154 results about "Spatial extent" patented technology

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

The invention belongs to the technical field of geographic information technology and remote sensing image processing and service, and discloses a slice-free image service system and method based on dynamic projection and real-time mosaic, which abandons the traditional pre-slicing mode, directly provides online service based on original image data, and improves the service efficiency. The problem of redundant storage caused by pre-generation and storage of massive tiles is fundamentally eliminated, and storage space is saved by up to 90%. Meanwhile, when the original data is updated, the system does not need to carry out a time-consuming re-slicing process, real-time updating and publishing of services can be realized, and the core pain points of long data updating period and high delay in the traditional technology are thoroughly solved. Through the integrated dynamic projection engine and the real-time mosaic module, the on-demand service request of the client for any coordinate system, any spatial range and any resolution can be responded.

The invention discloses a grid-coding-based video space-time retrieval method and device, the grid-coding-based video space-time retrieval method is executed in computing equipment, and the method comprises the following steps: mapping a coordinate range of a target area into a corresponding grid code set according to a Beidou grid standard on the basis of the coordinate range of the target area; acquiring video data with an intersection or inclusion relationship between the spatial range of the shot object and the target area; combining the shooting time of any video data with each grid code to generate a space-time grid code set; constructing an index structure corresponding to the video data by taking each time-space grid code in the time-space grid code set as a main key; and in response to query information of the user, analyzing a space-time range in the query of the user, mapping the space-time range into a corresponding space-time grid code range, executing retrieval based on the range, and returning hit video data. According to the method, rapid positioning and accurate retrieval of large-scale aerial photography video data are realized.

The invention belongs to the technical field of spatial data processing, and particularly discloses a multi-source remote sensing data gridding construction and processing method and system, a terminal and a medium. Comprising the following steps: acquiring remote sensing image data, vector data, point cloud data and TIN data to form a standardized data set; performing three-dimensional layered mesh generation on the target area to generate an initial multi-level mesh set; performing geometric superposition and coverage judgment on the standardized data set to form an element-grid mapping set; performing double-engine coding and decoding on the grid units in the mapping set, generating a unique grid code, and establishing a code-space range mapping table; constructing a three-dimensional space topological structure; obtaining a space intersection result, a space union result and a space difference result; a multi-scale grid set is generated, and a unified gridding integrated data set is formed. Through gridding fusion and self-adaptive multi-scale processing of multi-source data, integrated expression and efficient organization of multi-source remote sensing data in a three-dimensional space are realized.

The invention provides a robot dog intelligent inspection control method and system for an open environment, and relates to the technical field of data processing.The method comprises the steps that the space range of inspection environment data is divided into a plurality of space units, each space unit is marked as a region paragraph, and each region paragraph is marked as an area paragraph; the method comprises the following steps of: obtaining a plurality of regional paragraphs, establishing cross-segment marks among the regional paragraphs to form a recursive chain, calculating the route change transmission degree of each regional paragraph in the recursive chain to obtain recursive feature intensity, layering each regional paragraph in recursive chain data, generating regional paragraph sets with different intensity levels, and obtaining a recursive feature set; and calculating the periodic aggregation degree of each area paragraph in each inspection period to obtain periodic paragraph intensity, correspondingly binding each area paragraph in the rearrangement sequence data with a preset action set to obtain action control data, and generating a control instruction according to the action control data. According to the method, superposition and amplification of tiny offset in control circulation can be avoided, and the problem of interruption of the inspection process caused by frequent correction is solved.

The invention belongs to the technical field of computer vision, and discloses a semantic edge guided three-dimensional scene reconstruction method based on an SDF diffusion model. The objective of the invention is to solve the problems of high bandwidth occupation of input data, poor structural consistency and insufficient reconstruction quality in traditional three-dimensional reconstruction. A point fine segmentation and gradient semantic edge extraction method is provided, edge information with consistent semantics and rich geometric details is extracted, and lightweight scene representation retaining key geometric information is realized. On the basis, a three-dimensional reconstruction framework of staged diffusion is proposed: firstly, a global occupancy field is generated to determine an object space range; then executing high-resolution SDF diffusion in the local area to recover a fine geometric surface; and finally, carrying out space alignment and fusion to ensure the consistency of a reconstruction result under a global coordinate system. According to the method, the geometric accuracy, semantic consistency and resource overhead of reconstruction are effectively balanced, and a novel solution is provided for high-quality three-dimensional scene reconstruction under a low-bandwidth condition.

The invention discloses a structured corpus generation method and device for geological map multi-modal large model training, and the method comprises the steps: preprocessing geological vector data, and constructing a semantic aligned geological data set; randomly selecting a data viewport, and setting the geographic space range and size of a map viewport; configuring a plurality of visual parameter items, performing controllable random adjustment on text labels, element symbols and map maps, and customizing and producing geological map samples as required; extracting multi-dimensional attribute information of the geological map sample to construct structured meta-information annotation data, and constructing a geological element mask annotation file to obtain a structured corpus for geological map multi-modal large model training. According to the method, large-scale production of the training samples with diversity, high quality and image-text alignment features is realized, the geological map corpus meets the requirements of multi-modal large model training on data scale, diversity, labeling precision and modal alignment, and the training efficiency and generalization ability of a geological map intelligent interpretation understanding model are remarkably improved.

The invention relates to the technical field of three-dimensional vision and robot perception, in particular to a belt conveying line multi-camera point cloud real-time splicing method and system based on GPU acceleration. The point cloud real-time splicing method comprises the following steps of S1, collecting point cloud data of objects on a belt conveying line through a plurality of depth cameras; s2, performing spatial range filtering of the point clouds in parallel by using a GPU, and removing invalid points; s3, point cloud coordinate transformation is executed in parallel by using a GPU, and the point clouds of all the cameras are uniformly converted into the same world coordinate system; s4, performing triple filtering processing on the transformed point cloud to realize noise suppression, downsampling and density equalization; s5, fusing the filtered point clouds to form a complete scene point cloud; and S6, publishing the fused point cloud data through an ROS mechanism. GPU parallel computing is utilized, multi-camera point cloud high-speed real-time splicing is achieved, denoising equalization is effectively achieved through triple filtering, the processing efficiency and precision are remarkably improved, and the method is suitable for long-distance industrial monitoring.

The invention discloses a method and device for supporting multi-radar high-resolution networking and a storage medium, and relates to the technical field of radar data processing. The method comprises the following steps: determining a space range of a networking field; according to the spatial range and the target resolution, defining a grid structure of a networking field data body; allocating computing resources for high performance computing; aiming at each radar, pre-calculating a mapping area of the detection range of the radar in the grid structure; loading the original polar coordinate observation data of each radar into the computing resource in parallel; solving a networking value of each grid point in the grid structure layer by layer based on the mapping region and original polar coordinate observation data, and generating a networking result; and post-processing and storing the networking result, and releasing the computing resources. According to the method, the solution is directly solved from the original polar coordinate observation data based on the mapping region, so that the generation and conversion of massive intermediate data are avoided, and the technical bottleneck of huge memory and video memory consumption is fundamentally solved.

The application discloses a kind of forest scene voxel model construction method and system, the method includes: based on laser radar obtains the point cloud data of reference tree of the region to be measured and the point cloud data of canopy structure;According to the point cloud data of canopy structure, the point cloud data of reference tree is carried out point cloud conversion, and the point cloud conversion data of reference tree is obtained;According to the point cloud conversion data of reference tree, the blank of the point cloud data of canopy structure is filled with reference tree, and high-density point cloud data is generated;High-density point cloud data is voxelized, and forest scene voxel model is constructed.The system includes: data acquisition module, point cloud conversion module, data filling module and construction module.Through using the application, it can be realized in large space range to reconstruct the fine scale three-dimensional structure of forest scene and improve resolution.The application is a kind of forest scene voxel model construction method and system, and can be widely applied to laser radar data three-dimensional modeling technical field.

The invention discloses a multi-dimensional construction safety risk management and control method for a high-altitude highway, particularly relates to the field of construction risk management and control, and is used for solving the problems of slope monitoring lag and passive safety management and control caused by complex terrain in the existing high-altitude highway construction process. The method comprises the following steps: acquiring aerial survey images and laser point cloud data of a construction slope area according to a fixed period, generating a time sequence three-dimensional live-action model sequence, identifying and tracking natural feature points and artificial structure feature points in the model sequence, calculating a feature point displacement field based on precision registration among multiple periods of models, and obtaining a feature point displacement field. The method comprises the following steps: identifying risk deformation data in space-time association with a construction activity in combination with historical construction activity records, performing spatial clustering analysis on the risk deformation data to mark a potential instability region, and generating a prospective construction risk grading control signal according to the space range of the potential instability region and construction information data; and dynamic pre-judgment and graded response of high-altitude highway construction risks are realized.

The invention provides a multi-person posture estimation method and system based on topology perception dynamic association, and relates to the technical field of computer vision, and the method comprises the steps: synchronously obtaining a plurality of initial human body instance sets in an image and a corresponding initial key point set in each initial human body instance set based on a multi-scale feature map, generating a preliminary topological association between the human body instance and the key point; for each key point in the initial key point set, dynamically adjusting the spatial range of the key point based on the global context feature of the human body instance to which the key point belongs and the local semantic feature of the key point, and generating a dynamic key point frame; based on key point features extracted by the dynamic key point frame, key point relation reasoning is carried out based on a topology perception correlation model, key point positioning is optimized, grouping from key points to human body instances is completed, and a final multi-person posture estimation result is output. According to the method, the technical problems of poor robustness and insufficient precision of the existing multi-person attitude estimation method in a complex scene are solved.

This invention discloses a project status management method and system for intelligent point cloud modeling, relating to the field of project status management technology. The method includes registering point cloud block files, label files, and 3D model files generated during point cloud segmentation, point cloud partitioning, geometric fitting, and pipeline parameter adjustment as subsequent versions and recording operation impact information; constructing a 3D spatial index structure to associate and store point cloud block files, pipeline objects, and corresponding operation impact information; and when a rollback request is received, determining the set of objects that intersect with the target 3D spatial range and whose semantic category belongs to the target semantic category set, and determining the set of affected objects whose version number is later than the target version identifier. This invention can, upon receiving a rollback request containing the target spatial range and the target semantic category set, only locate the affected local object set and perform a local rollback, which helps to preserve the modeling results of unaffected areas and reduces redundant processing and manual verification work.

The invention discloses an urban space type identification method based on a mixed density clustering algorithm, and relates to the technical fields of smart cities, urban planning, tourism planning, urban traffic and the like, and the method comprises the steps: constructing a data set for urban space type identification for a research area; based on the constructed data set, urban space division processing is carried out by using a DBSCAN density clustering algorithm, and an obtained result is marked as a clustering result D; performing unique clustering processing on each category S in the D based on a C-means clustering or Gaussian mixture model (GMM) algorithm to obtain a central point position P of each category S; counting the number of facility POIs in a local spatial range for the central point position P of each category S, and recording an obtained result as a local spatial information counting result; and determining the type of each category in the clustering result D according to a local spatial information statistical result. According to the invention, clustering of the service point space data set and identification of the urban space type can be realized based on the design of the mixed density clustering algorithm.

The invention discloses a neutral atmosphere refractive index calculation method and system suitable for different heights, and belongs to the technical field of communication and navigation. During modeling, monthly average reanalysis data is used as input to calculate the three-dimensional refractive index field of the neutral atmosphere, and horizontal, vertical and time domain change characteristics of the refractive index are represented by adopting a regular grid, a third-order index and a trigonometric function; when in use, the refractive index of any target point at any time within a global near-surface space range of 85 km can be calculated through space-time interpolation and elevation normalization only by giving the longitude, latitude, geodetic height and simplified Julian day of the target point. According to the method, the empirical modeling of the global neutral atmosphere refractive index field is realized by utilizing reanalysis data for the first time, the refractive index calculation precision at different height positions is improved through third-order index elevation normalization, and the method has important application value in the communication and navigation fields such as electromagnetic wave signal propagation path simulation and neutral atmosphere delay calculation correction.

Methods and systems for performing K-nearest neighbour (K-NN) queries on a spatial dataset in a distributed or parallel computing system are described herein, where the spatial data comprises query data and object data. The spatial dataset is pre-processed, whereby the query space (i.e., the spatial extent of the query data) is partitioned according to a particular partition scheme. For each partition, an area (defined as the object range of the partition) is calculated based on the geometry of the partition and a set of K candidate objects allocated to that partition, with all objects within the object range defining the extent of any subsequent K-NN queries performed on that partition. When the query dataset is subsequently partitioned using the same partition scheme to perform a K-NN query, the pre-processed object range information can be used to retrieve the objects to be queried for each partition.

A method for determining target object extent data (40), which characterizes at least one spatial extent of at least one target object detected in at least one radar measurement with at least one radar system, a method for determining at least one point cloud (34), a method for determining a model (52) by machine learning, a radar system, and a model (52) are described. In the method for determining target object extent data (40), at least one model (52) determined by machine learning is executed. At least a portion of the raw radar data (50) acquired in at least one radar measurement is fed to at least one model (52) determined by machine learning, and corresponding target object extent data (40) are determined by the at least one model (52) based on the fed radar raw data (50).

The application discloses a map parallel superposition analysis attribute matching method and system based on Hilbert space coding, relates to the technical field of geographic information systems, determines a target region, reads and loads vector polygon data of a source layer and a target layer which need to be subjected to superposition analysis, calculates a geometric center point of each polygon, generates a globally unique Hilbert space code according to a space range, and analyzes the vector polygon data of the source layer. The application generates the globally unique Hilbert space code by calculating the geometric center point of the polygon, avoids the high complexity problem of geometric intersection operation in the traditional method, the uniqueness of the code ensures the accuracy of attribute matching, especially in the massive data scene, the parallel processing of the Hilbert space code significantly reduces the calculation time, meanwhile, the space filling characteristic of the code ensures that the codes of adjacent polygons are similar, further optimizes the locality of attribute transmission, and reduces invalid calculation.

The invention provides a high-temperature and drought composite disaster monitoring and early warning method and system, and relates to the technical field of disaster monitoring and early warning, and the method comprises the steps: 2, extracting a data subset from a fusion data set, constructing a feature analysis region according to a preset reference relation, selecting a core analysis unit in the feature analysis region as a focus of an ellipse, and carrying out the calculation of the data subset; selecting an external reference unit as another focus of the ellipse in an adjacent spatial range, and calculating the spatial form and dynamic change of an eccentricity quantization region of the ellipse; according to the incidence relation of the core analysis unit and the external reference unit along with the time change, an analysis path is generated, an environment parameter correction coefficient is calculated to calibrate related elements in the fusion data set, and a calibrated fusion data set is obtained. According to the invention, probability prediction and dynamic early warning of high-temperature drought composite disasters are realized, and the reliability and real-time performance of disaster early warning are improved.

This invention provides a method for security responsibility assessment and closed-loop traceability in smart governance scenarios, comprising: acquiring multi-source heterogeneous data in the smart governance scenario; generating a corresponding event identifier when an abnormal event is detected in the multi-source heterogeneous data; extracting a structured evidence package within a target time window and target spatial range based on the event identifier; extracting responsibility factors from the structured evidence package; inputting the responsibility factors into a responsibility assessment model and a responsibility scoring function to obtain the responsibility score, responsibility level, and responsibility confidence level corresponding to the abnormal event, and determining at least one of the direct responsible entity, related responsible entity, and management responsible entity based on the responsibility correlation; and automatically generating corresponding handling instructions based on the responsibility level and the category of responsible entity.

This invention relates to the field of artificial intelligence technology, providing an attention computation optimization method, electronic device, storage medium, and program product. The method includes: generating a bounding box list of an attention matrix based on mask information for attention computation, wherein the bounding box list includes at least one valid bounding box, which defines the spatial range of a target data block in the attention matrix to be subject to attention computation; loading the target data block corresponding to each valid bounding box from a query matrix, a key matrix, and a value matrix based on each valid bounding box in the bounding box list, and performing attention computation on the loaded target data block to obtain the attention output corresponding to each valid bounding box. This invention, by generating a bounding box list and loading and performing attention computation only on data blocks within valid bounding boxes in the list, avoids invalid computation and waste of computing resources, thereby improving overall computational performance.

The invention provides a method for establishing a spatial LCA data set, and relates to the technical field of life cycle evaluation and environment management, and the method comprises the steps: determining a data set target and a corresponding product / service, and defining a system boundary, a region boundary, a time boundary and a data quality requirement; then collecting basic unit process data including input / output streams and geographic positions; making a space division rule (including a space range, a grid shape and granularity) according to a region boundary and an application scene, and generating a grid system; attributing the basic unit data to the corresponding grids through space matching; aggregating the data in the grids according to a weighted average algorithm and other synthesis rules to generate synthesis unit process data; associating the grid geographic position identification to form grid unit process data; and finally, storing the original data and processing records in standard formats such as ILCD / EcoSpold2 and the like. The time-space consistency, traceability and multi-scene adaptability of data are ensured through a systematic process, and the method is suitable for the fields of environmental evaluation, carbon accounting and the like.

The present application relates to the technical field of marine engineering digital twin and three-dimensional visualization technology field, provide a kind of based on right triangle grid's dredging topography twin's generation method and system, comprising: obtaining regular grid elevation or sampling height data after pre-processing that have been unified to engineering plane coordinate system, determine the space range covered read space range and density parameter, based on space range and total row number and total column number of calculated grid;In the space covered, generate regularly arranged vertex, construct the topological structure of isosceles right triangle grid;With vertex as unit, several Chunk blocks are divided into global grid with fixed size, establish the triangular index mapping relationship of global row and Chunk block, the three vertices of isosceles right triangle and elevation or height data are filled in each Chunk block in parallel, generate local VBO, IBO and AABB;Batch rendering and coloring according to height color table are executed to all Chunk blocks, and the visualization of topography is realized.

The application discloses a deep learning magnetotelluric data inversion method and device based on sub-region coding, and the method comprises the following steps: dividing an inversion region of magnetotelluric data into multiple coding sub-regions; constructing a training data set according to prior information of the multiple coding sub-regions; training a deep learning model by using the training data set to generate coding with prior regions, and generating coding without prior regions by using direct mapping, so as to re-parameterize a geoelectric model and obtain a re-parameterization result; and optimizing the coding based on the re-parameterization result to realize magnetotelluric data inversion. The application can flexibly embed prior information with different complexities, degrees of certainty and spatial ranges; can adaptively improve inversion effect according to the quality of the prior information; can strengthen the reconstruction resolution in a specific region or direction; and has low complexity and low training calculation amount.

The invention relates to the technical field of remote sensing data processing and visualization, and discloses a three-dimensional rainfall reconstruction method based on GPM-DPR satellite observation data, and the method comprises the steps: obtaining a rainfall rate parameter representing a rainfall rate in GPM-DPR three-dimensional rainfall data, and generating a GeoTIFF format raster data set of a plurality of height layers; performing spatial range unified processing on the GeoTIFF format raster data set of each height layer based on a rectangular latitude and longitude boundary to obtain the GeoTIFF format raster data set of each height layer with a complete and consistent spatial range; and carrying out structured organization on the GeoTIFF data set of each height layer, and constructing a multi-layer nested GeoJSON structure. According to the method, automatic conversion from original HDF5 format data to a GeoJSON format required by WebGIS front-end visualization can be realized, and reconstruction and organization of three-dimensional rainfall data are realized.

A wide field of view video is split into multiple perspective projections, with individual perspective projections providing a two-dimensional view of a spatial extent of the wide field of view video. Object detection is performed within individual perspective projections to determine the placement of the objects within individual perspective projections. The placement of the objects are projected back into the wide field of view video to merge the detections. Redundant detection are filtered out and the remaining detections are used to perform object tracking in the wide field of view video.

The invention relates to the technical field of thermal ablation, in particular to a thyroid nodule ultramicro-invasive ablation precise puncture positioning method, which comprises the following steps of: acquiring continuous gray frames and extracting tissue gray to complete normalization and phase demodulation to generate phase time data; detecting a brightness peak value according to phase time data to obtain a tip echo, calculating a phase gradient in a neighborhood by using a fixed window to generate a stable domain data set, calculating a spatial range according to a stable domain, extracting a phase change signal along with time, and completing frequency domain dominant frequency extraction and period analysis to generate a period consistent region; according to the method, the continuous image phases are extracted, the time sequence is combined, the needle point recognition stability is enhanced, the echo region is restrained through the brightness peak value and the phase gradient, and the accuracy of positioning is improved. Position judgment is enhanced through spatial range and period analysis, and the guiding precision is improved by combining a three-dimensional distance and a symbol sequence.

Embodiments of the present application provide a kind of processing method, device, electronic equipment, storage medium and product of abnormal data.The method comprises: obtaining the two-dimensional grid map to be processed;At least one specular reflection area is contained in the two-dimensional grid map;For any specular reflection area, determine a plurality of first line segments located in the specular reflection area, and determine the target boundary line segment of the specular reflection area based on the plurality of first line segments;Determine the spatial range of the specular reflection area based on the target boundary line segment, and delete all pixel points in the spatial range of the specular reflection area, provide a kind of systematic way to identify and remove abnormal data, reduce the need for manual intervention, support the data cleaning process of automation, and the spatial range of specular reflection area can be accurately determined, improve the cleaning efficiency and accuracy of abnormal data.

The invention discloses an airborne ocean laser radar hierarchy and water bottom synchronous three-dimensional reconstruction method. The method comprises the steps of obtaining a three-dimensional point cloud of laser on a water surface according to airborne laser radar data; gridding is carried out on the three-dimensional point cloud of the laser on the water surface, multi-pulse averaging of spatial aggregation is carried out, and signals after gridding aggregation are obtained; and obtaining a water surface peak position, a level peak position and a water bottom peak position, and finally obtaining water bottom and level three-dimensional point clouds at the same time, thereby realizing high-precision three-dimensional reconstruction. According to the method, the thought of voxelized grids is combined, resolution sacrifice caused by multi-pulse averaging in a large space range is avoided, high-resolution three-dimensional reconstruction with adjustable resolution is achieved, a multi-target distinguishing algorithm architecture is designed, and the effect of multi-target simultaneous detection is achieved; the method has reference significance for shallow sea environment detection and shallow sea carbon cycle analysis, and has wide applicability.

Disclosed are a traffic data warehouse construction method, a storage medium, and a terminal. The method includes: creating a target monitoring task based on a creation instruction; loading a monitoring object and spatial range corresponding to the target monitoring task, and obtaining a sampling unit set of the monitoring object; calculating a spatial attribution relationship between a spatial range of each sampling unit in the sampling unit set and the spatial range of the target monitoring task, and determining a spatial coupling relationship; setting a label of the monitoring object based on the spatial coupling relationship, generating a labeled monitoring object, inputting the labeled monitoring object to a preset monitoring calculation function, and outputting a calculation result; and configuring the calculation result in the labeled monitoring object, and distributing the configured monitoring object to a task database corresponding to the target monitoring task.