1472 results about "Mine safety" patented technology

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 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 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 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 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 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 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.

The invention relates to the technical field of coal mine safety, in particular to a water inrush and rock burst composite disaster risk multi-source judgment method which comprises the steps that four fields of original data of a target underground area are collected in real time, and the four fields comprise a stress field, a vibration field, an energy field and a seepage field; extracting multivariate feature indexes from the four-field original data to obtain four-field multivariate indexes; constructing a core composite index based on the four-field multi-element indexes, quantifying the coupling strength of water inrush and rock burst, and judging a disaster dominant type; and calculating a comprehensive risk index based on the four-field multi-element indexes and the disaster dominant type, and judging the risk level of the water inrush and rock burst composite disaster according to the comprehensive risk index. According to the invention, accurate identification and graded prevention and control of composite disasters are realized, and the accuracy and timeliness of early warning of mine disasters are significantly improved.

The invention relates to the technical field of mine safety, and provides a cross-layer roadway multi-rock-stratum rigidity matching collaborative segmented supporting method which comprises the steps that plastic zone ranges of different rock stratum areas in a cross-layer roadway are determined, and rock stratum joint fissure zones and weak intercalated layers are identified; monitoring the elastic zone rigidity and the plastic zone weakening rate of the surrounding rock, constructing a surrounding rock mechanical model, obtaining the supporting structure rigidity for inhibiting the expansion of the plastic zone through the surrounding rock mechanical model, and constructing a supporting structure according to the supporting rigidity; and the reinforcing material is sprayed to the surface of the surrounding rock through high-pressure jet flow to form a protective layer. Through the cooperative control thought of weak layer control, rigidity adjustment and resistance transfer, breakthrough transformation of crossing roadway support from passive defense to active control is achieved, according to a scientific surrounding rock mechanical model, the rigidity of the support structure can effectively inhibit expansion of a plastic zone, the stability of a system is maintained, and the stability of the system is improved. And finally, an ultrahigh strength performance and gradient spraying layer structure technology is adopted, so that the compression strength and the shear strength of the surrounding rock are remarkably improved.

The invention provides an underground equipment fault real-time diagnosis method and system based on edge calculation, and relates to the technical field of coal mine safety production, and the method comprises the steps: collecting multi-modal data through a distributed sensor network, extracting multi-scale time sequence features, projecting the features to a Lie group manifold space, constructing a coupling mapping relation matrix, obtaining fusion features, and carrying out the real-time diagnosis of an underground equipment fault; and constructing a causal directed acyclic graph based on a topological connection relationship and a Granger causal coefficient, executing Bayesian probabilistic reasoning, determining an execution strategy in combination with entropy similarity matching, and performing deep time-frequency analysis and causal chain verification. High-precision real-time diagnosis of equipment faults in an underground complex environment is realized, and the fault early warning accuracy is improved.

The invention relates to the technical field of mine safety and geological environment evaluation, in particular to a mine goaf geological environment dynamic evaluation method based on digital twinning. The method comprises the following steps: constructing a digital twinborn model of a mine goaf, designing a data structure and a virtual-real mapping rule, collecting multi-source heterogeneous data, and performing spatio-temporal data indexing processing to form a spatio-temporal associated data stream; microseismic travel time data are extracted from the space-time associated data stream; performing parallel ray tracing and rapid inversion on the microseismic travel time data to obtain a wave velocity distribution initial value; carrying out wave velocity field self-adaptive grid refinement on the micro-seismic travel time data by utilizing the wave velocity distribution initial value to obtain a multi-resolution wave velocity field; and carrying out wave velocity change rate analysis on the multi-resolution wave velocity field to obtain a wave velocity variability tensor. According to the method, the abstract energy transfer process is concrete into a data field capable of being quantitatively analyzed, so that the geological environment of the goaf is accurately and dynamically evaluated.

The invention discloses a coal seam passive source seismic survey system and method based on deep learning, and relates to the technical field of coal mining safety monitoring and early warning, and the method comprises the following steps: S1, collecting the passive source seismic data of a coal seam area; s2, deep fusion features are extracted; s3, constructing a semi-supervised self-attention prediction network model; s4, extracting coal seam structure prediction parameters; s5, the corrected coal seam structure parameters are extracted; s6, constructing a fast seismic wave forward model, and calculating and outputting an initial model of the coal seam underground structure; s7, constructing a Transform optimization network, and extracting a depth feature vector; and S8, constructing a coal seam structure inversion model, and training iteration by using the improved batch depth Bayesian optimization algorithm. The method overcomes the limitation of manual analysis and slow response in a traditional coal seam seismic survey method, and provides an efficient and accurate solution for safe production of a coal mine.

The invention relates to the technical field of underground coal mine safety, and discloses an underground coal mine early warning system and method. The method comprises the following steps: collecting multidimensional monitoring data of an underground coal mine environment; performing dynamic window division on the multi-dimensional monitoring data to generate a data fragment set; performing abnormal mode recognition based on the data fragment set, and extracting abnormal feature points exceeding a dynamic threshold in each data fragment; inputting the abnormal feature points into a pre-constructed space-time correlation model, and analyzing diffusion paths of the abnormal feature points in a time dimension and a space dimension; according to the diffusion path, generating an abnormal propagation map containing the propagation direction and the intensity attenuation gradient of the abnormal feature points; hierarchical clustering is carried out on the abnormal propagation atlas, and abnormal clusters with similar propagation characteristics are divided; generating an early warning signal containing a priority label by using the spatial and temporal distribution characteristics of the abnormal cluster; performing synchronous verification on the early warning signal and underground equipment state data, and screening out a target early warning signal needing to be responded; and triggering a corresponding emergency control instruction according to the target early warning signal.

The invention discloses a coal mine safety monitoring data anomaly detection and intelligent alarm method and system, and relates to the technical field of coal mine well anomaly detection. The system adopts a layered architecture design and comprises a sensing layer, a transmission layer, a data processing layer, an intelligent analysis layer and an application layer. According to the coal mine safety monitoring system, multi-source data can be fused according to the extreme special environment of the underground coal mine, the system is adaptive to environment changes, abnormal modes are accurately recognized, and intelligent warning is provided.

The invention provides a mine safety training method and system based on digital twinning and virtual reality, and the method comprises the steps: obtaining laser scanning point cloud and geological exploration data of an underground roadway of a mine, and constructing a high-precision virtual underground environment model based on the data; on the basis of the virtual underground environment model, combined with explosion condition judgment, disaster simulation is triggered, and a virtual training environment is generated by applying real-time rendering and dynamic labeling technologies; obtaining operation behavior data of trainees in the virtual training environment, and evaluating the operation behavior data to obtain a behavior evaluation result; and based on the behavior evaluation result and the environmental parameter change data, constructing and training an evaluation model by utilizing machine learning, and generating customized training content in combination with geological conditions and mining process characteristics of different mining areas. According to the method, a high-reduction-degree virtual scene can be constructed, the problem that a traditional training scene is single is solved, quantitative evaluation of the training effect is achieved, and training pertinence and effectiveness are improved.

The invention relates to the technical field of mine safety monitoring and geophysical exploration, in particular to a mine water disaster intelligent alarm system responding to multi-dimensional physical field parameter abnormity, which comprises a full-space data acquisition module for acquiring monitoring area observation data and calling a background geological physical model; the ideal reference reconstruction module is used for constructing an ideal physical field reference in a non-abnormal state; the double difference extraction module is used for calculating an observation residual error of observation data relative to a reference and a theoretical residual error of simulation response relative to the reference; the inversion coupling verification module is used for driving the theoretical residual error to approach to the observation residual error and extracting parameter sensitivity characteristics; the intelligent alarm judgment module is used for analyzing sensitivity characteristics and convergence states; generating an alarm instruction if the feature is determined to be the entity fluid abnormal feature; if it is determined that the target abnormal interference feature is not the target abnormal interference feature, signal suppression processing is executed; according to the invention, the false alarm rate of mine water disaster detection is greatly reduced.

The invention provides a coal mine roof dynamic monitoring and early warning method and system based on artificial intelligence, and relates to the field of coal mine safety, and the method comprises the steps: collecting a stress waveform and a displacement waveform of a coal mine roof, extracting stress loosening characteristics, and constructing a three-dimensional stress conduction strength field; identifying a stress sudden change transition point by using a recurrent neural network to form a stress conduction chain; analyzing a regional topology migration rule and extracting a stress field instability path; matching with historical data to determine a prediction window; and continuously verifying the instability path and generating a support reinforcement scheme. The coal mine roof instability risk can be predicted in advance, and the early warning accuracy and timeliness are improved.

The invention relates to the technical field of mine safety monitoring, in particular to an anti-shielding real-time deformation monitoring method and system for roof fall and wall caving disasters. The method comprises the following steps: extracting a bounding box and a mask of a discontinuous region from roof caving and rib spalling point cloud data, generating complete point cloud data, and analyzing a preliminary deformation index; extracting a track list of the shielding target, a prediction shielding area, a deformation trend and a deformation prediction value; in combination with the bounding box, the mask and the initial deformation index, obtaining a displacement vector and curvature change of the repaired missing region; and in combination with the trajectory list, the predicted shielding region and the deformation characteristics after compensation completion of the initial deformation index, obtaining a compensated and corrected displacement vector and curvature change, and further evaluating a deformation state and an early warning level. The method combines shielding detection, dynamic object tracking, multi-view fusion and deep learning completion to realize anti-shielding deformation feature extraction, time sequence trend prediction and accurate early warning, and provides reliable technical support for mine safety production.

The invention provides a coal mine comprehensive guarantee capability dynamic coupling early warning method and system, and relates to the technical field of coal mine safety production assessment, and the method comprises the steps: constructing a multi-dimensional fusion assessment system comprising a first-level index and a second-level index according to a coal mine comprehensive guarantee demand and an assessment target; collecting corresponding multi-source heterogeneous index data under each dimension in real time; a two-stage dynamic weight distribution mechanism is adopted, an improved entropy weight method and a working condition sensing factor are combined, and the final weight of each index at the current moment is calculated; constructing a four-dimensional feature vector and forming a multi-dimensional tensor, calculating a comprehensive score of each dimension, and generating a coal mine comprehensive support capability index through weighted fusion and Sigmoid function mapping; and performing graded early warning according to the comprehensive support capability index of the coal mine and outputting an early warning signal. According to the method, dynamic coupling evaluation of multi-dimensional risks such as coal mine technology, safety, personnel and system can be realized, comprehensiveness and real-time performance of coal mine safety guarantee are improved, and closed-loop management of risk early warning and resource configuration is effectively supported.

The invention belongs to the technical field of mine safety and engineering earthquake monitoring, and particularly relates to a mine earthquake earth surface vibration intensity prediction method and system based on an improved sine and cosine algorithm, and the method comprises the steps: firstly constructing a mine earthquake intensity prediction model containing geometric attenuation, inelastic attenuation and an earthquake source energy item, and then constructing a target function based on measured data, and optimizing model parameters by adopting an improved sine and cosine algorithm. The algorithm improvement comprises the following steps: adaptively adjusting a search step size through a dynamic amplitude exponential decay strategy, and balancing global exploration and local development capabilities; and a periodic random restart mechanism is introduced, and the position of the individual with the worst fitness is reset to avoid premature convergence. Through the combination of the physical model and the intelligent optimization algorithm, the problems of fixed function form and low parameter optimization efficiency of the traditional empirical model are solved, and high-precision prediction of the mining induced earthquake surface vibration intensity is realized.

The invention relates to the technical field of mine safety, in particular to a rock burst prediction method based on dynamic frequency domain decomposition, relative event coding and dynamic sparse attention. According to the method, data decomposition of Fourier transform, adaptive frequency domain noise reduction, relative event coding and a dynamic sparse attention mechanism are combined, key features in micro-seismic data can be effectively extracted, and the accuracy and robustness of rock burst prediction are improved. The method is excellent in performance in rock burst prediction, the problems of noise interference, error accumulation and the like can be effectively solved, and reliable technical support is provided for accurate early warning of rock burst.

The invention discloses an intelligent dynamic prediction method for a coal and gas outburst dangerous area of a working face, and the method comprises the steps: building an integrated early warning information analysis platform, dynamically collecting key parameters in a mine, and constructing a dynamic data set; and calculating a coal seam firmness factor and a gas emission factor by utilizing a data fusion technology and combining historical data and regression analysis. Furthermore, in combination with a static data set, such as geological and ventilation system distribution data, a multi-parameter coupling prediction model is constructed. The model is trained and verified through a machine learning method so as to predict the coal and gas outburst risk of the working face in real time. The system automatically triggers early warning according to a model prediction result and a preset threshold value, a scientific basis is provided for coal mine safety management, and coal and gas outburst accidents are effectively prevented.

The invention relates to the technical field of geological information, and discloses a precise positioning method and system based on geological mineral product three-dimensional modeling, and the method comprises the steps: deploying a laser ranging array, a micro-seismic array and a fiber grating sensing array, and synchronously collecting multi-source deformation and stress data; after space-time alignment and filtering, an improved ICP algorithm is adopted to reconstruct a millimeter-level precision three-dimensional point cloud model; a safety coefficient cloud picture is calculated by combining a geomechanical constitutive equation, and a risk area is automatically delineated according to a three-level threshold value; and closed-loop control instructions such as voice alarm, thermodynamic diagram pushing, grouting coordinates and avoiding paths are generated. The system is composed of a sensing deployment unit, a data preprocessing unit, a point cloud reconstruction unit, a mechanical analysis unit, a risk delineation unit and an instruction distribution and execution unit. Millimeter-level positioning, sub-hour-level early warning and full-automatic intervention of goaf deformation are achieved, the early warning accuracy rate exceeds 95%, the emergency response is shortened to the minute level, and mine safety and mining efficiency are improved.

The invention, which belongs to the technical field of mine safety monitoring, provides a multi-source information fusion-based roadway surrounding rock roof separation tour-inspection early-warning system comprising a data acquisition module, an edge calculation module, a cloud analysis module and an early-warning feedback module. The data acquisition module acquires multi-source data of surrounding rock of a coal mine tunnel in an automatic mode; the edge calculation module performs spatio-temporal data synchronization on the collected multi-source data and extracts data features; the cloud analysis module performs feature weighted fusion based on the multi-source data to construct a risk early warning model; and the early warning feedback module triggers graded early warning according to prediction data of the risk early warning model, and provides visual feedback, so that a set of automatic, full-coverage and multi-source data fused roadway roof separation monitoring system is constructed.

The invention discloses a mining area disaster control-oriented dual-stage scheme construction method, and relates to the technical field of mine safety engineering, and the method comprises the following steps: S100, constructing a high-resolution multi-mode joint detection system, and carrying out the cooperative arrangement of a seismic wave array and an electrical method profile, and synchronously collecting the wave impedance response and electrical property anomaly characteristics, and obtaining a physical anomaly information set covering full depth. According to the method, through multi-mode joint detection and heterogenous data fusion, the precision and integrity of deep structure recognition are improved, and in combination with geological evolution reasoning and dynamic inversion, recognition blind areas are complemented, and accurate positioning of high-risk disaster-causing units is realized. Based on matching of a disaster index model and a parameterization strategy, automatic mapping of disaster partition and a treatment scheme is achieved for the first time, scientificity, targeting and resource allocation efficiency of disaster treatment are remarkably improved, and the method has important engineering application and popularization value.

The invention relates to the technical field of coal mine safety monitoring, and discloses a coal mine tunnel laser radar three-dimensional dynamic monitoring method. Aiming at the problems that in the prior art, data of a laser radar is easy to interrupt or unreliable under a severe working condition, and monitoring continuity is poor due to insufficient multi-source data fusion, the method is characterized in that laser radar surface data and optical strain gauge point data are synchronously acquired; constructing a roadway digital twinborn model; carrying out fusion and assimilation on the multi-source data by utilizing a Transform mechanism to obtain initial roadway deformation data; judging the credibility of the laser radar data based on Bayesian estimation; when the data are missing or uncredible, deducing full-field deformation by utilizing a gating mechanism in combination with optical strain gauge point data and a digital twin model; and finally, generating and outputting a three-dimensional dynamic deformation field. The system is mainly used for real-time, continuous and high-reliability deformation monitoring and safety early warning of the coal mine tunnel.

The invention relates to a method, a system and equipment for intelligently identifying unsafe behaviors of coal mine operating personnel and a medium. The method comprises the following steps: extracting individual trajectory data from an underground video stream through a multi-target tracking algorithm, decomposing continuous actions into an atomic behavior sequence with a space-time mark by using attitude estimation and a space-time diagram convolutional network, and capturing space-time relevance of a behavior chain through an attention mechanism enhanced long and short term memory network to generate a feature vector; risk reasoning is carried out in combination with the coal mine safety knowledge graph to predict the risk event type and probability, and finally early warning information is generated based on a multi-level early warning strategy. According to the scheme, the crossing from isolated action recognition to behavior chain risk prediction is realized, and the early warning capability of potential safety risks in coal mine operation is remarkably improved through fusion of space-time correlation analysis and domain knowledge of the behavior sequence.