48 results about "Functional dependency" patented technology

The invention provides an asset library model security risk situation assessment system and method, and the system comprises a data collection and processing subsystem, a multilayer asset association graph construction module which constructs the function dependence, data flow dependence and physical connection relation of assets in a resource library based on a graph database; the risk propagation probability calculation module is used for simulating a conduction path of an attack from an edge node to a core resource by adopting a Monte Carlo method; and the time sensitivity analysis module is used for dynamically adjusting threat weights in different time periods through a sliding window mechanism. According to the method, real-time comprehensive data is provided for evaluation through a multi-source streaming data fusion engine and distributed edge computing nodes; accurately analyzing asset association and risk propagation by applying a multi-layer asset association map and a Monte Carlo method; by means of a reinforcement learning decision framework and incremental model updating, self-adaptive decision optimization is achieved, and novel attacks are coped with in time; based on a visual interface and a closed-loop feedback control loop, the visual effect and the evaluation accuracy are improved.

The invention discloses a machine room monitoring method and system based on a multi-source data fusion intelligent inspection robot, and belongs to the technical field of machine room automatic monitoring, and the method comprises the steps: applying adversarial transfer learning on a four-dimensional fault semantic feature field, and generating a cross-modal causal atlas representing a fault evolution path through a graph neural network; according to the method, a loss function is combined to align feature distribution of a standard machine room and a current machine room, a gradient inversion layer is utilized to force feature distribution alignment of a source domain and a target domain, meanwhile, an attention mechanism and a causal strength weight are combined to generate a cross-modal causal atlas, and a graph neural network further models physical connection, functional dependence and time sequence association between nodes, so that the cross-modal causal atlas is obtained. A causal relationship is coded into an edge weight, noise correlation is filtered through a causal mask, the stability of the causal atlas is improved, and the cross-modal causal atlas can accurately capture a fault propagation path.

Embodiments of the present invention provide a code reconstruction method and system, a computing device, a storage medium and a program product wherein the code reconstruction method comprises: performing code analysis on a to-be-reconstructed initial code to generate a target reconstruction scheme wherein the target reconstruction scheme comprises a code reconstruction plan and a code test plan; the code reconstruction plan comprises a function dependency relationship of the target code; analyzing a function dependency relationship in the code reconstruction plan, and generating at least two code reconstruction tasks corresponding to the initial code; executing the at least two code reconstruction tasks in sequence to obtain each initial code snippet, and iteratively optimizing each initial code snippet through static analysis and dynamic verification until candidate code snippets conforming to a function dependency relationship are generated; and compiling a test case of each candidate code snippet based on the code test plan, and testing each candidate code snippet based on each test case to obtain a plurality of target code snippets conforming to the function dependency relationship. And the coherence and the consistency of the reconstructed codes are ensured.

The invention discloses a station yard state evaluation and early warning method and system based on an algorithm fusion technology, and relates to the technical field of station yard monitoring and early warning, and the method comprises the steps: carrying out the noise filtering and abnormal value detection of large-scale real-time station yard data collected from station yard equipment according to a frequency range and a noise type; performing rapid feature extraction and dimension reduction processing on the large-scale real-time station yard data subjected to noise filtering and abnormal value detection by using a quantum support vector machine to generate a low-dimensional feature vector; the method comprises the following steps: constructing an information weighted graph by utilizing physical connection, functional dependence and historical fault records among station equipment, representing each equipment as a node in the graph, and representing a data flow relationship, an energy transmission relationship and a physical connection relationship among the station equipment as edges; performing multi-round iterative training on the graph neural network by using the information weighted graph to obtain an equipment relation graph neural network model; and generating a final equipment state prediction vector in combination with the equipment relation graph neural network model and the low-dimensional feature vector.

The invention discloses a building operation and maintenance problem reasoning method based on a heterogeneous graph neural network. The method comprises the following steps: extracting BIM model space and component information; constructing a heterogeneous network graph; and model training and problem reasoning. Building space units are extracted from the building information model in the IFC format, space nodes are generated, components serving all the spaces are recognized, and component nodes are generated. And based on the space-component service relationship and the space-space adjacency relationship, establishing a heterogeneous network diagram containing multiple types of nodes and multiple edge relationships so as to comprehensively represent the space structure and function dependence in the building. A heterogeneous graph neural network model is used for training, and cross-space and cross-component feature aggregation and information reasoning are realized under a relation-aware message propagation mechanism. And applying a reasoning result to a building operation and maintenance stage to realize space anomaly detection and component function state diagnosis. According to the method, fusion modeling of spatial information and component information is realized, and the problem positioning precision and response efficiency in building operation and maintenance are improved.

The invention relates to the technical field of computers, in particular to a cross-architecture application program migration method and device, equipment and a medium. According to the embodiment of the invention, firstly, the source code is automatically analyzed, the function dependency relationship is obtained, and the specific code segment of the original architecture is accurately identified, so that high cost and low efficiency caused by manual intervention are avoided, and the problem of insufficient automatic migration capability is solved; secondly, a cross-architecture migration mapping library is utilized, an original architecture code segment is automatically converted into a target architecture compatible code according to a rule, third-party library dependence is processed in batches through a preset rule, and the risk of migration failure caused by the library compatibility problem is reduced; finally, the converted codes are reconstructed based on the function dependency relationship, it is ensured that calling logic among the modules is correct, and normal operation of the third-party library under the target architecture is further ensured. Through the series of automatic operations, efficient and stable application migration is achieved, and the success rate and reliability of cross-architecture migration are improved.

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 embodiment of the invention provides a server-free function deployment method for an air-space-ground integrated network, which is applied to the technical field of edge computing, and comprises the following steps: constructing a three-layer network model of the air-space-ground integrated network, and determining information of nodes and links thereof; establishing a function dependency graph of a server-free application and generating a task request queue; modeling a server-free function placement problem in the space-air-ground integrated network as a weighted sum of minimum request execution cost and end-to-end delay, and designing a constraint condition and a target function; establishing a reward function required by reinforcement learning according to the target function; and obtaining an optimal server-free function placement strategy through a deep reinforcement learning algorithm based on graph perception embedding. According to the method, the end-to-end execution cost and time delay are reduced, adaptive optimization can be carried out for the dynamic nature of the network and the heterogeneity of node resources, and it is ensured that a delay sensitive task obtains stable and high-quality services in a complex scene.

The application discloses a function dependency approximation discovery method suitable for big data, and comprises the following steps: clustering and partitioning tuples in original data set according to attributes, replacing all attribute values in the original data set with cluster serial numbers in the partition to obtain a new data set; creating a priority queue for the new data set by taking the clustering cluster as a unit, sampling tuple pairs in the priority queue, generating non-function dependencies by comparing the attribute value differences of the tuple pairs, and adding the non-function dependencies to a non-function dependency set; taking the growth rate of the set as a judgment standard to control the termination of the sampling process; structurally simplifying the non-function dependency set; verifying the legality of the current and subsequent function dependency sets by using the non-function dependency set, deleting the non-existing function dependencies, adding new attributes to the left part of the function dependencies to obtain new candidate function dependencies, and re-adding the new candidate function dependencies to a function dependency result set; and taking the growth rate of the result set as a judgment standard to control the flow direction. The method has small calculation cost.

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.

The invention provides a business processing method and device, equipment and a storage medium. The method comprises the following steps: analyzing a syntax tree of a business source code to obtain a function dependency relationship in the syntax tree and a first data query function used for accessing an external data source; determining a plurality of second data query functions from the first data query functions according to the function dependency relationship; wherein the plurality of second data query functions do not depend on one another and are used for accessing the same external data source; combining the plurality of second data query functions to obtain an optimized syntax tree; and generating an executable code according to the optimized syntax tree, and executing the executable code. According to the method, the data query efficiency of the business source code can be improved, so that the business processing efficiency is improved.

The present application relates to the field of edge cloud, more particularly, to a method and system for fast starting of Zygote container-based service in serverless edge cloud, wherein the method comprises: obtaining a function set to be invoked and function dependency package information of all functions in the function set; obtaining a frequent item set set composed of all frequent item sets; performing descending order sorting on the frequent item set set; generating a Zygote container; judging whether each function in the function set to be invoked can be started through the Zygote; if yes, starting the function through the Zygote; if not, starting the function through cold start. The present application performs descending order sorting on the frequent item set set, and finally generates a Zygote container according to the sorted frequent item set set; the Zygote container generated through the method is relatively simple and can be started quickly by as many function applications as possible, thereby improving the starting efficiency.

The present invention discloses a full life cycle management method for water conservancy projects based on BIM and big data, which specifically relates to the field of project scheduling management, including: extracting the water inlet and outlet attributes, geographic information and elevation of water conservancy structures, establishing a set of structural nodes with water-passing semantics, and analyzing their execution order to form functional dependency edges; combining historical construction records with typical plateau meteorological data, extracting construction offset nodes and constructing meteorological response offset attributes; fusing structural nodes, functional dependency edges and response attributes to construct a multi-dimensional construction management and control map, and subsequently dynamically adjusting the node order and accessibility status in the map according to the construction log and real-time meteorological conditions after the project is started; finally, embedding the dynamic map into the BIM plan task chain, and generating a construction task scheduling path re-arrangement plan through the linkage adjustment channel, so as to realize the active response and automatic planning capabilities of construction scheduling to meteorological conditions, and improve the construction coordination and task execution efficiency of plateau water diversion projects.

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.

The invention discloses a malicious software escape behavior detection method based on a high-order function and dependency algebra, and relates to the technical field of functional programming and program analysis. The method comprises the steps that source codes of the escape failure malicious software are received, dependency relationships among functions in the source codes are analyzed on the basis of the dependency algebraic theory, a related set is constructed, and the dependency relationships comprise data dependency, control dependency and parameter dependency; and constructing a function abstract by utilizing a high-order function so as to formally represent the dependency relationship. According to the method, the function calling relation, the data flow and the control flow in malicious software codes can be accurately analyzed by constructing the high-order function dependency matrix and the function abstract, compared with a traditional static analysis method, the malicious software key path can be rapidly extracted, and particularly under the condition that complex function calling and dependency relations exist, the malicious software key path can be rapidly extracted. The most critical malicious behavior path can be effectively identified, and the analysis efficiency and accuracy are remarkably improved, so that the detection capability of a related detector is enhanced.

The invention discloses a causal priori guided intelligent servo control method for a glass edge grinding machine, and relates to the technical field of servo control, the causal relationship between variables is learned from historical observation data of a servo control system of the glass edge grinding machine, a causal graph is constructed, a weighted adjacency matrix A is obtained, a neural network is used as a PID regulator, and a weighted adjacency matrix B is obtained; in the training process of the neural network, the weighted adjacency matrix A is used as priori knowledge, a matrix B capable of quantifying the function dependency relationship between input variables in the network is defined in the neural network, a causal regularization loss item is introduced, so that the matrix B approaches the matrix A, and the neural network is constrained to learn a mapping relationship consistent with a real causal mechanism. According to the method, false correlation caused by hybrid variables can be effectively avoided, and the generalization ability and robustness of a control strategy under variable working conditions are remarkably improved.

The application discloses a malware escape behavior detection method based on high-order functions and dependent algebra, and relates to the technical field of functional programming and program analysis.The application comprises receiving source code of failed escape malware, and analyzing the dependent relationship among functions in the source code based on dependent algebra theory, and constructing a relevant set, wherein the dependent relationship comprises data dependency, control dependency and parameter dependency; a function abstract is constructed by using a high-order function to form a formal representation of the dependent relationship.The application can accurately analyze the function call relationship, data flow and control flow in the malware code by constructing a high-order function dependent matrix and a function abstract, compared with the traditional static analysis method, the application can quickly extract the key path of the malware, especially in the case of complex function call and dependent relationship, the application can effectively identify the most critical malicious behavior path, significantly improves the analysis efficiency and accuracy, and thus enhances the detection capability of the related detector.

The invention discloses a database repair counting method based on function dependence and an interactive visualization system. The method comprises the following steps: step 1, determining function dependence; step 2, outputting an inconsistent visualization condition in the data; step 3, performing inconsistency detection; step 4, outputting the repair quantity; and 5, generating a repair progress by utilizing the repair quantity. The interactive visualization system comprises an inconsistency visualization generation module, an inconsistency detection module, a function dependency mining module, a repair calculation module and a progress indication module. Compared with the prior art, the method has the following advantages: 1, the calculation efficiency is improved: the repair counting complexity is reduced to a polynomial level from an exponential level through optimization of a Blocktree structure, and the calculation time is improved by 4 times according to experimental comparison display; and 2, dynamic adaptability is enhanced, user interaction is optimized, an interactive Web application provides visual restoration progress and restoration quantity display, and user experience is enhanced.

The invention discloses an automatic cleaning and labeling method for data assets, relates to the technical field of data processing, and solves the problems of high manual dependency degree, flow splitting, insufficient dynamic adaptability and poor traceability in existing data cleaning and labeling. According to the automatic cleaning and labeling method for the data assets, a topology analysis module, a containerization cleaning engine, a multi-modal labeling center, a feedback console and a block chain evidence storage module are included, and the topology analysis module comprises a matrix generation algorithm and a function dependency detector; the topology analysis module is used for outputting a dynamic topology map and positioning an abnormal propagation path. According to the method, high-precision automatic processing of multi-modal data is realized on the basis of an abnormal positioning and containerization cleaning engine of a data topological graph, and the preprocessing period is greatly shortened; the confidence arbitration mechanism of the intelligent labeling center significantly reduces invalid manual auditing.

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.

This application relates to the field of model building technology, and in particular to a software model building method, apparatus, computer device, and storage medium. The method includes: constructing functional sub-models for each software function based on the functional processing type of at least one software function of the target software; determining the functional dependencies between the software functions; and combining the functional sub-models according to the functional dependencies to obtain a software model of the target software. When performing reliability assessments on the target software, the software model obtained by this application can, with the help of the functional sub-models, evaluate the impact of each software function on the reliability of the target software, enabling the software model to accurately assess the software reliability of the software product.

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 belongs to the field of modular analysis technology, specifically relating to a modularization method for binary programs. This invention utilizes the community detection concept for binary program modularization, designing a binary program modularization model, BinMod. Through analysis of binary program functions, four types of function dependencies are extracted: direct call dependencies, co-reference dependencies, data reference dependencies, and local code dependencies. A function dependency graph is constructed based on these dependencies to better reconstruct the modules in the program. The community detection concept is then used to modularize the function dependency graph, employing similar neighbor movement, local movement based on module degree gain, and module combination to complete the modularization of the binary program. Results show that the method of this invention significantly outperforms the FCA and BCD modularization methods in terms of module partitioning effectiveness and time efficiency.

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 TOD (Transaction Order Dependence) vulnerability detection system and a TOD vulnerability detection method based on functional dependency analysis, which are used for systematically identifying potential transaction order dependency vulnerabilities in a smart contract by combining key links such as functional dependency analysis, transaction sequence generation, sequence rearrangement and differential analysis. According to the method, the conflict function pair is constructed, the diversified to-be-detected sequences are generated based on the conflict function pair, and then the contract execution results are subjected to differential comparison in different sequences, so that potential safety hazards caused by sequence change are captured. According to the transaction sequence dependency vulnerability detection method, static analysis, fuzzy testing and differential analysis are combined, key function dependency information extracted through static analysis is used as guidance, and a more targeted test case is generated. And through a transaction sequence variation strategy, dependence on traditional mode matching is avoided, so that the accuracy and applicability of detection are improved.

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.

This invention belongs to the field of report menu configuration technology, and particularly relates to an automated method and system for generating menu structures based on reporting task configuration. This method responds to the menu creation requirements of the target subsystem by using a relational parsing algorithm to perform directed hierarchical parsing from the root node, obtaining a hierarchical functional node tree, directed functional dependencies, and a functional permission constraint space. Based on the Kahn algorithm and decision trees, a directed decision growth forest is constructed, mapping the permission constraint space to functional association strength to control connection relationships, resulting in the directed decision growth forest. The menu structure is generated through hierarchical relational mapping and an automatic generation algorithm. Simultaneously, the completeness, accuracy, and stability of functions are debugged forward along the growth tree, while the permission constraints and anti-tampering properties are tested backward. After anomalies are located, real-time corrections are made using a strategy library and synchronized to the menu structure until the reporting subsystem requirements are met, achieving automated, accurate, and dynamic optimization of the menu structure.