137 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 provides a method and system for constructing a Neo4j knowledge graph based on an LLM natural language, and belongs to the technical field of smart tourism and knowledge graphs. The method comprises the steps that S1, text entities are recognized, and semantics are understood; s2, analyzing an entity relationship and generating a Cypher statement; s3, extracting entity attributes and performing constraint verification; s4, constructing a Neo4j knowledge graph; and S5, performing dynamic updating and quality control. According to the method, the natural language understanding ability of the large language model and the relation modeling advantage of the Neo4j graph database are fused, so that automatic construction, dynamic updating and culture compliance verification of the tourism knowledge graph are realized.

The invention discloses a global knowledge extraction method and system based on a large model and an RAG technology, and belongs to the technical field of large model data processing. According to the global knowledge extraction method and system based on the large model and the RAG technology, local sensitive hashing is adopted for duplicate removal, similar texts are combined through dynamic thresholds, and redundant data interference is reduced; a text segmentation strategy based on separator confidence ensures that segmented text blocks maintain semantic integrity and are adaptive to large model input length limitation, similarity retrieval and full-text retrieval are fused, results are reordered in combination with an RRF algorithm, semantic relevance and accuracy are considered, the multi-scene knowledge construction requirement is met, and the multi-scene knowledge construction efficiency is improved. High-correlation entity pairs are screened through mutual information, context knowledge is retrieved from a vector database in combination with an RAG system, semantic reasoning is conducted through a large language model, hidden logic relations among entities are generated, and jumping from data association to knowledge generation is achieved.

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 discloses a method for constructing a course ideological and political material library, and the method comprises the steps: obtaining data related to course ideological and political, and carrying out the preprocessing of the data, and obtaining a data set; performing entity extraction on the unlabeled data by using the fine-tuned UIE model, and identifying and extracting knowledge points and ideological and political elements in the unlabeled data; the extracted triple information is stored in a graph database Neo4j, knowledge points and ideological and political elements are associated through relation edges, and a course ideological and political knowledge graph is formed; displaying a course ideological and political material library on the basis of the course ideological and political knowledge graph, checking the association between knowledge points and ideological and political elements, and performing detailed display of specific cases; and providing extended case resources related to the knowledge points and the ideological and political elements according to a preset intelligent retrieval system. According to the invention, the problem of insufficient fusion degree of professional knowledge and ideological and political education can be effectively solved, and systematicness and effectiveness of course ideological and political construction are improved.

The embodiment of the invention provides a data processing method based on a three-dimensional hierarchical data model, a platform and a medium. The method comprises the following steps: firstly, acquiring multi-source hydrogeological data; classifying the data to obtain a first type of data and a second type of data; then, according to the three-dimensional hydrogeological hierarchical model, extracting a hierarchical association relationship of the two types of data; the first class data and the second class data are used for representing the incidence relation of the first class data and the second class data at different levels of geological observation angles; storing the first type of data in a relational database, and storing the second type of data in a non-relational database; and finally, according to the hierarchical association relationship, establishing a hydrogeological knowledge graph and storing the hydrogeological knowledge graph in an interactive coordination system. According to the method, after the multi-source hydrogeological data are classified, the incidence relations at different levels of geological observation angles are extracted and stored in the relational database and the non-relational database respectively, mapping between the databases is established by adopting the knowledge graph, the multi-source hydrogeological data are effectively integrated, and meanwhile query is facilitated.

The invention relates to a method and equipment for generating a code map for a project and a storage medium, and belongs to the technical field of software engineering. The method comprises the following steps: segmenting a code file in a code library into code snippets, generating a corresponding vector representation for each code snippet, and storing the code snippets and the vector representations thereof into a vector database; extracting a project structure tree, and adopting a large language model to generate a UML generation plan according to the project structure tree; generating a UML graph of the current project by adopting a large language model based on the UML generation plan and codes in the code library; displaying the UML diagram of the current item on a graphic interaction interface through a rendering engine; and responding to an instruction that a user clicks a selected node in the global UML graph, returning a code snippet related to the selected node, and generating a detailed view related to the selected node. On the basis of the method, the UML diagram of the whole project can be generated through one key, and the relation between the complete structure and the function module of the project can be quickly and visually known.

The invention belongs to the technical field of informatization management, and particularly relates to a data import method based on similarity calculation, automatic matching of file fields and database fields is realized by calculating the similarity of the file fields and the fields in a database, and the workload of manual configuration can be effectively reduced; a common editing distance calculation similarity algorithm is improved, the influence of the longest common subsequence on similarity calculation is considered, a similarity calculation formula is improved, and the matching success rate is increased; when the similarity is calculated, literal matching and semantic matching can be carried out by adopting a method of combining an improved editing distance similarity calculation algorithm and word semantic similarity calculation based on the Zhinetting, so that the matching success rate is effectively improved; besides automatic matching, a user configuration interface is provided, a user can manually adjust the mapping relation, and the flexibility and usability of the method are improved; user configuration content can be automatically recorded, and repeated configuration work of a user is avoided.

A large-scale language model for adapting to an industrial domain is provided, a graph database stores a domain-specific knowledge graph containing instance-level semantic information about the industrial domain, including true triple statements about physical entities from the industrial domain and their interrelation. A knowledge graph to corpus translator converts the domain-specific knowledge graph into a natural language corpus. A domain adaptation component pre-trains the large-scale language model with the natural language corpus to a provide a domain-adapted large-scale language model. This approach does not require any assumptions on the structure of the domain-specific knowledge graph, for example, it does not require the domain-specific knowledge graph to contain only instance data. Neither does it need linguistic templates since the verbalization of triples can be built on their existing labels or relation names. The triple-based information encoding of RDF facilitates a natural translation into language during the converting operation.

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 digital main line automatic modeling method and system based on a large language model, and the system comprises a database system, an embedded model, a vector database, a matching processing unit, a front-end interface, a cue word generation module, and a large language model interface. The digital main line automatic modeling method based on the large language model comprises the following steps: S1, extracting data information of a standard database and a database to be matched and converting formats; s2, storing the data information of the standard database in a vector database in a vectorization manner by using an embedded model; s3, vectorizing to-be-matched business database data information, searching similar fields in a vector database, and selecting candidate fields; s4, the user checks the candidate similar fields and confirms the mapping relation; s5, the data matching relation is input into the large language model, and a data transfer SQL is generated; and S6, executing SQL to realize data integration and automatic modeling.

A technique leverages a machine-trained pattern-completion engine to successively extract items-of-interest from unstructured data, categorize the items-of-interest, and identify relations in the unstructured data. The technique then generates a structured database based on the information it has identified. In some cases, the items-of-interest represent facts expressed by the unstructured data. The technique also leverages the structured database to perform various application tasks. In one approach, in the course of answering a query, the technique extracts supplemental information from the structured database. The technique then feeds the query and the supplemental information to the pattern-completion engine, and, in response thereto, receives output information that addresses the query. In some cases, the query is part of lengthy prompt information. Here, the technique first involves creating the structured database based on the prompt information, and then presenting the supplemental information extracted from the structured database and the query to the pattern-completion engine.

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 relates to an agent method and device for a vertical sub-database, equipment and a medium. The method comprises the following steps: when a database creation instruction of a user is received, querying a target routing strategy corresponding to the database creation instruction; selecting a target fragment from the created fragments according to a target routing strategy; executing the database creation instruction on the target fragment to create a logic database, and generating a creation log of the logic database; after the logic database is created, generating a mapping relation between the logic database and the target fragment; the mapping relation and the creation log are stored in metadata fragments, the metadata fragments are one fragment in the created fragments, and the mapping relation is a routing rule of an instruction for operating the logic database. According to the method and the device, the problems of data skew, data migration difficulty and the like caused by routing rules can be avoided, additional metadata service storage and management of the metadata do not need to be introduced, and storage and management of the metadata are more convenient.

The invention provides a data query method and device, electronic equipment and a storage medium. The method comprises the steps that a target SQL query statement is split, a plurality of target sub-queries are obtained, and the target SQL query statement is a statement used for conducting data query in a relational database; according to all the conditions in each target sub-query, a target graph query corresponding to each target sub-query is obtained through conversion, and the target graph query is a graph structure used for indicating the relation between any two conditions in all the conditions in the corresponding target sub-query; querying in a relational database through each target graph query to obtain a sub-query result corresponding to each target graph query; and obtaining a target query result corresponding to the target SQL query statement based on all the sub-query results. By means of the method and device, the problems that when SQL query is converted into graph query in the prior art, the workload of manually writing the conversion rule is large, and complex query is difficult to deal with are solved.

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.

The invention discloses a database transaction load sequencing method based on a conflict relationship, which is characterized in that non-conflict operations are grouped into operation batches in parallel by analyzing data access conflicts among the operations based on an original execution log of a database transaction load; the original execution sequence of conflict operations is deduced according to operation types, version dependence and lock competition conditions, then operation batches are sorted according to the sequence, an operation batch sequence is constructed, and therefore it is ensured that the execution result of the sequence is equivalent to the original execution result of a transaction load. Compared with the prior art, the method has the advantages that a kernel of a database system does not need to be intruded, the analysis speed is linearly expanded along with the scale of the original execution log, and good expandability and application prospects are achieved.

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 provides an SQL statement generation method and device based on a large model, equipment and a storage medium. The method comprises the following steps: constructing a cue word according to a first natural language question; the first natural language question is used for requesting to execute an operation on a first object, and the first object is obtained by the low-code platform from a source system; inputting the first natural language question and the cue word into the large model to obtain an SQL statement output by the large model; wherein the SQL statement output by the large model comprises object information of the first object in the source system; according to the mapping table, SQL statements output by the large model are translated to obtain SQL statements which can be executed by a low-code platform; the mapping table comprises the object information of each object under the source system and the corresponding relation between the object and the database of the low-code platform.

The invention relates to the technical field of databases, and discloses a data query method and device, equipment, a medium and a product, and the method comprises the steps: obtaining a plurality of query statements for a target database; the query statement is generated by the large language model according to an input natural language; according to the relation between the statistical tables corresponding to the multiple query statements, the multiple query statements are combined, and at least one combined statement is obtained; and executing the at least one merged statement to obtain query results corresponding to the plurality of query statements. According to the scheme, the multiple query statements can be merged according to the relation between the statistical tables corresponding to the query statements, so that the query result corresponding to the query statements can be obtained by executing the merged statements, compared with direct processing of the multiple query statements, the IO frequency of the database is reduced, the IO load of the database is reduced, and the query efficiency is improved. The unnecessary interaction overhead is reduced, and the data query efficiency is improved.

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 a data distributed storage method and device. The method comprises the steps that a to-be-stored relational database table is obtained, multi-dimensional data features of the relational database table are determined, and the dimensions of the data features comprise at least one of the table size, the field type, the field incidence relation and the access state; according to the multi-dimensional data features, a fragmentation strategy for the relational database table is determined, the relational database table is divided into a plurality of data fragments according to the fragmentation strategy, and the fragmentation strategy comprises at least one of range fragmentation, hash fragmentation and list fragmentation; and storing the plurality of data fragments to a plurality of storage nodes in a distributed manner, and recording a mapping relationship between each data fragment and each storage node. The technical problem that the data storage and use efficiency is easily influenced by unreasonable data fragmentation in the existing distributed storage technology is solved.

The embodiment of the invention discloses a construction method and device of a C + + code knowledge base and a medium, relates to the technical field of knowledge maps, and is used for solving the problem that information of an existing knowledge base is incomplete and inaccurate. The method comprises the steps that a compiling configuration file containing detailed compiling parameters is constructed according to a C + + project configuration file; analyzing file elements of the compiling configuration file based on a preset script analysis engine, preprocessing the C + + project source file to obtain a processed C + + project file, and generating a compiling unit based on the file elements; obtaining dependency information of the C + + project file based on a compiling unit, obtaining an abstract syntax tree of the C + + project file for node filtering, and mapping and storing the filtered nodes and the dependency information into a corresponding semantic mapping table to realize first persistence processing; and determining the incidence relation of the C + + project file based on each semantic mapping table, storing each incidence relation in a preset relation database, and realizing second persistence processing to obtain a C + + code knowledge base.

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 data processing, in particular to a data statistical method based on a large data volume, which comprises the following steps of: S1, defining a many-to-many first subject table and a second subject table of different data magnitudes in a relational database; s2, acquiring a mapping relation between the first main body table and a second main body table, traversing the second main body table, acquiring IDs of all records corresponding to each record in the second main body table in the first main body table, and splicing an ID list of nodes on a path by traversing a path from a node where the ID is located to a root node, after splicing is completed, an array is formed and stored in a field added to a corresponding record in the first main body table, and storage of the data mapping relation is completed; and S3, completing storage of a data mapping relationship between the first main body table and the second main body table for subsequent adjustment, statistics or query calling. According to the method, the data storage cost can be greatly reduced, the data maintenance cost can be reduced, and real-time statistics and query of a large amount of data can be realized.

The embodiment of the invention discloses a request processing method which is applied to a first database system and comprises the steps that the first database system obtains a first structured query language (SQL) statement, and grammar rules of the first SQL statement are the same as grammar rules of a second database system; the first database system converts the first SQL statement into a second SQL statement according to a configured mapping relation, and the second SQL statement conforms to a grammar rule of a second database system; the mapping relationship comprises a corresponding relationship between a first SQL statement expression conforming to a grammar rule of the first database system and a second SQL statement expression conforming to a grammar rule of the second database system; the first database system executes the second SQL statement, and the result that the first database system executes the second SQL statement is the same as the result that the second database system executes the first SQL statement. According to the scheme, the compatibility of the database can be improved.

The invention discloses a prediction method, device and equipment and a readable storage medium, and the method comprises the steps: obtaining a relational database containing a to-be-predicted object in response to a prediction request, taking a business object in an entity table in the relational database as an entity node, taking a business event in an event table as an event node, and carrying out the prediction of the to-be-predicted object. And determining edges between the entity nodes and the event nodes according to foreign keys corresponding to the business events in the event table, and constructing a target topological graph according to the entity nodes, the event nodes and the edges between the entity nodes and the event nodes. Therefore, the target features of the to-be-predicted object are automatically extracted from the whole target topological graph, original data in all tables in the relational database are fully utilized, human intervention of feature engineering is reduced, the prediction efficiency is improved, the complex relation between the to-be-predicted object and other data in the relational database can be better captured, and the prediction accuracy is improved. And the accuracy of model prediction is improved.