20214 results about "Coal" patented technology

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 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 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 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 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 relates to the technical field of coal mine safety monitoring, in particular to an underground coal mine dust concentration monitoring method and system based on multi-modal data fusion, and the method comprises the steps: synchronously collecting dust concentration time sequence data, dust image data, sound wave signal data and environmental parameters through a multi-modal sensor array deployed in an underground coal mine; carrying out preprocessing and feature extraction on the collected data; predicting the decomposed high-frequency and low-frequency component signals by adopting a long short-term memory neural network and a grey Markov model; when the environment humidity is greater than 80%, carrying out light scattering compensation on the predicted value; inputting various predicted values into an improved D-S evidence theory fusion device, and outputting a fusion dust concentration monitoring value; and when the threshold value is exceeded or the temperature and humidity composite condition is reached, an acousto-optic alarm is triggered and a spraying dust-settling device is started. According to the method, the problems of low precision of a single sensor, multi-source data conflict, high-humidity environment measurement deviation and insufficient time sequence and fusion precision in underground coal mine dust concentration monitoring can be solved.

The invention relates to the technical field of coal mine disaster monitoring, in particular to a gas coal mine goaf coupling disaster monitoring method, an electronic device and a storage medium, and the gas coal mine goaf coupling disaster monitoring method comprises the following steps: obtaining comprehensive data of a goaf through a sensor network, and carrying out data preprocessing to obtain standardized data; constructing a digital twinborn model through a 3D modeling and geographic information system technology; performing data synchronization and model correction processing on the digital twinborn model, and optimizing parameters and states of the digital twinborn model; constructing a fluid-solid-heat multi-field coupling model through a computational fluid mechanics and finite element analysis method; constructing a risk assessment model through an analytic hierarchy process and a fuzzy comprehensive evaluation method; training a disaster early warning model by using a machine learning algorithm based on the standardized data and the risk assessment model; a digital twinborn model, standardized data, a risk assessment model and a disaster early warning model are integrated, and a visual interaction platform is constructed. According to the monitoring method, multiple disaster elements can be coupled and dynamically evolved, and the disaster prediction accuracy and timeliness are greatly improved.

The invention relates to the technical field of coal mine water disaster prevention and control, and particularly discloses a hidden structure detection system for mine water disaster intelligent monitoring, early warning and prevention, which is used for solving the problems of low mine hidden structure detection precision, mine water disaster early warning lag and large seismic wave propagation path simulation error. Comprising a multi-source data comprehensive detection module, a real-time imaging module, a micro-seismic multi-scale intelligent analysis module, a seismic wave signal identification module, a regional difference analysis module and an intelligent data processing module. According to the invention, through fusion of multi-source data, real-time imaging, micro-seismic analysis, seismic wave identification and intelligent data processing technologies, precise detection of a mine hidden structure, high-precision prediction of mine earthquakes and water disasters and real-time risk early warning are realized, and timeliness and accuracy of mine water disaster prevention and control are improved.

The present invention relates to the technical field of coal mine water inrush disaster prevention and control, and relates to an artificial intelligence-based method for identifying a water inrush point in a mine and parameters of a simulation model, comprising the following steps: step 1: establishing a numerical model on the basis of observation data, and determining prior information of parameters to be identified including position coordinates of a water inrush point; step 2: on the basis of the numerical model and the prior information of the parameters, generating a training sample dataset and a test sample dataset for an alternative model; step 3: constructing and training an alternative model neural network; step 4: testing the accuracy of the alternative model; and step 5: executing a simulated annealing algorithm to identify the position of the water inrush point and parameters of a simulation model. By using the method provided by the present invention, the problem in accurate positioning of water inrush points in underground coal mines can be solved, which is of great significance in carrying out scientific disaster management and rescue efforts in a timely manner.

The present invention relates to a coal mine intelligent directional drill and its drilling method, belonging to the technical field of coal mine drills. It comprises a moving platform, machine frame, clamper, main manipulator, power head, drill pipe storage system, control system, and hydraulic system. The power head structure is improved by adding an angle adjuster with accurate angle adjustment, locking, and anti-rotation functions. The angle adjuster is positioned on the gearbox side opposite the main motor, connected via a driving shaft. The control system includes a toolface azimuth detection and initialization system that works with the angle adjuster. This solves prior issues such as the absence of specialized toolface azimuth adjustment devices, low precision, low track adjustment efficiency, and challenges in achieving automatic directional drilling in the prior art. The invention enhances precision and efficiency, enabling automated, intelligent directional drilling.

The invention discloses a three-dimensional panoramic intelligent monitoring method and device for a coal conveying gallery, and relates to the technical field of industrial automation and intelligent monitoring, and the method comprises the steps: obtaining initial point cloud data and an initial multispectral image of the coal conveying gallery through a rotary laser radar, a high-dynamic-range camera and an infrared thermal imager; inertial measurement units are arranged in the devices to collect vibration data, and the vibration data are used for carrying out vibration compensation on point cloud and images to generate target point cloud data and target multispectral images; thirdly, performing three-dimensional modeling and multispectral texture mapping on the coal conveying gallery based on the data to obtain a target three-dimensional model; and finally, mapping equipment, personnel and environment data into the three-dimensional model in real time, displaying each piece of state information, and realizing comprehensive monitoring. According to the application, high-precision three-dimensional panoramic monitoring can be realized in a complex vibration environment of the coal conveying gallery, and the safety management level and the operation and maintenance efficiency of the coal conveying gallery are effectively improved.

The invention discloses a rock burst global monitoring and dynamic and static far and near field cooperative early warning method and system, and the early warning method comprises the steps: determining a stope space key monitoring region and a stress wave type based on a dynamic load stress wave far field generation and propagation mechanism, delineating a working face surrounding rock high-stress region, and analyzing the structural characteristics of a rock stratum. Arranging a micro-seismic sensor in a far-field key area, and obtaining parameters such as seismic source fracture size, energy and magnitude through waveform inversion; an acoustoelectric system is arranged in a near-field high-stress area, coal rock fracture characteristics are dynamically monitored, and the static load energy storage state of surrounding rock is quantitatively analyzed. And in combination with a stress wave propagation attenuation coefficient, a far and near field fusion early warning criterion under the action of dynamic and static energy superposition is established, and advanced accurate early warning of the rock burst is realized through collaborative analysis of multi-source monitoring data. The disaster-pregnant space global monitoring technology fusing earthquake, sound and electricity is provided, the chain evolution disaster-causing process of dynamic load generation, cross-space propagation and working face surrounding rock damage is considered, and the accuracy of rock burst early warning can be remarkably improved.

The invention provides a coal quantity detection method and device based on laser radar and vision fusion, and belongs to the technical field of coal quantity detection of underground coal mine conveying belts. According to the technical scheme, the coal quantity detection method based on laser radar and vision fusion comprises the following steps: S1, sensor joint calibration; s2, data acquisition; s3, identifying a coal quantity area; s4, complementing the depth image; and S5, calculating the coal quantity. The invention further provides a coal quantity detection device which comprises a speed sensor, a laser radar, an industrial camera, a computer and a human-computer interface. The method has the beneficial effects that through multi-sensor data fusion and a deep learning algorithm, underground complex environment interference is overcome, and high-precision real-time detection of the coal quantity is realized; the detection device has the explosion-proof and dust-resistant characteristics, and the detection method comprises environment self-adaptive correction and lightweight calculation, and can be widely applied to intelligent mine transportation systems.

The invention relates to the technical field of deformation early warning of a coal mine TBM tunneling roadway, in particular to a simulation and monitoring method for intelligent early warning of large deformation of the coal mine TBM tunneling roadway. According to the technical scheme, the method comprises the steps of data acquisition and preprocessing, feature mining and fusion, intelligent early warning model construction and real-time monitoring and early warning, and the data acquisition and preprocessing specifically comprises the steps that multiple types of sensors are installed on a coal mine TBM tunneling roadway site to acquire multiple data, three-dimensional reconstruction is conducted on image data, and timestamps and space coordinates of the data are unified; a multi-dimensional spatio-temporal data set is formed by using wavelet transform denoising, and feature mining and fusion comprises the steps of extracting geologic structure image data features by using 3D-CNN and capturing spatio-temporal change features of other data by using LSTM. According to the method, high-quality data is provided, meanwhile, the model is more suitable for complex working conditions and more accurate, the generalization ability of the model is enhanced, real-time monitoring and early warning are timely, effective and visual, decision making is facilitated, and safety accidents and economic losses can be effectively reduced.

The invention discloses a deep water-rich weak-impact coal seam surrounding rock control method based on rapid tunneling, and relates to the technical field of surrounding rock control. The method comprises the steps that S1, a high-risk area is identified through address detection, a waterproof curtain is constructed, and the tunneling disturbance risk is reduced; s2, rapid tunneling and instant supporting are conducted, the surrounding rock exposure time is shortened, tunneling and supporting are completed synchronously, and deformation development is restrained; s3, sensing a surrounding rock-support response in real time, optimizing parameters and early warning risks, and realizing dynamic monitoring and feedback regulation and control; s4, later-stage continuous reinforcement maintenance is conducted, so that the roadway adapts to the rheological characteristics of deep surrounding rock, and the service cycle of the roadway is prolonged; according to the method, a high-risk area is identified through various detection technologies, and instant supporting is achieved through cutting process optimization and digging and supporting collaborative operation; real-time monitoring and dynamic feedback are achieved, and automatic optimization of supporting parameters is achieved; and later reinforcement and periodic maintenance are adopted to ensure that the surrounding rock supporting system can adapt to the rheological characteristics of deep surrounding rock for a long time.

The invention relates to the technical field of data processing, and discloses a coal mine area gas control and safety guarantee method and system under complex geological conditions. The method comprises the steps that geophysical prospecting, drilling and logging exploration are conducted on a coal mine area, and geological parameters are obtained; performing statistical analysis on the data, and constructing a three-dimensional gas model; identifying a high-gas area based on the model, and arranging a directional drilling network; differential extraction and anti-reflection measures are implemented according to the drilling network; fusing extraction and sensor data, and predicting a gas change trend by using a neural network; and performing intelligent diagnosis based on the early warning matrix to form a disposal scheme. Under the complex geological condition, the gas distribution rule is accurately mastered, scientific arrangement of directional drilling and optimization design of differential extraction parameters are achieved, meanwhile, a gas early warning decision support system based on artificial intelligence is established, the accuracy of gas treatment and the timeliness of early warning are improved, and therefore safe production of a coal mine is guaranteed.