19 results about "Functional dependency" patented technology

The invention belongs to the technical field of spacecraft product quality and reliability analysis modeling, and particularly relates to a fault tree generation method and device based on a knowledge graph and a large model fusion technology. The method comprises the following steps: constructing a spacecraft product fault domain ontology model, establishing a tag system comprising entity tags and relation tags, and extracting related knowledge from a technical document, a SysML model and an FMEA table by using a large language model to form a triple; according to entity types and relation types of different source knowledge, fusion is carried out, and a fault knowledge graph is constructed; obtaining a function dependence sub-graph, a fault behavior sub-graph and a redundant sub-graph of a function or a composition structure from the fault knowledge graph, and retrieving a specific sub-graph from the fault knowledge graph according to a user question; analyzing fault propagation relation and redundant structure information based on function dependence in the specific sub-graph to construct an OR gate and an AND gate, and mapping nodes in the specific sub-graph to a basic event, an intermediate event, a top event and a gate to generate a fault tree.

The invention discloses a software multi-defect positioning method and device based on variable dependency and clustering. According to the method, for a program block covered by a failed test case, a static analysis method is adopted, a data dependency, control dependency or function dependency relationship among variables is extracted, and a variable dependency network model is constructed to represent defects. Based on the above defect characterization, a graph editing distance is used to measure the similarity between the different characterization. And then selecting a proper clustering algorithm to adapt to fault characterization and similarity measurement modes, and automatically grouping failed test cases according to defect types to be used for positioning. According to the method, a more comprehensive and effective defect characterization algorithm is constructed, and a more effective failure test case difference measurement method is combined, so that the number of correctly predicted defects is effectively increased. By improving the accuracy of defect clustering, an independent defect focusing suspicious degree ranking is generated, and an effective solution is provided for a multi-defect positioning problem.

A method, apparatus, medium, device, and product for processing project component data are disclosed. The method includes: obtaining first data corresponding to a first function, wherein the first data is obtained based on the dependency information of the first function, the dependency information indicating a first file package that the first function depends on, the first file package being pre-associated with multiple first files, and the first files storing the data dependent on the first function; injecting a second function into the runtime environment of the first data; and obtaining the output result of the first function based on the first data. This disclosed solution is applied to the construction of application components in a project management system, achieving decoupling of component business logic from the backend, reducing component development costs, and improving system operating efficiency.

This invention discloses a real-time early warning method and system for road maintenance facilities, belonging to the field of intelligent transportation technology. The method includes: constructing a facility graph model based on dual rules of physical proximity and functional dependency; introducing a dynamic weight correction mechanism to generate a dynamic weighted adjacency matrix; real-time acquisition of multi-source sensor time-series data, which, after preprocessing, is input into a time-series graph neural network; extracting spatiotemporal features of nodes through spatial feature aggregation and multi-scale temporal evolution modeling; mapping the final hidden state to a future anomaly probability sequence; dynamically adjusting the early warning threshold based on the number of historical false alarms, and comparing it with the predicted probability to trigger tiered early warnings. This invention solves the problems of difficult identification of clustered anomalies and delayed early warning response in complex environments by quantifying the implicit functional coupling relationships between facilities and fusing residual graph convolution and multi-scale dilated convolution into a TGNN architecture, achieving high-precision, adaptive proactive early warning of road facility anomalies.

A project component data processing method and apparatus, a medium, a device and a product, the project component data processing method comprising: obtaining first data corresponding to a first function, the first data being obtained according to dependency information of the first function, the dependency information indicating a first file package dependent by the first function, the first file package is associated with a plurality of first files in advance, and data dependent on a first function is stored in the first files; injecting a second function into the operation environment of the first data; and obtaining a result output by the first function according to the first data. The scheme disclosed by the invention is applied to the establishment of the application component in the project management system, realizes the decoupling of the component business logic and the rear end, reduces the component development cost, and improves the system operation efficiency.

A method for three-dimensional reconstruction and parameter measurement of complex geometric bodies based on structural constraints. By introducing structural prior information in the modeling stage, the geometric constraints are converted into functional dependence between parameters, and combined with continuous and differentiable implicit representation, high-precision reconstruction and stable parameter optimization of complex structure targets are realized. In the presence of noise, local missing or uneven sampling of point cloud data, stable reconstruction and size parameter measurement of complex structure geometric bodies can be achieved. The output parameterized model has structural constraint relationship and can be widely used in precision measurement, industrial detection, reverse engineering modeling and digital manufacturing.

The invention discloses a multi-function code generation method and device, electronic equipment and a storage medium, and belongs to the multi-function code generation method and device, the electronic equipment and the storage medium. The functions are called one by one according to the generation sequence of the functions, the functions are tested through the test cases corresponding to the functions, and error accumulation is prevented. Under the condition that the function test fails, the call chain causing the function test failure is determined, the function dependency graph is determined according to the call chain, the function dependency graph represents the dependency relationship among the multiple functions, and the test failure caused by the fact that the dependency relationship is not met is reduced. And according to the function dependency graph, the generation sequence of the functions which are not tested is adjusted, and computing resources are saved. And according to the adjusted generation sequence, correcting the function which fails to be tested or continuing to test the next function, and changing the generation sequence from a static state to a dynamic state. Under the condition that the multiple functions meet the preset conditions, the target code is obtained according to the multiple functions, and the repair cost is remarkably reduced.

This invention provides an optimization method, storage medium, and device for database selectivity calculation. The method includes: extracting a first constraint clause using AND predicates; merging columns with functional dependencies in the first constraint clause into a first target column set based on these dependencies, and calculating the first selectivity for each merged target column in the first target column set; calculating the second selectivity for target columns outside the first target column set in the first constraint clause; sorting the first and second selectivity, and selecting a preset number of target columns according to preset rules, then substituting their selectivity into a preset formula to calculate the total selectivity. This method enables reasonable selectionivity calculation and incorporates data relevance into the selectivity formula, thereby improving the accuracy of the optimizer's selectivity calculation.

This invention discloses an intelligent code repository generation method and system based on a hierarchical planning graph, belonging to the fields of artificial intelligence and software engineering automation technology. The method includes: transforming unstructured natural language requirements into a structured information list containing functional requirements, constraints, and requirement intentions; constructing a high-level functional dependency directed acyclic graph (DAG) without logical conflicts using the structured information list as input; refining the DAG into a detailed code repository planning graph that can guide code generation, decomposing functional nodes into technical units and defining core attributes; parsing the code repository planning graph and scheduling AI agents to execute tasks according to dependency order; and generating target code, test code, and documentation. This invention can significantly improve the reliability, maintainability, and scalability of code generation, supporting the automated construction of large-scale complex software projects.

The invention discloses a data cleaning strategy selection method and system based on a feature map and metric learning, and belongs to the technical field of artificial intelligence. The technical problems that in the prior art, due to the fact that deep structured data set representation matched with cleaning strategy recommendation requirements is not constructed, and a targeted data set similarity measurement system is not established either, cleaning strategy recommendation accuracy is poor, and generalization ability is insufficient are solved. The method comprises the following steps: mapping a to-be-processed data set into a feature graph by taking attribute columns of a table type data set as nodes and a function dependency relationship between the attribute columns as edges; inputting the feature map into a depth metric learning model to generate an embedded vector; and recommending an optimal cleaning strategy for the current data set by using a preset knowledge base comprising a historical data set recommendation strategy and the embedded vector. The method is mainly used for recommending data cleaning strategies for target data sets.

The invention discloses a traditional dwelling decoration value factor construction and classification method and system, and the method comprises the steps: obtaining a decoration image and point cloud data through multi-source data collection, and generating a decoration element data set after semantic segmentation and standardization processing; multi-level features are extracted to construct a function dependency graph, and a large language model is used for filtering and reordering optimization; constructing a Text2SQL expansion framework based on the optimized function dependency graph, and extracting a multi-dimensional value factor of the decoration element; building a multi-granularity spatio-temporal flow mode through spatio-temporal mode enumeration and optimization, and forming a decoration value factor system; and importing the system into an atlas database, establishing a bidirectional mapping relationship, and realizing multi-dimensional classification and associated retrieval of the decoration elements. The problems that traditional decoration value evaluation is high in subjectivity and single in classification system are solved, and systematic and standardized construction and intelligent retrieval of the decoration value are achieved.

The application discloses a software security vulnerability detection method based on text features and function dependency features, and the method comprises the following steps: pre-processing the code in a project, extracting all function information in the project and saving, extracting and merging function text features and function dependency relationship features of the function information, using an Autoencoder to reduce dimensions and remove noise of the merged feature vectors, and finally using a local outlier factor detection method (LOF) to perform anomaly detection to obtain functions containing security vulnerabilities. The application performs software security vulnerability detection based on unsupervised learning, does not need to provide labels of whether samples contain security vulnerabilities, simultaneously considers text features of software and dependency relationship features between functions in the software, and improves the correctness of software security vulnerability detection.

The invention belongs to the technical field of power system operation and maintenance, and particularly discloses and provides a power equipment identification method based on deep learning, which comprises the steps of generating a pre-training lightweight model based on a constructed power equipment knowledge graph and a teacher-student model, and meanwhile, combining a semi-supervised pseudo-tag iteration strategy with manual recheck and an adaptive threshold value, so as to improve the identification accuracy of the power equipment. The problem of data scarcity of the novel equipment is solved, the recognition precision of the model on the novel equipment is continuously improved in a data driving mode by using the unlabeled data, and the dependence on high-cost manual labeled data is reduced; an isolated equipment detection task is converted into a reasoning task for structured graph data by constructing a spatial relationship graph of the power equipment, so that a model explicitly learns and utilizes a topological layout and a functional dependency relationship between the equipment; the error recognition rate that adjacent similar devices are easily misjudged as the same category due to independent detection of a single device is reduced, and the accuracy of operation and maintenance decision is improved.

The one or more embodiments of the specification provide a function dependency determination method and device, comprising: obtaining an original data set, the original data set containing a plurality of tuples, each tuple containing field values corresponding to at least two fields; generating a corresponding partition set for each field, the partition set containing at least one tuple set, the tuples in each tuple set having the same field values on the field corresponding to the partition set; sampling all tuple sets to obtain tuple pairs, each tuple pair containing two tuples belonging to the same tuple set; generating a corresponding invalid function dependency for each tuple pair and adding it to an invalid function dependency set to obtain all invalid function dependencies corresponding to the sampled tuple pairs; and inverting the invalid function dependency set to obtain a valid function dependency set, the valid function dependency set containing valid function dependencies corresponding to the original data set.

The method and apparatus for testing printed circuit board assemblies provided in this application include: acquiring the identification information of the printed circuit board assembly under test and determining a matching test configuration and functional dependency knowledge graph accordingly; performing electrical connection testing and static electrical parameter baseline acquisition on the printed circuit board assembly under test according to the test configuration to obtain static baseline data; applying dynamic load excitation to the associated power network of the printed circuit board assembly under test according to the test configuration and functional dependency knowledge graph to collect corresponding dynamic response data; determining a test execution strategy based on the functional dependency knowledge graph, static baseline data, and dynamic response data, and determining corresponding test data accordingly; and determining the root cause of the fault and generating a diagnostic report based on the functional dependency knowledge graph, test data, static baseline data, and dynamic response data; thereby improving the testing efficiency and accuracy of printed circuit board assemblies.

This application relates to the field of data processing technology, and in particular to a method for generating product alternative solutions centered on functional data. The proposed solution involves structurally modeling the functions of the product to be designed, constructing a functional data package containing functional inputs and outputs, performance indicators, resource requirements, and interface constraints, and forming a corresponding set of candidate implementation solutions based on this data. By introducing functional dependencies, the product solution generation process is organized as a controlled search process based on partially combined states. During the combination generation stage, interface consistency, resource budget, and dependency constraint checks are performed simultaneously. Furthermore, by combining extension priority and state deduplication mechanisms, invalid or duplicate combinations are pruned, thereby significantly reducing the risk of combination explosion while ensuring search coverage.

Methods for identifying data anomalies in data obtained by a condition monitoring system from an industrial asset are provided. The condition monitoring system monitors a plurality of data streams associated with the industrial asset and stores metadata that identifies functional dependencies between the data streams. The method generates a model of the dependency between data obtained from a first data stream and data obtained from a subset of the data streams and applies the model to generate a further time series. The further time series is used to identify anomalies.

The invention relates to a regression testing method and device, equipment, a storage medium and a program product. The method comprises the following steps: regularly scanning a to-be-tested project branch, and triggering an assembly line task when code change in the to-be-tested project branch is monitored; in the assembly line task, according to the target codes in the to-be-tested project branches and the codes of the main branches, determining at least one target function which is changed in the target codes and a file path where each target function is located; for each target function, determining a target user interaction function corresponding to the target function through the target function, the file path where the target function is located and the function dependency relationship; and determining a front-end user interface corresponding to each target user interaction function, and performing regression testing on each front-end user interface. By adopting the method, the regression testing efficiency can be improved in an agile development environment.

This invention relates to a method for constructing a multi-source heterogeneous data fusion model for the entire lifecycle of complex equipment. The method includes: establishing a method for identifying redundant attributes of multi-source heterogeneous data for complex equipment; establishing algorithms for reusable knowledge data association mapping based on product family metastructure trees, generating design structure trees and data association mapping for the design stage, generating manufacturing structure trees and data association mapping for the manufacturing stage, generating operation and maintenance structure trees and data association mapping for the operation and maintenance stage; and constructing a data model for the entire lifecycle of complex equipment that integrates multi-source heterogeneous data based on functional dependencies and foreign key constraints of relational models. This invention solves the problems of data silos caused by the differences in physical information data structures, feature redundancy, and lack of data relationships resulting from the independent application of various business systems in the design, manufacturing, and operation and maintenance stages of complex equipment by establishing data association mapping and inter-stage data mapping methods for the design, manufacturing, and operation and maintenance stages of complex equipment.