224 results about "Raster data" patented technology

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 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 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 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 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 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 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 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.

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 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 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.

A method for performing Tile to Raster (T2R) conversion includes: receiving tile input data including a stream of a plurality of tiles each having a tile height, a tile input width, a macroblock width (MBW), and data bits; segmenting the tile input data based on a total number of virtual square tiles; segmenting a Tile Buffer (TB) into one or more of the virtual square tiles based on the received tile input data; and performing a raster-scanning operation on each of the segmented tile input data and the segmented TB based on the total number of virtual square tiles to generate raster data.

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.

To solve a conventional problem, or, to provide a building information acquisition system capable of reducing effort required to generate building data as compared with conventional techniques.SOLUTION: A building information acquisition system which acquires building information related to a building plan view on the basis of on raster data of the building plan view comprises region extraction means and orientation specification means. When the building plan view is input into a first learned model, an "orientation symbol area (area in which an orientation symbol is displayed)" is extracted. When the orientation symbol area is input into a second learned model, a "drawing orientation (orientation of the building plan view)" is identified.SELECTED DRAWING: Figure 3

First, a device 100 acquires drawing data in a raster format (S201). Next, the device vectorizes the drawing data and converts the data into vector data (S202). Next, the device 100 generates dimension line data associated with first and second nodes included in the vector data (S203). Subsequently, the device 100 identifies, from the vector data, one edge or a plurality of connected edges close to a dimension line represented by the dimension line data (S204). The device 100 then performs character recognition on a corresponding region in the drawing data corresponding to a region including the identified one edge or connected edges (S205), and stores a recognized character as a dimension in association with the dimension line data (S206).

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 present invention provides a land use driving pattern mining method and related equipment, which processes the original raster data of the study area to obtain processed raster data; calculates the processed raster data to obtain land use change characteristics, neighborhood land use characteristics and land use scene characteristics of the study area; inputs the processed raster data, land use change characteristics, neighborhood land use characteristics and land use scene characteristics into the generated minimum spanning tree to partition, and obtain partition results; and uses the partition results to iterate the initial Bayesian network to obtain an iterated Bayesian network. The partition results are optimized according to the iterated Bayesian network until the iteration termination condition is met, and the optimal partition structure of the study area and the Bayesian network of each partition in the optimal partition structure are output as the land use driving pattern mining result of the study area; and accurate mining of the land use driving pattern can be 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 invention relates to a multi-source heterogeneous spatio-temporal data fusion modeling method and system. The method comprises the steps that a root directory is created on a computer system to serve as a physical address of a spatial data model; under the structure of the root directory, creating a subdirectory corresponding to each storage object type as a physical address of the storage object type; the storage object type comprises vector data, two-dimensional raster data and three-dimensional model data; adding related data of each storage object type in the core metadata system table; creating a database file corresponding to the storage object type under each subdirectory, initializing the database file, and generating an exclusive metadata table corresponding to the storage object type in the database file; the method comprises the following steps: acquiring two-dimensional and three-dimensional joint data of a live-action three-dimensional scene as to-be-stored data, calling a corresponding data conversion tool to convert the to-be-stored data based on the type of the to-be-stored data, storing the to-be-stored data in a corresponding database file in a corresponding storage mode, and associating the to-be-stored data with an exclusive metadata table in the database file.