1025 results about "Query statement" patented technology

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).

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 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 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 belongs to the technical field of semantic recognition, and discloses an extraction analysis method and system based on semantic expression understanding. The method comprises the following steps: performing semantic analysis and feature extraction on an original query statement input by a user in an interactive interface to generate a structured semantic unit; performing interaction scene judgment on the structured semantic unit based on a preset multi-dimensional judgment condition and then outputting a scene identifier; calling a corresponding target intention recognition strategy according to the scene identifier; inputting the structured semantic unit into a domain classification model, and outputting a plurality of candidate intentions and corresponding initial confidence; and determining a target user intention from the candidate intentions based on an interaction clarification result of the user and an intention query rule by using the initial confidence and a preset confidence threshold, and generating a standardized intention recognition result. According to the mode, the user semantics can be deeply understood, the recognition strategy is dynamically adjusted according to the context, and accurate, efficient and sustainable extraction analysis is carried out on the user intention through man-machine cooperation and closed-loop feedback.

Disclosed in the present application are a data processing method and apparatus, and a device and a readable storage medium. The method comprises: obtaining M candidate results which are sorted and displayed on a page, wherein the M candidate results are retrieved on the basis of an initial query statement; if an interaction operation meets a re-retrieval condition, acquiring historical interaction information, which comprises N historical query statements and a set of historical interaction results, wherein the N historical query statements comprise the initial query statement, and the set of historical interaction results comprises a candidate result which is included in an interaction record corresponding to an object; and generating context content vectors corresponding to the historical interaction results in the historical interaction information, and generating an updated query statement by means of the context content vectors and the N historical query statements, wherein the updated query statement is used for being displayed in a retrieval prompt box, and the retrieval prompt box is used for guiding the object to perform re-retrieval by means of the updated query statement. By means of the present application, the accuracy and efficiency of content retrieval can be improved.

The invention relates to the technical field of data query, can be applied to business scenes such as financial science and technology and medical health, and discloses a data query statement dynamic generation method and device, equipment and a medium, and the method comprises the steps: receiving query request information, matching business strategies, completing conflict resolution, and generating a target business strategy set; obtaining dynamic context information formed by a database structure, an index, statistical information and a system load; and generating an initial data query statement based on the target business strategy set, the dynamic context information and the query request information, and obtaining an executable data query statement through grammar verification, logic optimization and execution cost analysis. According to the method, service rule analysis, conflict resolution and environment perception are combined, so that the query statement generation process can be adaptively adjusted along with the change of the service rules and the change of the running state of the database, and the accuracy and execution efficiency of query statement generation are improved while the correctness of service logic is kept.

The invention relates to a data query method and system, computer equipment and a readable storage medium. The method is applied to a data query system, the data query system comprises an interaction module, a plurality of query engines, a distributed storage module and an analysis module, and the method comprises the steps that after the interaction module obtains a query condition input by a user and at least one target query engine selected by the user from the plurality of query engines, the target query engine is stored in the distributed storage module; converting the query condition into a target query statement, and sending the target query statement to each target query engine; each target query engine queries from the distributed storage module based on a dynamic pruning strategy according to the target query statement to obtain an initial query result; and the analysis module processes the initial query result queried by each target query engine based on a vectorization acceleration calculation strategy to obtain a target query result. By adopting the method, the query speed can be increased.

The invention relates to the technical field of equipment control, and provides an equipment control method and system based on a natural language and electronic equipment. According to the application, the machine language vector is generated through the intention recognition result of the natural language instruction, and the graph query statement is generated according to the semantic concept after the machine language vector is deconstructed, so that the equipment knowledge graph of the original equipment is queried through the graph query statement to obtain the equipment operation path meeting the semantic concept; the knowledge graph has a mesh topology structure, the equipment knowledge graph is used for replacing a rigid relational database structure, deep requirements in natural language instructions are mined through intention recognition, machine language vectors are obtained, the machine language vectors are deconstructed, and the control of the original equipment is achieved. And generating a graph query statement corresponding to the equipment knowledge graph according to the deconstructed semantic concept, thereby accurately and comprehensively querying the complex context semantic relationship between the nodes, and improving the understanding accuracy of the natural language instruction.

The invention discloses a Text2SQL (Structured Query Language) system and method based on cue words. The system comprises a natural language input module for receiving and preprocessing a natural language query statement; the metadata management module is used for managing database metadata information; the cue word generation module is used for generating cue words containing database modes, user problems and SQL requirements on the basis of the query statements and the metadata; the large language model module is used for generating a preliminary SQL based on the cue word; the SQL grammar verification module is used for verifying grammar and feeding back errors; the manual auditing module is used for auditing and editing the SQL statements passing the verification; and the SQL execution module is used for executing the SQL passing the audit and returning a result. According to the method, the accuracy is improved by combining cue word guidance and a large model, the field adaptability is enhanced by relying on metadata, the data security is ensured by introducing manual auditing, the accuracy and security of SQL generation in a complex query scene are remarkably improved, and the development and maintenance cost of the system is reduced.

The invention provides an intelligent question and answer semantic understanding and enhanced rewriting optimization method based on multiple rounds of dialogues, and relates to the technical field of artificial intelligence semantic understanding, and the method comprises the steps: obtaining a current query statement and a historical dialogue context, carrying out the time sequence analysis of the historical dialogue context, and constructing a dialogue context coding representation; and performing multi-level semantic disassembly on the query statement to identify dominant and implicit intentions, generating a semantic enhancement rewriting statement based on an intention hierarchical structure, and finally performing semantic matching and retrieval to output answers. The understanding accuracy of the real intention of the user in multiple rounds of dialogues can be effectively improved, and the interaction experience and the answering quality of the intelligent question answering system are improved.

The invention relates to the technical field of knowledge graph question answering, and discloses a knowledge graph fused question answering system which comprises a knowledge graph construction module, a natural language understanding module, a knowledge graph query module and an answer output module which are connected in sequence. The knowledge graph construction module performs knowledge extraction on the unstructured text to obtain triple data to construct a knowledge graph; the natural language understanding module converts a question input by a user in a natural language form into a structured query statement; the knowledge spectrogram query module performs retrieval in a knowledge graph according to the structured query statement and returns knowledge sub-graphs related to the query statement; and the answer output module inputs the knowledge sub-graph into an end-to-end generative model based on a copy network mechanism, generates a natural and smooth text answer and presents the text answer to the user. Through cooperative work of all the modules, efficient knowledge extraction, accurate intention understanding, efficient knowledge retrieval and high-quality answer generation are achieved, and the question and answer requirements of users can be met more efficiently and accurately.

The invention discloses an anaphora disambiguation method and system based on big language model enhanced text and structured query language generation. The method comprises the following steps: receiving a natural language question of a user, and analyzing and generating structured table field information through mode information; identifying and rewriting fuzzy time expression in the problem; extracting ambiguous entities, and replacing the ambiguous entities with database standard values through semantic matching and fuzzy matching; synthesizing the information to generate a structured query language statement and executing the structured query language statement; and if the execution result is null, automatically triggering an anaphora disambiguation process, performing error correction and re-matching on field values in the statement through a dual matching mechanism, and generating and executing a corrected query statement. According to the method, the problems of fuzzy anaphora, indefinite time expression, low matching accuracy, resource waste and the like in a traditional SQL system are effectively solved through a strategy of combining pre-processing and post-processing, and the accuracy, robustness and execution efficiency of complex query are remarkably improved.

The invention relates to a GPU-based adaptive compressed data direct query method, system and device and a medium, and the method comprises the steps: carrying out the data compression of input column data through employing an adaptive compression strategy, and obtaining a block-level structure suitable for GPU storage and calculation; loading the compressed data into a GPU memory, and performing Tile-level memory management by taking Tile as a basic scheduling unit; and according to the received query statement, executing direct query of the compressed data on the Tile level through the GPU, and outputting a query result. By designing a Tile-level direct query framework, a hardware-aware memory management and control flow coordination mechanism and a self-adaptive compression strategy, high-performance query execution is realized without decompression, and the computing potential of the GPU is fully released. The method can be widely applied to the technical field of big data processing.

The invention relates to the technical field of natural language processing, and discloses a structured query statement generation method and device.The method comprises the steps that a natural language problem input by a user and related information of a target database are received; performing mode linking processing on the natural language question and the related information of the target database to obtain database structured mode information related to the semantics of the natural language question; based on the natural language problem and the database structured mode information, self-driven exploration operation is executed, and a corresponding exploration result is generated; inputting the database structured mode information and the exploration result into a preset language model to generate an initial structured query statement; and performing verification and iterative correction on the initial structured query statement to generate a final structured query statement. According to the method, the structured query statements which are matched with natural language question semantics and can be effectively executed can be efficiently generated, and the accuracy and effectiveness of generation of the structured query statements are effectively improved.

The invention belongs to the technical field of intelligent traffic system and artificial intelligence crossing, and discloses an intelligent questioning and answering system and method for highway tunnel traffic events. The method comprises the steps of receiving a natural language query input by a user; guiding the context understanding to output a standardized event label in preset five types of events through a prompt project; calling a preset Cypher template, dynamically filling an event name, generating a structured query statement, executing query on a pre-constructed tunnel management and control knowledge graph Neo4j, and obtaining a structured sub-graph comprising a trigger condition, an influence region and response equipment; constructing a structured knowledge dictionary; guiding the large language model to generate a natural language answer through the strong constraint cue word; and if the identification result is a non-five-class event, directly performing answer rejection. According to the method, an interpretable, verifiable and landing technical path is provided for intelligent operation and maintenance of the expressway tunnel, and the method has important engineering application value and popularization prospect.

The invention provides a text-to-structured query statement method based on a large language model and reinforcement learning, and relates to the technical field of information, the method comprises the following steps: integrating a natural language problem and a database mode into a unified prompt template to obtain a candidate structured query, and generating a reference structured query by a reference model; constructing a multi-dimensional reward framework, and performing weighted aggregation on multi-dimensional rewards to obtain corresponding final reward scores; calculating a KL penalty value between the output of the strategy model and the output of the reference model based on the candidate structured query and the reference structured query to obtain a constraint of stable strategy update; updating the strategy model parameters through back propagation iteration to obtain an updated strategy model; and analyzing the prompt template by using the updated strategy model to obtain a text-to-structured query statement processing result, and completing the process from the text to the structured query statement. The problems that existing structured query generation is low in accuracy and semantic consistency is difficult to guarantee are solved.

A human-machine conversation method and apparatus, a device, and a storage medium. The method comprises: acquiring a user statement of a current round; determining, from one or more candidate tables, one or more target tables that match the user statement; parsing the user statement of the current round to obtain a first query statement; according to the first query statement, querying from the one or more target tables to obtain target data; and generating a system reply of the current round according to the target data.

The invention provides a prompt information evaluation method and device, electronic equipment, a medium and a program product, and can be applied to the technical field of artificial intelligence. The method comprises the steps that a pre-constructed query statement test data set is obtained, the query statement test data set at least comprises a plurality of error query statements, and each error query statement is used for triggering a target database to generate error prompt information in the execution process; inputting the error query statement into the target database, obtaining return information of the target database, and generating error prompt information based on the return information; based on the error prompt information, utilizing a large language model to obtain at least one corresponding correction query statement; and verifying the corrected query statement in the target database, and evaluating the quality of the error prompt information based on a verification result.

The invention discloses a graph database query acceleration method and system. The method comprises the following steps: constructing panoramic query observation data through non-intrusive log interception and structured processing; the query statement is analyzed into an abstract syntax tree, semantic normalization processing is carried out, and a unique hash value is generated; predicting a hotspot query template and high-frequency parameters thereof based on a popularity value score model, and asynchronously preloading a result to a cache; dynamically calculating adaptive survival time for each cache item according to the data change frequency and the access popularity; and constructing a multi-level cache architecture comprising a local cache, a distributed cache and a database built-in cache, and performing intelligent routing and collaborative backfilling based on a hash value. According to the method, intelligent and transparent acceleration of graph database query is realized, the query performance is remarkably improved, the database load is reduced, and the problems of redundancy, high cold start delay and difficulty in balancing cache consistency caused by grammar difference in traditional cache are solved.

The invention relates to the technical field of data analysis, and discloses a low-code data analysis platform and method based on multi-source data real-time access, and the platform comprises a data access module, a modeling module, a rendering engine and an early warning module. The method corresponds to the platform. According to the method and the device, the problems of real-time integration and standardization of the multi-source heterogeneous data are solved by obtaining the standardized data entity, and a high-quality data basis is provided for subsequent analysis; a dynamically constructed knowledge graph and a graph embedding technology are deeply fused into a data analysis process, so that semantic association among data entities is intelligently understood, an optimized query statement is automatically generated, intelligent recommendation and rationality verification are provided for establishing a data association relationship for a user, and the technical threshold of data modeling is reduced; a second-level response closed loop from data access to visual presentation is formed based on intelligent chart recommendation of data features and early warning rules and instant calculation and triggering in a real-time data stream.

The invention discloses an intelligent data question and answer method and system based on index semantic enhancement, and relates to the technical field of intelligent data question and answer. The method comprises the following steps: acquiring a data analysis demand in a natural language form; the method comprises the following steps: performing information extraction on a data analysis demand by utilizing an index semantic enhanced large language model, generating a query statement according to the extracted information, recalling and rearranging most relevant indexes and elements in an index semantic knowledge base by utilizing a knowledge retrieval enhancement layer according to the data analysis demand and a historical session on the basis of mixed retrieval, and generating a query statement according to the query statement. Generating a prompt template according to a retrieval result of the index semantic injection layer by utilizing the index semantic injection layer, and performing semantic understanding reasoning according to the prompt template to obtain corresponding information; and checking compliance according to the query statement, and returning a query result. The method aims at achieving efficient mapping, dynamic modeling, intelligent SQL generation and optimization, authority safety management and control and multi-round intelligent interaction of natural languages and enterprise-level index assets.

Implementations of this disclosure provide a computer-implemented method that includes operations of receiving user input being a natural language description of a search query through a graphical user interface (GUI) displayed on a display screen of a network device and generating an executable search query statement from the natural language description including providing the natural language description as input to a trained machine learning model, wherein the trained machine learning model, following processing of the natural language description, provides the executable search query statement as an output. Further operations include obtaining results of execution of the executable search query statement, automatically determining a graphical visualization configured to display the results of the execution of the executable search query statement, and updating the GUI by generating the graphical visualization populated with the results of the execution of the executable search query statement.

The invention discloses a query parameter generation method and device in an AI intelligent question scene. The query parameter generation method comprises the steps of obtaining natural language-based query statement data input by a user; performing semantic understanding and standardized rewriting on the natural language query statement data input by the user to obtain a rewritten query statement; on the basis of the rewritten query statement, recalling and screening related index information from an index knowledge base; based on the rewritten query statement and the index information, analyzing query parameters, and generating a standardized query parameter data structure; and executing data query based on the query parameter data structure and outputting a result. The method has the advantages that by defining a unified query parameter data structure, decoupling with a database is achieved, and universality and portability are improved.

The invention discloses a mixed retrieval method and device fusing keywords and semantic vectors and a medium, and the method comprises the steps that under the condition that a retrieval mode is a mixed retrieval mode, based on a mixed retrieval path corresponding to a query statement sent by a user, the query statement sent by the user is retrieved; respectively calculating semantic embedding similarity and keyword comprehensive similarity between the query statement and the paragraph text to be retrieved in the database; based on the text complexity and the text type requested by the query statement, determining weight coefficients respectively corresponding to the mixed retrieval paths; according to the weight coefficient, performing weighted summation on the semantic embedding similarity and the keyword comprehensive similarity to obtain matching degrees between different paragraph texts and query statements; and feeding back the paragraph texts meeting a preset matching degree threshold value to the user according to a descending order of the matching degrees.

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

The invention discloses a data query method and device, equipment, a storage medium and a product, relates to the technical field of artificial intelligence, and discloses a method for extracting a natural query statement from a data query instruction in response to the data query instruction; obtaining a first model cue word, wherein the first model cue word comprises a grammar rule of a search processing language SPL; converting the natural query statement into a target SPL query statement based on the first model cue word through a large language model; and executing a data query operation based on the target SPL query statement to obtain a data query result. The method can effectively reduce the data query difficulty and improve the data query efficiency.

The invention belongs to the field of cross technologies of natural language processing and database query technologies, and provides a method for generating database query through a natural language. Comprising the steps that table names and field names of all data tables are obtained, a Schema attribute graph used for describing the incidence relation between the tables is constructed based on the table names and the field names, and a structured Schema index database is established; receiving a query request input by a user, and calling an entity recognition model to recognize entity information related to the business from the query request to obtain corresponding candidate table names and candidate field names; determining an associated sub-graph structure, and constructing a local structured Schema index database; constructing a candidate association path set, and determining a target association path according to a preset priority rule; performing grammar and logic verification on the structured query statement to obtain an executable structured query statement; and updating to obtain the target local structured Schema index database. According to the invention, the stability and accuracy of query execution are improved.