1876 results about "Terrain" 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 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 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 relates to the technical field of geological monitoring, and discloses a side slope deformation monitoring method based on spaceborne synthetic aperture radar, which comprises the following steps: S1, data acquisition: acquiring multi-source spaceborne synthetic aperture radar (SAR) data of a side slope area, the multi-source spaceborne SAR data comprising C-band SAR data and L-band SAR data; s2, self-adaptive preprocessing: self-adaptive preprocessing is performed on the multi-source spaceborne SAR data according to a ground surface coverage scene of a slope area, and the ground surface coverage scene comprises a low vegetation area, a medium-high vegetation area, a flying dust interference area and a high and steep terrain area. According to the slope deformation monitoring method based on the spaceborne synthetic aperture radar, a self-adaptive wave band fusion strategy is adopted for different vegetation coverage scenes, a time sequence coherence enhancement algorithm is combined, the vegetation area monitoring precision is remarkably improved, and noise signals are effectively eliminated and interference errors are reduced through flying dust and construction interference dynamic identification and a multi-dimensional filtering algorithm.

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 relates to the technical field of wind field simulation, and provides a wind field numerical simulation method based on multiple meteorological data sources. The objective of the invention is to solve the problems of large simulation error, rough terrain boundary processing and poor turbulence model parameter adaptability caused by non-uniform coverage of a single data source, insufficient precision and unscientific multi-source fusion. The method is characterized by comprising the following steps: step 1, constructing a CFD three-dimensional grid based on a geometric model of a target area; 2, collecting original data of a ground station, satellite remote sensing, numerical forecasting and the like; 3, performing standardized preprocessing (abnormal value elimination, missing interpolation, radiation / geometric correction and resampling), determining a multi-source fusion weight by combining historical data analysis, and generating comprehensive meteorological data by adopting a weighted average method; and 4, inputting the comprehensive data into a CFD-RANS model, dynamically adjusting turbulence parameters, accurately setting terrain boundary conditions, and obtaining a wind field space-time distribution result through numerical solution. According to the invention, through combination of multi-source data fusion and CFD simulation, the wind field simulation precision and stability are improved.

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 provides a chemical restoration method for treating heavy metal-polluted terrain soil by ferromagnetic artificial nucleuses. In a ferromagnetic artificial nucleus soil restoration method, the ferromagnetic artificial nucleuses are applied to heavy metal-polluted soil; soil above a heavy metal pollution depth is overturned by cultivation equipment to uniformly mix with the ferromagnetic artificial nucleuses; and after heavy metal can be dispersed from the soil to the artificial nucleuses by a period of time, the soil containing the artificial nucleuses is overturned by using the cultivation equipment to increase the change of the artificial nucleuses contacting with other soil. After the soil overturning procedure is repeated by multiple times, the cultivation equipment provided with magnets can be used for separating the ferromagnetic artificial nucleuses, absorbed with the heavy metal, from the polluted soil; and the content of the heavy metal of the polluted soil is reduced due to shifting-out of the ferromagnetic artificial nucleuses, so that the purpose of soil restoration is gradually achieved.

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 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 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 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 discloses an unmanned aerial vehicle autonomous address selection method based on terrain evaluation and approach cost analysis, and relates to the technical field of unmanned aerial vehicle autonomous address selection. After the unmanned aerial vehicle receives a landing instruction in an unknown environment, the geometric safety of the terrain and the cost problem of the approach path can be comprehensively considered, and the landing point selection strategy of the unmanned aerial vehicle is optimized from the global perspective. Firstly, semantic segmentation is performed on an RGB image acquired by a visual sensor through deep learning to acquire a semantic image, then point cloud data acquired by a laser radar and the semantic image are fused to acquire a three-dimensional semantic point cloud, and a terrain safety area conforming to terrain geometric feature constraints is screened out on the basis of considering semantic tags. And finally, calculating the cost of the linear approach path of each candidate landing point in the terrain safety area through traversal, and screening out the landing point with the optimal comprehensive cost, thereby improving the accuracy and practicability of terrain safety area identification. According to the method, an unreachable or high-risk area is avoided, and the global point selection optimization effect is improved.

The invention relates to the technical field of snow melting disaster early warning, in particular to a snow surface rain disaster identification method based on a high and cold mountainous area and a related product, and the method comprises the steps: constructing a snow surface rain disaster single-point identification rule capable of being physically explained by using meteorological observation data and remote sensing inversion data of a small amount of weather stations in the high and cold mountainous area; the method is advantaged in that classification is carried out without depending on a black box model, precision and interpretability of single-point snow surface rain event discrimination are improved, regional popularization is carried out through dynamic downscaling simulation of a regional climate mode WRF coupling land surface process model Noah-MP, a point-surface fusion snow surface rain disaster discrimination and identification system is constructed, and the method is advantaged in that the method is simple and convenient to operate. The method is suitable for identifying snow rain disasters in complex terrain areas with high and cold areas, high altitude, large gradient, lack of data, even no data and the like, and solves the technical problems of insufficient snow rain disaster space identification capability and low precision caused by sparse observation data in the high and cold mountainous areas in the prior art.

The invention relates to an earthwork balance calculation method, system and device and a storage medium, and relates to the technical field of earthwork balance design. The method comprises the steps of performing fusion processing on oblique photography data and laser radar point cloud data to generate a live-action three-dimensional model; performing grid division on the live-action three-dimensional model to obtain a grid model; based on the gridding model, defining a decision variable set including the design elevation of each grid point, the overall slope of the site, the slope height of each slope unit and the earthwork allocation amount, and constructing a comprehensive objective function including an earthwork balance objective, an economical objective and a safety objective; and under the engineering constraint condition, performing multi-objective optimization solution on the comprehensive objective function to obtain a Pareto optimal solution set, and selecting a final implementation scheme from the Pareto optimal solution set. According to the method, the problem of topographic data missing of the dense vegetation region is effectively solved, the limitation of traditional single-target optimization is broken through, and the maximization of comprehensive benefits is realized.

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 provides an adaptive multi-view track roaming optimization method based on Cesium, and the method comprises the steps: obtaining an original path point, and carrying out the processing of the original path point, and generating a smooth path; dynamically adjusting the time rate of the smooth path movement; dynamically adjusting the number of interpolation points of the smooth path; loading the dynamically adjusted path, and binding with the path through a dual-mode view angle; and rendering the path in real time, and interacting with a user. According to the method, a Catmull-Rom spline interpolation and inflection point optimization method is adopted, so that turning is smoother; the terrain height is dynamically sampled, terrain adaptation is accurate, and the model is prevented from being suspended or falling into the ground; according to the invention, first person / third person switching is supported, and flexible visual angle control is realized; the speed calculation based on the path length ensures that the motion rhythm is natural.

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 discloses a mountain cloud sea landscape universalization forecasting method based on a vertical layering meteorological factor, and relates to the technical field of landscape forecasting, and the method comprises the following steps: obtaining forecast meteorological data of a target mountain, the meteorological data comprising a meteorological profile; based on the forecast meteorological data, predicting the dynamic evolution trend of the thermal inversion layer to obtain a thermal inversion layer evolution prediction result; correcting the initial meteorological profile based on a thermal inversion layer evolution prediction result; based on the corrected meteorological profile, analyzing and judging a space matching relationship among a thermal inversion layer, a wet layer and a wind field, and forming an ornamental index set of the cloud sea landscape; and comprehensively judging the ornamental value index set, and outputting a cloud sea landscape observability result of the target mountain land in the forecasting time period. According to the method, the dynamic evolution trend of the thermal inversion layer is identified, and the cloud sea landscape ornamental index is generated and predicted in combination with terrain disturbance correction, so that the problems of inaccurate generation and elimination depiction of the thermal inversion layer and difficulty in quantitative prediction of the cloud sea landscape in the prior art are solved.

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.