15 results about "Level structure" patented technology

A knowledge graph construction method based on ontology and large language model belongs to the field of artificial intelligence and knowledge graph construction, comprising the following steps: first, initial ontology construction for knowledge extraction task: step (1.1) domain requirement analysis and scope definition; step (1.2) ontology reuse; step (1.3) concept classification and hierarchical structure construction; step (1.4) relationship definition and attribute construction; step (1.5) attribute system design; second, ontology extension method based on large language model, comprising the following substeps: step (2.1) LLM automatically generates an extension candidate set; step (2.2) consistency verification of extension candidates; step (2.3) update domain ontology; third, automatic extraction of triples combined with adaptive chain thinking mechanism; fourth, triple fusion and knowledge graph construction oriented to heterogeneous data. The application effectively improves the structural standardization and semantic consistency of knowledge graph construction.

The application discloses a sequence construction method and system. The method constructs a switching matrix based on the number of various types of elements in the sequence, establishes a unified constraint system, and obtains a numerical solution of the switching matrix through optimization. Based on the numerical solution, a graph structure is constructed and connectivity is determined to determine whether a complete sequence can be formed; when the connectivity is satisfied, a target sequence that meets the switching relationship and quantity requirements is generated based on the graph structure. The method supports segment-level structure expression, modeling of the head-tail relationship of the augmented matrix, and sub-sequence combination expression, and is suitable for multiple sequence construction tasks. Accordingly, the application also provides a sequence construction system, which includes a matrix modeling module, a constraint construction module, a solving module, a connectivity determination module, and a sequence generation module.

Method for analyzing a mechatronic system, wherein the mechatronic system has one or more functions, the functions comprising one or more hardware and / or software functions, and in the method - a first network (20) in the form of a tree graph with several hierarchy levels and nodes (201, 202, 203, 204, 205, 206) arranged in the hierarchy levels is provided, wherein the nodes (201-206) are each representative of one of the functions of the mechatronic system and are linked to each other via one or more logical operators, such that the tree graph represents dependencies between the functions represented by the nodes (201-206), wherein the top hierarchy level has a single node (201) as the initial node, - a node of the tree graph of the first network (20) is specified as the first trigger node, - Software network data is provided that is representative of the software functions of the mechatronic system and their dependencies, - depending on the first trigger node and the software network data, a second network (30) in the form of a tree graph with several hierarchy levels and nodes (301, 302, 303, 304, 305) arranged in the hierarchy levels is determined, wherein the nodes (301-305) are each representative of one of the software functions of the mechatronic system and are linked to each other via one or more logical operators, such that the tree graph represents dependencies between the software functions represented by the nodes (301-305), wherein the top hierarchy level has a single node (301) as the initial node, and - depending on the first network of action (20) and the second network of action (30), the mechatronic system is analyzed, wherein an analysis of the mechatronic system includes that - the first network (20) is extended by the second network (30) such that the first trigger node of the first network (20) corresponds to the initial node of the second network (30), and - the mechatronic system is analyzed depending on the extended first network of action (40), wherein the mechatronic system has one or more diagnostic functions that are representative of one or more software functions for diagnosing the mechatronic system, in which - a node of the tree graph of the extended first network (40) is specified as the second trigger node, - one of the diagnostic functions is specified and assigned to the second trigger node, - Diagnostic network data is provided that is representative of the diagnostic functions of the mechatronic system and their dependencies, - depending on the second trigger node and the diagnostic network data, a third network (50) is determined in the form of a tree graph with several hierarchy levels and nodes (501, 502, 503, 504, 505) arranged in the hierarchy levels, wherein the nodes (501-505) are each representative of one of the diagnostic functions of the mechatronic system and are linked to each other via one or more logical operators, such that the tree graph represents dependencies between the diagnostic functions represented by the nodes (501-505), wherein the top hierarchy level has a single node (501) as the initial node, such that the second trigger node of the extended first network (40) corresponds to the initial node of the third network (50), and - depending on the extended first network (40) and the third network (50) the mechatronic system is analyzed.

A method for making predictions includes identifying, for each node in the heterogeneous graph structure, a set of node-target paths that connect the node to a target node; assigning, to each of the node-target paths, a path type identifier indicative of a number of edges and corresponding edge types in the associated node-target path; and extracting a semantic tree from the heterogeneous graph structure. The semantic tree includes the target node as a root node and defines a hierarchy of metapaths that each correspond to a subset of the node-target paths in the heterogeneous graph structure assigned to a same path type identifier. The semantic tree is encoded by generating a metapath embedding corresponding to each metapath in the semantic tree. A label is predicted for the target node in the heterogeneous graph structure based on the set of metapath embeddings.

The embodiment of the invention provides a structured information processing method, equipment and device, a storage medium and a program product. A hierarchical clustering mechanism with coordinated structure and semantics is provided, a plurality of nodes in a graph structure are coded into a plurality of feature vectors through a target coding model, and the feature vectors comprise text semantic information and hierarchical structure information between the nodes to lay a foundation for subsequent clustering. According to the method, clustering is carried out on multiple feature vectors and clustering clusters by adopting two-layer clustering operation, and in the hierarchical clustering process, category labels of the clustering clusters are generated through a language model, and the category labels have cross-hierarchical semantic interpretability. The hierarchical category label is generated based on the category label of each cluster, the semantic abstract hierarchical structure from fine granularity to coarse granularity is constructed, the hierarchical structure of the hierarchical category label is clear, the semantic interpretability is high, the context consistency and knowledge controllability of the text information retrieved from the graph structure are improved, and the text information retrieval efficiency is improved. And the accuracy of downstream tasks is improved.

This invention discloses a collision calculation acceleration method combining parametric geometry and collision algorithms, belonging to the field of physical simulation technology. The method includes: traversing a 3D scene to classify and label geometric objects as parametric entities or discrete mesh objects; constructing a hybrid bounding volume hierarchical structure tree, with leaf nodes storing algebraic equations or vertex data respectively; performing generalized collision detection to screen potential collision pairs; calculating a collision urgency index based on overlapping volume and complexity, and prioritizing collision pairs; using analytical geometry for parametric entities and primitive intersection testing for discrete meshes, and dynamically allocating computational resources according to priority; finally, using pose hash caching and a warm-start strategy to accelerate convergence. This invention effectively solves the problems of high computational load and inefficient resource allocation in traditional discretized collision detection by combining hybrid geometric representation and dynamic scheduling mechanisms, significantly improving the real-time performance of physical simulation.

The invention belongs to the field of graph data processing, and discloses a multi-modal graph representation learning method and system based on a dynamic hyperbolic hypergraph Transform model.The method comprises the steps that multi-modal data are obtained and preprocessed, and multi-modal graph data are obtained; generating hyperedges in a self-adaptive manner on the basis of the multi-modal graph data, and constructing a sparse incidence matrix between nodes of the graph data and the hyperedges; nodes and hyperedges of graph data are mapped to a hyperbolic geometric space, and bidirectional feature propagation and aggregation between the nodes and the hyperedges are executed according to a sparse incidence matrix to update node representation; performing global dependency modeling on the node representation based on the updated node representation to obtain a fused node representation; and based on a category weighted loss function and a multi-label threshold search strategy, performing classification prediction on the fusion node representation, and completing a graph representation learning task. According to the method, the problems of insufficient high-order relation modeling, hierarchical structure representation distortion, high calculation complexity, poor category imbalance adaptability and the like of a graph representation learning method in the prior art are solved.

The application discloses a kind of multi-dimensional graph tensor fusion representation and embedding of code and application, belong to artificial intelligence field.It includes: extracting the syntax information and hierarchical structure information of source code file and binary file;Abstract Syntax Tree abstract syntax tree (AST), Data Dependence Graph data dependence graph (DDG), Control Flow Graph control flow graph (CFG), Natural Code Sequence natural language sequence (NCS) four different heterogeneous code graph structures are generated simultaneously to source code file and binary file;Four kinds of heterogeneous code graph structures are combined to generate high-dimensional graph tensor;Using Graph Tensor Convolution Network interpretable graph tensor convolution neural network (GTCN) to generate accurate code semantic embedding and capture code internal features, and related technology is applied in various downstream tasks, such as malicious code identification, in detection efficiency and accuracy rate aspect, good balance is achieved.

The invention belongs to the field of software testing, and relates to a function-oriented knowledge graph construction and test scene automatic generation method and system based on a user manual. The method comprises the following steps: preprocessing a user manual of a to-be-tested application, including format unification and structured processing, and extracting a hierarchical structure and a content unit of a document; extracting knowledge from the preprocessed user manual through a large language model and constructing an initial knowledge graph; the initial knowledge graph is optimized through a graph-based reasoning technology, inconsistency caused by illusion of a large language model is relieved, knowledge implied in a user manual is deduced, and a function-oriented knowledge graph is obtained; and automatically generating a diversified test scene set based on the optimized function-oriented knowledge graph. According to the method, the high-quality knowledge graph can be automatically constructed from the user manual, and the test scene comprehensively covering normal functions, abnormal conditions and complex combinations is generated based on the high-quality knowledge graph.

The invention provides a sensitive data detection method and device based on hierarchical information enhancement and a graph convolutional network, and relates to the technical field of data processing. According to the method, text sequence semantics (BERT), an entity hierarchical structure (hierarchical embedding) and an entity association relationship (graph convolution) are combined, so that the modeling capability for a complex sensitive scene (such as multi-entity co-occurrence) is improved, and the detection accuracy of sensitive data in the complex sensitive scene is further improved; moreover, a tree-shaped hierarchical structure is constructed through the risk levels of the suspected sensitive entity types, so that risk weights associated with entities in different dimensions can be flexibly adjusted, and diversified sensitive data detection requirements are met; besides, based on a pre-trained BERT model, a graph structure comprising nodes and edges can be quickly migrated to sensitive data detection tasks in different fields (such as medical treatment and finance).

The application provides a sensitive data detection method and device based on hierarchical information enhancement and a graph convolutional network, and relates to the technical field of data processing. The method combines text sequence semantics (BERT), entity hierarchy (hierarchical embedding), and entity correlation (graph convolution) to improve the modeling capability for complex sensitive scenarios (such as multi-entity co-occurrence), thereby improving the detection accuracy of sensitive data in complex sensitive scenarios. Furthermore, by constructing a tree-like hierarchical structure based on the risk level of suspected sensitive entity types, the risk weights of different dimensional entity correlations can be flexibly adjusted to adapt to diversified sensitive data detection requirements. In addition, based on the pre-trained BERT model and the graph structure containing nodes and edges, the sensitive data detection task can be quickly migrated to different fields (such as medical care, finance, etc.).

A computer-implemented method includes obtaining a first kinematic graph that represents a first articulated object. The method includes generating a set of primitive structures by decomposing the first kinematic graph. The method includes generating a graph hierarchy by enumerating one or more valid combinations of the set of primitives. The method includes determining the production rules based on the graph hierarchy. The method includes determining kinematic graph grammar based on the set of primitive structures and the production rules. The method includes determining the continuous parameters for the discrete kinematic graph structure based on the production rules using a generative flow network. The method includes generating a second kinematic graph to represent a second articulated object based on the kinematic graph grammar and the continuous parameters.

The invention discloses an automatic optimization method for SysML model parameters. The method comprises the following steps that S1, a SysML architecture model facing system task constraints is constructed, and design variables and target attributes are marked; s2, modeling a system performance constraint relation based on the parameter diagram and establishing a target function expression; s3, analyzing the model semantics to automatically extract an optimization problem and converting the optimization problem into a standard mathematical form; and S4, solving based on a multi-objective optimization algorithm and feeding back a result. According to the method, the hierarchical structure of the SysML model, the relation semantics and the attachment characteristics of the parameters are fully considered, deep fusion of system modeling and performance optimization can be achieved, optimization targets, design variables and constraint conditions in the model are automatically recognized and extracted, a standardized mathematical optimization model is formed, and the optimization efficiency is improved. The parameter configuration efficiency and the design decision quality in the complex system design are remarkably improved, and the method has good universality and expansibility.

A computer-implemented method for automatically determining a neural network architecture represents a neural network architecture as a data structure defining a hierarchical set of directed acyclic graphs in multiple levels. Each graph has an input, an output, and a plurality of nodes between the input and the output. At each level, a corresponding set of the nodes are connected pairwise by directed edges which indicate operations performed on outputs of one node to generate an input to another node. Each level is associated with a corresponding set of operations. At a lowest level, the operations associated with each edge are selected from a set of primitive operations. The method includes repeatedly generating new sample neural network architectures, and evaluating their fitness. The modification is performed by selecting a level, selecting two nodes at that level, and modifying, removing or adding an edge between those nodes according to operations associated with lower levels of the hierarchy.

A method for making predictions pertaining to entities represented within a heterogeneous graph includes: identifying, for each node in the heterogeneous graph structure, a set of node-target paths that connect the node to a target node; assigning, to each of the node-target paths identified for each node, a path type identifier indicative of a number of edges and corresponding edge types in the associated node-target path; and extracting a semantic tree from the heterogeneous graph structure. The semantic tree includes the target node as a root node and defines a hierarchy of metapaths that each individually correspond to a subset of the node-target paths in the heterogeneous graph structure assigned to a same path type identifier. The semantic tree is encoded, using one or more neural networks by generating a metapath embedding corresponding to each metapath in the semantic tree. Each of the resulting metapath embeddings encodes aggregated feature-label data for nodes in the heterogeneous graph structure corresponding to the path type identifier corresponding to the metapath associated with the metapath embedding. A label is predicted for the target node in the heterogeneous graph structure based on the set of metapath embeddings.