3693 results about "Rock body" patented technology

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

A real-time quantitative characterization method, equipment and a medium for rock mass evolution are disclosed. The method includes: the multiphase field detection and monitoring data is fused; a deep learning model driven by physical principles is established based on the physical principles of fluid density; the deep learning model is trained using real-time fused data as input and corresponding evolution distribution images as output; a trained deep learning model is used to obtain an evolution distribution image based on multi-phase field detection and monitoring data of different time periods and types; a mathematical model is used to quantitatively characterize of physical and mechanical parameters in the whole process of progressive failure of the dynamic evolution of the rock mass based on the macroscopic mechanical parameters and evolution distribution images synchronized with multiphase field detection and monitoring data.

The invention discloses a grouting method and system for enhancing strip mine slope stability, and belongs to the technical field of slope protection. The grouting method comprises the steps that early-stage data are pre-collected, and an analysis model is established; grouting is designed according to a model analysis result; a pressure-resistant grouting pipe is installed and connected with a grouting pump; grouting parameters are adjusted in real time according to feedback of the sensor; after grouting, all data in the grouting holes are analyzed; outputting a slurry permeation three-dimensional cloud picture, a stress change curve and a stability evaluation index; and storing all monitoring data and model parameters. A cyclic self-adaptive system is formed, it is ensured that the grouting process is efficient and accurate, the overall stability is improved for heterogeneous rock mass, dependence on a static geological report is reduced, and therefore the problem that grouting parameters depend on engineering experience or conservative of the static report is avoided.

The invention relates to the technical field of reservoir bedrock anti-seepage grouting, and discloses a reservoir bedrock anti-seepage grouting method which comprises the following steps: acquiring fracture data through geological exploration, and constructing a reservoir bedrock fracture distribution model; simulating a flow path and permeability characteristics of the slurry in the fracture based on the fracture model; the grouting pressure and flow are determined through an optimization algorithm, and a grouting scheme is formulated; screening grouting materials according to fracture characteristics, and analyzing stress-strain and permeability of the grouting materials; a construction process is formulated, the grouting state is monitored in real time, and parameters are dynamically adjusted; and after grouting, carrying out permeation and pressure test to evaluate whether the curing effect reaches the standard or not. Based on the three-dimensional fracture structure modeling and flow field simulation technology, high-precision recognition and dynamic evolution tracking of the fracture permeation path in the complex rock mass of the reservoir bank are achieved, so that grouting hole layout is more accurate, the slurry flow direction is more controllable, and the method is different from a point-selecting grouting mode of a traditional empirical method.

The invention provides a tunnel rock mass dynamic disaster monitoring system based on digital twinning, and relates to the technical field of tunnel rock mass dynamic disaster monitoring based on digital twinning. By integrating a multi-mode sensing network, the system comprises a self-adaptive infrared structured light scanner, a distributed micro-seismic sensing array and a fiber grating strain sensor; comprehensive and real-time monitoring of the rock mass state is realized; meanwhile, an intelligent analysis algorithm based on deep learning is introduced, abnormal conditions are automatically recognized, a rockburst risk comprehensive index is generated, in addition, a physical entity and a virtual model are combined through a dynamic digital twinborn modeling technology, and visual analysis of rock mass state changes is achieved; and finally, an edge-cloud cooperative communication technology is adopted to ensure low delay and reliability of data transmission and ensure that the monitoring system can quickly respond and timely take emergency measures.

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 discloses a geological disaster monitoring system based on multi-modal data, and relates to the technical field of geological disaster monitoring, and the system comprises a crack analysis module which carries out the time-space correlation mining of the crack propagation rate of a monitoring region, and analyzes the nonlinear evolution characteristics and spatial differentiation rules of rock mass fracture; and the critical identification module is used for performing wavelet packet energy spectrum analysis on the inclination angle change rate of the geologic body, identifying a critical turning point of rigidity attenuation of the geologic structure in combination with a preset algorithm, judging whether the overall stability enters an instability acceleration stage or not, and performing multi-parameter collaborative detection, crack evolution cross validation and dynamic trend stability verification to obtain the stability of the geologic structure. The accuracy and reliability of geological structure rigidity attenuation critical turning point recognition are remarkably improved, a more accurate rigidity attenuation stage judgment basis is provided for an early warning module, and the capturing capacity of a multi-modal data geological disaster monitoring system for structure instability precursor is enhanced.

The invention relates to the technical field of geological exploration and three-dimensional modeling, in particular to a mining unmanned aerial vehicle high-precision three-dimensional geological modeling system which comprises a multispectral imaging acquisition module, a point cloud-image high-precision registration module, a geological feature fusion extraction module, a three-dimensional geological model construction module and a geological attribute visualization output module. Wherein the multispectral imaging acquisition module is used for acquiring registration image data and original point cloud data; the point cloud-image high-precision registration module is used for carrying out consistency matching and outputting a registration point cloud-image data set; the geological feature fusion extraction module is used for identifying geological boundaries and outputting geological feature vector data; and the three-dimensional geologic model construction module is used for generating a three-dimensional grid model with attribute topology. According to the method, through accurate three-dimensional modeling and lithology visualization, geological features and lithology information are efficiently combined, and reliable data support is provided for rock mass stability analysis.

The invention relates to the technical field of route planning, in particular to an underground cavern inspection method and system. The method comprises the following steps: acquiring geological survey data, micro-seismic monitoring data and historical deformation monitoring data; according to the geological survey data and the micro-seismic monitoring data, rock mass viscoelasticity local modeling is carried out, and a rock mass viscoelasticity model is obtained; performing deformation trend prediction according to the rock mass viscoelastic model and the historical deformation monitoring data to obtain deformation trend data; performing deformation disturbance resonance analysis according to the deformation trend data and the micro-seismic monitoring data to obtain deformation disturbance resonance data; performing risk area grading on the deformation disturbance resonance data to obtain risk area grading data; and generating a routing inspection graph according to the risk area grading data to obtain routing inspection graph data so as to carry out underground cavern routing inspection auxiliary operation. The method accurately identifies the potential risk area, dynamically plans the inspection task, and improves the inspection efficiency and decision timeliness.

The invention relates to the field of safety risk early warning, in particular to a BIM (Building Information Modeling)-based open-air goaf charging safety risk early warning method and a BIM-based open-air goaf charging safety risk early warning system. A three-dimensional BIM geologic model containing rock mass hardness data, joint density and fracture distribution data is constructed through unmanned aerial vehicle scanning and rock core sampling; determining a minimum effective explosive loading amount meeting a preset crushing lumpiness mean value in the three-dimensional BIM geological model, performing dynamic simulation through the three-dimensional BIM geological model to obtain a simulation result, and outputting a final explosive loading amount when the simulation result meets a preset safety constraint condition; and generating charge parameters according to the final charge amount, binding the charge parameters with space coordinates of the three-dimensional BIM geologic model, and outputting an early warning area through a visual interface. By determining the optimal explosive loading amount and optimizing the blast hole layout and the detonating sequence, the explosive loading safety risk is effectively reduced while the blasting effect is guaranteed.

The invention relates to the technical field of fracture network fracture water channel identification, and discloses an identification method for analyzing a dominant flow channel in a three-dimensional fracture network based on a topological network. According to the method, probability distribution parameters such as geometric occurrence, gap width and density of a rock mass multi-scale fracture system in a target area are obtained through field surveying and mapping and three-dimensional scanning, and a spatial topological structure of the three-dimensional fracture system is reconstructed by adopting Monte Carlo method simulation and discrete fracture network modeling technology; secondly, abstracting the fracture network into a three-dimensional topological graph model based on a graph theory principle, defining fracture center points and cross points as graph nodes, and converting fracture sections into weighted edges; the method breaks through the continuous medium hypothesis limitation of traditional seepage analysis, and has important engineering application value in the fields of deep geological energy storage reservoir seepage risk assessment, shale gas fracture network optimization design, rock slope stability analysis and the like; compared with a traditional numerical simulation method, the method has the technical advantages of being high in calculation efficiency, good in prediction precision, high in multi-scale applicability and the like.

The invention belongs to the technical field of tunnel safety monitoring, and particularly relates to a rock stratum stress-deformation coupled tunnel safety real-time dynamic modeling method, which comprises the following steps of: embedding a rock mass mechanical relationship into a neural network model as a hard constraint, and establishing explicit mapping of monitoring data and a physical field, a model parameter field is dynamically optimized by adopting ensemble Kalman filtering real-time general field detection data, and a data driving and physical mechanism collaborative deduction mechanism is designed, so that long-term prediction error accumulation is effectively inhibited; a stress field, a deformation field and other physical quantities deduced and output by the model are directly utilized to calculate disaster risk indexes with clear mechanical significance, and a physically interpretable early warning decision is realized by fusing fuzzy reasoning and multi-source risks; the technical problems of physical misalignment, weak long-term generalization and poor early warning interpretability of a data-driven model are solved.

The invention provides a rock mass high-temperature deformation simulation method and system based on deep learning near-field dynamics, and relates to the field of rock mass thermal-mechanical coupling analysis and research, and the method comprises the steps: obtaining the parameter information of a to-be-simulated target rock mass, including the geometric shape of the rock mass and the physical attribute of the rock mass; dispersing the target rock mass into a plurality of material points according to the parameter information, and constructing a thermal-mechanical coupling model of the target rock mass; based on the initialized thermal-mechanical coupling model, time step iterative simulation of a solid mechanical field is conducted, and finally the crack propagation process and temperature distribution of the target rock mass are obtained; according to the method, a solid field near-field dynamics theory based on strain energy density is introduced, a fluid field near-field dynamics equation is combined, temperature distribution in a rock medium is predicted through a physical information neural network, a deep learning near-field dynamics framework of rock mass high-temperature deformation damage is formed, and therefore rock mass high-temperature deformation is efficiently and accurately simulated.

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

The invention provides an intelligent optimization method and system for tunnel smooth blasting blast holes in layered rock mass, and belongs to the technical field of tunnel engineering and blasting. The method comprises the steps of collecting stratified rock geological data, evaluating and extracting features, constructing a multi-objective optimization function, solving by an intelligent optimization algorithm, performing construction feedback and dynamic optimization and the like. The method comprises the following steps of: acquiring data by using various geological exploration equipment, performing evaluation processing on the data by using a support vector machine model, constructing an optimization function considering multiple indexes, solving an optimal blast hole parameter combination by combining a genetic algorithm and a particle swarm optimization algorithm, and finally feeding back a blasting effect to dynamically adjust an optimization scheme. According to the intelligent optimization method and system for the smooth blasting blast holes of the tunnel in the layered rock mass, the blasting effect can be improved, over-break and under-break and concrete consumption can be reduced, the construction cost can be reduced, the construction safety and efficiency can be enhanced, and remarkable economic and social benefits can be achieved for layered rock mass tunnel engineering construction.

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 mining, and discloses a method for determining pre-splitting blasting parameters for mining blasting, which comprises the following steps of: 1, acquiring rock mass structure information of a blasting area through three-dimensional geological radar, laser radar and shallow seismic reflection data, and constructing a structural plane distribution map containing a soft and hard interbed and joint combination mode; 2, initial presplitting blasting parameters are generated based on the structural plane distribution map, wherein the initial presplitting blasting parameters comprise the hole distance, the linear charge density and the charge structure; according to the method, the technical scheme that the structural plane distribution map is constructed through multi-source geological data fusion and digital twinborn simulation dynamic correction is adopted, so that the technical effects that the influence of a complex structure combination on blasting energy transfer is accurately quantified, and the presplitting path control precision is remarkably improved are achieved; compared with the technical scheme depending on a static rock mass model or an empirical formula in the prior art, the defects of presplitting path offset and serious parameter mismatch caused by the fact that modeling is not carried out due to the dynamic effect of the structural plane are overcome.

The invention provides a fractured rock mass surrounding rock grouting quality evaluation method, and relates to the technical field of geotechnical engineering.The method comprises the following steps that permeability test data and acoustic velocity test data of fracture connectivity and mechanical states of a target surrounding rock area are obtained before grouting, and a characterization parameter data set of fractures before grouting is formed; synchronously acquiring three-dimensional point cloud data to calculate the volume of a fracture cavity; in the grouting process, electromagnetic field intensity data is collected to calculate the grouting filling rate, resistivity time sequence data is collected to calculate the cementation uniformity index, strain time sequence data is collected to calculate the strain gradient, and a grouting integrity index is formed through multi-feature fusion; after grouting, permeability and sound wave velocity data are obtained again, and the fracture characterization performance improvement rate is calculated; acquiring penetration test data to calculate a stone body strength parameter; and performing multivariate fusion on the grouting integrity index, the fracture characterization performance improvement rate and the stone body strength parameter to calculate a comprehensive quality score, and comparing the comprehensive quality score with a preset threshold value to perform grading evaluation.

The invention discloses an intelligent identification and grading early warning method based on mine production abnormity, and a system and equipment thereof. The method comprises the following steps: obtaining space-time evolution data, earth surface deformation data and environment monitoring data of rock mass stress in a target area; performing abnormal mode detection on the data by using a space-time convolutional network to obtain accident risk probability distribution data; using an ultra wide band positioning system to track the movement track of the person entering the target area in real time to obtain the movement track data of the person; and constructing a three-dimensional early warning index system in combination with the accident risk probability distribution data in the target area and a dynamic early warning mechanism, performing dynamic early warning grading on different positions in the target area by using the three-dimensional early warning index system, and generating a grading response strategy matched with the personnel activity trajectory data. According to the invention, the problems of incapability of tracking a personnel movement track in real time and low timeliness of mine production abnormity early warning are solved.

The invention discloses a deep resource fluidized mining disturbance effect evaluation model under a multi-field coupling effect, and the method specifically comprises the following steps: constructing a multi-field coupling parameter system: collecting geologic body basic parameters, covering rock mass mechanical parameters, thermal physical parameters, fluid parameters and chemical parameters in deep three-high, high ground stress, high ground temperature and high karst water pressure environments, and establishing a multi-field coupling parameter system; the method comprises the following steps: establishing a dynamic parameter database, realizing real-time updating of parameters along with a mining process, establishing a heat-flow-force-chemical full-coupling mathematical model, introducing the influence of a rock plastic damage correction term quantification temperature on heat conduction efficiency, associating a dynamic mapping relation between mineral reaction and rock physical property parameters, and establishing a dynamic parameter database. Simplification errors of a traditional model on multi-field effects are thoroughly eliminated, chained interaction of temperature, fluid, stress and chemical processes in a deep three-high environment is accurately described, a model prediction result better fits an actual mining working condition, and prediction deviation is greatly reduced.

The invention belongs to the technical field of underground engineering, and discloses an omnibearing intelligent monitoring system, method and device for an underground anchoring pull-out test. According to the method, electromagnetic signals generated in the stress process of the anchor rod are captured in real time through an electromagnetic sensor, and internal stress changes and energy distribution are reflected; the deformation and stress states of the anchor rod in the dynamic environment are evaluated through a strain gauge and accelerometer combined data combination method, the deformation condition of the anchor rod is monitored through the strain gauge, and the stress-strain relation is recorded; an accelerometer is used for detecting the dynamic response of surrounding rock mass so as to evaluate the overall stability of the rock mass; a coupling medium is injected, the computer terminal stores the collected signals locally or in a cloud end and analyzes the signals, stress and deformation parameters in an anchor rod pull-out test are extracted, and an anchor rod stress cloud picture is generated in real time. According to the method, the safety and reliability of underground engineering are remarkably improved, and a new technical support is provided for evaluation of anchoring quality.

The invention provides a mining area catastrophe early warning method, device and equipment based on a rock mass multi-dimensional quantitative characterization model, and the method comprises the steps: inputting the collected multi-field data of a stress field, a rock mass damage field and a penetration field into a multi-field co-evolution model to obtain a corresponding multi-field evolution coefficient, and updating the output of a catastrophe early warning model through the evolution coefficient. A stress-rock mass damage-permeation co-evolution mechanism is constructed, the prediction precision of disasters such as rockburst and water and mud inrush is guaranteed, and advanced early warning and active control of catastrophe are achieved.

The invention discloses a surface mine digital safety production management method and device and a medium, and relates to the technical field of intelligent mines, and the method comprises the steps: collecting rock mass physical and mechanical parameters of a gold mine mining area and three-dimensional geometric morphology data of a blasting working face, and taking the parameters as physical constraint conditions to construct a digital twinborn body; a preset target blasting scheme is called to be input into the element reinforcement learning agent, multi-round simulation deduction is carried out through a blasting scheme evaluation strategy, and a simulation deduction result is generated; when the peak particle vibration velocity in the simulation deduction result exceeds a preset peak particle vibration velocity threshold value, generating a parameter adjustment suggestion; and the parameter adjustment suggestion is fed back to the element reinforcement learning agent for iterative optimization until the peak particle vibration velocity does not exceed a preset peak particle vibration velocity threshold, and a blasting scheme is generated. Through autonomous exploration of the meta-reinforcement learning agent in the blasting parameter space, the blasting scheme evaluation strategy with adaptability and reliability can be efficiently generated.

The invention relates to the technical field of underground cavern construction, and discloses a layered rock mass cavern excavation parameter dynamic optimization decision system and method, and the system comprises a rock stratum database building module, an excavation simulation module and an excavation model optimization module. The method comprises the following steps: collecting basic data such as engineering geological conditions, rock mechanical properties and ground stress distribution; establishing an initial rock stratum parameter database through a three-dimensional ground stress test and rock mass permeability characteristic analysis; establishing a stratified rock mass excavation simulation model, simulating a failure mode of a stratified rock stratum under excavation disturbance, and analyzing stress redistribution and a potential failure area; determining a cavern axis direction, an excavation sequence and support parameters according to a simulation result; acquiring surrounding rock deformation, loosening circle expansion and seepage characteristic change; and utilizing a damage proximity index to predict the distribution of the loose circles, and optimizing the stratified rock mass excavation simulation model. The method can adapt to various complex working conditions from a sand shale interbed to an ultra-deep fractured stratum and the like.

The invention provides an automatic identification method and system for a linear structural plane in a complex rock mass outcrop, and belongs to the field of rock mass engineering, and the method comprises the steps: obtaining a DeepLabV3 + model of a rock mass outcrop image, carrying out the semantic segmentation of an input image, outputting a binary mask image of the trace pixel of the structural plane, and carrying out the topology reconstruction; establishing a mapping relation between the image coordinate system and the three-dimensional point cloud; calculating a space included angle between the direction vector of the trace and the average occurrence of the structural plane groups, classifying the three-dimensional point cloud data according to the structural plane groups according to an angle threshold value, and performing three-dimensional line segment extraction by adopting an improved iterative Hough transform algorithm; and realizing global multi-segment connection and trace length calculation of discrete segments based on a greedy algorithm. The method breaks through the limitation of a topographic region when the rock mass structural plane data is obtained through manual measurement, and improves the precision and efficiency of structural plane extraction in the complex rock mass.

The invention relates to the technical field of safety management, in particular to a mine safety production risk prevention and control method based on multi-source data fusion, and the method comprises the following steps: constructing a coupling parameter set through a micro-seismic sensor and a temperature and humidity sensor, detecting and extracting displacement points through Bayesian regression, and drawing a deformation diagram; determining a displacement area, counting an airflow velocity gradient, extracting a main fracture direction, generating an airflow path diagram, calculating a curvature change rate, positioning a sudden change area, extracting a thermal strain rate and an axial strain rate, generating a smooth curve, identifying a risk point, and expanding a boundary when the overlapping rate exceeds 60%, and redrawing a mine thermal-mechanical coupling risk map. According to the method, a multi-parameter analysis framework is constructed through micro-seismic and temperature and humidity data coupling to extract a deformation active band, difference statistics airflow velocity gradient is combined with a crack direction to correct an airflow path, a sudden change area is positioned based on a curvature change rate mean threshold, and thermal strain and axial strain are extracted to screen risk points. And constructing thermal variable amplitude mapping to represent the rock mass instability risk.

The invention provides a heat-water-force coupling experimental device and a testing method based on dynamic true triaxial electromagnetic Hopkinson bar system loading. The experimental device comprises an X-axis dynamic loading system, a Y-axis lateral anti-seepage loading system, a Z-axis normal static pressure servo control loading system, a seepage system, a temperature control system and a data monitoring and collecting system. According to the device, on the basis of a dynamic true triaxial electromagnetic Hopkinson bar system, a shear loading module, a temperature regulation and control module and a seepage control module are creatively integrated, so that the true working condition of a deep rock mass under the coupling action of dynamic disturbance, osmotic pressure and temperature is simulated. The device breaks through the limitation that a traditional experiment system is difficult to realize multi-field coupling loading under a high-speed shearing condition, and provides an experiment means for dynamic response research of fractured rock mass under a complex multi-field environment.

The invention provides a coaxial unequal force-based gas-containing coal and rock mass true triaxial indoor test method, which comprises the following steps of: preparing a sample, injecting CH4 into a confining pressure cavity, synchronously starting an acoustic emission device and a binocular miniature high-speed camera, and testing the gas-containing coal and rock mass true triaxial indoor test. Acoustic emission monitoring software and DIC analysis software are used for recording acoustic emission and strain field evolution data, stiffness loading systems in the sigma 1, sigma 2, sigma 3 + and sigma 3-directions are controlled through a controller and a servo valve, confining pressure is loaded in a force control loading mode, and sample data are processed and analyzed; according to the invention, the condition of unequal coaxial stress load in the free face direction in the mining process of an underground working face can be simulated, and the actual condition of a mine can be better reflected; a video probe side pressing plate is arranged, and a binocular miniature high-speed camera is embedded into the clamp, so that the structural change of a test surface in the loading process can be recorded in real time, three-way surface strain field dynamic monitoring is realized, and the change conditions of a crack field and a strain field of a coal sample under the gas-containing condition are better reflected.