339 results about "Dependency relation" patented technology

The invention discloses a crack segmentation method and system based on a dynamic receptive field and multi-scale semantic aggregation, and relates to the technical field of computer vision. The method comprises the following steps: inputting a crack image, and simultaneously capturing local details and global structural features of a crack through a dynamic snakelike Mama module: dynamically adjusting the shape of a convolution kernel to adapt to the geometric change of the crack, inputting the obtained features into a spatial pyramid pooling layer to extract multi-scale context information, and outputting feature representation fused with a long-range dependency relationship; based on the feature representation fused with the long-range dependency relationship, an interaction relationship between local details and global semantics is established through a multi-scale semantic aggregation module, background noise interference is suppressed through a parallel supervision attention mechanism, and a pixel-level crack segmentation result is generated through a lightweight segmentation head. According to the method, fine cracks can be segmented more accurately in a complex background environment, and meanwhile, the conditions of wrong segmentation and missing segmentation are effectively relieved.

The invention relates to a data query analysis method based on a natural language, which comprises the following steps of: analyzing a natural language query request input by a user into a dynamic semantic intention tensor which comprises an operation type, a comparison relationship, a time constraint and a data role semantic dimension, and dynamically combining according to task semantics to form a complete data query and analysis operation expression; mapping the natural language intention to a controllable data operation space of a database, and generating a preliminary data operation chain; performing side effect perception processing on the operation tensor, predicting a potential execution consequence of the operation, generating a side effect tensor for simulating a side effect of the database operation, and if any index in the side effect tensor exceeds a preset threshold value, performing local correction on the operation tensor; according to the method, a complex natural language query task is disassembled into a plurality of minimum semantic units by adopting a step-by-step semantic resolution mechanism, each unit is mapped into a specific tensor operation, and a dependency relationship between operation units is dynamically reasoned to generate an operation execution chain.

The invention discloses a software test automation report generation optimization method which comprises the following steps: automatically registering and authenticating a multi-source test data access endpoint, uniformly collecting and normalizing test data with a multi-dimensional attribute tag, and forming a high-quality grouped data set through structured modeling and automatic clustering; further extracting event and result feature nodes, and establishing an interpretable tree rule dependency relationship; according to the method, the minimum redundancy and reusable customized rule path is generated by applying a dependency tree traversal and optimization algorithm and automatic reasoning, and the continuous adaptive evolution of the rule base among multiple projects is realized by combining conflict detection and visual interaction, so that the efficiency and expandability of data processing in a complex test scene are improved, and the test efficiency is improved. And the flexibility and automation level of report customization rule configuration are enhanced.

The invention relates to the technical field of computer vision, computer aided design and artificial intelligence, and discloses a geometric feature recognition method based on a large language model, which comprises the steps of three-dimensional model natural linguistization, large language model feature reasoning, incremental learning and optimization, and feature verification and output. According to the geometric feature recognition method based on the large language model, three-dimensional geometric data are converted into structured text description, and a joint representation space of'geometric parameter-topological relation 'is constructed, so that the large language model can better understand the meaning of geometric features and the structure of parts; the method comprises the following steps: analyzing topological association among geometric features by utilizing long context understanding ability of a large language model, capturing dependency among the features through an attention mechanism, realizing hierarchical recognition of the features in a complex assembly, dynamically updating a geometric feature knowledge base of the large language model by continuously learning new geometric cases and engineering specifications, and realizing hierarchical recognition of the features in the complex assembly. Adaptive recognition of novel features is supported, and the model does not need to be retrained.

The invention discloses a cloud platform operation and maintenance method and device, equipment, a medium and a product, relates to the technical field of operation and maintenance, is applied to a server, and comprises the following steps: receiving a natural language instruction input by a user side; performing intention recognition and parameter extraction on the natural language instruction to obtain at least one atomic task, and generating an execution plan according to a target dependency relationship between the atomic tasks; screening an optimal execution path from at least two preset execution paths for each atomic task based on the task attribute of each atomic task in the execution plan; performing simulation drill analysis on the execution plan based on the optimal execution path to generate a risk pre-judgment report, and sending the risk pre-judgment report to the user side; and after execution confirmation information sent by the user side based on the risk pre-judgment report is obtained, a task executor corresponding to the optimal execution path is utilized to initiate calling to the cloud platform to execute the execution plan, and an execution result of the cloud platform is converted into a natural language response to be sent to the user side.

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 provides a Java-to-ArkTS code conversion method and system based on a large model, and belongs to the field of computers.The Java-to-ArkTS code conversion method comprises the steps that S1, a Java project to be converted is comprehensively analyzed, the dependency relationship and semantic information between files are extracted, and a code knowledge graph is constructed; s2, determining a code conversion sequence based on the code knowledge graph, extracting context information and multi-modal semantic information of a to-be-converted code, and completing conversion from Java to TS codes in combination with a multi-agent cooperation mechanism; and S3, comparing and analyzing the TS code with an official ArkTS coding specification, identifying a violation code, and correcting and verifying the violation code through a plurality of intelligent agents to generate an ArkTS code which accords with the ArkTS grammar specification and is consistent in semantics. According to the method, the automatic migration from the Java project to the ArkTS project can be effectively supported, the project migration efficiency and the code quality are improved, and the manual intervention cost is reduced.

The invention discloses a Text-to-SQL (Structured Query Language) generation method and device, a medium and a product, and relates to the field of geographic information technology application, the method comprises the following steps: analyzing a natural language query text input by a user to obtain a source field set and a target field set; determining corresponding nodes of each field in the source field set and the target field set in the river-lake long-system database mode knowledge graph to obtain a source node set and a target node set; the nodes in the source node set serve as starting points, the nodes in the target node set serve as terminal points, the knowledge graph is processed through the shortest path algorithm, and a path set is obtained; decomposing the natural language query text into a plurality of sub-problems by adopting a large language model and a thinking chain technology, and obtaining a dependency relationship among the sub-problems; according to the dependency relationship and the target field set, a public table expression is obtained, and then the SQL statement is obtained, and the accuracy of database query language generation under the river and lake long-term system scene can be improved.

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 belongs to the technical field of knowledge graph construction, and discloses a supply chain knowledge graph construction method based on time sequence dynamic perception and a large language model. Constructing a time sequence dynamic sensing model, and respectively generating a time-sensitive embedding matrix for the entity and relationship of each sub-graph in a historical time window; inputting the time-sensitive embedded matrix into an aggregator to further mine structure and semantic information of entities and relationships; establishing a dependency relationship of an autoregression model learning sub-graph in a time sequence, and generating a time sequence evolution representation; and inputting the time sequence evolution representation into a pre-trained large language model, generating candidate entities or candidate relationships to complement the fact tetrad, and updating the sub-graph sequence of the current timestamp. According to the method disclosed by the invention, the supply chain domain knowledge is adapted while the general semantic understanding capability is reserved, and the balance of dynamic evolution modeling, long-period dependency capture and efficient utilization of the domain knowledge is realized, so that the reliability and interpretability of a construction result are ensured.

The invention relates to the technical field of engineering cost, and discloses a data entry method for engineering cost software, which comprises the following steps of: S1, importing an engineering quantity list file in an Excel format through an interface of the engineering cost software, and automatically analyzing a data structure of the engineering quantity list file; s2, based on a preset intelligent matching algorithm, performing dynamic mapping on the Excel data field and a data field of a project cost software function module; s3, adopting a data type self-adaptive rule to automatically classify and convert numerical values, texts and compound data in the Excel; s4, based on a data association generation algorithm, automatically establishing a hierarchical relationship and a dependency relationship between the engineering quantity list items; s5, executing one-button data import, and writing the processed structured data into a project cost software database in batches; according to the method, high-precision automatic mapping of the Excel field and the software module is realized through an intelligent matching algorithm and a dynamic weight adjustment mechanism based on the feature vector similarity, the matching accuracy is greatly improved, and the manual correction frequency is greatly reduced.

The invention discloses an intelligent agent system and a control method thereof, and relates to the technical field of natural language processing. The system comprises an execution layer, a planning layer and an auditing layer, the planning layer identifies and extracts innovation task meta-information, constructs a task tree to complete operations such as hierarchical clustering, and determines a task execution path; the execution layer calculates the semantic similarity between the content of the knowledge graph and the user innovation question, determines the optimal question and answer, extracts heuristic information, and decomposes the heuristic information into sub-questions to construct a dependency graph; generating an answer set based on the graph solving sub-problems, and integrating answers by using a genetic algorithm to obtain an overall solution; and the auditing layer performs multi-dimensional scoring on the scheme, and determines a structured scheme report and optimization suggestions for users to use according to a scoring result. The method has the capabilities of structured reasoning, problem recursive decomposition and scheme closed-loop optimization, can generate a multi-dimensional evaluation result, and efficiently realizes systematic modeling of a clear path for analogy heuristic information support problem solution.

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 a knowledge graph semantic enhanced embedding optimization method based on a dynamic memory network, and belongs to the technical field of knowledge graph logic query questions and answers. Explicit modeling and dynamic updating of a historical logic mode are realized by constructing a structured dynamic memory network; a nested structure and a long-term dependency relationship in complex logic query are effectively captured, the semantic understanding and generalization ability of a model to diversified structure combinations is remarkably improved, basic semantics, logic structures and memory features are dynamically fused, a gated memory updating strategy is introduced, a memory calling path is clear in the reasoning process, and the reasoning efficiency is improved. And the optimization behavior is traceable, so that the interpretability and robustness of the model are enhanced, and the embedding space optimization effect is remarkable and has interpretability. And finally, on the premise that an original upstream encoder does not need to be modified, reasoning enhancement is realized through an external dynamic memory network module, the proportion of updated parameter quantity is small, the calculation overhead is low, and the method is suitable for efficient deployment of various reasoning platforms.

The invention provides an automatic test generation platform and method for an operating system kernel derived function. The method comprises the following steps: (1) intelligent symbol extraction and analysis; (2) generating an LLM-driven test code; (3) performing automatic compiling and virtualization testing; and (4) intelligent error diagnosis and repair. According to the method, the test coverage degree is greatly improved, through the multi-level symbol context extraction technology, single function information is extracted, the dependency relationship of related functions in the same element is analyzed, a function call graph and data flow analysis are constructed, rich context information is provided for LLM, and the problem that traditional manual test case writing is incomplete in coverage is solved.

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 apparatus employs adaptive machine learning for optimization using dynamically variable local parameters. It consists of a processor and memory. Initially, it access a first dataset corresponding to the first phenomenon and a second dataset corresponding to the second phenomenon. Then, it identifies a dependency relationship between at least one data cluster of the first phenomenon and at least one data cluster of the second phenomenon. Using the at least a processor, modify a processor, an attribute set, as a function of the second data cluster. Further, it optimize a target data cluster, as the function of the first phenomenon. Last, it modify using the at least a processor, the second data cluster as a function of the target data cluster.

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.

Methods, apparatus, and processor-readable storage media for determining local administrator rights-related (LAR-related) dependency relationships using artificial intelligence techniques are provided herein. An example computer-implemented method includes obtaining data pertaining to multiple actions performed across one or more software applications by one or more user devices; determining one or more action dependency relationships associated with at least one action performed by a given one of the user devices in connection with a given one of the software applications, by processing at least a portion of the obtained data using artificial intelligence techniques; modifying one or more LARs, granted for the given user device, to permit access to the given user device to perform one or more actions in connection with at least one of the software applications in accordance with the determined action dependency relationship(s); and performing one or more automated actions based on the modifying of the LAR(s).

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 discloses a multi-source data merging method and merging equipment based on rule chain configuration, and relates to the technical field of data fusion. In the method, merging equipment generates a structured configuration file through a visual configuration interface, and decouples a variable data source association relationship and a field mapping rule from a program code, so that a user can dynamically define a data merging task in a visual manner without modifying and redeploying a bottom program code, and the user experience is improved. And the execution triggering condition of each data source node is determined through the directed acyclic graph execution plan, so that the data processing tasks with the dependency relationship can be executed in series according to the dependency relationship, and the data processing tasks without the dependency relationship can be executed in parallel, thereby improving the merging efficiency of the multi-source data.

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 discloses a table and text numerical reasoning question-answering method based on joint modeling, which comprises the following steps: receiving a document containing a text and a table and a question proposed by a user, retrieving support evidence according to the question through a heterogeneous evidence retrieval module, reserving the two-dimensional structural integrity of the table, avoiding information loss caused by serialization, and obtaining a question-answering result on the basis of the retrieved evidence. Constructing a heterogeneous graph, encoding the heterogeneous graph by using a relationship perception graph neural network, capturing interaction information between table and text data and a hierarchical dependency relationship in a table, and generating a mathematical expression or a numerical answer for answering a question according to learned node representation, according to the table and text numerical reasoning question-answering method based on joint modeling, structured information of the table and semantic information of the text can be deeply integrated, an accurate mathematical expression or numerical answer is generated, and the accuracy, interpretability and applicability of a question-answering system are remarkably improved.

The invention discloses an unstructured text entity recognition and relation extraction method under small sample learning, which relates to the technical field of natural language processing, and comprises the following steps of: 1, preprocessing an unstructured text, including word segmentation, part-of-speech tagging and syntactic analysis, and converting the text into structured word vector representation; a pre-training language model is adopted to encode the text, and context semantic features of the text are obtained; 2, constructing a meta-learning model by utilizing a meta-learning algorithm based on context semantic features of the text, training through a small number of labeled samples and a large number of unlabeled samples, and learning common knowledge among different tasks; 3, based on the meta-learning model, word vectors in the text are processed, and entities including boundaries and types are recognized; and according to the identified entities, analyzing a semantic relationship between the entities, extracting a relationship type between the entities, and optimizing a dependency relationship between the entities and the relationship by adopting a dynamic planning method.

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.