1102 results about "Plane map" patented technology

Provided in the present invention is a three-dimensional reconstruction method based on LiDAR and vision multi-information fusion. The method comprises: calibrating a coordinate system of a LiDAR detection result and a visual detection coordinate system to unify the coordinate systems; obtaining sparse point cloud data of a LiDAR, and projecting data points onto a visual image; performing feature point extraction and depth estimation on an image collected by a monocular camera, so as to perform feature point extraction and depth estimation on a planar image, and thereby densifying a point cloud; fusing feature points, which are extracted from the image, with LiDAR point cloud data to form a dense point cloud, and mapping the dense point cloud to a three-dimensional space; and performing fast triangulation processing on the processed point cloud data to obtain a three-dimensional plane, performing spatial matching on image pixels and the three-dimensional plane, and restoring colors. In the method of the present invention, the LiDAR and the monocular camera are combined to complement each other, so that device costs are reduced, advantages in both robustness and stability are achieved, and the quality of point cloud data can be effectively improved.

The invention relates to the technical field of panoramic video processing, in particular to a multi-modal fusion panoramic video intelligent content analysis method, which comprises the following steps of: acquiring a panoramic video frame and a synchronous audio thereof; performing spherical projection transformation on the panoramic image data, and converting the equidistant projection image into a spherical image; generating a plurality of plane images based on the spherical image; performing candidate target position detection based on the plurality of plane images; based on a candidate target position detection result, an image target detection model is utilized to realize instance segmentation of a target, and target features are extracted to generate a target embedded description vector sequence; based on the synchronous audio, an audio recognition processing module is used for carrying out time sequence information extraction, and an acoustic attention sequence is formed; a spectrum sensor is adopted to realize association fusion of the target embedded description vector sequence and the acoustic attention sequence, and an audio and video collaborative attention sequence is generated; and based on the audio and video collaborative attention sequence, establishing an audio and visual event identification model, and obtaining event content described by the panoramic content.

The invention discloses a BIM-based two-dimensional data and three-dimensional model verification matching method and system. The system comprises a 2D drawing processing module, a 3D model processing module, a difference detection module and a report generation module. In the 2D drawing processing module, a user imports a process pipeline plan and a profile map, and through preprocessing and image semantic analysis, pipeline elevation labeling information is extracted and numbered. And the 3D model processing module loads a target BIM model, locates a process pipeline area, analyzes pipeline parameters and a space state, analyzes a pipeline topological relation and performs conflict verification. And the difference detection module binds the 2D drawing with a corresponding pipeline object in the 3D model, establishes a mapping relation, compares differences, and performs labeling and classification. And the report generation module is used for carrying out visual annotation in the 2D drawing and the 3D model based on the annotation difference, automatically generating a difference comparison table and outputting a report, so that efficient and accurate verification and matching of the two-dimensional data and the three-dimensional data are realized, the examination period is shortened, and the rapid collaborative correction efficiency of the construction team is improved.

The invention provides an intelligent inventory robot path optimization method based on environment modeling and collaborative optimization, and relates to the technical field of intelligent warehouse management, and the method comprises the steps: scanning a warehouse region through an unmanned plane platform, and generating three-dimensional warehouse point cloud data; after point cloud denoising, plane fitting, shelf boundary extraction and channel detection processing, a two-dimensional storage plane graph is generated and transmitted to a robot data base; generating an initial inventory planning path by adopting an A * algorithm, optimizing the initial path through a simulated annealing algorithm, and generating a target inventory planning path; based on the dynamic obstacle influence factors, the RFID signal intensity and the robot residual electric quantity are combined, and a target path is corrected through a mixed algorithm of multi-target optimization and PSO dynamic weight; and the operation end checks the checking progress through the AR equipment and manually adjusts the target path, so that the checking path of the intelligent checking robot can be optimized in real time in a dynamic environment, and the checking efficiency, the coverage rate and the cruising ability of the robot are improved.

The invention discloses a space identification method and system based on a YOLO model, a terminal and a medium, and the method comprises the steps: obtaining a CAD drawing, carrying out the space segmentation and coordinate conversion of the CAD drawing, obtaining a PNG image corresponding to a specified space region in the CAD drawing and a label file corresponding to the PNG image, and determining a training data set based on the PNG image and the label file; building a Python operation environment, installing a YOLOv8-seg model and a related dependency library thereof, and carrying out end-to-end training on the training data set based on a training script of the YOLOv8-seg model to obtain a YOLO space segmentation model; and based on the YOLO space segmentation model, carrying out space segmentation on the building plane drawing, outputting a segmentation result, and carrying out automatic labeling on a space region. According to the method, the YOLO space segmentation model is adopted, high-precision space recognition and classification are achieved, and the generalization ability is high.

The invention discloses a robot full-coverage path planning method based on cattle farming movement. The method aims at improving the path coverage efficiency of a robot in a complex environment. According to the method, a two-dimensional plane map of a real environment is obtained and converted into a binary image, and a grid map suitable for the size of a current robot is generated through rasterization processing. And dynamically adjusting a path planning direction according to a main direction and regional characteristics of a map / map region by adopting a motion direction priority judgment method of cattle farming type motion. And when the robot encounters an obstacle and falls into a dead zone, replanning a path by using a backtracking point set construction rule in combination with an A * algorithm, optimizing path selection of the robot for escaping from the dead zone, and ensuring that the robot can cover all reachable areas. The method effectively reduces path overlapping and calculation amount, improves efficiency and coverage rate of path planning, and is especially suitable for environments with complex boundaries and obstacles. The invention further discloses a system and application for implementing the path planning method, and the system and the application have wide application scenes.

Methods, systems, and bitstream syntax are described for a file container that supports the storage and transmission of multi-plane images. Examples are provided for coding texture and opacity information using HEVC or VVC coding and the HEIF container. Examples of carrying coded MPI images according to V3C and an example HEIF-based player are also presented.

Methods, systems, and bitstream syntax are described for a scalable 3D scene representation. A general framework presents a dual-layer architecture where a base layer provides a baseline scene representation, and an enhancement layer provides enhancement information under a variety of scalability criteria. The enhancement information is coded using a trained neural field. Example systems are provided using a PSNR criterion and a baseline multi-plane image (MPI) representation. Examples of bitstream syntax for metadata information are also provided.

The invention discloses a three-dimensional curved surface pattern layering processing method based on parameterized segmentation, and aims to solve the problem of high-precision pattern processing on the surface of a curved surface by using a laser scanning galvanometer. The method comprises the following steps: firstly, importing a three-dimensional model, and carrying out parameterization processing to obtain a two-dimensional parameter domain; positioning a to-be-processed plane pattern in the two-dimensional parameter domain to form a complete two-dimensional geometric grid covering a pattern area, generating a three-dimensional pattern grid attached to a curved surface, and calculating a center normal vector of the three-dimensional pattern grid; then, generating an equidistant plane group on the surface of the curved surface, and calculating an intersection line of the plane and the three-dimensional pattern grid as a laser scanning track; and the five-axis linkage platform-galvanometer system is started for machining. Through the parameterization domain, distortion of curved surface pattern machining is effectively solved, machining uniformity is improved, and the method is particularly suitable for laser machining of large-scale or high-complexity curved surface patterns.

The invention discloses a spatial multiplexing acoustic hologram design method based on a physical model neural network. Parameters are determined according to a target spatial multiplexing design scene, a sound wave propagation model and a deep learning model are established, the input of the deep learning model is a random noise image, the output of the deep learning model is a transducer array selection area graph and a phase plane graph, and the sound wave propagation model and the deep learning model are combined to establish an overall model to be combined with a target sound field graph for training; and after training, obtaining a transducer array selection area graph and a phase plane graph. According to the method, the problem that artifacts exist in multichannel information due to lack of global optimization in a traditional angular spectrum iterative design method is solved; the method overcomes the defects that an existing deep learning method consumes time and labor and is poor in robustness, and specific parameters cannot be designed according to the design requirements of the target holographic sound field when a data set is constructed, optimization design can be carried out on multiple target holographic sound fields at the same time, and any holographic sound field can be designed in a self-adaptive mode.

The invention provides a thermal simulation analysis method, device and equipment for PCB design and a storage medium, and the method comprises the steps: obtaining a finite element model of a PCB, the finite element model being obtained through modeling according to the electrical parameters of components, the typesetting parameters of the components and the structural layout parameters of a substrate; controlling the finite element model to operate under a preset power load to obtain a simulation heat diagram of the finite element model; partially hiding the finite element model to respectively obtain a component plane graph and a substrate plane graph; inputting the component planar graph into a preset image recognition model to obtain a first annotation graph, and inputting the substrate planar graph into the preset image recognition model to obtain a second annotation graph; and inputting the simulation heat map, the first annotation map and the second annotation map into a preset image scoring model, annotating a thermal anomaly region and thermal anomaly information, and using the thermal anomaly region and the thermal anomaly information to guide optimization of a finite element model.

The invention discloses a path planning method and system for curved surface integrated conformal jet printing, and belongs to the field of additive manufacturing. According to the method, the geometric data of the non-deployable curved surface are obtained and converted into the discretized grid model, then the plane pattern is designed and mapped to the three-dimensional curved surface, the mapped pattern is locally optimized, and the complete printing area model is generated. Path planning is divided into initial jet printing path generation and five-axis linkage motion path planning, conformal characteristics are achieved through an adaptive interpolation algorithm and NURBS curve optimization, five-axis motion parameters are calculated based on an inverse kinematics algorithm, and the positioning relation between a jet printing head and a curved surface is kept through the RTCP technology. The method solves the problems of precision and path planning efficiency in complex curved surface manufacturing, and is suitable for manufacturing heterogeneous complex curved surface functional structures in the fields of aerospace, medical instruments and the like.

The invention provides an effective room identification method, device and equipment and a storage medium, and the method comprises the steps: converting wall elements in an architectural plan into a line segment set, and carrying out the intersection detection and topology analysis to obtain an undirected weighted graph structure; determining an influence area in response to a user editing operation, and performing incremental updating on the influence area to obtain a locally updated graph structure; performing ring detection processing on the locally updated graph structure by applying a minimum ring basis algorithm to obtain a minimum ring basis containing a basic ring in the influence region; and identifying the effectiveness of the rings according to the geometric features and the topological features of each ring in the minimum ring basis, and identifying the effective rings as effective rooms. According to the method, through incremental updating and local processing, the calculation overhead of full-amount reconstruction is avoided, the calculation efficiency and real-time performance of room recognition are remarkably improved, and the method is suitable for interactive design scenes of large complex architectural planar graphs.

To provide a system.SOLUTION: The system includes: generation means for inputting a specific prompt sentence into a generative AI model, thereby automatically generating a blueprint of a housing desired by a user on the basis of user house-related information including the design, the style and the condition of the house and real estate information; means for proposing the housing on the basis of the generated blueprint; means for adjusting the interior design and the layout in the blueprint of the housing on the basis of an emotion engine for recognizing the emotion of the user and the emotion; and means for displaying the adjusted blueprint by using a virtual reality display device.SELECTED DRAWING: Figure 1

A splat generation system and associated methods generate a splat representation of a three-dimensional (3D) model with varying detail based on an unequal or non-uniform capture of the 3D model. The system receives the 3D model. The system analyzes variation in the position or visual characteristics of the 3D model primitives, attributes different levels-of-importance to different surfaces or regions of the 3D model based on the variation, and defines a non-uniform placement of virtual cameras around the 3D model according to the different levels-of-importance that are attributed to the different surfaces or regions of the 3D model. The system generates the splats for the splat representation that reconstruct the different surfaces or regions with differing amounts of detail that match the detail captured for the different surfaces or regions in images generated from the non-uniform placement of the virtual cameras.

The invention provides a rural infrastructure three-dimensional modeling method and system based on laser point cloud, and the method comprises the steps: collecting point cloud data at a rural infrastructure site through employing a standing laser scanner, and obtaining the space information of the infrastructure; preprocessing the collected point cloud data; the point cloud data is automatically segmented and processed by using a track extraction technology; performing circle fitting and automatic fine denoising on the segmented point cloud data; correcting the scanning intensity value of the point cloud based on a distance-intensity model; extracting and smoothing the central axis of the rural infrastructure, and providing a basis for the construction of a three-dimensional model; a lining plane image of the rural infrastructure is formed through a three-dimensional point cloud technology, and a basis is provided for texture mapping; rapid browsing and storage of the three-dimensional texture model of the rural infrastructure are realized, and subsequent use and analysis are facilitated. According to the invention, the acquisition and preprocessing process of the point cloud data can be optimized, the data quality is ensured, and the modeling efficiency and precision are improved.

The invention relates to the technical field of equipment control, in particular to an artificial intelligence-based disinfection and killing equipment control system, which comprises an operation area acquisition module for acquiring planar graph information of an operation area of disinfection and killing equipment; the plane coordinate construction module is used for constructing a work area coordinate system; the inspection information acquisition module is used for acquiring the inspection information and the operation information of the killing cancelling equipment; the routing inspection path analysis module is used for analyzing the routing inspection demand area and further used for analyzing routing inspection area parameters; the disinfection and killing target analysis module is used for analyzing the types of the disinfection and killing target area and the inspection area; the inspection collision analysis module is used for analyzing the path collision type; the inspection path optimization module is used for optimizing the inspection path of the disinfection equipment; and the equipment inspection control module is used for controlling the disinfecting and killing equipment to perform disinfecting and killing operation in the inspection demand area according to the inspection path. According to the invention, accurate control of the running track of the disinfection equipment is realized.

The invention discloses a transformer substation equipment identification method and system for image identification unmanned aerial vehicle route planning, and the method comprises the following operation steps: obtaining a layout plan of a transformer substation, and constructing a three-dimensional plan of transformer substation equipment for the layout plan of the transformer substation; generating an unmanned aerial vehicle flight route according to the three-dimensional plane graph; generating an unmanned aerial vehicle flight plan for the unmanned aerial vehicle flight route; when an unmanned aerial vehicle control system of the unmanned aerial vehicle flies according to the unmanned aerial vehicle flight plan, a transformer substation equipment image is collected according to the position of the transformer substation equipment in the three-dimensional planar graph; identifying the substation equipment from the substation equipment image and identifying a to-be-identified target object of the substation equipment at the target orientation; the to-be-identified target object is an electrical element on the substation equipment. According to the invention, the equipment image of the substation equipment is acquired through the unmanned aerial vehicle flight plan of the unmanned aerial vehicle, and the type of the substation equipment and the fault type when the fault occurs are further identified according to the equipment image.

The invention discloses a multi-floor personnel real-time positioning system based on UWB positioning, and belongs to the technical field of UWB positioning, and the system comprises a UWB base station module, a UWB label module, a central processor module, a positioning engine module, and a monitoring display module. In combination with efficient processing of the central processing unit module and the positioning engine module, real-time and accurate monitoring of positions of people in the multi-storey building is realized, electronic maps are integrated, position coordinate data are mapped to planar graphs of corresponding floors, and the positions of the people are displayed in real time in a point form. And historical track playback, electronic fence setting and alarm notification functions are supported, so that the efficiency, accuracy and real-time performance are greatly improved.

The invention discloses a railway CORS station selection method. At present, an experiential method is mostly adopted during site selection of the CORS. The method comprises the following steps: collecting DEM data of a target area, and calculating gradient, slope direction and surface roughness information of a research area; generating a DOM according to the remote sensing image type, and calculating a vegetation index; processing the SAR data through InSAR to obtain a deformation graph of the target area; communication network coverage conditions are collected and marked on a map, and a network signal coverage range map of the area is obtained; preprocessing the site selection factor of the CORS station; intelligent site selection is carried out according to the determined multiple factors; and according to the calculated site selection suitability plane graph, selecting a most suitable position as a CORS station deployment position every 50km along the railway, and guiding the construction of the CORS station. According to the method, the defects of a traditional on-site reconnaissance site selection method are overcome, and the arrangement position of the CORS station can be optimized.

The invention discloses a deep learning-based transmission and transformation project progress identification and analysis method and system, and the method comprises the steps: firstly collecting and preprocessing the multimedia data of a transmission and transformation site, constructing a project progress data set, training a deep learning model through a plane graph and the progress data set, inputting new progress data into the model, and identifying a current project state; comparing the plan progress, analyzing the deviation, and finally performing comprehensive trend prediction based on the model and the key influence factors. In the application of the design, key factors are obtained through a recursive transformer fusion network model fusing various modules and various differential data, and comprehensive trend analysis is realized in combination with differences of different voltages, so that deep-level features can be effectively extracted from various data sources, high-precision identification of the project progress is realized, and the accuracy of the project progress is improved. Compared with a traditional manual checking and recording mode, the method has the advantages that the recognition efficiency and accuracy are greatly improved, the interference of human factors is reduced, and the real-time performance and reliability of progress recognition are ensured.

The invention discloses an environment-friendly operation and maintenance robot vision system, and the system comprises an image collection module which comprises a 3D camera disposed on a mechanical arm of a robot, and is used for collecting a plane image and a depth image of a target object; the image processing module is used for preprocessing the plane image acquired by the image acquisition module and transmitting the plane image to the target detection module; the target detection module is provided with a rotating target detector obtained by training based on a YOLOv8OBB network model, and is used for detecting the image transmitted by the image processing module and outputting a target frame center point, a target frame width, a target frame height and a rotating angle; the data conversion module is used for obtaining a rotation translation matrix of the target relative to the 3D camera; and the result output module is used for converting the rotation translation matrix into a relative motion matrix of the mechanical arm and outputting the relative motion matrix to a mechanical arm control unit of the robot. Based on the scheme of the invention, an efficient and accurate water quality station robot vision system can be obtained.

A system and method for captures of physical object data for environment modeling is provided. Mesh data is displayed over an image of a physical space for which a floorplan is to be generated. Instructions to mark an object are received and the instructions include a voice command and eye gaze or gesture. The mesh data is annotated with a marker based on the instructions by placing the marker at a location at which an object is to be identified. A floorplan of the space is generated based on the mesh data. The marker is placed in the floorplan.

The present invention relates to the technical field of intelligent evacuation. Provided are a method and system for intelligently generating an emergency evacuation scheme. The method comprises: performing geographic information scene creation and data association on the basis of a park plane map, so as to obtain a digital visual map; sensing and marking the type, occurrence location, severity degree and influence range of an incident in real time, and performing initial information configuration on an evacuation scheme; determining an evacuation mode; automatically and manually creating or modifying evacuation routes, automatically merging the generated evacuation routes, and automatically supplementing unselected evacuation routes, so as to obtain an optimal evacuation path; and automatically generating key information of the evacuation scheme, so as to obtain an emergency evacuation scheme. The present invention reduces the evacuation design command difficulty of a complex incident site, improves and ensures the feasibility of all evacuation schemes, eliminates the misoperation possibly caused by human factors in the case of a large number of manual plans, and ensures the correctness of an evacuation indication direction, thereby making intelligent evacuation more reliable and safe.

The invention discloses a distributed urban and rural planning decision support method and system, and relates to the technical field of data analysis, and the method comprises the following steps: S100, drawing a planar graph of a to-be-planned region, dividing the planar graph of the region according to a Morse decomposition method, and constructing a region matrix, S200, calculating the coverage rate of the region matrix, and S300, calculating the coverage rate of the region matrix. And planning the surveying and mapping path. According to the method, planning units and surveying and mapping grids are unified into the same quadrilateral matrix through Morse complex, repeated zoning and edge matching errors are effectively avoided, the edge weight is inversely proportional to the building coverage rate, an unmanned aerial vehicle and a surveying and mapping vehicle automatically and preferentially encrypt a high built-up area, field working hours are saved, the data precision of a core construction area is guaranteed, and the construction efficiency is improved. The regional matrix naturally corresponds to edge nodes, and each node only communicates with an adjacent unit, so that the MST and the local route can be locally solved, the communication complexity is reduced, and the method is suitable for large-scale regional surveying and mapping.

The invention provides a virtual hotel guide customization system based on intelligent optimization and multi-mode interaction. The virtual reality and intelligent navigation system comprises a user preference acquisition module used for acquiring user preference data; the scene sensing module is used for outputting the short-time congestion degree weight of each interest point through a space-time diagram convolutional network congestion prediction model according to the hotel indoor plane graph and the real-time people flow data; and the path optimization module is used for generating a tour guide route which meets the time budget and has the minimum congestion degree and updating the tour guide route in real time. According to the virtual hotel guide customization system based on intelligent optimization and multi-modal interaction, not only can a crowded area be dynamically avoided within a second level and smooth and redundancy-free guide be ensured, but also the journey can be adjusted in real time according to the remaining time and interest preference of the user, and on-demand accurate service for different guest groups is realized.

The invention relates to an automatic grinding track generation method, a medium and equipment. The automatic grinding track generation method comprises the following steps: acquiring a workpiece point cloud, uniformly segmenting the workpiece point cloud to obtain a plurality of point cloud modules, obtaining a mean square error of each point cloud module, combining the point cloud modules into a plane according to the mean square error to obtain a workpiece plane image, and carrying out binary segmentation on the workpiece plane image to obtain a grinding track. Carrying out gradient calculation on the workpiece binary image to obtain a plurality of feature points, sequentially connecting all the feature points according to a set sequence to obtain an initial grinding track, fitting a curve of the initial grinding track, and determining a final grinding track. The problems that in the prior art, workpiece recognition and positioning are difficult, track calculation precision is low, and tracks are not smooth enough are solved.

In a method of providing an extended Reality (XR)-based composite image according to an embodiment, a planar image is received by a computing device from a camera photographing a display device, and the received planar image and a virtual space image are composited by the computing device, and a final image is output.

The invention relates to the technical field of image recognition, in particular to a building plane element recognition model construction method based on a convolutional neural network, and the method comprises the following steps: obtaining an input APN sample; elements in the APN sample are labeled, and a labeled file is generated; converting the annotation file into a format required by YOLO, and normalizing the annotation file; performing data enhancement on the APN sample, expanding the training data volume, and outputting to obtain an element detection result and a feature vector; installing dependency and carrying out data configuration; setting command line model parameters and customizing training scripts; and carrying out model training and model reasoning. According to the method, the plane layout is abstracted based on the machine learning method, and the vector data type has high flexibility and deformation capability, so that the vector output which keeps the original plane image form unchanged can be converted into various objects according to the purpose of a user. And classifying and identifying the plane elements by using the GNN.

The invention relates to the technical field of mechanical arm obstacle avoidance grabbing, and discloses an automatic obstacle avoidance grabbing system and method. The system comprises a mechanical arm, a stereo camera, a flexible grabbing unit and a control unit, the mechanical arm is arranged on a table top, the stereo camera is arranged on the mechanical arm, the flexible grabbing unit is arranged at the tail end of the mechanical arm, and the stereo camera is used for obtaining point cloud images and plane images of a target object and an obstacle; the control unit is used for obtaining point cloud data of the target object and the obstacle according to the point cloud images and the plane images of the target object and the obstacle, and processing the point cloud data of the target object and the obstacle to obtain the position of a grabbing point; envelope box description is carried out on the mechanical arm, the stereo camera, the flexible grabbing unit and the obstacle so as to carry out self-collision detection and obstacle collision detection, the path from the initial position to the target position is calculated under the condition that collision does not exist, and the mechanical arm is controlled to grab the target object according to the calculated path.