235 results about "Dependency relation" patented technology

The invention provides a code quality adaptive evaluation and optimization method, system and device and a medium, and belongs to the technical field of computers. The method comprises the following steps: performing redundant information cleaning and standardization processing on an original code; selecting an adaptive parser to convert the processed code into an abstract syntax tree, and optimizing the abstract syntax tree; performing embedding processing on each node of the abstract syntax tree by using a pre-trained deep learning model to generate a node semantic vector, and performing aggregation, feature extraction and vector normalization operation to obtain a semantic vector representing code semantic features; extracting a dependency relationship from the abstract syntax tree to construct a program dependency graph, extracting node features by using a neural network model, and generating a dependency relationship analysis result; based on the semantic vector and the dependency analysis result, identifying a code potential problem by using an optimization algorithm and generating an optimization suggestion; codes are automatically modified according to optimization suggestions, the optimization effect is re-evaluated, and related models and algorithms are iteratively optimized according to feedback information.

The invention relates to an enterprise data operation multi-source data virtualization access and index automatic management and control method and system, belongs to the technical field of computers, and aims to solve the problems of index definition fragmentation, frequent logic conflicts, dependency static management and verification rule rigid coupling in the prior art. Comprising the following steps: constructing a unified virtualized data access layer to integrate heterogeneous data sources; the indexes are disassembled into semantic atoms capable of being managed in a versioning mode and registered to an atom knowledge base; dynamically constructing an index dependency relation directed acyclic graph based on semantic atom combination; dynamically binding the data quality rules with semantic atoms; and analyzing an index request through a virtualized query engine, reversely traversing atlas aggregation dependency and rules, generating and executing local query, and automatically checking result data quality at the same time. According to the method, unified index definition, dependence on automatic conduction, quality embedded verification and multi-source transparent access are realized, and the data consistency, the treatment efficiency and the system flexibility are remarkably improved.

The invention relates to a multilevel semantic enhanced educational knowledge graph construction method, belongs to the technical field of artificial intelligence and educational information processing, and aims to solve the problems of insufficient semantic understanding, inconsistent structure, high labor cost and the like in existing educational knowledge graph construction. According to the method, an education text is preprocessed, a multi-level knowledge point catalog and a dependency relationship are constructed, entity relationship extraction and attribute completion are performed in combination with a large language model, a knowledge point precedence relationship is identified by utilizing a random forest model, and the structure normalization is further improved by adopting an entity disambiguation mechanism. And the constructed knowledge graph is managed in a centralized manner through a visual interface and imported into a graph database to realize structured storage. The method has the advantages of being clear in structure, accurate in semantics, high in construction efficiency and the like, and is suitable for automatic knowledge modeling and management of multidisciplinary education content.

The invention discloses a code change influence analysis method and device, equipment and a storage medium, and relates to the technical field of software, the method comprises the following steps: performing grammar analysis on a multi-programming language code to obtain a back-end abstract syntax tree and a front-end abstract syntax tree, and converting the back-end abstract syntax tree and the front-end abstract syntax tree into a uniform syntax tree structure through a front-end and back-end calling mapping chain; a cross-language dependency graph is constructed according to the unified syntax tree structure, the cross-language dependency graph is composed of nodes and edges, the nodes represent code entities, and the edges represent dependency relations; and when the code change of the bank business is detected, traversing the cross-language dependency graph through a graph traversal algorithm to obtain a code change influence result. Due to the fact that the back-end abstract syntax tree and the front-end abstract syntax tree of the multi-programming language codes are constructed and connected in series, the perfect cross-language dependency graph is constructed, the front-end link and the rear-end link are connected, cross-language tracking can be achieved when the codes of the banking business are changed, and the influence of code change can be accurately analyzed.

The invention relates to an airplane airborne BIT system testability verification method based on a correlation model, and the method comprises the steps: carrying out the testability modeling of an object system, and obtaining a dependency relation model which describes the relation between a fault mode of the object system and a test method of the object system; based on the dependency relationship model, obtaining a BIT theoretical fault report set of each fault mode under all test methods by utilizing reachability analysis, and deducing an optimal diagnosis fault fuzzy group; constructing a fault sample library, verifying the BIT theoretical fault report set by using samples in the library, and adjusting the dependency relationship model according to a verification result; and performing fault simulation based on the verified dependency matrix, and calculating a total fault detection rate and a fault isolation rate of the object system according to a generated fault report. According to the method, the isolation rate index of the fault can be effectively verified in the incomplete and unformed stage of the fault diagnosis strategy.

The invention relates to a query optimization method and device based on agent planning, electronic equipment and a medium. The method comprises the following steps: pre-retrieving a target tree structure index based on a user query request to obtain a pre-retrieval result set; generating a retrieval-reasoning double-stage directed acyclic graph task chain comprising a task type, a retrieval / reasoning target and a dependency relationship; executing each task according to a topological sequence, and obtaining a target text fragment through query rewriting and expansion; and performing multi-hop logical reasoning in combination with the task chain and the target text fragment, and outputting a final answer. Therefore, dynamic task planning of environment perception is carried out by constructing a retrieval-reasoning dual-stage directed acyclic graph task chain and combining a knowledge base tree structure index; the problems that when an existing retrieval enhancement generation system processes a multi-hop question and answer task, logic association understanding ability among retrieval fragments is insufficient, semantic matching precision is low, and a reasoning path is prone to deviation are solved, and the retrieval recall rate and the answer accuracy under a multi-hop question and answer scene are improved.

The invention discloses a multi-model inference chain data analysis method and device based on natural language input, a medium and a program product. The method comprises the steps that an analysis problem text in a natural language form is analyzed into a semantic task structure, and the semantic task structure is mapped into a structured analysis model conforming to a preset specification; registering the structured analysis model as an instance to an analysis model library, allocating a unique identifier ID to the structured analysis model during registration, and writing attribution relation information of the structured analysis model; an upstream model ID and a downstream model ID in the attribution relation information of the instance are read, the attribution relation information of the upstream model and the downstream model is analyzed, layer-by-layer expansion is carried out in the mode, and a multi-model reasoning chain of the instance is constructed according to the dependency relation between the models; and according to a sequence determined by the multi-model reasoning chain, executing each model instance in sequence or in parallel, and integrating the obtained final output into a comprehensive analysis report. According to the invention, automatic execution from a natural language to full-process data analysis can be realized.

The invention provides a software deployment method which can be applied to the technical field of artificial intelligence. The software deployment method comprises the following steps: in response to received code data uploaded by a user, analyzing the code data to obtain a dependency relationship among software items in the code data, and determining a priority corresponding to the dependency relationship; according to the priorities, the software projects are constructed in sequence to generate target software; according to the deployment requirement of the target software, searching from a pre-constructed knowledge base to obtain configuration information matched with the deployment requirement, and configuring a target deployment environment according to the configuration information; and extracting deployment parameters from the target deployment environment, and deploying the target software to the target deployment environment according to the deployment parameters. The invention further provides a software deployment device and equipment, a medium and a program product.

The invention relates to the technical field of education science and technology recommendation systems oriented to personalized learning path optimization, and particularly discloses an education science and technology recommendation system oriented to personalized learning path optimization. The method aims at solving the problems that an existing recommendation system is difficult to dynamically perceive a cognitive state, knowledge structure semantics and dependency relationships are ignored, and the recommendation precision is low due to data sparsity. The system comprises a multi-source data acquisition and fusion module, a dynamic cognitive state evaluation module, a knowledge graph construction and semantic enhancement module, a path generation and optimization decision module and a self-adaptive execution and feedback adjustment module. Through multi-source data fusion, real-time cognitive state quantification, knowledge graph semantic modeling, multi-target optimization path generation and closed-loop feedback adjustment, accurate recommendation of personalized learning paths is realized, and the continuity, rationality and educational effectiveness of the paths are effectively improved.

The embodiment of the invention relates to the technical field of natural language processing, and discloses a multi-task agent execution method and device based on a large language model, electronic equipment and a storage medium, and the method comprises the following steps: receiving natural language target task description and constraint conditions input by a user; generating a task tree with a hierarchical structure and a dependency relationship; calculating an initial priority for each task node based on a preset priority scoring model, and dynamically adjusting the real-time priority of the task node according to the execution feedback of the task node to form a scheduling queue; asynchronously executing each task node in the task tree according to the scheduling queue, and calling a large language model to complete the generation of subtask contents corresponding to each node; and integrating the execution results of the task nodes, and generating a final output result oriented to the target task. According to the method, the intelligent agent system with self-adaptive dynamic scheduling, hierarchical task decomposition and stable long-range context management capabilities is constructed, so that efficient, reliable and user-controllable multi-task automation is realized.

The invention discloses a risk decision scheme determination method and device based on financial service workflow, equipment and a storage medium, and relates to the technical field of credit extension businesses, and the method comprises the steps: adding a to-be-processed parameter identifier in an initial parameter relation table constructed based on a scheme request parameter of the financial service workflow, updating the to-be-processed parameter identifier and the parameter value to obtain a target parameter relation table; analyzing the consumption relationship of each task node in the target parameter relationship table, and generating a parameter dependency relationship graph by using a parameter dependency link constructed based on a directed acyclic graph structure and the consumption relationship; generating a relation algebraic expression by using a minimum parameter passing closure determined based on the parameter dependency relationship graph and a target parameter relation table; and processing the relational algebraic expression to obtain a to-be-transmitted parameter, transmitting the to-be-transmitted parameter in the minimum parameter transmission closure so that the task node executes a query task of the to-be-transmitted parameter, and determining a risk decision scheme based on an execution result. Therefore, the efficiency of determining the risk decision scheme can be improved.

The invention provides an abnormal information identification method and device, electronic equipment, a medium and a program product. The method can be applied to the technical field of artificial intelligence and big data, and relates to application of large models in financial science and technology scenes. The abnormal information identification method is characterized by comprising the following steps: acquiring abnormal information output in the operation process of a financial system; inputting the abnormal information into a recognition model, and obtaining a first feature vector, a second feature vector and a third feature vector output by the recognition model; splicing and fusing the first feature vector, the second feature vector and the third feature vector to obtain a target vector; determining an error type of the abnormal information based on the target vector; wherein the first feature vector represents a semantic association relationship among professional terms in the abnormal information; the second feature vector represents a dependency relationship between local features of the key phrases in the abnormal information; the third feature vector represents the logic relation of the long text in the abnormal information.

The invention provides a task execution method and device in the distributed computing process of an AI model, and relates to the field of distributed computing, the method is applied to a distributed computing system, and in the distributed computing process of the AI model, for a computing task and a communication task to be executed by acceleration equipment, the task execution efficiency is improved. In the embodiment of the invention, if the dependency relationship exists between the calculation task and the communication task, the calculation task and the communication task can be split into multiple pairs of subtasks with the same dependency relationship according to the dependency relationship, so that the time overhead of part of the subtasks can be covered by a host through reasonably scheduling the subtasks, and the task execution efficiency is further improved.

The invention relates to the technical field of semantic understanding, in particular to a special knowledge base fragmented slicing and semantic understanding answer matching method, which comprises the following steps of: obtaining document table images, identifying, segmenting and marking as a fragment set, generating a layered weight sequence by combining question comparison condition obligation and consequence aging reasoning, and obtaining a semantic understanding result; the method comprises the following steps of: identifying and segmenting a text to generate a fragment set, analyzing a connection relationship to form a trajectory link, segmenting candidate answers and performing semantic alignment to supplement missing, calculating a score accumulation result according to a weight sequence, adjusting a parameter sequence and priority, and generating and outputting an optimized answer, and comprises the following steps of: identifying and segmenting the text to generate the fragment set, comparing question contents with conditional obligations and combining consequence aging reasoning; a weighted logic chain is formed, a fragment connection relation dominantly constructs a link, candidate answers are segmented, tracks are aligned, a missing part dependency relation is supplemented, semantic precise alignment is performed on the whole, reasoning is coherent and complete, answer coverage is comprehensive, the integrating degree and precision are improved, and effectiveness and integrity in a complex scene are enhanced.

The invention relates to a data-in-channel-based cross-source data joint query method, which comprises the following steps of: S1, constructing a data-in-channel access strategy model, and automatically generating a most suitable access mode; s2, constructing a power data knowledge graph, and analyzing a dependency relationship, a traceability path and a blood relationship among the data by using a graph neural network; s3, seamless access is achieved through natural language understanding, natural language query translation is adopted, and the natural language query of the user is converted into a corresponding SQL statement; s4, according to the SQL statements, similarity is calculated through the embedded vectors, user problems and data models are matched, and according to historical query modes and service requirements, a virtual data view is generated; s5, based on the generated virtual data view, sensitive information fields possibly contained in the data set are recognized through a natural language model and regular pattern recognition, and automatic labeling and classification are carried out. According to the method, the technical threshold of data fusion and sharing is remarkably reduced, and the intelligence of a data platform system in the power industry is improved.

The invention discloses a task execution method and device, electronic equipment and a storage medium, and relates to the field of computers, in particular to the field of artificial intelligence such as cloud platforms. The specific implementation scheme is as follows: in response to a detected trigger operation on a target function of an application program, determining a first candidate path from a directed acyclic graph according to a dependency relationship between tasks in the directed acyclic graph of the target function; wherein the directed acyclic graph represents a dependency relationship and an execution sequence among a plurality of tasks split by the target function; determining a target path from the first candidate paths according to the weight information of the dependency relationship in the first candidate paths; wherein the weight information is used for indicating the weight of the execution cost of the task pointed by the dependency relationship in the first candidate path; and according to the execution sequence of the tasks in the target path, sequentially executing the tasks in the target path to obtain function response information corresponding to the target function.

The invention provides a loop dependency detection method and device, electronic equipment and a storage medium, and belongs to the technical field of computer software. The method comprises the following steps: acquiring a plurality of variables to be detected; based on the multiple to-be-detected variables, a first dependency graph is constructed, and the first dependency graph comprises each to-be-detected variable and information of a variable on which the to-be-detected variable depends; performing cyclic dependency detection on the basis of information of variables on which each to-be-detected variable depends in the first dependency relationship graph; and determining a loop dependence detection result. According to the invention, the efficiency of cyclic dependency detection can be improved.

The invention discloses a mathematical formula identification coding method, and particularly relates to the technical field of formula coding. The method comprises the following steps: acquiring mathematical formula image data to be identified, and performing symbol boundary extraction and preprocessing to generate symbol feature expression data; and performing visual spatial layout analysis and symbol type semantic classification based on the symbol feature expression data to generate formula layout structure data and symbol semantic classification data. And through graph structure analysis based on a topological relation, determining an inter-symbol topological relation of the mathematical formula, and generating symbol topological relation data. And deriving a dimension constraint relationship and an operator dependency relationship between symbols through a mathematical meta-knowledge mining technology, and generating mathematical meta-knowledge constraint data. Initial mathematical formula structure expression data is generated through decoding of structure and semantic constraint fusion, and a formula coding sequence is generated through formula consistency verification and semantic constraint reconstruction. According to the invention, the accuracy and efficiency of mathematical formula identification can be effectively improved.

The invention discloses a bid invitation file compiling method and system based on artificial intelligence, a medium and a product, and relates to the field of intelligent contract processing. The method comprises the steps that a file compiling system divides a natural language text into first text content containing computational logic and common second text content through semantic analysis; then, the first text content is converted into an executable formalized rule expression, and an association mapping relation between the executable formalized rule expression and the first text content is established; and calculating and generating a derivation record containing parameters, calculation steps and dependency relationships according to the expression. And finally, embedding a unique tracing identifier in the generated clause text, and querying and displaying a derivation record through the association mapping relationship by triggering the identifier by a user. According to the method, a complete traceable link from the natural language to the calculation result is realized, so that auditing personnel can clearly understand the logic source of the calculation result, the interpretability and the credibility of the generated calculation terms are improved, and the verification efficiency and the accuracy of the bid invitation file are improved.

The embodiment of the invention provides a code review method and a computer program product, and relates to the technical field of artificial intelligence. The method comprises the following steps: analyzing a to-be-reviewed code into an abstract syntax tree, extracting a dependency relationship of each code node from the abstract syntax tree, calculating the code complexity of each code node, generating a topological structure diagram according to each code node and each dependency relationship, marking each network node according to each code complexity and a preset code rule, and generating a topological structure diagram according to each network node. And performing visual review on the to-be-reviewed code. Therefore, the cognitive load and troubleshooting time of a developer for positioning the architecture level problem in a large-scale project are effectively reduced and shortened.

One or more embodiments of the invention provide a task processing method. The method comprises the following steps: receiving a to-be-executed task; the to-be-executed task is one of tasks which need to be executed and are determined by analyzing a question input by a user; creating a target node corresponding to the to-be-executed task in a state graph; the state atlas corresponds to the question and comprises a plurality of nodes, and edges among the nodes are used for representing dependency relationships among tasks corresponding to the nodes; determining a target tool or a target agent according to the to-be-executed task, and executing the to-be-executed task by using the target tool or the target agent; and under the condition that the execution of the to-be-executed task fails, recovering the to-be-executed task according to the state graph.

The invention relates to the technical field of data processing, and discloses a teaching content integration method and system based on a knowledge graph, and a storage medium. The method comprises the following steps: performing cognitive difficulty labeling on multi-source teaching resources, and constructing a concept ontology library; fusing the semantic similarity and the cognitive span through a pre-dependency matrix algorithm to generate a pre-dependency graph; carrying out six-layer cognitive layer coding to obtain a multi-layer semantic matrix; the cognitive competence of the learner and the semantic matrix are dynamically planned and recombined, and a self-adaptive content sequence is generated; and constructing a timestamp fusion space, and obtaining a dynamic personalized learning path through a relationship attenuation model evolution graph. According to the method, the problems that in an existing teaching content integration method based on the knowledge graph, cognitive load quantification is missing, concept dependency relation mining is inaccurate, static knowledge representation is limited, and dynamic updating is lagged are solved.

The invention relates to a task processing method and device, equipment, a medium and a product. The method comprises the following steps: receiving a code change request, wherein the code change request carries code change information and a code change type; generating a task dependency graph of the code change request based on the code change information and the code change type; analyzing a dependency relationship among the tasks in the task dependency graph, and generating an execution strategy corresponding to each task; and for each task, determining a target agent according to an execution strategy corresponding to the task under the condition that the task is determined to meet an execution condition based on the execution strategy corresponding to the task, and executing the task by using the target agent. By adopting the method, the task processing efficiency can be improved through a dynamic task arrangement mechanism in a complex multi-task and multi-environment parallel construction scene.

The invention belongs to the technical field of artificial intelligence education application and computing resource management, and discloses a course flexible construction and computing power elastic allocation method and system oriented to an education large model. According to the method, course contents are disassembled into knowledge unit plug-ins which can be independently deployed, and lightweight consistent deployment of course instances in different terminal environments is realized based on a dependency relationship between directed acyclic graph modeling plug-ins; by taking a course target as a constraint, analyzing a knowledge unit reasoning path on the dependency graph, only reserving components with weights higher than a threshold value, and cutting non-key sub-model components and a redundant prompt chain to generate a dynamically cut course instance; distributing an independent container operation environment for the course instance, and collecting an operation resource state through kernel-level monitoring; according to the method, tasks are divided into different types of task queues in combination with calculation characteristics of resource states and teaching tasks, resource demands are predicted based on historical resource use behaviors of users, and calculation resources are dynamically allocated or recycled.

The invention discloses a method and device for recommending a test case, and the method comprises the steps: obtaining a change function for code change in response to the monitored code change of a tested system; determining a to-be-recommended regression test case for testing code change according to the multi-dimensional information, the multi-dimensional information comprises a dependency relationship and a mapping relationship of a change function obtained in advance, function semantic information of the change function, function semantic information of a dependency function, a historical execution record of the regression test case, and any multiple items in importance attributes of the regression test case; the dependency function refers to a function having a dependency relationship with the change function, and the mapping relationship comprises a regression test case and a function which can be tested by the regression test case; and performing regression testing on the tested system according to the to-be-recommended regression test case.

The invention provides a multi-modal large language model construction method for automatic accounting document auditing, and belongs to the technical field of large language models.The method includes the steps that table graph structure data are constructed, a graph convolutional network is input for node feature propagation, and a sequential dependency relationship is modeled in combination with a bidirectional long-short-term memory network; a cross-modal semantic alignment encoder is constructed to map a visual token sequence and a language token sequence to a unified semantic space, a conditional generative adversarial network is adopted to carry out data enhancement, a time sequence modeling framework of a dynamic graph neural network is constructed to process time-varying graph structure data, an active learning framework and a noise adaptation network are designed, and time-varying graph structure data processing is carried out. A knowledge distillation technology and a model optimization method are used, a multi-task uncertainty weighted loss balance algorithm is adopted for training to obtain a multi-modal large language model for accounting document automatic auditing, and the problem that in the prior art, it is difficult to accurately understand complex table structures and cross-modal semantic association is solved.

The invention discloses an assessment method and device for a large model generated text, a readable storage medium and a program product, and relates to the technical field of artificial intelligence, and the assessment method for the large model generated text comprises the steps: obtaining a user-defined assessment template, and analyzing a dependency relationship and an execution priority of each preset assessment dimension in the user-defined assessment template, obtaining a directed acyclic graph used for representing the topological sequence of each preset evaluation dimension; according to a topological sequence of each preset evaluation dimension in the directed acyclic graph, evaluating the to-be-evaluated large model generation text on each preset evaluation dimension to obtain a score corresponding to each preset evaluation dimension; and determining a text evaluation result by counting the score corresponding to each preset evaluation dimension. According to the technical scheme, multi-dimensional evaluation can be carried out on the large model generation text, the multi-aspect capability of the model can be measured more comprehensively and accurately, and the scene adaptation capability of the text evaluation scheme is improved.

The application discloses a water conservancy knowledge differentiation retrieval and query decomposition method based on a large language model, and the method comprises the following steps: constructing a standardized retrieval channel for four types of heterogeneous data sources, namely a graph database, a relational database, a document library and a time series database; secondly, establishing a sub-query type classification system for the water conservancy field and forming a template library; thirdly, collecting training data from historical queries and expert samples, and adopting a multi-round labeling mechanism to label query decomposition results, channel mapping relationships and dependency relationships; finally, adopting a low-rank adaptation method to fine-tune the pre-trained large language model, so that the model can intelligently decompose a user natural language query into a sub-query sequence, automatically route to the corresponding retrieval channel and plan an execution sequence. The application realizes intelligent processing of a composite query in the water conservancy field, significantly improves the efficiency and reliability of cross-data source knowledge retrieval, and is suitable for decision support scenes such as flood control scheduling and reservoir group joint scheduling.

The invention discloses a large model function call optimization method based on code generation, electronic equipment, a computer readable storage medium and a computer program product, and the method comprises the steps: adopting a semantic understanding large model, and converting a natural language intention of a user into an executable rule; generating codes meeting scene requirements by adopting a code generation large model; a function call optimization method is adopted to call the codes meeting the scene requirements; the optimization method comprises the steps of obtaining a call function set in codes meeting scene requirements; constructing a function scheduling directed acyclic graph by taking the calling functions as nodes and the dependency relationship between the two calling functions as edges based on the calling function set and the dependency relationship of each calling function; performing linear sorting and layering on each node in the function scheduling directed acyclic graph; the nodes at the same level have no direct or indirect dependency relationship; and executing the calling functions at the same level in parallel.

Embodiments of the present disclosure relate to a method, device, and computer-readable storage medium for data processing. A method for data processing comprises obtaining a set of observed samples related to multiple factors, an observed sample in the set of observed samples comprising respective observed values of multiple factors. The method further comprises determining a set of dependency relationships between the multiple factors based on the set of observed samples, a dependency relationship in the set of dependency relationships indicating an interrelated factor pair among the multiple factors. The method further comprises determining a causality sequence of the multiple factors based on the set of dependency relationships, the causality sequence indicating that one factor is a cause of the other factor in the interrelated factor pair. Embodiments of the present disclosure further provide a device and computer-readable storage medium capable of performing the foregoing method.