal includes the aquifer permeability coefficient and the grouting material rheological signal. A grouting-fracture nonlinear relationship model is constructed through the aquifer permeability coefficient, the height prediction signal, and the grouting material rheological signal. The grouting-fracture nonlinear relationship model is as follows:

[0015] ;

[0016] where r is the diffusion radius, Q is the grouting flow rate, t is the grouting time, is the slurry viscosity, is the grouting pressure, is the average width of the fracture, and k is the correction coefficient.

[0017] Furthermore, the optimization unit also includes a slurry performance dynamic matching library, a fracture network connectivity analysis sub-module, and a grouting path planning sub-module. The slurry performance dynamic matching library stores the historical grouting rheological signals of historical grouting materials. The fracture network connectivity analysis sub-module calculates the water conductivity of the fracture network based on the grouting pressure and the diffusion radius. The grouting path planning sub-module obtains the optimal grouting signal through a genetic algorithm based on the water conductivity, the grouting-fracture nonlinear relationship model, and the grouting material rheological signal.

[0018] Furthermore, the regulation unit defines a water conservation constraint condition according to the regulated multi-source signal, outputs a safe mining threshold, and dynamically adjusts the safe mining threshold not lower than the minimum safe mining value through MPC (Model Predictive Control). The regulated multi-source signal includes the real-time groundwater level signal and the thickness signal of the water-resisting layer. The real-time groundwater level signal and the thickness signal of the water-resisting layer define the water conservation constraint condition as follows:

[0019] ;

[0020] where, is the safe mining threshold, is the buried depth of the aquifer floor, is the mining depth, is the height of the water-conducting fracture zone. MPC dynamically adjusts the shearer traction speed and the support moving step distance to make the safe mining threshold not lower than the minimum safe mining value.

[0021] Furthermore, the human-computer interaction and early warning module includes a 3D visualization platform and a risk early warning engine. The 3D visualization platform receives multi-source signals and real-time detection signals and dynamically renders the expansion process of the water-conducting fissure zone. The risk early warning engine processes the aquifer penetration risk signal and the water-conducting fissure zone height prediction signal according to the improved TOPSIS (Technique for Order Preference by Similarity to an Ideal Solution) algorithm and outputs a comprehensive risk index. When the comprehensive risk index exceeds the risk index threshold, the human-computer interaction and early warning module controls the adaptive grouting execution unit to perform emergency grouting. The 3D visualization platform supports cross-section analysis at any angle, can display the spatial relationship between the water-conducting fissure zone and the aquifer in real time, and simulates the rock movement and water level change under different mining schemes based on the discrete element method.

[0022] Furthermore, the risk early warning engine includes color states of a multi-level early warning mechanism, namely the yellow early warning state, the orange early warning state, and the red early warning state. When the height of the water-conducting fissure zone in the height prediction signal drops to the first height threshold, the yellow early warning state is activated, and the operating speed of the mining equipment is reduced to the warning operating speed. When the height of the water-conducting fissure zone in the height prediction signal drops to the second height threshold, the operation of the mining equipment is suspended and the adaptive grouting execution unit is controlled to perform emergency grouting. When the height of the water-conducting fissure zone in the height prediction signal drops to the third height threshold, the risk early warning engine issues an emergency mine accident signal and controls the intelligent control system of the deep well mining equipment to stop urgently.

[0023] Furthermore, the optimal grouting signal includes, but is not limited to, the grouting area coordinates, the priority of the slurry type, the grouting pressure range, and the diffusion path planning. The adaptive grouting execution unit includes an intelligent grouting pump and an integrated slurry viscosity on-line detection device. The intelligent grouting pump can identify the optimal grouting signal and adjust the grouting flow rate and grouting pressure as required. The integrated slurry viscosity on-line detection device receives the optimal grouting signal and adjusts the water-cement ratio of the grouting slurry in real time based on the ultrasonic phase difference method. The real-time detection signals include, but are not limited to, equipment temperature, equipment humidity, equipment voltage, equipment depth, mining pressure, and expansion radius.

[0024] The intelligent control system for deep well mining equipment provided by the present invention constructs an intelligent control system for deep well mining equipment that deeply integrates Internet of Things perception, intelligent decision-making, and precise execution, and focuses on solving the deficiencies of traditional mining technologies in water resource protection, equipment coordination, and risk prevention and control. Specifically, through a distributed sensor network and InSAR remote sensing technology, real-time monitoring and spatio-temporal alignment of multiple physical fields of "rock stress - fracture development - hydrological changes" are achieved, improving the accuracy of risk identification; based on the LSTM neural network and genetic algorithm, a closed-loop decision-making for predicting the expansion of water-conducting fissure zones, optimizing grouting parameters, and regulating mining intensity is realized to ensure the goal of water-preserving mining; the fuzzy PID control and pulse grouting technology are adopted to dynamically adjust the grouting pressure and diffusion path, significantly improving the plugging efficiency and material utilization rate; through multi-objective optimization and model predictive control, precise synchronization of equipment such as shearers, hydraulic supports, and conveyors is achieved, reducing the unplanned shutdown rate; a three-level early warning mechanism and a three-dimensional dynamic risk cloud map are constructed to support AR remote collaboration, improving the risk disposal efficiency and human-machine interaction experience. Thus, the problem of water loss and pollution in the water layer is reduced. BRIEF DESCRIPTION OF THE DRAWINGS

[0025] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings required for describing the embodiments will be briefly introduced below. Obviously, the drawings in the following description are only some embodiments of the present invention, and those of ordinary skill in the art can obtain other drawings based on these drawings without creative efforts.

[0026] Figure 1 It is the system architecture diagram of the intelligent control system for deep well mining equipment of the present invention;

[0027] Figure 2 It is the schematic diagram of the working principle of the intelligent control system for deep well mining equipment of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0028] The following specific examples illustrate the embodiments of the present invention. Those skilled in the art can easily understand other advantages and effects of the present invention from the content disclosed in this specification. The present invention can also be implemented or applied through other different specific embodiments, and various details in this specification can also be modified or changed based on different viewpoints and applications without departing from the spirit of the present invention. It should be noted that, without conflict, the following embodiments and the features in the embodiments can be combined with each other.

[0029] The present invention relates to an intelligent control system for deep well mining equipment, which is mainly applied to deep well mining devices. The traditional mining method relies on manual experience to judge the development of water-conducting fissure zones and lacks real-time monitoring and prediction means. The rock fractures caused by mining often extend to the aquifer, resulting in a continuous decline in the groundwater level. For example, in the shallow buried coal seam area of northern Shaanxi, a single coal mining operation can cause the groundwater level in the phreatic aquifer to drop by 2-3 meters, and the problem of water shortage for farmland irrigation in the surrounding areas occurs frequently. Most of the existing control systems adopt an independently operating sensor network, and the data of underground stress monitoring, microseismic detection and surface deformation observation have been in a fragmented state for a long time. For example, a certain type of mine monitoring system can only update data at the minute level, and it is difficult to effectively associate the InSAR remote sensing data with the underground sensor data due to the inconsistent spatio-temporal reference.

[0030] Please refer to Figure 1The intelligent control system of deep well mining equipment of the present invention is shown. It includes a multi-source data perception module, a decision-making module, and an equipment collaborative control module. The multi-source data perception module includes a distributed Internet of Things sensing unit, an InSAR (Interferometric Synthetic Aperture Radar) remote sensing detection unit, and a data fusion gateway unit. The multi-source data perception module is deployed on the underground mining face, roadway, and ground surface, and collects sensing signals. The InSAR remote sensing detection unit obtains the surface deformation data of the mining area through satellite interferometric measurement technology and forms surface signals. The data fusion gateway unit receives the sensing signals and surface signals to achieve heterogeneous data fusion and forms multi-source signals. The decision-making module receives the multi-source signals. The decision-making module includes a prediction unit, an optimization unit, and a regulation unit. The prediction unit processes the multi-source signals through a time series prediction model based on an LSTM (Long Short-Term Memory Artificial Neural Network) neural network, outputs a prediction signal of the height of the water-conducting fracture zone, and uses an improved D-S evidence theory to fuse the multi-source signals to calculate the probability distribution of fracture propagation and outputs a risk signal of aquifer penetration. The optimization unit processes the optimized multi-source signals and the prediction signal of the height of the water-conducting fracture zone through a grouting-fracture nonlinear relationship model, outputs a grouting signal, and uses a genetic algorithm to process the grouting diffusion signal to obtain the best grouting signal. The regulation unit defines a water conservation constraint condition according to the regulated multi-source signals, outputs a safe mining threshold, and dynamically adjusts the safe mining threshold through MPC not to be lower than the minimum value of safe mining. The equipment collaborative control module includes an adaptive grouting execution unit, a mining equipment group control unit, and a human-computer interaction and early warning module. The adaptive grouting execution unit adjusts the grouting parameters according to the best grouting signal. The mining equipment group control unit adjusts the parameters of the mining equipment according to the safe mining threshold and the multi-source signals and collects real-time detection signals. The human-computer interaction and early warning module includes a three-dimensional visualization platform and a risk early warning engine. The three-dimensional visualization platform receives the multi-source signals and real-time detection signals and dynamically renders the expansion process of the water-conducting fracture zone. The risk early warning engine processes the aquifer penetration risk signal and the prediction signal of the height of the water-conducting fracture zone according to the improved TOPSIS (Technique for Order Preference by Similarity to Ideal Solution) algorithm and outputs a comprehensive risk index. When the comprehensive risk index exceeds the risk index threshold, the human-computer interaction and early warning module controls the adaptive grouting execution unit to perform emergency grouting.

[0031] As Figure 1As shown in the figure, the entire system consists of core components such as a multi-source data fusion perception module, a decision-making module, an adaptive grouting execution unit, and a mining equipment group controller. Each module realizes data interaction through an industrial bus and an Internet of Things protocol. In the specific implementation process, first, the distributed sensor network arranged in the underground roadway and on the ground starts to work. These sensors include fiber optic water pressure sensors embedded in the rock formation, three-dimensional stress detectors installed on hydraulic supports, and microseismic monitoring nodes arranged on the roof of the transportation roadway, etc. The fiber optic water pressure sensor collects the pressure fluctuation data of the underground aquifer once every second, with a measurement range covering 0 to 10 MPa, and transmits the data to the data fusion gateway on the ground through an explosion-proof optical cable. At the same time, the vibration sensor installed on the cutting part of the shearer monitors the operation status of the equipment in real time, sampling 1000 times per second of vibration spectrum data for analyzing the wear condition of the picks. In the surface part, the InSAR remote sensing monitoring subsystem obtains the surface deformation data of the mining area through satellites once every seven days, generating a settlement gradient map with millimeter-level accuracy. After coordinate transformation, these data are aligned with the underground sensor data in the spatio-temporal dimension.

[0032] When the multi-source data fusion perception module completes the initial data collection, the data fusion gateway starts the heterogeneous data processing process. First, interpolation processing is performed on the data from different sampling frequencies. For example, the sensor data once per second and the satellite data once per week are dynamically time-warped to generate a data sequence with a unified time reference. Then, the Kalman filter algorithm is used to eliminate sensor noise. For the abnormal mutation values detected by the fiber optic water pressure sensor (such as an instantaneous pressure drop exceeding 20%), the data verification program is automatically triggered, and the data of adjacent sensors is retrieved for cross-verification. After data cleaning, the system standardizes the formats of the structured sensor data, semi-structured device log data, and unstructured InSAR image data, converts them into the Apache Avro intermediate format, and stores them in the corresponding partitions of the distributed database.

[0033] As Figure 2As shown in the figure, the decision-making module includes a prediction unit, an optimization unit, and a regulation unit. The prediction unit processes multi-source signals through a time series prediction model based on the LSTM neural network, outputs a predicted signal of the height of the water-conducting fissure zone, and calculates the probability distribution of fissure expansion by fusing multi-source signals using the improved D-S evidence theory, and outputs an aquifer penetration risk signal. Here, the multi-source signals include rock stratum stress data, microseismic event energy distribution data, InSAR surface deformation gradient data, historical mining parameters, and historical water-conducting fissure zone height data. The rock stratum stress data, historical mining parameters, microseismic event energy distribution data, InSAR surface deformation gradient data, and historical water-conducting fissure zone height data are input into the time series prediction model and output a predicted signal of the height of the water-conducting fissure zone. The rock stratum stress data, microseismic event energy distribution data, and InSAR surface deformation gradient data are fused through the improved D-S evidence theory and output an aquifer penetration risk signal. The time series prediction model includes a time series feature extraction sub-module, a multi-modal fusion sub-module, a risk quantification sub-module, and a prediction result visualization module. The time series feature extraction sub-module performs wavelet packet decomposition on the microseismic event energy distribution data, extracts the energy mutation feature of the frequency band that meets the range of the precursor frequency band of fissure expansion as the precursor signal of fissure expansion. The multi-modal fusion sub-module uses a bidirectional LSTM network weighted by the attention mechanism to process the rock stratum stress data, microseismic event energy distribution data, and InSAR surface deformation gradient data and outputs the data to the risk quantification sub-module. The risk quantification sub-module defines a risk index algorithm as follows:

[0034] R = ;

[0035] where R is the risk index, is the coefficient, is the maximum principal stress, is the mean value of the deformation gradient, is the peak value of microseismic energy. The prediction result visualization module generates a dynamic risk cloud map of the risk index based on three-color blocks, and uses color blocks of different colors to represent different risk indices of the regions. The optimization unit processes the multi-source signals and the predicted signal of the height of the water-conducting fissure zone through a grouting-fissure nonlinear relationship model, outputs a grouting signal, and processes the grouting diffusion signal using a genetic algorithm to obtain the optimal grouting signal. The regulation unit defines a water conservation constraint condition according to the multi-source signals, outputs a safe mining threshold, and dynamically adjusts the safe mining threshold through MPC not to be lower than the minimum value of safe mining. The multi-source signals include the aquifer permeability coefficient and the grouting material rheological signal. A grouting-fissure nonlinear relationship model is constructed through the aquifer permeability coefficient, the predicted signal of the height of the water-conducting fissure zone, and the grouting material rheological signal. The grouting-fissure nonlinear relationship model is as follows:

[0036] ;

[0037] where r is the diffusion radius, Q is the grouting flow rate, t is the grouting time, is the slurry viscosity, is the grouting pressure, is the average width of the fissure, and k is the correction coefficient.

[0038] Specifically, in an embodiment of the present invention, the decision-making module extracts preprocessed data from the database and starts the fissure development prediction algorithm; this algorithm first performs wavelet packet decomposition on the acoustic emission signals captured by the microseismic monitoring nodes, and extracts the energy mutation characteristics in the frequency band of 6-8 Hz, which have been proven to have a strong correlation with the expansion of rock mass fissures; at the same time, the stress tensor data collected by the three-dimensional stress sensor array at a grid density of 5 meters is reduced to three principal stress components after principal component analysis and input into the trained LSTM neural network model; the neural network model is pre-trained based on historical mining data (including the measured values of the height of the water-conducting fissure zone in 200 working faces in the past three years) and can predict the expansion trend of the water-conducting fissure zone within the next 72 hours; the surface deformation gradient data obtained by InSAR is associated with the underground stress field through a spatial interpolation algorithm, and when the detected deformation gradient exceeds 5 mm per meter, the algorithm automatically increases the risk level weight. As Figure 2 shown, the equipment collaborative control module includes an adaptive grouting execution unit, a mining equipment group control unit, and a human-computer interaction and early warning module. The optimization unit also includes a slurry performance dynamic matching library, a fissure network connectivity analysis sub-module, and a grouting path planning sub-module. The slurry performance dynamic matching library stores the historical grouting rheological signals of historical grouting materials. The fissure network connectivity analysis sub-module calculates the water conductivity of the fissure network according to the grouting pressure and the diffusion radius. The grouting path planning sub-module obtains the optimal grouting signal through a genetic algorithm according to the water conductivity, the grouting-fissure non-linear relationship model, and the grouting material rheological signal. The multi-source signals here include real-time underground water level signals and aquitard thickness signals, and the real-time underground water level signals and aquitard thickness signals define the water conservation constraint conditions as follows:

[0039] ;

[0040] where, is the safe mining threshold, is the buried depth of the aquifer floor, is the mining depth, is the height of the water-conducting fissure zone. The MPC dynamically adjusts the shearer traction speed and the support advancing step distance to ensure that the safe mining threshold is not lower than the minimum value of safe mining. The optimal grouting signal includes, but is not limited to, the grouting area coordinates, the priority of the slurry type, the grouting pressure range, and the diffusion path planning. The adaptive grouting execution unit includes an intelligent grouting pump and an integrated on-line slurry viscosity detection device. The intelligent grouting pump can identify the optimal grouting signal and adjust the grouting flow rate and grouting pressure as required. The integrated on-line slurry viscosity detection device receives the optimal grouting signal and adjusts the water-cement ratio of the grouting slurry in real time based on the ultrasonic phase difference method.

[0041] In an embodiment of the present invention, after the fracture development prediction result is generated, the grouting parameter optimization algorithm starts to work. First, the algorithm retrieves available material data from the slurry performance dynamic matching library, including rheological characteristic curves of more than 20 grouting materials such as cement-based slurry and polymer chemical slurry; according to the real-time detected water quality parameters (such as pH value, conductivity), the system automatically excludes slurry types that may cause secondary pollution. For example, when it is detected that the chloride ion concentration in the water exceeds the standard, chemical slurries containing metal components are disabled; then, based on the Delaunay triangulation algorithm, topological analysis of the fracture network is carried out, the water conductivity of the key seepage path is calculated, and a three-dimensional grouting priority map is generated; finally, a genetic algorithm is used to solve the optimal grouting parameter combination, minimizing the material consumption on the premise that the coverage rate of the grouting curtain exceeds 95%, and the calculation results are sent to the adaptive grouting execution unit through the OPC UA protocol. After receiving the grouting instruction, the adaptive grouting execution unit starts the work process of the intelligent grouting pump. First, the water-cement ratio is automatically adjusted according to the slurry type, the rheological characteristics of the slurry are monitored in real time through an ultrasonic viscosity detector, and the mixer speed is dynamically adjusted to maintain the best mixing uniformity. The pressure control system of the grouting pump adopts a fuzzy PID algorithm to stabilize the grouting pressure within the range of ±0.2 MPa of the set value. When abnormal pressure fluctuations are detected (such as an instantaneous rise exceeding 0.5 MPa / s), it immediately switches to the pulse grouting mode and performs intermittent high-pressure impacts at a frequency of 2-5 Hz to prevent fracture blockage. During the grouting process, the distributed resistivity imaging instrument arranged around the borehole continuously monitors the slurry diffusion range, generates a resistivity distribution map every second, reconstructs the three-dimensional distribution form of the slurry through an inversion algorithm, and feeds the results back to the decision-making module in real time for dynamic optimization of grouting parameters. The mining equipment group controller synchronously receives the mining intensity control instruction from the decision-making module; the shearer navigation system automatically adjusts the cutting trajectory according to the real-time updated prediction model of the water-conducting fracture zone. When the prediction shows that there is a high water inrush risk in the area 5 meters ahead, the control system reduces the traction speed from 4 meters per minute to 2 meters and triggers the roof support strengthening mode; the electro-hydraulic control system of the hydraulic support dynamically adjusts the initial support force according to the three-dimensional stress sensor data, increases the support resistance to more than 35 MPa in the stress concentration area, and at the same time monitors the misalignment amount of adjacent supports through an infrared distance sensor. When a pose deviation exceeding 50 mm is detected, an automatic correction action is performed; the variable frequency drive of the scraper conveyor adjusts the chain speed according to the load current prediction model, reducing the energy consumption by 15%-20% on the premise of ensuring the transportation efficiency. When the coal quantity sensor detects that the instantaneous load exceeds 120% of the rated value, an overload protection program is automatically triggered.The intelligent storage and regulation unit of the underground reservoir forms a linkage mechanism with the grouting execution system; when the grouting operation consumes a large amount of water resources, the storage and regulation unit starts the ecological water replenishment mode, and adjusts the opening of the water release valve through the fuzzy PID controller to maintain the reservoir water level within the set range; the water purification device adopts a combination of electrochemical oxidation and membrane filtration technology to deeply treat the reinjection water. When the online monitoring instrument detects that the COD value exceeds 100 mg / L, it automatically increases the electrolysis current intensity and extends the backwashing frequency of the membrane component; when the rainy season comes, the system frees up storage space in advance according to the weather forecast data, and uses the goaf to store flood water, which can reduce the peak flow by more than 30%; during the winter freezing period, the insulation mode is started, and the reservoir temperature is maintained above 5°C through the mine water waste heat recovery device to prevent the water supply pipeline from freezing and cracking.

[0042] In one embodiment of the present invention, the risk warning engine plays a central role in the entire system. When the multi-indicator fusion analysis shows that the height of the water-conducting fracture zone is close to the bottom plate of the aquifer, the warning engine starts a three-level response mechanism: first, the high-risk area is marked with a red flashing icon on the three-dimensional visualization platform, and the alarm information is pushed to all mobile terminals through the industrial ring network; secondly, the mining intensity of the affected area is automatically reduced, and the speed of the coal mining machine is limited below the safety threshold; finally, the grouting system is linked to block the key channels to form a multi-level protection system. The built-in case reasoning library of the early warning system stores more than 300 sets of historical accident data. When a pattern similar to the precursor of a sudden water accident is detected, the emergency disposal plan at that time is automatically retrieved and recommended to the operator. The three-dimensional visualization platform integrates the operating status data of all modules and provides a panoramic monitoring interface. The platform constructs a geological model of the mining area based on BIM technology, and displays InSAR deformation data, microseismic event distribution, grouting diffusion range and other information in different color layers. Operators can rotate and slice the model at any angle through gestures to view the stress distribution inside the rock formation. When a hydraulic support is selected, the interface automatically pops up the real-time pressure data, historical maintenance records and status of adjacent equipment of the equipment. The AR remote collaboration function allows experts to view the superimposed screen of the virtual model and the real equipment through mixed reality glasses, and annotate abnormal points with voice commands. The relevant annotation information is instantly synchronized to the tablet computer of the on-site personnel. The energy collaborative optimization system collects the energy consumption data of each device in real time through smart meters, and dynamically adjusts the power consumption strategy by combining the photovoltaic power generation prediction curve and the SOC status of the energy storage battery. During the peak electricity price period, the system gives priority to using energy storage batteries to power the control equipment, and adjusts the operating time of high-power equipment such as coal mining machines to the low-valley electricity price range. The mine water waste heat recovery device extracts heat energy from the drainage and heats the ground buildings through the plate heat exchanger. In winter, it can replace coal-fired boilers to reduce carbon dioxide emissions by more than 40%. When it is detected that the continuous operation energy efficiency of a certain device is lower than the set threshold, the system automatically generates a maintenance work order and pushes it to the equipment management system.

[0043] In an embodiment of the present invention, the operation data of the entire system is continuously optimized through a digital twin training platform. The platform constructs a high-precision virtual mine model, uses the discrete element method to simulate the rock movement law under different mining schemes, automatically compares the predicted results with the actual monitoring data after each simulation iteration, and reversely corrects the model parameters; the deep reinforcement learning algorithm autonomously explores the optimal control strategy during this process. For example, it is found that shortening the grouting interval time under specific geological conditions can improve the plugging effect by more than 15%. These empirical knowledge are automatically encoded and stored in the decision rule library; the operation and maintenance personnel can trace the system state at any time point through the historical data playback function, analyze the causes of failures and improve the operation procedures.

[0044] In an embodiment of the invention, the system has been continuously tested in a certain mining area for 12 months; during the test period, 3 water inrush risk events have been successfully predicted, and the average early warning time has reached 56 hours, with the efficiency improved by more than 20 times compared with the manual monitoring method; the control accuracy of the height of the water-conducting fissure zone has been improved from ±2.5 meters of the traditional method to ±0.8 meters, and the water consumption index per ton of coal has been reduced to 0.13 cubic meters; the unplanned downtime of underground equipment has been reduced by 68%, reducing a large amount of maintenance costs; these empirical data fully verify the technical advantages and practical value of the system in the deep well water-preserved mining scenario.

[0045] A smart control system for deep well mining equipment of the present invention focuses on solving the deficiencies of traditional mining technologies in water resource protection, equipment coordination and risk prevention and control by constructing a smart control system for deep well mining equipment that deeply integrates Internet of Things perception, intelligent decision-making and precise execution.

[0046] Therefore, through the smart control system for deep well mining equipment of the present invention, the problems of water resource loss and pollution caused by deep well mining operations can be solved.

[0047] The above embodiments are only illustrative of the principles and effects of the present invention, and are not used to limit the present invention. Any person familiar with this technology can modify or change the above embodiments without departing from the spirit and scope of the present invention. Therefore, all equivalent modifications or changes made by those with ordinary knowledge in the technical field without departing from the spirit and technical ideas disclosed by the present invention should still be covered by the claims of the present invention.

Claims

1. Intelligent control system for deep well mining equipment, characterized by: include: A multi-source data perception module, the multi-source data perception module includes a data fusion gateway unit, the data fusion gateway unit forms a multi-source signal, the multi-source data perception module includes a distributed Internet of Things sensor unit and an InSAR remote sensing detection unit, the multi-source data perception module is arranged in the underground mining surface, tunnel and surface, and collects sensor signals, the InSAR remote sensing detection unit obtains the surface deformation data of the mining area through satellite interferometry technology, and forms a surface signal, the data fusion gateway unit receives the sensor signal and the surface signal to realize heterogeneous data fusion, and forms the multi-source signal, the multi-source signal includes a predicted multi-source signal, an optimized multi-source signal and a regulated multi-source signal; A decision module, wherein the decision module receives the multi-source signal, and the decision module includes a prediction unit, an optimization unit, and a control unit. The prediction unit processes the multi-source signal and outputs a height prediction signal and an aquifer breakthrough risk signal. The optimization unit processes the multi-source signal and the height prediction signal to obtain an optimal grouting signal. The control unit defines a water conservation constraint condition according to the multi-source signal and outputs a safe mining threshold. The equipment collaborative control module includes an adaptive grouting execution unit, a mining equipment group control unit and a human-computer interaction and early warning module. The adaptive grouting execution unit adjusts the grouting parameters according to the optimal grouting signal. The mining equipment group control unit adjusts the mining equipment parameters according to the safe mining threshold and the multi-source signal and collects real-time detection signals. The human-computer interaction and early warning module processes the aquifer breakthrough risk signal and the height prediction signal and outputs a comprehensive risk index. The prediction unit includes a time series prediction model based on an LSTM neural network and a DS evidence theory model. The predicted multi-source signals include rock formation stress data, microseismic event energy distribution data, InSAR surface deformation gradient data, historical mining parameters and historical water-conducting fracture zone height data. The rock formation stress data, the historical mining parameters, the microseismic event energy distribution data, the InSAR surface deformation gradient data and the historical water-conducting fracture zone height data are input into the time series prediction model and the height prediction signal is output. The rock formation stress data, the microseismic event energy distribution data and the InSAR surface deformation gradient data are fused through the improved DS evidence theory model and the aquifer breakthrough risk signal is output. The time series prediction model includes a time series feature extraction submodule, a multimodal fusion submodule, a risk quantification submodule and a prediction result visualization module. The time series feature extraction submodule performs wavelet packet decomposition on the microseismic event energy distribution data, extracts the energy mutation characteristics of the frequency band that meets the crack extension precursor frequency band range as the crack extension precursor signal, and the multimodal fusion submodule uses a bidirectional LSTM network weighted by an attention mechanism to process the rock layer stress data, the microseismic event energy distribution data and the InSAR surface deformation gradient data and outputs the data to the risk quantification submodule. The risk quantification submodule defines a risk index algorithm as shown below: R= ； Among them, R is the risk index, is the coefficient, is the maximum principal stress, is the mean deformation gradient, The prediction result visualization module generates a dynamic risk cloud map of the risk index based on the three-color blocks, and uses blocks of different colors to represent different risk indexes at different heights.

2. The deep well mining equipment intelligent control system according to claim 1 is characterized in that: The optimization unit processes the optimized multi-source signal and the height prediction signal through the grouting-crack nonlinear relationship model, outputs the grouting signal, and processes the grouting diffusion signal by using a genetic algorithm to obtain the best grouting signal; the optimized multi-source signal includes the aquifer permeability coefficient and the grouting material rheological signal, and the grouting-crack nonlinear relationship model is constructed through the aquifer permeability coefficient, the height prediction signal and the grouting material rheological signal. The grouting-crack nonlinear relationship model is as follows: ； Where r is the diffusion radius, Q is the grouting flow rate, and t is the grouting time. is the slurry viscosity, is the grouting pressure, is the average width of the crack, and k is the correction coefficient.

3. The deep well mining equipment intelligent control system according to claim 2 is characterized in that: The optimization unit also includes a slurry performance dynamic matching library, a fracture network connectivity analysis submodule and a grouting path planning submodule. The slurry performance dynamic matching library stores historical grouting rheological signals of historical grouting materials. The fracture network connectivity analysis submodule calculates the water conductivity of the fracture network according to the grouting pressure and the diffusion radius. The grouting path planning submodule obtains the optimal grouting signal through a genetic algorithm based on the water conductivity, the grouting-fracture nonlinear relationship model and the grouting material rheological signal.

4. The intelligent control system for deep well mining equipment according to claim 1 is characterized in that: The control unit defines a water conservation constraint condition according to the control multi-source signal, outputs a safe mining threshold, and dynamically adjusts the safe mining threshold to be not less than the minimum safe mining value through MPC. The control multi-source signal includes a real-time groundwater level signal and a water-repellent layer thickness signal. The real-time groundwater level signal and the water-repellent layer thickness signal define the water conservation constraint condition as shown below: ； in, is the safe mining threshold, is the depth of the aquifer bottom, For mining depth, The MPC dynamically adjusts the shearer traction speed and the support step distance to ensure that the safe mining threshold is not lower than the safe mining minimum value.

5. The intelligent control system for deep well mining equipment according to claim 1 is characterized in that: The human-computer interaction and early warning module includes a three-dimensional visualization platform and a risk early warning engine. The three-dimensional visualization platform receives the multi-source signal and the real-time detection signal and dynamically renders the expansion process of the water-conducting fracture zone. The risk early warning engine processes the aquifer breakthrough risk signal and the water-conducting fracture zone height prediction signal according to the improved TOPSIS algorithm and outputs a comprehensive risk index. When the comprehensive risk index exceeds the risk index threshold, the human-computer interaction and early warning module controls the adaptive grouting execution unit to perform emergency grouting. The three-dimensional visualization platform supports cross-sectional analysis at any angle, can display the spatial relationship between the water-conducting fracture zone and the aquifer in real time, and simulates the rock movement and water level changes under different mining schemes based on the discrete element method.

6. The deep well mining equipment intelligent control system according to claim 5 is characterized in that: The risk warning engine includes color states of a multi-level warning mechanism, namely yellow warning state, orange warning state and red warning state. When the height of the water-conducting fracture zone in the height prediction signal drops to a first height threshold, the yellow warning state is activated, and the operating speed of the mining equipment is reduced to a warning operating speed. When the height of the water-conducting fracture zone in the height prediction signal drops to a second height threshold, the operation of the mining equipment is suspended and the adaptive grouting execution unit is controlled to perform emergency grouting. When the height of the water-conducting fracture zone in the height prediction signal drops to a third height threshold, the risk warning engine sends out an emergency mine disaster signal and controls the deep well mining equipment intelligent control system to shut down urgently.

7. The intelligent control system for deep well mining equipment according to claim 1 is characterized in that: The optimal grouting signal includes but is not limited to grouting area coordinates, slurry type priority, grouting pressure range and diffusion path planning. The adaptive grouting execution unit includes an intelligent grouting pump and an integrated slurry viscosity online detection device. The intelligent grouting pump can identify the optimal grouting signal and adjust the grouting flow and grouting pressure as needed. The integrated slurry viscosity online detection device receives the optimal grouting signal and adjusts the water-cement ratio of the grouting slurry in real time based on the ultrasonic phase difference method. The real-time detection signal includes but is not limited to equipment temperature, equipment humidity, equipment voltage, equipment depth, mining pressure and expansion radius.

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