23223 results about "Coal" patented technology

The invention discloses a coal mine dust diffusion simulation and control method based on artificial intelligence, and relates to the technical field of coal mine dust diffusion simulation and control, and the method comprises the following steps: collecting environmental data such as dust concentration, ventilation flow velocity, temperature, humidity and equipment operation state through a monitoring system in a coal mine; and cleaning and formatting processing are carried out in combination with historical working condition data. According to the method, reinforcement learning and virtual extreme scene simulation are utilized, the recognition and emergency response capability of the system to edge working conditions is improved, dust diffusion and explosion risks are effectively reduced, and mine safety is guaranteed. By dynamically adjusting the ventilation and water spraying system, efficient utilization of resources is achieved, energy consumption and operation cost are reduced, and the service life of equipment is prolonged. The system has an adaptive learning capability, can optimize a control strategy according to monitoring data and historical events, can automatically adjust even if the environment changes, improves robustness and flexibility, and reduces manual intervention.

The invention discloses an image enhancement method and system in a complex coal mine environment, and relates to the technical field of image processing, and the method comprises the steps: carrying out the preprocessing of a collected coal mine image of a target region, and dividing the coal mine image into different semantic regions, including a bright region, a dark region and a dust shielding region, through a deep learning semantic segmentation model; according to semantic region characteristics, a differentiation enhancement strategy is made; a traditional Retinex model is improved, non-local mean filtering is introduced, and an illumination component and a reflection component are decomposed through pixel similarity matching. According to the method, the image is divided into the bright area, the dark area and the dust shielding area through the deep learning semantic segmentation model, differential enhancement strategies are formulated according to different area characteristics, detail distortion caused by global adjustment is avoided, local contrast suppression is adopted in the bright area, illumination compensation is enhanced in the dark area, and the image quality is improved. Noise diffusion of the dust shielding area is inhibited through edge preservation smoothing, the image quality of each area is remarkably improved, and it is ensured that image details in a complex coal mine environment are clear and visible.

The invention provides a coal dressing whole process monitoring decision method and system based on Internet of Things sensing, and the method comprises the steps: firstly obtaining a real-time sensing data set of a plurality of monitoring nodes of a coal dressing process, covering equipment vibration, medium density and process flow data, and then carrying out the feature extraction, the method comprises the following steps of: acquiring equipment state, medium dynamic and flow stability characteristics of each monitoring node, performing abnormal correlation analysis on the characteristics based on a pre-trained monitoring decision model, generating abnormal probability distribution and flow adjustment parameters, determining a priority processing queue according to the abnormal probability distribution, and processing the flow according to the priority processing queue. And finally, the coal dressing process optimization strategy is issued to the edge execution terminal, coal dressing process adjustment operation is triggered, effective monitoring and decision optimization of the whole coal dressing process are achieved, and the stability and efficiency of the coal dressing process are improved.

The invention discloses a coal mine hole fracture seepage analysis and prediction system based on a deep learning model, and relates to the technical field of coal mine safety. Comprising a geological data acquisition and three-dimensional model construction module, a data preprocessing and cleaning module, a deep learning feature extraction and training module, a graph neural network optimization and topology modeling module and a seepage prediction and risk assessment module, geological exploration data and hydrogeological information of a coal mine area are obtained, and a three-dimensional geological model containing pores and fractures is constructed. Through the deep learning method combining the convolutional neural network and the graph neural network, the accuracy of coal mine fracture seepage analysis is remarkably improved. The CNN extracts local features, the GNN optimizes a global topological structure, and the seepage prediction precision is improved. The model not only can accurately predict a seepage path, but also can evaluate safety risks, help mine managers to identify hidden dangers in advance, reduce water damage and gas accumulation accidents, and optimize mine safety management.

The invention relates to the technical field of coal mine safety monitoring, and discloses a coal mine safety data comprehensive analysis and early warning system which comprises a data integration module, a three-dimensional visualization module, a risk assessment module, a linkage control module, a model training module and a central processing unit and can further comprise a decision support module, a storage cluster and a communication gateway. The data integration module constructs a multi-source heterogeneous data acquisition channel and performs dynamic topology modeling; the three-dimensional visualization module dynamically renders the monitoring data based on the space-time reference axis; the risk assessment module generates a danger situation map through space-time correlation analysis; the linkage control module establishes a multi-level response mechanism; the model training module optimizes the risk prediction model; and the central processing unit schedules each module to operate. According to the system, integrated analysis, dynamic visualization, risk prediction and cross-system linkage disposal of coal mine safety data are achieved, and the intelligent level and emergency capacity of coal mine safety monitoring are improved.

The invention relates to a coal mine safety production intelligent decision-making system based on digital twinning, and the system comprises a physical sensing layer which collects coal mine environment parameters, equipment states and personnel positioning data through the deployment of a multi-mode sensor network, and generates a structured data flow; the edge calculation layer is used for operating an incremental multi-objective evolutionary algorithm, quickly generating a cache strategy in combination with a strategy cache pool preloading mechanism, uploading the processed data to the digital twinborn layer, receiving a global instruction of the intelligent decision-making layer and decomposing the global instruction into a device-level control signal; the digital twinborn layer is used for receiving the real-time data uploaded by the edge calculation layer, updating the state of a digital twinborn body and feeding back an optimization demand to the intelligent decision-making layer; and the intelligent decision-making layer is used for generating a global strategy by means of digital twin-guided hybrid optimization and a special FPGA acceleration card for a coal mine, and fusing the cache strategy of the edge calculation layer and the global strategy of the intelligent decision-making layer to generate a global instruction.

The invention discloses a coal mine heat energy monitoring and cooling regulation and control method based on the Internet of Things, and relates to the technical field of coal mine heat energy monitoring and cooling regulation and control. The collected temperature data are transmitted to a central monitoring system in real time through an Internet of Things gateway; redundant temperature sensors are arranged at key positions, and data of the temperature sensors in the same area are regularly compared and verified. According to the invention, the monitoring robustness is improved through a redundant temperature sensor and a data fusion algorithm, a monitoring blind area caused by a single-point fault is avoided, and the accident risk is reduced; a self-adaptive learning algorithm is adopted to dynamically optimize a detection threshold value and a compensation strategy, so that the anomaly detection accuracy is improved, and false alarms and missing alarms are reduced; the temperature trend is pre-judged through a predictive control algorithm, cooling is conducted in advance, balance between safety and energy efficiency is achieved, overheating accidents are prevented, and energy consumption is reduced.

The invention belongs to the technical field of coal mine risk early warning, and particularly relates to a coal mine goaf multi-risk comprehensive early warning method and system based on machine learning, and the method comprises the steps: collecting mine pressure, gas and hydrological real-time data in real time through a multi-temporal-spatial-scale sensor, and obtaining a dynamic coupling relation basic data set based on the real-time data; preprocessing noise and missing values according to the dynamic coupling relationship basic data set, and modeling node connection between a geological structure and mine pressure change by adopting a graph neural network to obtain space-time heterogeneous feature representation; non-linear features are analyzed through spatial-temporal heterogeneous feature representation, and a multi-scale dynamic mode is determined; acquiring a risk conduction path in the multi-scale dynamic mode, and acquiring an early recognition signal of a potential disaster chain; based on the early recognition signal, a long-short-term memory network is used for processing a sequential sequence, and the probability of the compound disaster is judged; a high-risk area is extracted from the composite disaster probability, and real-time early warning model parameters are obtained; and generating alarm output according to the real-time early warning model parameters.

The invention discloses a data-mechanism dual-drive-based rock burst early warning method and system, and the method comprises the following steps: deploying a multi-modal sensor network to collect coal and rock stratum data, building a rock burst disaster precursor information sample database, providing a rock burst disaster multi-modal data precursor feature recognition algorithm, and carrying out the recognition of rock burst disaster multi-modal data precursor features. Mining the relevance between the multi-modal data and disaster-causing key risk indexes, and establishing a rock burst disaster multi-modal data prediction model; establishing a three-dimensional geological geometric model, fusing a multi-field coupling dynamics constitutive model and a catastrophe criterion, constructing a PINN physical information neural network prediction model of the rock burst disaster, and obtaining a time-space evolution rule of an energy field of a target area; providing a loss function coupling calculation method of a multi-modal data driving sample error and a physical driving control equation residual error, dynamic data and mechanism prediction result weight, comprehensively calculating a risk score, and accurately judging a top disaster danger level.

The invention provides a coal mine goaf coal spontaneous combustion risk intelligent monitoring and early warning method and system, and relates to the technical field of coal mine safety management. A dot-matrix wireless sensor network is deployed in a target area, temperature and gas concentration data are collected in real time, and data processing is carried out through edge calculation. A dynamic characteristic matrix is generated based on transfer learning and a space-time diagram convolutional network, a comprehensive risk index is calculated, four early warning levels are divided, efficient and accurate coal spontaneous combustion risk monitoring and early warning are achieved, and an innovative solution is provided for mine safety management.

The invention relates to a coal mine safety risk intelligent management and control method, device and equipment and a medium. The method comprises the following steps: constructing a multi-source heterogeneous coal mine safety data system according to received data of a target coal mine park; the multi-source heterogeneous coal mine safety data system comprises static structure data, dynamic environment data, personnel behavior data and management data; constructing a coal mine three-dimensional space model according to the static structure data and the dynamic environment data; risk indexes in the dynamic environment data are extracted based on multi-algorithm fusion for evaluation, and a risk level is obtained; and if the risk level reaches a preset threshold value, triggering a corresponding linkage response mechanism, and forming a visual result in the coal mine three-dimensional space model. By the adoption of the method, closed-loop logic from sensing, evaluation to linkage treatment can be achieved, and the real-time performance, predictability and controllability of coal mine safety management are effectively improved through algorithm support of each stage and fine design of implementation details.

The invention discloses a tunneling equipment position calibration method and system adopting a cursor frame, and relates to the technical field of coal mine tunnel construction tagging. Geologic feature data in a coal mine tunnel are collected in real time through a dynamic environment data set, an environment adaptability index Szp is calculated, and the influence of the environment on equipment calibration is evaluated; a calibration error evaluation coefficient Gz and an error growth trend value Wczs are combined, a calibration strategy is dynamically optimized, adaptive filtering and a multi-modal fusion algorithm are adopted to improve positioning precision, and stable work in an environment with large geological change is ensured; meanwhile, calibration parameters are adjusted in real time by using a deep learning model and a self-adaptive path calibration algorithm, and the adaptability to complex geological conditions is enhanced; the interference compensation module processes electromagnetic interference through Kalman filtering, particle filtering and other algorithms, optimizes the error coefficient Ins of the inertial navigation system, corrects the deviation of the sensor, reduces the influence of external interference on the calibration precision, and guarantees the high precision and stability of the system.

The invention discloses a coal mine power supply intelligent monitoring system based on the Internet of Things, belongs to the field of coal mine power supply monitoring, and aims to solve the problems that an existing coal mine power supply intelligent monitoring system is lagged in response, high in false alarm rate and large in manual dependence degree. According to the invention, through the end-side global sensing module, the data advanced analysis module, the edge data processing module, the data transmission module, the cloud data analysis and model construction module and the fault early warning and closed-loop control module, the real-time acquisition of the equipment state is realized by deploying multiple types of intelligent sensors; local data preprocessing and abnormal pre-judgment are carried out by combining edge computing nodes, an equipment health degree model is established by adopting a time sequence data association analysis algorithm, closed-loop control of overload prediction, electric leakage positioning and energy consumption optimization is realized through multi-source data fusion analysis, and finally a three-level intelligent monitoring system of end side sensing-edge computing-cloud decision is formed. The system response efficiency and accuracy are improved, and the personal labor intensity is reduced.

The invention relates to the technical field of equipment fault prediction, in particular to a coal mine equipment fault prediction system and method based on big data, and the method comprises the following steps: collecting the thickness of a biological membrane on the surface of equipment, synchronously collecting the vibration acceleration of the equipment and the environmental hydrogen sulfide concentration, and constructing a corrosion multi-mode data set; performing electrochemical noise separation on the corrosion multi-modal data set, and constructing a corrosion activity fingerprint spectrum; decoupling a coupling effect of mechanical vibration and microbial corrosion, and generating a corrosion-fatigue damage correlation map; and constructing a dual-channel life prediction model based on the corrosion-fatigue damage correlation map, and setting an early warning mechanism. Microbial corrosion characteristic current is accurately extracted through wavelet packet decomposition and wavelet entropy analysis, a corrosion-vibration heterogeneous graph is established by adopting a graph attention network, corrosion-induced structural vibration enhancement effect analysis is realized, and compared with a traditional prediction method based on a single signal, the method has the advantages that the method is simple and convenient to operate. And the problems of limited local corrosion influence range, unstable vibration response and the like are effectively solved.

The invention discloses a non-impact grid-connected recovery method, system and device for a coal mine isolated network power supply system and a medium, and belongs to the technical field of non-impact grid-connected recovery of power supply systems, and the method comprises the steps: monitoring the operation state of an isolated network, and obtaining voltage, frequency and phase parameters; preparation before grid connection is executed, and main network parameter confirmation and equipment verification are completed; performing parameter synchronous adjustment based on the controller to complete dynamic matching of voltage, frequency and phase; pre-synchronization control is implemented to realize phase prediction and power pre-balance; executing closing operation and performing impact suppression; carrying out stability regulation and control after grid connection, wherein the stability regulation and control comprise oscillation suppression and smooth load loading; and when grid connection is abnormal or fails, an emergency processing branch is entered, and standby power supply switching or island splitting control is triggered. Through multi-dimensional synchronous control, impedance reconstruction and hierarchical coordination mechanisms, high-precision and low-impact grid connection of multiple energy units under extreme working conditions is realized, and the grid connection stability and success rate of a coal mine isolated network power supply system are remarkably improved.

The invention relates to the technical field of fire early-stage recognition, in particular to a multi-source information-based conveying belt fire early-stage recognition and early-warning system, which comprises a sudden change detection module, a synchronous analysis module, a spatial trend recognition module, a coupling fluctuation screening module and a probability evaluation module. According to the invention, through multi-source information linkage acquisition, collaborative monitoring of parameters such as along-line temperature, smoke, gas, images, load and heat source temperature difference, combined characteristic analysis among parameters and synchronous response trend determination are driven, and active revelation of early risk hidden dangers and dynamic discrimination of spatial distribution consistency and fluctuation continuity are realized. Potential abnormal focusing locking under a high-interference complex working condition is promoted, collaborative fluctuation between a load and a heat source temperature difference further eliminates environmental noise influence, risk weight dynamic adjustment strengthens classification sensitivity of probability identification, fire risk clustering division promotes accurate mastering of distribution of tiny initial hidden dangers, and the probability identification accuracy is improved. And the reliability of fire early warning in a coal mine area conveying belt scene is obviously improved.

The invention discloses a coal and rock mass fracture instability multivariate signal fusion analysis method, and belongs to coal and rock mass analysis. The method comprises the following steps: carrying out time-frequency domain processing on an acoustic emission signal to obtain an accumulated ringing characteristic value # imgabs0 # and an accumulated b characteristic value # imgabs1 #; a main control frequency characteristic value # imgabs2 # and an amplitude characteristic value # imgabs3 # are accumulated; a spatial characteristic value # imgabs4 # of acoustic emission; fitting a fitting function # imgabs7 # from the characteristic value # imgabs5 # to the characteristic value # imgabs6 # respectively; according to the fitting function # imgabs8 #, calculating a correlation coefficient matrix R between the characteristic values # imgabs9 # to # imgabs10 #; according to the matrix R, determining a weight coefficient # imgabs11 # of each characteristic value; a first-order derivative function # imgabs13 # and a second-order derivative function # imgabs14 # of the fitting function # imgabs12 # are obtained, and according to the first-order derivative function # imgabs15 # and the second-order derivative function # imgabs16 #, a demarcation time point # imgabs17 # in the coal and rock mass damage process is determined; and the boundary time point # imgabs19 # is corrected by using the weight coefficient # imgabs18 #, and a corrected boundary time point # imgabs20 # is obtained. In order to solve the problems that an existing coal and rock mass fracture instability judgment index is single, and coal and rock mass fracture instability early warning is low in single index precision, the early warning precision is improved through multi-element signal fusion.

The invention discloses a pressure-controlled drainage gas production method and system for a coal-bed gas well. The pressure-controlled drainage gas production method comprises the steps that drainage is conducted through the first daily drainage amount, wherein the first daily drainage amount meets the eliminating demand for production interference objects in a coal bed; and a pressure-supplementing medium is injected while drainage is conducted, and the downhole flow pressure daily drop amount is maintained as a first differential pressure value so as to avoid retracting and closing of pores and cracks in the coal bed. According to the pressure-controlled drainage gas production method for the coal-bed gas well, the drainage speed at the drainage state can be conveniently determined, the eliminating demands for drilling and completion fluids, pulverized coal and slack coal in the coal bed can be met, and meanwhile, retracting and closing of the pores and the cracks in the coal bed can also be avoided; and compared with the prior art, the pressure-controlled drainage gas production method is simple in process, the production efficiency of the coal-bed gas well can be greatly improved, and quite high practical value and popularization value are achieved.

The invention discloses an underground coal mine video monitoring method, which relates to the technical field of image communication, and comprises the following steps of: acquiring historical video data, extracting historical key feature data for analysis, establishing a feature baseline range of an underground coal mine normal operation state, constructing an anomaly rule base for different anomaly types, and establishing a feature baseline range of the underground coal mine normal operation state; establishing a mapping relation between historical key feature data and abnormal rules, acquiring real-time video data and audio data of the underground coal mine, extracting real-time key feature data, comparing the real-time key feature data with a feature baseline range, matching corresponding rules in an abnormal rule base based on the established mapping relation, outputting an abnormal analysis result, and if it is judged that an abnormality exists, outputting the abnormal analysis result. If yes, triggering a corresponding alarm according to the exception rule, associating an exception handling process, and realizing intelligent safety monitoring by collecting multi-period underground video extraction features, establishing baseline and differentiation judgment conditions, constructing an exception library and a triggering mechanism, comparing real-time data to judge exception and performing graded response handling.

The invention discloses a coal rock fracture intelligent extraction method based on improved U-Net. The method comprises the following steps: S1, constructing a coal rock fracture CT image data set; s2, constructing an improved U-Net segmentation model, specifically comprising the following steps: S2.1, taking VGG16 as a backbone network, and introducing a depth separable convolution module; s2.2, a PPA attention module is added after each layer of depth separable convolution of the decoder, the PPA attention module is introduced after each up-sampling stage of the decoder, and the output of the PPA attention module is subjected to batch normalization and Dropout layer processing; s2.3, defining a composite loss function; s3, training and optimizing a segmentation model, wherein the specific steps comprise: S3.1, setting hyper-parameters; and S3.2, training the model by using the training set, adjusting hyper-parameters by using the verification set, and evaluating the performance by using the test set, wherein the evaluation indexes comprise MIoU, MAcc and FWIoU. According to the method, the problems of difficult identification of small fractures, large model calculation amount, poor multi-scale information fusion and class imbalance in the coal rock fracture image can be solved, and the robustness, segmentation precision and practicability of the model are improved.

The invention relates to a coal conveying control system and method for a thermal power plant. The method comprises the following steps that S1, operation data of a coal conveying system are collected in real time through a preset multi-source sensor network; s2, constructing a time sequence prediction model based on an attention mechanism, and dynamically predicting the operation state of the coal conveying system; s3, executing real-time optimization calculation at the edge calculation node, and generating a control instruction set including the belt speed, the coal feeder opening degree and the transfer station cooperation strategy; s4, the control instruction is issued to an execution mechanism through a 5G-MEC network, and meanwhile key data is uploaded to a cloud end for model iteration updating; and S5, dynamically adjusting a safety protection threshold value based on an equipment vibration spectrum analysis result, and triggering a hierarchical protection mechanism when a coal blockage risk is detected. The method has the effects of effectively improving the performance of the coal conveying system and being green and energy-saving.

The invention provides a management method and system for coal preparation quality intelligent control, and the method comprises the steps: firstly obtaining raw material quality, equipment operation and environment state index sets of a target production line, then carrying out the dynamic association processing of the index sets, and generating a technological process feature set; then calling a preset optimization decision model to carry out parameter prediction on the technological process feature set to obtain a process regulation parameter set, generating a quality optimization instruction set based on the process regulation parameter set, transmitting the quality optimization instruction set to a production control unit to execute dynamic regulation operation, and finally, according to a quality feedback index set after regulation execution, carrying out dynamic regulation operation. And the parameter weight distribution of the optimization decision model is updated, intelligent control and continuous optimization of the coal dressing quality are realized, and the stability of coal dressing production and the product quality are improved.

The present invention discloses a coal mine disaster simulation prediction system based on multi-dimensional data collection, which relates to the field of coal mine safety technology. A variety of monitoring equipment such as geological radar and microseismic monitoring are integrated to build a real-time monitoring network covering key areas of coal mines, and comprehensively collect multi-dimensional data. The system includes modules such as data preprocessing, model construction and training, prediction and decision-making, early warning and emergency response, and user interaction interface. Data is processed through data cleaning, filtering, standardization and fusion technology, and disaster prediction is performed using a CNN and LSTM hybrid model to output disaster probability and risk areas, and automatically trigger early warning and emergency plans. The user interaction interface displays real-time monitoring, prediction results and early warning information, providing a comprehensive and intelligent solution for coal mine disaster prevention and control.

The invention discloses a coal mine ventilation and heat reduction integrated control method based on intelligent scheduling, and relates to the technical field of coal mine ventilation and heat reduction control, and the method comprises the following steps: arranging a plurality of monitoring devices on a ventilation path and a standby ventilation path of a mine, and collecting temperature, humidity, gas concentration and wind speed and volume data; the central control system gathers monitoring data in real time, analyzes the ventilation condition in the air duct and identifies whether abnormal changes exist or not. Continuous ventilation is ensured through multi-path redundancy and dynamic switching, risks of gas explosion and the like are reduced, early warning is performed by means of the airflow simulation model, and safety of miners is guaranteed. And in combination with a reinforcement learning algorithm and a simulation model, quick response and self-adaptive adjustment are realized, air distribution is optimized, shutdown is avoided, and economic benefits are improved. Meanwhile, by intelligently planning a ventilation path and dynamically adjusting the fan load, energy consumption and maintenance cost are reduced, the service life of equipment is prolonged, and sustainable operation of a mine is promoted.

The invention discloses a coal mine pressure risk assessment method based on complex system dynamics, belongs to the technical field of mine pressure risk assessment, is used for mine pressure risk assessment, and constructs a complex system dynamics model, combines various sensor data, and carries out real-time monitoring and risk assessment of mine pressure changes. The method comprises the steps of geological data acquisition, sensor data real-time monitoring, data preprocessing, mine pressure prediction model construction, model training and optimization, real-time risk assessment and the like, data of a stress sensor, a displacement sensor and an acoustic emission sensor are comprehensively utilized, and data preprocessing is performed by adopting a Kalman filtering algorithm; and optimizing the mine pressure prediction model by using a graph convolutional network and an attention mechanism, and performing real-time risk assessment through an adaptive graph neural network. According to the method, the adaptive graph neural network is adopted for real-time risk assessment, potential risks in mine pressure changes can be found in time, risk scores are generated, and scientific decision support is provided for coal mine managers.

The invention provides a coal seam anti-reflection effect prediction method based on multi-source data fusion, and belongs to the technical field of machine learning prediction. According to the method, coal seam anti-reflection multi-source data is collected in real time, normalization processing is carried out on the data, then a coding mode is designed for different types of data, and key features are extracted; then performing unified serialization on the multi-source representation data, and aligning a time scale; a self-attention mechanism is adopted to calculate the time dependence weight of single-mode data, and the importance of different data sources is dynamically adjusted; and finally, capturing information complementarity of cross-modal data by adopting a cross attention mechanism. And inputting the fused data into the trained coal seam anti-reflection multi-parameter combined prediction model, outputting four key parameters including permeability, gas yield, pressure variation and fracture density, and performing prediction analysis on the coal seam anti-reflection effect. According to the method, comprehensive evaluation of the coal seam anti-reflection effect is achieved, compared with an existing method, the coal seam anti-reflection process can be described more accurately, and prediction precision is improved.

The invention discloses a construction method and system for a coal mine geological transparent working face, and relates to the technical field of intelligent mine and geological information, and the method comprises the following steps: collecting and preprocessing visible light, near-infrared images and point cloud data, extracting texture, reflectivity, curvature and density features, and calculating the texture, reflectivity, curvature and density features; the method comprises the following steps: constructing a three-dimensional structure reconstruction model, a rock stratum physical property inversion model and a geological stress prediction model, generating a three-dimensional geometric structure coordinate set, a rock stratum moisture content matrix and a vertical stress distribution matrix of each region, setting an anomaly judgment condition, outputting three-dimensional coordinates of an abnormal region, carrying out graded early warning, synthesizing a three-dimensional visual image, and predicting the geological stress. And holographic transparent expression of the working face geological state is realized. According to the method, centimeter-level three-dimensional reconstruction of the geological transparent working face of the coal mine, precise positioning of a hidden water-containing area and a stress abnormal area and construction of a full-attribute dynamic visual scene are achieved, the timeliness and accuracy of early warning of disasters such as water inrush and rockburst are remarkably improved, real-time geological decision support is provided for intelligent mining, and safe and efficient production is guaranteed.

The invention discloses an intelligent fusion classification and grading advanced early warning method for major disasters of a coal mine. Firstly, coal mine safety monitoring data are collected to construct a data set; secondly, an Angle-Transform model and an E2GAN model are trained, abnormal values and missing values in the data are recognized and interpolated, and data preprocessing is achieved; and thirdly, a new multi-index fusion prediction model is introduced in combination with a BiLSTM and MMOE multi-task learning network, and the future trend of each index can be synchronously predicted after training optimization. And then, improving an AdaTT network to realize a multi-disaster risk fusion early warning model, and optimizing training times and an early warning threshold value by using an F1 index. And inputting real-time monitoring and fusion prediction data into the optimized early warning model to obtain the risk probability of each disaster. And finally, in combination with the optimal early warning threshold and the risk probability, providing a multi-disaster grading early warning method, and realizing grading identification of different disaster risks. According to the method, disaster risks such as gas, fire disasters, dust and roofs can be effectively warned, risk information is provided in advance, and the method is of great significance to advanced management and control of multiple disaster risks of a coal mine.