2644 results about "Landform" patented technology

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 method for simulating and forecasting flood in a cold highland area based on hydrological and hydrodynamic coupling, and belongs to the technical field of disaster forecasting. The method specifically comprises the following steps: S1, multi-source basic data collection and preprocessing: collecting multi-type and multi-scale basic data for a target cold and cold mountainous area drainage basin; and S2, deep learning correction and fusion of the satellite rainfall data: aiming at the local overestimation and underestimation problems of the satellite rainfall data, a deep learning algorithm is adopted to carry out hour scale correction and fusion. Four types of core data of satellite remote sensing, reanalysis, ground observation and geographic space are collected, total factors of'rainfall-runoff-terrain-underlying surface 'required by flood simulation in the cold and cold mountainous area are covered, simulation one-sidedness caused by lack of data types in traditional modeling is avoided, rainfall and runoff abnormal values are eliminated by adopting a 3-sigma criterion, data formats and spatial-temporal scales are unified, and the modeling efficiency is improved. A standardized data set is formed, and interference of abnormal values, format incompatibility and space-time mismatching on subsequent model input is avoided.

The invention belongs to the field of geotechnical engineering and engineering geological information modeling, and relates to a stratum-parameter coupling randomness three-dimensional random field modeling and shield construction ground surface settlement rapid prediction method and device based on multi-source data fusion. According to the method, a three-dimensional joint random field acting on stratum types and rock-soil key physical property parameters at the same time can be constructed under the joint constraint of multi-source data such as drilling, geophysical prospecting, geotechnical tests and terrains, and a calibrated, explainable and updatable geological section automatic generation and uncertainty quantification channel is formed; on the premise that geological rationality is guaranteed, section geometry and parameter distribution under the conditions of complex structures and lateral phase change are restored robustly, the accuracy, consistency and interpretability of the three-dimensional geological section are remarkably improved, and a combined uncertainty result which can be directly used for rapid prediction and risk assessment of shield construction settlement is output. The technical problems that a geological section generated by an existing method is not accurate enough, uncertainty is difficult to quantify, and the extrapolation capacity of a complex scene is weak are solved.

The invention, which belongs to the technical field of image analysis and geological disaster early warning, discloses a high-precision topographic change monitoring and geological disaster early warning image analysis system comprising a deformation spatio-temporal feature sensing module, a geomechanics constraint optimization module, a multi-scale disaster evolution prediction module and a self-adaptive early warning decision module. Through deep coupling and closed-loop feedback between modules, dynamic matching of deformation confidence and mechanical constraint weight, physical enhancement of stress distribution and disaster prediction, and dual-path parameter optimization driven by early warning performance are realized, and the system adopts an InSAR technology and deep learning fusion to extract millimeter-level deformation. The physical embedded neural network is utilized to realize anomaly recognition under mechanical constraints, multi-scale disaster evolution is predicted through space-time convolution Transform, and the early warning accuracy rate can reach 95% or above after closed-loop iterative optimization.

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 belongs to the technical field of crossing of environmental engineering and ocean dynamics, and particularly relates to a tidal dynamics prediction method and control device for a seawater desulfurization system, and the method comprises the steps: constructing a time-space coupling prediction model fusing multi-source hydrological observation data, and introducing a nonlinear dynamic weight distribution mechanism; the influence of terrain constraint, wind stress disturbance and upstream runoff on tidal propagation is quantified in real time, and a rolling prediction sequence of tide level phase, flow velocity gradient and salinity disturbance in the next three hours is output; the prediction result drives the scheduling of a desulfurization pump set, the adjustment of spraying density and the matching of aeration intensity, and the model weight is corrected on line based on the actually measured feedback of desulfurization efficiency to form closed-loop control. By means of the technical scheme, accurate cooperation of the operation parameters of the desulfurization system and tidal dynamics is achieved, meaningless energy consumption is reduced while the desulfurization efficiency is guaranteed, and the utilization rate of the desulfurization agent and the stability of the system are improved.

The invention discloses a wharf shoreline terrain reconstruction method based on remote sensing data, and belongs to the technical field of three-dimensional terrain reconstruction. The method comprises the following steps: acquiring a multi-temporal multispectral remote sensing image, a synthetic aperture radar image, a laser radar point cloud and synchronous tidal water level data of a target wharf area; processing the multispectral image to generate an initial land and water segmentation map; carrying out tide correction by utilizing the tide water level data, fusing the multi-polarization characteristics of the synthetic aperture radar image, and correcting the ground feature type to obtain an accurate land and water boundary diagram; processing the laser radar point cloud to generate a digital surface model, extracting a shoreline contour with sub-pixel-level precision from the digital surface model, and endowing the shoreline contour with an elevation value; and superposing the shoreline contour with the elevation with the multispectral image to generate a three-dimensional terrain model. According to the method, the problems of low terrain reconstruction precision, large tide influence and insufficient automation degree of a traditional method in a complex wharf environment are solved, and high-precision and high-efficiency wharf shoreline three-dimensional automatic reconstruction is realized.

The invention relates to a multi-source data fusion rainfall revision model construction method based on dynamic physical constraints, and belongs to the technical field of artificial intelligence and meteorology and hydrology crossing. The method comprises the steps that multi-source heterogeneous data such as satellites, reanalysis, sites and terrains are acquired and fused, and a standardized space-time sample set is constructed; constructing a special deep learning network comprising a double-flow encoder, a cross-scale physical attention fusion module and a decoder; constructing a composite loss function fusing data fidelity, terrain uplift constraint and wind field advection constraint; designing a dynamic constraint gating mechanism, and adaptively adjusting a physical constraint weight according to a real-time atmospheric state; and a high-precision precipitation revision model is obtained by optimizing the composite loss function training network. According to the method, the key physical process is embedded into the model in a dynamic adjustable mode, the problems that a traditional method is poor in physical consistency and generalization ability are solved, and a revised precipitation field which is physically reasonable and higher in precision can be generated under complex terrains and changeable weather.

The invention relates to the technical field of marine surveying and mapping and submarine topography modeling, and discloses a submarine topography and landform construction method based on multi-source water depth data fusion, which comprises a multi-source data acquisition module, a data preprocessing module, a feature extraction module, a fusion algorithm matching module, a three-dimensional modeling module and an uncertainty evaluation module. A historical submarine topographic map is converted into grid data, namely, submarine topography is discretized into digital representation of regular grid units, water depths of other sources are converted into the grid data, the precision of the water depths of different sources is evaluated by adopting a superposition comparison method, and an error distribution model is established to reasonably correct and fuse the water depth data of various sources. The innovation of the method lies in that topographic feature parameters including the gradient, the curvature matrix and the bionic optimization algorithm are introduced, the defect that a single algorithm is preset in a traditional method is overcome, and the precision of coral reef and fault zone complex areas is improved.

The invention discloses a flood disaster risk intelligent early warning method fusing multi-source information, and relates to the technical field of flood disaster prediction.The method comprises the steps that multi-source data of meteorology, hydrology, terrain, remote sensing, ground monitoring terminals and the like are obtained, and a unified data set is constructed through time synchronization, space resampling, abnormal value elimination and missing data filling; constructing multi-dimensional spatio-temporal features based on factors such as rainfall process, topographic features, surface coverage, drainage capacity, river network structure and soil humidity; inputting the spatial-temporal characteristics into a multi-source information fusion model, and predicting a flood occurrence probability in a future time period; flood risk indexes are generated according to the prediction probability, real-time monitoring and historical samples, and regional risks are divided into different grades; early warning information is automatically generated and issued according to the risk level; and the early warning result is fed back and corrected through field monitoring and patrol data, so that self-adaptive updating of early warning parameters and a model structure is realized.

The invention relates to the technical field of terrain reconstruction and path planning, and particularly provides a night terrain reconstruction and path planning method and system based on multi-sensor collaboration, and the method comprises the steps: obtaining the multi-source sensing data of a night environment through a data collection module; performing multi-source data fusion processing on the multi-source sensing data to generate a night environment reconstruction model fusing multi-dimensional features; performing obstacle semantic analysis on the night environment reconstruction model, extracting static obstacle distribution information and a dynamic obstacle movement track, and constructing a composite environment map; according to the composite environment map, a passable path is generated through a dynamic optimization algorithm in combination with terrain passability parameters and kinematics constraint conditions of a mobile carrier, and path parameters are dynamically adjusted based on real-time update data of a night environment reconstruction model; when it is detected that the obstacle distance or the motion state exceeds a safety threshold value, path real-time correction and graded obstacle avoidance instruction output are triggered through multi-sensor confidence evaluation and path collision risk prediction.

The invention relates to the technical field of terrain wind resource assessment, in particular to a complex terrain wind resource assessment method and system based on CFD enhancement, and the method comprises the steps: processing terrain, weather and vegetation data, and generating a complex terrain feature vector; constructing bidirectional interaction between a mesoscale meteorological mode and computational fluid dynamics, and iteratively optimizing a flow field; fluid dynamic parameters are calculated through reinforcement learning automatic optimization; turbulence simulation is enhanced by combining the generative adversarial network with large eddy simulation; dynamically allocating resolution according to the grid priority of the multi-feature calculation; the lightweight model pre-judges errors, and after the errors reach the standard, evaluation indexes are calculated to complete wind resource evaluation; the system comprises a multi-source data fusion module, a dynamic coupling module, a parameter optimization module, a reinforced turbulence simulation module, an adaptive grid module and an error prediction and evaluation module, and all the modules cooperate to achieve full-process evaluation. The problems that in the prior art, one-way coupling precision is low, manual parameter selection is low in efficiency, turbulence simulation precision and efficiency are difficult to balance, and static grids are wasted are solved.

The invention relates to the technical field of coast engineering, in particular to an estuary sandy coast erosion and deposition simulation system based on the coupling effect of flood peak runoff, waves and tide, which comprises a training data generation module, an intelligent agent model construction module, a dynamic prediction engine module and a scene evaluation module. The training data generation module is used for outputting a hydrodynamic state field, a wave characteristic field, a bed surface shear stress field and a bed surface elevation variable quantity by utilizing a traditional numerical model to construct a training data set; the intelligent agent model building module is based on a deep learning network and introduces physical constraint loss function training to obtain an intelligent agent model; the dynamic prediction engine module loads the trained deep learning network to realize bed surface elevation variation prediction and update terrain boundary conditions; and the scene evaluation module executes erosion and deposition evolution simulation according to different hydrological boundary condition combinations and extracts terrain evolution data to quantitatively evaluate the coast stability and the channel deposition risk. The invention relates to the field of estuary dynamic landform simulation and coast engineering.

The invention relates to a temperature forecast correction method based on a PSD-Net and a PSD-Net, and belongs to the technical field of weather forecast, and the method comprises the steps: obtaining a dynamic meteorological variable based on the PSD-Net, and loading topographic data and a report starting time observation truth value at the same time; preprocessing the dynamic meteorological variable, the topographic data and the report starting time observation truth value; inputting the preprocessed data into a temperature forecast correction model to obtain temperature correction field data; wherein the temperature forecast correction model is obtained by training a PSD-Net model based on a training data set, and the training data set comprises historical dynamic meteorological variables, topographic data and a report starting time observation true value. Compared with the traditional numerical mode and the original output of the archaeological model, the temperature forecast MAE corrected by the method is reduced by more than 37.6%, the accuracy rate within 2 DEG C is improved by 14%-19%, and the precision advantage is more prominent especially in complex terrain areas and extreme weather events.

The invention relates to the technical field of computer information processing, and discloses a high-fidelity terrain surface interpolation method and system for large-scale point cloud data, and the method comprises the steps: carrying out the multi-level cleaning of original point cloud data, and generating a cleaned point cloud data set; performing adaptive tile grid division based on the cleaned data to generate a tile set covering the target area; in each tile, taking the to-be-interpolated grid point as a query point, constructing and optimizing a local Gaussian process model, and obtaining a real elevation predicted value of the query point; processing all query points in the tiles in parallel to obtain a tile elevation interpolation result set; performing weighted average on each tile result to generate a global seamless terrain surface; according to the method, the problems of poor expandability and surface discontinuity caused by local interpolation in large-scale point cloud data processing are effectively solved, and high-precision and high-fidelity terrain surface construction is realized.

The invention discloses an InSAR (Interferometric Synthetic Aperture Radar) deformation monitoring system for landslide and a method thereof, and relates to the field of geological disaster monitoring. The method has a high-precision deformation monitoring effect, through multi-source SAR data fusion, interference pair screening optimization and multi-source error correction (such as troposphere and ionosphere delay correction), the fidelity of a deformation phase sequence is remarkably improved, and the precision limitation under a complex terrain is overcome; the method has an intelligent geological constraint inversion capability, introduces geological prior knowledge (such as fault and fracture characteristics), enables a deformation field to better conform to the actual geomechanical law through deep learning semantic segmentation and a Bayesian inversion model, and reduces misinformation and missing report. The method has self-adaptive landslide recognition, adopts deformation gradient field calculation and a dynamic threshold segmentation algorithm, automatically recognizes a potential sliding zone boundary, improves the landslide partitioning efficiency, and is suitable for large-range monitoring.

The invention relates to the technical field of urban planning, and particularly discloses an urban planning construction land management and control system based on big data, and the system comprises the steps: obtaining geological environment, landform, engineering construction and human activity data through multi-source data collection; multi-source data fusion analysis is adopted to construct a construction land comprehensive evaluation field including geological stability, engineering suitability and environmental bearing capacity; a development suitability partition is generated based on a density peak value spatial clustering method; updating the development suitability level by dynamically monitoring the verification process; according to the method, accurate analysis and early warning of the three-dimensional deformation field are realized, and scientificity and safety of urban planning in a complex geological environment are improved.

The invention relates to the technical field of ocean engineering digital twinning and three-dimensional visualization, and provides a method and system for generating dredging terrain twinning based on a right-angle triangular mesh, and the method comprises the steps: obtaining regular grid elevation unified to an engineering plane coordinate system or preprocessed sampling height data, determining a covered space range, reading the space range and density parameters, and calculating the total row number and the total column number of the grids based on the space range and the density parameters; generating regularly arranged vertexes in the covered space, and constructing a topological structure of an isosceles right triangle grid; dividing the global grid into a plurality of Chunk blocks according to a fixed size by taking a vertex as a unit, establishing a triangular index mapping relationship between global rows and columns and the Chunk blocks, filling three vertexes of an isosceles right triangle and elevation / height data in each Chunk block in parallel, and generating local VBO, IBO and AABB; and performing batch rendering and coloring according to a height color table on all Chunk blocks to realize visualization of the terrain.

The invention relates to the technical field of topographic change analysis, in particular to an unmanned aerial vehicle-based topographic change detection system, which comprises a slope direction sensing track control module, a texture structure extraction module, a crack evolution track construction module, a direction trend comparison module and a patrol recheck positioning module. According to the method, a continuous elevation point column of an unmanned aerial vehicle scanning area is extracted, laser reflection point coordinates are fused, a space relation of transition point distribution is constructed, dynamic adjustment of a ground-imitated flight path is achieved, and a texture structure area with continuous directivity is recognized in combination with a high-angle image boundary communication relation; texture boundary evolution is compared at different time nodes to form a crack path, the stability of the path and the slope direction is judged through an included angle sequence, recognition and sorting of areas with the consistent direction are completed, a space comparison result is registered in a three-dimensional coordinate system, terrain change areas are accurately marked, and rapid positioning and continuous tracking of high-risk areas are achieved.

The invention discloses a vegetation-submerged embankment coupled tough coast protection method and system and a storage medium, and the method comprises the steps: firstly, determining a research region, and systematically collecting the near-shore terrain, water and sand power conditions, vegetation parameters and other data; secondly, constructing a water-sand-vegetation coupling numerical simulation model based on the collected data, and performing precision verification on the model; then selecting a suitable vegetation type in combination with environmental characteristics of the research area, carrying out artificial seeding and planting work in a vegetation potential habitat of the tidal flat, and determining a design elevation and an offshore distance of the submerged dike; and finally, evaluating the coast protection effect under the synergistic effect of the submerged embankment and the vegetation, and quantitatively analyzing the improvement degree of the coast protection effect on wave attenuation and beach stability. A sustainable tough coast protection system can be formed under the climate change background through a collaborative protection mechanism integrating natural vegetation and an artificial submerged embankment. The method has the advantages of low cost, remarkable wave reduction effect and the like, and the capability of coping with climate change challenges and comprehensive protection of coastal zones can be effectively improved.

The invention relates to the technical field of unmanned aerial vehicle path planning, in particular to an unmanned aerial vehicle automatic navigation method and system for power transmission line inspection. Comprising the steps of collecting terrain elevation data and a wind speed fluctuation signal, generating a line corridor boundary elevation distribution diagram, analyzing the influence of a terrain gradient on a conductor sag, and determining a curvature limitation adjustment demand; re-planning the flight path in combination with the obstacle position information, and generating a detection path sequence; calculating a height abrupt change value and an attitude angle deviation value, judging whether an acceleration impact suppression requirement exists or not, and generating an inspection risk score of the vegetation invasion area; determining a cross span distance risk quantitative index, and generating a flight control instruction sequence; and verifying whether the path meets a boundary integrity standard, and generating an automatic navigation path sequence. According to the method, the problems of instability, insufficient coverage and poor safety of an unmanned aerial vehicle routing inspection path under a complex terrain condition are solved, and stable, continuous and efficient automatic generation of the power transmission line routing inspection path is realized.

The embodiment of the invention discloses a three-dimensional landform model reconstruction method and device based on geometry and texture combination. A specific embodiment of the method comprises the following steps: carrying out self-adaptive grid division on a landform three-dimensional point cloud and then carrying out geometric super-resolution to obtain a target three-dimensional landform sub-grid; performing color texture mapping and texture correction enhancement on the target three-dimensional landform sub-grids to obtain an enhanced landform texture map; performing collaborative bidirectional optimization on the enhanced landform texture map and the target three-dimensional landform sub-grids to obtain a super-resolution landform texture map and a super-resolution three-dimensional landform sub-grid; performing model recombination and fusion on the plurality of super-resolution landform texture maps and the plurality of super-resolution three-dimensional landform sub-grids to obtain a three-dimensional texture landform grid model; and performing density control reconstruction processing on the three-dimensional texture landform grid model to obtain a three-dimensional density texture landform model, and storing the three-dimensional density texture landform model. According to the embodiment, the quality of the three-dimensional density texture landform model can be improved, and waste of storage resources is reduced.

The invention discloses a summer rainfall forecast correction downscaling method and system based on deep learning, and belongs to the technical field of meteorological prediction and climate simulation. In order to solve the technical problems of systematic deviation and spatial detail missing in forecasting, a deep learning downscaling model is constructed for summer multi-mode climate forecasting data published six months ahead of time in combination with high-resolution observation data and topographic data, deviation correction and spatial super-resolution reconstruction are performed on a summer rainfall forecasting result, and a deep learning downscaling model is constructed. And outputting the kilometer-level summer precipitation field of the target area. The model improves the spatial feature capture capability of multi-scale climate elements through an attention mechanism, and realizes the adaptive fusion of meteorological elements and topographic factors through a topographic perception module, thereby enhancing the medium and long term prediction precision of summer rainfall in a complex topographic region. According to the method, global climate information and local topographic features can be effectively integrated, and high-precision technical support is provided for medium-and-long-term prediction of regional summer rainfall, disaster prevention and reduction in flood seasons and water resource scheduling.

The invention discloses a high-resolution peak accumulated snow depth inversion device and method based on a geographically weighted random forest, and the device comprises a data obtaining module which is used for obtaining the multi-source data of a research region, and the multi-source data comprise MODIS remote sensing accumulated snow data, Sentinel-1VV / VH backscattering data, topographic data, and meteorological station snow depth observation data; the high-resolution snow coverage data set construction module is used for processing the MODIS remote sensing snow data to obtain a high-resolution cloudless binary snow coverage data set; the feature construction module is used for constructing a comprehensive feature set containing a snow space-time process feature index, a back scattering feature index and a topographic feature index; and the model construction and analysis module is used for training a geographically weighted random forest model through the comprehensive feature set, and inverting the peak snow depth of the research area according to the optimal model. According to the invention, estimation of high-resolution peak snow depth can be realized.

The invention relates to the technical field of artificial intelligence and electric power system prediction, and discloses a wind power short-term output prediction method based on multi-modal data, and the method comprises the steps: obtaining the multi-modal data, such as historical output, numerical weather forecast, actually measured weather of an anemometer tower, landform and fan operation state; performing sliding window segmentation on the output sequence and identifying a mutation interval; calculating a local optimal alignment path of each mode in the mutation interval based on a dynamic time warping algorithm; non-uniform resampling is carried out in this way, and a time-synchronized multi-modal alignment feature sequence is generated; and inputting a hybrid neural network formed by a gating circulation unit and an attention mechanism, and outputting a high-precision output prediction value in the next 15 minutes. The system comprises corresponding function modules. According to the method, through dynamic time alignment and cross-modal feature fusion, the wind power short-term prediction precision is remarkably improved, the root-mean-square error in a sudden change scene is reduced by 23.7%, and reliable support is provided for power grid dispatching.

The disclosure provides an improved canopy height correction method. The method includes: obtaining GEDI LiDAR data, airborne canopy height data, GDEM with high resolution and land cover product within the selected target area and timeframe; performing quality filtering and spatial-scale filtering on GEDI footprints; extracting laser pointing parameters and waveform parameters; extracting reference canopy height from airborne data for each footprint; extracting laser pointing parameters and waveform parameters; preprocessing the GDEM and calculating topographic parameters, including topographic variability index (TVI); constructing the Laser Pointing and Topographic Index (LPTI) according to the 3D forest-ground geometry model; inputting the waveform parameters, even topographic parameters, TVI and LPTI as independent variables, and the reference canopy height as the dependent variable to modeling an improved forest canopy height extraction, and utilizing the improved canopy height extraction model to correct the twofold influence of topographic on GEDI canopy height extraction.

The invention discloses an indoor water tank sediment pushing characteristic acquisition system and method, the system mainly comprises a water tank main body, a buffer unit, a drainage adjusting unit, a sediment throwing device, an imaging unit, an adjustable support and a data processing unit, and according to the method, non-contact data acquisition is carried out through a high-definition camera and a three-dimensional terrain measuring instrument; automatic identification, parameter extraction and migration speed calculation of landforms such as sand waves are achieved, the sediment bed surface is not touched in the whole process, damage to the bed surface form caused by traditional contact type measurement is eliminated, and the authenticity and reliability of data are ensured. Meanwhile, the buffer design of the combination of the flow slowing plate and the multi-stage water baffle is adopted, the kinetic energy of water flow is effectively dissipated through the graded water outlet structure, the impact force of injected water flow is greatly reduced, the bed surface is prevented from being scoured in the initial water injection stage, and the stability of the initial test condition is powerfully protected.

The invention discloses a meteorological data assimilation method fusing topographic features, and relates to the technical field of meteorological data assimilation, and the method comprises the steps: combining a meteorological variable-topographic factor correlation map with an atmospheric state numerical field, and obtaining a multivariable initial meteorological field model fusing topographic factors; identifying space-time dynamic deviation distribution characteristics of the multivariable initial meteorological field model by using real-time multi-source environment observation data, and generating a terrain disturbance response matrix; carrying out dynamic correction and space-time error reconstruction based on a terrain disturbance sensitive weight on the terrain disturbance response matrix and the multivariable initial meteorological field model through an assimilation optimization algorithm to obtain a meteorological assimilation result; according to the method, dynamic correction and space-time error reconstruction are carried out on the initial meteorological field through the terrain disturbance sensitive weight, directional perception and intelligent correction of observation deviation are realized, and the response capability and the error control level of the model in a dynamic change scene are effectively enhanced.

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 discloses a submarine topography reconstruction system and method based on optical mobile measuring and scanning, and relates to the technical field of ocean exploration, and the system comprises the following units: an image collection unit, which is used for coordinating the emission of 532nm pulse laser and the collection of a gating camera image through a synchronous sequential circuit, a triangulation measurement optical structure with separated emission and reception is adopted, and a submersible can be carried for mobile measurement and scanning; the system calibration unit is used for carrying out joint calibration on camera parameters and a laser plane so as to determine system calibration parameters, so that the spatial position of a laser stripe accurately corresponds to the actual coordinate of the target surface; and the image processing unit is used for preprocessing the image acquired by the system and further extracting the coordinates of the center line of the laser stripe in the preprocessed image. According to the invention, adjacent splicing can be carried out through continuously collected laser stripe images to construct two-dimensional submarine landform information, and three-dimensional reconstruction of submarine topography can be carried out by using generated point cloud data.