400 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 field of geological disaster prediction, in particular to a landslide dynamic evolution prediction method and system based on a biological neural network. The method comprises the following steps: acquiring comprehensive monitoring data of a landslide area; establishing a biological neural network architecture for simulating and learning a landslide dynamic evolution rule; obtaining a dynamic prediction result of the chaotic edge state of the landslide system; generating a landslide spatio-temporal evolution path in combination with a topological manifold generation algorithm; and through the landslide spatio-temporal evolution path, realizing visual prediction of landslide dynamic evolution. According to the method, the biological neural network architecture and the critical state neurodynamics model are established, so that deep fusion and high-precision dynamic prediction of multi-source data in the landslide evolution process are realized; a dynamic prediction result is converted into a four-dimensional space-time voxel density field by utilizing a quantum dot neural fluorescence labeling system and a topological manifold generation algorithm, and a high-resolution landslide space-time evolution path is generated, so that multi-dimensional visual expression of a landslide evolution process is realized.

The present invention relates to the technical field of prevention and control of geological disasters in mountainous coal mining areas. Disclosed are an InSAR-based measurement method for the angle of critical deformation of a coal mining area, and a system. The method comprises: acquiring a mining-induced surface deformation field image by means of small baseline subset-interferometric synthetic aperture radar; in the mining-induced surface deformation field image, separately drawing profile lines along a coal seam strike and a coal seam dip, and selecting target deformation points on the profile lines as points to be monitored; according to the incident angle, the flight direction and the azimuth angle of a satellite, obtaining vertical and horizontal deformation values; using a probability integral method to calculate parameters such as inclination, curvature and horizontal deformation; and drawing a curve graph on the basis of the vertical deformation of a target region and the above parameters, so as to obtain a corresponding angle of critical deformation. The InSAR-based measurement method for angle of critical deformation of a coal mining area provided by the present invention has the advantages of improved temporal resolution and reduced phase noises and the like, can achieve high precision and high efficiency, and can generate clear and visible Excel tables for surface deformation data of mining areas and corresponding profile data.

The invention relates to the technical field of data processing, and discloses a coal gangue storage yard environment risk real-time monitoring method and system based on edge calculation. The method comprises the following steps: acquiring environmental risk original data collected by a multi-source sensor; performing data preprocessing through the edge computing node; constructing a multi-modal recognition model to obtain pollution and geological disaster risk results; generating graded early warning information and a dynamic risk map; inputting an intelligent decision-making system to generate an emergency response plan; and updating the model parameters based on the execution data, and optimizing the risk monitoring system. According to the application, by introducing an edge computing technology, localization processing of data is realized, the data transmission quantity is reduced, the response time is shortened, meanwhile, unified monitoring, prevention and control of pollution risks and geological disaster risks are realized by integrating a multi-modal recognition model, and timeliness, accuracy and response efficiency of real-time monitoring of the environmental risks of the coal gangue storage yard are 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 discloses a landslide geological disaster multi-index fusion forecasting method based on ERT monitoring. The method comprises the following steps: S1, carrying out long-term ERT monitoring on a slope body and acquiring monitoring data; s2, performing statistical analysis on the monitoring data to obtain the average resistivity, the correlation coefficient, the average resistivity change rate and the correlation coefficient change rate at different moments; s3, performing inversion based on the neural network to obtain an inversion resistivity profile map and an inversion resistivity change rate profile map, and performing water flowing fracture identification on the inversion resistivity change rate profile map; s4, carrying out landslide risk grade division according to the fracture development condition parameters of the section, the average resistivity and the correlation coefficient, and carrying out landslide risk early warning by taking the weighted sum of the obtained three risk grades as a comprehensive prediction index; and carrying out critical sliding prediction according to the absolute value product of the average resistivity change rate and the correlation coefficient change rate. According to the method, more accurate early warning and forecasting of medium-and-long-term, short-term and temporary landslide of the landslide can be realized.

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 rock core drilling sampling equipment for geological disaster investigation, and relates to the technical field of sampling, and the rock core drilling sampling equipment comprises a composite mechanism. According to the rock core drilling and sampling equipment for geological disaster investigation, through the design of a composite mechanism, the motor is connected with the butt joint block, and the drill rod is inserted into the inner side of the butt joint block, so that drilling and sampling are conducted on rocks, in the drilling process, the movable frame slides on the outer side of the sliding column so as to ascend and descend, and therefore the work requirement is met; a drill bit mechanism is arranged on the outer side of the drill rod, large rocks are crushed through the drill bit mechanism in the drilling process, meanwhile, rock blocks are guided to enter the inner side of the drill rod, so that a sample is taken out when the movable frame body is lifted subsequently, subsequent treatment is facilitated, and after sampling is finished, the drill bit mechanism is used for crushing the large rocks and guiding the rock blocks to enter the drill rod. When the sample is taken out from the inner side of the drill rod, the sample is taken out more cumbersomely and difficultly, the pushing and pressing mechanism is pressed by the electric push rod, so that the effect of discharging the sample is achieved, and normal operation of equipment is kept.

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 discloses a sky-ground integrated remote sensing emergency monitoring method for post-flood road safety, and relates to the technical field of remote sensing monitoring. The method comprises the following steps that an unmanned aerial vehicle collects highway pavement video image data after flood, and a terrain flood model is constructed; rainfall early warning sites are marked in the terrain flood model, and a corresponding flood early warning requirement is marked for each rainfall early warning site; and obtaining a road model in the terrain flood model, and generating an obstacle model on the road model according to the real-time data collected by the unmanned aerial vehicle. According to the invention, the terrain flood model is constructed for the video image collected by the unmanned aerial vehicle, the rainfall threshold value of each area is marked in the terrain flood model, and when the geological disaster occurs, the actual condition of the obstacle model is displayed on the terrain flood model, so that the timeliness and accuracy of flood early warning are improved, and the occurrence of the geological disaster is displayed in time. Obstacles on the road are visually displayed, and rescue after disasters can be conveniently and rapidly carried out.

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 discloses a mine geological disaster dynamic identification method and system based on multi-source data, and relates to the technical field of geological disaster identification. The method comprises the following specific steps: acquiring multi-source monitoring data of a target area to form a multi-source heterogeneous data set; performing spatial registration and data fusion on the multi-source heterogeneous data set to obtain fused data; acquiring information features of the mine geological disaster from the fused data; inputting the information features into a mine geological disaster hidden danger recognition model, performing recognition analysis on the geological disaster hidden danger of the target area, and determining a mine geological disaster recognition result; and monitoring the mine geological disasters in real time, feeding back the real-time monitoring data to the mine geological disaster hidden danger recognition model, and updating the mine geological disaster recognition result in real time. According to the method, a mine geological disaster dynamic identification framework based on multi-source data is constructed, and the accuracy and the working efficiency of multi-source geological disaster information processing are improved, so that effective support is provided for disaster prevention and reduction of geological disasters.

The invention relates to the technical field of seepage monitoring, and particularly discloses a rock mass seepage monitoring system based on an optical fiber sensing technology. Comprising a rock mass seepage track output module, a rock mass seepage information acquisition module, a rock mass seepage risk analysis and evaluation module, a rock mass seepage stability analysis and evaluation module, a rock mass seepage comprehensive analysis module, a rock mass seepage abnormity early warning module and a rock mass seepage state monitoring module. Through the optical fiber sensing technology, the rock mass seepage track is determined, rock mass seepage information data are collected, the rock mass seepage risk defense coefficient and the rock mass seepage stability evaluation coefficient are obtained through analysis, and then the rock mass seepage safety early warning index is obtained through analysis; high-precision and real-time monitoring and comprehensive analysis of rock mass seepage activities are realized, powerful technical support is provided for prevention and emergency response of geological disasters, and the efficiency and accuracy of engineering safety monitoring are greatly improved.

The invention discloses a digging and filling monitoring method and system for a pumped storage power station reservoir area, and relates to the technical field of geological disaster prevention and control. According to the method, deformation characteristics of a digging and filling area are analyzed through remote sensing interpretation and a time sequence technology, and meanwhile time sequence prediction is conducted on the analyzed deformation characteristics; according to the method, potential collapse characteristics of the excavation and fill are determined and predicted, and finally possible sliding surfaces of the excavation and fill are determined through future deformation characteristics obtained through prediction, so that excavation and fill construction has quantitative graphic data support, and follow-up prevention and treatment have perspectiveness; certain prevention and treatment measures can be adopted when the excavation and fill are not actually deformed and damaged, and the safety of excavation and fill construction is guaranteed.

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 a regional geological disaster risk assessment system and method based on big data, and the method comprises the following steps: S1, obtaining geological data and related data, and carrying out the preprocessing of the geological data and related data, and generating a geological disaster data set; s2, training Bi-LSTM through the preprocessed geological disaster data set, and constructing a geological disaster risk assessment model; and S3, collecting a new geological disaster data set, inputting the new geological disaster data set into the geological disaster risk assessment model, and generating a geological disaster risk prediction result. According to the geological disaster risk assessment method, the convolutional neural network, the Bi-LSTM and the attention mechanism layer are combined to construct the geological disaster risk assessment model, geological disaster assessment is realized, and compared with a current geological disaster risk assessment method, the PSO algorithm is utilized to perform optimal matching on hyper-parameters of the Bi-LSTM model, so that diversity characteristics of geological data and related data are captured more comprehensively, and the geological disaster risk assessment method is more efficient. Therefore, the geological disaster risk prediction precision and reliability are improved.