1773 results about "Query statement" patented technology

The invention relates to an intelligent question-answering system optimization method and device based on a knowledge graph, and the method comprises the steps: obtaining original knowledge data of a target knowledge domain, and constructing a knowledge graph structure model; extracting term information of entity nodes in the knowledge graph structure model, and constructing an entity term set; receiving a natural language question input by a user, executing a semantic understanding operation based on the standardized expression set to obtain a structured question semantic representation, and matching the question semantic representation with the case training set to obtain context semantic features; constructing a cue word template, and executing a query instruction generation operation to obtain a target query statement of the graph database; submitting the target query statement to a graph database to execute data retrieval operation, and obtaining query result data corresponding to the question semantic representation; and performing personalized rendering processing on the query result data based on the user portrait information to generate final question and answer return content. The method has the effect of improving the query accuracy.

The invention provides an intelligent SQL generation method and system for business query, and belongs to the technical field of artificial intelligence. Related data of query statements are acquired, and a corresponding query intention knowledge graph is constructed by semantic clustering; the method comprises the following steps: analyzing a historical SQL statement structure, extracting a natural language template and an SQL template, and expanding through a large language model to generate a feed-shot example set; and constructing a composite cue word template by combining task setting guidance, a feed-shot example and CoT chain thinking reasoning guidance. An intention completion module is arranged in a large language model, a natural language query statement of a user is combined with a composite cue word template context, a structured query statement is generated through entity recognition, semantic completion, parameter filling and fuzzy intention training, and the structured query statement is converted into a standard SQL statement through a knowledge graph and a template. According to the method, the use threshold of business personnel is remarkably reduced, and efficient conversion from natural language questions to SQL statements is realized.

The invention relates to the technical field of image data processing, and discloses a power grid drawing intelligent review method and system based on a knowledge graph, and the method comprises the steps: driving a multi-mode large language model based on a domain specific prompt project, extracting a power grid domain knowledge triple from structured text data, and then carrying out the quality evaluation and conflict detection, performing automatic resolution on the conflict knowledge to obtain a candidate knowledge set; checking and confirming the candidate knowledge set through a man-machine cooperation verification mechanism, and constructing a power grid design specification knowledge graph; and identifying to-be-reviewed elements in the to-be-reviewed power grid drawing through the multi-modal large language model, querying the power grid design specification knowledge graph according to the generated structured query statement, performing compliance judgment on the to-be-reviewed power grid drawing based on the obtained query data packet, and generating a review report. According to the method and the device, the efficiency and the reliability of automatic drawing review can be improved, and meanwhile, the interpretability and the traceability of review results are ensured.

The invention discloses a report chart automatic generation method based on natural language analysis and data query, which comprises the following steps: acquiring a natural language text, and constructing a slot set and an initial semantic fragment; executing consistency verification and ambiguity identification, and constructing a semantic framework; executing field standardization and mode alignment, generating a data query statement and establishing a traceability chain identifier; querying a target database to obtain an original data set, completing data quality self-correction, and generating a cleaned data set; calling an improved Mamba model to construct a constraint judgment matrix and outputting a target chart subset; generating a chart description grammar and an image annotation template, and executing image rendering, feature point recognition and image sidebar construction; and performing layout optimization and double compilation, and outputting a structured report file. According to the method, the chart generation automation level and the report compiling efficiency are improved, and the accuracy, traceability and specialty of the output content are ensured.

According to the artificial intelligence-based data analysis system and method provided by the invention, a set of novel data analysis system architecture is designed, and key technical components such as natural language processing, a large language model, database query optimization, multi-modal visualization and a semantic knowledge graph are fused; data transmission between modules is realized through unified intermediate data objects such as a structured query intention, an analytic tree and a structured query language template object, and asynchronous collaboration is realized through event driving and a message queue mechanism, so that a non-professional user can input an analysis request through a natural language; semantic analysis, structured query language query statement generation, data query and result visualization presentation are automatically completed, the method can be widely applied to data analysis scenes in the industries of government affairs, traffic, finance, education and the like, the data use efficiency is improved, the technical threshold is reduced, and the digital decision-making ability is enhanced.

The invention discloses a document analysis and query method based on a knowledge graph, and the method comprises the steps: carrying out the part-of-speech tagging of a received to-be-analyzed document, and obtaining a part-of-speech tagging result; extracting knowledge element information from the part-of-speech tagging result based on a preset power grid domain ontology knowledge base, and constructing an initial knowledge graph based on the knowledge element information; combining nodes in the initial knowledge graph to obtain a fused knowledge graph; constructing a mapping table according to the fused knowledge graph, and generating a candidate query template set based on the mapping table; receiving a natural language query statement input by a user, selecting a target query template from the candidate query template set based on the natural language query statement, and generating a target query statement; querying from the fused knowledge graph by using the target query statement, and outputting a query result; according to the method, the accuracy and comprehensiveness of information analysis can be effectively improved, the query intention of the user can be accurately understood, and the accurate query and analysis requirements of professionals on project documents are met.

The invention relates to the technical field of natural language processing, in particular to an intelligent search engine system and method based on NLP and vector hybrid retrieval, and the system comprises a query analysis module which is used for receiving a query statement input by a user and obtaining structured query information based on the query statement; the symbiotic index module comprises a sparse extension index unit which is used for constructing a sparse inverted index table based on keywords in the structured query information; the vectorization hypergraph index unit is used for forming a hypergraph index based on the business data; the recall arrangement module is used for acquiring a candidate object set according to the structured query information; the constraint rearrangement module is used for calculating a joint priority score between query and candidate objects based on the candidate object set, and obtaining a sorting result under constraint conditions of meeting a supplier proportion, a category proportion and a price interval; and the evidence generation module is used for generating evidence information based on the sorting result and outputting the evidence information to the user interface.

The invention discloses a data production and application method based on index management, and the method comprises the steps: receiving an index query request containing a target index identifier, a dimension constraint condition and a time range parameter, carrying out the analysis and semantic verification of the target index identifier based on a global index asset library, and obtaining the index definition information; generating a standardized query statement according to the index definition information, and selecting an optimal data calculation engine to execute query; performing multi-level cache optimization and parallel computing acceleration on the query process to obtain an original data set; processing the original data set in real time according to a preset analysis model to generate structured index data containing trend analysis, anomaly detection or attribution inference results; and filtering and desensitizing the data based on a fine-grained permission control strategy, only returning contents in a permission range and recording an audit log. By means of the method, unified management, efficient query and intelligent analysis of the index data are achieved, the automation level and query performance of data production are improved, and meanwhile data safety and access controllability are guaranteed.

A structured query language statement generation method, executed by an electronic device, and comprising: determining a natural language text representing a query task, and determining a preset database, the preset database comprising at least one data table, and each data table comprising at least one field (101); on the basis of the natural language text, determining, from the preset database, at least one task-associated field and at least one task-associated data table which are associated with the natural language text (102); rewriting the natural language text on the basis of the task-associated field to obtain a rewritten natural language text (103); acquiring prompt information for the rewritten natural language text, the prompt information being used for guiding a query statement generation model in generating a structured query language statement (104); performing question classification on the rewritten natural language text to obtain question type information of the rewritten natural language text (105); and generating the structured query language statement of the rewritten natural language text by means of the query statement generation model on the basis of the task-associated field, the task-associated data table, the prompt information, and the question type information (106).

An information processing method, an electronic device, and a storage medium. The method includes: obtaining a query statement of a user, determining at least one model identifier of at least one candidate service model based on the query statement; generating at least one first prompt word based on the query statement and the at least one model identifier, inputting the at least one first prompt word into a pre-trained target large model, and outputting, by the target large model, at least one screening parameter of the at least one candidate service model based on the at least one first prompt word; determining a target service model from the at least one candidate service model based on the at least one screening parameter; and inputting the query statement into the target service model, and obtaining feedback information corresponding to the query statement.

The present application provides a large language model-based query statement generation method, apparatus, and device, and a medium. The large language model comprises a plurality of encoders. The method comprises: acquiring a query text; performing vectorization processing on the query text to obtain a target vector representation corresponding to the query text; inputting the target vector representation into the plurality of encoders, to perform encoding processing on the target vector representation by means of weight matrices of the encoders, so as to obtain context vectors corresponding to the encoders, wherein different encoders have different weight matrices; and generating a target query statement on the basis of the context vectors corresponding to the encoders. A model is allowed to focus on different information features in different attention heads, independently capture different aspects of a query text, and process information at multiple abstraction levels, thereby better capturing complex and abstract semantic relationships, effectively distinguishing the importance of information when processing complex and fuzzy questions, improving the accuracy of query statement generation.

The invention discloses an intelligent data query system and method based on natural language processing, and relates to the technical field of natural language processing and database query. According to the method, natural language input and database mode information are received, a historical query log is combined to construct mode knowledge representation, a query skeleton is generated on the basis, a fine-tuned large language model is called to generate candidate SQL statements, error detection and ambiguity recognition are carried out on the candidate statements, and the query result is obtained. And if necessary, triggering interaction clarification and updating a query result according to user feedback. And meanwhile, the wrong clauses are locally repaired through a gating mechanism, and the clauses are returned and regenerated when multiple times of repair fails, so that the correctness of the query statement is ensured. And finally, after the SQL is executed in the database, a result is fed back to the user. Besides, the system records generation and repair tracks in the operation process, and continuously optimizes the model based on reward shaping, comparative learning and self-game training, so as to improve the generalization ability in different business scenes.

The invention relates to the technical field of document processing, and discloses a document auditing method, device and equipment and a medium, the method comprises the following steps: obtaining a to-be-audited document containing at least one of a text, an image and a chart, and analyzing the to-be-audited document to obtain corresponding to-be-audited content; obtaining an auditing rule document, extracting an auditing rule described in a natural language from the auditing rule document, and converting the auditing rule into a structured query statement; and extracting and auditing multi-dimensional elements of the to-be-audited content based on the structured query statement to obtain a corresponding auditing result, and outputting the auditing result. According to the technical scheme, the multi-modal document is automatically analyzed, the natural language rule is converted into the structured query statement, full-automatic analysis and auditing of the document are achieved on the basis of a multi-dimensional element extraction and auditing mechanism, manual work is not needed, and the document auditing efficiency and accuracy can be effectively improved.

The invention provides a private intelligent code collaborative optimization method and system based on graph retrieval, and relates to the technical field of software engineering intelligent auxiliary tools, and the method comprises the steps: obtaining a source code file and operation log data of a private project; the method comprises the following steps: performing syntax tree analysis on a source code file through a Tree-siter analyzer to generate a corresponding abstract syntax tree; constructing a code knowledge graph by analyzing a function call relationship, an inheritance relationship and a module dependency relationship; monitoring the change of a project file in the code knowledge graph through a code change event processor, and updating the code knowledge graph in real time when the file is changed; establishing an association relationship between the running log data and a corresponding function node in the code knowledge graph; the method comprises the following steps: analyzing a natural language problem of a developer into a Cypher query statement through an intelligent Agent; obtaining a query result from the graph database; and formatting the query result to generate a final result.

The embodiment of the invention provides a multi-modal fusion entity retrieval enhancement generation method and device, and the method comprises the steps: carrying out the blocking and adaptive text extraction of multi-modal data in an offline stage, obtaining the text block data corresponding to each modal data, extracting the entity and relation of the text block data according to a language model, constructing an entity triple, and carrying out the segmentation and adaptive text extraction of the entity triple. Fusing the entity triad with the text block data to obtain an offline knowledge graph, and constructing a data index; in the present stage, a query statement of a user is received, text block data most similar to the query statement are retrieved in a knowledge graph through a data index, after entity aggregation is carried out on the retrieved text block data, the text block data are reordered according to retrieval scores, an entity aggregation result is obtained, entity ordering is carried out according to the entity aggregation result, and the entity aggregation result is obtained. By means of the multi-modal retrieval method and device, the efficiency and accuracy of multi-modal retrieval can be improved.

The invention relates to the technical field of information retrieval, and provides a multi-dimensional heterogeneous knowledge recall enhanced hybrid retrieval method and system.The method comprises the steps that texts recalled through keyword retrieval, sparse vector retrieval and dense vector retrieval are screened through a reciprocal sorting fusion algorithm, and a text type candidate knowledge list is obtained; based on user query, generating and checking a query statement through a large language model, and retrieving an entity-relationship-attribute triple from the knowledge graph library; based on user query, generating enhanced knowledge through a knowledge graph enhanced retrieval method fusing keyword retrieval, vector retrieval and community retrieval; and carrying out format alignment and duplicate removal on the text type candidate knowledge list, the triple result and the enhanced knowledge to form a multi-modal candidate pool, and carrying out reordering score calculation and ordering on each piece of recall knowledge in the multi-modal candidate pool through a reordering model and a business rule to obtain a final retrieval result. And the coverage blind area of single retrieval on heterogeneous knowledge is solved.

The invention relates to the technical field of computer data processing, and discloses a data segmentation and query processing system under a distributed architecture. The system comprises a data segmentation module which divides data blocks based on a self-adaptive partitioning algorithm; the distributed storage module is used for distributing data blocks by using a consistent Hash algorithm and dynamically adjusting the distribution of virtual nodes; the index construction module is used for generating a global index table through a multi-layer inverted index algorithm; the query optimization module is used for converting query statements by utilizing a query rewriting algorithm; and the result aggregation module is used for processing the sub-query results by adopting a parallel aggregation algorithm. The system can dynamically process data according to data characteristics, optimizes the data storage and query process, effectively improves the data processing efficiency and query performance of a distributed system, and is suitable for scenes such as big data storage and analysis.

The invention relates to a multi-agent search enhancement generation method and system. The method comprises the following steps: analyzing a task instruction through a first agent to generate a search feature set; matching a plurality of corresponding second intelligent agents and performing directional semantic conversion on the original semantic vector to generate a query statement set; screening a plurality of third intelligent agents, and executing query operation through the third intelligent agents to obtain a preliminary retrieval set; inputting the preliminary retrieval set and the retrieval feature set into a fourth agent for verification, and generating an optimized retrieval result; in conclusion, according to the multi-agent search enhancement generation method and system provided by the invention, intelligent analysis, cross-modal semantic conversion, dynamic database partition scheduling and search result optimization verification of task instructions are realized through a multi-agent cooperation mechanism; the method has the effects of improving the retrieval precision and efficiency, enhancing the adaptability to complex query intentions and optimizing the collaborative retrieval capability of the heterogeneous database.

The invention discloses a financial data query method and device, electronic equipment and a storage medium, and relates to the technical field of artificial intelligence or other related fields, the method comprises the following steps: receiving a data query demand, and retrieving a knowledge document according to the data query demand to obtain index information of a target data table indicated by the data query demand; based on the data query requirement, N reasoning tasks associated in sequence are generated, the N reasoning tasks are executed in sequence to obtain N reasoning results, and N is a positive integer; generating a prompt text based on the data query demand, the retrieval result and the N reasoning results, inputting the prompt text into the target large language model, and outputting a database query statement; and executing the database query statement to obtain a financial data query result. Through the method and the device, the technical problem of low financial data query accuracy caused by difficulty in accurately understanding user query requirements under the condition of complex financial system database environment in related technologies is solved.

The present application relates to the technical field of electric digital data processing, and provides a data preparation method, system, and device for AIGC-based interaction analysis, and a medium. The method comprises: by means of a pre-trained text label recognition model, determining a label sequence corresponding to an interaction statement having experienced first word segmentation processing; on the basis of a preset expression construction rule, determining a label combination sequence corresponding to the label sequence and a conditional expression corresponding to the label combination sequence; by means of a preconfigured database candidate text dictionary and a preset matching rule, determining a strictly matched keyword and a fuzzily matched keyword corresponding to the interaction statement having experienced second word segmentation processing; on the basis of the strictly matched keyword and a similar candidate text corresponding to the fuzzily matched keyword, determining a word segmentation result corresponding to the second word segmentation processing; on the basis of a plurality of preset SQL statement rule matching templates, generating an interaction SQL query statement, so as to determine, on the basis of the interaction SQL query statement, interaction response information corresponding to the interaction statement.

The invention discloses a query statement generation method and electronic equipment, and relates to the technical field of query statement generation, and the query statement generation method comprises the following steps: obtaining a historical query record in response to a received natural language query request of a user; judging whether query relevance exists between the natural language query request and a historical query record or not, and if yes, determining a candidate data table set based on a historical query data table; calling a pre-trained language model, and performing first reasoning on the natural language query request and the candidate data table to generate an initial query statement; executing the initial query statement on a first data table set constructed based on the candidate data table set, verifying whether a query score of an initial query result meets a preset availability standard threshold value or not, and if yes, taking the query score as a target query statement. According to the method and the device, the problems of input information redundancy, high overhead loss, low statement verification efficiency and insufficient correction capability in related technologies are solved, and efficient and accurate generation from the natural language query request to the query statement is realized.

The invention provides an SQL generation method and system combining GraphRAG and a large model in the technical field of natural language processing and artificial intelligence crossing, and the method comprises the steps: S1, obtaining an input natural language query statement, and recognizing the user intention of the natural language query statement through a TextCNN text classification model; s2, on the basis of the user intention, matching similar indexes of the natural language query statement through a text sliding block similarity algorithm; s3, querying associated table fields from a preset data knowledge vector library on the basis of the similar indexes through a GraphRAG technology, and filtering each table field; s4, through a preset cue word template, generating a query cue word based on the natural language query statement and the filtered table field; and S5, inputting the query prompt word into a large model to obtain an SQL statement corresponding to the natural language query statement. The method has the advantage that the SQL generation accuracy and efficiency are greatly improved.

The invention provides an intelligent question and answer method and device based on large model multi-source information fusion and electronic equipment, relates to the technical field of computers, in particular to the fields of big data, intelligent search and the like, and can be used for application scenes such as intelligent question and answer. According to the specific implementation scheme, pre-retrieval is conducted from a data table structure information base and a data table field information base according to a user problem, and a pre-retrieval result is obtained; retrieving from a business knowledge base, a business rule base and a question number template base according to the user question to obtain a knowledge enhancement result; according to the pre-retrieval result and the knowledge enhancement result, utilizing a query instruction generation model to obtain a query statement; and querying from the data table set according to the query instruction to generate an answer corresponding to the query word. The scheme can improve the professionality of intelligent questioning and answering, and is suitable for application scenes of various industries.

The invention discloses a structured query statement generation method and system, and the method comprises the steps: obtaining a keyword of a user question, and obtaining a target keyword through calculating the similarity between the keyword and database data; based on the target keyword, rewriting and reconstructing the user question, performing retrieval in a database based on the rewritten and reconstructed user question, and constructing a minimum link table and a most relevant example according to a retrieval result; component identification is carried out on the user problem based on the large language model, a structured statement about the user problem is obtained and optimized, and the optimized structured statement is obtained; and obtaining a cue word based on the minimum link table, the most relevant example and the structured statement, inputting the cue word into the large language model to obtain a pre-generated structured query statement, and executing the pre-generated structured query statement in the database to obtain an answer to the user question. According to the method, the accuracy of generating the structured query statement can be improved, and the adaptability and reliability of processing the natural language query are enhanced.

The invention discloses a natural language database query method and system based on large-model two-channel context enhancement, and the method comprises the steps: carrying out the parallel execution of a business link and a mode link on a query statement, and respectively outputting business context information and mode link information; carrying out vectorization fusion on the two types of information to generate combined context information; generating a query intention vector based on the combined context information, and outputting a complexity label of the query statement through a complexity calculation model; and generating a prompt template according to complexity label differentiation, inputting a large model to generate an SQL statement, and executing query. Through a double-channel cooperation mechanism, the problems of semantic ambiguity, database structure complexity, poor field adaptability and the like in natural language query are solved, query accuracy and response efficiency are improved, and the method is suitable for multi-industry complex database scenes.

The invention discloses a product data question and answer method based on a large model and mixed retrieval and related equipment. The method comprises the following steps: constructing a double-track knowledge base based on multi-source heterogeneous product data; performing multi-dimensional analysis on the current query statement in combination with the large model to obtain a multi-dimensional analysis result; the multi-dimensional analysis result comprises a query vector, a structured query instruction and a keyword; inputting the query vector into a vector database for retrieval to obtain a vector retrieval result; inputting the structured query instruction into a graph database for retrieval to obtain an entity relationship retrieval result; inputting the keyword into the text knowledge fragment for keyword matching to obtain a keyword retrieval result; and combining the vector retrieval result, the entity relationship retrieval result and the keyword retrieval result, and sorting and screening the combined mixed retrieval result through a large language model to obtain target answer data. The method can significantly improve the accuracy and reliability of the retrieval result, and can be widely applied to the technical field of artificial intelligence.

The invention provides a well drilling and completion report self-adaptive SQL generation and intelligent question and answer data processing method. The method comprises the steps that S1, natural language query input of a user is received; s2, inputting user query input into a 'Zhongqi-7b-chat-1m' model, understanding a query intention, and outputting a most matched database table name; s3, accessing database metadata according to a table name output by the model, and obtaining structure information corresponding to the table; s4, inputting the natural language query and the obtained table structure information into a 'Zhongs-Code-32B-Instruct' model together, and generating an SQL (Structured Query Language) query statement conforming to semantic logic by the 'Zhongs-Code-32B-Instruct' model; and S5, executing the generated SQL query statement, and returning a structured query result. According to the method, the advanced natural language processing technology and the deep learning model are combined, so that automatic conversion from natural language query to standardized SQL is realized. According to the method, the query efficiency and accuracy are greatly improved, and the method is particularly suitable for non-technical personnel to perform complex drilling data query.

The invention provides a big language model knowledge graph question answering method and system combined with semantic correction, and the method comprises the steps: generating a logic form according to an input question through a fine-tuned open source big language model, and executing a query statement corresponding to the logic form to obtain an answer; if the answer cannot be obtained by the query statement corresponding to the execution logic form, extracting a main line entity and a relationship from the non-executable logic form, and performing semantic similarity comparison on the extracted relationship and a relationship in the knowledge graph through an unsupervised dense searcher to generate a candidate relationship set; based on the main line entity and the candidate relation set, iteratively retrieving triples in a knowledge graph to construct a reasoning path; and screening the constructed reasoning path by using a closed source large language model, or selecting a tail entity in the multi-answer path, and outputting a final answer. According to the method, the non-executable logic form is corrected, the semantic analysis effect based on the large language model is optimized, and efficient knowledge graph question answering is achieved.

The invention provides a query statement generation method and device, electronic equipment and a storage medium, and relates to the field of artificial intelligence, in particular to the field of computers, the field of databases and the field of deep learning. The query statement generation method comprises the steps of performing intention recognition on a demand text based on business data to obtain a demand intention; in response to the demand intention meeting a preset demand condition, processing the demand intention by using a language model to obtain an execution logic structure, the execution logic structure comprising a plurality of execution logic elements based on structured representation, and a structured relationship among the plurality of execution logic elements representing an execution process of executing the query statement; determining target mode information matched with the text semantics represented by the execution logic element from preset database mode information; performing semantic understanding on the plurality of execution logic elements and the target mode information by utilizing a language model, and outputting a plurality of pieces of intermediate structured data for describing the query statement; and generating a query statement according to the intermediate structured data.

The invention relates to a query method based on multi-agent collaboration, and the method comprises the steps: generating a query structured representation after obtaining a natural language query text input by a user, and obtaining an associated database structure representation in combination with a constellation data model of a target database; and inputting the query structured representation and the database structure representation into a preset query statement generation agent to generate a plurality of first candidate query statements. And checking and correcting the first candidate query statement, and carrying out iterative checking and correcting on the corrected query statement which does not meet the condition. And obtaining a second candidate query statement with higher reliability through an iterative correction mechanism. And then, carrying out confidence evaluation on each second candidate query statement, selecting the second candidate query statement with the highest confidence as a target query statement, and executing query in the target database. According to the embodiment of the invention, the accuracy, reliability and practicability of natural language query are improved, and more convenient, efficient and accurate query experience is provided for users.