140 results about "Raster data" patented technology

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 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.

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 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 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.

Methods and systems for identifying stormwater management opportunities in accordance with embodiments of the invention are disclosed. In one embodiment of the invention, a method for identifying stormwater management opportunities comprises gathering spatial data for a region; rasterizing the spatial data into a set of raster data layers; forming a plurality of benefit maps using subsets of the set of raster data layers, wherein the plurality of benefit maps corresponds to a plurality of benefit categories; generating an opportunity map by combining a subset of the plurality of benefit maps using weights corresponding to the plurality of benefit categories; and displaying the opportunity map on a user interface.

A spatial analysis method for vector and raster data, applied on a server side, includes: receiving vector data and raster data from a client; performing gridded representation of the vector and raster data respectively in a reference coordinate system based on a first partitioning level to obtain grids related to the vector data and grids related to the raster data, wherein the first partitioning level is used to indicate the granularity level of grid division; and performing spatial analysis on the vector and raster data based on the grids related to the vector and raster data. In this way, by unifying the representation of both vector and raster data through gridded representation during spatial analysis, the spatial distribution of vector and raster data can be represented using data of a single modality, reducing the difficulty of cross-modal spatial analysis of vector and raster data. Furthermore, the method can filter out computationally irrelevant grids during spatial analysis, improving the efficiency of spatial analysis.

The invention relates to the technical field of urban hydrological simulation. The invention discloses an urban community surface ponding simulation method considering a roof structure, and the method comprises the steps: obtaining a regional multi-angle picture through an oblique photography unmanned plane, generating an oblique photography model through image processing, and reconstructing a digital surface model according to the oblique photography model; ground data are separated from the DSM through a PMF method, roof data are extracted in combination with building features, mask data are output, other ground features are removed, and building roof elevation grid data are obtained; calculating the gradient of the roof, dividing the gradient into flat and sloping roofs, building drainage structures into roof drainage models, integrating the roof drainage models with the urban community hydrological model considering the roofs, and running to realize surface ponding simulation. The method comprises the following steps: building a high-precision model through an oblique photography unmanned aerial vehicle and a GIS, separating ground data through a PMF, and accurately dividing a flat / slope roof through median filtering and an SVM; special drainage structures are arranged for the two types of roofs; and integrating the roof drainage model and a two-dimensional coupling hydrological model, and outputting an accumulated water thematic map.

The invention discloses a map conversion method and apparatus. The method comprises the steps of obtaining original data of unmanned aerial vehicle patrol flight; performing conversion processing on the coordinate data to obtain target coordinate data, and performing correction processing on the original image according to the target coordinate data and the flight attitude parameters to obtain an orthoimage; performing feature preprocessing on the orthoimage to obtain target vector data and target raster data; carrying out topological relation map construction according to the target vector data and the target raster data to obtain electronic map data; performing error correction on the electronic map data according to the high-precision reference map to obtain a corrected electronic map; and carrying out data integration on the corrected electronic map and the electronic map data to obtain a target electronic map. Therefore, by integrating the corrected electronic map data and the uncorrected electronic map data, the integrity and consistency of the data can be further improved, and the precision of the generated final electronic map is ensured to be higher.

The application provides a space-time big data distributed privacy computing system and equipment based on homomorphic encryption, and relates to the technical field of data processing. It comprises a space privacy computing operator, which is used for the computing characteristics of vector data and raster data, and realizes homomorphic encryption computing of vector data and raster data by using TFHE homomorphic encryption algorithm and CKKS homomorphic encryption algorithm respectively; a distributed space privacy computing framework is used, each node of the distributed space privacy computing framework is respectively provided with a space privacy computing operator, and the distributed space privacy computing framework realizes multi-node task parallelization and safe cooperation based on a key management mechanism under a distributed environment and a remote Boolean decryption protocol based on inadvertent transmission; each node in the distributed space privacy computing framework is provided with a GPU acceleration architecture, the GPU acceleration architecture is respectively provided with a corresponding CUDA heterogeneous computing optimization scheme for the Boolean logic operation of vector calculation and the matrix operation characteristics of raster calculation, and the processing efficiency of a single node is improved.

The invention discloses a geothermal target area intelligent delineation method, device and system and a storage medium, accurate constraint correction is carried out on imported plane grid data through well point deviation field correction, a finite difference heat-flow coupling solver is adopted to solve a temperature field and a pressure field, and a fluid potential field and a flow velocity field are calculated according to the temperature field and the pressure field. Target region identification and grading are realized based on hot fluid potential index HFI comprehensive evaluation, HFI probability distribution estimation and confidence grading are realized through Bayesian uncertainty quantification, extrapolation protection is realized through risk evaluation based on streamline convergence degree, and GUI one-key operation, data import, visualization and report generation are provided. By adopting the technical scheme of the invention, the calculation cost is reduced while the physical rationality is maintained, the confidence quantification of target region identification is provided, and the reliability of terrestrial heat target region delineation and the engineering decision efficiency are improved.

The invention discloses a method for quickly generating high-resolution carbon emission raster data, which comprises the following steps of: fusing national carbon emission panel data and night light remote sensing images, establishing a provincial-scale logarithmic regression model, extracting parameters of the logarithmic regression model and constructing a parameter characteristic matrix; a space-time constraint K-Means algorithm is adopted to perform clustering analysis on provincial administrative units throughout the country and divide the provincial administrative units into K carbon emission type areas, for each carbon emission area, an area regression model is established by using logarithmic carbon emission and night light data, and a clustering center parameter is used as Gaussian prior to obtain a clustering result; and performing parameter updating through Bayesian maximum posteriori estimation, estimating the carbon emission by using the model, performing correction on a pixel scale by adopting a zero error method, and finally generating a national carbon emission grid data set. The space-time constraint clustering and Bayesian maximum posteriori estimation method is introduced on the basis of carbon emission modeling, and balance of few models and high precision is achieved.

The invention relates to the field of remote sensing image processing and terrain correction, and particularly discloses a radar geometric field-driven SAR distortion information adaptive neighborhood propagation identification method, which comprises the following steps of: S1, acquiring a Golden digital elevation model of a research area and a pixel-by-pixel ellipsoid incident angle graph generated by SAR satellite precision orbit parameters and imaging geometry, and carrying out geometric unified processing on the Golden digital elevation model and the pixel-by-pixel ellipsoid incident angle graph; s2, calculating a terrain parameter, an effective slope angle, an R geometric response field and an R field directivity gradient based on the unified raster data; s3, main distortion region classification is carried out according to the effective slope angle and the pixel-by-pixel ellipsoid incident angle; s4, defining an adjacent gradient and constructing a self-adaptive threshold value and a shadow self-adaptive threshold value; and S5, performing passive region propagation from the boundary of the main distortion region, and generating a final classification grid map after space overlapping correction. According to the method, the defect of blind propagation of the existing method is overcome, and high-precision automatic identification of SAR geometric distortion under complex terrains is realized.

The application discloses an intelligent early warning method for slope landslide, and comprises the following steps: a. obtaining high-definition open slope orthographic images and terrain raster data; b. installing a slope radar on the opposite side of the open slope; c. installing a first intelligent cruise camera; d. increasing the number of cameras, and synthesizing panoramic photos of the early warning area by using photos taken by i cameras; e. increasing the number of cameras according to the monitoring period of the slope radar; f. determining the three-dimensional coordinate range and the central three-dimensional coordinate of the covered area of each photo; g. screening historical and real-time photos corresponding to the perspective of the early warning area, screening interference factors in the area based on an image artificial intelligence algorithm, and realizing intelligent early warning. The application establishes a mapping relationship between the camera perspective and the three-dimensional coordinates of the mine slope based on computer vision, verifies the early warning information quickly by using an artificial intelligence image processing algorithm, greatly improves the work efficiency, and reduces the labor intensity of personnel.

The invention relates to the technical field of computers, and discloses a meteorological data visual rendering system, which comprises an acquisition module used for acquiring meteorological data of a first spatial scale; the data rasterization module is used for mapping the meteorological data into a target grid structure according to a geographic range of a second spatial scale and a second resolution; the meteorological interpolation module is used for performing bilinear interpolation and / or inverse distance weighted interpolation on missing points and / or irregular sampling points in the target grid structure to obtain grid numerical values corresponding to grid pixels in the target grid structure; the region cutting module is used for cutting the target grid structure based on the inclusion relation between each pixel in the target grid structure and the to-be-rendered target region to obtain target grid data corresponding to the to-be-rendered target region; the meteorological data of the to-be-rendered target area can be rendered and displayed.

This invention discloses a multi-source deformation monitoring data fusion method and system based on scene awareness, belonging to the field of deformation monitoring data fusion technology. The invention involves: S1: acquiring GNSS time-series data, InSAR raster data, LiDAR point cloud data, and ECMWF meteorological data of the dam monitoring area; S2: constructing a multi-dimensional scene feature vector containing signal quality and environmental geometry dimensions based on the acquired data; S3: calculating a first weight, a second weight, and a third weight corresponding to the GNSS time-series data, InSAR raster data, and LiDAR point cloud data respectively, based on the multi-dimensional scene feature vector and a preset physical threshold indication function; S4: calculating a first deformation value, a second deformation value, and a third deformation value of the dam monitoring point based on the acquired data; S5: fusing the first deformation value, the second deformation value, and the third deformation value according to the first weight, the second weight, and the third weight to obtain the fused deformation value of the dam monitoring point.

The application discloses a road disease point pile number positioning method and system based on a UAV, and comprises the following steps: constructing a road center line based on a road orthographic image and a design file; taking each whole hundred pile number point as an initial reference point, constructing a plurality of vertical offset lines along the road center line, and distributing pile number data of each vertical offset line; taking each vertical offset line as a central axis to construct a rectangular buffer zone, and merging all the rectangular buffer zones into a strip-shaped buffer zone surface; performing grid division on the strip-shaped buffer zone surface according to the resolution of the orthographic image, and distributing pile number data of each grid; aligning each grid of the strip-shaped buffer zone surface with the orthographic image pixel by pixel, and fusing the pile number data of each grid with image data of corresponding pixels to generate corresponding fusion grid data; combining image data of the road disease point and the fusion grid data of each grid, searching for a grid closest to the road disease point by using a KD tree, and accurately positioning the pile number of the road disease point according to corresponding pile number data.

The embodiment of the invention discloses a dynamic landslide assessment method and system based on ensemble learning and phase gradient analysis, and a medium. A dynamic landslide assessment method based on ensemble learning and phase gradient analysis comprises the following steps: acquiring whole-area raster data, disaster-inducing factor raster data and landslide and non-landslide vector data of a target area, and respectively constructing a training data set and a prediction data set through spatial association; training the Stacking integrated model by using the training data set, inputting the prediction data set to obtain the landslide risk probability of each grid, and further determining the landslide susceptibility grade; the method comprises the following steps: acquiring SAR image data, calculating and stacking phase gradients in different preset space directions, combining stacked gradient maps to obtain a phase gradient index, determining a final deformation level of each grid according to the phase gradient index, constructing a dynamic evaluation list by taking a landslide susceptibility level as a column and the final deformation level as a row, and determining an evaluation level of each grid. Therefore, the high-risk area is accurately locked, the range of the high-risk area is reduced, and targeted decision support is provided for disaster prevention and reduction.

The invention relates to the field of agricultural irrigation monitoring, and provides a crop area water level determination method and device and a storage medium. The determination method comprises the following steps: generating a digital elevation model of a crop region, wherein the crop region comprises a plurality of sub-regions; determining elevation data of each sub-region from the raster data of the digital elevation model based on a predefined field boundary vector diagram to obtain elevation matrix data of each sub-region; performing visualization processing on the elevation matrix data of each sub-region to generate an elevation distribution diagram, and marking pixel points of a preset elevation in the elevation distribution diagram; determining a target mounting position of the water level gauge based on the elevation distribution diagram; acquiring water level data acquired by a water level gauge and elevation data at a target mounting position; and determining a water layer coverage area proportion and / or an average water layer height in the crop area based on the digital elevation model, the water level data and the elevation data at the target installation position. The method can provide accurate irrigation decision support for crops.

The invention discloses a flood risk real-time early warning method based on radar echo region nesting analysis. The method comprises the following steps: obtaining and preprocessing radar data; coordinate conversion and image generation: converting radar data into a Cartesian coordinate system, generating three-dimensional raster data through spatial interpolation, and extracting a height layer of 1-1.5 kilometers to generate an equal-height plane position display image; region nesting cell division: defining the effective detection range of the radar as a large region, and dividing the large region into a plurality of square cells; extracting radar echo characteristic parameters; global rainfall type identification and quantitative estimation; judging a gridding rainstorm early warning grade; and carrying out early warning fusion and generating an early warning product. According to the method, dual-scale division is carried out on a radar detection area, machine learning classification and a localized statistical threshold are fused, automation and objectification of the whole process from data preprocessing to early warning product generation are achieved, and finally a refined flood risk early warning guidance product which is high in spatial resolution, high in updating frequency and highly synchronous with the real condition is produced.

The present disclosure provides a point cloud noise identification method, device and unmanned vehicle, comprising: performing feature identification on the point cloud data of the current frame to obtain semantic point cloud; performing rasterization processing on the semantic point cloud, and removing the grid unit representing the dynamic target category according to the category to which each point in the semantic point cloud belongs, to determine the target semantic grid data of the current frame; fusing the target semantic grid data of the current frame with the target semantic grid data of the historical frame to generate terrain data; and checking the point cloud data of the current frame according to the terrain data to determine the noise of the point cloud data of the current frame.

A method for visualizing a heart valve and blood flow through the heart valve for the purpose of assessing valve backflow. A visualization is generated based on both the 3D B-mode data and the 3D Doppler data. The visualization includes generating a combined drawing of each of a heart valve structure, an upstream confluence region, and an output jet region. Drawing parameters applied in drawing each of these structures may preferably be independently adjustable to allow tuning of the visualization. The rendering may be performed by a render operable to render both the raster data and the grid data.

The application discloses a kind of regional water resource consumption spatialization method, comprising: 1) constructing research area database, integrating the grid data of water consumption data and influence factor in region;2) data is preprocessed, and eliminate dimensional difference;3) screening key influence factor;4) constructing multiple linear regression model;5) based on ArcGIS platform, in combination with the grid data of key factor, generate regional water resource demand quantity spatial distribution diagram;6) according to spatial distribution diagram, the result of rasterization is verified with original observation data, guarantee the accuracy and applicability of model.The application method integrates multiple influence factors, filters out key influence factor, uses linear regression optimization model, realizes the high-resolution spatial distribution expression of regional water resource demand quantity by means of GIS technology, solves the problem that traditional method ignores the coupling relationship of multiple influence factors, and can provide important scientific basis for water resource management, land use planning and ecosystem protection.

This application relates to the field of stockpile quantity technology, and discloses a method, apparatus, and machine-readable storage medium for determining the stockpile quantity. The method includes: acquiring point cloud data of a target stockpile; performing dimensional transformation and rasterization on the point cloud data to obtain raster data corresponding to the point cloud data; constructing a quadtree for each raster based on the points in each raster; determining the type of each leaf node in the quadtree of each raster based on the boundary information of the target stockpile, wherein the type includes boundary raster, internal raster, and external raster; determining the area of ​​each leaf node based on its type; determining the elevation of each leaf node based on its attributes in the quadtree of each raster, wherein the attributes include void nodes and non-void nodes; and obtaining the stockpile quantity of the target stockpile based on the area and elevation of each leaf node in the quadtree. This application can improve the accuracy of stockpile quantity estimation results.

This invention proposes a method for constructing manhole node data based on Geographic Information System (GIS) data, relating to the technical field of drainage system data analysis. First, vector data containing drainage pipes and manhole nodes, as well as DEM raster data covering the modeling area, are read. Based on the vector data, the flow direction of the drainage pipes is determined, and new manhole nodes are inserted along the flow direction according to a preset interpolation step size, calculating the inner bottom elevation of each new node. Based on the DEM raster data, the ground elevation of all nodes is extracted, and the manhole depth of each node is calculated in conjunction with the inner bottom elevation. Based on the inner bottom elevation and manhole depth of each new node, a complete geographic information dataset of manhole nodes is constructed. This solution addresses the problems of large workload and complex parameter calculations in existing SWMM modeling methods, improving the spatial resolution of the manhole node model while achieving efficient fusion and parsing of the original data, thus improving modeling efficiency.

This application provides an automatic mapping method and system for lunar impact crater micromorphology, belonging to the field of image data processing technology. The method involves acquiring a candidate raster dataset, including lunar digital terrain data, lunar image data, and terrain-derived data; the terrain-derived data includes multiple terrain-derived factors; filtering the candidate raster dataset to determine the optimal feature combination; combining the optimal feature combination with the labels of impact crater micromorphology subclasses to construct an impact crater micromorphology subclass dataset; inputting the impact crater micromorphology subclass dataset into a pre-built deep learning model for training to obtain a target model; and using the target model to automatically extract impact crater micromorphology subclasses across the entire lunar surface, thereby completing the mapping of the entire lunar landscape. This solution alleviates the current problem of insufficient efficiency in extracting impact crater micromorphology subclasses primarily through manual extraction, providing the possibility for high-precision and rapid mapping of the entire lunar landscape.

This invention pertains a to computer-implemented data structure, a computer-implemented data structure collection, computer-readable storage media, methods, and a computer program for storing and retrieving geographic data comprised in a multi-level tiled map. The invention further pertains to a geographic information system comprising the computer-implemented data structure and / or the computer-implemented data structure and configured to execute the methods for storing and retrieving geographic data comprised in the multi-level tiled map. The computer-implemented data structure is suitable for storing and retrieving geographic data organized in a tile pyramid of the multi-level tiled map, wherein coordinates of a tile of the tile pyramid of the multi-level tiled map in a tile coordinate system of the tile pyramid of the multi-level tiled map define a metadata location, within the geographic metadata comprised in the computer-implemented data structure, of a data pointer to a data location, within geographic data comprised in the computer-implemented data structure, of geographic tile data associated with the tile of the tile pyramid of the multi-level tiled map. The computer-implemented data structure collection for storing and retrieving geographic data of the multi-level tiled map comprising a plurality of tile pyramids, the computer-implemented data structure collection comprising a plurality of computer-implemented data structures.

The invention relates to the technical field of computers, and provides a point cloud-based planar graph generation method, device and equipment and a storage medium, and the method comprises the steps: obtaining to-be-detected point cloud three-dimensional data, and carrying out the two-dimensional projection of the point cloud three-dimensional data, and obtaining point cloud raster data; performing skeleton refinement on the point cloud raster data to obtain a skeleton diagram; processing the skeleton diagram to obtain gradient change characteristics, and detecting to obtain a wall line candidate line segment set based on the gradient change characteristics; obtaining a point chain topological relation graph based on the skeleton graph, and determining a connection relation of each wall line candidate line segment in the wall line candidate line segment set based on a preset wall line connection strategy by taking the point chain topological relation graph as a constraint model; generating final wall line data based on the connection relation of the candidate wall line segments; the wall line data is used for generating a plane graph. According to the method, the plane graph with high precision and good connection quality can be automatically generated.

To provide a device, method and program for recognizing a value of dimension within a drawing represented by drawing data for managing the drawing data including raster data.SOLUTION: A device acquires drawing data of a raster type (S201), vectorizes the drawing data to be converted into vector data (S202), generates dimension line data associated with first and second nodes included in the vector data (S203), specifies, from the vector data, one or more coupled edges adjacent to a dimension line represented by the dimension line data (S204), applies character recognition to a corresponding region within the drawing data corresponding to a region including the specified one or more coupled edges (S205), and stores a recognized character as a dimension in association with the dimension line data (S206).SELECTED DRAWING: Figure 2