1265 results about "Digital elevation model" patented technology

The invention relates to a multi-model space-time combination flood peak prediction method fusing physical constraints, which comprises the following steps of: acquiring static space data, dynamic time sequence data and boundary data of a research drainage basin, converting the static space data of the digital elevation model into a grid matrix, and calculating the dynamic time sequence data of the research drainage basin according to the grid matrix and the dynamic time sequence data of the research drainage basin; representing an elevation value of each geographic position, extracting gradient features by using gradient calculation according to the elevation values, and performing normalization processing on the gradient features to obtain normalized gradient features; a CNN-Bi-LSTM-Transform prediction model is constructed, the prediction model comprises a spatio-temporal data alignment module, a CNN convolutional network, a spatio-temporal feature splicing module, a bidirectional long and short term memory network and a Transform module, the prediction model is trained, and a loss function during training adopts a physical constraint loss function composed of mean square error loss and water conservation constraint terms and is used for flood peak prediction.

The invention relates to a disaster multi-source situation intelligent sensing management and control method and system. According to the method, hydrometeorological and topographic data are collected, and a standardized data set is generated through space-time alignment and anomaly cleaning; constructing a directed topological graph containing node and edge attributes based on the extracted river network topological relation; designing a neural network model, and training through a physical constraint loss function embedded in a water balance principle to obtain a flood dynamic routing prediction model; inputting real-time hydrological data into the model for graph convolution operation, and predicting water level, flow and split ratio changes of each node in a future time period; and finally, carrying out submerging simulation analysis in combination with a digital elevation model, and generating a flood control scheduling scheme and risk early warning information. The deep fusion of a physical mechanism and data driving is realized, the flood propagation rule under the river network topology constraint is effectively captured by using the graph neural network, the calculation efficiency is remarkably improved while the prediction precision is ensured, and real-time and reliable decision support is provided for flood disaster prevention and control in a complex river network region.

A control method and system for precise landing of an unmanned aerial vehicle. The method comprises the following steps: on the basis of a multi-sensor fusion technique, using a lidar and a stereo vision algorithm to collect environmental data and perform primary processing, and generating comprehensive environmental perception data (step S1); on the basis of the comprehensive environmental perception data, using a digital elevation model algorithm to re-construct a three-dimensional terrain of a landing area, and generating a three-dimensional terrain model (step S2); on the basis of the three-dimensional terrain model, using an A* search path-planning algorithm to perform risk assessment and plan a safe landing path, and generating an optimized landing path (step S3); on the basis of the optimized landing path, using an adaptive control algorithm to adjust a flight attitude in real time by means of a fuzzy logic controller, and generating flight parameter adjustment (step S4); on the basis of the flight parameter adjustment, using machine vision and a decision tree algorithm to execute autonomous obstacle avoidance and emergency response, and generating a safe landing execution scheme (step S5); and on the basis of the safe landing execution scheme, using an ultrasonic sensor and a ground feedback system to confirm and fine-adjust a landing point, and completing final landing confirmation (step S6).

Provided are a method, system and device for global path planning for an unmanned vehicle in an off-road environment. The method includes: obtaining satellite elevation data and a satellite remote sensing image of a current off-road environment; constructing a digital elevation model (DEM); determining slope and land surface relief of each grid in the current off-road environment; performing gray processing on the satellite remote sensing image to obtain grayscale values of the grids; determining traversal costs of the grids corresponding to different ground types; constructing a global grid map based on the slope and the land surface relief of each grid, as well as the traversal costs corresponding to the different ground types; determining a rugged terrain potential field and path costs; and searching for paths using a Bresenham's line algorithm and Theta* algorithm based on the rugged terrain potential field and the path costs, to generate a global path.

The invention relates to the technical field of agricultural machinery automation control, and discloses an agricultural machinery operation path intelligent planning method based on Beidou positioning. The method comprises the following steps: firstly, acquiring Beidou positioning data and a farmland geographic data set, and eliminating positioning noise by using a dynamic filtering algorithm to obtain a calibrated agricultural machine track sequence; performing rasterization processing on the farmland geographic data to generate a digital elevation model, and dividing a job priority region; generating an initial operation path by adopting a hybrid optimization algorithm, and dynamically adjusting the path based on real-time operation state information; and finally, a path conflict detection module is combined with obstacle coordinates to calculate a collision probability, and a final agricultural machine operation path is output. According to the method, various factors are comprehensively considered, real-time dynamic adjustment of the path can be realized, path conflicts are effectively avoided, the operation precision and efficiency of agricultural machinery are improved, energy consumption and equipment loss are reduced, and agricultural mechanization and intelligentization development is promoted.

The invention relates to the technical field of topographic mapping, in particular to a DEM super-resolution reconstruction method and system combined with a depth map, and the method comprises the steps: constructing a multi-modal data set which comprises an optical image, a depth map, a low-resolution DEM image and a high-resolution DEM image which are paired; designing a multi-modal fusion super-resolution network; designing a pseudo Siamese feature extraction module to extract low-level features and high-level features of the optical image, the depth map and the low-resolution DEM image; fusing the low-layer feature map generated by the pseudo Siamese feature extraction module in a shallow layer through a multi-scale feature fusion module, and fusing the deep-layer feature map in a deep layer; outputting a high-resolution DEM image through a DEM reconstruction module; and training the multi-modal fusion super-resolution network by using the multi-modal data set and the collaborative loss function. According to the method, the depth map generated by the optical image is used as priori knowledge to guide the DEM super-resolution reconstruction task based on the basic depth large model, a complex fine adjustment process is not needed, and the DEM reconstruction precision and efficiency are improved.

The invention relates to the technical field of meteorological data processing and geographic information, in particular to a near-surface meteorological field downscaling method based on a topographic constraint Transform model, which comprises the following steps of: extracting multi-modal fusion features from reanalysis meteorological data and digital elevation model data; mapping to a target resolution by means of a topographic constraint Transform network to obtain initial downscaling meteorological data; latent variable modeling is carried out on the initial data through a variational auto-encoder to describe multi-scale uncertainty, physical constraints such as energy balance and mass conservation are incorporated in combination with an iterative updating mechanism based on meta-learning, and core downscaling meteorological data are generated; according to the invention, detail capture and dynamic adaptation of local meteorology are realized through self-supervised pre-training and online updating.

The invention discloses a plateau mountain road disaster identification method and system based on multi-source remote sensing image restoration and super-resolution reconstruction. The method comprises the following steps: acquiring and preprocessing a multi-source remote sensing image of a plateau mountain region, and extracting landform measurement parameters based on a digital elevation model; a super-resolution reconstruction network fusing deformable convolution and Transform is constructed, and a low-resolution image is reconstructed by using constraint training of a composite loss function containing geomorphic measurement parameters; performing feature extraction and adaptive weighted fusion on the preprocessed image and the reconstructed high-resolution image; based on the fused image, utilizing a multi-task deep learning model to identify landslide, debris flow and roadbed subsidence disasters along the highway; and carrying out morphological optimization and boundary refinement under GIS constraint on an identification result, and outputting a disaster thematic map. According to the invention, the precision and reliability of road disaster identification in a complex terrain environment are effectively improved.

The invention relates to the technical field of risk analysis, in particular to a safety engineering comprehensive management system which comprises a geological parameter dynamic monitoring module, a risk threshold early warning module, a stability weight evaluation module and a risk comprehensive management and control module. According to the method, lithology data are extracted through a drilling profile to calculate the layer thickness sudden change rate and the strength attenuation rate, a lithology abnormal area is positioned through a variable coefficient method, lithology sudden change marks are analyzed through spatial clustering, a dynamic trend is captured through sliding window mean value comparison, and gradient amplitude and slope direction dispersion are calculated through a digital elevation model. Fusing the abnormal point density to generate a terrain instability index; establishing a multi-dimensional quantitative evaluation framework; carrying out fuzzy comprehensive evaluation and cross validation on the instability index and an engineering parameter trend; generating a dynamic priority sequence, carrying out stake number interval matching to construct a risk measure table, carrying out analytic hierarchy process dynamic resource allocation support drainage scheme, and carrying out real-time monitoring and prediction closed-loop iteration. And adaptively adjusting the control strategy.

The invention discloses a three-dimensional wind field spatial-temporal feature reconstruction and efficient prediction method, and relates to the field of spatial-temporal feature reconstruction and efficient prediction. Constructing a three-dimensional terrain computational domain based on the digital elevation model of the target mountain region, performing multi-scene numerical simulation by adopting a fluid mechanics method or a mesoscale meteorological model, generating a wind field training data set, and constructing and training a wind field spatial feature mapping model; training a wind speed and wind direction short-time prediction model based on the actually measured data set; inputting the monitoring data obtained in real time into the wind speed and wind direction short-time prediction model to obtain a future wind speed and wind direction prediction value of each monitoring station; and inputting the wind speed and direction predicted values into the wind field spatial feature mapping model to obtain the mountain overall wind field distribution of the target mountain region at the future moment. By constructing an'actual measurement-simulation-modeling-prediction-reconstruction 'integrated technical framework, high-temporal-spatial-resolution short-time prediction from observation of local wind speed and wind direction to the overall three-dimensional wind field of the mountainous region is realized.

The invention discloses a road slope modeling method based on an unmanned aerial vehicle inspection route, and relates to the technical field of unmanned aerial vehicle inspection. The method comprises the following steps: measuring the road slope length, the landform and the geological complexity, and selecting an adaptive unmanned aerial vehicle type and sensor combination according to the road slope length, the landform and the geological complexity; planning unmanned aerial vehicle surface inspection and shallow geological exploration routes, ensuring space matching, and synchronously obtaining high-resolution images and ground penetrating radar data; and processing image data through a photogrammetry technology, generating a digital elevation model and an orthophoto map, and combining to construct a three-dimensional model of the earth surface. Geophysical inversion is carried out on the ground penetrating radar data, reflection features are extracted and projected to the ground surface three-dimensional model, and space correlation between the ground surface and a shallow layer structure is established; the reflection features are digitalized and geometrically reconstructed into three-dimensional surface elements, the three-dimensional surface elements are embedded into an earth surface model to form a three-dimensional geologic body model, time dimensions are introduced, and a dynamic evolution model is constructed. According to the dynamic evolution model, the dynamic analysis of the slope stability is realized.

The invention discloses an air-ground integrated measurement method for an original terrain, and relates to the technical field of engineering surveying, and the method comprises the steps: S1, carrying multi-sensor aerial survey by an unmanned aerial vehicle, obtaining an image and POS positioning data, scanning a blind area of the unmanned aerial vehicle by using a three-dimensional laser scanner, obtaining laser point cloud data, and carrying out the scanning of the laser point cloud data; constructing a multi-source data set including optical images, POS positioning data and laser point cloud, and forming an air-ground complementary data acquisition mode; s2, geometric correction and aerial triangulation encryption are carried out on the unmanned aerial vehicle image, denoising and multi-station registration are carried out on the laser point cloud data, and the data quality is improved; s3, unifying a multi-source data space reference through coordinate system unification, feature fusion and decision-level fusion, and realizing feature complementation; s4, constructing a digital elevation model and a digital surface model based on the fused data, and generating a topographic mapping map; the method breaks through the limitation of a single technology through air-ground technology complementation, and ensures the global data coverage of complex terrains.

The invention discloses an earth geology physical three-dimensional modeling method based on multi-data fusion, which comprises the following steps: A, data acquisition: acquiring geological data through geological exploration and measurement technologies, comprising geological profile, underground water level, lithology, structure, surface digital elevation model (DEM) data, remote sensing image data, surface geographic information data, drilling data, geological plane data, profile data, stratum isoline data, fault data, geophysical prospecting data, dynamic data and related document data. According to the invention, data of geological profile, underground water level, lithology, structure, earth surface digital elevation model (DEM) data, remote sensing image data, earth surface geographic information data, drilling data, geological plane data, profile data, stratum isoline data, fault data, geophysical prospecting data, dynamic data and related document data are collected. And the data can be effectively integrated.

The invention relates to the technical field of topographic survey, in particular to an automatic surveying and mapping method and system for a complex topographic land parcel for urban and rural planning. The method comprises the following steps: acquiring satellite remote sensing data and a digital elevation model corresponding to an urban and rural planning survey area, and performing survey area terrain pre-detection to generate an urban and rural planning complex terrain survey area plot; the method comprises the following steps: carrying out element semantic recognition and segmentation on an urban and rural planning complex terrain plot to obtain terrain obstacle distribution corresponding to each building, vegetation and water body element in the urban and rural planning complex terrain plot; obtaining a corresponding three-dimensional surveying and mapping boundary constraint of the complex terrain land parcel and carrying out surveying and mapping path obstacle planning to generate a terrain constraint surveying and mapping planning path corresponding to the urban and rural planning complex terrain land parcel; and performing automatic navigation surveying and mapping processing on the urban and rural planning complex terrain plot to generate urban and rural planning complex terrain plot surveying and mapping data. According to the invention, the efficient, accurate and automatic navigation surveying and mapping process of the plot under the complex terrain condition can be realized.

The invention discloses a high-precision topographic mapping system and method based on multi-source remote sensing data, and relates to the technical field of topographic mapping, and the method comprises the steps: obtaining an optical remote sensing image, a radar remote sensing image, LiDAR point cloud data and meteorological index data of a target region, and carrying out the data preprocessing of the optical remote sensing image, the radar remote sensing image and the LiDAR point cloud data; obtaining the surface feature types of the target area and the coverage area of each surface feature type, and carrying out the weighted fusion of the optical remote sensing image and the radar remote sensing image of the target area according to the surface feature types of the target area and the coverage area of each surface feature type, and obtaining a self-adaptive fusion remote sensing image; according to the method, points in LiDAR point cloud data are divided into distinguished ground points and non-ground points, a digital elevation model is constructed according to the ground points in the LiDAR point cloud data, three-dimensional modeling of the terrain is performed on the basis of the digital elevation model in combination with texture information of a self-adaptive fusion remote sensing image, a three-dimensional terrain model is generated, and the terrain surveying and mapping precision is remarkably improved.

The invention provides a geological BIM modeling and engineering quantity calculation method based on point cloud sparse optimization, and relates to the technical field of geological engineering, and the method comprises the steps: obtaining high-resolution point cloud data through an unmanned aerial vehicle oblique photography technology, combining the high-resolution point cloud data with drilling exploration data, and carrying out the denoising, classification, segmentation, lithology discrimination and registration, thereby obtaining a point cloud point cloud model; a geological BIM model before excavation is constructed; area point cloud data are obtained again after excavation, the point cloud precision of the slope toe area is optimized through an inverse distance weighted interpolation algorithm, and a geological BIM model after excavation is generated; and in combination with a classified and optimized digital elevation model, detecting the elevation difference of the model before and after excavation pixel by pixel by adopting a robust elevation difference calculation method based on a neighborhood window, and accurately calculating the volume change of each lithologic region to obtain the engineering quantity. Through the point cloud sparse optimization and elevation change detection technology, the precision of the geological BIM model and the reliability of volume calculation are improved, and the method is widely suitable for geological engineering and earth volume calculation scenes.

The invention discloses a distributed model-oriented medium and small watershed sub-watershed division method, which comprises the following steps of S1, collecting digital elevation model data, performing depression filling processing, and extracting preliminary river network information; s2, extracting an actual water system, and calculating a river network main stream grid elevation difference; s3, adjusting digital elevation model data according to the elevation difference, and optimizing a river network extraction result; s4, basin morphological parameters are calculated, and basin classification is carried out through a manifold learning method; s5, constructing a sub-basin optimal threshold calculation model, and dynamically adjusting a sub-basin division threshold; s6, flow direction calculation, confluence analysis and sub-basin division are carried out, and sub-basin boundaries are optimized; and S7, verification is carried out in combination with the distributed hydrological model, the influence of the division result is analyzed, and sub-basin division parameters are optimized. According to the method, multi-scale features, dynamic threshold optimization and hydrological network topology are fused, sub-basin division precision, boundary continuity and hydrological simulation stability are improved, and intelligent basin division is realized.

The invention discloses a metal ore body identification method based on remote sensing interpretation, relates to the technical field of mineral resource exploration, and aims to solve the problems of low identification precision, neglect of geological laws and poor generalization caused by dependence on single remote sensing data in the prior art. After standardization processing, spectrum and texture features are fused to generate a composite vector; extracting a multi-scale mineralization signal by using a convolutional neural network, and interpreting and constructing an ore control area in combination with a terrain gradient and a curvature; alteration zoning is identified through spectrum angle matching, and a favorable mineralization area is determined by combining lithology combination analysis; and dynamically weighting geological elements by adopting a deep learning fusion network, outputting an ore body position according to a comprehensive score, and finally optimizing a boundary through spatial clustering and morphological filtering. The method significantly improves the automation degree and reliability of ore body identification in a complex environment, and is suitable for metal mineral exploration target area delineation.

The invention discloses a complex terrain three-dimensional modeling and earthwork volume calculation method based on a multi-source fusion point cloud, belongs to the technical field of surveying and mapping and engineering surveying, and mainly solves the problems of low terrain modeling precision and insufficient earthwork calculation efficiency in a complex scene. The method comprises the following steps: constructing a ground-air integrated multi-source sensing network to synchronously acquire laser point cloud, multi-view images and positioning data; adopting PointNet + +-based initial registration and multi-scale ICP fine registration fusion to generate a unified point cloud; combining semantic segmentation and penetration probability filtering to accurately extract a digital elevation model; constructing a similar triangular prism voxel model by using a constrained Delaunay triangulation network; and finally, the earth volume is rapidly calculated by adopting a GPU parallel voxel cutting and filling algorithm, so that high-precision terrain modeling and rapid engineering quantity calculation are realized, and the method can be efficiently and reliably applied to large-scale projects such as roads and mines.

A system for extracting glacier boundaries using multiparametric analysis, consisting of: a data acquisition module configured to acquire Synthetic Aperture Radar (SAR) data of a glacial region; a data processing unit configured to process the SAR data to generate coherence images and backscatter intensity maps, to extract terrain parameters including slope and curvature information from a digital elevation model (DEM), to apply thresholds to the coherence images, backscatter intensity maps, and terrain parameters, to generate a multi-band stack comprising the thresholded coherence images, the thresholded backscatter intensity maps, the thresholded slope information, and the curvature information, and a principal component analysis module configured to standardize the multi-band stack, calculate a covariance matrix from the standardized data, calculate eigenvalues and eigenvectors of the covariance matrix, and transform the original data into a principal component space with reduced dimensionality; a texture feature extraction module configured to calculate first- and second-order statistics from the output of the principal component analysis; generating a texture feature set comprising at least one of the following: sum average, entropy, difference entropy, sum entropy, variance, difference variance, inverse difference moment, contrast, correlation, information measures of correlation, and maximum correlation coefficient; a connected component segmentation module configured to: convert the set of textural features into a binary format that distinguishes glacier ice from the background; group spatially connected pixels of similar intensity into segments; extract a vector shape file corresponding to the glacier boundary; an output generation module configured to generate a glacier boundary delineation output; and a user interface having a display configured to display the output generated by the output generation module.

The invention is suitable for the technical field of earth surface monitoring, and provides an earth surface deformation monitoring method and system based on a time sequence InSAR, and the method comprises the following steps: carrying out the SAR image screening and interferogram generation, and collecting corresponding meteorological data and thermal infrared data; performing space-time adaptive atmospheric phase correction, taking the air pressure vertical gradient and the temperature anomaly as driving factors of atmospheric delay, and separating an atmospheric phase through a space-time weighted model; dynamic deformation modeling is carried out, and deformation is decomposed into linear deformation and nonlinear deformation; the method comprises the following steps: taking a mining area road network as a geometric constraint, unwrapping a coherent region by adopting a minimum cost flow algorithm, converting an unwrapping phase into sight-line-direction deformation, and calculating horizontal and vertical deformation components in combination with InSAR sight-line-direction deformation and a digital elevation model. The method adapts to a complex deformation mechanism of a mining area by capturing linear and nonlinear deformation. Through sight line deformation and DEM geometric projection, vertical and horizontal deformation separation is realized, and the deformation direction is determined.

The invention discloses an SAR (Synthetic Aperture Radar) soil surface water content inversion method and device based on deep learning, and the method comprises the steps: constructing a deep learning regression model for predicting the volume content of soil water through combining SAR data, auxiliary data and in-situ sensor data; a ConvNeXt model is adopted to construct a parallel convolution encoder to solve multi-source data with different input resolutions, and back scattering coefficients, spectral information, digital elevation model high-resolution data and soil sand content, sludge content, clay content and stacking density low-resolution data are fused into soil moisture estimation. And the inference capability of unknown data is improved. The model obtained by training can realize large-range and high-resolution automatic estimation of the water content of the soil surface layer. According to the invention, the influence of radiation transmission noise is avoided, the inversion precision of a vegetation coverage area is improved, and large-range high-resolution automatic processing of soil humidity products is realized.

According to the intelligent rolling construction method and system for the concrete face rockfill dam and the computer readable storage medium, self-adaptive adjustment of the rolling speed and frequency is achieved through three-dimensional digital elevation model generation, optimal rolling path planning, rolling parameter dynamic optimization model and real-time compaction degree prediction. The system integrates satellite positioning, BIM model verification, cloud data management and an abnormal protection mechanism, can correct path deviation in real time, generates a pressure supplementing instruction and stores construction data, provides an intelligent solution for high-precision and high-reliability rock-fill dam construction, and is suitable for popularization and application. The technical problems that in traditional rolling construction, manual experience is relied on, parameters are static, monitoring lags behind, and the rework rate is high can be solved.

The invention relates to the technical field of unmanned aerial vehicle intelligent control and path optimization, in particular to an unmanned aerial vehicle multi-target path planning method based on dynamic fitness adjustment, which comprises the following steps: topographic data acquisition and processing: acquiring data of a digital elevation model (DEM) and a digital orthoimage (DOM) through an unmanned aerial vehicle, generating a slope map and an obstacle density map, a dynamic fitness function and a dynamic weight adjustment mechanism are designed by combining factors such as terrain gradient, obstacle density and path length, and the weight of the fitness function is adjusted in real time according to different terrain characteristics so as to adapt to a variable environment. And the efficiency, the robustness and the adaptability of complex terrain path planning are remarkably improved.

The invention relates to the technical field of geological disaster detection, in particular to a geological disaster hidden danger point detection method and system. Comprising the following steps: S1, synchronously acquiring vegetation canopy penetration data, multispectral soil humidity spatial distribution data and lithology distribution data by adopting an unmanned aerial vehicle carried image data acquisition device and a multi-band laser radar, and generating an orthoimage and a three-dimensional point cloud data set; s2, fusing the orthoimage and the three-dimensional point cloud data through an aerial triangulation encryption algorithm to establish a three-dimensional model, loading the multispectral soil humidity spatial distribution data into the three-dimensional model to generate a dynamic digital elevation model, and generating a contour line topological structure through a TIN triangulation network algorithm; s3, extracting a slope factor, topographic relief, vegetation coverage, fracture distance and a lithologic factor based on the three-dimensional model to construct a geological disaster hidden danger system; according to the system, the geological disaster occurrence time can be estimated, and the rescue and protection treatment period can be effectively planned based on the geological disaster occurrence time.

The invention relates to an engineering geological exploration and evaluation method based on a remote sensing technology, and relates to the technical field of remote sensing, a laser radar and an unmanned aerial vehicle are used for aerial photography to obtain ground three-dimensional point cloud data, soil samples are collected through geological exploration to obtain geological data, and the obtained point cloud data and geological data are cleaned and denoised to obtain an evaluation result. According to the processed point cloud data, generating a digital elevation model to analyze topographic features, respectively performing feature extraction on the point cloud data and the geological data, establishing a landslide risk assessment model by using a machine learning method, and performing landslide risk assessment and grade division on a target area according to a result of the model. The point cloud data and the geological data are combined, fusion of multi-source data can be achieved, richer input is provided for a landslide risk assessment model, and the precision of landslide risk assessment is improved.

The invention belongs to the technical field of seismic structure models, and discloses a seismic structure model construction method and system, and the method comprises the steps: constructing a database; constructing a three-dimensional digital elevation model; constructing a three-dimensional geological structure model; constructing an earthquake three-dimensional earthquake structure model; constructing a three-dimensional P-wave velocity structure model; and analyzing a pregnancy and earthquake environment. The three-dimensional seismic geologic model of the Changning seismic region is constructed by taking the Changning seismic region in southern Sichuan as a modeling object and integrating superficial geology, landform (DEM), seismic fine positioning data and main fracture distribution, a deep seismic detection result, a two-dimensional seismic reflection profile, drilling data and the like, the spatial distribution and structure of the three-dimensional seismic geologic model are finely determined, and the three-dimensional seismic geologic model of the Changning seismic region is constructed. Analysis of deep and shallow tectonic deformation characteristics and tectonic styles has important guiding significance for discussing the earthquake-pregnancy environment and evaluating the earthquake risk of the deep and shallow tectonic deformation.

The invention discloses a mine high and steep slope landslide machine learning prediction method based on an unmanned aerial vehicle technology and multi-source data fusion. The method comprises the steps that a high-resolution digital surface model is shot by an unmanned aerial vehicle; removing non-topographic features from the digital surface model data in ArcGIS software, then converting the digital surface model data into digital elevation model data, obtaining a surface refining model of the surface mine in the digital elevation model, taking the surface refining model as a constraint to trim the surface of the three-dimensional geologic model, and fusing factors of ArcGIS software and three-dimensional geologic model analysis by using Python to obtain a three-dimensional geologic model surface refining model; complete slope data are constructed; inputting the fused complete side slope data into a machine learning algorithm training model, and generating a side slope landslide susceptibility prediction model; the optimized model is used for carrying out landslide susceptibility prediction on new slope data. According to the method, various factors influencing the slope stability can be comprehensively analyzed, high efficiency can be kept during large-scale data processing, and the slope stability in mine engineering can be evaluated more accurately.

The invention relates to the technical field of slope stability analysis and landslide disaster prevention and control, in particular to a slope three-dimensional stability calculation method and system based on a potential sliding direction. The method comprises the following steps: acquiring slope point cloud data, and constructing a digital elevation model according to the slope point cloud data; geologic structure data are obtained, and a side slope three-dimensional geologic model is constructed according to the geologic structure data and the digital elevation model; according to the digital elevation model, side slope flow direction and slope runoff characteristics are obtained, and according to the side slope flow direction and the slope runoff characteristics, the potential sliding direction of the side slope is determined; and according to the potential sliding direction of the slope and the three-dimensional geological model of the slope, constructing a three-dimensional stability calculation model of the slope, and according to the three-dimensional stability calculation model of the slope, calculating the three-dimensional stability of the slope. According to the method, the problem that the calculation result is not accurate enough due to the fact that calculation of the three-dimensional stability of the side slope is not comprehensively considered in the prior art is solved.

The invention provides a long-distance moving path planning method and device for a lunar rover, and the method comprises the steps: carrying out the classification of topographic features based on the whole-moon typical landform features through the wide-angle camera image of a lunar investigation orbiter; dividing the to-be-planned moving path into a plurality of local passable areas based on the classified passable areas; for each local passable area, generating a digital elevation model and a digital orthoimage by using a narrow-angle camera image of a moon investigation orbiter; performing global path planning to generate a global reference path; constructing a local digital elevation model and a local digital orthoimage by using a three-dimensional lunar surface image sensed by the lunar rover in real time; and according to the local digital elevation model, the local digital orthoimage, the measured data of the lunar rover and the prior lunar soil mechanical parameters, verifying the global reference path based on the kinetic model, and outputting a global planning strategy. The method can be used for long-distance moving path planning of the lunar rover, and is high in accuracy.