120 results about "Connected component" patented technology

The invention discloses a handwritten element automatic segmentation and extraction method for a complex layout, relates to the technical field of handwritten element automatic segmentation and extraction, and aims to solve the technical problem that the recognition and separation precision of handwritten contents in a mixed image-text layout is insufficient. S201, a dynamic threshold segmentation algorithm is carried out; s202, context sensing connected domain analysis is carried out; s203, judging whether the elements are handwritten elements or not; s204, if the judgment result is yes, the handwriting region candidate is reserved; and S205, if not, filtering and eliminating. According to the method, the dynamic threshold segmentation algorithm and the context sensing connected domain analysis technology are cooperated, the segmentation threshold can be adaptively adjusted according to the pixel mean value and the standard deviation of the image local window through dynamic threshold segmentation, and the context sensing connected domain analysis is combined with the context information of the document to perform semantic analysis on the connected domain. The problem that the recognition and separation precision of the handwritten content in the mixed image-text layout is insufficient is solved.

The invention relates to the technical field of artificial intelligence, in particular to a question and answer method and system based on LLM fusion knowledge graph and retrieval enhancement generation. The entities which are connected with the known entities and do not appear in the local knowledge graph are marked as related entities through the graph structure connection relation, a new local knowledge graph is formed, descending sorting is carried out according to the edges and the weights in the connected components, when the weights are the same, the lexicographical order of the entity names is used as a second sorting key, and the second sorting key is used as a third sorting key; and after sorting is completed, according to the sorting sequence, until the accumulated number of the documents reaches a preset number, combining the finally obtained individual documents with the questions, and inputting the combined documents and the questions into a large language model to generate a final answer. According to the method, the accuracy and interpretability of questions and answers can be effectively improved, the illusion problem of a large language model is solved, and reliable knowledge service support is provided for a complex decision-making scene.

The invention discloses a CAE (Computer Aided Engineering) workflow control subsystem and method based on cloud computing, and belongs to the technical field of crossing of computer simulation and cloud computing, and the subsystem comprises a workflow configuration analysis module which is used for converting a CAE workflow configuration file possibly containing cyclic dependence into a directed acyclic graph state machine through strong connectivity component analysis; the workflow state machine operation module is used for dynamically deriving task groups which can be executed in parallel in the next stage in the state machine according to the current state; and the workflow scheduling module is used for submitting the tasks to a cloud solving platform in parallel and realizing fault recovery through state persistence. Through deep fusion of a graph theory method and a cloud computing architecture, automatic scheduling, efficient parallel execution and reliable fault tolerance of the CAE workflow are realized, and the computing resource utilization rate and the system reliability are remarkably improved.

A method for decoding a mesh includes receiving a bitstream including coded information of the mesh that includes a first group of connected components that has a first connectivity. The method includes determining at least one portion of the first connectivity. The at least one portion of the first connectivity is shared by at least one region of each connected component in the first group. The method includes for each connected component in the first group, determining attribute values of vertices in the at least one region of the respective connected component.

The invention discloses an orbit foreign matter detection method and system, and relates to the technical field of orbit detection.The method comprises the steps that an unknown foreign matter model is built through Gaussian distribution and a convex hull algorithm, and a known foreign matter model is built in combination with prior data; extracting a flat area of the ballast bed, projecting the foreign body model into a depth map and generating a shadow, and superposing to obtain a synthetic image with a mark; training a segmentation network by using the synthetic image and a real image, and obtaining a potential foreign matter mask through thermodynamic diagram binaryzation, connected domain analysis and size screening during reasoning; and inherent facility false alarms are eliminated through a secondary classification network, a real foreign matter detection result is finally output, alarm is triggered according to a detected threshold value, and efficient and accurate detection is realized. According to the method, on the premise that real foreign matter samples are scarce and ballast bed scenes are complex, efficient, accurate and real-time ballastless track foreign matter detection is achieved, meanwhile, the false alarm rate is reduced, and the generalization ability of the model to unknown foreign matter is improved.

The invention provides an online dynamic water quality fingerprint rapid acquisition and preprocessing device and method. The method comprises the steps that intelligent flow path selection is achieved through graph theory modeling, a water quality monitoring system is modeled into a directed weighted graph, bilateral connected component decomposition is carried out to recognize system vulnerabilities, and a redundant flow path is configured to improve the fault-tolerant capability; adopting an optimal transmission theory to optimize dilution control, modeling a dilution problem as an optimal transmission problem of spectral distribution, and calculating an optimal dilution ratio through a linear space super-gradient algorithm; a matrix flipping graph search algorithm is applied to accelerate spectrum calculation, a spectrum data matrix is abstracted into a flipping graph model, and an optimal matrix flipping operation sequence is found through heuristic search. According to the method, the efficiency, precision and robustness of water quality monitoring are remarkably improved, the monitoring efficiency is improved by 50%, the measurement error is reduced by 70%, and the calculation speed is improved by 5-10 times.

The invention discloses a fault propagation path determination method and device, a storage medium and electronic equipment, and relates to the field of Internet of Things. The method comprises the following steps: acquiring a target data set; a service calling graph is constructed according to the target data set, then strong connectivity vectors in the service calling graph are determined, and the service calling graph is used for representing a topological structure and a dependency relationship of service interaction in the distributed system; the strong connectivity vector is used for representing a connectivity component of which the bidirectional calling frequency is greater than a preset frequency and / or the data interaction strength is greater than a preset strength in the service calling graph; performing neighborhood feature aggregation on the service call graph to obtain a time sequence feature matrix; and determining a fault propagation path of the distributed system according to the strong connectivity vector and the time sequence characteristic matrix. According to the method and the device, the technical problem of poor fault propagation path prediction accuracy caused by lack of quantification of dependency relationship and topological association between services in fault propagation prediction in the prior art is solved.

The invention relates to the field of computer vision, in particular to a road defect self-adaptive detection method and system. The method comprises the following steps: acquiring an original road surface image, and generating a plurality of feature views by using the original road surface image; based on the feature view, performing reasoning by using a plurality of pre-constructed defect detection models to obtain an initial detection frame and a corresponding initial confidence coefficient; constructing a detection frame topological association graph by using the initial detection frame and the initial confidence coefficient, and obtaining a connected component of a potential defect target based on the detection frame topological association graph; calculating a fusion defect position and a fusion confidence coefficient based on the connected component; and carrying out adaptive updating on the defect detection model by utilizing the fusion defect position and the fusion confidence coefficient. The problems of insufficient robustness and long-term effectiveness of model identification caused by insufficient environmental adaptability, easy attenuation of model performance due to data drift and single decision-making mechanism in road defect detection in the prior art are solved.

The application relates to a key node identification method, device, equipment and medium of an electric power coupling network. A power station and a power-on relay in an electric power system are taken as an application node and a transition node of a physical layer network respectively, a logical node is constructed by the application node, a logical layer network of a structure of an undirected and weightless graph representing communication capability is built, the electric power system is modeled into a physical-logical coupling network, a logical edge centrality score in the logical layer network is calculated by using a hybrid centrality measurement, a logical edge criticality list is arranged in descending order according to the scores, a target logical edge is selected according to the sequence, corresponding transition nodes in the physical layer network are determined, a plurality of shortest paths between the two are calculated, path nodes are taken as candidate nodes to be removed, the candidate nodes and corresponding logical edges are removed, a change of a maximum connected component of the logical layer network is taken as a damage rate, and when the damage rate reaches a preset value, the removed nodes are key nodes. The method can effectively and quickly identify key nodes of an electric power network.

This invention provides a drawing processing method and system based on scene text recognition. It identifies all graphic and non-graphic elements within the global scope of a drawing. Based on the shape features of the graphic elements, it divides the drawing into several sets of non-graphic elements. Based on the connected components of all non-graphic elements within each set, it generates several text strings corresponding to those sets and performs fuzzy classification to identify the text results of the non-graphic element sets. Based on the spatial distribution information of the graphic and non-graphic elements on the drawing, it preprocesses the text results and overlays them onto the corresponding areas of the drawing, distinguishing between non-graphic and graphic elements. It also performs connectivity recognition on the non-graphic elements, initially labeling the text strings within them to obtain their corresponding text results. This achieves synchronous association recognition of graphics and text within the drawing, improving the efficiency and accuracy of drawing editing.

Hardware unit (10) for determining a non-linear ground surface, the hardware unit (10) comprising: at least one data input interface (1) via which the hardware unit (10) receives measurement points (M) from a 3D point-supplying sensor (20); a computing device (2) which maps the measurement points (M) into a two-dimensional grid (G) and determines a height difference for each grid cell (C) based on the measurement points (M) assigned to that grid cell (C), and classifies one of these grid cells (C) as invalid if the respective height difference exceeds a predetermined threshold; if the respective height difference of one of these grid cells (C) does not exceed the threshold and the respective height difference of at least two grid cells (C) directly adjacent to that grid cell (C) also does not exceed the threshold, classifies that grid cell (C) as a flat surface;wherein the computing device (2) performs a connected component analysis stepwise over the grid cells (C) classified as planar surfaces, separating planar surfaces belonging to obstacles from planar surfaces belonging to a ground surface and obtaining the nonlinear ground surface; a data output interface (3) that outputs the nonlinear ground surface.

We provide a system and method for autofocus and automated cell counting. [Solution] A system and method for autofocus using artificial intelligence includes (i) capturing multiple monochrome images over a nominal focal range, (ii) identifying one or more connected components within each monochrome image, (iii) sorting the identified connected components based on the number of pixels associated with each connected component, (iv) generating focus quality estimates for at least some of the sorted connected components using a machine learning module, and (iv) calculating a target focal position based on the focus quality estimates of the evaluated connected components. The target focal position can be used to perform cell counting using artificial intelligence, for example, by (i) generating seed likelihood images and whole cell likelihood images based on the output of a convolutional neural network, and (ii) generating a mask indicating the quantity and / or pixel location of objects based on the seed likelihood image.

The application discloses a generative AI industrial design topology closed-loop optimization method and system, and relates to the technical field of computer-aided design, comprising: obtaining an initial three-dimensional geometric representation of an industrial product and design constraints, and constructing a geometric parameter offset field; in iterative deformation, calculating discrete curvature, deformation energy, time sequence mutation and topology anomaly index, triggering and positioning a topology sensitive area; in the area, performing edge collapse, edge split, local re-meshing, Loop subdivision, constraint Laplace smoothing or SDF local reconstruction, and verifying and repairing the results by Euler characteristic number, genus, boundary ring, connected component and topology anomaly; calling a physically consistent proxy model to predict performance and evaluate confidence, triggering finite element sampling when confidence or safety margin is insufficient, error risk and topology sensitive area overlap, updating the proxy model by using finite element true value increment, and finally completing layered optimization and complete finite element verification.

This invention provides a method for identifying surface defects in graphite bipolar plates, belonging to the field of defect detection technology in computer vision. First, it collects and standardizes multi-source heterogeneous image data of aligned flow channels to construct a physically consistent benchmark dataset that excludes environmental noise. Then, based on a self-supervised encoder-decoder architecture, it builds a graphite surface topology repair and reconstruction model. By using masked image modeling, the network learns prior knowledge of flow channel distribution and micro-texture, and a dynamic feature enhancement module repairs missing information in masked areas. Subsequently, it performs residual analysis on the reconstructed reference image and the actual observation image, constructing a defect discrimination feedback loop using pixel-level comparison and structural similarity measurement. This accurately identifies key defect indicators such as microcracks, edge chipping, and scratches without a standard physical template. Finally, it completes defect classification and localization through connected component quantization features, decoupling the residual mapping map into quantifiable geometric parameters, effectively reducing the false alarm rate and improving the level of detection intelligence.

The invention relates to the field of image processing, and provides a medical ultrasonic image segmentation method based on geometric guidance and concept perception fusion, which is characterized in that a geometric guidance text generation module is designed, and high-quality medical text description is automatically generated according to connected component analysis and multi-dimensional geometric analysis; according to the method, text-free segmentation is converted into text-guided segmentation, the problems that high-quality text description is limited in number, difficult to manufacture and high in cost are solved, a concept-aware cross-modal fusion module is designed, multi-scale space alignment and medical knowledge graph modeling are utilized, the problem that cross-modal fusion is insufficient is solved, and the cross-modal fusion efficiency is improved. According to explicit modeling of medical priori knowledge, professional knowledge in the medical field is fully utilized to guide a fusion process and a segmentation decision, a self-adaptive segmentation decoding module is further provided, an optimal strategy is dynamically selected according to feature complexity, the efficiency is improved while the precision is ensured, and the method is suitable for large-scale popularization and application. According to the invention, the precision and efficiency of medical ultrasonic image segmentation are improved.

This invention discloses a deformation analysis method for multi-bolt structural connections based on displacement field equivalence and machine learning, relating to the field of advanced aircraft connection technology, and aimed at solving the technical problems of high computational load and low efficiency in existing technologies. The method includes: establishing a refined model of a single bolt connection process; analyzing the deformation of the connected components during the connection process based on the refined model; obtaining the equivalent load based on the deformation displacement field; constructing a multi-bolt connection deformation analysis model and creating connection elements within the analysis model; simulating the overall component by inputting the equivalent load to obtain a sample dataset; training a machine learning model using the sample dataset and learning the mapping relationship between load boundary conditions and deformation response from the simulation samples; and predicting the deformation of the multi-bolt structural connection based on the trained machine learning model.

This invention belongs to the field of robot path planning technology, specifically disclosing a robot path planning method guided by a large language model. The method includes: discretizing the environmental space to establish a configuration space for the robot and obstacles; calculating the collision region in real time and segmenting the free space into connected components; generating prompt text; calling the large language model to parse the text and output a recommended obstacle list; constructing a path tree; generating new nodes; backtracking to generate candidate paths based on the new nodes, and allowing node poses to shift within a preset range through a tolerance verification mechanism; when the target region is connected to a connected component, adding the path to the solution set library, and selecting the optimal solution based on a weighted cost function that minimizes the path length and the total distance moved by the obstacles. This invention utilizes the semantic understanding capability of the large language model to optimize the search strategy, significantly improving the efficiency and adaptability of path planning in dynamic environments, and is applicable to scenarios such as warehousing logistics and disaster relief.

The application discloses a kind of topological network fast disintegration method, device, equipment and storage medium, comprising: the entity in target system data is abstracted as network node, and the interaction between entity is abstracted as network directed edge and constructs directed graph model;Identify the connected component of directed graph model, select the connected component containing the most nodes as the initial structure integrity reference of network;Based on directed graph model, extract the structural feature information of node, generate the embedding representation of node;Based on the embedding representation of node, the influence degree of node in network disintegration is evaluated using strategy model, generate node removal priority to remove node, update the structure state of residual network, calculate the maximum connected component size of residual network;When the maximum connected component size is lower than the preset threshold or the number of removed nodes reaches the preset proportion, terminate node removal and output node removal sequence.The application improves the accuracy of key node identification and the efficiency of node removal.

This application provides a network channel routing verification method and apparatus. The method constructs a directed graph based on the directed connection relationships of ports in the routing data, and uses graph algorithms to partition connected components and add audit rules based on data dimensions for verification. This allows for rapid and accurate detection of the connectivity and integrity of channel routes. During the verification process, it does not rely on the segment group order attribute in the routing data, avoiding shallow data quality issues that could prevent verification. Furthermore, it can verify the integrity of the channel carrying structure according to the business channel dimension, quickly aggregating all hierarchical channel routing anomaly audit results. For business channels with abnormal aggregated audit results, a secondary verification is performed to ensure the accuracy and reliability of the verification results. This helps vendors rectify data according to the business channel dimension and achieve rapid results.

The invention provides an electromechanical system model end equipment statistical method and device based on a natural language, and relates to the technical field of electromechanical system data processing. The method comprises the following steps: acquiring a BIM model of the electromechanical system, receiving a natural language statistical instruction input by a user and performing semantic analysis to obtain a target system type, a starting object screening range and an end equipment statistical condition, and obtaining an end equipment statistical result according to the target system type and a preset service rule; the method comprises the following steps: determining an initial object limiting parameter, screening out an initial object meeting the limiting parameter, starting from the initial object, carrying out recursive search on communicated components according to a connection relationship in a BIM model, ending when an end component or a repeated component is searched, obtaining a candidate component set, screening out components meeting end equipment statistical conditions, and obtaining a candidate component set; and obtaining the target end equipment, and outputting a statistical result according to the screened target end equipment. According to the invention, the statistical efficiency and accuracy of the electromechanical system model end equipment are improved.

The application provides a kind of online social platform topic propagation topology fingerprint graph construction method, comprising: constructing topic propagation network graph, determining source node, extracting maximum connected subgraph, calculating subgraph each node forwarding volume, node hop count, adjacent edge forwarding intensity;From each subgraph source node, with forwarding intensity as weight, the shortest weighted path from source node as filter function, forward search out connected component sequence, record each connected component start time step and duration, the same connected component is merged;From each subgraph recorded search end node, start reverse search, get ring sequence from reverse search, record each ring structure start time step and duration, the same ring structure is merged;According to connected component sequence set, ring sequence set constructs topic propagation fingerprint graph.The application can effectively extract the significant topology of connected path and loop formed in the process of online social platform topic propagation, and quantify its duration.

This invention discloses a method for estimating the pose of stacked workpieces based on the combination of 2D images and 3D point clouds. Using depth images as the processing object, the method performs 2D image processing, including background separation, hole filling, noise removal, and connected component analysis, to remove useless background information. The 2D pixel coordinates of each stacked workpiece are extracted, and point cloud data for each workpiece is generated using mapping relationships. The quality of the generated data is improved through downsampling, filtering, and surface smoothing. The model point cloud is obtained by multi-angle stitching. Key points are extracted from both the model and workpiece point clouds to establish FPFH feature descriptions. An improved Kuhn-Munkres algorithm is used to pair the feature points of both, and clustering is performed based on geometric constraints. The precise pose of the workpiece is obtained using SVD and ICP nearest point iteration. This method solves the problems of poor accuracy in 2D image detection and slow 3D point cloud registration, enabling stacked workpiece recognition to achieve good accuracy and real-time performance without requiring a large amount of training data.

This invention relates to the field of machine vision inspection technology, specifically a machine vision-based online image detection method for material particle size. The method includes: acquiring a grayscale image sequence of material flow from a conveyor belt; enhancing the image by suppressing background noise; segmenting particle regions and extracting particle contours based on edge information; constructing pseudo-boundaries by combining particle edge curvature changes and local grayscale minimum distribution characteristics; and separating adhered and overlapping particles using an improved connected component analysis algorithm. The method calculates geometric features such as particle projected area and equivalent diameter, determines particle size levels by comparing with grading thresholds, and statistically analyzes the proportion of particles at each size. Finally, it calculates continuous mass flow distribution data by combining the conveyor belt running speed and generates an online detection report. This method effectively solves the problem of particle stacking and segmentation, stabilizes and refines particle recognition accuracy, and achieves integrated real-time detection of material particle size and conveying flow rate, making it suitable for continuous material conveying operations in industrial settings.

This application discloses a method, apparatus, and related products for reverse analysis of CAN signals. The method calculates the entropy value of each bit in the original CAN message data, the covariance matrix between every two bits, performs connected component analysis, and calculates the temporal variance and range of each bit, resulting in a multi-dimensional feature matrix. This multi-dimensional approach more accurately and completely identifies signal boundaries, reducing the inaccurate signal segmentation caused by segmenting signals using a single feature. The feature matrix is ​​input into a prediction model to obtain candidate parsing results represented in a multi-dimensional form of six-tuples, solving the problem of missing parameter dimensions. LLM (Logical Learning Model) is introduced for logical verification, effectively filtering out erroneous parsings that violate common sense, and obtaining a logical score for each candidate parsing result, further improving accuracy. Furthermore, the method requires no manual intervention, improving reverse analysis efficiency, and is transparent throughout the process, enhancing the credibility and interpretability of the parsing results.

The present application relates to mobile robot path planning technical field, especially to a kind of grinding machine full area coverage path planning method.The technical scheme includes:by obtaining work environment grid map and carrying out inflation treatment to obstacle to obtain safety grid map, the passable area is marked and connected component is constructed Room adjacency graph, determine room access order;In each room, according to the aspect ratio of circumscribed rectangle to generate parallel sweep section, and the path connection between sweep section and room is realized using A star algorithm, while path straightening and circular arc smoothing processing are carried out.The present application divides room by work area and combines sweep path generation and path smoothing optimization, realizes the full area coverage of grinding machine work path, reduces path redundancy and turning number, improves path smoothness and work efficiency.

The invention discloses a symbolized polyphonic data anomaly detection and error correction method based on a heterogeneous graph neural network and related equipment. The method comprises the following steps: analyzing a symbolized polyphonic data stream, and extracting a multi-dimensional acoustic feature vector; a heterogeneous topological graph G = (V, Et, Ec) is constructed, Et is a horizontal directed edge, and Ec is a vertical undirected edge; extracting and fusing space-time features through a space-time diagram convolutional network; calculating a matching degree weight w based on the statistical constraint matrix, and positioning a topological distortion node cluster through threshold judgment and a connected component algorithm; and generating a candidate node set for the distorted node cluster, optimizing and correcting errors by using a global cost function, and outputting a reconstructed sequence. According to the method, the structural sensitivity and robustness of the abnormal positioning of the polyphonic data are improved, the error correction efficiency is improved, and the method can be widely applied to scenes such as music education, creation assistance and music score editing.

This application provides a method, apparatus, and electronic device for determining image region attribution. The method includes: obtaining a first binary matrix and a corresponding second binary matrix; constructing a first-order matrix and a second-order matrix based on the first and second binary matrices; performing dilation processing on the first-order and second-order matrices to obtain a first dilated matrix and a second dilated matrix; in an image processing scenario, the dilation processing includes adaptive structuring element dilation processing combined with boundary features after image preprocessing; performing an intersection operation on the first and second dilated matrices to obtain candidate associated regions; and generating a region attribution determination result through connected component analysis. This application can optimize the adaptability of image processing scenarios and meet the high-precision and real-time requirements of image region attribution determination in different scenarios such as industrial inspection.

This specification describes a method for segmenting a mesh into submeshes based on the properties of triangles. First, triangles are classified according to certain properties using their respective areas. A filtering process can be used to modify the classification according to adjacent triangles. Next, connected components are generated, and adjacent connected components are merged according to specific criteria. In some embodiments, connected components that share the most edges are merged. Using this technique, submeshes can be automatically generated without prior knowledge of the mesh generation stage.

A face image clustering method and apparatus, an electronic device, and a storage medium are provided. The method includes: performing feature extraction on samples in a face data set by using a face recognition model to obtain a feature; calculating a cosine distance between each two features, and constructing a connected graph covering all the samples; on the basis of a connected component, searching the connected graph to obtain low-level subgraphs, and aggregating the low-level subgraphs, to obtain first candidate clusters; calculating quality scores and intersection scores, and screening the first candidate clusters to obtain second candidate clusters; outputting probability values of vertexes in the second candidate clusters by using a graph convolutional neural network, and removing noise points to obtain third candidate clusters; and searching for a shared vertex between each of the other third candidate clusters and a reference cluster, and removing the shared vertex.