5650results about "Mining devices" patented technology

The invention discloses a real-time surrounding rock deformation monitoring and data acquisition method and system, which is applied to long-distance weak surrounding rock tunnel construction, and comprises the following steps: determining the dynamic change trend of underground water seepage rate and ground stress distribution gradient by adopting a time sequence analysis method; based on the trend, carrying out risk partitioning on the tunnel construction section by adopting a K-means clustering algorithm, determining a deformation sensitive area, and optimizing the spatial distribution of the monitoring points according to the deformation sensitive area; monitoring data are acquired in real time, and when the data fluctuation period exceeds a threshold value, the data acquisition frequency of the corresponding monitoring point is automatically improved; processing high-frequency acquired data by adopting a long-short-term memory network to obtain a real-time surrounding rock deformation prediction result; the prediction result and the multi-source real-time geological parameters are fused, a Bayesian updating method is adopted for processing, a quantitative surrounding rock stability evaluation result is obtained, closed-loop self-adaptive optimization of a monitoring scheme and accurate risk prediction are achieved, and the safety early warning capacity of tunnel construction and the utilization efficiency of monitoring resources are remarkably improved.

The invention discloses an underground mine operation state analysis system and method based on video monitoring data, and the system comprises a data collection module which is used for collecting mine video and environment parameter data through a distributed sensor network, and generating a multi-dimensional data fusion set based on a space-time label technology; the edge analysis module is used for extracting feature parameters through a convolutional neural network algorithm based on the multi-dimensional data fusion set and generating a mine operation state recognition result; the fence construction module is used for constructing a three-dimensional digital model and a dynamic safety boundary based on the mine operation state recognition result to form a real-time monitoring reference framework; and the decision execution module is used for performing hierarchical risk assessment on the monitoring data in the security boundary based on the real-time monitoring reference framework, and generating a security early warning and disposal scheme with a tracing identifier. Each piece of early warning and disposal information is attached with a unique tracing identification code, so that follow-up event backtracking analysis is facilitated, and the risk management and control capability is continuously improved.

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.

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

The invention relates to a dynamic regulation and control method and system for mine ventilation. The method comprises the following steps: acquiring multi-source data in a mine, and preprocessing the multi-source data to obtain a standardized data set; calculating the risk of each region in the mine based on the standardized data set to obtain a risk prediction matrix; based on the standardized data set and the risk prediction matrix, the air volume demand of each area is calculated, and an air volume demand table is obtained; based on the air volume demand table, an optimization proposition corresponding to the air volume demand is constructed and solved, and a solution set of the optimization proposition is obtained; mapping the solution set based on a preset rule to obtain a feasible allocation scheme; and based on the feasible allocation scheme, generating an equipment cooperation instruction, and obtaining a security instruction set. According to the method, dynamic factors can be considered, the air volume is dynamically adjusted, multi-fan cooperation is achieved, and the effects of real-time sensing and autonomous dynamic decision making of mine ventilation are achieved.

The invention relates to the technical field of disaster monitoring, in particular to a mine inrush water image processing system based on AI recognition, which comprises a fissure water flow tracking module, a voiceprint disturbance extraction module, an acceleration mapping module, a form analysis module and a risk output module. According to the method, a fine voiceprint perturbation sequence is formed by extracting crack water flow boundary pixel coordinates from continuous frame images, accurately capturing water flow micro-displacement and generating a deviation track in combination with an inter-frame difference between a gray gradient amplitude and a direction angle, and synchronously extracting a liquid level perturbation wave crest position and a voiceprint primary and secondary frequency amplitude difference; the acceleration is calculated according to the pixel displacement speed and mapped to a time axis, so that the boundary movement trend and morphological change are synchronously analyzed in the time dimension, risk frame locking is completed by multi-source signals in the same time period under double-threshold judgment, and three-dimensional presentation of the water flow dynamic state is achieved. The accuracy and reliability of sudden water gushing risk judgment in a complex mine environment are remarkably improved, and the probability of missing detection and misjudgment is reduced.

The invention belongs to the technical field of underground engineering unfavorable geological disaster prediction and intelligent control, and provides a tunnel unfavorable geological physical field-hydrological field fusion holographic detection method and system. Detection information of various physical fields such as an induced electric field, a seismic electric field, a natural electric field and a hydrological field is used as a fusion data source; establishing a coupling objective function taking cross gradient inversion as a physical constraint condition; a CNN-GNN-Transform hybrid network is constructed, and multi-level feature fusion is carried out; through contribution of physical constraint conditions in a self-adaptive weight dynamic balance coupling objective function and deep learning data driving feature learning capability of a hybrid network, multi-field holographic interpretation with a hybrid deep learning model as a carrier is realized, and the fusion degree of multi-source heterogeneous data is improved. Three-dimensional holographic imaging and water gushing prediction of a water gushing disaster source can be achieved, and the accuracy of unfavorable geological disaster detection and the reliability of intelligent decision making are improved.

The invention relates to the technical field of mine environment control, and discloses an intelligent environment monitoring and control method in a mine laneway. The method comprises the steps that a real-time environment parameter set of a mine laneway is collected, the set contains gas concentration data, dust concentration data and temperature and humidity data, and real-time capture of laneway environment multi-dimensional parameters is achieved; then, based on the collected real-time environment parameter set, calculating an environment safety evaluation index so as to scientifically and objectively reflect the roadway environment safety condition; a dynamic control strategy is generated according to the environmental safety evaluation indexes, and the strategy comprises ventilation equipment regulation and control parameters and early warning level parameters and can be flexibly adjusted according to environmental changes; and finally, the ventilation equipment regulation and control parameters are sent to a mine ventilation system, and roadway environment regulation and control are executed. According to the method, environmental parameters can be comprehensively monitored, safety risks can be accurately evaluated, ventilation equipment can be flexibly regulated and controlled, the early warning function can be perfected, energy waste can be reduced, labor intensity can be reduced, and underground operation safety and mining efficiency can be guaranteed.

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

The invention provides a tunnel surrounding rock grouting control method and system, and relates to the technical field of tunnel engineering.The tunnel surrounding rock grouting control method comprises the steps that grouting parameters are obtained, and the grouting parameters comprise grouting pressure and slurry viscosity; performing multi-dimensional real-time monitoring on the surrounding rock fracture, and constructing a dynamic three-dimensional geologic model through the multi-dimensional real-time monitored data; performing grout diffusion path prediction based on the three-dimensional geologic model, and generating an initial grouting scheme through the predicted grout diffusion path; the fractal dimension is optimized according to a genetic algorithm, the grouting parameters are dynamically corrected through the optimized fractal dimension, and a grout diffusion result is obtained; establishing a multi-field coupling equation based on the slurry diffusion result, and optimizing the initial grouting scheme through the multi-field coupling equation to obtain an optimal grouting scheme; and according to the optimal grouting scheme, power pressure compensation is conducted, and grouting control is conducted. The problem that slurry diffusion is difficult to accurately control is solved.

The invention belongs to the technical field of highway tunnel ventilation control, and provides a highway tunnel ventilation control method and system. The method comprises the following steps of multi-source heterogeneous data acquisition, multi-source data fusion, state estimation, traffic flow prediction, dynamic pollution index calculation and multi-target weight generation, rolling optimization with constraints, control execution, model updating and feedback correction. The state sensing precision is improved through multi-source data fusion, real-time accurate estimation and error reduction of a pollutant concentration field and a traffic flow state are realized through a heterogeneous sensor and a Rayleigh fusion technology, the problem of sensing deviation caused by single data in a traditional method is solved, future traffic flow is predicted based on a graph neural network, and the traffic flow sensing accuracy is improved. The fan power is optimized through model prediction control, the ventilation intensity is adjusted in advance, pollutants are prevented from exceeding the standard, the response speed is increased compared with threshold value control, and energy consumption is reduced.

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 discloses a tunnel ventilation control system and method based on artificial intelligence, and relates to the technical field of ventilation control, and the method comprises the steps: in a tunnel design stage, building an unsteady-state computational fluid dynamics model based on tunnel three-dimensional linear parameters, calculating turbulence structures under different traffic conditions through a large eddy simulation method, and calculating an unsteady-state computational fluid dynamics model; determining an optimal space configuration scheme of the fan group according to the distribution of the velocity field and the pressure field; in the tunnel construction stage, multiple types of sensor arrays are arranged along the vault and the side wall of a tunnel in a layered mode; in the tunnel operation stage, real-time vehicle tracks and speed distribution information of a traffic monitoring system are obtained; establishing a ventilation demand dynamic prediction model based on space-time correlation analysis, constructing a fan cooperative control model considering airflow organization optimization, and solving an optimal operation strategy by adopting a multi-target adaptive weight distribution algorithm; when a fire characteristic signal is monitored, the multiple sets of fans are coordinated to form a relay type smoke exhaust airflow organization.

The invention discloses a tunnel monitoring method and system based on a vibrating wire sensor, and relates to the technical field of tunnel engineering construction safety monitoring, and the method comprises the steps: collecting the vibration frequency data and spatial position coordinates of the vibrating wire sensor in a monitoring region; the method comprises the following steps: calculating a time difference and an energy attenuation ratio between adjacent sensors by extracting spectrum energy density distribution, and constructing a stress wave propagation field; dividing grids in the propagation field to calculate node stress values, generating a stress field distribution cloud picture and extracting a stress concentration area; calculating a gravity center position coordinate change rate and a stress time sequence curve of the stress concentration area, and determining fracture extension parameters; and calculating a surrounding rock bearing capacity attenuation curve based on the parameters, and outputting an early warning signal when the slope exceeds a threshold value. According to the invention, real-time monitoring and early warning of the tunnel surrounding rock instability risk are realized.

The invention relates to the technical field of coal mining equipment, and discloses a mining roadway stability real-time monitoring and early warning system which comprises the following steps: S1, multi-source sensor network deployment, S2, data transmission and preprocessing, S3, stability index dynamic calculation, S4, fusion early warning model construction and S5, graded early warning triggering. Roadway surrounding rock deformation, stress, vibration and environmental parameters are collected in real time through a multi-source sensing network, efficient data transmission and preprocessing are achieved in combination with an industrial looped network, surrounding rock strain energy density, displacement convergence rate, support failure coefficient and other key indexes are dynamically calculated, a fusion early warning model is constructed based on machine learning, and the early warning accuracy is improved. According to the method, accurate evaluation of the stability level is achieved, grading alarm and emergency control are automatically triggered when a threshold value is reached, a'perception-analysis-decision-response 'closed loop is formed through the design, the real-time performance of roadway stability monitoring and the early warning reliability are remarkably improved, and accidents such as roof fall and wall caving are effectively prevented.

The invention relates to the field of coal mine exhaust control, in particular to a coal mine exhaust control method and device based on environmental perception. The method comprises the following steps: acquiring multi-dimensional monitoring signals based on environment sensing sensors at multiple positions of a mine, performing distributed sensing marking, and constructing a global gas concentration sensing graph; the method comprises the following steps: acquiring position information of operating personnel in real time based on a miner positioning sensor, performing short-time movement prediction on the operating personnel, and generating a miner movement prediction path; obtaining a coal mine internal structure chart, and carrying out average oxygen consumption rate budget and instantaneous oxygen supply demand calculation based on the miner movement prediction path to obtain oxygen supply demand indexes of each unit; and according to the oxygen supply demand index of each unit, intelligent exhaust power calculation adjustment is carried out, and a conventional exhaust control strategy is constructed. Ventilation efficiency of the coal mine area is improved, emergency blast treatment is carried out under the risk condition, the risk problem response capacity is greatly improved, and safety of the coal mine area is enhanced.

The present invention belongs to the field of multi-equipment safety management and control for personnel and equipment clusters at an underground coal mine driving working face, and solves the problem of safety hazards caused by human-machine collaborative operations as the mechanization level of underground equipment increases. Provided are a system and method for safety monitoring and early warning of human-machine collaborative operation at a working face. The system comprises a safety management and control system for mobile equipment at a working face, which is used for performing in real time position, orientation and environmental sensing of individual equipment and behavior analysis of drivers; working-face collaborative intelligent wearable devices, which are used for environmental sensing of operating personnel and individual equipment and acquiring in real time the motion postures of the operating personnel; and a working face output control subsystem, which is used for identification and management of operating personnel entry and exit, position monitoring of personnel and individual equipment, operating personnel behavior identification and monitoring and working environment monitoring, and generating corresponding alarm instruction information. The present invention can realize human-equipment and equipment-equipment safety management and control during multi-equipment collaborative operation at a driving working face.

The invention provides a coal mine goaf early warning method and system, equipment and a medium, and belongs to the technical field of coal mine monitoring. The method comprises the steps that multi-field monitoring data of a target goaf at the current moment are acquired; constructing a multi-field coupling relationship based on the multi-field monitoring data, each coupling relationship being a coupling relationship formed by every two fields of monitoring data and used for representing a mutual influence relationship between the two fields of monitoring data; performing prediction based on the multi-field monitoring data and the multi-field coupling relationship to obtain target multi-field monitoring data and a target early warning state; the target multi-field monitoring data and the target early warning state are the multi-field monitoring data and the early warning state corresponding to the future target moment. According to the coal mine goaf early warning method, system and equipment and the medium provided by the invention, accurate prediction of future multi-field data of the goaf and accurate early warning of the future state of the goaf can be realized.

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 belongs to the technical field of geophysical exploration in an extremely complex tunnel environment, and provides an air-ground and hole-tunnel multi-angle combined tunnel unfavorable geology advanced detection system and method.A first main control unit is used for establishing a unified time reference and controlling an air-ground capacitive induced polarization detection unit to collect air-ground electric field data under the unified time reference; the second main control unit is used for controlling the hole-tunnel induced electric field and natural electric field combined detection unit to collect underground induced polarization response signals and continuous natural electric field signals and controlling the tunnel seismic electric field detection unit to collect seismic electric field signals and seismic wave signals; and the second main control unit is used for receiving the air-ground electric field data sent by the first main control unit, uniformly integrating the air-ground electric field data with an underground induced polarization response signal, a continuous natural electric field signal, a seismic electric field signal and a seismic wave signal which are acquired by the second main control unit to obtain a multi-field data set, and performing interpretation and inversion to obtain a potential unfavorable geologic body identification result. And the disaster source detection precision and efficiency in the extremely complex tunnel environment are obviously improved.

The invention relates to the technical field of mine environmental protection and intelligent control, and discloses an intelligent dust suppression system and method for an open-pit coal mine, which are used for solving the problems of fixed spraying mode, poor real-time performance and waste of water resources in the dust suppression process of the open-pit coal mine. According to the intelligent dust suppression system and method for the open pit coal mine, a dust concentration sensor, a wind speed and direction sensor and a meteorological sensor are arranged in a mining area, real-time monitoring of a dust diffusion source and environmental parameters is achieved in combination with a video monitoring unit, and multi-source data are fused in a control system for diffusion trend prediction; according to the method, a dust high-risk area can be dynamically identified, then a targeted spraying strategy is generated according to a prediction result, and accurate control over the spraying position, the spraying time and the spraying flow is achieved, so that the problems that in the prior art, a spraying operation mode is fixed and is difficult to adapt to environmental changes are solved, the water consumption per unit time is reduced, and the working efficiency is improved. The air quality of an operation area is guaranteed, and the operation environment safety and the environmental protection standard-reaching rate 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 field of surrounding rock supporting, and provides an aging cracking and multi-source dynamic response self-adaptive supporting system and method in order to improve the supporting reliability, and the state of surrounding rock is sensed in real time through a multi-source sensing module; the aging damage model can comprehensively and dynamically evaluate the damage state of the surrounding rock of the mixed rock, and progressive instability accidents such as hysteresis rock burst can be effectively prevented; the dynamic disturbance model can analyze the damage effect of dynamic loads such as TBM tunneling and blasting on mixed rock surrounding rock in real time, a millisecond-level dynamic risk assessment result is provided for a self-adaptive support system, and instant instability accidents are effectively restrained. The self-adaptive support of the dynamic execution module is realized by combining the parallel decision of the aging damage model and the dynamic disturbance model, the problem that the traditional support depends on static geological data and is difficult to adapt to the dynamic damage change of the surrounding rock is solved, the stability and reliability of the support system are remarkably improved, and the engineering cost and the safety risk are reduced.

The invention relates to the technical field of data analysis, in particular to a multi-source data-based concealed vanadium titano-magnetite bed target area delineation method and system, which comprehensively considers various factors possibly influencing drilling, such as geological condition complexity and drill bit wear risk, and has relatively low potential risks in various aspects of trajectory selection. The probability of unexpected conditions such as drill bit damage and borehole wall collapse in the drilling process can be effectively reduced, smooth drilling work is guaranteed, the drilling effect abnormity prediction result and the difficulty abnormity reflect the advantages and disadvantages of the drilling track from different angles, and the drilling efficiency is improved. The drilling effect anomaly prediction considers the influence of various abnormal conditions possibly occurring in the drilling process on the final drilling result, the difficulty anomaly focuses on the drilling difficulty of the trajectory, the two are fused and analyzed, the feasibility and the risk degree of each trajectory are evaluated more comprehensively and accurately, and the prediction accuracy is improved. And the influence degree of the track drilling process on ore deposit target area detection is reduced.

The invention provides a deep coal seam roadway surrounding rock stability intelligent early warning method, and relates to the technical field of coal mine dynamic disaster prevention and control. According to the intelligent early warning method for the stability of the surrounding rock of the deep coal seam roadway, a basic sample library is constructed through a dynamic and static combined loading rock test; synchronously collecting field surrounding rock static stress and micro-seismic data and carrying out time sequence polymerization; constructing an equivalent dynamic and static combined stress index based on a dynamic and static load superposition principle, and forming a seismic-force coupling feature vector; establishing an LSTM early warning model, adopting a transfer learning strategy, pre-training by using laboratory data, and then performing fine tuning by using field data; inputting the real-time feature vector into a model and outputting an instability probability; and verifying and correcting the probability based on actual deformation data of the surrounding rock, and applying a missing report sample to online incremental updating of the model. According to the method, the dynamic and static load coupling effect is quantified, the difficulty of small sample training is overcome, and accurate and self-adaptive early warning of the deep roadway surrounding rock instability risk is realized through a physical feedback closed-loop correction mechanism.