2171 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 provides an intelligent data query method based on a natural language, and relates to the technical field of intelligent data processing and natural language interaction.The intelligent data query method comprises the steps that firstly, enterprise original data is subjected to standard treatment, and a standardized theme database and a data directory and index definition document are constructed; key semantics are extracted based on unstructured knowledge, and a domain knowledge vector library is fused and constructed by combining document text fragments and vector representation of a mapping relation between historical questions of a user and an SQL (Structured Query Language). And after receiving a natural language question of a user, calling a large language model to identify a task type, and distinguishing knowledge questions and answers, data query and complex analysis. Executing corresponding operations according to different types: directly retrieving a vector library by knowledge questions and answers to generate answers; extracting keywords in data query and generating a query request in combination with context; and in the complex analysis, predefined workflow is judged and executed or intelligent agent processing is called, and query or analysis requirements are output.

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 provides a data query method and system for converting a natural language into a database query language, and the method comprises the steps: analyzing the natural language input of a user through a multi-modal understanding agent on the basis of constructing a dynamic knowledge graph based on metadata, combining a historical session with a business term table, eliminating ambiguity, and generating a standardized Query, a retrieval routing agent selects a query strategy according to Query complexity, simple query directly matches a cache template, complex query traverses a knowledge graph, and related tables, fields and service constraints are returned; then, an expert committee agent generates an SQL (Structured Query Language) by adopting multi-stage collaboration, executes plan pre-evaluation, and selects a version with the highest comprehensive score; the test agent simulates and executes the SQL in the isolation environment, and verifies the grammar legality and the field permission; and finally, executing the detected SQL, and processing a result. According to the method, the accuracy of converting the natural language into the SQL (NL2SQL) in a complex database scene can be effectively improved.

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 relates to a data query analysis method based on a natural language, which comprises the following steps of: analyzing a natural language query request input by a user into a dynamic semantic intention tensor which comprises an operation type, a comparison relationship, a time constraint and a data role semantic dimension, and dynamically combining according to task semantics to form a complete data query and analysis operation expression; mapping the natural language intention to a controllable data operation space of a database, and generating a preliminary data operation chain; performing side effect perception processing on the operation tensor, predicting a potential execution consequence of the operation, generating a side effect tensor for simulating a side effect of the database operation, and if any index in the side effect tensor exceeds a preset threshold value, performing local correction on the operation tensor; according to the method, a complex natural language query task is disassembled into a plurality of minimum semantic units by adopting a step-by-step semantic resolution mechanism, each unit is mapped into a specific tensor operation, and a dependency relationship between operation units is dynamically reasoned to generate an operation execution chain.

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 an automatic data modeling and optimizing system and method fusing a knowledge graph and ChatBI, and relates to the technical field of data analysis. In order to solve the problems of high interaction threshold and insufficient analysis depth of a traditional BI tool, the scheme adopted by the invention comprises a data layer which has the capabilities of multi-source data access, domain knowledge graph construction, intelligent mapping recommendation, federal calculation and data mild governance, and realizes data integration and semantic unification; the analysis layer realizes accurate conversion from a natural language to an SQL and semantic reasoning of a knowledge graph through graph vectorization, multi-model cooperation, natural language understanding, intelligent SQL generation and graph dynamic updating, and supports efficient data query and analysis; and the application layer has the functions of natural language interaction, visual recommendation and generation, root cause analysis and intelligent early warning, and provides a visual interaction interface and data display service for a user. According to the invention, intelligent data analysis and visualization based on natural language interaction can be realized.

The invention discloses a government affair data dynamic authorization management method based on secure multi-party computing, and relates to the field of government affair data security authorization management, and the method comprises the steps: distributing a unique identity identifier for each participant, generating an asymmetric key pair through employing a national secret SM2 algorithm, registering a public key and mechanism attribute information to an alliance chain smart contract, and generating an identity certificate package; and based on attribute information in the identity credential packet, the participants cooperatively generate anti-quantum dynamic attribute-based encryption key fragments through a secure multi-party computing protocol, and the anti-quantum dynamic attribute-based encryption key fragments are distributed to the participants by adopting a threshold secret sharing technology. Threshold decryption and token verification are achieved through an alliance chain verification intelligent contract, an authorization record is generated, an access control strategy of an edge computing node is configured according to the authorization record, an oblivious transmission protocol is adopted to respond to data query, and an audit node monitors an access log, dynamically adjusts attribute weight parameters and synchronizes the attribute weight parameters to a key fragment.

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 an industrial PCB cooperative control method based on open-source and Feiteng CPUs, particularly relates to the technical field of industrial automatic production line control, and is used for solving the problem that a quality control instruction and a board card physical flow in an existing PCB production line are difficult to accurately synchronize, so that an adjustment instruction is lagged or mistakenly acted. A unique identifier is generated for each printed circuit board, the position of each printed circuit board is tracked in real time, and when a board card enters a synchronous control area, a Feiteng CPU obtains the state of each execution device; selecting a data query strategy based on state consistency, and collecting node state feedback and quality data; according to the method, the consensus convergence rate and the phase-frequency characteristic distortion index of the system are calculated, the triggering time point of a control instruction is dynamically adjusted when the index is abnormal, and a correction instruction is sent to execution equipment through an open source gap distributed soft bus, so that high-precision cooperative control of the instruction and board card flow is realized, and the real-time performance and reliability of closed-loop quality control are 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 natural language intelligent analysis and data query instruction generation method based on a large model, which relates to the technical field of natural language processing and comprises the steps of performing semantic analysis by pre-training a large-scale language model, constructing a dynamically enhanced query semantic graph, performing semantic enhancement in combination with an external knowledge graph, and generating a data query instruction. And extracting a structured feature vector and mapping the structured feature vector into a database query syntax tree, and finally optimizing an execution plan based on deep reinforcement learning to generate a standardized query instruction. According to the method, intelligent conversion from natural language query to efficient database instructions is realized, and query precision and execution efficiency are improved.

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.

Provided in the embodiments of the present disclosure are a data query method for sensitive data, and an electronic device, a storage medium and a computer program product. In the embodiments of the present disclosure, when an access terminal queries sensitive data in a database system, the database system sequentially performs desensitization processing and encryption processing on the sensitive data. Thus, provided is a data protection solution in which encryption is combined with desensitization, what flows out of the database system is encrypted desensitized data, and what the access terminal obtains after decryption is desensitized data rather than sensitive data. Sensitive data is protected by means of encryption during data transmission. In addition, what an access terminal obtains is desensitized data rather than sensitive data, thereby effectively reducing the risk of leakage of the sensitive data.

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 discloses a target retrieval method and system. The method comprises the following steps: S1, data access and preprocessing: acquiring multi-source data, performing cleaning, formatting and time-space standardization, and performing target detection and cutting on an image / video to generate a structured target object; s2, feature extraction: extracting deep semantic feature vectors and auxiliary understanding information, which have discriminability and adapt to complex scenes, from the target image; s3, constructing a data index, including constructing a spatio-temporal semantic hypergraph index of multiple types of nodes and hyperedges based on deep features and spatio-temporal information of the targets to express a complex relationship between the targets; and S4, data query: receiving multi-modal query information of a user, performing candidate region screening, feature matching and relation reasoning by utilizing a hypergraph index, and finally outputting a high-confidence target retrieval result. According to the method, the robustness of complex scenes and target changes and multi-dimensional query of depth are improved.

The present application provides a data query method and apparatus, and a related device. The data query method comprises: semantically parsing a natural language inputted by a user; converting the semantically-parsed natural language to generate a structured query language; querying a data report library or a database on the basis of the generated structured query language, wherein the data report library comprises one or more data reports, and the data reports are obtained by performing sorting on the basis of the service logic relationship of data; displaying to the user data reports, in the data report library, matching the natural language; or retrieving data, in the database, matching the natural language inputted by the user to form data reports, and displaying the data reports to the user. By means of the natural language inputted by the user, data reports can be directly and rapidly retrieved. In this way, the obtained data reports are user-customized, thereby improving the efficiency of data query.

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 discloses a multi-modal data retrieval method and device under dynamic collaboration and a medium, belongs to the technical field of information retrieval and natural language processing, and is used for solving the problems that unstructured data is difficult to process efficiently in traditional data retrieval, an information island phenomenon exists, and the data retrieval efficiency is low. And an effective connection and integration mechanism is lacked between data from different sources. The method comprises the following steps: performing task allocation processing on a corresponding Agent cluster on a question type to be queried to obtain an Agent task list; performing data query processing on the Agent task list under different data sources, and obtaining query result data; performing data primary processing on the query result data to determine initial retrieval data; performing multi-modal consistency check on the initial retrieval data, and integrating verified integrated retrieval data; and performing information strategy generation processing based on a fact verification mode and an explanation enhancement mode on the integrated retrieval data to obtain final answer information.

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 discloses a natural language query method and system for the field of rail transit, and relates to the field of data query, and the method comprises the steps: constructing a candidate data table based on an ElasticSearch database, a Milvus vector database and a user permission dictionary, extracting entities in a user query problem by using a large model in combination with a cue word template, and obtaining a query result; in combination with the Milvus vector database and the candidate data table, constructing an MQL statement based on the basic index library; generating sample data, an MQL statement and a candidate data table in combination with SQL, calling a large model, generating a first SQL statement in combination with a cue word template, performing data query in combination with a synonym mapping dictionary of retrieval conditions constructed by rail transit field experts, writing data of the whole interaction process into a Mysql database, and storing the Mysql database into a database. Accurate, safe and efficient natural language query service can be provided for non-technical personnel.