1980 results about "Mine safety" patented technology

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 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 provides a mine abnormal event real-time identification method and system based on time sequence characteristics, and relates to the technical field of mode identification, and the method comprises the steps: carrying out the time-space alignment and semantic annotation of multi-modal monitoring data, and constructing a time sequence knowledge graph; calculating a dynamic association weight between entities, and analyzing a risk propagation path; predicting a risk situation based on a historical evolution rule; and dynamically generating a differential early warning strategy and establishing a closed-loop tracking system. According to the invention, early identification, accurate prediction and efficient disposal of mine safety risks can be realized, and the mine safety management level is improved.

The invention discloses a mining rock mass structure degradation monitoring method and system based on deep learning, particularly relates to the technical field of mine safety monitoring, and is used for solving the problems of monitoring lag and misjudgment caused by the fact that an existing static model cannot automatically adapt to rock mass damage dynamic evolution. The method comprises the following steps: constructing a dynamic damage incremental data set by collecting multi-source heterogeneous monitoring data under mining disturbance in real time, extracting newly-added damage features, and calculating a distribution offset degree between the newly-added damage features and historical features; when the deviation degree exceeds the limit, verifying and screening a damage feature subset according with a mechanical law based on a rock constitutive equation, and blocking non-physical noise interference; a geological structure evolution mode is matched through damage path dependence modeling, and a potential damage path thermodynamic diagram is generated in combination with a space gradient; performing cross-stage association on the thermodynamic diagram and a historical feature library by adopting a knowledge distillation mechanism to generate a dynamic weight matrix fused with a mining time sequence; and finally, updating the damage assessment model through a parameter reweighting mechanism to realize adaptive assessment of the rock mass degradation risk.

The invention discloses a self-adaptive grouting control plugging method based on mining-induced fracture real-time monitoring, and relates to the technical field of mine safety engineering and hydrogeology. Through cooperative work of the grouting mechanism, the data monitoring system, the data collecting and processing module and the self-adaptive control module, automation and intellectualization of grouting protection can be achieved, and through introduction of the fracture roughness coefficient and the time-dependent viscosity, rough fracture flow resistance and grout rheology and time-varying characteristics are quantified, and the grout diffusion radius calculation error is reduced. The data weight is dynamically adjusted through a micro-seismic travel time residual error and sound wave velocity field joint inversion algorithm in combination with a weighted robust LM algorithm, and accurate fracture positioning is achieved. And a PID gain compensation and saturation function mechanism is adopted, so that self-adaptive safe and efficient grouting is realized. The fracture dynamic state is monitored in real time through the multi-source data fusion technology, grouting parameters are dynamically optimized in combination with an intelligent algorithm, complex geological interference is effectively restrained, slurry waste is reduced, and efficient and accurate plugging of mining-induced fractures is achieved.

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 the technical field of mine rock slope analysis, and in particular to a freeze-thaw damage analysis method and system for mine rock slopes. The method comprises the following steps: selecting mine rock slope samples, performing freeze-thaw cycle tests on the mine rock slope samples, and obtaining rock freeze-thaw test information; on the basis of the rock freeze-thaw test information, setting displacement analysis indices, and on the basis of the displacement analysis indices, obtaining displacement variations of rock slopes under freeze-thaw cycles; on the basis of the rock freeze-thaw test information, analyzing plastic damage conditions and mine safety coefficients of the rock slopes under the freeze-thaw cycles; on the basis of the rock freeze-thaw test information, establishing a stress analysis model, and by means of the stress analysis model, obtaining stress distribution conditions of the rock slopes under the freeze-thaw cycles; and by combining the displacement variations, the plastic damage conditions, the mine safety coefficients and the stress distribution conditions, analyzing damage results of the mine rock slopes. In the present invention, multi-objective analysis of freeze-thaw damage conditions of mine rock slopes is performed, thereby ensuring the stability of the rock slopes.

The invention discloses a mine safety production risk monitoring and early warning system and method, and relates to the technical field of mine safety, and the system comprises a data collection module, a data integration and storage module, a correlation analysis and model construction module, a real-time monitoring and early warning module and an emergency decision support module. According to the method, a multi-dimensional data acquisition system is constructed, multi-source data of geology, production, equipment, personnel and the like are fused, deep analysis is carried out by applying a multi-dimensional association algorithm based on mutual information, potential relationships among the data are comprehensively mined, comprehensive and accurate assessment of mine safety production risks is realized, and a real-time dynamic risk assessment model is utilized to realize comprehensive and accurate assessment of mine safety production risks. The risk state is updated in real time according to latest collected multi-dimensional data, a server-side timed task obtains data from a data integration and storage module according to the minute-level frequency and inputs the data into a model, an early warning mechanism is triggered immediately when the risk level calculated by the model exceeds a preset threshold value, risk mutation can be perceived in time, and the hysteresis of a traditional assessment mode is overcome.

The invention belongs to the technical field of mine safety monitoring and geotechnical engineering stability evaluation, and relates to a mine filling body performance detection and stability evaluation method and system.The method comprises the steps that S1, a first type of monitoring signals are collected in real time through a distributed optical fiber sensor array, and meanwhile vibration waveform signals serve as second type of monitoring signals; s2, inputting the first type of monitoring signals and the second type of monitoring signals into a multi-source data fusion model, and generating fusion state signals representing the three-dimensional damage degree and the real-time deformation state of the filling body; s3, inputting the fusion state signal into a parameter dynamic identification module to generate an updated numerical model parameter signal; and S4, based on the updated numerical model parameter signal, executing stability quantitative calculation to generate a risk level signal. According to the mine filling body performance detection and stability evaluation method and system, the problem of inaccurate psychological state recognition such as large individual difference, strong environmental interference and weak dynamic adaptability can be solved.

The invention discloses a mine disaster prediction method based on multi-source data, and relates to the technical field of mine safety, and the method comprises the following steps: collecting original data from different types of sensors in a mine, the data types comprising gas concentration, temperature, humidity, wind speed, ground pressure, water level and vibration information; each type of data is provided with a corresponding timestamp and a spatial position identifier. According to the method, time resampling and space mapping standardization of multi-source data are realized, so that the time-space consistency of data fusion is remarkably improved, and the accuracy of disaster prediction model input is ensured. Meanwhile, a dynamic feature matrix is constructed and a high-precision position weight mechanism is introduced, so that the sensitivity of the model to key areas and key parameters is enhanced, the real-time performance and accuracy of mine disaster prediction are effectively improved, and the risk of missing report and false report of an early warning system is remarkably reduced.

The invention relates to the technical field of mine safety monitoring, and discloses a roadway surrounding rock stability evaluation method, which comprises the following steps: step 1, collecting roadway surrounding rock geological data, step 2, constructing a three-dimensional geomechanical model, step 3, inverting mechanical parameters, step 4, calculating a stability coefficient, and step 5, outputting an evaluation result. According to the method, rock mass types, structural plane occurrence, crustal stress vectors and permeability parameters are integrally collected through geological radar scanning, drilling coring and crustal stress measuring devices, the problem of model distortion caused by single data in a traditional method is solved, deformation of a stress concentration area is accurately captured by adopting a non-uniform grid division strategy, inversion mechanical parameters are combined, and the method is suitable for large-scale popularization and application. The network is trained by using in-situ direct shear and triaxial test data, the inversion efficiency is improved compared with manual trial and error, the whole surrounding rock fracture process is simulated based on a finite element-discrete element coupling algorithm, plastic zone distribution and a comprehensive index S are output, and automatic judgment of the stability level is realized.

The invention relates to the technical field of mine safety analysis, and discloses a mine personnel trajectory prediction method, system and device based on artificial intelligence. The method comprises the following steps: collecting mine personnel positioning data, and performing standardization processing; calculating trajectory quality through a neural network; converting the spatio-temporal feature vector to perform image recognition; performing multi-time scale prediction by applying an activation function; matching with a mine space structure, and clustering to identify a group behavior mode; and calculating a safety index, and generating a safety scheduling strategy. According to the method, the future trajectory of the mine personnel can be accurately predicted through the special environment constraint of the mine and the working behavior mode of the miner, and the prediction result is converted into a safe scheduling strategy.

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 relates to an abandoned mine methane source detection-monitoring-analysis method and system based on air-ground cooperation, and belongs to the technical field of coal mine safety monitoring. The method comprises the following steps: carrying a high-sensitivity CH4 detection device and a camera by an unmanned aerial vehicle, carrying out aerial inspection according to a route, and carrying out source detection and inspection on methane distribution around the abandoned mine by returning three-dimensional concentration; the method comprises the following steps: deploying holder laser telemetering equipment and meteorological sensors in a wellhead, a fracture dense area and an air flow path, and transmitting monitoring data through LoRa / ZigBee; the method comprises the following steps: fusing air-ground data, constructing a methane concentration gradient field and a diffusion model, and predicting a leakage trend while performing large-range and continuous monitoring in combination with geological and meteorological data; and the system triggers multi-stage early warning and automatically starts emergency response. According to the invention, all-directional and high-precision monitoring of CH4 in the abandoned mine is realized, the leakage source positioning error is less than 5m, the response efficiency is improved, the operation and maintenance cost is reduced, the explosion and environmental pollution risks are effectively prevented and controlled, and the safety management level of the abandoned mine is improved.

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 invention relates to the technical field of mine safety early warning, and particularly provides a mine risk early warning method based on multi-parameter fusion and trend prediction, and the method comprises the steps: collecting multi-dimensional safety parameters including mine environment, equipment operation and production working condition parameters in real time; acquiring mine historical data, and generating a parameter association feature set through time sequence analysis in combination with real-time multi-dimensional parameters; inputting the multi-dimensional parameters and the feature set into a trained risk trend prediction model, and outputting a current risk level and an evolution trend; according to the risk level, the evolution trend and the real-time parameters, early warning information and a response strategy are dynamically generated; and the communication link is adapted based on the risk level, and the early warning information and the strategy are pushed to the target terminal. The method breaks through single parameter monitoring limitation, breaks through traditional alarm hysteresis through multi-parameter fusion and trend pre-judgment, constructs a perception-pre-judgment-response-transmission complete closed loop, solves the problems of early warning and disposal disjunction, transmission failure and the like, and improves the active prevention and control capability and management and control accuracy of mine risks.

The invention relates to the technical field of coal mine safety monitoring, and discloses a coal mine heterogeneous data visualization processing method combined with artificial intelligence, and the method comprises the steps: collecting multi-source heterogeneous data above and under a coal mine to construct a real-time heterogeneous data matrix; feature categories are divided based on data dynamic relevance, and stable data areas are screened through a dynamic analysis window to generate an optimization matrix; performing pattern recognition on the unstable region to extract an abnormal data cluster, and calculating an abnormal feature factor of a data point; analyzing the feature distribution difference to generate a stable feature index, and calculating an abnormal risk value in combination with the distance, the abnormal feature factor and the stable feature index; and mapping the center coordinate of the stable region to a three-dimensional space of the coal mine, and positioning a key information region in combination with the abnormal risk value. An apparatus includes a memory, a processor, and a computer program. The coal mine heterogeneous data processing efficiency and the safety monitoring accuracy are improved, intelligent positioning of the key information area is achieved, and powerful support is provided for coal mine safety production.

The invention provides a remote alarm risk studying and judging method and device based on underground coal mine multi-mode data, and relates to the technical field of coal mine safety. Obtaining time sequence data of abnormal sensors in an underground coal mine area, associated time sequence data of a plurality of associated sensors influencing the data of the abnormal sensors, states and behaviors of feature objects influencing underground coal mine safety in image videos, and working tracks of personnel; and judging the alarm reason of the sensor data abnormity, and performing multi-angle alarm risk research and judgment through a retrieval enhancement generation large model to obtain the risk level of the alarm reason and the corresponding alarm reason coping strategy. Therefore, multi-modal data fusion analysis is performed through the sensor data, the image video and the working track in the underground coal mine area, intelligent risk research and judgment of coal mine safety alarm are realized, the alarm risk research and judgment time is shortened, and the alarm risk research and judgment precision is improved.

The present disclosure provides an artificial intelligence-based method for enhancing mine safety by identifying and predicting locations of water inrush points in a mine, including the following steps: S1: constructing a numerical model to determine priori information of parameters to be recognized based on observation data, including coordinates of locations of water inrush points; S2: generating a training sample dataset and a test sample dataset of an alternative model based on the numerical model and the priori information of the parameters; S3: constructing and training a neural network of the alternative model; S4: testing an accuracy of the alternative model; and S5: performing a simulated annealing algorithm to identify the locations of water inrush points and simulation model parameters.

The invention provides a miner safety early warning system based on UWB and AI cameras, and relates to the technical field of mine safety, the miner safety early warning system comprises a positioning module, a video monitoring and analysis module, a data fusion and processing module and an early warning and communication module, the positioning module is composed of a plurality of UWB base stations arranged in a mine and UWB positioning cards worn by miners, and the video monitoring and analysis module is connected with the early warning and communication module. The centimeter-level real-time positioning of miners is realized through ultra-wideband pulse signals; the UWB technology is applied, the positioning precision is improved to the centimeter level, the positions of miners can be accurately mastered, precious time is won for emergency rescue, intelligent recognition and analysis of personnel behaviors and environmental risks are achieved through the AI camera, the defects of traditional monitoring are overcome, potential safety hazards can be found in advance, and through multi-source data fusion and real-time processing, the safety of the miners is improved. The system can give an early warning at the first time when a danger occurs, thereby effectively avoiding the occurrence of an accident or reducing the harm degree of the accident.

The invention discloses a smart mine safety production violation behavior identification method, system and device and a medium, belongs to the technical field of smart mine safety identification, and aims to solve the technical problem of how to improve the accuracy and efficiency of mine safety production violation behavior identification, realize real-time and accurate safety supervision of the whole process of mine operation and improve the safety of mine safety production violation behaviors. According to the technical scheme, the method comprises the following steps: data acquisition and preprocessing: installing a camera in a key operation area of a mine to acquire video image data, and carrying out denoising, graying and normalization preprocessing operation on the video image data to obtain preprocessed video image data; and constructing a deep learning model based on a convolutional neural network: introducing an attention module into the network structure of the deep learning model, and training the deep learning model by using the marked video image data including the safety production violation behavior and the normal operation behavior, and adopting a transfer learning method in the training process.

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 relates to the technical field of information processing, in particular to a mine microseism positioning control system and method.The mine microseism positioning control method comprises the steps that microseism signals are synchronously collected through a geophone, an accelerometer, an acoustic emission sensor and an optical fiber sensor, and data timestamps are aligned based on a GPS / Beidou clock; blind source separation is carried out on the signals after wavelet filtering, independent seismic source components are extracted, multi-sensor features are fused through a graph neural network, and a space-time joint feature matrix is constructed; classifying micro-seismic event types by using a ResNet-LSTM hybrid model, generating a seismic source coordinate initial solution based on deep reinforcement learning, and iteratively converging to an optimal solution through a particle swarm optimization algorithm; and mapping a positioning result to a three-dimensional geological grid model, simulating an energy diffusion path, triggering a three-level alarm according to the event energy density, and automatically pushing an emergency instruction to a mine safety management system. Therefore, the problem of low precision of the existing mine microseism positioning control system is solved.

The invention relates to a coal mining safety prediction visualization method and system, and the method comprises the steps: determining a sensor deployment scheme based on mine geological structure information and operation environment characteristics; collecting multi-modal monitoring data according to the sensor deployment scheme, and generating a fusion data set of time-space alignment; extracting corresponding spatio-temporal features, and generating a corresponding risk assessment matrix based on the spatio-temporal features; constructing a four-dimensional tensor field containing a time dimension, and generating a dynamic risk visual image based on the four-dimensional tensor field; in the dynamic risk visual image generation process, receiving a feedback instruction input by an expert, and updating the coal mine safety domain knowledge graph by adopting a fuzzy cognitive mapping mode based on the feedback instruction; and optimizing the space-time convolutional network, updating the dynamic risk visualization image, obtaining an updated risk visualization image result, and executing hierarchical early warning processing. According to the method, the richness and accuracy of coal mining safety prediction visualization can be improved.