1539 results about "Seismology" 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 provides a BIM (Building Information Modeling)-based rail transit construction geological exploration profile generation method, which belongs to the technical field of exploration measurement and comprises the step of comprehensively improving the accuracy of rail transit construction geological exploration through an advanced data acquisition and intelligent analysis method. In a construction site, multi-dimensional data of geological drilling, geological radar, geophysical prospecting testing and the like are acquired through various precise instruments, and a three-dimensional coordinate system is firstly established; and deep feature extraction is performed on complex data by using a deep learning algorithm, and key information such as geological stratification, rock and soil parameters and underground water level is accurately obtained. Intelligent fusion of multi-source data is realized by constructing a geological stratification model, a rock and soil parameter model and an underground water level model, and a comprehensive geological information model is generated. And importing the extracted geological profile data into a BIM platform, and carrying out strict precision verification and optimization by adopting a professional geological information collaborative equation set to finally form a highly accurate and reliable rail transit construction geological exploration profile.

The invention belongs to the technical field of mine safety monitoring and geotechnical engineering stability evaluation, and relates to a mine filling body performance detection and stability evaluation method and system.The method comprises the steps that S1, a first type of monitoring signals are collected in real time through a distributed optical fiber sensor array, and meanwhile vibration waveform signals serve as second type of monitoring signals; s2, inputting the first type of monitoring signals and the second type of monitoring signals into a multi-source data fusion model, and generating fusion state signals representing the three-dimensional damage degree and the real-time deformation state of the filling body; s3, inputting the fusion state signal into a parameter dynamic identification module to generate an updated numerical model parameter signal; and S4, based on the updated numerical model parameter signal, executing stability quantitative calculation to generate a risk level signal. According to the mine filling body performance detection and stability evaluation method and system, the problem of inaccurate psychological state recognition such as large individual difference, strong environmental interference and weak dynamic adaptability can be solved.

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 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 discloses a seismic source inversion data driven rock burst prediction method and system, and the method comprises the steps: carrying out the quantitative inversion calculation of the core parameters of a coal rock fracture seismic source mechanism through original sound wave data collected on site; a rock burst seismic source data prediction model is constructed based on an Informer algorithm, and the occurrence time and position of rock burst are accurately predicted. A rock burst risk evaluation model is constructed by combining the predicted occurrence time and position of a future fracture source and seismic source mechanism core parameters, the magnitude and property of rock burst are predicted, and a dual-drive rock burst intelligent model for seismic source data prediction and rock burst risk evaluation is established; and multi-dimensional intelligent prediction of sequential response, spatial distribution and evolution attributes in the rock burst catastrophe process is realized. According to the method, seismic source data prediction and risk evaluation are combined, the rock burst catastrophe risk can be predicted, and the rock burst evolution process and the disaster-causing mechanism can be visually represented.

The invention discloses a building structure anti-seismic toughness optimization method and system based on deep learning, and relates to the technical field of anti-seismic toughness optimizing.In operation of the system, a sensing network is deployed, multi-dimensional dynamic response parameters in the vibration process of a building structure are collected, preprocessing and sliding window characterization are carried out on a collected parameter set, and the anti-seismic toughness of the building structure is optimized; encoding the input tensor TXF, extracting a deep space-time coupling feature generation coefficient, performing multi-dimensional composite calculation on an anti-seismic stress energy coefficient SEC, a response co-motion coefficient RSC and a damage evolution index DEI to generate a system-level evaluation core index toughness spectrum evolution index REX, and comparing the toughness spectrum evolution index REX with a preset threshold value to obtain the evaluation core index. Grading and risk positioning are carried out on the current state of the building structure, a generated control strategy is sent to an execution layer, a damping adjusting device, a shock insulation layer controller or component repairing equipment is linked to complete structure regulation and control, and response is carried out after real-time acquisition and optimization.

The invention belongs to the technical field of geophysical methods, and particularly discloses a geophysical electromagnetic method data inversion method and system based on entropy constraint, and the method comprises the steps: dividing an inversion space, setting a conductivity value, and constructing an initial inversion model; a magnetotelluric regularization inversion objective function based on entropy constraint is constructed; and adopting a Gaussian-Newton method to minimize the magnetotelluric regularization inversion objective function, updating the initial inversion model, obtaining a conductivity model, and realizing magnetotelluric three-dimensional inversion. The method can achieve the effect of focusing inversion so as to obtain a high-resolution conductivity model with a clear boundary, and serves the fields of petroleum, mineral resource exploration and the like.

The invention relates to a coal mining subsidence area plant foundation monitoring method and system based on multi-source data fusion, and the method comprises the steps: obtaining multi-source monitoring data of a target coal mining subsidence area plant area, the multi-source monitoring data comprising satellite remote sensing data, ground measurement data and interference radar data; performing time synchronization and space unification on the multi-source monitoring data to obtain monitoring data with unified space; performing fusion processing on the monitoring data with uniform space to obtain comprehensive deformation data; constructing a three-dimensional settlement deformation field according to the comprehensive deformation data, and extracting deformation rate and acceleration information of each monitoring point through a gradient calculation method; and according to the deformation rate and the acceleration information, obtaining risk assessment of the plant foundation in the target coal mining subsidence area. The technical problems of multi-source heterogeneous data fusion and real-time settlement monitoring and early warning are effectively solved, and the precision and timeliness of foundation settlement monitoring are improved.

The invention discloses a hydraulic fracture monitoring system and method based on an underground micro-seismic sensor network, and belongs to the field of coal mining, and the system comprises a plurality of micro-seismic sensors which are used for collecting micro-seismic signals generated by rock fracture; the plurality of data acquisition substations are used for receiving and processing the micro-seismic signals; the data transmission substation is used for converting the micro-seismic signal processed by the data acquisition substation into an optical signal; the micro-seismic server performs micro-seismic event analysis according to the optical signal; the mining network switch is connected with the data transmission substation and the micro-seismic server and is used for network data exchange; the in-mine ring main unit is connected with the mining network switch and the micro-seismic server to form a communication network of the system; in order to solve the problem that hydraulic fracture detection precision is low due to the fact that micro-seismic signals generated by hydraulic fracture and coal mining activities are difficult to accurately distinguish in a complex underground environment, the hydraulic fracture detection method and device distinguish the micro-seismic signals generated by hydraulic fracture and coal mining activities by dividing a fracture area and a mining area, and the real-time monitoring precision of hydraulic fracture expansion is improved.

The invention discloses an anti-seismic and anti-power-frequency-interference system and method for a vortex shedding flowmeter, and relates to the technical field of flow measurement, the system comprises a signal receiving module, a vibration monitoring module, a frequency identification module, a temperature and pressure measurement module and an interference signal processing module, interference signals in a flow-free state can be accurately processed, whether actual flow exists or not can be accurately judged by measuring the temperature and pressure of fluid in a pipeline and combining the temperature and pressure with vibration and frequency characteristic information, wrong measurement data generated due to the interference signals in the flow-free state are avoided, and the measurement accuracy is improved. According to the method and the system, the measurement problem caused by misjudgment can be reduced, meanwhile, information about the equipment operation state and the pipeline environment is provided for different types of interference signals through comprehensive analysis and judgment of the system, potential equipment faults and abnormal working conditions can be found in time, and a powerful data basis is provided for maintenance and management of the equipment.

The invention relates to the technical field of submarine engineering and information processing, in particular to a submarine cable positioning method and system, and the method comprises the steps: synchronously collecting magnetic field, structured light and underwater acoustic signal data, and fusing inertial navigation information to construct a unified space-time reference system; precise registration of heterogeneous data is realized by adopting sliding time window screening, multi-modal noise reduction and rigid body transformation; constructing a graph structure model for expressing cable space-signal features based on the fused data, and reasoning cable path topology by using a graph convolutional network; reconstructing a cable center line through three-dimensional visualization, embedding the cable center line into submarine topography, and realizing risk area dynamic clustering by combining signal integrity score and burial depth estimation; historical time sequence data are fused, a long short-term memory network is adopted to predict cable drift, detectable attenuation and burial depth change trends, risk levels are divided based on prediction results, and spatial distribution early warning and disposal suggestions are output. According to the invention, the cable positioning precision and the risk assessment intelligent level in a complex seabed environment are improved.

The present invention belongs to the technical field of geological exploration deep Earth. The system of the present invention is configured to: collect seismic data, and preprocess original seismic data to obtain standardized gamma ray logging parameters; extract features from the standardized gamma ray logging parameters to obtain a variance attribute curve while drilling; obtaining a formation thickness prediction model of each set based on the variance attribute curve while drilling; acquire frequency-division seismic data based on the standardized seismic data by means of a frequency-division processing method based on wavelet transform; acquire optimal seismic frequency band information based on a predicted formation thickness value of each set; establish a geological prediction model of the current formation based on the frequency-division seismic data, the optimal seismic frequency band information, historical gamma ray logging parameters and the standardized gamma ray logging parameters.

The invention provides a deep-buried tunnel TBM vibration and excavation unloading fracture micro-seismic discrimination method and system, and relates to the technical field of tunnel construction, and the method comprises the steps: obtaining micro-seismic event data in a target area; spatial classification is carried out according to the micro-seismic event data, and the influence range of the excavation unloading effect is obtained; performing region division according to the influence range to obtain a rock fracture signal after noise reduction; performing multi-layer decomposition on the rock fracture signal by using wavelet transform to obtain a characteristic parameter set; performing energy grading according to the characteristic parameter set to obtain a grading result; constructing a discrimination standard according to the grading result to obtain the discrimination standard; and performing discrimination based on the discrimination standard to obtain a discrimination result. According to the method, the dynamic model is constructed based on the multi-energy-level discrimination standard, main control factor classification is performed on the real-time micro-seismic event, real-time dynamic discrimination of TBM vibration and excavation unloading fracture in the construction process is achieved, and the early warning timeliness of time-delay rock burst is remarkably improved.

The present invention relates to the field of mine surveying and mapping technology, and in particular to a mine surveying and mapping method and system based on unmanned aerial vehicle remote sensing technology, the method comprising: obtaining terrain data of a mine; generating a flight trajectory of an unmanned aerial vehicle; using an unmanned aerial vehicle to collect image data and hyperspectral data of the mine; constructing a three-dimensional surface model of the mine; obtaining geological structure data and mineral distribution data of the mine; and generating a three-dimensional structural model of the mine based on the geological structure data, mineral distribution data and the three-dimensional surface model. The present invention generates a flight trajectory of an unmanned aerial vehicle to ensure that the unmanned aerial vehicle takes photos at a reasonable flight altitude and speed in different areas, thereby reducing repeated coverage and maximizing regional coverage. By generating a three-dimensional structural model, detailed geological structure and mineral distribution data can be obtained without actually entering the mining area, greatly reducing the safety risks and economic costs that may be caused by on-site surveys.

The invention provides a deep high-precision underground disease detection method based on GPR, and relates to the technical field of geophysical exploration. Establishing a road disease forward model, and initializing the road disease forward model; obtaining an initialized road disease forward model and adding preset noise information into a result to generate fused echo data; preprocessing the fused echo data to generate target echo data; constructing a road disease data set according to the target echo data, and performing multi-frequency data fusion in the road disease data set to generate multi-frequency data; constructing a super-resolution convolutional neural network according to the multi-frequency data, and training the super-resolution convolutional neural network; inputting detection data into the trained super-resolution convolutional neural network model, and obtaining a detection report; and the road disease radar detection performance is improved.

The invention relates to the technical field of hydraulic ring geological survey, in particular to a hydraulic ring geological survey system and method based on GPS positioning, and the method comprises the following steps: obtaining the three-dimensional coordinate data of a survey area through a GPS positioning device, generating an initial geological positioning point set, inputting the initial geological positioning point set into a three-dimensional modeling system, generating an initial three-dimensional geological model, and carrying out the three-dimensional modeling of the initial three-dimensional geological model. Dynamically correcting the initial three-dimensional geologic model through an environment compensation algorithm to generate a corrected geologic model, dividing a geologic abnormal region through a spatial clustering algorithm based on a rock stratum permeability coefficient abnormal gradient value in the corrected geologic model, comparing the geologic abnormal region with an engineering parameter threshold library to generate a visual investigation report, and performing the visual investigation on the geologic abnormal region. An internet of things monitoring node network is established, displacement monitoring is carried out on a geological abnormal area, if a monitoring value exceeds a preset threshold value, an early warning signal is triggered, and the method can be widely applied to the fields of hydraulic ring geological survey, engineering construction and disaster early warning and has remarkable technical advantages.

The invention belongs to the technical field of data acquisition, and provides an underground pipeline detection data acquisition method and system, and the method comprises the following steps: collecting underground pipeline drawing data of a target region, and determining the information parameters of underground pipelines; performing field exploration, marking a ground outcrop point of the underground pipeline, and preliminarily judging the trend of the pipeline; detecting the underground pipeline through an electromagnetic method or a ground penetrating radar, and determining the position and the burial depth of the underground pipeline; reflected and scattered sound wave signals propagated through the internal fluid of the underground pipeline are collected through sound wave detection, and the state of the internal fluid is evaluated; fusing different detection data to generate an underground pipeline space model; by comprehensively using the electromagnetic method, the ground penetrating radar and the sound wave detection technology, the position, the burial depth and the internal fluid state information of the underground pipeline can be comprehensively collected, the physical position of the pipeline is determined, the state of the internal fluid of the pipeline is evaluated, and the accuracy and the reliability of the detection result are effectively improved.

The invention relates to the technical field of mining, in particular to a mine disaster prediction method and system. The method comprises the following steps: acquiring geologic structure data corresponding to a mine, wherein the geologic structure data comprises rock stratum distribution, fault occurrence and underground water occurrence state; constructing a coupling model corresponding to the mine based on the rock stratum distribution, the fault occurrence and the underground water occurrence state, and determining a rock mass constitutive relation and coupling parameters in the coupling model; determining a prediction boundary condition of the coupling model based on the rock constitutive relation and the coupling parameters; and acquiring field monitoring data of the mine, correcting the coupling model based on the field monitoring data and the prediction boundary condition so as to determine a potential disaster point based on the corrected coupling model, and generating a risk early warning strategy and a risk recovery strategy corresponding to the potential disaster point. According to the embodiment of the invention, a full-chain prevention and control system from disaster prediction to emergency disposal is constructed, and the safety management level and disaster prevention and control capability of mine engineering are remarkably improved.

The invention discloses an underground space surrounding rock excavation damage area testing method, which belongs to the technical field of rock mass damage testing, and comprises the following steps: sampling rocks with different depths, carrying out ultrasonic testing, and determining rock sample attenuation characteristics and wave velocity; determining a comprehensive damage index range of the rock samples with different damage grades; then, sensors are arranged in the excavation process to collect ultrasonic and acoustic emission data; then, an initial calculation model is constructed, and a surrounding rock three-dimensional wave velocity model is obtained through inversion of collected data; and finally, determining a damage grade according to the comprehensive damage index, and marking in the model to realize damage area testing. According to the method, the mapping relation between the damage degree and the wave velocity change is established, the comprehensive damage index range threshold value of different subareas is provided, quantitative and gradable classification is achieved, the three-dimensional wave velocity model is inversed based on test and monitoring data, and three-dimensional presentation and grading labeling of the damage area are achieved.

An approach for calibrating prestack seismic inversion is provided. This method includes selecting various features from inverted elastic properties to generate reservoir properties; fully connected neural network models are used to learn the mapping between the features and ground truth data at well locations; and the prediction is applied to generate one or more final models for the reservoir characterization of the whole survey region.

The invention discloses a foundation settlement early warning method and system based on an intelligent algorithm, and relates to the technical field of settlement early warning. During operation of the system, through combined use of various sensors, foundation settlement related data from multiple sources are collected and processed and fused in real time, foundation settlement is predicted through the intelligent algorithm, and the early warning effect is achieved. The settlement risk of different areas is evaluated by combining a space-time big data analysis technology, a settlement risk index GSI is calculated, an early warning grade is output, early warning decision making is carried out according to a prediction result and a set dynamic threshold value, the severity of the settlement risk is judged and classified, a system automatically generates an early warning report, and once early warning is triggered, the early warning is automatically carried out. The system automatically starts an emergency response scheme, dynamically adjusts reinforcement and intervention strategies according to the actual change of foundation settlement, provides a real-time visual interface of settlement data and early warning information, and displays the settlement state, the early warning level and historical data.

The invention relates to the field of geological disaster monitoring, and discloses an alpine region ice rock collapse potential dangerous area identification device comprising an acquisition and preprocessing module used for acquiring and preprocessing alpine region ice rock multi-source data to form basic data; the fusion extraction module is used for performing data fusion processing and feature extraction on the basic data to form fused feature data; the identification and prediction module is used for identifying a potential risk area of the ice rock in the alpine region and predicting an ice rock collapse scene according to the fused feature data to form a risk area judgment result and ice rock collapse simulation and prediction information; and an optimization layout module. Basic data are formed by collecting and preprocessing ice rock multi-source data in the alpine region, fused feature data are formed by conducting data fusion processing and feature extraction on the basic data, and therefore spatial features of satellite images, time sequence features of sensor monitoring data, environment features of meteorological data and the like are organically combined.

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 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 provides a tunnel surrounding rock time sequence deformation monitoring data anomaly detection method and device combined with an LSTM and an auto-encoder, and a medium. The tunnel surrounding rock time sequence deformation monitoring data anomaly detection method combining the LSTM and the auto-encoder comprises the following steps: step 1, cleaning original monitoring data; 2, constructing a data set; step 3, constructing a self-encoding LSTM neural network model; 4, model training; 5, performing model testing and performance evaluation; and 6, deploying the model. According to the tunnel surrounding rock time sequence deformation monitoring data anomaly detection method and device combined with the LSTM and the auto-encoder and the medium, the problem that in existing tunnel automatic monitoring, due to the fact that the data quality is poor and noise is large, traditional threshold value early warning is low in reliability can be solved, the efficient deep learning model is constructed, and the reliability of the tunnel surrounding rock time sequence deformation monitoring data anomaly detection is improved. And automatic and accurate anomaly detection of tunnel surrounding rock monitoring data is realized, so that reliable guarantee is provided for tunnel construction safety.

The invention relates to the technical field of reflected waves, in particular to a coal mine disaster comprehensive evaluation system based on three-component micro-motion detection, which comprises a frequency direction extraction module, a propagation included angle calculation module, a sensitive path identification module, a critical turning identification module and a structural disturbance mapping module. According to the method, the continuous sequence is formed in the space through the direction vector constructed through the main frequency value of the measuring point, an included angle screening strategy is used for eliminating incoherent direction sections, it is ensured that the path has trend consistency, the frequency amplitude difference value and symbol inversion are used for detecting a mutation point, and the response recognition capacity for directional changes is enhanced; the normalized frequency ratio is combined with a standard deviation and a difference value to carry out joint calculation so as to quantify a structure jump degree and improve the accuracy of reflex position expression, disaster boundary points form an image set, and a structure identification channel is established by taking a frequency direction as a principal line; and the spatial expression definition and precision are improved in the aspects of path aggregation, boundary precision, disaster recognition and partition and continuous structure drawing.

The invention discloses a shield tunnel ground subsidence real-time prediction method considering spatio-temporal information, and the method comprises the steps: constructing an input feature system, including classifying model input into geometric information, multi-ring geological condition information, multi-time step shield parameter information and historical subsidence information; a multi-source information fusion model architecture is designed, geometric information, geological condition information, shield operation parameter information and historical settlement information serve as input of the model, and feature extraction is conducted through multiple encoders. Then feature fusion is carried out, nonlinear mapping is carried out by using a residual network (ResNet), and finally a real-time settlement prediction value is output; according to the scheme, the space-time characteristics of ground subsidence induced by tunneling are deeply excavated, and real-time prediction of subsidence of any position in a disturbance range is realized through deep fusion of multi-source characteristics.

The invention discloses an open-pit mine slope risk prediction method and system based on multi-source data, and relates to the technical field of intelligent geological engineering, and the method comprises the steps: collecting geological data, displacement monitoring data and environmental activity data of an open-pit mine slope, carrying out the preprocessing, and constructing a three-dimensional digital twinborn body; carrying out fusion calculation on the preprocessed displacement monitoring data and environmental activity data through a physical information neural network, predicting a displacement value and a stress value of the strip mine slope, and obtaining full-field physical quantity prediction data; according to the strip mine slope slip crack surface damage evolution path, the stability risk levels of the whole strip mine slope and different areas in the strip mine slope are judged, high-risk areas are positioned, and a strip mine slope risk prediction report is generated. According to the method, the damage evolution path of the slip crack surface of the strip mine slope is dynamically deduced, so that space-time continuous modeling of the damage evolution process, early recognition of the slope instability precursor and accurate insight of the risk evolution trend are realized.

The invention relates to the technical field of electric digital data processing, in particular to a dangerous rock instability monitoring and early warning method. The method comprises the following steps: collecting multi-modal original data related to the dangerous rock in real time, and preprocessing the multi-modal original data to obtain multi-dimensional core parameters; lithologic partitioning is carried out on the monitoring area based on the borehole acoustic logging data, and a lithologic partitioning result is obtained; carrying out data association and fusion by adopting a D-S evidence theory based on the lithologic partitioning result and the multi-dimensional core parameters, and generating a comprehensive support degree of each catastrophe state; multi-modal characteristic spectrum mathematical characterization is carried out based on the multi-dimensional core parameters, a precursor mode corresponding to the multi-modal characteristic spectrum subjected to mathematical characterization is identified, and space-time correlation verification is carried out on an identification result; and according to the comprehensive support degree, the identification result and the time-space correlation verification result, outputting a graded early warning instruction of dangerous rock instability. According to the invention, the problems of one-sided consideration, low precision and poor real-time performance of dangerous rock instability early warning in the prior art can be solved.