345 results about "Geologic hazards" patented technology

The invention discloses an automatic geological disaster recognition system and method based on multi-source remote sensing data, and particularly relates to the field of geological disaster recognition, and the system comprises a multi-modal remote sensing data acquisition module, a cross-domain physical fusion module, a spatio-temporal evolution decision module, a multi-cascade early warning decision module, an optimization control module and a visualization module. According to the geological disaster automatic identification system and method based on the multi-source remote sensing data, virtual features are generated through a cross-domain physical fusion module by using a domain adversarial network, the model generalization ability during cross-domain application is improved, physical association among the multi-source remote sensing data is deeply mined, and dependence on manual design rules is eliminated; through a three-layer processing chain technology composed of a spatial-temporal feature extraction layer, a dynamic graph evolution layer and a critical recognition layer, the capability of capturing disaster features in a complex geological environment is effectively improved, especially the recognition precision of precursor tiny deformation is improved, and the risk of missing report is reduced.

The invention discloses a landslide susceptibility assessment method and system fusing feature interaction and dynamic weighting, and belongs to the field of geological disaster assessment, and the method comprises the steps: building an evaluation index system, and carrying out the factor classification; acquiring landslide and non-landslide point sample data, evaluating factor importance by adopting a random forest to obtain a preliminary weight, and preliminarily evaluating the susceptibility based on an I-RF model; detecting double-factor interaction by using a geographic detector, screening interaction factors for collaborative enhancement, and optimizing an expansion index system; retraining the random forest model, obtaining a new weight containing a single factor and an interaction factor, and constructing an I-RF optimization model; for spatial heterogeneity, sub-regions are divided by using spatial clustering, and the weight of each sub-region is dynamically corrected according to the sensitivity of a main control factor, so that self-adaptive adjustment is realized; and calculating susceptibility indexes of the sub-regions and the whole region, and grading to form spatialized and multi-level risk partitions. According to the method, the scientificity and accuracy of evaluation are effectively improved through feature interaction optimization and partition dynamic weighting.

The invention discloses a multi-source sensing fusion highway slope geological disaster real-time early warning method, particularly relates to the technical field of safety early warning and monitoring, and is used for solving the problem that an existing fusion system fails to report due to multi-source data false consistency in the early stage of deep hidden catastrophe. By collecting slope multi-source geological parameter time series data, energy conversion efficiency differential entropy mutation is analyzed to generate an abnormal association mark; constructing a co-evolution matrix in a marking state; searching a space-time overlapping region of a main deformation parameter second derivative zero point and an auxiliary parameter frequency domain extreme point in the matrix, and marking a phase change critical state by comparing geological history envelope lines; performing hidden variable decoupling on the critical state matrix, and generating a hidden catastrophe risk index based on the spatial similarity of the main feature vector and the catastrophe mode vector; and when the risk index continuously exceeds the dynamic critical value and the frequency domain characteristic is in an unstable evolution state, an early warning signal is generated, the deep catastrophe critical state is effectively identified, and missing report caused by safety illusion is avoided.

The present disclosure provides a landslide identification method, a device and a storage medium based on multi-path feature fusion, and relates to the field of geological hazard monitoring and early warning. The device and storage medium are used to implement the method. The beneficial effects of the present disclosure are as follows: a landslide identification method based on multi-path is provided, deep feature-level interaction among different types of landslide image data is achieved, high-resolution feature information is preserved, landslide identification accuracy is significantly improved, the computational costs are reduced, the real-time performance of landslide identification is significantly enhanced and the real-time monitoring and early warning are achieved.

The invention discloses a slope settlement data processing method under an infiltration surface effect based on a water-soil coupling model, and relates to the technical field of civil engineering geomechanical analysis. And establishing a water-soil coupling numerical model containing a mutual coupling relationship between water flow permeation and soil deformation. According to the method, the multi-source monitoring data and the sudden change recognition algorithm are fused, so that the recognition precision and real-time performance of the sudden change point of the slope infiltration surface are improved. And a parameter adaptive correction mechanism is introduced, so that the model can dynamically adjust key parameters, and the adaptability to a complex geological environment and the prediction stability are enhanced. Meanwhile, a dynamic processing flow integrating data driving, model calculation and risk identification is constructed, intelligent linkage early warning of the side slope settlement risk is achieved, the landslide accident risk is effectively reduced, and the scientificity and safety of geological disaster monitoring are improved.

The invention relates to the technical field of coal mine geological safety monitoring and disaster early warning, in particular to a coal mine disaster early warning method and system based on a geological model and a storage medium, and the method comprises the steps: firstly obtaining high fractal fracture information and monitoring data such as gas, hydrology and stress through chaotic wave excitation and fractal analysis; and a three-dimensional fractal grid with multi-level fracture representation is constructed. And discretizing a mechanical field and a fluid field by using a fractal partial differential equation operator, and globally solving a large-scale nonlinear equation through a quantum annealing mode or a quantum and classical combined annealing mode to form a quantum fractal coupling simulation result. And finally, integrating the grid units and monitoring data in the fuzzy hypergraph structure, carrying out fuzzy membership analysis on a multi-disaster element coupling sign, and outputting early warning information once the risk is judged to reach a threshold value. According to the invention, the precision and timeliness of coal mine geological disaster monitoring can be obviously improved.

The invention relates to the technical field of landslide geological disaster hidden danger point identification, in particular to a landslide hidden danger point identification method based on an air-space remote sensing fusion neural network, which comprises the following steps: acquiring satellite SAR data, a digital elevation model, a vector boundary map and a precision orbit ephemeris of a research area; carrying out interference processing on the InSAR data, and carrying out deformation rate inversion, terrain residual error correction and atmospheric delay phase modeling and correction; preprocessing an original high-resolution remote sensing image, interpreting a high-resolution image, and outputting spatial distribution of suspected landslide hidden danger points in a research area; planning an aerial survey route of the unmanned aerial vehicle, and performing distortion correction, feature matching, dense point cloud generation and orthoimage generation processing; performing multi-dimensional feature analysis, and performing comprehensive interpretation in combination with an expert knowledge base; according to the method, the key effect of the multi-source remote sensing fusion technology in landslide hidden danger monitoring is displayed from data acquisition to hidden danger recognition.

The invention relates to the technical field of side slope geological disaster monitoring, and discloses a side slope long-term monitoring early warning and threshold value dynamic adjustment method, which comprises the following steps: S1, deploying a plurality of sensors on a side slope; s2, preprocessing the collected original data; s3, setting an early warning threshold value for the key feature index; s4, dynamically evaluating and predicting the stable state of the slope in real time; s5, dynamically correcting and optimizing the initial early warning threshold value set in the step S3; s6, when the key characteristic index value exceeds the newest early warning threshold value, early warning is triggered; and S7, generating early warning information of different levels. The method solves the problems of tedious configuration of slope monitoring early warning threshold values and false alarm and missing alarm, achieves the adjustment and configuration of a unified threshold value through an efficient early warning trigger judgment disposal and threshold value dynamic adjustment mechanism, effectively improves the alarm success rate and early warning disposal efficiency, remarkably reduces the false alarm rate / missing alarm rate of slope early warning, and improves the safety of slope early warning. And the interference of the early warning system on residents is greatly reduced.

The invention provides a dangerous rock risk early warning method and system based on static force-dynamic force-environmental quantity fusion, and relates to the technical field of geological disaster monitoring and early warning, the method comprises the following steps: obtaining rock mass monitoring data and environmental monitoring data of a dangerous rock to be measured, the rock mass monitoring data comprising rock mass vibration time domain signals and rock mass statics parameters; according to the rock mass vibration time domain signal, extracting a dynamic index of the rock mass to be measured; and according to the dynamic indexes or the rock statics parameters, constructing a dangerous rock risk fine evaluation model based on a deep learning algorithm, inputting the environmental monitoring data into the dangerous rock risk fine evaluation model, and performing fine evaluation and early warning on the dangerous rock risk. The method not only considers a single physical index for monitoring and does not depend on an empirical threshold for stability discrimination, but also fully considers the comprehensive action of rock mass dynamic response characteristics and multi-source environment information, and can realize high-precision and early instability early warning.

The invention discloses an underground coal mine geological disaster early warning method and system and a medium, and relates to the technical field of disaster early warning. The method comprises the following steps: constructing a multi-parameter cooperative monitoring field by arranging multi-modal sensing equipment in an underground coal mine; deploying N geological disaster recognition nodes for the N monitoring fields; the collaborative operation node performs risk identification on the returned data and outputs a geological disaster pre-judgment result; and the early warning management and control middle station receives the spatial topology of the monitoring field, executes disaster evolution prediction on a pre-judgment result according to the spatial topology, and outputs real-time early warning. According to the method, the technical problems of difficulty in real-time cooperative processing of multi-source data and insufficient disaster evolution prediction capability in underground coal mine geological disaster early warning are solved, and the underground coal mine disaster risk is dynamically analyzed through cooperative monitoring of a multi-modal sensor and cooperative work of time sequence data analysis and geological disaster recognition nodes, so that the underground coal mine disaster early warning efficiency is improved. And the accuracy and the response speed of geological disaster early warning are improved.

The invention relates to the technical field of geological disaster recognition, in particular to a power transmission line geological disaster recognition method, system and device and a medium, and the method comprises the steps: regularly obtaining satellite InSAR data and unmanned aerial vehicle LiDAR point cloud data of a target area; performing first preprocessing on the satellite InSAR data, and extracting earth surface deformation information; performing second preprocessing on the unmanned aerial vehicle LiDAR point cloud data to generate a digital elevation model; carrying out geological disaster feature extraction based on a digital elevation model to obtain a disaster type classification result; and analyzing the surface deformation information and the disaster type classification result, and outputting geological disaster state information. The beneficial effects of the invention are that data support and technical guarantee are provided for safe and stable operation of the power transmission line, operation and maintenance cost is reduced, monitoring efficiency is improved, timely prevention measures are ensured, and influence of geological disasters on the power transmission line is reduced.

The invention relates to the field of coal mine geological disaster monitoring, in particular to a coal mine surface slope remote sensing data fusion monitoring method and system, and the method comprises the steps: collecting multi-source data through a satellite-unmanned aerial vehicle-ground three-stage network, and carrying out the cleaning, correction and standardization preprocessing; utilizing an improved LSTM model which introduces an attention mechanism and a geological parameter correction item to predict slope displacement and risk levels; distributing monitoring resources through a PSO (Particle Swarm Optimization) algorithm, dynamically adjusting an equipment state through a fuzzy PID (Proportion Integration Differentiation) algorithm, and fusing multi-source data through a Bayesian algorithm to generate a high-dimensional matrix; and finally, storing data based on the alliance chain and performing early warning based on a dynamic threshold value. The system correspondingly comprises six core modules. The problems of narrow coverage, poor timeliness and low data utilization rate of traditional monitoring are solved, millimeter-level precision monitoring is realized, the early warning response is less than or equal to 5 minutes, the cost is reduced by more than 40%, and the method is suitable for safety and ecological monitoring of various coal mine slopes.

The invention discloses a geological disaster risk evaluation method and system based on big data, and the method comprises the following steps: S1, obtaining multi-source geological disaster data, including geological structure data, landform data, meteorological and hydrological data, human activity data and historical disaster data; s2, performing space-time alignment and standardization processing on the multi-source geological disaster data, and constructing a geological disaster space-time data set; s3, constructing a geological disaster risk evaluation model based on the deep learning network, wherein the geological disaster risk evaluation model comprises a feature extraction module, a space-time correlation module and a risk prediction module; s4, calculating a weight coefficient of each evaluation index by adopting a dynamic weight distribution method; s5, calculating a geological disaster risk index through the geological disaster risk evaluation model, and generating a risk grade zoning map; according to the invention, more intelligent geological disaster risk evaluation of the urban area is realized.

A plateau mountain area road slope geological disaster automatic identification method fusing multiple data comprises the following steps: S1, acquiring multi-source heterogeneous data of a target slope area, including a time sequence satellite optical image, synthetic aperture radar interferometry (InSAR) deformation data, laser radar (LiDAR) point cloud data, meteorological and hydrological data and basic geological data, and generating fusion data; s2, constructing a space-time registration module, unifying the multi-source data obtained in the S1, establishing a unified space-time coordinate system for the multi-source data, and generating fusion data; and S3, processing synthetic aperture radar interferometry (InSAR) deformation data through a freeze-thaw cycle noise compensation model, and eliminating deformation noise caused by seasonal frost heaving and thaw collapse. According to the plateau slope disaster identification method, the plateau freeze-thaw compensation model is combined with a multi-modal dynamic decision-making mechanism, and physical mechanism complementation of InSAR and LiDAR is adopted, so that'earth surface deformation-rock mass structure 'two-dimensional verification is realized, and multi-data fusion is realized, and therefore, the automatic identification precision of plateau slope disasters is improved.

The invention provides a method and system for analyzing a highway risk range based on geological disaster point data, and the method comprises the steps: obtaining geological disaster point data and highway network data, and storing the geological disaster point data and the highway network data; searching and determining a road section closest to each geological disaster point, determining a vertical point of each geological disaster point on the corresponding closest road section, and calculating a relative position of the vertical point on the road section; automatically determining a stake number corresponding to a vertical point according to a starting point stake number, a stopping point stake number and a relative position of a road section stored in the road network data; and generating a road risk range analysis result at least based on the stake number corresponding to the vertical point. According to the method, through rapid intelligent interpretation of geological disaster point space data, related units can be assisted to rapidly position a disaster risk range, then the potential threat range of the related units to a highway network is accurately defined, and the scientific and refined level of highway geological disaster prevention and engineering management is improved.

The invention relates to the technical field of geotechnical engineering and geological disaster monitoring, in particular to a rockburst tendency dynamic discrimination method, and aims to solve the limitation caused by the fact that a dynamic evolution process is simplified into a quasi-static attribute in traditional rockburst tendency evaluation. According to the method, rockburst tendency is defined as a hidden state variable evolved along with time, a physical constraint state space model containing a hidden state kinetic equation and a multi-modal observation equation is constructed, continuous stress-strain and discrete acoustic emission data are fused, hidden state probability distribution is estimated in real time through online Bayesian inference, and the probability distribution of the hidden state is estimated in real time. And in combination with adaptive information weight updating and physical consistency constraint, predicting a future state trajectory, calculating a risk probability exceeding a critical threshold, and outputting a dynamic rockburst tendency level. According to the scheme, continuous, dynamic and prospective judgment of the rockburst tendency is realized, and the accuracy and reliability of early warning are improved.

The invention relates to the technical field of mine restoration, and discloses an intelligent surface mine environment monitoring and treatment system, which comprises an environment monitoring and multi-modal data acquisition module, a data processing module and a data processing module, the data acquisition module is used for acquiring various types of environmental data such as air quality, noise, water pollution, soil erosion and geological disasters through a sensor network, an unmanned aerial vehicle, satellite remote sensing and other detection devices, and performing local data processing; and the environment optimization and decision-making module based on edge computing and deep learning is configured to deploy computing resources at a data acquisition end close to a mining area by utilizing an edge computing technology and perform real-time processing and complex multivariate analysis on the multi-source environment data through a deep learning algorithm. Through multi-modal data acquisition and an environment optimization and decision module based on edge calculation and deep learning, multi-dimensional environment data from a mining area can be rapidly processed.

The invention relates to the technical field of landslide geological recognition, in particular to a reservoir area landslide geological disaster recognition method and system, and the method comprises the steps: collecting SAR images and DEM data of a reservoir area at each time point; forming the SAR images of the adjacent time points into master and slave SAR images, obtaining an interferogram of the master and slave SAR images, and dividing the interferogram of the master and slave SAR images into an incoherent water area, a terrain transition area and a high-coherence stable area; determining a terrain phase gradient of each pixel point in the interferogram, obtaining a terrain gradient amplitude of each pixel point, correcting an objective function of a least square phase unwrapping algorithm, and obtaining an interferometric phase after phase unwrapping; elevation information corresponding to the interferogram is determined, the average deformation rate of each pixel point is calculated, and the potential landslide risk of the reservoir area corresponding to each pixel point is identified. Therefore, the accuracy of reservoir area landslide risk identification is improved.

The invention belongs to the technical field of satellite navigation and geological disaster monitoring, and particularly relates to a landslide deformation planar continuous monitoring method integrating unmanned aerial vehicle photogrammetry and GNSS, which uses an unmanned aerial vehicle to carry a camera to carry out image acquisition on a landslide area, and uses a base station in a GNSS monitoring system to provide a reference for an unmanned aerial vehicle airborne receiver. Unifying unmanned aerial vehicle image coordinates to a GNSS monitoring system based on a double-difference model, a cubic spline interpolation function and a coordinate compensation value of an unmanned aerial vehicle photographing record file, and performing aerial triangulation; a joint monitoring model is constructed based on the point cloud derived from the multi-stage unmanned aerial vehicle image data and the GNSS monitoring sequence; the combined monitoring model is used for calculating the elevation type variable of the deformation area in the monitoring time period and is combined with the DOM for display, the elevation deformation condition of the landslide surface-shaped monitoring area is visually displayed, continuous and visual monitoring of landslide deformation is achieved, and the monitoring precision is high.

The invention discloses a mining area watering cart driving control method based on animal avoidance real-time re-planning, and the method achieves the intelligent obstacle avoidance through multi-modal sensor fusion detection, deep learning driven target recognition, three-dimensional dynamic path planning, a multistage acousto-optic early warning system and adaptive speed adjustment. And a radar, a camera, a bionic compound eye vision module and the like are adopted, so that the detection response time is shortened, and the false alarm rate is reduced. Constraint conditions such as terrain gradient and soil humidity are integrated through an RRT algorithm, continuous processing of path curvature is achieved, the watering cart can complete path reconstruction without complete stop in an animal penetration scene, a graded early warning mechanism triggers three-level acousto-optic warning according to the animal approaching degree, and a dynamic speed adjusting model is matched to adjust the speed of the watering cart. And the spraying operation continuity is ensured while the safety allowance is ensured. According to the invention, through the environment interference compensation, the geological disaster early warning interface and the driver behavior simulation unit, the operation safety and the operation efficiency in the complex environment of the mining area are obviously improved.

The invention discloses an extreme rainfall induced geological disaster chain risk evaluation method, and relates to the technical field of geological disaster risk evaluation. Comprising the steps of defining extreme rainfall induced geological disaster chain danger, selecting susceptibility evaluation indexes and grading, constructing a geological disaster susceptibility evaluation model based on a multi-element machine learning model, constructing a geological disaster susceptibility evaluation model based on a multi-element machine learning integrated model, and evaluating the susceptibility of an extreme rainfall induced geological disaster chain. And assessing the risk of the geological disaster chain induced by the extreme rainfall. According to the method, based on an optimal model, the comprehensive susceptibility and danger of extreme rainfall-collapse, extreme rainfall-landslide, extreme rainfall-debris flow and extreme rainfall induced geological disaster chains are evaluated respectively; the comprehensive risk assessment method for the extreme rainfall-collapse chain, the extreme rainfall-landslide chain, the extreme rainfall-debris flow chain and the extreme rainfall induced geological disaster chain is constructed, and a new thought is provided for risk assessment research of the extreme rainfall induced geological disaster chain.

The invention discloses a device for high-precision soil loss amount monitoring, relates to the technical field of water and soil conservation monitoring, and solves the problems that an existing monitoring method cannot comprehensively and accurately reflect the soil loss amount of a monitoring area, a monitoring point is fixed and cannot be moved and reused, and the accuracy of a monitoring result is low. A device for high-precision soil loss monitoring comprises an integrated controller, a transverse rod, a data acquisition unit, a track system, a power wheel, a transverse support, supporting legs and auxiliary wheels, and the integrated controller integrates a power unit, a power supply unit, an intelligent control module and a data transmission unit. The monitoring device provided by the invention is quick to install, is completely composed of modules, can adapt to the requirements of fields with different sizes and gradients, can realize full-automatic monitoring and remote control monitoring of soil loss amount, can also be used for monitoring slope displacement and geological disasters, and has a monitoring result precision far higher than that of a traditional method.

The invention provides a dumping type slope dangerous rock body inherent frequency analytical calculation method and system, and relates to the technical field of geotechnical engineering and geological disaster prevention and control. The method comprises the following steps: acquiring basic parameters of the toppling slope dangerous rock mass; according to the basic parameters, the safety coefficients of the gravity center of the toppling type slope dangerous rock body inside and outside the toppling point are determined; based on a modified Timoshenko beam theory, constructing a beam model; according to the beam model, analytic calculation is conducted on the toppling type side slope dangerous rock mass, and the inherent frequency of the toppling type side slope dangerous rock mass is determined; constructing a dynamic model of the toppling type slope dangerous rock mass; determining the relationship between the inherent frequency and the relative depth of the crack according to the dumping type slope dangerous rock mass dynamic model; according to the relation between the safety coefficient and the inherent frequency and the relative depth of the crack, a dumping type slope dangerous rock mass stability evaluation model is constructed; and carrying out stability analysis on the dumping type slope dangerous rock mass through the dumping type slope dangerous rock mass stability evaluation model.

According to the slope geological disaster risk assessment method and system provided by the invention, the feature information of the slope object can be accurately assessed by obtaining the non-natural vibration data and combining the hidden danger description factor information of the slope object, so that the adjacent slope objects around the target slope object are considered together in the slope object set, and the risk assessment accuracy is improved. Comprehensive analysis of a target slope object is formed through slope hidden danger data features in the slope object set, and then feature extraction of important hidden dangers is carried out on the target slope object set to obtain a description vector of the target slope object set; and according to the description vector regression of the target slope object set, whether the slope object attribute of the target slope object affects the slope geological disaster risk assessment result is analyzed, the slope object attribute is used for representing the attribute affecting the hidden danger of the slope object, and the regression analysis accuracy of the attribute change of the slope object is improved. And the accuracy and confidence of evaluation are ensured.

The invention discloses a landslide geological disaster deduction method and system based on an MPM method, and the method comprises the steps: collecting the multi-source data of a disaster scene, and synchronously constructing a digital twinborn model; a qualitative and quantitative two-stage inversion calibration model is adopted; a qualitative layer combines a monitored displacement direction and a deformation mode, and preliminarily inverts key parameters such as slip band cohesive force and an internal friction angle; the quantitative layer comparison model calculates the actual deformation range, collapse position and displacement, and parameters are optimized to match the actual landslide state; in landslide long-term evolution deduction, a rock-soil body aging characteristic model is embedded, an exponential function is used for describing annual scale parameter deterioration, prediction is divided into continuous analysis steps, MPM calculation is carried out based on a material point method, full-cycle simulation from the current year to the future years is completed, and deformation range, displacement and outburst risk results are output. And reliable decision support is provided for disaster prevention and reduction.

The invention provides a collapsed rockfall accurate identification and monitoring method and device based on AI and machine vision, and relates to the technical field of geological disaster monitoring, and the method comprises the steps: collecting the multi-modal visual data and environmental parameters of a collapse hidden danger region through a cooperative collection system; performing recognition, contour extraction and spatial parameter calculation on the multi-modal visual data through the recognition and positioning model to obtain a contour sequence, a volume sequence, a spatial coordinate sequence and a spatial attitude sequence of the collapsed rockfall; on the basis of the space coordinate sequence, the space attitude sequence and the multi-modal visual data, predicting the movement track and the speed sequence of the collapsed rockfall through a mixed time sequence AI model; inputting the environment parameters, the contour sequence, the volume sequence, the motion trail and the speed sequence into a kinetic energy calculation model, and calculating to obtain the impact energy of the collapsed rockfall; according to the contour sequence, the volume sequence, the motion trail, the speed sequence and the impact energy of the collapsed rockfall, the collapsed rockfall risk level is obtained through calculation.

The invention provides a debris flow risk assessment method and system based on debris flow parameter probability inversion calibration, and relates to the technical field of geological disaster risk assessment. According to the technical scheme provided by the embodiment of the invention, an effective means is provided for scientifically identifying debris flow parameters, and the method is particularly suitable for setting single gullies which are limited in detailed observation and cannot directly measure the parameters. A traditional method depends on statistical parameters or deterministic back analysis, the former introduces errors due to poor regional adaptability, and the latter has the problem of non-uniqueness of parameters; according to the scheme, the parameter posteriori distribution is updated by combining the Bayesian framework with historical observation data (debris flow influence areas), so that the standard deviation of key parameters is generally reduced. In the embodiment of the invention, the influence area is taken as core observation information, the practical limitation that the flow depth and the flow velocity are difficult to observe in real time in the debris flow event is avoided, the observation information can be obtained through post-event field investigation or satellite images, and the scheme is high in practicability.

The invention belongs to the field of geologic disaster prevention and control of geotechnical engineering, and provides a multi-scale numerical simulation method for a slope instability mechanism of a ductile shear zone, which comprises the following steps: acquiring physical and mechanical parameters of a rock-soil body material, and establishing a numerical model based on a particle flow discrete element method; a Johnson-Kendall-Robert contact model is adopted as a contact model between particles, the contact force between the particles is calculated, and meanwhile, a pressure interference parameter is defined to control the limit value of normal contact rigidity; performing dynamic modeling of the discrete element particle system based on a particle generator; static modeling of a discrete element particle system is carried out based on a Lagrange body continuous unit component; the acting force of discrete element particles is converted into equivalent grid node force through an equivalent node force conversion method, and surface attributes of all unit sets possibly involved in contact are defined in a solver.

The invention discloses a landslide hazard hidden danger point intelligent identification method based on unmanned aerial vehicle multispectral remote sensing, and relates to the technical field of geological disaster monitoring and unmanned aerial vehicle remote sensing intelligent identification. Comprising the following steps of (1) data acquisition and preprocessing, (2) trajectory disturbance feature mining, (3) spectral feature enhancement and space-time filtering, (4) bimodal coupling analysis and intelligent identification, and (5) closed-loop verification and route optimization. According to the method, the problem of data dimension splitting and error processing unidirectionality is solved by reserving the trajectory offset original value and constructing the fusion data set, and the data utilization rate is increased; through multi-disciplinary cross modeling and coupling knowledge base construction, a disciplinary blank is filled, and an unmanned aerial vehicle-environment-geology dynamic mutual feedback mechanism is established; by means of bimodal feature enhancement, anti-factual reasoning and route optimization, the recognition efficiency of the early hidden landslide hidden danger is effectively improved, the development stage of the hidden danger can be distinguished, and the high-precision and early recognition requirements are met.

The invention relates to the technical field of coal mine gas geological disaster prevention and control and intelligent identification, and discloses an underground coal mine gas geological abnormal body intelligent identification and grading early warning method and system, and the method comprises the steps: S1, collecting multi-source heterogeneous monitoring data in an underground coal mine target region, and carrying out the standardization processing and time-space reference unification; s2, constructing a multi-dimensional feature fingerprint database based on a historical or accumulated standardized data set; s3, acquiring real-time multi-source monitoring data for the current tunneling process or detection process, and extracting features of the real-time multi-source monitoring data; based on the multi-dimensional feature fingerprint database, adopting an evidence theory to carry out fusion calculation on a plurality of extracted real-time feature evidences to obtain a comprehensive confidence identification result of a specific type of gas geology anomalous body existing in a front unrevealed area; and S4, generating graded early warning information according to the comprehensive confidence identification result and a preset risk grade threshold. According to the invention, advanced identification, risk assessment and targeted early warning of geological disasters such as underground gas outburst are realized.