174 results about "Graph match" patented technology

The invention relates to a self-localization and motion perception method based on deep learning, and the method comprises the following steps: multi-modal perception: collecting RGB frames and event streams through employing a DAVIS346 event camera, obtaining texture and depth information through employing an RGB-D camera, and supplementing 3D structure data through a laser radar; hybrid optical flow driven motion perception: adopting a double-branch architecture of an improved RAFT network and an event optical flow private network; multi-modal 3D detection and trajectory management: fusing multi-modal data based on VoxelNeXt-Lite to generate a 3D detection frame, filtering false detection by combining point cloud density clustering and an event density threshold, then constructing a space-time diagram associated trajectory through a Spatio-Temporal Graph Transformer, and optimizing pedestrian trajectory prediction continuity by using an LSTM (Long Short Term Memory) model perceived by a gait cycle; multi-view semantic graph matching: constructing a 3D semantic voxel map containing vertical features, calculating scene similarity in combination with top view NetVLAD features and deformable graph matching, and dynamically adjusting semantic weight by using a situation encoder and knowledge graph reasoning; and carrying out multi-sensor fusion and robust positioning.

The invention discloses a real-time dynamic trajectory tracking method and system for millimeter wave radar gesture recognition, and relates to the technical field of gesture recognition tracking, and the method comprises the steps: receiving an echo signal reflected by a gesture, extracting a potential target point cloud, and carrying out the clustering generation of a gesture point cloud sequence; establishing a multi-modal motion model library, dynamically selecting an optimal motion model by adopting graph matching, and generating a prediction state in combination with a gesture point cloud sequence; on the basis of a Poisson multi-Bernoulli hybrid filtering framework, according to the signal-to-noise ratio and spatial distribution of the current gesture point cloud sequence, dynamically adjusting the observation weight, optimizing the observation point cloud, and carrying out optimal association by combining Mahalanobis distance with dynamic time warping; a multi-hypothesis tracking strategy is adopted to maintain trajectory hypothesis, an optimal trajectory is selected through a trajectory scoring mechanism, and Kalman filtering smoothing processing is performed on the optimal trajectory. According to the method, high-precision and low-delay tracking of gesture motion is realized, gesture habits of different users and complex environment interference can be adapted, and meanwhile, relatively high track precision is kept.

The invention discloses a single tree trunk structure extraction method and system based on deep learning, and the method comprises the steps: obtaining two-dimensional image data of a single tree, marking the two-dimensional image data, and constructing a single tree trunk segmentation data set; constructing a spatial domain and frequency domain double-branch network based on the segmented data set, respectively extracting spatial domain features and frequency domain features, and fusing the double-branch features to generate a coding feature map; the coding feature map is decoded, in the decoding process, coordinate convolution CoordConv is adopted to enhance position perception, and mask segmentation and semantic label extraction are respectively carried out through a dynamic mask reconstruction branch DRMask Branch and an instance branch Inst Branch; based on a bipartite graph matching strategy, associating results of the mask segmentation and instance branches, and realizing segmentation of a single tree trunk and matching of instance-level labels; according to the method, the boundary precision and the detail reconstruction capability of trunk segmentation are remarkably improved, and the problem of feature loss of a traditional method in a complex under-forest environment is solved.

The invention discloses a cross-language code semantic alignment method, which constructs a shared semantic space through a unified abstract syntax tree (AST) and a graph matching network (GMN) so as to reduce the difference of different programming languages in syntax structure and node representation. The method comprises the following steps: (1) mapping a multi-language AST node to a unified general label set and performing structure enhancement; (2) performing node feature coding on the unified AST, and realizing cross-language interaction in combination with a cross-graph attention mechanism; (3) node representation is generated through intra-graph loop updating, and an overall semantic vector is obtained through global attention pooling; and (4) through comparative learning training in the shared space, the distance between semantically equivalent positive sample vectors is shortened, and the distance between non-equivalent negative sample vectors is shortened, so that the discrimination capability of cross-language semantic representation is enhanced. According to the method, the semantic consistency of the functional level can be effectively captured, and the accuracy and efficiency of cross-language code understanding, multiplexing and retrieval are remarkably improved.

The invention provides a query graph sub-graph matching method based on a graph neural network, relates to the technical field of data processing, and aims to avoid direct global aggregation of a whole graph by adopting a query graph division strategy based on betweenness centrality so as to reduce the calculation complexity. By designing a multi-round bidirectional cross attention mechanism, the matching results of different sub-graphs are effectively fused, and the problems of node representation smoothing and global structure information loss in the existing method are solved, so that the consistency of the overall matching results is improved. By introducing the reinforcement learning pruning strategy, the decision in the matching process is adaptively optimized, so that the efficiency and precision of the algorithm in processing complex graph data are improved.

The invention discloses a new energy power station inspection method based on a multi-modal large model small sample open set, and the method constructs a multivariate text learnable prompt, and guides a detection model to form a finer-grained category decision boundary through the text information related to an aggregation task. Due to the lack of real unknown class samples in the training process, the mining of unknown class pseudo samples is regarded as a bipartite graph matching task for the first time, and the model is optimized to form a compact unknown class decision boundary by adding unknown class virtual nodes, constructing a cost matrix and mining the unknown class pseudo samples. In order to solve the problem that known classes and unknown classes of small samples are prone to confusion, the method proposes unknown class optimization loss based on cost perception, considers the classification and positioning quality of the unknown classes, and improves the open set detection performance of the model. According to the method, transformation from a single-mode vision small model to a multi-mode vision large model is realized, and good generalized small sample open set target detection performance can be obtained only by a small amount of training data.

The invention relates to the field of relation triple extraction, in particular to a relation triple joint extraction method based on information enhancement and bidirectional modeling. According to the method, through entity-to-relation and relation-to-entity double-branch collaborative modeling, semantic information is enhanced through double-branch potential information complementation: a bipartite graph matching method is combined with subjects and objects extracted by an entity extraction module, and relation classification is optimized through an entity boundary mask attention enhancement method; a potential relation extraction module is used for guiding subject and object entity extraction, and entity boundary information is utilized to crosswise mask attention of a large related area; the two branches filter redundant triads through a relation triad cutting module; combining bidirectional results and outputting a complete triple set; according to the method, a bidirectional interaction method is introduced to realize mutual enhancement of entities and relationships, and attention distribution is optimized in combination with entity boundary masks, so that the problem of high dependence on an initial extraction result caused by unidirectional modeling in traditional joint extraction is effectively solved.

A graph representing the source code is computed, the graph comprising a plurality of nodes connected by edges, each node representing a software component of the source code and each edge representing a relationship between software components. A domain model of a domain related to the legacy code base is computed, the domain model comprising a plurality of concepts and relationships between the concepts. A node of the graph matching one of the concepts is identified. A functional role of model is assigned to the identified node. The knowledge of the assigned functional role is used to trigger any of: migration, deployment as a microservice, deployment as a containerised service, deployment as a cloud-native application, maintenance, refactoring.

The invention relates to the technical field of computer vision and pattern recognition, and discloses a multi-component object matching method and system based on graph matching. The method comprises the following steps: identifying each discrete component and component type label in a to-be-detected object image; forming a hierarchical set structure corresponding to the hierarchical relationship of the preset template; establishing undirected full-connection edges between adjacent levels, and filtering invalid edges to construct a dynamic graph; searching candidate sub-graphs matched with the topological structure of the preset template in the dynamic graph; calculating an affine transformation matrix between each candidate subgraph and a preset template; calculating a matching degree between the remaining candidate sub-images and a preset template, taking a matching result of which the matching degree exceeds a preset threshold value as effective matching, and outputting an object position coordinate and a geometric transformation parameter corresponding to each effective matching; according to the method, deep learning detection and graph matching technologies are fused, and hierarchical dynamic graph construction and backtracking affine two-stage verification are combined, so that the detection rate and the processing efficiency are greatly improved, and the mismatching rate is effectively reduced.

The invention provides a deep learning-based dynamic early warning analysis method for disseminated intravascular coagulation, which comprises the following steps of: acquiring and structuring a core physiological mechanism knowledge graph of the disseminated intravascular coagulation, preprocessing standardized and clean multi-modal clinical time sequence data, generating a physiological consistency constraint vector by utilizing a dynamic graph matching and graph embedding technology, and performing dynamic early warning analysis on the disseminated intravascular coagulation core physiological mechanism knowledge graph. Inputting the medical logic correlation variable into a time sequence encoder fused with a knowledge graph attention mechanism to realize efficient modeling of the medical logic correlation variable; anti-factual reasoning is carried out based on model output, the risk of dispersive intravascular coagulation of a patient is dynamically scored, a grading early warning signal is triggered, and clinical decision making is supported; the model can update the knowledge graph and optimize parameters according to new cases and clinical feedback increments, the adaptability and generalization are enhanced, and the accuracy, early warning ability and medical interpretation of risk assessment of disseminated intravascular coagulation are effectively improved.

The invention discloses a timing sequence statement positioning model training method and device based on proposal selection and anchor point distribution, and relates to the technical field of timing sequence statement positioning. The method comprises the following steps: initializing a fixed number of learnable queries according to a first static anchor point set; on the basis of the first learnable query, according to the unpruned long video and the natural language description text, performing proposal generation through a time sequence statement positioning model; based on the proposal selection module, redundant proposal filtering is carried out by using a non-maximum suppression algorithm; based on an anchor point distribution module, according to the untrimmed long video, the first static anchor point set and the candidate proposal set, performing bipartite graph matching by using a Hungary algorithm; and carrying out loss function weighting calculation according to the unpruned long video and the optimal proposal set, and carrying out parameter optimization on the time sequence statement positioning model to obtain an optimized time sequence statement positioning model. The method is an efficient and accurate time sequence statement positioning model training method combining proposal selection and anchor point distribution.

The embodiment of the invention provides a network surveying and mapping behavior anomaly detection method and system based on machine learning, and belongs to the technical field of network surveying and mapping behavior anomaly detection. The method comprises the steps of collecting double-source flow data, and generating a structured log data set through a double-source log fusion engine; performing sub-graph matching calculation to obtain a surveying and mapping behavior deviation degree; generating communication data containing the watermark identifier in a communication path corresponding to the session; and verifying whether an attack event carries the watermark identifier, and generating a network surveying and mapping behavior anomaly detection report. According to the method, the self-adaptive attack behavior model is constructed through the multi-modal feature vector based on the structured log and the graph protocol mapping rule base, the cognitive robustness of protocol camouflage and path drifting is fundamentally enhanced, a detection result real-time verification chain is established, and through cross verification of a watermark carrying state and a behavior track, the detection accuracy is improved. And traditional passive detection is converted into self-proof active defense.

Systems and methods for entity linking using a graph neural network are disclosed. In one aspect, a method for entity linking can include extracting a first attribute set of an unknown entity from an information source and retrieving second attribute sets of known entities from a database, wherein each of the second attribute sets corresponds to one of the known entities. The method can further include generating an unknown entity graph based on the first attribute set, generating known entity graphs based on the second attribute sets, generating an unknown entity graph embedding by applying the unknown entity graph to a graph neural network, and generating known entity graph embeddings by applying the known entity graphs to the graph neural network. The method can further include assigning the information source to one of the known entities based on the unknown entity graph embedding and the known entity graph embeddings.

The invention discloses a sheet metal part manufacturability reasoning method based on space-semantic map alignment, and relates to the field of manufacturing-oriented design evaluation and industrial knowledge reasoning, and the method comprises the following steps: carrying out geometric analysis on a CAD geometric model of a sheet metal part to be evaluated; abstracting the geometric features and the topological / metric spatial relationship thereof into a computable spatial semantic graph; performing semantic analysis on the process specification described by a natural language, converting the process rule into formalized logic check expression by using a large language model through context learning, and generating an executable domain-specific language check script; executing the script on a spatial semantic graph, realizing deterministic reasoning through graph matching and attribute verification, and completing accurate mapping and violation detection of text rules and geometric features; and outputting an interpretable diagnosis result containing violation feature positioning, triggering rules and numerical evidence. In order to solve the problems that a process rule'natural language-geometric model 'has a semantic gap, a traditional rule system is poor in adaptability, and an end-to-end learning method is high in data dependence and cannot be explained, a new rule can be quickly adapted under the condition that a large amount of data does not need to be labeled and a model does not need to be retrained; the method can accurately identify the violation of the micro-size and spatial relationship, and has reasoning preciseness, interpretability and engineering availability.

The invention discloses a traditional Chinese medicinal material defect detection method based on machine vision, relates to the technical field of intelligent detection, and solves the problems that in the prior art, a threshold cannot be migrated, so that an inter-batch judgment standard is unstable, and high-resolution block reasoning causes boundary pseudo defects and instance wrong affiliation. According to the method, a reference area is constructed in a batch domain, a statistical descriptor is extracted, and monotone mapping calibration is performed on defect scores so as to realize cross-batch unified judgment; meanwhile, a seam continuity field is introduced in the image block splicing process, defect connected domains are fused through a cross-window adjacency relation, and accurate attribution of defects and medicinal material pieces is achieved based on bipartite graph matching; according to the method, the judgment stability and the statistical reliability in the traditional Chinese medicinal material defect detection process are remarkably improved.

The invention discloses a vehicle path problem solving method, system and equipment and a medium, and particularly relates to a vehicle path problem solving method based on clustering decomposition and graph matching, which comprises the following steps: S1, receiving a large-scale vehicle path problem instance to be solved, modeling into a weighted undirected graph, and combining a series of constraint conditions and optimization targets; s2, constructing an offline knowledge base with a diversified structure, inputting a group of preset parameters in the step, and outputting a knowledge base stored in the memory; s3, aiming at the large-scale vehicle path problem instance to be solved, executing initialization operation to generate a global initial solution; and S4, by taking the global initial solution as a starting point, executing an iterative local search framework to carry out deep optimization on the solution until a preset termination bar is met. The technical problems that in the prior art, when a large-scale complex logistics network is processed, the calculation time consumption is long, the planning cost is high, and the result is unstable are solved.

The invention provides a discrete manufacturing intelligent scheduling method based on a graph theory. The discrete manufacturing intelligent scheduling method comprises the steps of 1, constructing a heterogeneous graph model of scheduling elements; step 2, static scheduling optimization based on a critical path method; step 3, resource allocation optimization based on a multi-resource bipartite graph matching method; and 4, dynamic response and rescheduling are carried out. Based on a graph theory method, a complex'process-resource-constraint 'relationship is converted into a visual heterogeneous graph model, and a systematic scheduling solution based on the graph theory is constructed, so that the scheduling solution is suitable for a discrete manufacturing scene with multi-process, multi-equipment, multi-constraint and dynamic disturbance characteristics; the method is used for realizing static planning of production plan scheduling, resource optimization distribution and dynamic adjustment full-process optimization.

The application relates to a robust guarantee method for graph matching, which is used for solving the problem that existing image matching produces obvious matching errors under external disturbance. In the method, joint Gaussian distribution is constructed based on key point position information in a pair of images to be matched, and N1 joint sampling noises and N2 joint sampling noises are obtained by sampling the joint Gaussian distribution; a first matching result set Y1 is obtained based on the N1 joint sampling noises and the pair of images to be matched, and a second matching result set Y2 is obtained based on the N2 joint sampling noises and the pair of images to be matched; a key point matching matrix of the first matching result set Y1 is smoothed to obtain a smoothed key point matching matrix y, the similarity between the smoothed key point matching matrix y and elements in the second matching result set Y2 is calculated, and a robust guarantee range is calculated based on the similarity and a preset similarity threshold value, so that when the disturbance of the input key point position is within the range, the output matching result is unchanged or the result matching similarity is higher than the set threshold value.

The application discloses a graph similarity search method based on subgraph matching, divides data graphs into a plurality of mutually exclusive half-edge graphs, constructs an inverted index by taking subgraph embedding as a key and a data graph set containing the subgraph as a value, constructs a coordinate index based on size information of the data graph, realizes hierarchical filtering by using a mapping relationship between the two indexes, processes the half-edge by using a self-loop or loop-free strategy, designs a sequential embedding model, so that embeddings of two graphs with subgraph isomorphism have the same partial order position in a high-dimensional space, inputs a query graph, screens out a preliminary candidate set through the coordinate index, further screens the candidate set through the inverted index, accurately calculates a graph edit distance between the query graph and the candidate graph, and obtains a final result set, which not only avoids traditional subgraph isomorphism testing, but also can generate subgraph embedding in an offline stage, greatly shortens inference time of the model.

The invention relates to the technical field of telecommunication network security, in particular to a network element association method and system fusing attributes, behavior sequences and topological relations, and the method comprises the steps: carrying out the coarse-grained screening based on network element attributes, constructing the vector representation of the network element attributes, and calculating the similarity of embedded vectors or screening similar network elements in a rule matching mode; performing fine-grained screening on the basis of the network element behavior sequence, modeling the network element behavior sequence by using an attention mechanism, generating low-dimensional vector representation, and further screening candidate network elements according to the similarity of behavior sequence embedded vectors; based on depth map matching of a network element topological relation, constructing topological sub-graphs for interaction generated between the seed network elements and the same batch of users as well as between the candidate network elements and the same batch of users, and calculating the similarity of network element nodes through a graph matching algorithm; and outputting similar network elements obtained by matching. According to the method, a hierarchical progressive matching strategy is adopted, the association detection precision and efficiency of the threat network elements in the telecommunication network are remarkably improved, and attack traceability and security defense are assisted.

The application discloses a mathematical expression diagnosis method and device based on an abstract syntax tree and a semantic network, and belongs to the technical field of mathematical expression processing. The method comprises the following steps: establishing formal grammar rules of a mathematical expression based on an extended Backus-Naur form; performing lexical analysis on an input mathematical expression to generate a token stream; performing syntax analysis on the token stream based on the formal grammar rules to construct an abstract syntax tree; in the process of constructing the abstract syntax tree, running a structural diagnosis process, wherein the structural diagnosis process comprises constructing a semantic network based on a mathematical knowledge base, and performing graph matching on nodes in the abstract syntax tree and the semantic network to identify structural errors in the mathematical expression; and generating error correction suggestions based on the identified structural errors. The application can realize deep analysis from syntax correctness checking to mathematical rationality, and improve the accuracy of mathematical expression processing.

The invention relates to the technical field of multispectral image fusion, and discloses a real-time multispectral image fusion system based on deep learning. The system comprises a multispectral image spectrum module, a process spectrum synthesis module, a personalized process extraction module, a process vertex labeling module and a fusion process verification module. The multispectral image map module stores various image category templates of a map structure, and each template corresponds to a fusion flow chart containing a processing vertex and a connecting edge; the process map synthesis module fuses a plurality of templates to generate a unified hypergraph through vertex clustering and edge redirection; the personalized process extraction module analyzes a user demand text and converts the user demand text into a demand graph, and a unified hypergraph is matched with a sub-graph to serve as a basic fusion process; the flow vertex labeling module identifies adjustable vertexes and adds marks; and the fusion process verification module verifies logic through a deep learning model, detects abnormal vertexes and generates a report. The system adapts to multiple scenes, and the flexibility and reliability of the fusion process are improved.

The invention provides a graph matching network-based vulnerability and patch semantic association evaluation method, which comprises the following steps of S1, performing standardization processing on a vulnerability report and patch description to obtain a preprocessed text; s2, constructing a word co-occurrence graph of the preprocessed text by utilizing a sliding window mechanism; s3, coding initial features of nodes and edges of the word co-occurrence graph into hidden vectors by using a multi-layer perceptron, and generating a vulnerability graph and a patch graph; s4, introducing an intra-graph attention mechanism and a cross-graph attention mechanism, and aggregating neighbor information in and between the vulnerability graph and the patch graph; and S5, respectively aggregating the vulnerability graph and the patch graph into graph-level representation vectors by using the patch correctness evaluation model, and calculating the Euclidean distance between the vulnerability graph and the patch graph. According to the method, the graph structure representation of the vulnerability report and the patch description is constructed, and the cross-graph attention mechanism is introduced to carry out joint semantic modeling, so that the problems of text structure information missing and lack of joint reasoning in the existing method are effectively solved.

The invention discloses a sub-graph matching method and device, and the method comprises the steps: obtaining a query graph containing N query nodes and a data graph containing M data nodes, and determining a candidate set, containing candidate data nodes corresponding to the query node in the data graph, of each query node; according to a first sequence used for traversing the N query nodes, the candidate sets of the N query nodes are filtered in sequence, and the step of filtering the first candidate set of the first query node comprises the substeps that a first set of all second query nodes which are contained in the query graph and are adjacent to the first query node is determined; for each candidate data node in the first candidate set, determining whether the candidate data node has at least one connection with the candidate set of each second query node in the first set through a plurality of threads; if the check result is no, removing the candidate data nodes from the first candidate set to update the first candidate set; the updated first candidate set is used for determining sub-graphs isomorphic to the query graph in the data graph.

The invention relates to the technical field of tower identification, and discloses a similar tower retrieval method and system based on vertex neighborhood attributes, and the method comprises the steps: obtaining vertex coordinate data of a tower model, calculating the vertex neighborhood attributes according to the vertex coordinate data, determining the neighborhood attribute information entropy of vertexes, and screening structural key points based on the neighborhood attribute information entropy; constructing a local reference frame for the structure key points, converting coordinates of each point in a neighborhood of the structure key points to the local reference frame, and generating a rotation invariant descriptor; constructing a local adjacency graph based on the structure key points and the rotation invariant descriptors; and obtaining a local adjacency graph of the tower to be retrieved and a local adjacency graph of a target tower in a preset database, performing sub-graph matching on the local adjacency graph of the tower to be retrieved and the local adjacency graph of the target tower, and determining a similar tower retrieval result according to a sub-graph matching result. By replacing original point cloud data with vertex neighborhood attributes, similar structures can be quickly positioned.

The invention provides a territorial space knowledge evolution model construction method, and belongs to the technical field of territorial space knowledge evolution models.The territorial space knowledge evolution model construction method includes the steps that a distributed graph storage framework is constructed, sub-graph partitions are divided through a community detection algorithm, and external event information is collected and converted into a graph operation instruction sequence; positioning a k-hop neighborhood sub-graph by using an incremental sub-graph matching algorithm and executing local updating to generate an intermediate knowledge graph, calculating node state embedding representation of a fusion time coding vector, capturing a time sequence dependence mode through a time perception graph attention aggregation algorithm, executing semantic consistency constraint detection to identify conflicting nodes, and obtaining a time perception graph attention aggregation algorithm; when the conflict degree exceeds a threshold value, the multi-version concurrency control protocol is triggered to roll back to the snapshot version, otherwise, the intermediate knowledge graph is reserved as final output, and the problem that it is difficult to give consideration to updating efficiency and semantic consistency guarantee when the territorial space knowledge graph responds to an external event for evolution updating is solved.

The invention belongs to the related technical field of large-scale graph matching, and particularly relates to a suspicious transaction sub-graph matching calculation method based on UPMEM storage and calculation integrated equipment, which comprises the following steps: generating sub-tasks according to the granularity of candidate vertex-neighbor vertex sub-segments, estimating the cost of the sub-tasks by using a recursive price expansion model, and executing logic segmentation on high-out-degree candidates; and then statically distributing the fine-grained subtasks to a plurality of DPUs by adopting a greedy method. In a single DPU, threads are specially divided into a loading type and a computing type, and asynchronous data flow is achieved through DMA prefetching and lockless annular FIFO; and in the set intersection stage, distributing to microkernels such as bitmap parallelism, bipartite detection and double-pointer merging according to vertex degree topology. Through strict evaluation, the scheme effectively relieves load unevenness and access delay, reduces control divergence, is suitable for multi-seed graph mode query, and has the advantages of high throughput, expandability and stability.

The invention discloses a monocular vector high-precision map data association method based on geometric context, and relates to the technical field of map data association. Extracting a map landmark set in a current frame view range from the map through the initial vehicle pose, and projecting a point set to a pixel plane; performing structured modeling on each point set in the detection road sign set and the projection road sign set; constructing a cost matrix for the current estimated vehicle pose; introducing a gating mechanism based on map distance and a self-adaptive gating range based on map depth, and eliminating matching pairs with inconsistent geometric structures; complete data association is obtained through cost matrix calculation, optimal bipartite graph matching is performed on the cost matrix by adopting a Hungary algorithm, and a data association relationship meeting global optimum is obtained. According to the method, feature information of road signs in a vector high-precision map is fully utilized, outlier matching relation pairs are eliminated through self-adaptive gating, finally, data association is conducted through a Hungary algorithm, and matching robustness and accuracy are improved.

The invention provides a sub-graph matching method based on decomposition query, which relates to the technical field of data processing, and comprises the following steps: decomposing a query graph and a data graph into a plurality of query sub-graphs and data sub-graphs; establishing a filtering strategy based on the feature vectors, filtering the data sub-graphs based on the filtering strategy, and obtaining a first candidate data sub-graph set which comprises a plurality of candidate data sub-graphs; generating an R tree based on the candidate data sub-graphs, and excluding unmatched candidate data sub-graphs in the first candidate data sub-graph set to obtain a candidate result set; and calculating a matching sequence based on the candidate result set, obtaining a preliminary matching result of the query sub-graph, combining the preliminary matching result into a candidate sub-graph, and taking the verified matching sub-graph as a final sub-graph matching result through refinement and verification. A data graph and a query graph are decomposed into a plurality of sub-graphs for efficient query of vertexes in the graphs. The generated candidate vertex set can provide more accurate information for the sorting method.

The invention discloses a connectionless dynamic balance-oriented edge calculation method, which relates to the field of edge calculation, and comprises the following steps: I, an edge management layer constructs and maintains a group of intention maps, and when a task flow reaches an edge network, the metadata features of the task flow are analyzed, and online sub-graph matching is carried out to obtain a task intention; iI, in an operation process, periodically acquiring a resource entropy value of each edge node, and constructing a dynamic network entropy field by each edge node through a connectionless broadcast protocol; according to the method, high-affinity matching from tasks to nodes is achieved, the resource utilization rate and task completion efficiency are improved, dependence on central control is remarkably reduced, meanwhile, the transparency of system behaviors is improved, auditing, anomaly detection and continuous optimization are facilitated, the stability of edge calculation is enhanced, state information is stored in a decentralized mode, and the system performance is improved. It is guaranteed that node faults cannot cause calculation state loss, and the task recovery and recombination capacity is improved.