2547 results about "Rock body" patented technology

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 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 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 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 discloses a method and a system for predicting the lumpiness of open-air throwing blasting, and relates to the technical field of rock blasting engineering. The invention discloses a new calculation relation about a lumpiness similarity proportion, relates to the technical field of open-air loose blasting mining, and can predict blasting lumpiness of projects such as mines, tunnels and the like by scaling a lumpiness distribution proportion. The method comprises the following steps: firstly, acquiring a rock sample from a blasting site, measuring parameters such as rock density and fracture toughness, and acquiring blasting parameter information of a to-be-blasted area; and then, a numerical simulation model under an open-air throwing blasting similar material is constructed by using a numerical simulation method, the change rule of blasting fragmentation lumpiness along with explosive unit consumption in a scale model test is studied, and the blasting lumpiness in a field blasting area is predicted according to the blasting lumpiness similarity ratio. According to the method, the change rule along with the unit consumption of the explosive is analyzed, the utilization rate of crushing energy of the explosive in surface blasting engineering is improved, reference is provided for blasting lumpiness control, and prediction of field blasting lumpiness before blasting is achieved.

The invention discloses a rock damage assessment method fusing wave velocity and microscopic fracture, and belongs to the technical field of rock damage assessment, and the rock damage assessment method comprises the following steps: arranging a wave velocity testing device on a rock sample, and respectively making an unloaded rock sample and a damaged snapshot rock sample under different stress loading; microcosmic fracture information of the damaged snapshot rock sample is identified, a three-dimensional wave velocity model of the damaged rock sample under different stress loads based on a wave velocity test is constructed, and then a macro-micro scale fusion multi-dimensional data set is constructed; based on the macro-micro scale fusion multi-dimensional data set, constructing and training a rock damage identification model; and inputting the characteristic parameters of the rock sample to be evaluated into the rock damage identification model, identifying the damage distribution characteristics of the rock sample on the spatial scale, and evaluating the rock damage degree. According to the method, accurate prediction and evolution process evaluation of the rock damage degree and spatial distribution can be realized, and a reliable technical means is provided for disaster early warning, stability evaluation and risk prevention and control in complex rock mass projects such as deep mines and tunnels.

The invention discloses an evaluation and improvement method and device for a tunnel blasting effect and a medium, and relates to the technical field of tunnel engineering.The evaluation and improvement method comprises the steps that a tunnel coupling data set is collected, space registration and three-dimensional shape reconstruction are conducted, fracture space distribution is formed, risk probability mapping is conducted on the fracture space distribution, and a fracture risk thermodynamic diagram is output; the fracture risk thermodynamic diagram and tunnel rock mass data are input into a digital twin blasting model, a load mapping layer carries out load gradient distribution, a rock mass response deduction layer carries out stress wave propagation simulation and damage accumulation quantification, an optimized blasting number instruction set is generated, and a drill jumbo is driven to execute blasting operation and vibration monitoring according to the optimized blasting instruction set. And actual blasting effect parameters are obtained. According to the method, accurate simulation of geological conditions and optimal adjustment of blasting parameters are realized through the digital twin blasting model, and meanwhile, refined and interpretable evaluation of the blasting effect is realized through vibration monitoring and error propagation modeling of the actual blasting effect.

The invention provides a deep rock mass creep-seepage coupled near-field dynamics simulation method and system, and relates to the technical field of geotechnical engineering.The method comprises the steps that a near-field dynamics numerical calculation model is established based on geological survey data; based on the Burgers creep constitutive model, a near-field dynamic creep-seepage coupling basic motion equation is formed; and carrying out rock mass creep process simulation considering the seepage effect by utilizing the near-field dynamics numerical calculation model and the creep-seepage coupling basic motion equation, and carrying out iterative solution on stress, strain, displacement, water pressure and damage of each time step in the simulation process until iteration stop conditions are met, so as to obtain the rock mass creep process. And finally, obtaining distribution characteristics of a rock mass displacement field, a damage field and a seepage field and a creep characteristic curve, and realizing near-field dynamic simulation of a rock mass creep-seepage coupling process. According to the method, the technical blank of near-field dynamics in the field is filled, and the long-term stability of the engineering rock mass and the disaster evolution process are accurately reproduced and predicted.

The invention relates to the field of geological exploration and geotechnical engineering, provides a rock mass dynamic mechanical parameter sensing method and system based on while-drilling parameters in order to dynamically obtain rock mass dynamic mechanical parameter characteristics in situ in real time in the drilling process, and provides a rock mass dynamic mechanical parameter sensing method and system based on while-drilling parameters by using multi-modal signals such as vibration, acoustic emission, torque and bit pressure through uniform characteristic engineering and multi-task learning. The instantaneous elastic modulus, the dynamic Poisson's ratio, the transient uniaxial compressive strength, the cohesive force and the internal friction angle can be synchronously and dynamically predicted in single drilling, and the limitation that only a single static parameter is estimated in a traditional method is broken through; a rock mass dynamic mechanical parameter acquisition model constructed on the basis of a multi-modal adaptive graph attention sequential network can effectively couple the physical proximity and the statistical association relationship of a sensor, and integrates expansion sequential convolution and a time perception Transform module, so that the characterization capability of emergencies (such as jamming / friction resistance) and long-term trends is considered on the premise of low time delay; therefore, the prediction accuracy is improved.

The invention discloses a grouting parameter intelligent feedback method based on rock mass groutability evaluation, which comprises the following steps: collecting geological condition data, rock mass quality data, field test data and grouting process data, and constructing a rock mass groutability grading data set; an SMOTE algorithm is adopted to process data imbalance, input indexes are screened through Pearson correlation analysis, and data are normalized through deviation standardization; an XGBoost classification model is constructed, hyper-parameters are optimized through grid search, the model is trained through K-fold cross validation, and the rock mass groutability grade is evaluated; constructing an XGBoost regression model by taking a rock mass groutability grade, grouting construction data and monitoring data as input, and predicting the maximum value and the minimum value of grouting pressure and slurry density by using an optimal model; and generating a grouting parameter interval according to the predicted maximum value and minimum value, and dynamically feeding back to the grouting equipment to regulate and control parameters. According to the method, scientificity and accuracy of rock mass groutability evaluation and grouting parameter regulation and control can be effectively improved.

The invention provides a multi-source geophysical prospecting data fused Zenzenite rock mass contact zone anomaly method and system, and relates to the technical field of geophysical prospecting, the method comprises the following steps: executing joint denoising processing and time-frequency feature enhancement processing on a TFEM data set in a three-dimensional fused data volume, and outputting the optimized three-dimensional fused data volume; and based on the optimized three-dimensional fusion data body, by establishing a mapping relationship between magnetic, electric and polarization anomalies and a geologic body, quantifying the correlation between a multi-parameter combination and a mineralization index and screening an up-to-standard abnormal region, and finally delineating a magnetic anomaly gradient zone, a low-resistance region and a high-polarization anomaly co-occurrence ore prospecting target region three-dimensional distribution diagram. Through multi-source geophysical prospecting method collaboration, data deep fusion, quality optimization and accurate mapping, full-chain technology upgrading from data acquisition to target area delineation is realized, and the high efficiency of mineral exploration is improved.

A rock mass mechanical parameter prediction method based on an underground powerhouse of a pumped storage power station relates to the technical field of mechanical parameter prediction, and comprises the following steps: identifying an embedding position and distribution characteristics of a sulfur-containing shale interlayer by constructing a three-dimensional geologic structure model and lithologic information of a construction area, and establishing a structural domain partition parameter prediction area system; constructing a nonlinear regression function model taking infrared spectrum characteristic factors and environment control parameters as input and taking elasticity modulus and the like as output by combining infrared spectrum characteristic data and a historical evolution track; the model is embedded into a regional system for dynamic assignment, and a parameter space-time change atlas is constructed; the laser point cloud data and the micro-seismic monitoring data are fused to invert the actual response of the surrounding rock; when the monitoring value deviates from the prediction map, triggering parameter degradation function re-calibration to complete parameter dynamic updating and prediction closed loop; according to the method, the accuracy of rock mass mechanical parameter prediction of the pumped storage power station underground powerhouse can be improved.

The invention relates to a rock slope support model construction scheme generation method and device, equipment and a medium. According to the method, a three-dimensional geological model fusing geological information and rock mass parameters is constructed, an initial damage field is obtained in combination with micro-seismic monitoring data and statistical learning inversion, and then a constitutive model capable of reflecting the damage and plastic coupling evolution law is established and calibrated; the model is used for dynamically predicting a spatio-temporal evolution path of a potential slip plane in the excavation process, the supporting opportunity and position are accurately judged based on the stress and damage state in the path, a spatio-temporal sequence scheme is generated, and finally optimal supporting parameters are solved through the multi-objective optimization model. And finally, a set of dynamic support construction scheme capable of actively controlling damage development and giving consideration to safety and economical efficiency is integrated and output, technical spanning from passive reinforcement to active intervention and from static design to dynamic optimization is achieved, and the accuracy and reliability of slope support are effectively improved.

The invention provides a tunnel hard rock non-blasting excavation method. The tunnel hard rock non-blasting excavation method can realize efficient excavation of complete or relatively complete hard rock without adopting large excavation equipment such as a heading machine and the like , and is characterized by comprising the following steps of 1, drilling contour holes along a tunnel face contour line so as to determine a tunnel excavation range, and determining each drilling and grooving position according to the excavation range, then, forming and drilling a plurality of cavity expanding holes in the position close to the positions; 2, drilling a plurality of rows of guide holes between the contour holes and the cavity expanding holes; 3, drilling groove forming holes which are meshed and overlapped with each other at the determined drilling and grooving positions, and finally forming a groove cavity; 4, enabling the cavity expanding holes near the groove cavity to be subjected to impact crushing, and therefore expanding excavation is conducted on the groove cavity; 5, according to a certain excavation sequence, starting from the guide holes close to the expanded and excavated groove cavity, adopting a hydraulic breaking hammer for conducting impact breaking on the guide holes, so that excavation of the rock mass of a main body part is completed; and 6, conducting impact crushing on the contour holes.

The invention relates to the technical field of artificial intelligence and data processing, in particular to an automatic rock stratum identification method and system based on multispectral remote sensing, and the method specifically comprises the following steps: employing a continuum removal-based adaptive normalization method for a collected multispectral remote sensing data sample; decomposing the normalized sample based on a mixed pixel spectrum of a physical mechanism of a rock mass, and calculating an abundance coefficient of each end member; calculating a first derivative value of each normalized sample in each wave band, and calculating a fusion feature value of each dimension by combining an abundance coefficient; constructing a rock stratum identification model based on deep learning, and inputting fusion feature values of all dimensions to carry out rock stratum category probability prediction; performing optimization training on the rock stratum identification model; newly collected data are processed and then input into the trained model, and a final recognition result is obtained. The accuracy, the stability and the geological rationality of the automatic rock stratum identification result can be improved.

The invention relates to the technical field of geotechnical engineering disaster monitoring and early warning, and discloses a stratum rainfall seepage deformation coupling numerical simulation method and system and a storage medium, and the method comprises the steps: building a three-dimensional geologic model reflecting soft and hard rock interbed characteristics; converting the on-site rainfall data into hydraulic boundary conditions in real time; calculating a rock mass shear expansion volume change rate, determining a chemical damage acceleration factor, and updating a total damage variable; reconstructing a permeation tensor representing the dominant flow channel according to the space gradient of the damage variable; solving a fluid-solid coupling equation based on the permeability tensor, obtaining pore water pressure and calculating a displacement field; building a likelihood function by using field monitoring data to perform inversion correction on model parameters; and evaluating the stability based on the corrected calculation result and outputting an early warning signal. By constructing a mechanical coupling mechanism and a dominant flow channel model, precise simulation of a landslide evolution process in a complex geological environment is realized.

The invention relates to the technical field of rock mass earthquake dynamic response, and discloses a layered rock mass earthquake dynamic response multi-scale monitoring system and method, and the system comprises a rock mass three-dimensional geological module, a simulation setting module and an earthquake simulation module. The method comprises the following steps: establishing a layered rock mass three-dimensional geologic model; the method comprises the following steps: identifying a dangerous rock mass structural surface, monitoring slope displacement, and installing sound emission monitoring equipment at a key position to form a three-dimensional monitoring network; a layered rock mass and a broken area in the layered rock mass three-dimensional geological model are divided into discrete element areas, and a continuous medium area is divided into finite difference areas; coupling the discrete element region and the finite difference region; inputting the seismic wave as a dynamic boundary condition into the finite difference region, transmitting a response to the discrete element region through a coupling interface, and simulating a rock stratum and rock mass dynamic fracture process; and the crack propagation particle size and the original stress propagation are transmitted to the cloud platform. The geometric morphology and the internal structure of the rock mass can be accurately reflected.

The invention discloses a deep well rock mass instability failure time accurate forecasting and early warning system, and relates to the technical field of deep well rock mass failure early warning, and the deep well rock mass instability failure time accurate forecasting and early warning system comprises the following steps: collecting a plurality of deep well rock samples to carry out a uniaxial compression experiment, and collecting acoustic emission signals in real time based on an acoustic emission sensor to obtain original acoustic emission signal data; carrying out self-adaptive preprocessing, and carrying out data classification and integration to obtain rock sample acoustic emission parameter data; constructing an original damage forecasting model, and performing model training and verification optimization based on the rock sample acoustic emission parameter data to obtain a rock damage forecasting model; collecting an acoustic emission signal of the deep well rock mass, and predicting the instability failure time of the deep well rock mass in real time based on the rock failure forecasting model; the method is used for solving the problems that when an existing deep well rock mass damage early warning technology carries out early warning on rock mass instability damage through acoustic emission monitoring, single or few characteristic parameters are manually processed and analyzed, and the early warning reliability and timeliness are not high.

The invention relates to the technical field of data processing, and provides an unmanned aerial vehicle-mounted LiDAR geological modeling and intelligent blasting parameter optimization system, which is characterized in that a laser point cloud and a multispectral image are deeply correlated through a data acquisition module based on a tight coupling algorithm, the sampling quality of a point cloud data set is evaluated, and a supplementary flight instruction is judged and generated; a complete data source is provided for subsequent geological modeling; the geological modeling module divides the point cloud data set through an adaptive threshold algorithm to identify rock mass structural surfaces, analyzes a spatial topological relation of the rock mass structural surfaces based on a graph neural network, performs clustering to form a control structural surface group, and associates the control structural surface group with spectral features through a convolutional neural network to cooperatively discriminate rock mass properties; geological indexes of the three-dimensional geological model are extracted through a parameter optimization module, blasting parameters are generated after genetic algorithm processing, the blasting effect is judged through laser point cloud obtained again after blasting and change characteristics are extracted, the parameters of the genetic algorithm are reversely adjusted, and the three-dimensional geological model is updated.

The invention relates to the technical field of tunnel construction, and discloses a coal mine goaf karst tunnel construction method which comprises the following steps: S1, performing three-dimensional scanning on the axis and the periphery of a tunnel by adopting a high-frequency geological radar in combination with aerial survey of an unmanned aerial vehicle, and accurately positioning the boundary of a goaf, the development form of a karst cave and filler distribution; and S2, integrating a seismic wave reflection method, drilling and coring data and a geological sketch result, constructing a three-dimensional geological model, and dynamically predicting a potential water inrush and mud inrush risk area. According to the method, a high-frequency geological radar is combined with aerial survey of an unmanned aerial vehicle, three-dimensional scanning is conducted on the axis and the periphery of a tunnel, geological radar data, a point cloud model and tunnel design BIM are integrated, the boundary of a goaf (the error is smaller than or equal to 0.3 m) and the karst cave form are marked, and the collapse risk level is predicted based on rock mass mechanical parameters (compressive strength and fracture density) and hydrological data (seepage paths). Therefore, the method has the advantages of advanced detection, multi-source data fusion and the like, and the resolution of geological forecast is improved.

The invention provides a tunnel support self-adaptive adjusting method and system based on rock mass-support feedback, and relates to the technical field of tunnel engineering support. The method comprises the following steps: acquiring surrounding rock strain data and stress data through a sensor network arranged in tunnel surrounding rock, and generating original monitoring data; calculating the surrounding rock strain energy density by using a data processing unit; matching the strain energy density with a preset energy stage feature through an adaptive control algorithm to generate an energy stage matching result; determining an energy state stage based on a stage confirmation module and generating an energy stage identifier; a supporting mode switching instruction is generated through a supporting mode mapping querier; and finally, the supporting control unit adjusts the working parameters of the supporting structure, real-time monitoring of the energy state of the surrounding rock and dynamic matching of the supporting parameters are achieved, the adaptability and safety of tunnel supporting are effectively improved, and the hysteresis of manual intervention is reduced.

The invention discloses a deep mine composite disaster risk prevention and control method, and relates to the technical field of mineral engineering and safety engineering, and the method comprises the following steps: constructing a ground stress disturbance recognition model, collecting deep rock stress change data in real time based on a multi-point stress sensor array arranged in a mine, and calculating the stress change data of the deep rock in real time; and a stress abrupt change point is identified through a dynamic sliding window algorithm. According to the invention, through disaster time sequence coupling modeling and cross trigger identification, accurate identification and early warning of a multi-disaster composite evolution path are realized, the defect of traditional single-disaster independent monitoring is made up, and the perspectiveness and accuracy of disaster prediction are improved. Meanwhile, in combination with a deep learning model and a multi-system linkage response mechanism, closed-loop control from risk assessment to intelligent response is realized, response time is greatly shortened, personnel and equipment losses are reduced, and a systematic and intelligent composite disaster prevention and control solution is provided for a deep mine.

The invention provides a hydraulic fracturing fracture net distribution form near-field monitoring method, which comprises the following steps of: arranging a plurality of roof monitoring holes in roadways on two sides of an underground coal mine working surface, installing electromagnetic signal sensors in each roof monitoring hole and on anchor rods of roadway stoping sides, and constructing a full-space monitoring network covering a hydraulic fracturing layer; a data interpretation processing module of the electromagnetic signal time-shifting imaging analysis system comprises a multi-field coupling mathematical model for analyzing and deducing real-time imaging of electromagnetic signals; in the hydraulic fracturing process, an electromagnetic signal sensor is used for monitoring a current field, an electromagnetic field and an ion diffusion field in the rock mass fracture development and expansion process, a monitoring result is substituted into an electromagnetic signal time-shifting imaging analysis system for calculation and feature value extraction, and the fracture net distribution form is deduced through the feature value. According to the invention, the expansion path of the fracturing fluid in the rock stratum can be monitored in real time, the three-dimensional distribution form of the hydraulic fracturing fracture network can be inverted, and a new technical support is provided for the inspection of the hydraulic fracturing pressure relief effect of the underground coal mine.

The invention relates to the technical field of geological early warning, in particular to a dangerous rock mass instability analysis method, system and equipment based on a space-time diagram neural network, by fusing unmanned aerial vehicle LiDAR, multispectral data, meteorological radar data and the space-time diagram neural network (ST-GNN), the system realizes sub-meter spatial resolution and minute-level time response, and the stability of dangerous rock mass instability analysis is improved. The four-dimensional (time and space) analysis result of the instability probability of the dangerous rock mass is obtained through high-precision space-time modeling, the problems that a traditional geological disaster early warning system is low in resolution ratio, slow in response and high in misinformation are solved, the comprehensiveness, accuracy and reliability of instability prediction of the dangerous rock mass are improved, and the early warning effect is good. And full-chain intelligent closed-loop management of real-time data acquisition-dynamic prediction-early warning push-feedback optimization is supported, the emergency decision time is shortened by real-time rainfall superposition risk thermodynamic diagrams, and the attenuation rate of long-term prediction precision is reduced by dynamically fusing newly added geological data and instability events through incremental learning.

The invention discloses a preparation method of a green high-performance industrial solid waste-based grouting material, and belongs to the field of industrial solid waste treatment and grouting reinforcement, and the preparation method comprises the following steps: performing dry mixing on red mud, slag, fly ash and cement which are dried and ground to specified fineness according to a preset proportion to obtain a uniform dry material; preparing a composite alkali activator from water glass and sodium hydroxide according to a molar ratio of 3: 1, premixing the composite alkali activator with water, and adding the dry material in batches to form wet slurry; and carrying out high-speed shearing and stirring on the wet slurry, and standing and curing to obtain the solid waste-based slurry which can be directly used for grouting. The method obviously improves the solid waste utilization rate, relieves the environmental pressure, and is suitable for related projects such as stratum settlement control, gushing water blocking, mine roadway broken rock mass supporting and underground cavity concrete lining crack repairing in tunnel and subway projects.

The present disclosure relates to the technical field of stability control of surrounding rock of tunnelling, and provides an excavation compensation method for tunnelling in deep rock engineering, including: acquiring an engineering geological information of the tunnelling in deep rock engineering; determining an engineering hazard type based on the engineering geological information; determining an excavation compensation support strategy for the surrounding rock of the tunnelling in deep rock engineering based on the engineering hazard type; and performing a supplementary support control on the surrounding rock of the tunnelling in deep rock engineering based on the excavation compensation support strategy. Through the supplementary support strategy, the difference value between a radial stress of the surrounding rock of the tunnelling in deep rock engineering and an initial crustal stress can be reduced to within a preset approximate value range, further effectively preventing a stress concentration phenomenon occurred in a tangential stress.

The invention provides a large cavern group design method for a hydropower station under an extremely high crustal stress condition. The method comprises the following steps: S1, analyzing a three-dimensional initial crustal stress field of a factory; s2, rock mass mechanical characteristic analysis; s3, underground powerhouse cavern group surrounding rock stability analysis; s4, carrying out design analysis on an anchor cable pre-tightening coefficient and a supporting opportunity; s5, performing micro-seismic early warning analysis on deformation and fracture of the surrounding rock of the cavern group of the underground powerhouse; and S6, providing underground powerhouse rockburst criteria in combination with measured rockburst data: providing underground powerhouse rockburst occurrence and evolution mechanisms based on rock mass characteristics, rockburst characteristics, micro-seismic data and monitoring results, analyzing surrounding rock stress release, relaxation and deformation characteristics, and evaluating surrounding rock stability. The design safety and reliability of the large cavern group of the hydropower station are improved, the construction risk and cost are remarkably reduced, and meanwhile the requirement for environmental protection is met. The improvement measures act together to ensure smooth implementation and long-term stable operation of an engineering project.

The invention provides a tunnel three-dimensional geologic body modeling method and system based on in-hole camera shooting and while-drilling lithology perception, and relates to the technical field of tunnel and underground engineering and intelligent geological information processing.The method comprises the steps that firstly, a drill hole wall optical image sequence and while-drilling engineering parameters are synchronously collected and preprocessed respectively; then establishing a depth-time mapping relation to realize accurate alignment of the multi-source data in a depth domain; then fracture intelligent identification and parameter extraction are realized through an improved U-Net network, and lithology classification is completed by using an XGBoost model; feature level and decision level fusion is carried out on the fracture features and the lithologic features, and rock mass quality grades are generated; and finally, constructing an integrated geologic model fusing lithologic distribution and a three-dimensional fracture network by adopting an implicit modeling and discrete fracture network technology. According to the method, the problem of splitting of in-hole visual information and while-drilling physical information is solved, automation and precision of geological recognition are improved, and reliable geological information support is provided for tunnel construction safety and support design.