904 results about "Mine safety" patented technology

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 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 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 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.

The invention relates to the technical field of mine safety monitoring, and particularly discloses a metal mine safety risk avoiding multi-source data fusion monitoring method, which comprises the following steps of: receiving multi-source data streams from environment, equipment and personnel positioning and video monitoring subsystems under a unified time reference and roadway coordinate system; a subsystem identifier, a roadway node identifier and a timestamp are written into each record; constructing a space-time diagram containing an observation layer and a roadway layer, mapping multi-source data to observation layer nodes, and aggregating the multi-source data to corresponding roadway nodes according to time slices; in the sliding time window, credibility indexes are generated for observation layer nodes according to fault records, sampling intervals and spatial correlation, the credibility indexes are input into the credibility mapping model, the dynamic credibility of the observation layer nodes is obtained, and the dynamic credibility is summarized into node credibility according to roadway nodes. According to the invention, through the multi-source monitoring space-time diagram and the risk-avoiding decision model driven by the credibility, reliable evaluation of the monitoring result during the metal mine disaster and dynamic optimization of the risk-avoiding path are realized.

The invention discloses an automatic monitoring device and method for an open pit coal mine slope, particularly relates to the technical field of mine safety monitoring, and is used for solving the problem that existing equipment and methods are insufficient in monitoring precision and real-time performance. Slope displacement, vibration, stress and visual image data are collected in real time through a multi-source sensor array; performing cross-modal feature extraction and noise suppression on the multi-modal data to generate a structured time sequence feature vector and spatial gridding image feature data; fusing the time sequence and the visual data based on a space-time alignment algorithm and an edge calculation framework to generate high-precision space-time synchronization data; performing deep correlation analysis through a multi-head attention mechanism by using an AI large model, and outputting slope deformation characteristic data and a stability risk assessment value; and when the risk value exceeds the preset threshold value, real-time early warning is triggered, and an interactive monitoring report is output through a three-dimensional visual interface, so that the problem that the existing equipment and method are insufficient in monitoring precision and real-time performance is effectively solved.

The invention provides a mine operation safety early warning method and system, and relates to the technical field of mine safety monitoring. Through multi-source data fusion, dynamic scene portrait construction, machine learning risk identification and a grading early warning response mechanism, the technical problem that information integration is difficult due to fragmentation of a traditional monitoring means, stiffness of a static threshold value alarm mechanism and data island is solved, and the mine safety early warning method has the remarkable advantage of improving mine safety early warning precision and timeliness.

A mine earthquake adaptive positioning method and system based on multi-index fusion discrimination belong to the field of mine earthquake positioning, and the system comprises a data acquisition module, a feature extraction and quality evaluation module, a comprehensive index calculation module and an adaptive decision module. The method comprises the following steps: firstly, carrying out multi-dimensional physical examination on input waveform data, station layout and a speed model by the system, and generating a quantized physical examination report comprising a signal-to-noise ratio, a pickup error, station geometric coverage, model credibility and the like, namely a multi-dimensional feature vector; and then, a built-in rule engine automatically screens out an optimal single method or combined strategy under the current condition from multiple positioning methods according to the report and a demand target set by a user, and outputs a decision result with a clear basis. Through the system and the method, full-process automation from data input to strategy output is realized, and efficient and reliable technical support is provided for mine safety monitoring and disaster early warning.

The invention relates to the technical field of mine safety monitoring and early warning, and provides a non-coal mine risk situation deduction system based on digital twinning and reinforcement learning, which comprises at least two twinning spaces; wherein the first twinborn space is used for building a mine digital twinborn body mapped by the current mine entity, and the second twinborn space is used for mapping a target dynamic event containing at least one risk factor in dynamic update of the mine digital twinborn body; the at least one reinforcement learning agent generates a third twinborn deduction model used for predicting the risk evolution trend of the mine; the risk early warning module is used for receiving the real-time multi-source data, determining a target dynamic event in the real-time multi-source data through the first twinborn space, judging whether the target dynamic event has a risk factor through the second twinborn space, and if the risk factor exists in the target dynamic event, performing early warning on the target dynamic event; and generating deduction data of the target dynamic event through a third deduction model, and determining a risk area and a risk probability.

The invention provides a complex mine radar adaptive anti-interference detection method based on reinforcement learning. Relates to the technical field of mine safety monitoring and intelligent sensing. Multi-domain features are extracted based on the echo signals; constructing a reinforcement learning state space design composite reward function; an incomplete information game adversarial reinforcement learning algorithm is introduced to carry out iterative training on the radar strategy network; selecting and executing the action of the initial anti-interference strategy according to the current environment state in the actual detection process; on-line fine tuning and updating are carried out on parameters of the radar strategy network based on the real-time feedback data; performing performance evaluation; and if the preset performance threshold is not met, adjusting the weight of the composite reward function and the radar strategy network parameters, and performing iterative optimization until an optimal anti-interference strategy is obtained. According to the invention, multi-source interference adaptive suppression and stable detection are realized, the target detection precision is improved, the false alarm rate is reduced, and the environmental adaptability and long-term robustness of the system in unmanned tunneling, intelligent transportation and safety monitoring scenes are enhanced.

The invention relates to the field of coal mine safety and mine system intellectualization, in particular to a multi-source information fusion supervision method for an underground coal mine drilling process, which comprises the following steps: S1, acquiring continuous synthesized monitoring pictures in the underground coal mine drilling process to form a supervision video stream; s2, performing region segmentation on each frame of synthesized monitoring picture in the supervision video stream to obtain a corresponding static UI region, a dynamic data region and a real-time video region; s3, compressing a static UI region, a dynamic data region and a real-time video region of each frame of synthesized monitoring picture in the supervision video stream by adopting corresponding compression strategies to obtain corresponding compressed data; s4, fusing the compressed data of the static UI area, the dynamic data area and the real-time video area to generate a regional compression supervision video stream; and S5, storing the regional compression supervision video stream to realize subsequent inspection and supervision. According to the invention, the storage efficiency of supervision videos and the efficiency and accuracy of coal mine underground drilling process supervision can be improved.

This invention provides a method and system for monitoring surrounding rock deformation in underground coal mines based on three-dimensional point clouds, relating to the field of coal mine safety monitoring. The invention includes the following steps: installing a reference target in the monitoring area and collecting point cloud data; preprocessing the point cloud data, performing coarse registration using the reference target and fine registration using the iterative nearest point algorithm, and generating a triangular mesh model using the Poisson surface reconstruction algorithm; parameterizing the triangular mesh model of the monitoring area into a two-dimensional analysis mesh, calculating the three-dimensional displacement vector of each node, and establishing a displacement field matrix; calculating the displacement gradient tensor based on the displacement vector, deriving the Green-Lagrange strain tensor, and obtaining the full-field strain distribution; recording the strain value of each mesh node, and determining whether an instability warning is triggered based on the relationship between the strain value and the warning threshold; this method can achieve high-precision monitoring of surrounding rock deformation in underground coal mines.

The invention relates to the technical field of mine safety engineering and hydrogeological information processing, and discloses a multi-scale coal seam water spatial variation analysis and main control factor identification system for group-mine combined mining, which comprises a multi-source spatio-temporal data benchmark unification and preprocessing module for generating a standardized data set; the macro-micro orthogonal collaborative dynamic permeability tensor field construction module is used for establishing a permeability tensor field; a non-Euclidean hydraulic distance calculation module based on Riemannian manifold calculates a manifold hydraulic distance; a variable-lag embedded convergence cross-mapping causal identification module calculates causal coupling strength; a causal topology feedback driving model correction module iteratively corrects the model to convergence according to the difference; and the spatial variation characteristic analysis and main control factor output module outputs a main control factor list. According to the method, a causal topology feedback-driven model self-correction closed loop is constructed, a causal intensity reverse constraint physical model obtained through convergence cross mapping is used, and the problems that a traditional physical model cannot respond to mining-induced fracture evolution in real time, and pure data mining lacks physical interpretability are solved.

The invention relates to the technical field of mine safety monitoring, in particular to a high-precision monitoring and early warning method and system for a deformation field of an underground mine goaf. Comprising the following steps: integrating multi-source data features to obtain cross-modal preliminary fusion features; based on an environment interference compensation feature, the cross-modal preliminary fusion feature and the multi-source data feature, obtaining an accurate multi-source fusion feature after interference compensation through attention weighted fusion and feature screening optimization; combining the time sequence deformation trend characteristics and the space distribution characteristics of the goaf, and extracting space-time aggregation deformation characteristics; and introducing a dynamic deformation baseline feature adaptive to a real-time working condition, the precise multi-source fusion feature and the space-time aggregation deformation feature into Kalman filtering, and constructing a working condition self-adaptive early warning threshold to complete monitoring and early warning. The method is suitable for severe mining construction environments such as high dust raising, high water mist and high vibration in underground goaf production operation areas, and submillimeter-level precision all-weather continuous automatic continuous monitoring of the deformation field of the goaf is achieved.

The invention provides a metal mine water inrush channel multi-field cooperative monitoring and grading early warning method, and relates to the technical field of mine safety monitoring, and the method comprises the steps: laying a monitoring subsystem in a surrounding rock of a metal mine excavation working face, and collecting data; processing the data of each subsystem by adopting a feature extraction method to obtain each feature parameter; processing each characteristic parameter by adopting a cross-time scale parameter ratio method to obtain a mutation identification index and a mutation mark; according to each characteristic parameter, processing by adopting a multi-field collaborative discrimination strategy to obtain a current evolution stage; according to the abrupt change mark, the abrupt change identification index of each characteristic parameter and the current evolution stage, a grading early warning judgment mechanism is adopted for processing, and an early warning grade is obtained; and early warning information is displayed in real time in the monitoring center, and emergency response measures are pushed. According to the invention, quantitative evaluation and graded early warning of water inrush disasters can be realized, and a more accurate, timely and reliable monitoring and early warning means is provided for mine safety production.

The invention discloses a coal mine tunneling roadway advanced geological exploration and ground stress inversion method, particularly relates to the technical field of seismic signal processing in geophysical exploration, and is used for solving the problems of geological exploration signal distortion and insufficient stress inversion precision caused by strong noise interference of tunneling equipment in the prior art. Rock mass vibration signals are collected through an optical fiber sensor arranged on a roadway wall, equipment noise characteristics are identified through spectral analysis, a quantitative mapping relation between working condition parameters and noise characteristics is established, a noise reference time period is determined according to the quantitative mapping relation, a spatial coherence characteristic template is constructed, high-coherence noise components are inhibited through template comparison, and the noise reference time period is determined according to the spatial coherence characteristic template. Finally, the geological structure and stress field distribution in front of the working face are inverted based on the purified seismic wave signals, a stress concentration area and a disaster risk area are identified, the signal-to-noise ratio of the seismic signals in the strong noise environment and the reliability of the geological inversion result are effectively improved, and accurate technical support is provided for safe mining of a coal mine.

The invention provides a large model and retrieval enhanced intelligent gas risk studying, judging and handling method and device.The method comprises the steps that a gas over-limit alarm semantic description text is obtained, and structured risk elements are extracted through a large language model subjected to instruction fine adjustment; mapping the risk elements into standard terms based on a gas over-limit risk standardized ontology model; executing multi-dimensional weighted retrieval in the coal mine safety domain knowledge base according to a standardized result, calculating a comprehensive correlation score by combining semantics, ontology, regulations, cases and trend consistency, and screening knowledge fragments; and finally, constructing a scenario semantic context, driving the large language model to perform causal reasoning and generating a risk disposal strategy. Through ontology standardization and a multi-dimensional retrieval mechanism, the problems of insufficient specialty and illusion of a general model are solved, accurate study and judgment and scientific disposal of the coal mine gas risk are realized, and it is ensured that the finally generated risk disposal strategy has a clear regulation source and data support.

The invention relates to the technical field of coal mine safety and intelligent power supply, and discloses a coal mine safety risk data driving evaluation system, method and device and a medium. And then ultra-short-term prediction and risk level evaluation of load fluctuation are realized by using a data driving technology, a dynamic detection model is constructed by combining the importance degree of the load and voltage sensitivity characteristics, and the single / double-loop power supply quality is accurately analyzed. The system has a network topology change online identification capability, network equivalent impedance in different scenes can be rapidly calculated, and an energy storage device configuration strategy is formulated for a high-risk area to optimize power supply quality. Three operation modes of normal state detection, early warning disposal and catastrophe emergency are innovatively set, the working state is automatically switched through an online identification result, and the whole-process intelligent management and control from real-time monitoring and risk assessment to active disposal is realized.

The present application relates to the technical field of mine safety monitoring, and discloses a mine digital gas parameter early warning system based on gas analysis. The system comprises data acquisition and feature extraction, spatial topology correlation, gas state intention analysis, risk deduction based on a knowledge graph, and hierarchical early warning generation, which realizes intelligent monitoring of mine gas safety. The gas behavior mode is dynamically correlated with the roadway space structure, forming networked situation awareness, and the multi-source precursor information is deeply semantically correlated and probabilistically inferred using a disaster knowledge graph, thereby generating a comprehensive risk conclusion with causal logic. The system realizes the leap from discrete point alarm to spatial situation analysis, from simple threshold judgment to intelligent causal deduction, and improves the accuracy of early warning and decision support capability.

The invention discloses a mine safety monitoring method, device and equipment based on unmanned aerial vehicle scanning mapping and 3DGS scene reconstruction, and a medium, and the method comprises the steps: obtaining multi-view image data, laser point cloud data and unmanned aerial vehicle attitude data, carrying out the preprocessing, obtaining a standardized mine observation data set, and carrying out the calculation of the standardized mine observation data set; generating a three-dimensional scene model of the mine monitoring area based on the standardized mine observation data set, marking safety risk areas in the three-dimensional scene model, extracting safety risk parameters in the three-dimensional scene model based on the safety risk areas, and determining risk levels corresponding to the safety risk areas based on the safety risk parameters, and sending the early warning instruction and the monitoring strategy corresponding to each risk level to a mine control system so as to carry out safety monitoring and dynamic early warning on a mine monitoring area, thereby solving the problems of low risk identification precision and identification lag caused by low precision of traditional mine modeling, and greatly improving the real-time performance and reliability of mine safety monitoring.

The invention provides a gas concentration time sequence distribution prediction method based on multivariable data driving. Based on the theoretical relationship between the mean square displacement and the mean scattering angle cosine of neutrons in an infinite homogeneous medium, the mean square displacement is directly counted through Monte Carlo simulation, the mean scattering angle cosine is reversely deduced, and a first-order scattering matrix meeting the mean square displacement conservation is constructed accordingly. In order to be suitable for a finite geometric model, a correction factor is further introduced to correct an average scattering angle cosine obtained by a traditional method. A verification result shows that the method remarkably reduces the calculation deviation of effective proliferation factors, improves the neutron flux distribution precision, and is particularly suitable for high-precision multi-group calculation of high-anisotropy fast spectrum reactor cores such as pebble-bed high-temperature gas cooled reactors and sodium cooled fast reactors. According to the method, the time sequence probability distribution prediction of the gas concentration can be realized, the prediction uncertainty is quantified, the risk indexes such as the over-limit probability are directly output, and the scientificity and the reliability of coal mine safety early warning are improved.

The invention relates to the technical field of working face mine pressure multi-parameter intelligent monitoring and early warning systems, and particularly discloses a working face mine pressure multi-parameter intelligent monitoring and early warning system. The system comprises a multi-source sensing data acquisition device, a pulse sequence coding unit, a bionic pulse neural network early warning model, a dynamic attention regulation and control module and an early warning information output terminal, multi-source heterogeneous data such as hydraulic support resistance, micro-seismic energy and acoustic emission frequency are converted into a pulse event sequence similar to a biological neural signal, a rock stratum instability precursor is identified by using a bionic model simulating a neuron discharge mechanism and synaptic plasticity, key signals are self-adaptively strengthened in combination with a dynamic attention mechanism, and noise is suppressed. According to the technical scheme, multi-parameter coupling characteristics can be captured with high sensitivity, the early warning accuracy and advance are improved, the false alarm rate is reduced, and reliable guarantee is provided for deep coal mine safety.

The invention relates to the technical field of mine operation early warning, and particularly discloses a mine personnel positioning and environmental parameter linkage early warning method and system, and the method comprises the steps: building a multi-dimensional data collection model of a mine operation area, and carrying out the construction of a multi-dimensional data collection model through arranging personnel positioning nodes, an environmental monitoring unit and a ventilation parameter sensing device, a comprehensive sensing network covering the working face, the roadway and the key nodes is formed; acquiring and fusing the spatial position information, the motion state information and the environmental parameter information of the underground personnel through a comprehensive sensing network to obtain fused data; and constructing a personnel-environment association feature model based on the fused data, and analyzing a coupling relationship between a personnel behavior state and a local environment parameter change trend by using the personnel-environment association feature model. By constructing a linkage early warning mechanism of personnel positioning and environmental parameters, intelligent identification and dynamic response of the mine safety state are realized, and the accuracy and timeliness of early warning and the self-adaptive capability of the system are improved.

The invention relates to the technical field of coal mine safety production monitoring and intelligent early warning, and discloses a coal mine hidden danger recognition and intelligent early warning system based on multi-modal feature fusion, which comprises a multi-modal sensing data acquisition module, an edge calculation preprocessing module, a central analysis server and a linkage alarm execution module, the central analysis server integrates a three-dimensional flow field construction unit, a feature extraction unit, a cross-modal fusion unit and a hidden danger decision-making unit. The method comprises the following steps: constructing a three-dimensional limited flow field by using monocular depth estimation and an optical flow field, and inverting gas concentration into a virtual gas concentration distribution diagram; performing cross-modal depth correlation on visible light, infrared, audio and gas distribution characteristics by using a Transform network; and the hidden danger decision-making unit identifies categories based on the fused features and generates a visual heat map. According to the method, visual reconstruction of invisible gas and deep complementation of heterogeneous data are realized, the problem of high false alarm rate of single-mode monitoring is solved, and the accuracy and response speed of coal mine hidden danger early warning are improved.

The present application relates to a kind of carbon monoxide sensor with resistance to multiple gas and detection method, belong to coal mine environment gas detection sensor field.The present application includes carbon monoxide sensor, and the method of gas detection of the sensor.Therein, sensor mainly includes carbon monoxide / hydrogen sulfide dual gas detection module, nitrogen oxide detection module, modular mainboard, main shell and main shell display window, wherein carbon monoxide / hydrogen sulfide dual gas detection module and nitrogen oxide detection module measure the gas to be measured.The present application carries out gas concentration value calculation using cross interference compensation algorithm, and the concentration of carbon monoxide gas in coal mine environment is accurately detected by setting different working modes.The present application realizes the real-time monitoring of carbon monoxide, nitrogen oxide and hydrogen sulfide multi-component gas concentration by carbon monoxide, nitrogen oxide, hydrogen sulfide composite electrochemical element design, improves the precise perception ability of intelligent mine, and guarantees coal mine safety production.

The invention discloses a mine working face stress monitoring and disaster early warning system and method, particularly relates to the technical field of coal mine safety monitoring, and is used for solving the problem of insufficient disaster early warning reliability caused by model uncertainty of an existing stress field inversion method. Accurate early warning of working face dynamic disasters is achieved by constructing a stress field analysis system containing uncertainty evaluation, surrounding rock strain data are obtained through an optical fiber sensing network, advanced stress field distribution is obtained through inversion calculation, uncertainty transmission and amplification effects in the inversion process are analyzed, confidence intervals of stress values of all areas are calculated, and the dynamic disasters of the working face are obtained. The critical degree of the current stress state is evaluated in combination with historical stress data, disaster early warning information is generated based on a multi-dimensional risk evaluation result, the reliability of stress field analysis and the accuracy of disaster early warning are effectively improved, and technical guarantee is provided for safe production of a working face.