17006 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 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 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 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 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 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 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 relates to the technical field of coal mine exploration, in particular to a coal field geological anomalous body accurate positioning and intelligent prediction method based on machine learning, which comprises the steps of S1, integrating multi-source geological data; s2, data processing and feature enhancement; s3, machine learning modeling; s4, target spot optimization and dynamic verification; and S5, geological modeling and risk grading. According to the coal field geological anomalous body accurate positioning and intelligent prediction method based on machine learning, a depth domain joint data set is constructed, multi-dimensional features such as seismic amplitude, lithology coding, fracture index and fault distance field are integrated, and multi-source data fusion and physical constraint machine learning are realized through a feature enhancement technology; locking a high-uncertainty region based on the prediction variance, and dynamically updating a target spot through a Gaussian process proxy model to realize targeted drilling and dynamic closed-loop optimization; a multi-attribute risk fusion model is constructed, a four-level risk map is output through the risk factors, grouting resources are guided to be preferentially put into a high-risk area, and risk grading early warning processing is achieved.

The invention provides a high-negative-pressure gas extraction drill hole full-life-cycle efficiency evaluation method, and belongs to the technical field of mineral exploitation. A ground stress distribution model is established through microseism monitoring to determine drill hole arrangement parameters; a distributed optical fiber sensing system and a multi-parameter gas flow meter are used for monitoring the stress change around a drill hole and gas extraction data in real time, the coal fracture development degree is obtained in combination with sound wave testing, and all the data are input into a physical numerical value coupling model to calculate a permeability dynamic evolution curve. Establishing a permeability influence coefficient matrix and determining a key parameter weight, constructing an evaluation index system including the extraction amount, the permeability change rate, the drilling life and the coverage range, and performing multi-objective optimization configuration by applying a Pareto optimal solution theory. The technical problem that full-life-cycle dynamic monitoring and comprehensive efficiency evaluation of the high-negative-pressure gas extraction drill hole are lacked at present is solved.

The invention relates to the technical field of coal mine safety, in particular to a coal mine water disaster prediction system based on a data analysis and machine learning technology, which comprises a multi-source data acquisition module, a dynamic data preprocessing module, a multi-modal feature engineering module, an integrated prediction model construction module and a prediction optimization control module, the multi-source data acquisition module fuses geological and hydrological data, micro-seismic data and equipment working condition data, the dynamic data preprocessing module constructs a noise feature library and realizes noise elimination and data standardization, and the multi-modal feature engineering module extracts dynamic causal feature vectors of a water diversion coefficient change rate and a micro-seismic energy release rate based on convergence cross mapping; the integrated prediction model construction module fuses and outputs a water disaster risk probability value through a meta-learner; and the prediction optimization control module triggers a sampling rate adjustment and disaster response linkage mechanism according to the risk probability value. The method has the advantages of high reliability, high adaptability and timely response, and is suitable for real-time prediction of water disasters in a complex coal mine environment.

The invention relates to the technical field of coal mine telemetering, in particular to a mining laser methane telemetering system based on multispectral fusion, which comprises a multispectral laser emission module, an optical receiving and signal conversion module, a multispectral fusion processing module, an intelligent algorithm compensation module, a data transmission and display module and a power supply module. According to the method, accurate and real-time monitoring of the methane concentration in the coal mine environment is achieved, reliable guarantee is provided for coal mine safety production, compared with a traditional neural network compensation model, the optimized neural network compensation model has the advantages that the methane concentration compensation precision is greatly improved, the generalization ability of the model to different environment conditions is obviously enhanced, and the method is suitable for popularization and application. Accurate measurement of methane concentration can be realized in a more complex and changeable coal mine environment. Meanwhile, due to the application of a dynamic weight compensation mechanism and an uncertainty quantification and compensation adjustment method, the reliability and the stability of a measurement result are further improved, and a more powerful guarantee is provided for safe production of a coal mine.

The invention relates to the technical field of mine safety, and discloses a coal mine underground gas monitoring method and system. The method comprises the following steps: acquiring methane absorption intensity data by adopting a tunable diode laser absorption spectrum, converting the methane absorption intensity data into gas concentration based on a Lambert-Beer law, performing temperature and humidity compensation, calculating a transmission priority according to concentration deviation to form a data packet, and establishing a spatial interpolation model by utilizing multipoint data to predict gas concentration distribution. Calculating a risk assessment index based on the predicted distribution, automatically adjusting the rotating speed of the ventilator, and controlling the electrical equipment to be powered off. According to the method, the technical problems of low detection precision, incomplete monitoring coverage, low data transmission efficiency and lack of prediction and early warning capabilities in the existing underground coal mine gas monitoring method are solved. And the accuracy of gas concentration detection and the intelligent level of gas monitoring are improved.

The invention discloses a gas concentration prediction method based on micro-seismic monitoring data, and belongs to the technical field of coal mine safety monitoring. Comprising the steps that micro-seismic sensors and gas sensors which are distributed in a net shape are arranged in a mining area, and data are collected in real time and subjected to denoising and standardization processing; the method comprises the following steps of: constructing a gas concentration prediction model fusing a long-short-term memory network and a convolutional neural network by extracting multi-dimensional characteristic parameters such as a microseismic energy gradient, a seismic source aggregation degree and a gas concentration change rate, and dynamically distributing spatial-temporal characteristic weights by utilizing an attention mechanism; a safety threshold value is dynamically adjusted based on coal seam permeability, mining depth and geological parameters, score calculation of comprehensive danger evaluation indexes is combined, and three-level early warning of underground sound-light alarm, ventilation regulation and control and remote expert cooperation is triggered; and meanwhile, through periodic model calibration and parameter retraining, the prediction precision is improved. According to the invention, space-time collaborative perception and self-adaptive early warning of the gas risk are realized, and the real-time performance and reliability of coal mine safety prevention and control are obviously improved.

The invention provides a multi-physics field cooperative regulation and control method in coal mine rock burst prevention and control, and relates to the technical field of coal mine rock burst prevention and control, and the method comprises the steps: synchronously collecting stress field, fracture field and vibration wave field data in real time through a distributed optical fiber sensor network and a micro-seismic monitoring system, and constructing a dynamically updated three-dimensional geomechanical model; based on an adaptive Kalman filtering algorithm, noise reduction multi-source data are fused, a dynamic stress concentration factor and an energy accumulation critical index are extracted, and a rock burst risk probability model is established; a multi-objective optimization algorithm is adopted to generate graded regulation and control instructions of grouting reinforcement, mining speed adjustment and pressure relief drilling, and the graded regulation and control instructions are executed in real time; and iteratively optimizing the model weight by using a transfer learning algorithm in combination with a historical case library to form a closed-loop feedback adaptive prevention and control system. According to the method, the analysis precision of rock mass fracture evolution under complex geological conditions is improved through multi-physics field collaborative perception and dynamic modeling, and risk quantification and real-time regulation and control are realized through multi-algorithm coupling analysis.

The invention discloses a coal and rock mass fracture seismic source and stress field joint inversion method and system, and the method comprises the following steps: arranging a multi-channel sonic sensor in a coal and rock mass, and collecting original acoustic emission / microseismic signal data; the head wave arrival time / amplitude of the filtered effective waveform data is automatically picked up through an improved AIC (Akaike Information Code), quantitative evaluation is carried out on the quality of a picked-up result, and high-precision positioning is carried out on waveform signals with a high signal-to-noise ratio and a low signal-to-noise ratio; constructing a tension fracture seismic source model based on a displacement discontinuous tensor, obtaining a moment tensor component of a fracture event, and calculating to obtain a spatial orientation, a tension-shear attribute, a deviation angle alpha, energy release and a moment magnitude of the fracture; establishing a stress field inversion model of the tension-shear fracture seismic source, and realizing the inversion of the stress direction and size of the tension-shear fracture seismic source; and a slip direction included angle minimization objective function is established for iteration until convergence conditions are met, and joint iteration inversion of the fracture and the stress field is realized.

A coal mine risk early warning system based on big data analytics, the coal mine risk early warning system comprising: a data collection module, used for collecting data in real time during coal mine operation; a data storage module, configured to store historical data records collected by the data collection module; a data processing module, which uses big data analytics technology to process the stored data and identify potential risk factors; a risk assessment module, which assesses the risk level of coal mine operation on the basis of analysis results of the data processing module, there being three risk levels: low, medium, and high; and an early warning module, which sends an early warning signal to relevant personnel when the risk level reaches a preset threshold.

The invention discloses a coal fire area fracture active enhancement detection test system and method, and belongs to the technical field of mine safety and geophysical exploration, and the system comprises a fracture generation module which is used for simulating a heat-force-gas coupling condition of an underground coal fire area, and inducing a coal rock mass test piece to generate a fracture; the fracture detection module is used for synchronously acquiring multi-physics field data of the coal and rock mass test piece in real time; the fracture intervention module is used for injecting a natural medium simulant and an enhanced medium foam gel material into the fracture so as to simulate a real environment and enhance a detection signal; and the intelligent feedback and data fusion module is used for controlling operation parameters of each module, synchronously acquiring detection data, and performing fusion processing, intelligent inversion and four-dimensional visualization on the detection data to realize whole-process monitoring detection of fracture evolution. The invention provides a scientific method for precise delineation and targeted plugging treatment of hidden fractures in a coal fire area, and solves the problems of multiplicity of solution in traditional fracture detection and easy reburning in coal fire treatment.

The present invention belongs to the technical field of anchor protection apparatuses. Provided is an automatic anchor protection platform having combined support of anchor rods and anchor cables. The automatic anchor protection platform comprises a movable chassis, a multi-cycle anchor rod drilling rig, an anchor rod drilling rig posture positioning control device, an anchor cable support device, a temporary support device, an electrical system and a hydraulic system. Said automatic anchor protection platform integrates the multi-circle anchor rod drilling rig, the anchor cable support device and the temporary support device, and therefore meets the anchoring and support requirements of roadways under different geological conditions, allows anchor rod and anchor cable parallel support operations to be conducted at the same time, and achieves efficient coordinating matching linkage integrated design, thus further promoting efficient operation processes in limited spaces such as coal mines and tunnels.

The invention discloses a coal mine rock burst risk assessment system which comprises a data acquisition module, an energy accumulation rate calculation module, a roadway network modeling module, a risk assessment module and an early warning regulation and control module. The method comprises the following steps of: firstly, calculating an energy accumulation rate of a roadway, then constructing a roadway network diagram, identifying a high stress conduction path, calculating a dynamic risk index and delimiting a risk area by integrating factors such as a historical impact event and an energy index, and automatically triggering corresponding early warning and regulation measures by a system according to a risk level so as to realize accurate early warning and dynamic regulation. And mine operation decision making and accident prevention are assisted.

The invention belongs to the technical field of power station boiler waste heat recycling energy-saving equipment and relates to a plate-and-shell type coal economizer. The plate-and-shell type coal economizer comprises a corrugated plate heat exchange core body (1), a heat exchanger shell (2), a first and a second water inlet / outlet water tank (3, 3') and a first and a second exhaust gas inlet / outlet pipeline (4, 4'), wherein the corrugated plate heat exchange core body (1) is mounted in the heat exchanger shell (2), the first and second water inlet / outlet water tanks (3, 3') are respectively arranged at the upper and lower ends of the front side of the corrugated plate heat exchange core body (1), and the first and second exhaust gas inlet / outlet pipelines (4, 4') are respectively arranged at the upper and lower sides of the corrugated plate heat exchange core body (1). The plate-and-shell type coal economizer solves the problem that the length of a water passage needs to be increased to solve the problem of unapparent water heating due to the great difference between an exhaust gas density and a water density, and meanwhile, a corrugated passage at the exhaust gas side enables the exhaust gas flow turbulivity to be increased and the heat transfer efficiency to be improved; the blockage due to ash accumulation happens rarely, even if the ash is accumulated, the cleaning is easy to finish.

The present application provides an underground site safety state monitoring method and apparatus for a coal mine, applicable to a site monitoring hardware unit group. The site monitoring hardware unit group comprises a plurality of site monitoring hardware units, and the plurality of site monitoring hardware units are used to achieve digitization of an underground site space of a coal mine. The method comprises: constructing digitization result data of an underground site space of a coal mine, and performing semantic segmentation on digitization result data at each moment, and obtaining multiple pieces of semantic geological structure information corresponding to each moment; and, on the basis of multiple pieces of historical semantic geological structure information corresponding to a historical time sequence of the same position, performing safety evaluation on a geological structure at the same position, obtaining a geological evaluation result, and providing a safety early warning on the basis of the geological evaluation result. The underground site safety state monitoring method for a coal mine provided by the present application achieves real-time monitoring of and early warning for the underground site safety state for a coal mine.

The invention relates to the technical field of gas extraction drilling machine vibration signal processing, in particular to a gas extraction drilling machine vibration signal self-adaptive noise reduction method based on multi-scale feature fusion. The method comprises the steps of collecting a vibration signal, converting the vibration signal into a two-dimensional waveform image, recognizing a drilling working condition area, constructing a multi-scale objective function to optimize VMD parameters, decomposing the signal and screening a dominant IMF component for reconstruction. Through multi-objective optimization and multi-feature fusion strategies of energy distribution and transient impact characteristics, the problems that in a traditional method, the noise reduction effect is poor, and the coal rock character recognition precision is insufficient are solved, the waveform similarity, the power spectrum density coincidence degree and the correlation coefficient are remarkably improved, and reliable guarantee is provided for coal mine safety production.

The invention discloses a coal mine fire and gas coupling disaster multi-source information intelligent monitoring and early warning system, and relates to the technical field of coal mine safety. A data acquisition module is used for acquiring underground related data, and an image acquisition module is used for acquiring a roadway area image and a visible light video; the data image processing module is used for processing underground related data to obtain a comprehensive environment index, the regional analysis module is used for carrying out feature fusion on the processed data and images to obtain a fused feature index, and then the comprehensive monitoring module is used for collecting and calculating to obtain comprehensive equipment performance parameters. And performing comprehensive risk prediction calculation based on the comprehensive equipment performance parameters, the comprehensive environment indexes and the fusion feature indexes to obtain a comprehensive risk index coefficient, performing comparison through a preset dynamic risk threshold value, determining a risk state according to a comparison result, and performing execution operation according to different levels of early warning signals through an execution module. The function of determining whether the underground coal mine is safe or not through complete early warning based on the multi-source data is achieved.

The invention provides a coal mine operation and maintenance monitoring method and device, electronic equipment and a storage medium. Environmental parameters and equipment state data are collected in real time through the Internet of all things deployed in an underground coal mine; preprocessing the environment parameters and the equipment state data by utilizing an edge computing node; the method comprises the following steps: acquiring point cloud data and a multi-view image of an underground coal mine, and splicing the point cloud data and the multi-view image to construct a three-dimensional scene model; the preprocessed data and the three-dimensional scene model are dynamically fused, and the equipment state features, the environment features and the risk early warning parameters are displayed in the three-dimensional scene model in a three-dimensional visualization mode in an overlapping mode; under the abnormal condition, abnormal data are received, maintenance steps or fault points are marked in a virtual interface of the monitoring center, data islands are broken, and multi-source data are deeply fused; and real-time guidance can be provided through interaction in modes of gestures, voice and the like, so that the maintenance efficiency is improved.