78 results about "Relation (database)" patented technology

The invention provides an intelligent data query method based on a natural language, and relates to the technical field of intelligent data processing and natural language interaction.The intelligent data query method comprises the steps that firstly, enterprise original data is subjected to standard treatment, and a standardized theme database and a data directory and index definition document are constructed; key semantics are extracted based on unstructured knowledge, and a domain knowledge vector library is fused and constructed by combining document text fragments and vector representation of a mapping relation between historical questions of a user and an SQL (Structured Query Language). And after receiving a natural language question of a user, calling a large language model to identify a task type, and distinguishing knowledge questions and answers, data query and complex analysis. Executing corresponding operations according to different types: directly retrieving a vector library by knowledge questions and answers to generate answers; extracting keywords in data query and generating a query request in combination with context; and in the complex analysis, predefined workflow is judged and executed or intelligent agent processing is called, and query or analysis requirements are output.

The invention discloses a multi-modal data retrieval method and device, a storage medium and computer equipment. The method comprises the following steps: collecting multi-modal original data; on the basis of description information of metadata of original data, all metadata belonging to the same associated items and logic relations among all the metadata are obtained, a metadata chain is constructed, a distributed graph database is constructed on the basis of the metadata chain, the metadata chain is expressed in the distributed graph database in the form of a graph, the graph comprises nodes and edges, the nodes represent the metadata, and the edges represent the metadata. The edge represents a logical relationship between the metadata; and when a data retrieval instruction is received, traversing each node in the metadata chain along the logical relationship of the metadata chain in the distributed graph database, obtaining a target node matched with a data retrieval requirement corresponding to the data retrieval instruction, and returning original data corresponding to metadata represented by the target node. Multi-modal data dynamic association retrieval can be realized, and cross-modal information mining efficiency and accuracy are improved.

The invention discloses a query method based on a knowledge base and a large model. The method comprises the following steps: constructing the knowledge base of a database; obtaining target language information which is input by a user and contains a user question; based on the knowledge base, generating a target pseudo mode corresponding to the target language information; based on the target pseudo mode, screening out a minimum table set associated with answering the user question from the knowledge base; packaging the multi-table connection logic in the minimum table set into a query view; based on the query view and the target language information, a reference example pair list which is most similar to the user problem and indicates the mapping relation between the problem and the query statement is retrieved from a knowledge base, and each example pair in the reference example pair list represents the mapping relation between the problem and the query statement; and generating a target query statement corresponding to the target language information based on the user problem, the target pseudo mode, the query view and the reference example pair list, and performing retrieval in a database based on the target query statement to obtain a query result.

The invention relates to a data correction method and device, computer equipment, a computer readable storage medium and a computer program product. The method comprises the following steps: acquiring an original structured statement and original annotation information; determining knowledge data corresponding to the original structured statement in a target knowledge database based on a metadata query engine; the knowledge data comprises graph relation metadata and semantic vector metadata; according to the data correction large model, the graph relation metadata and the semantic vector metadata, error recognition is carried out on the original structured statement and the original annotation information, data correction is carried out on error data obtained through error recognition, and a target structured statement and target annotation information are obtained. By adopting the method, the accuracy of structured statement and annotation error correction can be improved.

The invention discloses a multi-modal retrieval method and device, and relates to the technical field of multi-modal retrieval, and the method comprises the steps: obtaining a query vector corresponding to a user query text, and carrying out the query in a vector database according to the query vector, and generating a query result; vector codes corresponding to the multi-level content block structures corresponding to the multiple multi-modal table documents are stored in the vector database; the multi-level content block structure of any multi-modal table document comprises an atomic layer, a semantic layer and a relation layer; the atomic block comprises target element content and structure information of any cell in the multi-modal table document; the semantic block comprises aggregation content corresponding to any row, any column or any whole table in the multi-modal table document; the relation block comprises a text for describing the incidence relation between the multiple atomic blocks and the incidence relation between the multiple semantic blocks; the structure information comprises row and column indexes corresponding to each cell and a cross-modal association relationship among the multi-modal elements. According to the method, the multi-modal retrieval accuracy can be improved.

The invention belongs to the technical field of databases, and particularly relates to an LLM-Text2SQL-oriented database table relation exploration method. According to the method, a multi-stage cooperative processing strategy is adopted, database metadata and data content features are integrated, and semantic enhancement and structured completion are performed on original information through a large language model. And on the basis, a potential association candidate set is screened in combination with an algorithm based on feature similarity, multi-dimensional verification and judgment of an association relationship are performed by fusing a predefined rule and a large language model reasoning mechanism, and finally an entity relationship graph supporting interactive editing and iterative optimization is generated. According to the method, end-to-end automatic processing from a heterogeneous database with constraint missing and data integrity impaired to a standardized ER graph is realized, and a solid foundation is laid for remarkably improving the success rate of natural language SQL generation based on a large language model.

The invention discloses a wind turbine generator abnormal knowledge association method and device based on a knowledge graph and a medium, and the method comprises the steps: carrying out the collection of distributed multi-source heterogeneous data and knowledge based on the operation and maintenance demands of the wind power industry; establishing an OPC UA information model of the wind power equipment node; the method comprises the following steps: guiding an industrial multi-modal industrial large model to carry out standardized description and identification on node data through cue word engineering, completing entity extraction and attribute extraction, carrying out relation extraction and expression, and importing into a graph database to construct an abnormal knowledge graph; the semantic relation between the node entities is optimized and updated, knowledge merging and processing are completed, and the abnormal knowledge graph is dynamically updated; and for an abnormal knowledge application scene, performing knowledge reasoning based on abnormal knowledge graph mining entity association. According to the multi-modal knowledge graph construction method based on graph structure learning and fine tuning of the multi-modal industrial large model, relevance of different anomalies is explored, the knowledge base rich in abnormal semantics is constructed, and it is ensured that industrial abnormal faults are correctly processed.

The invention discloses an equipment fault diagnosis method and device based on a dynamic knowledge graph and a large model fine tuning technology. The method comprises the following steps: firstly, identifying a core entity from multi-source heterogeneous equipment fault data through a named entity identification model for fine tuning of domain data and a relation extraction model for special fine tuning of a fault diagnosis domain corpus, mining deep semantic association, and injecting the deep semantic association into a graph database after cleaning to form an initial knowledge graph; receiving user natural language fault description, realizing term and standard entity linking through editing distance fuzzy matching and Sension-BERT semantic vector similarity calculation, and combining bidirectional retrieval and attention mechanism fusion to obtain an enhanced context; and finally, generating a structured diagnosis report containing thinking chain reasoning based on an enhanced context by utilizing a specialized fine-tuning fault diagnosis large language model. According to the method, the limitation of a traditional diagnosis method is effectively solved, high-precision and interpretable equipment fault diagnosis is realized, and the diagnosis efficiency and reliability are improved.

A computer-implemented method is provided of generating a system prompt for a large language model to convert a natural language query (NLQ) to a structured query language(SQL). 5 The method includes generating (1606) the system prompt for the NLQ based on information extracted from a first database. The information identified from a knowledge graph includes a structured view of data including telecommunications domain knowledge and relations within the data filtered by a chain of thought prompt that represents a logical relation between a plurality of nodes in the knowledge graph. The method further includes passing (1614) the system prompt to 0 the LLM to convert the NLQ to the SQL.

The invention discloses a knowledge indexing and retrieval method for an AUTOSAR hierarchical architecture, and the method comprises the steps: taking a hierarchical structure of an AUTOSAR CP knowledge system as a trunk, carrying out the vectorization of an input document, constructing an entity-relation hierarchical tree, obtaining a knowledge graph indexing structure, carrying out the query matching through the similarity during retrieval, and carrying out the retrieval. Sorting retrieval output entities and relationships, constructing a context, and outputting a structured answer; comprising the following steps: step 1, indexing: intelligently partitioning external database content input by a user and performing vectorization storage, operating general entity extraction and / or AUTOSAR special extraction and combining results, constructing a tree structure, performing hierarchical clustering to form a knowledge graph, and generating a community report; and 2, retrieval: receiving user query, finding related entities through vector similarity search, aggregating contexts in combination with tree and graph dual structures, and starting universal cue words and / or AUTOSAR special cue words to generate structured answers. The invention further discloses a system for implementing the method, and the system has wide application value.

The invention discloses an NL2SQL and NL2VIS method based on large model collaborative intelligent agent, and belongs to the technical field of natural language processing. The NL2VIS method comprises the steps that candidate database modes related to natural language query are obtained from database modes, and task complexity corresponding to the natural language query is obtained according to the number of tables contained in the candidate database modes; in combination with the task complexity, natural language query is converted into a preliminary SQL query statement; verifying, repairing and feeding back the SQL query statement based on the initial SQL query statement until a final SQL query statement is generated; and converting the final SQL query statement into a visual query language. According to the method and the device, the database table, the field and the association relation of the database table and the field involved in the query intention of the user can be more accurately identified, and connection errors, field mistaken selection and information omission caused by mode misunderstanding are fundamentally reduced.

The invention relates to the technical field of knowledge maps, and provides a screening type dynamic classification self-adaptive relation extraction method, device and equipment, and the method comprises the following steps: defining an entity type list, inputting a text by a user according to a cue word template in a labeling format, judging the length of the text, and extracting the entity type list according to the length of the text; selecting a corresponding strategy according to the text length to generate a candidate entity pair; the association strength of each candidate entity pair is calculated by combining static corpus statistics and a dynamic semantic hybrid scoring model, and the candidate entity pairs with low association are filtered; constructing a dynamic prompt request LLM to generate a natural language abstract describing the subject entity and the object entity, and classifying the abstract into a predefined relationship type to obtain a structured triple; and importing the triple into a graph database to construct the knowledge graph. According to the method, the knowledge extraction efficiency is improved, and the problems of calculation redundancy and high cost are solved.

The invention discloses an intelligent mining method for dispatching knowledge of a water-wind-light complementary system based on a large language model, which belongs to the technical field of dispatching of the water-wind-light complementary system and comprises the following steps: S1, constructing an ontology model in the field of water-wind-light dispatching according to'demand traction-reuse verification-concept extraction-semantic modeling-formalized implementation '; s2, collecting and processing data; s3, the RoBERTa-BiLSTM-CRF architecture is trained, and entity mining is achieved; s4, realizing relation mining by combining a mixed relation mining method with a three-layer semantic constraint mechanism; s5, performing multi-dimensional fusion on entity semantics; s6, relying on Neo4j graph database storage, combining a man-machine natural language interaction interface of the generative LLM and realizing knowledge base iteration updating. According to the intelligent mining method for the scheduling knowledge of the water-wind-light complementary system based on the large language model, multi-source data collaborative fusion and mining are achieved, and core knowledge support is provided for multi-energy collaborative scheduling and optimization decision making.

The invention provides a cross-database approximate nearest neighbor search method and system and a calculation framework, and the method comprises the following steps: constructing a graph index, storing the graph index in a relation table, and obtaining a graph index table; acquiring and storing a data set and a query set in a structured relation table form; the dismantling approximate nearest neighbor search process comprises a plurality of SQL operation stages including a candidate node screening stage, a neighbor expansion stage, a distance calculation stage, a result combination stage and a priority queue maintenance stage; and based on the graph index table, executing an iterative search process of the plurality of SQL operation stages on each query point in the query set, and finally outputting an approximate nearest neighbor search result of the query set. According to the method, the graph index is combined with the relational database, and the approximate nearest neighbor search is realized by adopting a plurality of SQL operation stages, so that the high-dimensional vector retrieval efficiency and the cross-database compatibility are remarkably improved, and the large-scale application of the vector data in a multi-element scene is promoted.

The invention discloses a large model illusion suppression intelligent question answering system and method based on deep learning, and the method comprises the following steps: constructing a database Schema, and configuring an entity mapping relation; configuring cue word information reasoned by the large model to form a task cue word sequence; complete system input is formed; executing structured query language conversion by adopting a large model of an improved NASNet network architecture to generate an SQL query statement; generating natural language description information; performing consistency verification to obtain a consistency verification result; generating corresponding calculation prompt information; according to the method, the hallusion and semantic offset generated by a large model in a structured question and answer scene are effectively inhibited, and the accuracy of SQL generation and the reliability of the question and answer result are improved.

The invention provides a parameterized three-dimensional modeling method and system based on natural language and database driving, and belongs to the technical field of civil engineering modeling. According to the method, a performance function library (comprising a structure creation unit, a section operation unit and a parameter management unit) is constructed, a natural language dialog box is used for receiving a user instruction, the instruction is analyzed into modeling parameters by means of a natural language processing technology, and corresponding performance functions in the performance function library are automatically called. And then, according to a performance function calling result, generating information such as node coordinates, a geometric topological relation and section attributes, storing the information into a database, and finally, generating a three-dimensional model based on the database. According to the parameterized three-dimensional modeling method and system based on the natural language and database driving, professional software operation is not needed, the learning and modification cost is remarkably reduced, manual input errors are reduced, the modeling efficiency and the team cooperation ability are improved, and the parameterized three-dimensional modeling method and system can be widely applied to building, bridge, tunnel and steel structure engineering.

The invention discloses a production data query method and device based on a graph, and the method comprises the steps: extracting a metadata relation set from an equipment production database, and carrying out the semantic coding of a query text, and obtaining an instruction semantic vector; constructing an enhanced graph by the metadata relation set; aligning a preset optimized semantic vector with the enhanced atlas to identify an entity association path; generating a query instruction according to the entity association path; performing data query in the enhanced atlas by using the query instruction, and monitoring the state of data query in real time so as to update the enhanced atlas according to the state; and performing data query in the updated enhanced atlas by using the instruction semantic vector. The enhanced atlas constructed by the method not only can store the attributes of the entities, but also can show and store the association relationship between the entities in a displayed manner, so that efficient and accurate data query is realized.

The invention belongs to the technical field of intelligent question counting, and discloses a large model and knowledge graph combined intention recognition method and system and a medium, and the method comprises the steps: constructing a semi-structured language system MLS for a big data platform intelligent question counting scene, and taking the MLS as a semantic bridge of a natural language and an SQL (Structured Query Language); receiving and analyzing natural language query through a large model LLM, and extracting three elements including known conditions, a target object and a limited relation; performing semantic mapping and reasoning on the three elements by using a predefined knowledge graph containing sets, items, values and various semantic relationships in the MLS; natural language query is converted into an MLS expression containing operators such as I, C, V and Q according to the reasoning result; and finally, the MLS expression is translated into a relational calculation form and an SQL statement which can be executed by a database, so that intention recognition and conversion from a natural language to a computer instruction are realized, and the accuracy and efficiency of intelligent number asking are improved.

The embodiment of the invention provides a conversational intelligent analysis method and system fused with time-space relation modeling, and the method comprises the steps: carrying out the light ontology atomization extraction and time-space evidence binding of an input text, and generating a structural unit carrying an evidence anchor point; constructing a three-library index architecture in which a vector library, a graph database and a relational library are coordinated, and realizing responsibility separation of semantic retrieval, graph structure propagation and space-time constraint; when a user query is processed, time-space and semantic intentions are analyzed, time-space hard filtering and semantic hybrid retrieval are executed, a personalized initial vector fusing time-space semantic signals is constructed based on a retrieval result, and a context evidence set of steady-state sorting is obtained through propagation sorting of a time-space perception graph and is assembled into a structured evidence packet; an evidence packet constraint large model is used to generate an answer with reference and confidence; and performing machine self-inspection on the answers, starting attribution re-check on high-risk items, constructing a performance portrait based on audit feedback and driving self-evolution of strategy parameters to form a full-link audit closed loop.

The invention discloses a multi-modal database-oriented adaptive index structure selection method, which comprises the following steps of: analyzing statistical characteristics such as data dimensions, variances, sparseness and distance distribution of each modal, calculating hidden dimensions of the modals, and describing effective data complexity of the modals; based on a preset index adaptation rule, automatically mapping each mode to an optimal index type in the candidate index structure set, and completing automatic construction of a local index and binding of the local index and a global routing structure according to the optimal index type; on the basis, the multi-modal query can be automatically routed to the corresponding index to execute retrieval according to the index mapping relation. According to the method, an index structure self-adaptive selection mechanism without manual configuration is realized, the index maintenance cost of the multi-modal database can be remarkably reduced, and the query efficiency and expandability in a complex retrieval scene are improved.

The invention relates to the technical field of material informatics, and discloses a method for constructing a knowledge graph based on a material database, which comprises the following steps: S1, collecting multi-source basic data and carrying out standardization processing, identifying a knowledge graph entity based on the standardized data and establishing entity attributes, defining an explicit association relationship between the entities and generating corresponding relationship data; s2, performing association mining on the basis of the explicit association relationship and the entity attributes to obtain a hidden association relationship used for representing potential relationships between entities; and S3, fusing the entity, the explicit association relationship and the hidden association relationship, and constructing a material database knowledge graph. According to the method, raw materials, a formula, a process, performance and an application scene serve as an entity framework, explicit modeling is conducted on the relation between entities, and a computable association network is constructed, so that research and development personnel can achieve cross-link retrieval, reasoning and positioning based on unified semantics, and the efficiency and consistency of formula development and performance analysis are improved.

This invention discloses a cross-platform business system integration method based on knowledge graphs, comprising the following steps: S1, reading data from the business platform database and generating field text, entity identifiers, and relation records; S2, inputting the field text into an ALBERT model, generating field semantic vectors through a domain word vector gating layer; S3, forming triples based on entity identifiers and relation records and writing them into the knowledge graph; S4, inputting the triples into a TransH model, generating entity structure embedding vectors through dynamic rotation of relation vectors and hyperplane normal vectors; S5, generating entity embedding vectors based on field semantic vectors and entity structure embedding vectors; S6, calculating various distances based on entity embedding vectors to obtain the similarity of cross-platform candidate entity pairs; S7, performing duplicate entity determination on the similarity and updating the knowledge graph. This invention achieves accurate alignment of cross-platform business entities, improves data fusion efficiency and consistency, and is suitable for multi-source business integration scenarios.

The embodiment of the invention provides a method and device for generating a structured query language (SQL), a product and a storage medium, the method is used for generating the SQL, a target database corresponds to an auxiliary database, and a corresponding relation between table information of a data table and a preset vector of the table information is stored in the auxiliary database; obtaining an input natural language question, and identifying a target text related to the table information; performing retrieval in a target database, and determining whether a first type of candidate table information matched with the target text can be obtained or not; obtaining a vector corresponding to the target text and performing retrieval in an auxiliary database, and determining whether a second type of candidate table information corresponding to a candidate vector matched with the vector of the target text can be obtained or not; under the condition that the first type of candidate table information and / or the second type of candidate table information are / is obtained, SQL prompt data are constructed based on the natural language problem and the obtained candidate table information, and a target SQL generated by a preset language model through the SQL prompt data is obtained.

The invention relates to a quick scheme recommendation method based on a knowledge graph. The method comprises the following steps: dividing and clustering cases of a case library on the basis of a trusted database through a k-means clustering method; performing entity extraction and relationship definition on the divided case library data so as to construct a knowledge graph; the method comprises the following steps: constructing a text representation of an entity according to parameter information of the entity, processing the text representation through a language model to obtain a parameter vector representation of the entity, and processing through a graph model according to graph structure information of the entity to obtain a graph structure vector representation of the entity, respectively storing the parameter vector representation and the graph vector representation into a parameter database; and analyzing task requirements, searching a historical case of the most similar scene as a baseline case, replacing an entity in the baseline scheme, adjusting the scheme, and completing rapid recommendation of the scheme. According to the method and the device, the correlation between the entities can be accurately reflected, then scheme adjustment is carried out on the cases generated by clustering, and available schemes can be quickly generated.

The invention discloses an asset full-period management method based on a knowledge graph. The method comprises the following steps of obtaining multi-source asset data and performing preprocessing; semantic coding is carried out on the asset full-period time sequence modeling data set; generating a structured triple candidate set through a subject filtering CasRel entity-relation joint extraction model; performing CasRel result re-scoring according to a preset constraint set and performing correction; mapping time information into nodes and associating the nodes with the modified structured triple set; and importing the time sequence multi-tuple set into a graph database to construct the asset full-cycle time sequence knowledge graph. According to the method, the subject filtering CasRel and the life cycle-time prior gating mechanism are introduced, the asset full-cycle time sequence knowledge graph is constructed, and the technical effects that asset relation extraction is more accurate, stage semantics are more consistent, and a full-cycle structure is more reliable are achieved.

The invention relates to a multi-table association SQL (Structured Query Language) generation method based on graph structure reasoning, and belongs to the fields of artificial intelligence, natural language processing and databases. The method comprises a coding end double-alignment modeling stage, a graph structure reasoning and semantic constraint path reasoning stage and a decoding end efficient generation and structure constraint stage. According to the method, double-alignment modeling of semantics and structures is firstly realized, and then graph structure reasoning and hierarchical SQL generation are carried out, so that the problem of inaccurate mapping of the natural language and the SQL structure in a complex multi-table association scene is effectively solved, and cross-modal information of a database mode and the natural language is better utilized. Meanwhile, interactive reasoning is carried out on graph structure features and semantic features, so that the model can fully mine topological association between tables and deep relation of query semantics, structured knowledge is obtained from limited multi-table data, and the method has good generalization ability in a multi-table database query scene.

The application discloses a cross-endpoint associated path retrieval method and system driven by RDF class relation, and belongs to the technical field of semantic web data association. The application carries out preprocessing on data in a SPARQL endpoint, extracts RDF class relation from an ontology and entity relation; the extracted RDF class relation is stored in a graph database in the form of graph data; when a user carries out relation query between entities, inputs a retrieval word and selects a data source; the entity URI of the retrieval word is determined, the entity URI is parsed to determine the class to which the entity belongs, and the associated path of the class is queried in the graph database; the associated path information between the classes that are queried is dynamically encapsulated into a SPARQL federated query statement; the SPARQL federated query statement is executed, and the associated path result of the query is dynamically visualized and displayed. The application can retrieve the associated path between entities across SPARQL endpoints, supports multiple data source endpoints and any associated direction, and improves the efficiency and quality of cross-endpoint associated path retrieval.

The invention provides an intelligent labeling relation reasoning method and system based on a knowledge graph, and belongs to the technical field of data processing. Code submission, version and vulnerability information are obtained from an open source software code warehouse and a vulnerability database to construct an initial knowledge graph containing codes, versions and vulnerability entities. By parsing the differential content and time series of the code submission records, modification, movement, and multiplexing events of the code blocks are tracked to generate a code evolution path graph. And mapping the semantic features of the code entities and the evolution features of the code blocks to the same vector space to generate vector representation fusing semantics and evolution modes. And based on the vector, calculating semantic similarity and evolution association degree between code entities, and performing multi-hop association reasoning to determine a vulnerability propagation link. According to the evolution path diagram and the propagation link, the vulnerability propagation path and the risk level are marked for the affected target code and the version entity, and the vulnerability propagation path analysis accuracy and the risk assessment comprehensiveness can be improved.

The invention provides a knowledge question and answer method for cross-source guidance of entity selection and knowledge assimilation, and relates to the technical field of knowledge question and answer. Firstly, subject entities are identified and selected according to questions of a user; after the subject entity set is determined, entering a retrieval stage of the knowledge graph; retrieving correlations and candidate entities in a graph database source using the selected entities; filtering the retrieved correlativity according to the question, and further screening candidate entities; the method specifically comprises the steps that in a collected relation set, a big language model LLM is guided through prompt to select and score the relation, so that a key relation beneficial to solving a problem q is found; wherein W is the number of subject entities in the current iteration; guiding document retrieval according to the filtered atlas, and guiding selection of candidate entities in the atlas according to the retrieved context; judging a termination condition and obtaining external knowledge; after external knowledge is obtained, conflicts are solved through multi-agent knowledge assimilation.