820 results about "Digital elevation model" patented technology

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.

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 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 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.

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 provides a landslide classification method and system based on a visual language model and a cross attention mechanism. The method and the system specifically comprise the following steps: data preprocessing: carrying out Canny edge detection on an RGB image, and calculating terrain attributes such as a gradient and a slope direction for a DEM (Digital Elevation Model); feature extraction: capturing local features by adopting a reflection filling convolution layer and multi-scale residual connection; a visual language model is introduced, wherein semantic enhancement features are extracted through image-text alignment by means of the visual language model; cross self-attention fusion: capturing a global context through self-attention, and focusing heterogenous data complementary information by cross attention; and classifying and outputting: outputting a result by using global average pooling and a linear classifier. Through the visual language model and the cross self-attention mechanism, the landslide recognition capability under the complex terrain is effectively improved, an efficient and reliable technical means is provided for geological disaster monitoring, and the method can be widely applied to the fields of landslide recognition, risk assessment and the like.

The invention provides an atmospheric precipitable water quantity inversion method and system based on multi-source satellite remote sensing data, and relates to the technical field of remote sensing data. After clear sky pixels are screened, radiation brightness temperature data, digital elevation model data and spatio-temporal feature coding data are processed through three feature extraction branches, obtained feature representations are spliced and input into a feature fusion network, a spatial attention module is introduced in the feature fusion stage, finally, a composite loss function is adopted for training, and the feature fusion network is constructed. And high-precision atmospheric precipitable water quantity inversion is realized. According to the method, the multi-source data characteristics are fully utilized, and the inversion precision and the space continuity are improved.

The invention belongs to the technical field of unmanned aerial vehicle path planning, and particularly discloses an unmanned aerial vehicle path planning method based on a multi-strategy fusion information acquisition optimization algorithm, and the method comprises the steps: obtaining the digital elevation model map data of a target region, and constructing a three-dimensional environment space model based on a task environment; constructing a path cost function and initializing an optimization algorithm population, wherein the initialization adopts three strategy fusion mechanisms of hypercube initialization, chaos initialization and random initialization to enhance the diversity of the population; a crisscross strategy is introduced, the adaptability and stability of the algorithm in the dynamic search process are enhanced, and a sine disturbance mechanism is adopted, so that the global optimization capacity and local development precision of the algorithm are enhanced; the flight path of the unmanned aerial vehicle is constructed by optimizing the path point sequence, and the path is smoothed by introducing B spline interpolation, so that the path is ensured to have continuity and feasibility, the flight requirement of the unmanned aerial vehicle is met, and the robustness and environmental adaptability of path generation are remarkably improved.

The invention relates to an urban live-action three-dimensional construction method based on a high-resolution satellite image. The method comprises the following steps: carrying out stereo image pair screening and stereo image pair preprocessing on a high-resolution multi-view satellite image; carrying out adjustment optimization based on a rational function model on the satellite stereo image pair; generating an epipolar ray image based on the optimized rational function model; performing dense matching on the epipolar line image by combining building edge straight line segment constraint and an MGM algorithm to generate a disparity map; generating a three-dimensional point cloud and a three-dimensional model formed by a plurality of stereo image pairs through forward intersection based on the disparity map and the optimization parameters; generating a digital elevation model, a digital surface model and a three-dimensional Mesh model based on the three-dimensional point cloud; and carrying out precision evaluation and integrity evaluation by adopting pixel proportions of plane precision, elevation precision and height difference. According to the method, the urban live-action three-dimensional model and the corresponding digital product can be constructed quickly, efficiently and accurately by using the high-resolution satellite image.

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 discloses a glacier area surface water resource distribution image extraction method based on deep learning, and belongs to the field of surface water resource image extraction, and the method comprises the steps: obtaining a multispectral remote sensing image covering a glacier area and digital elevation model data, and carrying out the data preprocessing; building a YOLOv12 instance segmentation model comprising a backbone network, a neck network and a detection head, and constructing an improved YOLOv12 segmentation model; carrying out model training and optimization; segmenting and extracting surface water resources through the optimized segmentation model; processing is performed after segmentation is completed, and finally an optimized multi-class surface water resource distribution diagram is generated. According to the method, based on an improved YOLOv12 network architecture, the glacier area surface water resources in the remote sensing image are comprehensively and automatically extracted by fusing vision, a convolution block attention module and a space-to-depth convolution technology, the problems of high reflection of ice and snow, ice cracks, complex stone glacier textures and fuzzy edges are effectively solved, and the segmentation precision is improved.

The invention discloses a drainage basin water quality pollution source flux identification method based on an SWAT model, computer equipment and a storage medium, and the method comprises the steps: obtaining topographic data, land utilization data, soil type data and meteorological data, and enabling the topographic data, the land utilization data, the soil type data and the meteorological data to form a drainage basin spatial database; based on the preprocessed digital elevation model, determining a water flow direction and setting a reasonable water collection area threshold value, and dividing sub-basins to construct a basin calculation unit network topology structure; obtaining pollution source data, and constructing an SWAT-CUP model according to the pollution source data; performing system calibration and verification on the watershed hydrological process by adopting an SUFI-2 algorithm in the SWAT-CUP model; carrying out system calibration and verification on the total nitrogen load of the drainage basin by adopting an SWAT-CUP model; and based on the calibrated SWAT hydrological model, the input condition of the pollution source is changed. The method can cover the whole watershed and quantify the contribution rate of different pollution sources; cost is high, operation is simple, and long-term change can be dynamically simulated.

The invention relates to the technical field of computer vision and power equipment detection, in particular to a power transmission line conductor sag intelligent judgment method based on an unmanned aerial vehicle inspection image, which comprises the following steps: S1, identifying a conductor area based on an unmanned aerial vehicle visible light image, and extracting a continuous conductor edge; s2, an insulator string is positioned in a wire area, and a wire hanging point is accurately positioned through gradient direction aggregation analysis; s3, generating a space datum line based on the digital elevation model and the line direction; s4, conducting wire form reconstruction of physical constraint is executed, the fracture edge is connected with the physical characteristics of a conducting wire catenary as constraint, and the reconstructed conducting wire is projected to a datum line vertical plane; and S5, calculating the maximum vertical distance from the projection point to the reference line as a sag value, and dynamically diagnosing abnormality in combination with the environment temperature. Through combination of image processing, deep learning, a physical model and a terrain adaptive technology, the precision and reliability of conductor sag detection can be effectively improved.

The invention discloses an ecological restoration effect evaluation method for a land and space comprehensive improvement area, and relates to the technical field of ecological environment monitoring. A terrain structure data set and a moisture dynamic data set are obtained after data analysis and image feature extraction; the method comprises the following steps: carrying out terrain disturbance evaluation by constructing a terrain disturbance response index Rtz, carrying out hydrological back-up state evaluation by constructing a hydrological back-up trend index Gsh when the terrain has an unsteady disturbance region, carrying out ecological adaptation evaluation by constructing an ecological adaptation comprehensive index SSZ when the hydrological back-up meets the standard, and generating a landform base map of a renovation region by calling DEM (Digital Elevation Model) data. And each evaluation result is subjected to visual partition display through GIS space rendering, and a repair effect report is automatically generated. According to the method, multi-dimensional quantitative evaluation of terrain disturbance, hydrological refilling and ecological adaptability of the renovation area is realized.

The invention relates to the technical field of deep learning, in particular to a building target intelligent extraction method and system based on multi-source surveying and mapping data fusion, and the method comprises the steps: obtaining multi-source surveying and mapping data of a target region; preprocessing the multi-source surveying and mapping data; based on a pre-constructed multi-source data fusion module, performing feature level fusion on the spectral feature map, the geometric feature map and the elevation feature map of the preprocessed high-resolution optical remote sensing image to generate a multi-dimensional fusion feature matrix; inputting the multi-dimensional fusion feature matrix into a trained model, and outputting an initial extraction result of the target region; and post-processing the initial extraction result to obtain a target extraction result. Compared with traditional single data extraction, high-resolution optical remote sensing images, laser point cloud and digital elevation model data are obtained synchronously, spectrum, three-dimensional geometry and elevation dimension information can be covered at the same time, the defect of single data is avoided from the source, and comprehensive data support is provided for subsequent accurate extraction.

The invention provides a pollutant three-dimensional monitoring method and system combined with wind field perception, and relates to the technical field of pollutant monitoring, and the method comprises the steps: enabling obtained three-dimensional wind field scene data to be aligned with digital elevation model data, and obtaining fusion grid data of a monitoring region; determining a sampling point layout strategy according to the fused grid data, a preset wind field weight coefficient and a preset terrain weight coefficient; taking the three-dimensional wind field scene data and the acquired sampling data as input of a pre-constructed matching model to obtain an interpolation strategy of the monitoring area; based on the three-dimensional wind field scene data, the digital elevation model data and the sampling point layout strategy, performing optimization planning on an unmanned aerial vehicle sampling path by adopting a multi-target optimization algorithm to obtain a target three-dimensional route; and performing spatial interpolation operation on the target pollutant concentration data by adopting an interpolation strategy, and reconstructing a three-dimensional pollutant concentration distribution model of the monitoring area. According to the method provided by the invention, three-dimensional monitoring of pollutants in the monitoring area is realized.

The invention provides a seawall terrain extraction method and system fusing a vehicle-mounted laser point cloud and an inclination model. The method comprises the following steps: forming a seawall range according to a seawall vector diagram and a satellite image; dividing a seawall range into a vehicle-mounted laser radar acquisition range and an unmanned aerial vehicle oblique photogrammetry range; acquiring a vehicle-mounted laser point cloud collected by a vehicle-mounted laser radar, and performing preprocessing and precision optimization on the vehicle-mounted laser point cloud; obtaining oblique photography image data collected by a five-splicing camera carried by the unmanned aerial vehicle, and generating an oblique photography model and an oblique three-dimensional model point cloud; extracting edge line elements and angular point feature points of a common overlapping area in the inclined three-dimensional model point cloud and the vehicle-mounted laser point cloud, and performing point cloud fusion based on the edge line elements and the angular point feature points; filtering the fused point cloud to screen seawall terrain point cloud data; according to seawall terrain point cloud data, resolution-adaptive digital elevation models are constructed for different areas of a seawall, and seawall structure features can be accurately represented.

The invention is suitable for the technical field of road construction, and provides a wind power plant road intelligent line selection method based on multi-dimensional constraint dynamic modeling, and the method comprises the steps: obtaining a multi-dimensional constraint condition of wind power plant road line selection, and constructing a constraint model; generating a slope grid map based on a digital elevation model, and fusing the slope grid map with a constraint map layer reflecting geological stability and a land cover type to generate a dynamic cost surface; adopting an improved heuristic search algorithm to perform parallel path search from the road starting point to each target fan position, generating a plurality of initial feasible paths, and calculating a comprehensive cost value; screening a candidate line set according to a comparison result of the comprehensive cost values; receiving real-time updating data in a construction process and environment monitoring, inputting the real-time updating data into the constraint model to trigger model updating, performing iterative optimization on the candidate line set based on the updated constraint model, and outputting a final line selection scheme; and the line selection efficiency, economy and reliability are effectively improved.

The invention relates to the field of water conservancy and hydropower engineering construction land acquisition and physical index investigation, in particular to a method and a system for quickly generating a mountainous area riverway type reservoir inundation area based on a high-precision DEM (Digital Elevation Model), and aims to solve the problems of poor reservoir inundation area generation precision, long time consumption and large personal error in the prior art. The method comprises the following steps: acquiring or generating a reservoir reach DEM, a river section line and a water return meter, and preprocessing; expanding section lines to divide river channel units; calculating a grid water level based on inverse distance weighting interpolation, and comparing the grid water level with DEM elevation to determine a submerged area grid; and generating a closed surface domain through boundary tracking and smoothing processing, and combining to obtain a whole submerged region. The method gives consideration to efficiency and precision, supports second-level dynamic generation of multiple schemes, improves analysis precision and efficiency, is suitable for mountainous river type reservoirs, and provides data support for scheme comparison and selection and object investigation.

The invention is suitable for the technical field of multi-source data processing, and provides a water body adaptive extraction method based on multi-source remote sensing data fusion, and the method comprises the steps: obtaining multi-source data of a target region, constructing an improved water body index based on optical remote sensing image data, and enhancing the spectral separation degree of a deep water region and a shallow water region; constructing a scattering feature distinguishing rule set based on synthetic aperture radar image data, and separating a water body from typical interference ground features; generating a terrain gradient constraint condition based on the digital elevation model data; generating a fusion data set according to the improved water body index, the scattering feature distinguishing rule set and the terrain gradient constraint condition; dynamically segmenting the fused data set through an adaptive threshold segmentation algorithm to obtain an initial water body extraction result; and performing mixed pixel decomposition and morphological boundary optimization processing on the initial water body extraction result to generate an optimized result, and outputting a water body spatial distribution diagram, thereby effectively improving the water body extraction precision, the adaptability to complex scenes and the anti-interference capability.

The invention relates to the technical field of beam fusion, and discloses an irregular water area underwater topographic surveying and mapping method based on multi-beam fusion, which comprises the following steps: acquiring multi-voyage sounding data of a target irregular water area by using a multi-platform multi-beam sounding system such as a coastal patrol ship and an unmanned surveying ship, and performing sound velocity profile correction, attitude correction and tide level reduction to obtain a target irregular water area; unifying to the same space-time reference and plane rectangular coordinate system, and constructing an initial underwater terrain digital elevation model in an irregular water area range by adopting Gaussian weighted interpolation; defining an influence area by taking a hydraulic structure as a center, decomposing a local terrain into a background terrain and a scouring residual error, performing polar coordinate conversion and angular terrain spectrum analysis in the influence area, and calculating an axisymmetric form dominance index of the scouring pit; and constructing a topographic form shaping factor based on the axisymmetric form dominance index of the scouring pit, the maximum scouring depth and the influence radius, performing frequency domain modulation on the Fourier coefficient, and reconstructing the corrected local topography to obtain a final underwater topography DEM.

The invention discloses a surface water storage amount monitoring method and system based on space-air-ground water multi-source collaboration, and relates to the technical field of water resource investigation, and the method comprises the steps: obtaining multi-source collaboration collection data of a target water body, and generating a multi-source data set of a unified space-time reference; carrying out automatic extraction and change detection on a surface water area range by using a water body index method and deep learning to obtain a water area boundary; taking a water area boundary as a spatial constraint, extracting ashore terrain and underwater terrain data, and inputting the ashore terrain and underwater terrain data into a deep learning fusion model based on an encoder-decoder structure to generate a water-land integrated digital elevation model; and according to the water body types, based on the model, respectively adopting corresponding deep learning models to carry out storage volume monitoring, and calculating the water storage volumes and dynamic changes of different water body types. Efficient fusion and intelligent processing of multi-source data are achieved, the precision and efficiency of surface water storage capacity monitoring are remarkably improved, and the method is suitable for refined dynamic monitoring of various water bodies such as rivers, reservoirs and ponds.

The invention relates to the technical field of water resource management, in particular to a flow hydrograph simulation method based on coupling of a Xinanjiang model and a depression, and the method comprises the steps: obtaining the geographic space data and meteorological data of a target drainage basin; the geospatial data at least comprises a digital elevation model, land utilization data and soil type data; and carrying out sub-drainage-basin division on the target drainage basin based on the digital elevation model, identifying depressions in the digital elevation model by adopting an improved priority flood algorithm, and constructing a spatial topological relation of the depressions. According to the method, the hydrological characteristics of the depression are fused into the Xinanjiang model, the regulation and storage function of the depression group on the drainage basin runoff is quantified, the hydrological simulation precision of the depression dominant drainage basin is remarkably improved, and by improving the depression recognition algorithm, pre-construction of the slope confluence time matrix based on the cells and rapid judgment of the depression merging mechanism, the stability of the depression confluence time matrix is improved. The calculation speed of the distributed hydrological model is greatly improved, and the efficiency requirement of the real-time hydrological model can be met.

The invention discloses a power distribution equipment point cloud multi-target classification extraction method, system, equipment and medium, and belongs to the technical field of power distribution line three-dimensional visualization, and the method comprises the steps: obtaining three-dimensional laser point cloud data covering a power distribution line region, and carrying out the noise point elimination and spatial density resampling, and obtaining first point cloud data; extracting ground points in the first point cloud data, and constructing a digital elevation model; removing the ground points to obtain second point cloud data containing the power distribution equipment and the environment target; executing clustering processing, and extracting structural feature parameters of each cluster; inputting the extracted structural feature parameters into a pre-trained classification model to complete classification; and correcting a classification result, and outputting a power distribution equipment point cloud classification data set with a category label. The method has the beneficial effects that in the ground point identification and elimination stage, a sliding window area consistent with the distribution line layout characteristics is constructed, an initial ground point set is identified by using a height gradient trend, and a misjudgment point set with obvious terrain disturbance or local ground characteristics is screened out through joint verification of a fitting residual error and a ground structure template, so that the accuracy of the ground point identification and elimination is improved. And the stability of subsequent structure extraction and the precision of ground object separation are enhanced.

The invention discloses an arid region groundwater reserve estimation method and system based on multi-source remote sensing data, and relates to the technical field of groundwater monitoring, and the method comprises the steps: collecting land water reserve data and remote sensing image data of a target arid region, obtaining a land water reserve change sequence according to the land water reserve data, carrying out the region weighting of the target arid region, and obtaining a target arid region groundwater reserve change sequence; obtaining a land water reserve change sequence of the target arid region; based on the remote sensing image data of the target arid region, using a digital elevation model to obtain a surface water reserve change sequence of the target arid region, using a land surface process model to obtain a soil water reserve change sequence of the target arid region, and establishing an underground water reserve model of the target arid region; and performing error analysis on an output result of the groundwater reserve model by using a Monte Carlo simulation method. According to the method, through collaborative inversion of multi-source remote sensing data and integration of the groundwater reserve models, accurate quantification of spatial-temporal dynamic of the groundwater reserves in the arid region is realized.

The invention discloses a mountain area power transmission and transformation project downhill slag slip detection method based on remote sensing three-dimensional width change. The method comprises the following steps: acquiring a satellite remote sensing image and digital elevation model data of a target area, then carrying out data preprocessing, and generating a slope grid map and a processed image; extracting edge lines on the left side and the right side of a road from the processed image, generating vertical section lines along the center line of the road, calculating horizontal plane coordinates of intersection points of the vertical section lines and the edge lines, and calculating elevation values of the intersection points according to digital elevation model data, calculating a three-dimensional road width corresponding to each pair of intersection points according to the horizontal plane coordinate and the elevation value, and comparing the three-dimensional road width with a reference width to obtain a width variable quantity; and if the width variable quantity is not smaller than the width threshold value and the slope value of the corresponding position in the slope grid map is not smaller than the slope threshold value, judging a downslope slag sliding event and carrying out risk early warning. According to the method, the measurement error of a traditional plane method in the abrupt slope terrain is reduced, and the judgment accuracy is improved.

The invention discloses a steep cliff three-dimensional modeling method and system based on structural plane constraint, and relates to the technical field of three-dimensional geographic information system.The steep cliff three-dimensional modeling method comprises the steps that steep cliff area geological data including steep cliff multi-view images and steep cliff surface dense point cloud data are collected, and a geological prior knowledge base is constructed; an image recognition technology is used for processing a steep cliff multi-view image, point cloud geometric analysis is combined, and the structural plane spatial orientation including a rock stratum interface and a joint plane is recognized. According to the method, geological priori knowledge is fused, the geometric distortion problem of a traditional digital elevation model in a cliff area is effectively solved, the spatial orientation of a rock stratum interface and a joint surface is extracted by using an image recognition technology, and the spatial orientation is used as a hard constraint condition to construct a control triangulation network framework; the problems of forced smoothing errors of a regular grid DEM and a suspended surface of a triangular irregular network are avoided, and the rock stratum structure, the crack distribution and the dangerous rock body form of the cliff can be precisely restored through the self-adaptive triangulation algorithm.

The invention relates to the technical field of underwater topographic surveying and mapping, in particular to a sea area water taking channel scanning survey method and system based on multi-beam measurement, and the method comprises the following steps: constructing a rigid support, fixing a depth finder through a three-way cable fastening device, and building a Boolean parameter model; sound velocity, tide level and original echo data are collected; performing space-time registration, coordinate conversion and filtering denoising on the data to generate corrected water depth; and constructing an irregular triangulation network by using hard boundary constraint, generating a digital elevation model, and extracting a siltation area. According to the invention, multi-beam wide sector scanning and multi-sensor attitude correction technologies are fused, a limited water area full-coverage measurement system is constructed, and the problems of terrain blind areas and detail loss are solved. A boundary constraint reconstruction algorithm is utilized to realize closed-loop processing from coordinate reference unification to micro-landform restoration, the accuracy of channel bottom pit capture and edge topography expression is improved, and data support is provided for sedimentation analysis.

The invention discloses a rainstorm ground waterlogging prediction method, device, equipment and product, and relates to the technical field of urban waterlogging prediction.The rainstorm ground waterlogging prediction method comprises the steps that ephemeris and ground observation station data are obtained, the atmospheric moisture delay amount and the high-altitude water vapor conversion factor are calculated respectively, the historical water vapor content is calculated, and the future atmospheric precipitable water amount is predicted; rainfall time distribution data are obtained through rainstorm intensity decomposition; calculating the rainwater overflow amount of the catchment area in combination with the water discharge amount time distribution data of the urban drainage pipe network; and iterative calculation is carried out by using a digital elevation model grid to obtain the waterlogging depth, and waterlogging early warning information is sent out. By constraining the total rainfall amount, the rainfall amount can be effectively constrained by predicting the precipitable water amount based on the satellite ephemeris; the underground pipe network drainage is restrained, and a water flow condition in a pipe channel is simulated by adopting an SWMM model; and finally, the waterlogging accumulated water volume is calculated, constraint verification is carried out on each link of urban waterlogging, and relatively high precision and relatively long timeliness are ensured.