3094 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 relates to an AI Agent agent based on a big language model collaborative knowledge graph, which comprises a natural language dialogue module, a big language model module, an RPA SOP process knowledge graph module and an RPA execution engine, and is characterized in that the natural language dialogue module receives a natural language and converts the natural language into a structured query statement; the big language model module interacts with the natural language dialogue module and the RPA SOP process knowledge graph module respectively, and the big language model module understands user requirements, retrieves a knowledge graph according to query statements and generates an RPA process; the RPA SOP process knowledge graph module stores standard SOP knowledge of vertical business of a specific industry, and provides professional knowledge and standard constraints for knowledge query and reasoning; the RPA execution engine executes the generated RPA process; the invention further comprises an agent method and a storage medium. The RPA process knowledge graph is utilized to enhance the knowledge and reasoning ability of the large language model, RPA process steps meeting requirements are generated, and the RPA process generation accuracy and efficiency 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 discloses an enterprise knowledge base query method based on a large language model, and relates to the technical field of information retrieval, and the method comprises the following steps: converting a user query statement into vector representation, and carrying out semantic understanding and intention analysis based on the large language model to generate a query vector; refining the query vector by using the multi-round dialogue capability of the large language model to obtain a comprehensive query vector; the user identity is obtained, and the user access permission is controlled in combination with the semantic understanding and intention analysis results of the large language model; performing parallel retrieval according to the comprehensive query vector in combination with the user access permission to generate a final query result; personalized recommendation is carried out on the user, the query effect is judged according to the user feedback score and the personalized recommendation accuracy, and the model is optimized according to the query effect. The problems that in the prior art, flexibility is insufficient, efficiency is low, and natural language query is difficult to process are solved; and the query accuracy and efficiency are improved by utilizing a large language model.

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.

Disclosed are a data query statement generation method, apparatus and device, and a storage medium. The method comprises: acquiring a process presentation requirement and a natural statement to be converted; on the basis of the process presentation requirement, constructing a prompt instruction; and on the basis of the prompt instruction, inputting said natural statement into a pre-trained data query statement generation model, and on the basis of a model output result, determining and outputting an intermediate generation result and a target data query statement which correspond to the process presentation requirement, wherein the pre-trained data query statement generation model is a generative large language model that is trained on the basis of a pre-constructed chain-of-thought, and the pre-constructed chain-of-thought is a chain-of-thought that is constructed after logic segmentation is performed on a logic plan corresponding to a data query statement.

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 natural language processing, and provides an LLM-based complex language question SQL query generation method and system, and the method comprises the steps: responding to a target language question request; splitting a requested target language question sentence into one or more core clauses through a language model LLM, converting the core clauses into semantic vectors, calculating a matching score of each core clause and a question normal vector of each table in the schema knowledge document, performing sorting and screening according to the score size and a score threshold, and returning a recall result of the core clauses; combining and sorting the recall results of all the core clauses, and screening to generate a recall result of the target language question; and generating a Prompt through a Prompt script, and calling an LLM (Language Model) to automatically generate an SQL (Structured Query Language) query statement. The method does not depend on field fine tuning, does not need to reconstruct a wide table, and effectively solves the problem that a target table is difficult to position when a large language model processes coded value mapping, multiple tables and complex business logic by constructing a virtual table and splitting a question sentence into clauses containing complete semantics and matching the clauses.

The invention discloses a knowledge graph analysis and question answering method and device based on a large language model. The method comprises the steps of obtaining original data, performing data processing based on a large language model to obtain target data, constructing a knowledge graph according to the target data, and storing the knowledge graph into a graph database; acquiring an open source data set, and processing the open source data set by using the large language model to obtain a training data set; according to the graph database, constructing a dictionary which is used for reflecting a graph structure of the graph database; utilizing the training data set, the dictionary and a preset prompt instruction to finely adjust the large language model to obtain an optimized large model; generating a query statement by utilizing the graph database and the optimization large model according to a query problem of a user, and determining a query result in the graph database according to the query statement; and based on the optimization large model, carrying out integration processing on the query question and the query result to obtain a final answer. According to the method, the construction process of the knowledge graph is simplified, and the accuracy of analysis and question answering is improved.

The invention provides a document knowledge questioning and answering method, a document knowledge storage method and a document knowledge questioning and answering device, and relates to the technical field of intelligent questioning and answering. All layers of nodes of a knowledge tree are traversed according to a target retrieval mode on the basis of the query statement to obtain a query result, the knowledge tree comprises multiple layers of nodes, the multiple layers of nodes at least comprise first-layer nodes, second-layer nodes and third-layer nodes, semantics of the first-layer nodes are semantics of text contents of knowledge point levels in the document, and semantics of the third-layer nodes are semantics of text contents of knowledge point levels in the document; the semantics of the second-layer nodes are the semantics of the paragraph-level text content in the document, and the semantics of the third-layer nodes are the semantics of the full-text-level text content in the document; and outputting an answer text corresponding to the query statement based on the query result. The method provided by the invention not only improves the recall rate of knowledge and the correlation of answers, but also enhances the overall experience of the user, so that the user can obtain more comprehensive and accurate answers in complex queries.

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.

A text-to-structured query language conversion method based on a large language model comprises the steps that database outline information is cut, and tables and columns needed by query of a non-target structured query language SQL are eliminated through correlation scoring and Steiner tree problem solving; screening context samples, predicting a similarity score from a text to a structured query language Text-to-SQL by using a cross encoder language model, and selecting a sample most related to a query problem; and injecting the cut database table and column information and the screened context sample into a cue word template, and generating an SQL query statement by using a large language model. The method can be used for optimizing the automatic conversion process from the natural language to the SQL and improving the conversion accuracy, and is used for providing a quick and convenient database retrieval mechanism for a non-SQL user and providing applications such as a data access mode in a wider form for mechanisms such as big language model retrieval enhancement generation RAG and the like.

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 provides a hybrid retrieval method, device and equipment based on a knowledge graph and a vector and a storage medium, and the method comprises the steps: firstly obtaining a query statement of a user, converting the query statement into a Cypher query, sending the Cypher query to a Neo4j engine, and receiving a first query result of the knowledge graph; then, converting the query statement into a query vector, and matching a second query result from a vector database according to the query vector; then, a pre-constructed AC automaton is called to automatically match the query statement to generate a third query result, and the AC automaton is constructed based on a knowledge graph; and finally, calling a recursive ranking fusion model, fusing the first query result, the second query result and the third query result based on a preset weight, and outputting a retrieval result, thereby solving the problem that the retrieval capability of the existing knowledge graph and large language model is limited.

The invention provides a query statement conversion method and device based on a natural language and a storage medium. The method comprises the steps that a natural language query statement input by a user is received, a pre-trained large language model is used for decomposing the natural language query statement into a plurality of subtasks, and each subtask corresponds to one query operation; analyzing each subtask, and performing step-by-step reasoning based on a mode knowledge base and a large language model in a database to generate an intermediate representation corresponding to each subtask; and summarizing the intermediate representation corresponding to each subtask, and converting the summarized intermediate representation into a complete target query statement by utilizing a large language model. According to the method, the accuracy of semantic understanding can be improved, the reliable target query statement is generated, the conversion efficiency is improved, and system debugging and optimization are facilitated.

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.

The invention discloses a multi-modal enhanced retrieval method and system, and the method comprises the steps: obtaining heterogeneous data, and executing data processing to obtain a meta-information set; encoding the meta-information set into a high-dimensional meta-information vector, and constructing a vector index; receiving a query statement of a user, calculating the similarity between the query statement and the meta-information vector, and obtaining a meta-information query result; and calling a large language model based on the obtained meta-information to generate a summary result, and merging and outputting the summary result and heterogeneous data corresponding to the meta-information. According to the method, multi-modal data is processed into unified semantic vector representation, efficient vectorization indexing and semantic generation and expansion capacity of a large-scale language model are combined, high-value information can be rapidly extracted from multi-source heterogeneous data, and real-time, cross-modal and high-precision retrieval requirements are supported. A reordering and traceability mechanism introduced in the scheme further enhances the correlation and reliability of the information, and provides comprehensive and accurate knowledge support for the generation of query results.

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.