351 results about "Connected component" patented technology

The invention discloses a load transfer path regulation and control method and system based on a maintenance topology rule base, and relates to the technical field, and the method comprises the following steps: constructing a graph model, obtaining a load node of a shutdown device in the graph model, and obtaining a load path switching node based on a connected component analysis method; correcting the graph model to obtain a power grid topological graph of load path switching nodes, and generating a load path switching scheme based on a shortest path algorithm; a pre-switching adjustment strategy is set, and a load transfer action instruction is generated and executed step by step based on a load path switching scheme; carrying out local control on load transfer path adjustment based on a load transfer action instruction and a distributed control technology; the optimal load switching path is identified through connected component analysis and the shortest path algorithm, and the load transfer path of the maintenance equipment is controlled and switched according to the load transfer action instruction in combination with the distributed control technology, so that the stability, the flexibility, the response speed and the regulation and control precision of load path regulation and control are improved.

The invention discloses an intelligent construction and retrieval method for a medical knowledge graph based on GraphRAG and LLM. The intelligent construction method of the medical knowledge graph comprises the steps of data collection and processing, text segmentation, entity and relation extraction, entity analysis, weak connected component identification, word distance filtering, LLM evaluation, element abstract generation, graph community generation and community abstract generation. The intelligent retrieval method comprises the steps of knowledge graph vector library construction, text vector library construction, user question entity extraction and vectorization processing, multi-layer entity and relation retrieval, text retrieval, LLM summarization, result output and user feedback collection. According to the method, the GraphRAG and the LLM large model technology are combined, and efficient retrieval and accurate presentation of the medical information are achieved. According to the system, the accuracy and efficiency of medical literature retrieval are remarkably improved through deep integration of graph structured data and natural language processing capacity.

The invention discloses a multi-level batch text parallel deduplication method, system and device and a storage medium, and the method comprises the steps: carrying out the preprocessing of text data, and carrying out the similarity text representation and content deduplication of sensitive hashing at a chapter level, a paragraph level and a statement level; adding a priority score and a sorting rule in a duplicate removal process to eliminate duplicate removal randomness; during content deduplication, a graph structure is created according to similarity text representation of sensitive Hash, a plurality of connected components are established on the graph structure by utilizing combination and separation of union sets, and a target text is selected and reserved in each connected component by adopting a dynamic Hash threshold value; in the process, data frame objects are respectively constructed for chapter-level, paragraph-level and statement-level texts, unique identifiers are allocated, and the time complexity and the required memory space are optimized by adopting a parallel deduplication strategy of block delay calculation and pointer storage. According to the method, the duplicate removal speed and accuracy can be improved, and meanwhile, the problem of memory shortage faced by mass duplicate removal is solved.

The invention provides a layout file geometric data processing method, a computer device and a storage medium. The method comprises the steps that a layout file of a three-dimensional chip is obtained, polygonal entities and layer information are extracted, the polygonal entities are distributed to corresponding layer containers, and an independent R tree index is created for each layer; performing connectivity analysis on the polygonal entity, and returning a connected component set and performance analysis statistical information; when an independent R-tree index is created for each layer, data of all polygonal entities of a single layer is obtained, a minimum boundary rectangle is generated for each polygonal entity, R-tree indexes are constructed for different layers in parallel by adopting a plurality of threads, whether the number of node entries of the R-tree indexes exceeds a splitting threshold value or not is judged, and if yes, splitting processing is carried out on nodes; and verifying the consistency of the created R tree index and the original data. The computer device and the storage medium can realize the method. The geometric data processing efficiency of the layout file can be improved.

The invention belongs to the technical field of AUV formation control, and particularly relates to an AUV formation tracking control method based on a dynamic topology switching strategy, a program, equipment and a storage medium. According to the method, a new communication topological structure is generated by using 2-Delaunay, and the problem of non-unique connected components is avoided by solving the shortest path between different sets in combination with the optimal communication distance and the maximum communication distance, so that topological network optimization is completed; a dynamic topology switching scheme based on a Jaccard coefficient is adopted for control, when an intelligent agent communication topology in a formation is interrupted or reconnected, a topology network structure is switched to cover a formation network, and therefore the purpose of achieving real-time monitoring and dynamic management of the topology through measurement between an expected topology and a self topology is achieved. A DMPC is adopted as a formation controller, and efficient and accurate formation control of a plurality of AUVs in a complex environment is realized by utilizing the excellent capability of the DMPC in the aspects of system dynamic characteristic modeling and future behavior prediction.

The invention discloses a rapid local repair method for a self-intersecting triangular mesh. According to the method, local processing and rational number calculation are combined, aiming at an input low-quality triangular mesh, firstly, the mesh quality is optimized through a preprocessing step, intersection areas are positioned, then the mesh is partitioned based on octree division and connected component analysis, and the intersection areas and non-intersection areas are separated. In each intersection area, a shape-preserving boundary three-dimensional grid optimization method based on rational numbers is adopted to ensure the robustness of intersection calculation, and surface grids are extracted after successful conversion into floating-point number representation. And finally, seamlessly splicing the repaired local grid and the non-intersecting grid, and improving the overall grid quality through an edge re-gridding method based on curvature self-adaption. According to the method, the non-intersecting high-quality grid can be quickly generated, the method is suitable for downstream applications such as finite element analysis, meanwhile, the calculation overhead is remarkably reduced, and the algorithm robustness is improved.

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 provides a method for eliminating metal artifacts in a cone beam CT (computed tomography) projection domain, which comprises the following steps of: converting a current frame projection image IC into a binarization image IL and an edge detection image IG, marking connected domains in the IL and the IG, and deleting non-metal artifact area connected domains, so that binarization and edge detection results can be mutually verified; the accuracy of metal artifact area detection is improved; obtaining an extended bounding rectangle Ri, ext of each connected domain in the IM, and effectively eliminating metal artifacts according to the extended bounding rectangles Ri, ext; by segmenting the metal artifact area in the projection domain image, the resolution of the image is protected, and the artifact elimination effect is improved.

The invention discloses an individual tree segmentation method based on machine learning, and particularly relates to the field of individual tree segmentation. The method comprises the following steps: respectively acquiring forest point cloud data through a ground scanner and an unmanned aerial vehicle, registering the forest point cloud data into a complete data set, preprocessing the point cloud data, extracting geometric, reflective and spatial distribution multi-dimensional features, and fusing the geometric, reflective and spatial distribution multi-dimensional features into a comprehensive feature vector; training a model by using a machine learning method based on the labeled data, taking the feature vector as input, and outputting whether the point belongs to a tree or not; predicting a point category by using the trained model, performing individual tree instance segmentation by using a connected component clustering and graph segmentation method through CRF post-processing optimization, constructing a 3D model and calculating key parameters; and finally, performing post-processing on a preliminary segmentation result, filtering noise by using connected region analysis, filling the deficiency by using a three-dimensional hole, optimizing a boundary based on graph segmentation, and optimizing a block seam by using an ICP (Inductively Coupled Plasma) algorithm.

The present invention relates to a method for analyzing the connectivity of communication nodes based on a knowledge graph, and relates to the field of communication network analysis. The present invention manages communication nodes and the relationships between them through a graph database, and dynamically screens the node relationships in combination with time attributes and communication types. The specific steps include: first, obtaining all nodes and relationships that meet the specified time conditions, and grouping them by communication type; then, constructing a graph for each communication type and applying a breadth-first search algorithm to identify connected components; finally, processing the relationships between nodes based on the attributes of the communication type, and identifying the set of interconnected nodes under each communication type. The present invention aims to efficiently analyze the connectivity between nodes in a communication network. The present invention can efficiently process large-scale network topologies, is flexible, and is particularly suitable for dynamic and real-time changing communication network environments. It has broad application value, especially in communication network modeling and analysis.

Embodiments of the present disclosure relate to the field of deep learning, and provide a model evaluation method and apparatus, a device, a program, and a medium. The method comprises: acquiring a test image, the test image comprising a foreground area and a background area; inputting the test image into a target classification model, to obtain a target classification result, the target classification result at least comprising target feature information and target attribute information; inputting the test image into an attribution analysis model, to acquire an attribution graph corresponding to the test image; using the test image as a reference, analyzing a connected component in the attribution graph, and determining a type of the connected component, the type of the connected component being a foreground type or a background type; on the basis of the type of the connected component, determining whether the target classification model is a qualified target classification model, and on the basis of the type of the connected component, determining whether the target classification result is a trusted result.

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 provides a node clustering method based on communication network connectivity analysis, and belongs to the technical field of network analysis. The method comprises the following steps: constructing a graph model according to input nodes and a connection relationship thereof, wherein the construction of a graph supports a single network or multi-network relationship; traversing each node in the graph by using a depth-first search algorithm, recording an access path, and classifying the nodes with a communication relationship into the same communication component; and outputting a set of all connected components. According to the method, when the graph is constructed, topology analysis of various network conditions in the undirected graph is supported by dynamically judging the nodes and the neighbor relation of the nodes. The method has the advantages of low calculation complexity and high expansibility, is suitable for the fields of network communication, equipment connectivity check, complex command and control system information transmission modeling and the like, and provides an efficient and accurate solution for connectivity analysis.

The invention discloses a trunk breast height section reconstruction and breast diameter determination method and device, a medium and a product, and relates to the field of point cloud data processing, and the method comprises the steps: carrying out the normalization of point cloud height information; adopting a height threshold method to extract a trunk slice point cloud based on the normalized point cloud; performing individual segmentation on the trunk slice point clouds by adopting a connected component analysis method to obtain individual trunk slice point clouds; extracting a point with the maximum local point cloud density and a trunk center point based on the individual trunk slice point cloud to construct a reference direction, and performing polar coordinate conversion on the trunk slice point cloud to obtain a polar coordinate trunk slice point cloud; performing point cloud reconstruction based on the polar coordinate trunk slice point cloud by using a Kalman filtering algorithm, and performing Cartesian coordinate conversion to obtain a chest height section reconstruction result; and constructing a convex hull for a chest height section reconstruction result, and determining an equivalent diameter to obtain the diameter at breast height. According to the method, the trunk breast height section reconstruction reliability can be improved while the accuracy and efficiency of trunk breast height section reconstruction and breast diameter determination are improved.

The invention discloses a subway power distribution system fault prediction and operation and maintenance method based on big data analysis. The method comprises the steps of S1, data preprocessing; s2, constructing a topological graph of the power distribution network; s3, extracting node space embedding based on multi-head GAT; s4, generating time embedding through bidirectional LSTM and time attention; s5, fusing spatio-temporal features across spatio-temporal attention; s6, only using normal samples to train an auto-encoder to learn a reconstruction function; s7, adaptively generating a threshold value in the sliding window by using the GMM, and mapping a reconstruction error into a risk score; and S8, when the risk score exceeds a dynamic threshold value, automatically dividing a suspected fault area according to a connected component, and issuing early warning information including a risk grade, the suspected fault area and a disposal suggestion. According to the method, prospective prediction of the fault risk of the subway power distribution system can be realized, so that operation and maintenance personnel can take preventive measures in advance, and efficient and stable operation of the subway power distribution system is ensured.

The invention relates to the technical field of process control, in particular to a radiation environment laboratory full-process traceable system and a processing method.According to the radiation environment laboratory full-process traceable system and processing method.By executing timestamp sequence extraction, attribute difference screening and path structure decomposition operation on detector records and shielding cover logs, causal, time sequence and role attachment relation edges between nodes are established; and classifying attribute values to construct a unified mapping set, converting the multi-source heterogeneous data into a flow chart structure with relevance, executing node embedding operation through a graph attention network algorithm, and performing adjacent node feature weighted aggregation and edge weight screening processing based on an embedding result. And calling a Tarjan strong connected component algorithm to analyze a node branch structure, identifying a heterogeneous sub-graph in a flow path by branch index difference, executing priority ranking and task insertion actions in combination with the number of available tokens and lead plate thickness grade parameters, and generating a dynamic scheduling sequence under a multi-task coexistence condition in a control path.

The invention relates to the technical field of data cleaning, in particular to an automatic heterogeneous data cleaning method and system based on a configurable rule engine. The method comprises the following steps: acquiring a heterogeneous data source, uniformly converting a data mode and an instance into a multi-dimensional heterogeneous fact graph, calculating an execution weight for each conflict rule according to the authority of the data source and the priority of the rule when a write operation conflict is detected, segmenting data partitions by utilizing the betweenness centrality of a connected component and a node, the method comprises the following steps of: generating a plurality of data partitions, forming rule subsets corresponding to the data partitions, creating isolated Drools session instances to perform parallel cleaning, generating traceability logs in the parallel cleaning process, aggregating the traceability logs after all the data partitions are cleaned, and re-evaluating and updating the authority of each data source to solve rule conflicts in the next cleaning period. According to the scheme, the writing conflict can be precisely processed, the safety of parallel cleaning is improved, the processing period is shortened, and a continuously optimized processing closed loop is formed.

The invention belongs to the technical field of robot path planning, and particularly discloses a robot path planning method based on large language model guidance, and the method comprises the steps: discretizing an environment space, and building a configuration space of a robot and an obstacle; calculating a collision area in real time and segmenting a free space into connected components; generating a prompt text; calling a large language model to analyze the text and outputting a recommended obstacle list; constructing a path tree; generating a new node; generating candidate paths based on new node backtracking, and allowing node poses to shift in a preset range through a tolerance verification mechanism; and when the target area is connected with the connected component, adding the path into the solution set library, and selecting an optimal solution according to a weighted cost function of a minimum path length and a total obstacle moving distance. The search strategy is optimized by using the semantic understanding ability of the large language model, the efficiency and adaptability of path planning in a dynamic environment are remarkably improved, and the method is suitable for scenes such as warehouse logistics and disaster rescue.

The invention relates to a single-stage multi-view large-scale network behavior analysis system based on potential subspace anchor point learning. The invention discloses a single-stage multi-view large-scale network behavior analysis system based on potential subspace anchor point learning (SAGL), and belongs to the field of network behavior analysis. The method comprises the following steps: firstly, dominantly mapping a collected network data sample to a high-dimensional kernel space through a plurality of different empirical kernel functions; secondly, learning an anchor point of a sample in a consensus potential subspace; secondly, introducing a projection matrix to establish a relation between an anchor point in the consensus potential subspace and an original sample, and constructing a multi-view consensus anchor point diagram in a multi-view scene based on the idea of an adaptive neighbor method; then, strictly constraining connected components of the constructed multi-view consensus anchor point diagram; and finally, optimizing each parameter by adopting a coordinate descent method until convergence, and finally obtaining a cluster where the network data sample is located according to the connected component of the anchor point diagram. According to the method, information loss caused by mutual separation of stages of a large-scale network behavior analysis system is reduced, and a high-quality network behavior analysis result can be obtained through a single-stage strategy.

The invention discloses an Internet of Things distributed monitoring data synchronization method based on port machinery equipment state, which comprises the following steps: taking a fault sign as a node, and if the co-occurrence frequency between two nodes is greater than a preset value, constructing an undirected edge; constructing a fault group according to each connected component; acquiring image data through a plurality of information acquisition terminals, and analyzing whether a fault sign exists or not; obtaining residual fault signs in the corresponding fault group, generating a to-be-detected task, and broadcasting the to-be-detected task in the information acquisition terminal group; the information acquisition terminal calculates the spatial distance between the information acquisition terminal and each to-be-detected task, and the information acquisition terminal corresponding to the minimum value is used as a task execution terminal to confirm residual fault signs; summarizing residual fault sign confirmation conditions, generating a fault confidence coefficient, and forming a recheck message; according to the method, isolated alarm is converted into group early warning, the fault advanced sensing capability is remarkably improved, and the calculation amount of a data center is reduced through co-occurrence relation mining and group division of fault signs.

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.

Described herein is a method to segment meshes into sub-meshes based on triangle properties. The triangles are first classified according to some characteristic using their respective areas. A filtering process may change the classification according to the neighboring triangles. Then connected components are generated, and neighboring connected components are merged following a certain criteria. In some embodiments, connected components that share the most amount of edges are merged. With this technique, sub-meshes can be automatically generated without any previous knowledge of the mesh generation stage.

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 relates to the technical field of old-age nursing task automatic generation and distribution, and discloses an old-age nursing task automatic generation and distribution method. The overall process comprises the steps of collecting basic data of the old people and nursing personnel, defining original variables, performing standardization processing on the data, and calculating feature distances between the old people; then adaptively determining distance scale parameters, constructing a similarity matrix and extracting connected components, and realizing demand clustering of the elderly; and on the basis, a greedy strategy is adopted to select the least nursing personnel to cover the service demand, tasks are accurately distributed according to the capability proportion, and a result is output. According to the method, a whole-process closed loop from data acquisition to task allocation is realized, and the method has an end-to-end automation capability. Through standardized modeling, similarity calculation, graph theory clustering and a greedy optimization algorithm, the scheme realizes intelligent nursing management in a resource limited environment.

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 relates to the technical field of digital mechanical design, and particularly provides a two-dimensional multi-communication surface domain construction method, device and equipment and a medium, and the two-dimensional multi-communication surface domain construction method comprises the steps: obtaining a geometric figure file of a to-be-analyzed target aero-engine; thirdly, constructing an undirected graph to be processed based on geometric feature data in the geometric graph file, and performing triangulation processing on a first endpoint set in the undirected graph to be processed to obtain a triangulation graph; finally, constructing a dual graph of the triangulation graph, and determining a multi-connected surface domain information set according to a plurality of connected components in the dual graph; according to the invention, the recognition accuracy and efficiency of the two-dimensional multi-connected surface domain can be improved.

A system processes images of documents, for example, identification documents. The system transforms an image of a document to generate an image that represent the document in a canonical form. For example, if the input image has a document that is tilted at an angle with respect to the sides of the image, the system modifies the orientation of the document to show the document having sides aligned with the sides of the image. The system stores user accounts that include user information including images. The system generates a graph of nodes that represent user accounts with edges determined based on similarity scores between user accounts. The system determines connected components of user accounts, such that each connected component represents user accounts that have a high likelihood of being duplicates.

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 provides an image foreign matter detection method capable of reducing false detection probability. The method comprises the following steps: searching a difference region between a to-be-detected image and a standard image; binarizing the image according to the difference region to obtain a binarized abnormal image; inputting a to-be-detected image into the false detection model, and marking false detection points and non-false detection points; binarizing the image according to the false detection points to obtain a binarized false detection image; searching a connected domain Ai and a connected domain Bj in the two binarized images respectively, wherein the connected domains are recorded as the connected domain Ai and the connected domain Bj; if the consistency of a certain connected domain Ai and any connected domain Bj meets the requirement, deleting the connected domain Ai; marking the connected domain Ai which is not deleted in the to-be-detected image as a foreign matter region; according to the method, a deep learning model classification method and an image processing method are fused, the robustness of the foreign matter detection method is enhanced, suspected areas are screened through fusion of the two methods, and the false detection rate of foreign matter detection is effectively reduced; the anti-interference capability is stronger, and the foreign matter area positioning is more accurate.

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.