2484 results about "Query statement" patented technology

The invention discloses a traffic engineering large model intelligent question answering system and method based on a knowledge graph, and the method comprises the steps: extracting a structured degree feature, a semantic ambiguity feature and a context association feature through receiving and analyzing a natural language query statement inputted by a user, generating a retrieval intention vector, and carrying out the retrieval of the retrieval intention vector; and dynamically selecting a retrieval path according to the intention classification model. And according to the retrieval path, constructing a structured query statement or a semantic vector, and respectively retrieving in the knowledge graph and the vector database to obtain a first retrieval result and a second retrieval result. Further performing bidirectional verification through entity consistency, semantic similarity and relation connectivity indexes, screening a candidate result set, and constructing a reasoning chain; if the inference chain is broken, a large model inference gap complementation mechanism is adopted to generate relay nodes, a complete inference chain is formed, and inference type answer output is generated based on the complete chain. According to the method, the retrieval accuracy and reasoning continuity of the question-answering system are improved.

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 relates to a brilliance large-scale model culture knowledge generation and retrieval method combined with a knowledge graph. The method comprises the steps that entity extraction, relation modeling and dynamic completion processing are carried out according to multi-modal culture data, a culture knowledge graph is constructed in combination with a fuzzy time reasoning algorithm and a cross-modal alignment mechanism, and the multi-modal culture data comprises an unstructured culture text, image data and space-time metadata; the space-time metadata comprises text space-time metadata and image moment metadata; performing semantic alignment processing by combining a large model through a user query statement and a culture knowledge graph to obtain joint representation of cross-modal semantic alignment; and performing multi-modal retrieval-generation joint optimization based on the joint representation to obtain cultural knowledge description and associated multi-modal content. According to the method, through multi-modal data fusion, dynamic knowledge completion and fuzzy time reasoning, culture knowledge description can be accurately generated, associated multi-modal content can be retrieved, and the accuracy and the intelligent level of culture knowledge service are improved.

The invention discloses an intention classification method and device based on vector retrieval and context awareness and a medium, and relates to the technical field of artificial intelligence. The method comprises the steps of extracting business metadata and associating the business metadata with typical problem examples to generate a standardized service description document; encoding the standardized service description document into a high-dimensional semantic vector through a pre-training language model, and constructing a neighbor search index to store the high-dimensional semantic vector; splicing the user identity information and the current question text into an enhanced query statement, and encoding the enhanced query statement into a context-aware dynamic query vector through a semantic model; performing similarity retrieval based on the dynamic query vector to obtain candidate intelligent services, performing business domain filtering, context weighted sorting and dynamic priority rearrangement, and outputting target recommendation services; by collecting interactive behavior data of a target recommendation service, quality scoring is performed on service descriptions and problem examples based on a preset evaluation rule, and the service descriptions and the problem examples of which the quality scores are lower than a quality threshold value are updated.

The embodiment of the invention discloses an interactive intelligent analysis method and device based on a knowledge graph and a medium, and relates to the technical field of data processing.The method comprises the steps that a natural language query text input by a user is obtained, intention detection is conducted on the natural language query text, the corresponding intention fuzzy confidence degree is determined, and the knowledge graph is obtained according to the intention fuzzy confidence degree; matching a target query channel; when the target query channel is a guide query channel, performing path derivation on the natural language query text through a business knowledge graph constructed by pre-learning to generate a personalized analysis card sequence so as to obtain a target analysis dimension in the personalized analysis card sequence triggered by a user; and under the triggering of the target analysis dimension, switching the guide query channel into a precise query channel, and through the precise query channel, calling a pre-learned query mode knowledge base to generate a target query statement corresponding to the target analysis dimension, so as to determine a corresponding visual analysis result through the target query statement.

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 discloses an RAG enhanced Text-to-SQL (Structured Query Language) query method and system for a large-scale database environment. The method comprises the following steps: constructing a vector database; based on the original query of the user, generating query enhancement description aiming at Schema recall and query enhancement description aiming at SQL (Structured Query Language) by utilizing LLM (Logistics Language Model); carrying out Schema recall and historical question and answer pair recall operations in a double-way parallel manner by utilizing an RAG technology and relying on the constructed vector database; based on query enhancement description, recalled Schema and historical question and answer pairs, generating an SQL query statement by using LLM in combination with RAG, and realizing Text-to-SQL conversion; the generated SQL query statement is subjected to post-processing optimization through a database interface, the post-processing optimization comprises grammar verification, performance optimization and error correction, the SQL query statement is combined for execution and feedback, efficient, accurate and reliable natural language query is achieved, and continuous optimization can be conducted through user feedback.

The invention discloses a structured query language generation method and device based on a large model, equipment and a medium, and relates to the technical field of artificial intelligence. The method comprises the steps of performing unified index definition and dimension definition for different business scenes, and combining indexes and dimensions in the business scenes according to the index definition and the dimension definition to obtain different structured query language templates; obtaining a natural language query statement input by a user in the current service scene, and performing semantic analysis on the natural language query statement by using a preset large model to obtain a semantic analysis result; and determining a target structured query language template matched with the natural language query statement from the structured query language templates according to the semantic analysis result, and filling the target structured query language template to generate a structured query language statement result. According to the technical scheme, the natural language query of the user can be efficiently and accurately converted into the structured query language statement.

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 provides a cache-based data query analysis result optimization method and system, and relates to the technical field of data query analysis result optimizing.The cache-based data query analysis result optimization method comprises the steps that user query statements are understood through a semantic understanding model and an intention recognition model, query features are constructed and matched with a preset template, and query rules are determined; based on data distribution prediction and index optimization technologies, an optimal execution path is determined, a database executor is scheduled to obtain a query result, the result is cached, anomaly detection and resource scheduling optimization are performed according to performance data, and a preloading strategy is generated to improve query efficiency.

The invention belongs to the field of intelligent question-answering systems, and relates to a financial intelligent question-answering method and system based on mixed retrieval and dynamic query, and the method comprises the steps: constructing an FAQ library and a financial office database; receiving a user question, calling a historical chat record of the user, and calling the large language model to perform intention recognition; calling a large language model to perform semantic rewriting in combination with the real intention of the user to generate a standardized query statement; obtaining a candidate list from the FAQ library by adopting a mixed retrieval mode; if no candidate questions and answers exist in the candidate list, replying the standardized answers; otherwise, screening candidate questions and answers of Top-N, judging whether the candidate questions and answers of Top-1 are personalized questions or not, if yes, calling a financial office database to generate a query result, and replying according to the query result and the candidate questions and answers of Top-N; according to the method, semantic retrieval and keyword retrieval are mixed through the user intention classification result and the conflict detection rule, the candidate list is obtained according to the mixed retrieval mode, and the retrieval recall rate is increased.

The invention relates to the technical field of large models, and discloses a knowledge graph and retrieval enhancement-based government affair question-answering method and system, and the method comprises the steps: carrying out the information extraction of each government affair document, and obtaining text information, multi-modal information and metadata; fusing the information to obtain document information; performing entity relationship extraction based on the document information to obtain entity, relationship and text key value pairs; constructing a government affair knowledge graph based on the entity, the relationship and the text key value pair corresponding to each government affair document; based on the type of the user query statement and the government affair knowledge graph, adopting a retrieval enhancement technology to generate a query answer; and optimizing the query answer to generate a target answer. According to the method, through the multi-modal information and the knowledge graph, the document content and the query intention are comprehensively understood, the question and answer accuracy is improved, the knowledge graph and a retrieval enhancement technology are fused, the illusion phenomenon of a large language model is reduced, the answer accuracy is improved, the safety, accuracy and readability of the answer are ensured through optimization, and the user experience is improved.

The invention relates to the technical field of artificial intelligence, can be applied to business scenes such as financial science and technology and medical health, and discloses an intelligent optimization method, device and equipment for query statements and a medium. The method comprises the steps of recording execution plan information and resource consumption information of an original query statement, generating original execution information, preprocessing the original query statement and the original execution information to generate preprocessing data, extracting feature information from the preprocessing data, generating optimization information through a pre-trained optimization model based on the feature information, and sending the optimization information to a server. And according to the optimization information, adjusting the content or execution mode of the original query statement, and generating an optimization query instruction. According to the method, the feature information is extracted by monitoring the actual query behavior and combining the execution performance, and the optimization strategy is generated by utilizing the training model, so that the automatic optimization of the original query statement is realized, the manual intervention cost is reduced, and the intelligent adaptation capability of the query performance is improved.

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.

The invention discloses an intelligent question answering method and device based on a large model, a storage medium and a server, and relates to the field of information retrieval. The method comprises the steps of receiving a to-be-answered question input by a user; according to formats indicated by a plurality of preset retrieval algorithms, converting the question to be answered into query statements respectively corresponding to the plurality of retrieval algorithms; according to the generated multiple query statements, retrieval is performed in a knowledge base, and candidate document sets corresponding to the multiple query statements are obtained; fusing the candidate document sets according to a preset fusion rule to obtain a fused document set; extracting a target answer from the fusion document set according to a pre-trained generation large model; according to the method and the device, the candidate document sets generated by fusing different algorithms are balanced between accuracy and diversity, so that the answer is enabled to be related to the question, and rich information is provided.

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 an automatic NL2SQL data set construction method and system based on a large language model. The method comprises the steps that service-oriented SQL query data collection is carried out; the method comprises the following steps of: collecting SQL query statements related to business through a channel, and cleaning and standardizing an obtained SQL sample by adopting a data preprocessing technology; generating NL2SQL data based on a large language model; a mapping pair between the natural language and the SQL is generated by using a large language model, corresponding natural language description is generated through reverse derivation according to a specific business problem and a table connection mode, and the conversion process from the SQL to the natural language is realized; optimizing the quality of the NL2SQL data set; and dynamically updating a data set for private deployment. According to the method, an automatic SQL sample generation mechanism and a multi-dimensional quality control process are introduced, so that manual intervention is remarkably reduced, and the diversity and accuracy of a data set are improved.

The invention relates to a method for generating steel field TEXT2SQL based on LLM enhanced by RAG, which comprises the following steps: firstly, establishing connection with a steel database, and defining user semantic information and a database mode; then, obtaining user input and carrying out natural language processing; thirdly, guiding a large language model to generate a more accurate SQL query by utilizing the generated group of guiding words prompt; then, natural language query is converted into SQL query in combination with a large language model and an RAG technology; when a new user query is received, searching a historical query most similar to the new user query and a corresponding SQL query from the vector database; and finally, performing grammar correctness and logic rationality verification on the generated SQL statement, and outputting the SQL statement. According to the method, the problem that table names and field names are difficult to obtain when a large language model generates accurate SQL statements according to user description is solved. According to the method, the high-quality SQL query statement can be effectively generated, and the accuracy and the use efficiency of steel database query are improved.

The invention discloses an intelligent traffic data analysis and decision generation system based on multi-model dynamic collaboration, and the system comprises a multi-mode collection module which collects voice, text, video and structured data and loads a traffic knowledge graph; the feature processing module is used for extracting semantic, time sequence and spatial features and generating a fusion vector; the semantic modeling module is used for generating an intention vector in combination with the knowledge graph and the interaction record; the dynamic routing module selects an edge or cloud model according to the intention vector to generate a query statement; the Prompt memory module fuses the historical template and the alignment parameters to generate a query draft; the query verification module is used for executing semantic and structure verification and outputting a correction statement; the query execution module generates a response vector; the causal interpretation module generates an interpretation vector; and the response generation module outputs a multi-granularity result according to the user role. According to the method, the cooperative processing capability and semantic analysis precision of complex traffic query are improved.

The invention provides a Cypher-stack type alignment generation method based on a large language model, and belongs to the field of knowledge graph questions and answers. The method comprises the steps that user query and knowledge graph mode information are input into the large language model to generate an initial Cypher query statement; based on a thinking chain prompt technology, retrieving from the knowledge graph to obtain a retrieval result related to user query, and correcting the initial Cypher query statement by the large language model based on the retrieval result to obtain a corrected Cypher query statement; and executing the corrected Cypher query statement. The invention further provides a Cypher-stack type alignment generation device based on the large language model. Through the semantic analysis capability of a large language model and a thinking chain guiding technology, a Cypher query statement is automatically generated and corrected in two stages, and the problem that the query intention of a user and knowledge graph data are difficult to accurately align when the Cypher query is generated due to the diversity of query requirements of the user and a semantic gap between the knowledge graphs is solved.

The invention discloses a method and a device for adaptively generating a structural query statement by using a natural language. The method comprises the following steps: acquiring a natural language query statement input by a user; vectorizing the natural language query statement to obtain a first query statement and a data table and an SQL statement corresponding to the first query statement; rewriting the natural language query statement to obtain a second query statement; recalling a candidate data table and a candidate table field based on the second query statement and a preset to-be-queried data table; the first input data and the first sample data are input into the preset large language model, the query statement and the reasoning chain are structured, the natural language query statement input by the user is corrected, the difference between the natural language query statement and the database table structure is reduced, and the accuracy of data table recall is improved; by recalling the candidate data table, the reasoning difficulty is reduced, so that the accuracy of generating the structured query statement according to the natural language query statement is improved.

The invention provides a method and a device for converting a natural language statement into an SQL (Structured Query Language) and a storage medium, relates to the technical field of data processing, and is used for improving the accuracy of converting the natural language statement into the SQL statement. The method comprises the steps that semantic analysis is conducted on a data query text, a target entity and at least one query field are obtained through extraction, and the target entity indicates a query intention corresponding to the data query text; determining a comprehensive similarity between each query field and each first candidate node according to a semantic similarity and a structural similarity between each query field in the at least one query field and each first candidate node in a pre-stored knowledge graph; determining a target node corresponding to each query field according to the comprehensive similarity between each query field and each first candidate node; according to the information, the data query text can be converted into the structured query statement.

Disclosed herein are systems and methods for improving the auto-generation of pipelined search query statements by a large language model (LLM). In some examples, such a method includes operations of receiving a user-provided prompt, wherein the user-provided prompt is provided in natural language, identifying an objective of the user-provided prompt, and based on the objective, providing the user-provided prompt to a first operational pipeline of a plurality of operational pipelines, wherein each operational pipeline is associated with a unique prompt template. Additionally, the method may include generating, by the first pipeline, an auto-generated prompt based on a first unique prompt template of the first pipeline, providing the auto-generated prompt to a large language model (LLM), and receiving a response to the auto-generated prompt from the LLM. A graphical user interface (GUI) may then be generated that displays the response to the auto-generated prompt.

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 provides an intelligent AI question answering method based on domain knowledge graph and table paraphrasing enhancement generation, which comprises the following steps: constructing a domain knowledge graph, and performing paraphrasing enhancement on a domain database table to obtain a table paraphrasing enhancement knowledge base; receiving a user question, and analyzing a keyword of the user question through a natural language processing technology; performing intention understanding analysis on the keyword based on a domain knowledge graph to obtain a knowledge graph retrieval result, the knowledge graph retrieval result at least comprising N pieces of similar path information matched with a user question in the knowledge graph; searching M reference tables in a table paraphrasing enhancement knowledge base based on the N pieces of similar path information; and generating an SQL query statement according to the N pieces of similar path information and the M reference tables by utilizing a language large model, and querying in a database based on the SQL query statement to obtain an answer to the user question.

The invention discloses a query statement conversion method and device based on a large model, equipment and a medium, and relates to the field of computer science, and the method comprises the steps: obtaining a natural language query statement sent by a user side, comparing the natural language query statement with a high-frequency problem in a target knowledge base, and obtaining a query statement conversion result; whether a high-frequency problem with the similarity with the natural language query statement exceeding a preset similarity threshold value exists in the target knowledge base or not is judged; if the high-frequency problem that the similarity with the natural language query statement exceeds a preset similarity threshold does not exist, determining a target database table corresponding to the natural language query statement based on the target knowledge graph, and extracting a creation statement corresponding to the target database table, so as to determine target information corresponding to the target database table according to the creation statement; and analyzing the target information by using a preset large model to generate an SQL statement corresponding to the natural language query statement. According to the method and the device, the natural language query statement can be efficiently and accurately converted into the structured SQL statement.

The invention relates to the technical field of artificial intelligence large language models, and discloses a data attribution analysis task processing method, system and device based on a large language model and a storage medium. The data attribution analysis task processing method is applied to data attribution equipment and specifically comprises the following steps that S101, user input is received through a multi-mode input interface, and the user input comprises natural language problems, structured data files or API data streams; by means of the natural language understanding ability of the large language model, the system can directly analyze service problems put forward by a user in a daily term, professional data query languages are not needed, non-technical personnel can conveniently use the system, the data retrieval time is shortened to be within 3 minutes from 30 minutes on average through the automatic SQL query generation technology, the efficiency is improved by 10 times, and the method is suitable for large-scale popularization and application. The system can intelligently identify the database fields corresponding to the business indexes and generate optimized query statements.

The invention relates to the technical field of financial large models, and provides an RAG method and system for compliance analysis of a multi-modal financial document, and the method comprises the steps: carrying out the preprocessing of an input multi-modal financial document, generating a corresponding text block through the multi-modal data in the multi-modal financial document, and constructing a corresponding vector database; responding to the target query request; according to the target query statement, entering a multi-path retrieval process to generate a binding packet for responding to the sub-query statements; based on the binding packages generated by the multi-path retrieval module, the semantic alignment degree of each binding package and a sub-query statement is calculated, a corresponding time reward score is calculated in combination with a time reward mechanism to obtain a matching score, ranking optimization is conducted on the matching score through a direct preference optimization mechanism, and a preference text block set is obtained to respond and generate a corresponding answer; by integrating the preprocessing module, the retrieval module and the reordering module of the financial document, the defects of the traditional method in the aspects of processing heterogeneous financial data and improving the retrieval recall rate and correlation are effectively overcome.

The invention discloses an intelligent access API generation method, system and device and a medium, and the method specifically comprises the steps: generating a dynamic dragging interface based on user role information, and obtaining function access parameters customized by a user through the dynamic dragging interface; according to semantic features and operation sequences in historical user interaction data, user intentions are analyzed, and target data topic logic is generated in combination with function access parameters; converting the subject logic of the target data into an executable query statement by utilizing an SQL splicing tool, and recommending an index optimization scheme to a database engine based on a high-frequency condition and field combination in a historical query mode; and distributing the optimized executable query statements to Serverless workflow nodes adapted to different query engines for execution, and packaging returned data into a standardized API (Application Program Interface) for output. According to the method and the device, full-process automation from parameter customization by a user to standardized API output is realized, and the efficiency and the flexibility of data acquisition are remarkably improved.

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.