494 results about "Vector diagram" patented technology

The invention relates to the technical field of navigation and positioning, in particular to an intelligent positioning and navigation early warning system and method for ships and boats. The method comprises the following steps: collecting and carrying out sea condition sensing and positioning signal evaluation according to a multi-source sensor synchronous data set to obtain a sensor reliability score table; performing target motion situation analysis according to the sensor reliability score table to obtain a fusion situation vector diagram; performing target motion state judgment according to the fused situation vector diagram to obtain a target motion state judgment result; performing multi-target collision risk assessment according to the target motion state judgment result to obtain a risk degree assessment matrix; performing navigation maneuvering performance constraint according to the risk assessment matrix to obtain a route planning maneuvering constraint rule set; and performing route trajectory search according to the route planning manipulation constraint rule set to obtain a navigation trajectory scheme. According to the invention, through the positioning navigation early warning technology, the positioning precision, the navigation safety and the early warning system practicability of the ship in a complex environment are obviously improved.

The invention relates to a remote sensing image ground feature recognition method based on deep learning, and the method comprises the steps: carrying out the data collection and preprocessing of a ground surface target region, eliminating the position deviation through geometric correction, processing the illumination difference through combination with radiation equalization, and generating a ground feature registration image; performing multi-dimensional feature fusion processing on the image, performing tensor fusion on vegetation spectral features, earth surface texture features, point cloud data features and linear ground feature features, and constructing a ground feature fusion matrix; a bilateral convolutional neural network is adopted to extract spectral response characteristics and spatial correlation characteristics, and characteristic interaction is realized through an attention mechanism to generate a ground feature probability distribution diagram; and finally, carrying out noise filtering, boundary refining and vectorization conversion processing on a classification result, and outputting a ground feature classification vector diagram. According to the method, three technical bottlenecks of insufficient cooperative utilization of multi-source heterogeneous data, insufficient spectrum-space feature fusion and poor GIS compatibility are solved, and the operation efficiency of territorial investigation, disaster monitoring and other scenes can be remarkably improved.

The invention discloses a seabed sonar image processing method based on semantic segmentation. The method comprises the following steps: S1, obtaining preprocessed sonar image blocks; s2, generating a submarine landform boundary priori map corresponding to the preprocessed sonar image blocks pixel by pixel; s3, an improved U-Net double-branch semantic segmentation network is constructed, the improved U-Net double-branch semantic segmentation network comprises a semantic branch and a boundary branch, and a landform mutual feedback gating module is configured in each scale jumper connection layer; s4, obtaining a fusion feature map; s5, deploying to the autonomous underwater vehicle to form an onboard reasoning model; and S6, performing online semantic segmentation reasoning on the preprocessed sonar image block acquired in real time by using the onboard reasoning model, and outputting a semantic category diagram and a boundary vector diagram corresponding to the preprocessed sonar image block. According to the method, the average mIoU index of the model is improved in a small-target and multi-category hybrid scene.

The invention provides a method for generating multi-dimensional landform vector data based on remote sensing image semantic segmentation, and belongs to the technical field of remote sensing image ground object recognition and artificial intelligence, and the method comprises the steps: training a landform recognition deep learning model based on a remote sensing image, adjusting a pre-training weight to optimize an image encoder, and generating a classification result; the landform recognition deep learning model comprises an encoder formed by a multi-layer convolutional network and a decoder of an up-sampling and feature fusion architecture; extracting a single-band label corresponding to each landform type from the classification result, and generating a binary raster image with the single-band labels; and converting the binary raster image with the single-band label into a vector diagram, and combining the vector diagram with the elevation information to generate multi-dimensional geographic data.

The invention belongs to the technical field of engineering information intelligent processing, and relates to a highway bridge drawing multi-modal information extraction and semantic understanding method and system.The method comprises the steps that firstly, self-adaptive judgment is conducted on a vector drawing and a scanning drawing, geometric distortion correction, drawing frame and title bar positioning and layout segmentation are completed, and then the vector drawing and the scanning drawing are obtained; constructing a hierarchical document structure comprising texts, tables, images and two-dimensional drawing objects; then, a front-end target detection network and a rear-end cross-modal document understanding model are fused, and detection and relation reasoning of elements such as view blocks, labels, symbols and tables are achieved; and image-text feature alignment is further performed by using a visual coding network and a text coding network, structural description conforming to engineering semantics is generated by means of a multi-modal language model, an engineering parameter database is established, and parameter query and multi-modal question and answer output are supported. According to the method, the accuracy and efficiency of automatic acquisition and semantic understanding of the key information of the highway bridge drawing are improved.

Systems and methods allow prediction of semantic information and generation of a 3D model from one or more raster images of a structure. In embodiments, the raster images may be converted to vector images, and received semantic information may be used for generation of predicted semantic information. The combination of provided and predicted semantic information, along with the vector images, can facilitate generation of a 3D model that is scale-accurate to the structure illustrated by the raster images. Other embodiments may be described and / or claimed.

The invention discloses a cable outer semi-conductive layer stripping defect detection method and device, terminal equipment and a storage medium, and belongs to the field of power equipment detection. The method comprises the following steps: acquiring polarization images of a to-be-detected cable joint at a plurality of angles and corresponding camera pose parameters; performing stokes parameter calculation according to the polarization image and the camera pose parameter to obtain a stokes vector diagram; performing three-dimensional morphology reconstruction according to the camera pose parameters, a polarization derived parameter diagram generated by the Stokes vector diagram and the de-reflective polarization fusion image to obtain a submillimeter-level precision point cloud; according to a preset channel attention mechanism, carrying out weighted fusion on geometric features extracted from the submillimeter-level precision point cloud and texture features extracted from the de-reflective polarization fusion image to obtain a defect type probability and a surface topography parameter; and obtaining a stripping defect type and a defect position according to the defect type probability and the surface topography parameter. According to the invention, the problem of low defect detection accuracy in the prior art can be solved.

The invention relates to the technical field of fire safety inspection, in particular to a fire safety inspection method and system based on machine vision recognition. The method comprises the following steps: carrying out inspection equipment deployment on an inspection area to obtain a camera deployment scheme and a sensor network deployment scheme; performing multispectral dynamic sensing according to the camera deployment scheme to obtain a time sequence spectral flow; performing multispectral channel motion estimation on the time sequence spectral flow, and performing motion vector noise suppression to obtain a filtered motion vector diagram; performing spectral characteristic-based motion enhancement on the filtered motion vector diagram to obtain a spectral enhanced motion vector; and performing motion amplification algorithm application on the time sequence spectrum flow according to the spectrum enhanced motion vector, and constructing an enhanced motion vector field to obtain the enhanced motion vector field. According to the invention, through multi-modal information fusion, context awareness and capturing of early fine features, early and accurate fire hazard identification and early warning are realized.

The invention discloses a query condition analysis method and device and a medium, and the method comprises the steps: receiving a natural language query statement input by a user for a human resource management system, and extracting a core semantic unit in the natural language query statement; based on a preset knowledge base, mapping the core semantic unit into a semantic structure corresponding to an Elasticsearch database to generate a corresponding key vector diagram; based on a preset pseudo code normal form, generating a pseudo code according to the key vector diagram; wherein the pseudo code comprises a query node set and a logical relationship between query nodes; and performing code verification on the pseudo-code, and converting the pseudo-code into a DSL query statement in an Elasticsearch database under the condition that the pseudo-code passes the code verification, so as to realize semantic analysis on the natural language query statement through the DSL query statement.

The invention discloses an identification and verification method and system for a secondary drawing of a transformer substation, and belongs to the technical field of image identification, and the method specifically comprises the steps: analyzing a CAD vector diagram, obtaining four-corner references and pixel coordinates of a scanning graph, building initial mapping of pixels and engineering millimeter coordinates by taking an engineering millimeter coordinate system as a reference, and generating a unified intermediate representation model; constructing a terminal strip parameterization template based on the vector anchor point set and the visual anchor point set; a composite deformation field is calculated through vector-bitmap-vector closed-loop rendering calibration, terminal row alignment and terminal sequence binding are completed in the composite deformation field, and a terminal pixel position-terminal sequence-engineering millimeter coordinate consistent mapping table is formed; comparing the topological netlist to obtain an electrical node corresponding table; and performing source decomposition on the terminal position residual error based on the composite deformation field and generating a self-adaptive tolerance band, and finally executing wiring verification by taking the self-adaptive tolerance band, the electrical connectivity and the serial number consistency as criteria and outputting a verification report.

The invention discloses a microplastic transportation path prediction method, device and equipment based on a complex hydrodynamic environment and a medium, and relates to the technical field of environment monitoring and water pollution governance, and the method comprises the steps: integrating a watershed digital elevation model, a river channel vector diagram, microplastic attributes and real-time hydrological data; a dynamic adjacency matrix is used for capturing water flow direction changes, multi-modal feature fusion is combined for modeling micro-plastic physical attributes and topographic resistance, the problem of monitoring data scarcity is solved through a cross-basin transfer learning strategy, a micro-plastic transfer path is accurately predicted, high-precision simulation of micro-plastic transfer in a complex hydrodynamic environment is achieved, and high-precision simulation of micro-plastic transfer in a complex hydrodynamic environment is achieved. The pollution prevention and control efficiency is improved.

The invention provides a seawall terrain extraction method and system fusing a vehicle-mounted laser point cloud and an inclination model. The method comprises the following steps: forming a seawall range according to a seawall vector diagram and a satellite image; dividing a seawall range into a vehicle-mounted laser radar acquisition range and an unmanned aerial vehicle oblique photogrammetry range; acquiring a vehicle-mounted laser point cloud collected by a vehicle-mounted laser radar, and performing preprocessing and precision optimization on the vehicle-mounted laser point cloud; obtaining oblique photography image data collected by a five-splicing camera carried by the unmanned aerial vehicle, and generating an oblique photography model and an oblique three-dimensional model point cloud; extracting edge line elements and angular point feature points of a common overlapping area in the inclined three-dimensional model point cloud and the vehicle-mounted laser point cloud, and performing point cloud fusion based on the edge line elements and the angular point feature points; filtering the fused point cloud to screen seawall terrain point cloud data; according to seawall terrain point cloud data, resolution-adaptive digital elevation models are constructed for different areas of a seawall, and seawall structure features can be accurately represented.

The invention relates to the technical field of bridge monitoring, in particular to a bridge displacement monitoring method based on unmanned aerial vehicle visual identification, which comprises the following steps: S1, generating a flight path covering all key parts of a bridge, and collecting a bridge image by adopting an unmanned aerial vehicle according to the flight path; the method comprises the steps of S1, preprocessing a bridge image, S2, carrying out feature extraction after the bridge image is preprocessed, S3, carrying out matching on image feature points at different time points, and calculating the displacement of a bridge, S4, carrying out model optimization by adopting a convolutional neural network to optimize image features, and S5, carrying out hierarchical monitoring on displacement magnitude differences of different components of the bridge, and superposing a displacement calculation result on the bridge image in the form of a thermodynamic diagram or a vector diagram, and representing the displacement by using a color gradient. The method adopts the unmanned aerial vehicle visual identification technology, only needs the unmanned aerial vehicle and the camera, combines the image processing algorithm and the deep learning model to calculate the bridge displacement, is high in precision, is low in cost, and is suitable for various types of bridge structures.

The invention discloses an operation state evaluation method and system for the deep peak regulation process of a fluidized bed unit, and relates to the technical field of fluidized beds, and the method comprises the steps: collecting the combustion condition data of the deep peak regulation process of the fluidized bed unit; generating a particle flow velocity vector diagram, and identifying and marking different flow modes and paths in the particle flow velocity vector diagram; generating a non-uniform index of the whole flow field through particle flow velocity and particle concentration distribution of each flow mode area in the flow field; generating a bed temperature distribution diagram, recognizing a high temperature-flow mode region by combining with the particle flow velocity vector diagram, and calculating a coking risk index of the high temperature-flow mode region; and calculating a coking risk index of the whole flow field, comparing the coking risk index of the whole flow field with a risk threshold value, and when the coking risk index of the whole flow field is greater than or equal to the risk threshold value, giving out an early warning, identifying potential risks, and avoiding serious problems such as coking.

The invention relates to a graph drawing method, a graph drawing device, electronic equipment and a storage medium, and belongs to the technical field of graph drawing. The method comprises the steps of obtaining user drawing board operation event information, obtaining a user drawing board operation element method through a predefined drawing board operation method mapping table, obtaining user drawing board operation element change data through drawing board updating logic calculation, and obtaining user drawing board operation element change cache data through caching of a data storage mechanism, the method comprises the steps of generating a real-time preview image through WebGL, determining user drawing board operation element difference data through data comparison, obtaining user drawing board operation element difference data vector diagram data through SVG format conversion, and performing drawing board local rendering through a Canvas API. The rendering precision of the drawing board is dynamically adjusted according to equipment performance data, multiple rendering tasks are calculated in parallel through a multi-core processor, and a next drawing board operation type label of a user is predicted through a user drawing board operation prediction model, so that related operation controls are preloaded, and the response speed is increased.

The invention relates to the technical field of artificial intelligence and digital modeling, and particularly discloses an automatic equipment modeling method and equipment based on artificial intelligence, and the method comprises the steps: obtaining data to construct a multi-modal fusion data set; extracting target features and integrating the target features into a unified high-dimensional feature vector so as to generate a semantic feature map for modeling equipment; obtaining geometric modeling parameters and assembly constraint conditions of modeling equipment by using a parameter optimization model constructed based on a reinforcement learning framework; based on the fault data, optimization is carried out through a Markov decision principle to generate a preliminary 3D model; and performing key point detection and feature alignment on the planar projection of the model and the BIM drawing vector diagram through a hierarchical matching strategy, correcting model deviation based on vertex information to generate a 3D model, and generating a digital twin 3D model. Thus, by means of multi-source data collaborative acquisition and deep feature fusion, the precision, efficiency and adaptability of equipment modeling are remarkably improved in combination with dynamic optimization and a closed loop verification mechanism.

The described systems and methods are configured generate a 3D virtual representation of a physical scene at a location, and output isometric and / or orthographic vector drawings based on the 3D virtual representation. The vector drawings are generated by rendering views of the 3D virtual representation in a scalable vector graphics (SVG) format, so that the views can be zoomed without blurring or other decreases in image viewability. Further, sub-rooms, tags, labels, and / or dimensions may be added to the output. Rather than taking hours to generate drawings, the described systems and methods enable generation in a few milliseconds, among other advantages.

The invention relates to the technical field of transmission shaft machining, and discloses an intelligent clamping method and system for universal joint transmission shaft machining. The method comprises the steps that the geometric dimension, the form and location tolerance number, vibration signal data, unbalance data and pressure distribution data of a transmission shaft are obtained; performing precision correction based on preset machining precision to obtain transmission shaft positioning parameters; adjusting parameters in real time through an adaptive control algorithm to obtain real-time positioning parameters; performing feature extraction to obtain vibration spectrum features; outputting a vector diagram to obtain an unbalanced vector diagram; performing Kalman filtering processing to obtain filtering parameters; calculating by adopting a fuzzy control algorithm to obtain a vibration compensation amount and an unbalance correction amount; performing data processing to obtain a corrected pressure matrix; inputting into a pre-trained neural network model to obtain a predicted risk level; and risk prediction and judgment are carried out, clamping strategy adjustment is carried out, a corrected clamping strategy is obtained, machining of the transmission shaft is carried out, and the machining accuracy is improved.

The invention relates to the field of homogenizer intelligence, and discloses a homogenizer intelligent pressure increasing and reducing control system based on servo synchronous transmission, which comprises the following steps: extracting the operation inertia difference of each plunger mechanism under a multi-pump parallel structure, generating a synchronous offset vector map, and calculating the pressure increasing and reducing of the homogenizer based on the synchronous offset vector map. Calling a slip registration algorithm and an inertia field decoupling network, extracting reconstructed plunger stroke and pressure response mapping data, constructing a three-dimensional flow-path-stress tensor model, judging whether fluctuation-wear coupling sensitive points exist or not, activating a frequency conversion regulation and control strategy set of a servo driving layer based on a disturbance window and the coupling sensitive points, and carrying out the control of the servo driving layer. And judging whether structural thermal drift feedback unbalance exists or not, dynamically adjusting servo redundancy and a buffer response strategy in combination with a preset life curve and an abnormal response backtracking channel, and outputting a synchronous regulation and control instruction set. The method has the advantage of improving the long-term operation reliability of the homogenizer.

The invention relates to the technical field of three-dimensional map recognition, and discloses a semantic segmentation-based low-altitude three-dimensional map element autonomous recognition method and system. The method comprises the following steps: acquiring a low-altitude remote sensing image and preprocessing to generate a multi-channel image matrix containing geographic coordinates and spectral characteristics; extracting multi-scale features through a pyramid feature extraction network in combination with cavity convolution, and obtaining an adaptive weighted feature tensor through a cascade attention mechanism fusion channel and a spatial weight; adopting a bidirectional feature fusion strategy to generate fusion features, and outputting an initial category probability distribution diagram by a semantic segmentation header network; obtaining a refined mask through edge perception optimization and superpixel segmentation correction, and mapping the refined mask to a three-dimensional coordinate system to generate a vector layer with a semantic tag; a constraint rule is deduced through a topological relation inference engine, logic conflicts are eliminated through rule-driven post-processing, finally, a standardized three-dimensional map element database meeting the geographic information standard is generated, and efficient, accurate and autonomous recognition of low-altitude three-dimensional map elements is achieved.

The invention discloses a multi-modal depth perception and adaptive segmentation full-spectrum primitive intelligent identification method, system and device for a primary main wiring diagram of a plant station, and a medium, and belongs to the technical field of intelligent identification of engineering drawings of power system plant stations, and the method comprises the steps: fusing the dual-source data of a vector diagram and a grating diagram, and constructing a standard primitive library; secondly, primitive detection is realized by adopting a recognition and segmentation collaborative multi-stage network, and the contour precision is improved through model distillation; secondly, iteratively optimizing the recognition capability of the model on the difficult sample by using an active learning mechanism; and finally, finishing primitive classification and result optimization in combination with deep metric learning and electrical topology constraints. According to the invention, through multi-modal data fusion and multi-scale feature adaptive fusion, the recognition capability of a small target and a fine primitive is improved; by means of a double-branch heterogeneous architecture and a structural distillation mechanism, pixel-level high-precision edge segmentation is realized, and the identification problem of dense adhesion primitives is effectively solved.

The invention provides a document image-text integrated intelligent understanding and processing method and system, and relates to the technical field of artificial intelligence. Comprising the following steps: separating image-text elements and establishing context association; calling a graph vectorization engine, and executing intelligent vector conversion processing on the rasterized illustration; performing hierarchical classification, attribute endowing and structured reconstruction on the vector primitives in combination with text semantic analysis to generate vector entity objects with complete attributes; the text elements and the vector entity objects are jointly input into a pre-trained multi-mode large language model, unified knowledge expression containing vectors, topology, attributes and document contexts is generated, and applications such as question answering, editing, abstracting and reporting of image-text integration are supported. According to the method, the problems of graph semantic loss, difficulty in fine analysis and the like caused by vector-to-grid illustration in papers, reports and other types of documents are solved, the overall understanding depth and interpretability of AI for complex image-text documents are remarkably improved, and high-performance semantic understanding capacity is provided for document-based training and intelligent processing.

The invention discloses a process manufacturing method of a cut image, which belongs to the technical field of laser cutting, and comprises the following steps of: acquiring a grid area covered by a cut graph of a material to be processed, dividing the cut graph into a plurality of vector diagrams according to the grid area, and performing slotting compensation processing on each vector diagram to obtain a corresponding cutting track; obtaining attribute information of the multiple primitives, determining a reference point of the vector diagram according to the attribute information of the primitives and the cutting trajectory, determining a light-off position on the primitives according to the reference point, the included angle and the vertex, and performing path optimization on the vector diagram according to the light-off position and the cutting trajectory to obtain a target cutting trajectory, the laser machining parameters of the cutting pattern are set based on the target cutting track and the to-be-machined material, hole blasting or hole punching caused by unstable laser of the cutting starting point can be prevented, it is guaranteed that the cutting effect of the to-be-cut material is optimal, the size precision meets the requirement, and the laser cutting efficiency is also improved.

The invention relates to the technical field of image data processing, and discloses an intelligent drawing auditing method and device based on a vector atlas and an active perception closed loop, and the method comprises the steps: obtaining a to-be-audited engineering drawing file and a compliance specification text; analyzing vector data of the engineering drawing file, and converting the vector data into an attribute graph used for representing a topological structure and semantics of the drawing; generating an audit instruction through a multi-modal large language model planner based on the attribute graph and the compliance specification text; executing the auditing instruction on the attribute graph through a graph signal processing verifier so as to carry out iterative compliance verification and diagnostic analysis on the attribute graph, and generating a corresponding compliance verification result and a diagnostic result; and generating a compliance audit report containing the traceable evidence based on the compliance verification result and the diagnosis result. According to the method, the accuracy and the reliability of the auditing result can be improved while automatic auditing of the engineering drawing is realized.

The invention discloses a weighted Voronoi graph vector generation method and device, equipment and a medium, and relates to the technical field of graph processing. The method comprises the steps of generating a first initial isoline set, a first initial to-be-divided region, a first target isoline set and a maximum weight point set based on a first initial seed point set; according to the maximum weight point set and the first initial to-be-divided region, performing weighted Voronoi division to obtain a maximum weight point region; when the current maximum weight point set does not include all points in the first initial seed point set, performing weighted Voronoi division according to the updated second initial isoline set, the second initial region to be divided and the second initial seed point set to obtain a new maximum weight point region; and outputting a vector diagram of the current maximum weight point area until the current maximum weight point set comprises all points in the first initial seed point set. And good vector generation efficiency and vector generation precision of the weighted Voronoi graph can be ensured at the same time.

The present invention provides a method for identifying upstream and downstream hydrological stations of a river channel based on existing GIS data, belonging to the field of digital twins of hydrological information. According to the hydrological station coordinate GIS data and the river network vector map, the present invention finds any number of stations upstream and downstream of the river channel corresponding to a given station number; displays the topological relationship diagram of the upstream and downstream stations of the given station; determines whether other stations are located upstream of the given station and which of the main stream and tributaries in the upstream basin they are in; saves the upstream and downstream topological relationship of the stations and the river network edge table in a text file for subsequent analysis. The present invention only needs to construct two layer files based on the existing GIS data to be able to identify the upstream and downstream hydrological stations of the river channel, which can greatly save the workload of manually searching for the upstream and downstream hydrological stations of specific stations. For large river networks and station layers, it has high accuracy and fast operation speed, and can be used as a favorable basis for the digital twin of the river network.

The invention provides a graph structure guided remote sensing image interpretation method and system, a terminal and a medium, and the method comprises the steps: carrying out the interpretation through employing a visual language model based on an obtained remote sensing image and expert priori knowledge; the model training process comprises the steps of obtaining a graph structure based on a vector graph corresponding to a remote sensing image, and rapidly synthesizing a large number of samples for training by randomly modifying the graph structure; model training is carried out based on the synthesized training remote sensing images and the corresponding training graph structures and training vector diagrams, graph structure features obtained by the training graph structures are adopted as training priori knowledge, a large number of image features obtained by the training remote sensing images are interpreted, and feedback optimization is carried out with the training vector diagrams as labels. The visual language model adopted by the invention can obtain the spatial information and semantic information of the remote sensing image, avoids inaccurate interpretation caused by spatial relationship deficiency, and has a better interpretation effect.

The invention discloses an SVG vector diagram-based primitive matching and intelligent recognition system and method, and relates to the technical field of computer graphic processing and mode recognition. The system stores a PDF file through SHA-256 Hash duplicate removal, format conversion is achieved through a tool, an 800DPI PNG image is generated, and the resolution is verified; the affine transformation matrix is analyzed, coordinate normalization and graphic element classification conversion are executed, and complex paths are split, deduplicated and merged; detecting a character area, and deleting corresponding character elements in the SVG; geometric features are extracted to construct a multi-dimensional index, and a template topological structure is analyzed; and selecting anchor points according to priorities, adapting to multiple rotation directions, matching non-anchor point elements through dynamic tolerance, verifying connection points in combination with adaptive topology tolerance, and finally outputting a matching result containing confidence. The system realizes efficient identification and accurate matching of technical drawings through standardization processing and an intelligent matching algorithm, and is suitable for scenes such as industrial drawing analysis and document digitization.

The invention relates to the field of phasing instruments, in particular to an airborne wireless phase checking and phasing instrument which comprises an unmanned aerial vehicle, a host and a detector (composed of an X detector and a Y detector), a 4.3-inch 65K-color LCD color screen is selected for the host, the phase, frequency, phase sequence, phase checking results and dynamic vector diagram indication can be displayed on the same screen, and a voice prompt module is arranged in the host. The device can be used inside or outside a transformer substation and is not affected by topographic conditions.