317 results about "Raster data" patented technology

The embodiment of the invention provides a landslide disaster negative sample optimization method based on an improved frequency ratio, and belongs to the technical field of data processing, and the method specifically comprises the steps: collecting disaster-pregnant environment data, and carrying out the preprocessing of the disaster-pregnant environment data, and obtaining raster data; defining two parameters of grading precision and grading bandwidth, introducing information entropy and a maximum frequency ratio to adaptively set parameter values of the grading precision and the grading bandwidth, and calculating a comprehensive frequency ratio; based on the statistical distribution of the comprehensive frequency ratio, taking a peak point as a negative sample screening threshold value, dividing a negative sample screening area based on the threshold value, then realizing calculation of a positive and negative sample ratio, and further determining a negative sample label under the constraint of a buffer area; according to the frequency ratio distribution of each raster data, an attribute interval with a frequency ratio greater than 1 is extracted as a significant feature, then the significant features of each raster data are classified, and an optimal feature element is determined through multi-collinearity analysis. Through the scheme of the invention, the accuracy and reliability of negative sample selection are improved.

The present invention relates to the technical field of digital twin, and in particular to a private network park digital twin visualization method, apparatus and device, and a computer storage medium. Disclosed in the present application is a private network park digital twin visualization method, comprising: using a clustering algorithm to classify ground objects in a remote sensing image of a park, dividing grids on the basis of formed pixel clusters, and calculating dilution ratios thereof; mapping grid data and point cloud data into a unified coordinate system, and using the clustering algorithm to cluster the point cloud data in each grid; sampling each point cloud cluster on the basis of the dilution ratio of the corresponding grid, and finding out non-edge points by means of a BoundED algorithm; on the basis of the proportion of the non-edge points in each point cloud cluster, calculating an initial dilution threshold of each point cloud cluster; and, after each point cloud cluster has been diluted, rendering and presenting same by means of a three-dimensional rendering engine and returning a rendering frame number and, if the rendering frame number meets a preset condition, ending the process.

The invention discloses a goaf collapse risk assessment data fusion system based on big data processing, and particularly relates to the technical field of geological disaster assessment, and the system comprises three core modules: a multi-source data adaptive weighted fusion module which establishes a unified space-time coordinate system, converts non-raster data into a continuous field through Kriging interpolation, and performs data fusion on the continuous field; combining the information entropy and the correlation coefficient to dynamically distribute weights, and generating an enhanced feature field through self-supervised pre-training; the physical-space-time neural network dynamic prediction module is integrated with elastic-plastic mechanical constraint loss and multi-task learning, and outputs a future multi-time step risk probability field and a deformation prediction field through a space-time convolution-memory network; and the risk field three-dimensional subdivision and emergency response module is used for clustering three-dimensional voxels in a high-risk area, automatically calculating risk body parameters, generating an emergency scheme in combination with DEM data and an A * algorithm, and improving evaluation accuracy and emergency scheme practical operability through digital twinborn deduction evaluation.

The invention discloses an urban district regulation construction management operation and maintenance method and system based on three-dimensional digital twinning, and the method comprises the steps: collecting multi-source data, including geographic raster data, BIM model data and mobile phone signaling data, of an urban district, and achieving the coordinate alignment of the multi-source data through an LM algorithm; the multi-source data generates feature data composed of coding tables defining urban features through feature engineering, and an octree is constructed based on the feature data; defining leaf nodes of the octree as voxels, and applying multi-scale convolution to each voxel to obtain super voxels with space-time labels; constructing a constraint network based on the super voxels and a construction, management or operation and maintenance specification constraint library; and judging the risk area of the city district by calculating the space-time constraint intensity of the constraint edge, and outputting a corresponding risk area list. According to the invention, the problems of multi-source data integration, real-time dynamic monitoring and risk prediction in the planning, construction, management, operation and maintenance processes of the urban district are solved.

The invention discloses a sea area monitoring method and system based on a satellite remote sensing technology, and relates to the technical field of remote sensing, and the method comprises the steps: firstly, generating multi-stage and multi-band remote sensing raster data through a resource set, and secondly, obtaining joint sample vectors at different times through monitoring external environment data in a target monitoring region, a multivariable kernel density estimation algorithm is adopted to obtain a four-dimensional joint probability density distribution function, and an abnormal coverage area is obtained in combination with multi-stage and multi-band remote sensing raster data; then, based on the abnormal coverage area, identifying an enhanced abnormal unit, and performing dynamic correction operation; and finally, after dynamic correction, obtaining an abnormal connected region, analyzing an abnormal unit with an unstable form, determining an abnormal evolution sensitive region, and realizing a local adaptive optimization means based on the change of the abnormal evolution sensitive region.

The invention discloses an AI-based illumination control system self-adaptive dimming method, which comprises the following steps of: acquiring a two-dimensional coordinate, a motion track curve and voice communication data of a personnel position, and identifying social interaction scene categories of different time slices; determining a social interaction strength score according to the voice communication data, and predicting the most adaptive spectrum configuration data in the current social interaction scene in combination with the social influence factor matrix and the social interaction scene type; according to a historical dimming log and an energy consumption metering curve, identifying a short-term high-frequency dimming segment, and adjusting a real-time response strategy and a disturbance classification processing mechanism of the spectrum configuration data; and adjusting the illumination power distribution values of the high-frequency use area and the low-frequency use area according to the occupation raster data generated by the millimeter-wave radar and the infrared array in combination with the lamp area mapping relation and the power budget constraint. Through multi-source data fusion and a self-adaptive regulation and control mechanism, unification of real-time performance, pertinence and energy-saving performance of spectrum regulation is realized, and the dynamic response capability and the user perception experience of the intelligent illumination system are improved.

The invention discloses a GIS-based main body function area evaluation index system construction method, and relates to the technical field of land resource management, and the method comprises the steps: obtaining multi-source geographic data of a target main body function area, and carrying out the preprocessing to form a unified geocoding evaluation database; generating an evaluation index system of the main body function area by adopting a composite weighting algorithm according to the evaluation database and the main body function area type division result; inputting the multi-dimensional raster data of the evaluation index system into the spatial decision tree classification model to obtain a classification result; performing overlay analysis on the multi-dimensional raster data to obtain comprehensive evaluation raster data; according to the classification result and the comprehensive evaluation raster data, obtaining a function goodness of fit thermodynamic diagram of the main body function area; and according to the function goodness of fit thermodynamic diagram, performing dynamic updating based on the time sequence, and when the change rate of the monitoring data exceeds a preset threshold, triggering an iterative optimization process. According to the invention, land space planning and main body function area optimization adjustment are realized.

The invention provides a flood simulation method driven by physical and data fusion, and belongs to the technical field of flood simulation, the method comprises the following steps: pre-training to obtain a qualified urban flood simulation model, a target loss function comprising a data supervision loss item and a physical constraint loss item, the physical constraint loss item is composed of a mass conservation residual error loss item, a momentum conservation residual error loss item and an energy conservation residual error loss item; inputting ground elevation data and historical meteorological data of a target area in a city into the hydrological model to obtain water depth grid data and flow velocity grid data, and inputting the water depth grid data and the flow velocity grid data into the city flood simulation model for prediction processing to obtain water depth grid data and flow velocity grid data of the target area at different time points in the future; and based on the data at different time points, generating an urban flood ponding dynamic evolution graph of the target area. The invention aims to improve the efficiency and accuracy of urban flood scene prediction.

The invention discloses a soil attribute intelligent prediction method and system based on a multi-dimensional dynamic scale, and relates to the technical field of soil prediction.The method comprises the steps that remote sensing images, terrain factors, meteorological factors and soil actual measurement sample data are collected and stored in a multi-source raster data bin in a unified mode; preprocessing the multi-source data, and constructing raster data in a unified format; combining terrain difference, land utilization and climate fluctuation, dynamically dividing a spatial scale, a time scale, a terrain layering scale and a land utilization pattern scale, and constructing a multi-dimensional dynamic scale feature expression vector; carrying out soil attribute modeling and training based on a multi-task deep neural network model; and applying the model to a non-actually-measured area, and outputting a prediction result and a spatial distribution map layer. The system comprises a multi-source raster data construction and management module, a multi-dimensional dynamic scale feature construction module and a soil attribute modeling and prediction module. The method can be used for forestry management and protection, resource management, ecological monitoring, resource evaluation and other scenes.

The invention relates to the technical field of non-point source pollution decision, and discloses a non-point source pollution simulation method and a non-point source pollution intelligent interaction method based on an agent, and the simulation method comprises the steps: extracting land and river attribute data of a target region, and constructing a nitrogen emission coefficient database; associating the land attribute type with a nitrogen emission coefficient library, calculating the plot emission amount through a reference area correction model nitrogen emission coefficient, converting the plot emission amount into emission amount grid data, and generating a nitrogen emission distribution map; converting the emission raster data into point data, calculating each pollution point and a river reach influence area, and constructing a river directed graph network; calculating a river reach absorption coefficient by adopting an exponential decay model, and calculating a pollution load transmission quantity and a river reach absorption quantity step by step along a river digraph network topology through breadth-first search by combining the river reach absorption coefficient based on the digraph network; the total consumption amount of the influence river reach of each pollution point is accumulated, the net discharge load distribution diagram and the consumption amount distribution diagram are generated, the calculation process is high in real-time performance, and the method is suitable for various application scenes.

The invention discloses a heterogeneous feature fusion-based multi-task traffic situation cognitive calculation method, and belongs to the field of intelligent traffic. The method specifically comprises the following steps: firstly, preprocessing multi-source traffic observation data to obtain normalized traffic flow, OD raster data and traffic state data; then, for a current frame, carrying out dynamic space-time convolution and diffusion diagram convolution on traffic flow and OD raster data to obtain node-level space-time features; meanwhile, carrying out feature extraction on the traffic state data by utilizing 3D convolution to obtain grid-level spatial-temporal features; and then, inputting the features and the common features into a heterogeneous cross attention fusion module to obtain a unified fusion feature h of the current frame, performing multi-task prediction, and respectively obtaining flow, OD and traffic state prediction results of future K steps. And finally, inputting the prediction result of each task and a true value to carry out multi-task loss calculation, carrying out parameter updating and completing model training. According to the invention, multi-task prediction can be realized by using multiple prediction heads.

The invention discloses a soil organic carbon density spatial non-stationary dominant factor identification and high-resolution mapping method, which uses raster data of characteristic variables as input to train an RF model, so that the trained RF model can correspondingly output and predict spatial distribution of soil organic carbon density, and high-resolution prediction of SOCD in space is realized. Meanwhile, the SHAP value spatial distribution diagrams of all the feature variables are superposed, the SHAP value of each feature variable on each pixel can be obtained, and then the dominant factor of each pixel is obtained, so that the specific influence of the feature variables of different regions on SOCD spatial distribution is obtained, and interpretive analysis of the spatial level is provided.

The invention discloses a DEM landform analysis method based on a large language model, and relates to the technical field of DEM data analysis, and the method comprises the steps: loading a DEM semantic mapping tool set, and achieving system initialization; after the system receives user input, questions are divided into single-tool questions, multi-tool collaborative questions and non-tool answerable questions through a question classification Agent on the basis of a pre-training model; respectively processing a single tool question, a multi-tool collaborative question and a non-tool answerable question; and performing existing memory retrieval on each problem based on a bidirectional knowledge evolution memory iteration mechanism, and performing multi-dimensional analysis in combination with the large language model to realize DEM landform analysis based on the large language model. The technical problems that in the prior art, LLM is difficult to directly and effectively analyze the spatial topological relation contained in DEM raster data, and iteration updating of geographic knowledge in a complex dynamic analysis task is difficult are solved.

The invention provides a mine land occupation instance segmentation method, and relates to the technical field of image processing, and the method comprises the steps: inputting obtained mine land occupation remote sensing image raster data into a trained mine land occupation instance segmentation model, and outputting a corresponding instance mask, the mine land occupation instance segmentation model comprises a boundary enhancement module and a boundary optimization module; the boundary enhancement module is used for processing the feature map of the raster data of the mine land occupation remote sensing image to obtain an initial mask of the remote sensing image; and the boundary optimization module is used for extracting boundary blocks along the boundary of the initial mask according to a preset sliding window, optimizing the boundary blocks by fusing different scale features through multi-resolution branches to obtain optimized boundary blocks, and splicing the optimized boundary blocks to generate an instance mask. According to the invention, the segmentation precision of the mine land occupation instance can be effectively improved.

The invention belongs to the cross technical field of disaster emergency management, remote sensing information processing and intelligent traffic scheduling, and particularly relates to a snow disaster emergency snow removal path intelligent planning method fusing a multi-target ant colony algorithm, and the method comprises the steps: S1, inputting multi-source heterogeneous data: receiving and integrating the input data, weight parameters such as operation workload, operation difficulty, passing urgency and rescue urgency are set; s2, constructing a road network line segment structure: based on the road vector data, generating basic unit road network line segments by extracting cross points and carrying out topology cutting; s3, calculating the snow removal volume and the operation time consumption: constructing a line segment buffer area according to the snow depth grid data and the road snow removal width, obtaining snow depth slices, calculating the volume, estimating the snow removal time consumption of each line segment in combination with the snow removal capacity of the rescue team, and calculating the snow removal volume and the operation time consumption by adopting the fusion of the road buffer area and the snow depth grid slices; and the accumulated snow volume and the operation time consumption of each road segment are accurately estimated.

The invention belongs to the technical field of meteorology, and particularly relates to a sand storm monitoring method based on aerosol window analysis and spectral indexes. Preprocessing the Himawari-8 data, and generating a raster data set including channel data, latitude and longitude information, elevation angle information and zenith angle information; a lookup table is constructed through a 6S radiation transmission model, and the optical thickness of the aerosol is inverted by using spectral information in the remote sensing image; scanning an image through a sliding window, and screening a sand storm feature region in combination with weighted average, standard deviation and abnormal judgment of aerosol concentration; establishing a sand-dust spectral index by using the sand-dust sensitive wave band; and combining the aerosol concentration with the sand and dust spectral index to extract sand and dust storm information. According to the method, the aerosol concentration and the sand dust spectral index are combined, sand dust storm information can be effectively identified and extracted, and important data support is provided for environmental protection and disaster early warning; the diversity of the sand storm detection method is increased, and the obtained result is accurate and reliable.

The invention discloses a co-seismic landslide susceptibility evaluation method and system based on heterogeneous ensemble learning, relates to the field of co-seismic landslide susceptibility evaluation, and solves the problem in the prior art that a deep learning model is difficult to capture the internal relationship between landslide occurrence and disaster-inducing factors through single-machine learning under complex and hard mountain conditions. According to the method, landslide boundary vector data and disaster-inducing factor raster data are integrated, and invalid disaster-inducing factors are removed according to correlation analysis and importance analysis to obtain a training data set; constructing a CNN-RXStack landslide susceptibility evaluation model to predict co-seismic landslide susceptibility of an earthquake area, converting the predicted landslide occurrence probability into a landslide susceptibility grade to obtain a landslide susceptibility map, and verifying and analyzing a landslide susceptibility result; according to the method, the heterogeneity of the disaster-inducing factor characteristics is improved, and the model can better capture the characteristic difference between the landslide and the non-landslide, so that the accuracy and the reliability of model prediction are improved.

The invention discloses an unmanned aerial vehicle image ground object recognition method based on an improved DeepLabV3 + network, and the method comprises the steps: obtaining a visible light sequence image of an unmanned aerial vehicle, carrying out the preprocessing and dividing, carrying out the data enhancement and pixel-level marking of a training set, and obtaining a marking data set; inputting the annotation data set into the improved DeepLabV3 + network for training, and monitoring a training process and performance indexes by using the verification set; an ASPP module of the improved DeepLabV3 + network adopts an initial voidage combination, and the voidage of the ASPP module is dynamically adjusted based on a verification set performance monitoring index in a training process to obtain a trained semantic segmentation model; inputting the test set image or the to-be-identified unmanned aerial vehicle image into a semantic segmentation model to obtain an initial segmentation result; the method comprises the following steps: firstly, carrying out semantic segmentation on a model, then, carrying out automatic optimization on hyper-parameters of the model based on a Bayesian optimization framework, carrying out retraining or prediction on the model by utilizing the optimized hyper-parameters to obtain an optimized semantic segmentation result, and then, converting a picture format into a raster data format with geographic information to obtain a ground feature recognition graph.

The invention discloses a method and system for quantifying the influence of human activities on surface vegetation, and belongs to the technical field of environmental monitoring, and the method comprises the steps: firstly processing obtained remote sensing vegetation index data to obtain NDVI raster data, and then obtaining KNDVI raster data according to the NDVI raster data; then, a buffer area is constructed for the research area according to the radiation range to obtain an affected area, on this basis, the buffer area continues to be constructed, the original area is removed to obtain a contrast area, raster data of the area except natural vegetation is removed for the contrast area, original KNDVI raster data is used for the affected area, and the original KNDVI raster data is used for the contrast area; and the influence of human activities on the ground vegetation of the research area is quantified. A novel remote sensing vegetation index is adopted, and the defects that a traditional observation period is long, a model is difficult to simulate, and a remote sensing calculation method is large in processing workload and has no universality are overcome.

The invention provides a moon DEM upscaling method and system based on expert knowledge and a random forest model, and belongs to the technical field of digital processing. Marking different types of landform areas under the initial resolution (high resolution) in the initial moon DEM data by an expert, and quantifying the marked areas to generate second raster data; the second raster data comprises labels of different types of landforms under the target resolution, so that expert knowledge is quantified into the labels under the target resolution. On the basis, training sample data sets corresponding to different types of landforms are constructed in combination with initial lunar DEM data, and the feature importance of the different training sample data sets output by the random forest model is multiplied by the feature vectors of the training samples, so that the feature importance of the different training sample data sets is obtained. The expert knowledge is transmitted from the initial lunar DEM data to the DEM data after upscaling. According to the scheme, the practicability of the lunar DEM upscaling method in lunar appearance multi-scale mapping can be improved.

The invention discloses a multi-source rainfall data fusion method for a regional landslide early warning threshold. The method comprises the following steps: improving the spatial resolution of long-sequence raster data by adopting a statistical downscaling technology; according to the landslide position, selecting an optimal neighborhood method or a Thiessen polygon method to generate a site and grid representative precipitation sequence pair; establishing a dry day / wet day discrimination model based on the characteristics of the rainfall event triggering the landslide; correcting daily precipitation based on a quantile mapping method; and constructing a regional rainfall landslide threshold curve and performing precision test. According to the method, the defects of low precision of long-sequence grid data, short site data time sequence and space-time difference between a site rainfall observation position and a landslide occurrence position can be improved, and an effective means for establishing a rainfall type landslide early warning system is provided for a data lacking area. Meanwhile, the method can be migrated to rainfall type landslide risk assessment in a global future climate situation mode, and scientific support is provided for constructing a climate adaptation type disaster reduction system.

The invention relates to the technical field of satellite remote sensing, and discloses a kNDVI-based object-oriented karst mountainous area stony desertification classification method and system, and the method comprises the steps: obtaining a remote sensing image and elevation data of a target region, and carrying out the preprocessing; obtaining vegetation coverage raster data, rock coverage raster data and texture parameters based on the preprocessed remote sensing image, and obtaining a slope and an altitude based on the preprocessed elevation data; fusing the vegetation coverage raster data, the rock coverage raster data, the texture parameters, the gradient, the altitude and a plurality of wavebands of the preprocessed remote sensing image, and outputting a multi-waveband remote sensing image; removing an area of which the building index is greater than 0 in the multiband remote sensing image, and segmenting the multiband remote sensing image after removal processing; and according to the segmentation result, carrying out stony desertification grade division by using the membership degree, and outputting a classification result. According to the invention, the extraction precision of stony desertification information can be improved, so that the classification accuracy is improved; meanwhile, the regional adaptability is improved.

A method and a system for networking a water network system are provided, including: collecting digital elevation raster data of a study area, identifying rivers within the study area, and dividing the study area into several subnets, each contains several sub-basins; constructing evaluation models for each subnet and optimizing model parameters and hyperparameters, and calculating comprehensive evaluation results of a coordinated state of water resources-ecological environment-economic and society; sorting the characteristic values and evaluation indicator values, constructing a feasible networking scheme set, constructing complementary indicators, forming a matched networking scheme set and calculating engineering construction cost, constructing an optimal scheduling model, and calculating to obtain an optimal scheduling mode and networking benefit; setting a networking cost range, determining the networking scheme and inputting it into a pre-constructed optimal networking decision model, and calculating to obtain a networking scheme of the water network system.

The invention discloses a natural grassland state identification method, and the method comprises the steps: obtaining a remote sensing image of a natural grassland, and obtaining the spatial distribution data of the grassland through a trained grassland state identification model. And constructing a grass mowing field state recognition model based on the grass mowing characteristic indexes. The grass trimming characteristic indexes comprise a topographic characteristic index, a spectral characteristic index, a texture characteristic index and a user-defined characteristic index. And for each grass trimming characteristic index, generating corresponding characteristic index raster data through calculation, and constructing grass trimming field characteristic index data through a wave band synthesis tool.

The invention belongs to the field of ocean remote sensing information, and particularly relates to a laver culture area rapid extraction method based on sentinel No.1 radar images. Comprising the following steps: 1) acquiring a sentry No.1 radar image, and analyzing and processing pixels in the image to obtain a nori raft frame initial product; 2) pretreating the initial product of the laver raft frame to obtain a continuous raft frame product; 3) extracting three masks by using the auxiliary optical image and confused pixels around the high-reflection area; and 4) performing mask processing on the continuous raft frame product by using an extracted result to obtain a spatial distribution grid data set of the laver culture raft frame. According to the invention, the optical image is prevented from being limited by a cloud layer and a turbid water body, the time sequence is integrated through a maximum value synthesis algorithm, and the complete spatial distribution of the raft frame submerged by the high tide level is captured.

The invention provides an urban road network robustness evaluation method, device and equipment under a disaster and a storage medium, belongs to the technical field of road network information processing, and solves the problem that the urban road network robustness evaluation is inaccurate under a rainstorm waterlogging disaster risk. The method comprises the following steps: acquiring environmental factor data and road network topology data of urban roads; inputting the environmental factor data into a waterlogging risk prediction model for processing to obtain waterlogging sensitivity raster data; performing mapping processing on the waterlogging sensitivity raster data and the road network topology data to obtain a road network node failure probability; determining a road network robustness index according to the road network node failure probability and the road network topology data; and determining urban road network robustness evaluation data according to the road network topology data and the road network robustness index. According to the scheme, road network robustness evaluation under disaster impact and disaster reduction decision optimization under resource constraint are realized.

The invention relates to a fertilization prescription map rapid generation method and system based on multispectral detection, and belongs to the technical field of agriculture. Comprising the following steps: multi-source data acquisition: synchronously acquiring a 450-900nm wave band crop multi-spectral image acquired by a multi-spectral camera carried by an unmanned aerial vehicle, laboratory detection nutrient data of a preset soil sampling point, real-time soil humidity data, plot boundary vector data and a crop recommended fertilization model; data preprocessing: carrying out radiation correction, geometric correction and de-noising processing on the multispectral image; performing crop nutrient demand inversion; according to the method, the multi-spectral image of the unmanned aerial vehicle, the soil sampling data, the real-time soil humidity and the plot boundary vector data are synchronously integrated, a raster data set of a unified coordinate system is constructed, and crop nutrient requirements and environment variables are comprehensively covered. In combination with vegetation index combinations such as NDVI, RENDVI and RVI, the crop nutrient content is inverted through the PLSR algorithm, and the prediction precision R2 is greater than 0.85 and is significantly superior to that of a single index model.

The embodiment of the invention discloses a sub-basin division method of a target area and related equipment, and the method comprises the steps: fusing the elevation model data of the target area with a multispectral remote sensing image according to a preset terrain sensitivity coefficient, a preset river elevation constraint weight and a preset dynamic river reference elevation; the fused data can effectively reflect the topographic structure characteristics of the target area; historical meteorological raster data, future predicted meteorological raster data and fusion data serve as a data set to be input into an ST-Mama model for water system extraction, so that the ST-Mama model can fully combine topographic features and meteorological conditions to optimize the water system recognition effect, and the probability value of each raster output by the ST-Mama model belonging to the water system is more accurate. Therefore, an accurate and accurate water system graph is obtained when the probability value is used for water system division, so that the accuracy of the obtained sub-basins is improved.

The invention belongs to the technical field of image processing, and particularly relates to a geological disaster risk area dynamic updating method based on slope unit automatic segmentation. Comprising the following steps: 1, acquiring digital elevation model raster data covering a target area and remote sensing image raster data spatially aligned with the digital elevation model raster data, and outputting slope unit vector data; 2, acquiring disaster-bearing body vector data, performing spatial superposition on the disaster-bearing body vector data and the slope unit vector data, and combining outer boundaries of slope units of all geological disaster risk areas to generate risk area vector boundary data; 3, generating new year risk area vector boundary data based on the new year data; and updating the risk area vector boundary data of the last year based on the risk area newly-added area and the risk area reduced area. According to the method, the slope unit division precision, the disaster-bearing body exposure description capability and the timeliness of risk area range updating are remarkably improved.

Embodiments of the invention provide a data processing method and apparatus, and an intelligent driving device. The method comprises the steps of obtaining point cloud data; points in the point cloud data are mapped to a two-dimensional grid coordinate system from a three-dimensional space coordinate system, grid data are obtained, and the grid data comprise measured values of the points; according to the raster data, the type of the point is determined, and the type of the point comprises a noise point or a non-noise point; and de-noising the point cloud data according to the type of the point. The embodiment of the invention can be applied to an intelligent automobile or an electric automobile, a large amount of random access is not needed in the processing process of the point cloud data, and the data processing efficiency can be improved.