430 results about "Dependency relation" patented technology

The invention discloses a cue word compression method and device of a large language model and a medium. The method comprises the steps that redundant vocabularies and redundant statements are recognized and removed according to the size relation between the vocabulary importance score and the importance threshold value of each vocabulary after original cue word segmentation; constructing a local syntax tree of each statement, and constructing a global analysis tree by the local syntax trees according to the dependency relationship among the cue words; calculating a local information entropy so as to align the LLM word segmentation device with nodes on the global analysis tree and adjust node values of the global analysis tree; trimming the global analysis tree through a recursive algorithm according to the adjusted node value, and generating a compression prompt corresponding to the prompt word; according to the method, the compression prompt is evaluated, the compression prompt is iteratively optimized according to an evaluation result, the cue word is reconstructed according to the optimized compression prompt, and compression of the cue word is achieved.

The invention relates to the technical field of process optimization, in particular to a knowledge graph-driven manufacturing process optimization system, which comprises a graph node generation module, a dependency relationship construction module, a semantic entity matching module, a process path screening module and a trend-driven early warning module. According to the method, semantic implicit relation recognition is achieved through sequential alignment and numerical difference comparison, quantitative screening between the machining size and the tolerance grade is introduced in path construction, and it is ensured that a path connection structure is optimized under the condition that the size precision constraint is met; in combination with the time sequence trend splitting and continuous fluctuation consistency discrimination mechanism of the equipment operation state, the dynamic marking and early warning annotation set generation of the process deviation trend can be completed at the node path level, and the closed-loop processing from knowledge modeling, path screening to trend pre-judgment is realized. And the process path adaptability, the parameter configuration precision and the abnormality identification advancement are effectively improved.

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 relates to the technical field of artificial intelligence, in particular to a task execution method and device based on prompt information, equipment and a medium, and aims to improve the flexibility of a task model prompt project and the accuracy of task processing. The method comprises the following steps: extracting each piece of key information related to a task target from task description information of a task to be reasoned; determining a task scale of the to-be-reasoned task according to the task description information, and selecting a topology type matched with the task scale; based on each piece of key information and the topology type, constructing a reasoning topology structure; reasoning nodes and edges in the topological structure, wherein the nodes and the edges respectively represent the reasoning steps of the task to be reasoned and the dependency relationship of the reasoning steps; generating prompt information of each node based on the reasoning topological structure, wherein the prompt information comprises an execution mode of a reasoning step represented by the corresponding node; and based on each piece of prompt information, guiding the task model to gradually execute each reasoning step according to each node and the dependency relationship thereof to obtain a task execution result.

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 high-dimensional time series data classification method and device based on multi-scale diffusion denoising. According to the method, a multi-scale condition guidance strategy is introduced to guide the denoising process of time sequence labels, and condition priori of different scales is used in the diffusion process to adjust each step. The method comprises the following steps of: encoding time sequence data from two angles of variable and time, extracting features, obtaining multi-scale feature representation of a time sequence, mining a dependency relationship of the sequence on a time dimension and a variable dimension by using a double attention mechanism, and generating condition priori on different scales; a standard denoising diffusion probability model is used for training a process, a fusion vector in a potential space is obtained by using a multi-scale encoder, then the fusion vector and a time step are embedded, and a full connection layer is used for predicting noise. According to the method, the result accuracy of the high-dimensional multivariable time series in the classification task and the robustness of processing irregular time series data containing missing values are improved.

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 discloses a high-precision geographical name translation method integrating artificial intelligence and multi-language syllable segmentation, and relates to the technical field of geographical name translations, the method comprises the following steps: firstly splitting an input geographical name into a sub-word Token sequence, and using a context coding model of a word embedding model to realize semantic context associated coding of a to-be-translated geographical name; and then modeling internal structured information and a dependency relationship in context semantics of the token to be translated in a local semantic relevance reconstruction enhancement mode, identifying recessive syllable relevance in compound words and adhesive words, and enhancing local morphological characteristics near syllable boundaries, so as to solve the problems of irregular spelling and cross-language interference, and improve the translation efficiency of the token to be translated. And further decoding and outputting the geographical name character string after syllable segmentation to carry out geographical name translation, generating a corresponding geographical name target language translation text, and realizing end-to-end effective conversion from an original geographical name to a target translation name.

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 invention discloses a power load prediction model establishment method based on a convolutional bidirectional long-short-term memory network and an attention mechanism, and the method comprises the steps: carrying out the full feature extraction of original complex power load data through a one-dimensional convolutional network; the factors influencing the load and the characteristics of trend, periodicity, randomness and the like of load change can be more fully mastered by considering the multivariate characteristics; compared with the traditional LSTM, the bidirectional structure of the BiLSTM can more comprehensively capture the bidirectional dependency relationship in the load sequence data through forward propagation and backward propagation, and the understanding of the model on the data is improved. Besides, an attention mechanism widely applied in the field of natural language processing is applied to research on multi-element short-term power load prediction, key information in a load sequence can be focused by introducing the attention mechanism, and the prediction precision is further improved; experiments are carried out on a real multivariate load data set, and the precision of multivariate short-term load prediction can be effectively improved by adding the one-dimensional convolutional network and the attention mechanism.

The invention relates to the technical field of law informatization, and particularly discloses a law decision semantic deviation recognition method based on artificial intelligence, and the method comprises the steps: data collection and preprocessing, semantic unit segmentation, multi-level semantic analysis, semantic deviation recognition, decision consistency evaluation, and result interpretation and application. According to the scheme, semantic features are extracted from the three levels of vocabularies, syntax and context, the overall semantic representation of the judgment document is enhanced through an attention mechanism and a dynamic context window mechanism, it is ensured that semantic connection of all parts is close, the long-term dependency relationship is captured, misjudgment caused by the fact that a single semantic unit is separated from the context is avoided, and the detection accuracy is improved; a semantic deviation recognition model is constructed on the basis of a Transform architecture, hidden semantic deviation is effectively recognized through a deep semantic feature matrix, deviation scores of all semantic units are calculated through an adaptive weight distribution mechanism, and the objectivity and consistency of scores are improved.

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.

The embodiment of the invention provides a Gradle-based compiling method and device, equipment, a medium and a program, and the method comprises the steps: detecting a module of a project to obtain compiling reference information of a first source code module without source code change in the project, and the compiling reference information comprises an identifier without source code change and information of a binary file; obtaining a dependency relationship tree of each module of the project, and determining a target module which has no source code change and has a dependency type of source code dependency in the dependency relationship tree of each module according to the compiling reference information of the first source code module, according to the binary file of the target module, modifying the source code dependency of the target module in the dependency relationship tree of each module into binary dependency; and compiling each module according to the modified dependency relationship tree of each module. The dependency type of the module is changed from source code dependency to binary dependency, and source code compiling does not need to be performed on the modified module, so that the time required for source code compiling is saved, and the engineering compiling efficiency is improved.

The invention provides a task processing method and device, relates to the technical field of computers, and aims to quickly process a grid computing task and improve the task processing efficiency. The method comprises the steps that a grid computing task in an application is obtained, the grid computing task comprises a plurality of subtasks, and the dependency relationship among the subtasks conforms to a dependency rule; analyzing a dependency relationship between a first sub-task in the plurality of sub-tasks and other sub-tasks, and determining a dependency rule; the dependency relationship among other sub-tasks in the plurality of sub-tasks is analyzed according to a dependency rule; and allocating computing resources to the plurality of sub-tasks according to the dependency relationship among the plurality of sub-tasks, and executing the plurality of sub-tasks.

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).