1624 results about "Data query" patented technology

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 a verifiable medical data query method based on a DAG block chain architecture, and belongs to the technical field of block chains and medical information management. According to the method, medical events such as diagnosis and treatment records and image reports are modeled into DAG nodes, an on-chain topology with time sequence and causal association is constructed by encrypting a hash pointer, dynamic indexing and multi-keyword verifiable query are achieved in combination with an off-chain authentication data structure, a verification object containing a matching node set and a verification path is received, and the verification object is subjected to dynamic indexing and multi-keyword verifiable query. The authenticity, integrity and timeliness verification of the result is independently completed based on the authentication root stored on the chain without depending on a third party. According to the method, through an on-chain and off-chain collaborative storage and incremental authentication updating mechanism, on the premise of ensuring privacy and tamper-proofing of medical data, query efficiency, verification performance and expandability under high concurrency are remarkably improved, communication and resource expenses are lower, and the method is suitable for large-scale popularization and application. The method is suitable for application scenes such as cross-institution medical data sharing, remote diagnosis and treatment and compliance auditing.

The invention discloses a natural language-to-SQL (Structured Query Language) method and system based on RAG (Random Access Gateway) enhancement, and the method comprises the steps: constructing a query intention graph through dependency syntax analysis, recognizing and complementing semantic missing components, and forming complete semantic representation; vector representation is carried out on the business term segments by adopting vectorization coding, accurate definitions of business terms are obtained from a factory structured knowledge base, and an enhanced context set is formed through expansion retrieval in a low-confidence region; an SQL template mapping network is established based on historical query records, a mapping relation matrix is generated through field candidate matching, mapping conflict positions are identified, and multiple SQL candidate sequences are generated; grammar verification is carried out on the candidate sequence to identify grammar errors, semantic consistency verification is carried out to calculate the intention alignment degree, and an optimal SQL statement is selected through a deviation correction factor; and performing formatting processing and statistical abstract on an execution result, and providing data query capability for scenes such as factory quality management and equipment maintenance.

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

The invention relates to the field of data query, and particularly provides a medical big data query method and system based on dynamic desensitization, a terminal and a medium, and the method comprises the steps: collecting multi-source heterogeneous medical data, carrying out semantic normalization, constructing a unique identifier of a patient, and generating standardized intermediate plaintext data; constructing an adaptive isomorphic index structure based on the data, constructing a semantic hash B + tree index for structured data, constructing a feature vector index for unstructured data, and dynamically optimizing index hierarchy and parameters according to access frequency; and after a query request is received, the permission context is analyzed, intermediate plaintext data is positioned and read by using the index, and a differential privacy protection strategy is dynamically applied according to the access permission. According to the method, the problem of balance between privacy protection and data effectiveness as well as between retrieval efficiency and storage cost in medical data processing is effectively solved, and data availability and access performance are improved while safety compliance is guaranteed.

The invention discloses a water conservancy intelligent question-answering system and method based on knowledge enhancement and data driving, and aims at water conservancy business structured and unstructured data query, through loop optimization driven by positive and negative examples, precise classification of secondary intentions of water conservancy problems is realized, knowledge questions and answers, data query and professional water conservancy subclass questions and answers can be distinguished, and the system and the method can be applied to water conservancy business. And the semantic analysis efficiency is improved. Aiming at the problem of low query accuracy of retrieval enhancement generation in the field of water conservancy, a differential water conservancy knowledge base oriented to professional books, industrial standards and laws and regulations is constructed, local and networking information is processed through a multi-source knowledge fusion and conflict resolution mechanism, and the accuracy, interpretability and traceability of question and answer content are improved. Aiming at the problems of complex operation and low semantic query accuracy in query of massive water conservancy business data and monitoring data, multi-layer constraint Text-to-SQL conversion is performed based on water conservancy business knowledge, high-precision semantic query and automatic visual output of water conservancy structured data are realized, and query efficiency is improved.

The invention discloses a data analysis method and system based on a large model. The method comprises the steps that natural language query of a user is received; performing semantic coding and intention analysis on the natural language query based on a pre-trained large language model; based on metadata retrieval of vector similarity, obtaining metadata related to natural language query semantics from a vector database; generating corresponding structured query parameters through the large language model in combination with the obtained metadata and the natural language query; according to the structured query parameter, generating an executable structured query language statement; and executing the structured query language statement to obtain a data query result. According to the method, the problems of insufficient semantic comprehension ability, low metadata retrieval precision and poor query generation controllability in the prior art are solved.

Disclosed are a data query method and system, and a related device, relating to the field of artificial intelligence. The method comprises the following steps: a data query system acquires input data; obtains, on the basis of the input data, metrics to be queried as requested by a user; uses, on the basis of said metrics, a multi-agent collaborative processing strategy to confirm a data query result, the multi-agent collaborative processing strategy being used for indicating a manner of implementing data retrieval on the basis of at least one of a graph model and a vector database; and presents the data query result, wherein the graph model expresses, in a graph-based form, correspondences between the metrics and between the metrics and database table data, and the vector database expresses, in a vector-based form, descriptive texts and computational formulas of the metrics. In this way, even if the expression of input data differs from the expression of fields in the database, or if metrics to be read are not present in the database, corresponding SQL statements can still be obtained by means of the graph model or the vector database, thereby reducing the error rate of natural language query, and meeting the query requirements of users.

The invention provides a question answering method and device based on a large language model, equipment and a storage medium, and relates to the technical field of artificial intelligence. The method comprises the following steps: acquiring a question text; performing semantic understanding on the question text through a large language model to obtain a structured data query instruction; retrieving in a database based on the data query instruction to obtain target data related to the question text, the data included in the database being obtained by integrating first business data obtained from at least one business system; and obtaining a target answer of the question text through the large language model according to the question text and the target data related to the question text.

The invention provides an intelligent number asking method suitable for a sales data query scene, which belongs to the technical field of data processing and comprises the following steps of: (1) inputting a natural language; (2) dialogue type number asking; (3) accessing a plug-in model; (4) carrying out multi-modal analysis; and (5) carrying out platform service. According to the intelligent data asking system architecture, an industry knowledge graph is used as a cognitive footstone, a large language model is used as an interaction engine, and dynamic data association is used as execution blood vessel three-in-one. The core pain point of'water and soil disability 'in vertical industry application of a general AI model is solved, the method is a key for really changing the AI from'chatting' to'drying ', and unprecedented agility and accuracy are provided for data-driven decision making.

The invention provides a medical data query optimization method and system based on semantic understanding, and relates to the technical field of medical data query, and the method comprises the steps: firstly obtaining a user medical data query request, a historical query record set and a current medical scene description; performing semantic intention scene deconstruction and historical association mapping on the query request to generate a deconstruction result; constructing a semantic path dynamic adaptation model based on the deconstruction result, and generating a model parameter set; calling the initial query path, performing path reconstruction according to the model output adaptation relation, and generating an optimized query path; and executing query operation to obtain an initial result, collecting user feedback for semantic calibration, generating an optimization result, feeding back the optimization result to the user terminal, and associating related information to a historical record set. According to the invention, the accuracy and efficiency of medical data query are 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 provides a large language model-based finance and taxation data intelligent dialogue and query system, and relates to the technical field of finance and taxation data processing and natural language processing.The method comprises the following steps of: firstly, constructing a middle adaptation layer for connecting a large language model and a finance and taxation service database, and converting database information into a form which can be recognized by the large language model; and performing finance and taxation field special training on the large language model, so that the large language model can process finance and taxation problems of a user and generate a query instruction. And executing data query through the middle adaptation layer to obtain an original numerical result. The model is called to analyze the result to generate preliminary interpretation, and a finance and tax rule text retrieval library is combined for correction to form a final result and push the final result to the user. The finance and taxation data query efficiency and accuracy are improved, and comprehensive and professional finance and taxation information is provided for users.

The invention relates to an operation and maintenance alarm root cause positioning method and system fusing a knowledge graph and a large model, and belongs to the technical field of data processing and intelligent operation and maintaining.The method comprises the steps that original alarm data are obtained from a plurality of heterogeneous monitoring sources based on alarm data query service of time period parameters and subjected to cleaning and structural processing, and the original alarm data are obtained; obtaining alarm data to be analyzed; performing unsupervised clustering analysis to realize alarm automatic classification and noise filtering to obtain classified alarms and category labels; extracting corresponding object topological relation information from a pre-constructed knowledge graph; and inputting the classified alarms, category labels and object topological relation information into a large language model, guiding the large language model to perform semantic reasoning through a preset cue word project, and generating a structured diagnosis report containing root cause diagnosis, an influence range and repair suggestions. According to the method, the alarm storm is inhibited, the fault root cause is quickly, accurately and interpretably positioned, and the operation and maintenance efficiency and the system stability are improved.

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 an edge computing network-oriented micro-service and distributed database collaborative deployment system. The system comprises a database copy number dynamic optimization unit which obtains a dynamic optimization strategy of the copy number of a database based on a queuing gradient projection elastic scaling algorithm; a deployment strategy dynamic adjustment unit calculates end-to-end time delay based on the micro-service routing path and the database routing path; calculating a data inconsistency measurement index based on the number of each copy of the database; dynamically adjusting the deployment strategy based on the resource constraint between the end-to-end time delay and the data inconsistency measurement index and the dynamic optimization strategy of the number of copies of the database; the deployment strategy comprises micro-service instance deployment, database copy deployment and optimal routing path selection; according to the MEC scene-oriented distributed database design and fine-grained collaborative deployment optimization method, a message queue, a queuing theory and a Canal database incremental updating mechanism are integrated, and the micro-service application performance and the data query efficiency / reliability are improved.

The invention provides a hierarchical storage method for multi-source data, a storage medium, a program product and electronic equipment, and the method comprises the steps: obtaining the multi-source data of a service, and converting the format of the multi-source data into a column storage format; performing embedded space pre-regularization processing on the multi-source data after format conversion to construct an n-dimensional simplex geometric constraint space of the multi-source data, and according to the n-dimensional simplex geometric constraint space, generating a multi-layer composite label, at least comprising a business semantic label and a cold and hot aging label, of the multi-source data; dividing the multi-source data into hot data, temperature data and cold data according to the cold and hot aging labels; the hot data, the temperature data and the cold data are stored in different storage layers respectively; and obtaining a data query request, and routing the data query request to one of the three storage layers according to the service semantic tag. The problems of semantic confusion, low storage efficiency and low access efficiency of bank data lakes in multi-source heterogeneous data fusion in the prior art are solved.

The invention relates to the technical field of power two-ticket management, and discloses a two-ticket full life cycle optimization and management method and system based on workflow, and the method comprises the steps: obtaining the basic data of personnel qualification, equipment state and flow rule, and constructing a typical work ticket library and a typical operation ticket library; calling a work ticket template or an operation ticket template to perform information supplement, performing double verification, and then completing graded auditing and electronic signature according to preset nodes to generate an effective work ticket and an executable operation ticket; and monitoring the execution process of the effective work ticket and the executable operation ticket, and archiving execution data to a historical database to realize multi-dimensional two-ticket full-process data query, generation of an analysis report, update of basic data, a work ticket typical ticket library and an operation ticket typical ticket library. The standardized typical ticket library is constructed by integrating basic data, monitoring and archiving traceability of two tickets are achieved, standardized intelligent management of the two tickets is achieved, and the problems that existing data are scattered, compliance is poor, traceability is difficult, and no improved closed loop exists are solved.

The invention discloses a power distribution network semantic analysis and intelligent number asking method and system based on NL2DSL, and relates to the technical field of power distribution network data query. The method comprises the following steps: receiving a natural language question of the power distribution network, clarifying fuzzy terms through multiple rounds of asking, and outputting a clarified question; based on the historical dialogue and the terminology library optimization problem, generating a standardized and optimized problem; taking a semantic layer of the power distribution network as a unique true phase source, and converting an optimized problem into an initial DSL (Digital Subscriber Line) by combining RAG and Prompt engineering; a compliance DSL is output through double-layer protection of static rule verification and dynamic service verification; and analyzing the compliant DSL and associating the pre-calculation model to generate an optimized SQL, and outputting a structured result after execution. The method can improve the query accuracy and the data security, reduces the maintenance cost, and is suitable for the full-scene intelligent number asking demand of the power distribution network.

The embodiment of the invention provides a data query method and system. The data query method comprises the steps that a data query request associated with a target field is received; identifying query intention information corresponding to the data query request, and constructing a query prompt word corresponding to the data query request according to the query intention information; the query prompt word is input into a target distillation model to be processed, a data query sequence corresponding to the data query request is obtained, and the data query sequence comprises a data query statement and a data query reasoning chain associated with the data query statement; and executing the data query statement to obtain data query information, and reading target data in a target data table associated with the target field based on the data query information to serve as a response of the data query request.

The invention discloses a data query method and device, a nonvolatile storage medium and electronic equipment. The method comprises the following steps: determining domain information corresponding to a to-be-processed document; determining a preset cleaning rule base corresponding to the field information, and identifying and correcting an error text in the to-be-processed document according to the preset cleaning rule base; segmenting the to-be-processed document after the error text is corrected to obtain a plurality of text blocks, and storing the text blocks and text block vectors corresponding to the text blocks into a database; and after a query instruction sent by a target object is received, determining a target text block corresponding to the query instruction according to the query vector and the text block vector corresponding to the query instruction, and generating a query result according to the target text block. The technical problem that the documents cannot be effectively corrected due to the fact that the same set of universal cleaning rules is adopted for all the documents in the prior art is solved.

The invention provides a data decoupling centralized query method and system based on MQ message triggering, and relates to the technical field of data query, and the method comprises the steps of receiving a message queue data change message, constructing a field, dividing check nodes by depending on a directed acyclic graph, carrying out data consistency verification, and pushing a transaction identifier to carry out concurrent operation scheduling. And collecting hotspot information by adopting a Bloom filter to adjust data fragmentation and cache distribution, generating a distributed query routing table, and distributing query requests according to the routing table. According to the method, the data consistency is improved, and the query performance is optimized.

The invention relates to a data query statement method and device, equipment, a storage medium and a program product, and relates to the technical field of computers. The method comprises the steps of obtaining a target demand description text; searching a historical demand description text similar to the target demand description text from a historical script knowledge base; obtaining a historical data query statement associated with the historical demand description text from a historical script knowledge base; on the basis of the target demand description text and the historical data query statement, constructing prompt words; and generating a target data query statement based on the cue word through the large language model. By adopting the method, the data query statement generation efficiency can be improved.

The invention discloses a blockchain zero-knowledge proof collaborative credit risk control data privacy protection query method, and belongs to the technical field of financial science and technology and information security. According to the method, a four-layer architecture of a feature importance analysis layer, a zero-knowledge proof verification layer, a block chain intelligent contract layer and an audit log layer is constructed, so that the contradiction between privacy protection and data availability in a traditional credit risk control system is solved. According to the system, an SHAP value is adopted to calculate feature importance, a zk-SNARKs verification circuit is deployed for high-sensitivity features, zk-STARKs lightweight verification is adopted for low-sensitivity features, and differential privacy protection is achieved. And dynamically adjusting the complexity of a verification algorithm according to the credit risk score of the user, and realizing dynamic authority control based on a verification result through an intelligent contract. The verification metadata is recorded to the block chain to form a tamper-resistant auditing log, and it is ensured that the query process is transparent and traceable. According to the method, the real-time risk control requirement is met while the privacy of the user is protected, and a compliant and efficient data query solution is provided for financial institutions.

The invention provides a knowledge question-answering method and device based on a large model and a medium, and relates to the technical field of knowledge questions.The method comprises the steps that a data query period corresponding to a question text is obtained through a first large language model; if the duration of the data query time period is smaller than the preset duration, determining a target operation and maintenance tool from a plurality of preset operation and maintenance tools through a second large language model; controlling the target operation and maintenance tool to perform data acquisition, and taking the acquired data as intermediate data corresponding to the target operation and maintenance tool; performing data compression on the intermediate data based on the character number of the intermediate data to obtain target data; merging the target data into the context information, and obtaining an answer text according to the context information and a specified large language model after merging is completed; according to the method, input overflow, model reasoning failure or response delay caused by too large data volume or exceeding of the context length limit of the large language model can be avoided, the stability of answer generation is ensured, and the user experience is improved.

The invention provides a multi-modal data joint query analysis method and system supporting natural language interaction, and relates to the technical field of data query analysis. Historical operation data, audio data and text data of a user on an intelligent search platform supporting natural language interaction are collected; modeling the historical operation data to obtain a user preference vector, performing voice recognition and text standardization processing on the audio data to obtain standard query data, and integrating the standard query data and the text data into context information; an attention mechanism-based algorithm is used for semantic understanding and intention recognition to obtain an intention feature vector, and the intention feature vector is fused with a user preference vector to obtain a classification result; and finally, based on the result, querying in a preset multi-modal database through a collaborative filtering algorithm to obtain a joint query result, so that personalized accurate query of the multi-modal data under natural language interaction can be realized, and the result fits the intention and long-term preference of the user.

The invention discloses a new energy station operation index multi-dimensional comparative analysis display method and system, and belongs to the field of new energy station operation management and data analysis. The method comprises the following steps: fusing and standardizing multi-source heterogeneous data, collecting and cleaning multi-station data, and classifying and storing the multi-station data as a standardized data pool; rasterized multi-dimensional indexes are calculated, grids are divided, data are mapped, and grid-level and hierarchical multi-dimensional indexes are generated; performing dynamic weight and TOPSIS evaluation, constructing an evaluation model to calculate the score of a single station, extracting a core index intersection, and transversely comparing the ranking of the stations; the evaluation result is subjected to instantiation storage and hierarchical storage to guarantee data query and tracing; and performing multi-dimensional visual display, displaying the data by multiple terminals, and generating a decision report. The system comprises corresponding function modules. According to the method, the problems of data fragmentation, evaluation system deficiency, non-visual display and the like are solved, multi-station data integration, quantitative evaluation and visual decision making are realized, and the operation management efficiency is improved.

The invention discloses an intelligent data calling method based on an MCP protocol, and the method comprises the steps: carrying out the intention recognition and key entity extraction in response to a natural language request of a user, and obtaining a plurality of user intentions and at least one key entity; judging based on the confidence coefficient of the key entity, and screening the user intentions according to the confidence coefficient of the user intentions to obtain an execution sequence containing a plurality of subtasks; and determining at least one target data source tool from a plurality of data source tools packaged based on the MCP protocol according to the description of the subtasks one by one so as to perform data query based on the key entity, obtain a query result and generate a final response. According to the method, the system decision reliability can be improved, the tool calling intelligence is enhanced, the process automation is realized, and the two major problems of low task planning quality and inaccurate tool calling are solved.

The invention relates to a coal mine fire early warning field knowledge question-answering system based on a large language model and a construction method thereof, and belongs to the technical field of coal mine data query and analysis. The method comprises the following steps: collecting environment, sensor and equipment state monitoring data; a fire sensor analog quantity-discrete magnitude hybrid interpolation method is adopted to clean enhanced data, and abnormal values are optimized and processed; converting structured data by a keyword association labeling improvement method based on a coal mine fire term dictionary; constructing a domain knowledge base and matching query questions; constructing question and answer pairs based on the knowledge base and the training corpus, and finely adjusting the large model to generate a scene knowledge model; and constructing a question answering system, and visually realizing gas abnormity identification, alarm pushing and knowledge question answering. According to the method, the intention of the user is accurately understood through matching of the large language model and the knowledge base, knowledge retrieval is achieved through the vectorization technology, resources are optimized, the query efficiency, the response speed and the intelligent level are improved, and reliable technical support is provided for coal mine fire early warning and safety management.

The invention discloses an insurance intelligent question and answer method based on a large model RAG technology. The method comprises the following steps: constructing a vectorization knowledge base; receiving a question of a user, extracting an insurance proprietary entity from the question by combining the big language model with the cue word project, and labeling an entity type; the entity is linked with the standard product full name in the knowledge base, and question sentences are automatically clarified or rewritten based on a confirmation result; performing intention recognition by using a prompt word project in combination with a preset intention type, executing semantic routing according to a recognition result, and guiding a question sentence to a vectorized knowledge retrieval, structured data query or FAQ processing flow; in vectorization knowledge base retrieval, a multi-path recall and reordering method is adopted, and top-K reference knowledge blocks are output; and calling a large language model to generate a natural language answer. According to the method, high-precision, low-cost and extensible intelligent question answering can be realized in an insurance service scene, and the agent query efficiency and question answering experience are remarkably improved.