314 results about "Query optimization" patented technology

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 relates to the technical field of distributed agent collaboration and graph database query optimization, in particular to a graph query processing method and system based on multi-agent collaboration, and the method comprises the following steps: reasoning a coordination agent to analyze the semantics and intention of user query by using a large language model; a structured multi-agent execution plan is output, and an entity search agent or a relation analysis agent needing to be called is explicitly called; the agent coordinator receives the multi-agent execution plan and selects agents according to task types and task allocation; obtaining a distributed intermediate result set; starting a result aggregation mechanism, and performing typed processing, priority ranking and deduplication merging on the distributed intermediate result set to obtain an arrangement result; and outputting an answer for answering the initial question of the user. The system comprises an agent execution plan output module, a multi-task execution module and an arrangement result reasoning module. According to the method, efficient, extensible and fault-tolerant graph query processing is realized.

The invention provides a household appliance knowledge question-answering method and system based on retrieval enhancement generation. The method comprises the following steps: acquiring household appliance field multi-modal data from a multi-format document library; extracting text information, table information and chart information in the multi-modal data; performing domain term injection processing on the extracted information, and constructing a packet domain enhancement index; receiving a natural language question input by a user; the natural language problem is analyzed through a query optimizer, and semantic retrieval and keyword retrieval are executed in parallel; carrying out fusion processing on the semantic retrieval result and the keyword retrieval result; selecting matched document fragments by adopting a relevancy sorting algorithm; inputting the matched document fragments into a large language model to generate candidate answers; verifying the compliance and traceability of the candidate answers through a credibility evaluation module; outputting a final answer with a reference source; and storing the high-frequency questions and the final answers into a cache library to solve the problems that the answer accuracy of a knowledge question-answering system is reduced and the response efficiency is limited.

The invention discloses a retrieval enhancement generation method and device of an electric power system, a medium and equipment, and the method comprises the steps: constructing an electric power knowledge graph according to an original corpus of the electric power system, and constructing a structure index and a semantic index according to the electric power knowledge graph; performing query optimization on the original query content input by the user to obtain professional query content; performing neighbor sampling on the electric power knowledge graph according to the professional query content to obtain a query related sub-graph; according to the professional query content, the query related sub-graph, the structure index and the semantic index, performing mixed retrieval of semantics and maps to obtain a query retrieval result; and performing enhanced generation on the query retrieval result based on an attribution mechanism to obtain a user query answer. According to the technical scheme, the credibility of the generated result can be improved, meanwhile, the retrieval efficiency is improved, the query threshold is lowered, and the interpretability of the generated result is improved.

An adaptive random access system and method with learned query optimization for compacted data files that enhances random access performance through machine learning and pattern recognition. The system incorporates a query pattern learning module that analyzes historical access patterns and user behavior to build statistical models of data usage. An adaptive estimator module improves location estimation accuracy by incorporating learned patterns rather than relying solely on mathematical calculations. A predictive boundary detector uses learned codeword patterns to more accurately identify boundaries in compacted data, reducing misalignment errors. An intelligent search engine coordinates optimization strategies including context-aware search string parsing and encoding strategy selection based on learned performance data. A dynamic codebook optimizer reorganizes sourceblock layout based on access frequencies and co-occurrence patterns to improve retrieval speed. An enhanced search cache implements predictive caching algorithms that anticipate user queries and proactively load relevant data.

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.

A hypergraph workload manager in a server is configured for failure tolerant and explainable state machine driven hypergraph execution. The hypergraph executor comprises a query optimizer, a hypergraph enlister, a pipeline analyzer, and a state machine generator. The query optimizer translates a user query into a query operator graph. The hypergraph enlister enlists the query operator graph into a hypergraph containing a set of query operator graphs representative of already submitted user queries. The enlistment is configured to join query operator graphs where it makes sense to optimize query executions. Updates to the hypergraph based on the enlistment results in a set of disconnected graphs. The pipeline analyzer performs an analysis of all operators of all queries in the hypergraph to find an optimal sequencing of execution. The state machine generator is configured to generate a hierarchical state machine for all operators of a disconnected graph of the hypergraph.

The invention relates to the technical field of big data storage, and discloses a multi-level data storage and mixed query method and system, a storage medium and equipment, and the method comprises the steps: collecting original buried point data, carrying out real-time stream processing, storing the processed real-time data in a hot storage layer, carrying out timing offline batch processing on historical buried point data, and storing the processed real-time data in a hot storage layer; converting into an optimized storage format and storing in an object storage system; an extended metadata management system is constructed, and structure information, partition information and access frequency information of data in the object storage system are automatically extracted and managed; executing query, and reducing the data scanning amount through a query optimization strategy; automatically migrating data among different storage layers through a dynamic cold and hot data layering mechanism; a unified query interface is provided, a query request is distributed to a corresponding real-time processing system or an offline query system based on an intelligent routing strategy, and a query result is cached and returned, so that high-performance and low-cost storage and second-level query of data are realized.

The invention provides a visual form construction method, which comprises the following steps: receiving user form operation data, carrying out business logic calculation, state change control and inter-service coordination operation on the user form operation data based on domain-driven design, and generating domain events, state data and service governance data. Domain events and state data are recorded to an event storage system, a query optimization view is constructed, event logs and query view data are generated, and dual optimization of data persistence and query efficiency is ensured. According to a preset security policy rule, real-time security monitoring is performed on the service governance data, a system health state index and security audit data are generated, and a system-level observability and security protection mechanism is established. And performing interface rendering and synthesis based on the event log, the query view data, the system health state index and the security audit data to generate a user interactive form interface. Through fusion processing of multi-source data, deep integration of interface rendering and security policies is realized.

The invention discloses an Internet of Things full data persistent storage system and method based on multi-stage time granularity. The system comprises a data acquisition module, a multi-stage storage management module, a dynamic index module, a compression engine module and a query engine module. The data acquisition module is used for receiving equipment data in real time, the multi-level storage management module is used for dynamically dividing time windows and executing a cold and hot data migration strategy, the dynamic index module is used for constructing a joint index of time dimensions and equipment IDs, and the compression engine module is used for selecting an optimal compression algorithm according to data features and transmitting the optimal compression algorithm to the storage management module. And the query engine module is used for analyzing a query condition, positioning a data block through an index and optimizing an IO reading sequence. Compared with a traditional time sequence database, the storage cost is reduced through a dynamic time granularity division and hierarchical compression algorithm; and on the basis of query optimization of multi-level indexes, the query efficiency in the complex time range is improved by several times, and the query response time in the complex time range is reduced from the minute level to the sub-second level.

The disclosure describes an enhanced distributed retrieval augmented generation (RAG) system. The enhanced distributed RAG system provides RAG data to a local small foundation system to form prompts. The enhanced distributed RAG system creates local query optimized representations (QORs) that are matched with queries sent to the local small foundation system to create prompts. The enhanced distributed RAG system is located on one edge site and is connected to the global federated RAG module. The global federated RAG system is connected to other enhanced distributed RAG systems in other edge sites. QORs can be distributed across the enhanced distributed RAG systems and the global federated RAG module by promoting local QORs to global QORs and vice versa.

The invention discloses a news detection method and system based on a dual-model collaborative optimization framework, and a medium. The method comprises the following steps: screening news materials and news descriptions from a news material library to construct a training data set; respectively inputting the training data set into a student model and a teacher network model to obtain a first image feature and a second image feature, and aligning the first image feature to a space of the second image feature to obtain a third image feature, inputting the third image feature into a decoder of a teacher network model to perform target query optimization so as to obtain a fourth image feature, taking a prediction score obtained by the teacher network model as a soft label, supervising training of a student model until a preset condition is met, and determining a trained target detection model; and inputting the news material obtained in real time into the target detection model, and determining the news category and the sensitive element, so that the news detection accuracy and efficiency can be improved.

The invention discloses a natural resource big data off-line comprehensive analysis, annotation and proof system and method, and belongs to the technical field of natural resource informatization and geographic information processing. The system comprises the following components: a spatial data fusion module, which is used for realizing unified management and efficient processing of heterogeneous spatial data; the spatial index optimization module is used for realizing quick retrieval and high-performance query optimization of spatial data; the intelligent annotation and arrangement module is used for realizing low-code rapid configuration and business process automatic arrangement of different natural resource investigation scenes; the off-line analysis module is used for realizing buffer area analysis, space superposition and terrain analysis in an off-line environment; the real-time visualization module is used for realizing multi-level map display, track recording, vector element editing and multimedia evidence collection; and the data quality management module is used for realizing automatic quality inspection of geometry, topology and attributes and statistical analysis, version control and export management of annotation results.

The invention relates to the field of data processing, and discloses a database SQL query optimization method, device and equipment and a storage medium, and the method is used for intelligently generating and optimizing an SQL query. The method comprises the following steps: acquiring a user request, and generating an SQL statement; analyzing the SQL statement to extract a related table, and obtaining structure information of the related table from a database; obtaining a historical query log, performing multi-modal feature fusion on the SQL statement, the structural information of the table and the historical query log based on a pre-trained Transform model, and generating a context-aware joint embedded vector; inputting the joint embedded vector into a pre-trained optimization strategy generation model to obtain optimization suggestions applicable to cross platforms; and collecting user feedback data, updating parameters of the optimization strategy generation model to obtain an updated optimization strategy generation model, and replacing the existing optimization strategy generation model with the updated optimization strategy generation model.

The invention provides a query optimization method, electronic equipment, a storage medium and a program product. The method includes. Based on the left deep tree, the optimization problem of the table connection sequence in the database is converted into a secondary unconstrained binary optimization QUBO problem; the relationship in the left deep tree is a table in the database; solving a QUBO problem through a quantum solving method to obtain a connection relation of the tables in the left deep tree; and determining an optimized table connection sequence based on the connection relationship of the tables in the left deep tree.

The invention discloses a lake and warehouse integrated Internet of Things time series data storage architecture system and method in the crossing field of Internet of Things time series data storage and engineering machinery intelligent technology. The system comprises a data source layer, a data access layer, a data access and preprocessing layer, a lake and warehouse integrated storage layer, a data fusion and analysis layer and an application layer. The data source layer collects multi-source heterogeneous time sequence data, after the multi-source heterogeneous time sequence data is buffered and temporarily stored by the data access layer, an adaptation module of the data access and preprocessing layer analyzes and converts the data, and a preprocessing module performs quality cleaning to form standardized data; the lake-warehouse integrated storage layer carries out classified storage according to data types; the data fusion and analysis layer extracts fusion features and constructs an equipment health state evaluation model; and the application layer realizes the functions of equipment monitoring, fault prediction, construction optimization and the like according to an analysis result, effectively solves the problems of storage, fusion and query optimization of multi-source heterogeneous time series data, improves the storage efficiency and the query speed, and assists in intelligent operation, maintenance and management of equipment.

A method and system process a construction domain query. A user query relating to a construction specification document is obtained. The construction specification document is obtained A Table of Content (ToC) page detection module autonomously classifies each page of the construction specification document as a table of content (ToC) or not. A text extraction module autonomously extracts text content from the construction specification document. A document sectioning module autonomously outputs section titles based on the ToC page classification results and the extracted text content, and sections the construction specification document into sections based on the section titles. The user query is processed based on the sectioned construction specification document.

The invention discloses a column storage-based big data compression and query optimization method, which relates to the technical field of big data compression and query optimization, and comprises the following steps of: performing sliding window division on original data of column storage to obtain a plurality of data windows; calculating a feature vector based on the value distribution characteristics in the multi-data window, wherein the feature vector is used for representing a local statistical rule of the data; performing dynamic merging according to the similarity of the feature vectors of the adjacent data windows to form a compressed block; selecting a corresponding compression coding mode based on the data type and the distribution characteristic of the compression block, and generating a metadata index containing a minimum value, a maximum value, a coding type and a compression size; after receiving a query request, mapping a predicate condition to a compressed coding space, and executing non-decompressed filtering judgment on a compressed block by utilizing the metadata index and a compressed coding type; and determining a decompression range based on a filtering result of the compressed blocks, and only performing local decompression and data reduction on the possibly hit compressed blocks.

The invention discloses a data knitting cross-library view efficient query method and system, and relates to the technical field of data processing. The system comprises a unified query interface module, a metadata management module, an intelligent distributed query optimizer, a multi-level cache module, a materialized view management module, an execution engine module and a performance monitoring and feedback module. According to the efficient query method and system for the data knitting cross-library view, a dynamic cost model is constructed through an intelligent distributed query optimizer, and a push-down strategy is decided and queried in combination with source capability metadata; a multi-level cache and materialized view collaboration mechanism is adopted to reduce source data access; maintenance overhead of materialized views is reduced with incremental refresh driven by CDC.

The invention discloses a query optimization method and system for a DBMS (database management system), and relates to the technical field of database query processing, and the query optimization method comprises the following steps: by means of the strong natural language processing capability and formatting processing capability of pre-training LLM, taking an SQL (structured query language) statement as a natural language; the method comprises the following steps of: constructing diversified thinking chain data by capturing a plurality of different query optimization rules executed in a DBMS (database management system) by SQL (structured query language) and corresponding query optimization paths, and exciting the strong query optimization thinking ability of LLM by using the data, so as to provide a high-quality query optimization path. According to the method, a simple and universal database query optimization method can be constructed.

The invention discloses a semantic-driven intelligent query method, system and device based on a security event library and a medium, and the method comprises the steps: improving the query accuracy through deep semantic understanding and cross-modal correlation analysis; potential threat association difficult to recognize can be found through multi-modal semantic association and threat graph construction; the query habit and professional domain knowledge of the user can be automatically learned in combination with a query optimization mechanism driven by reinforcement learning, personalized customization of a query strategy is realized, the manual adjustment frequency is reduced, and the man-machine cooperation efficiency is improved; on the whole, based on a dynamic optimization mechanism of a plug-in architecture and a large vertical model, the system can quickly adapt to changes of a network security environment of a power system, the response speed to novel threats is increased, and the adaptability of the system is enhanced.

The invention discloses a dynamic SQL (Structured Query Language) optimization method based on consanguinity dependency, and relates to the technical field of database query optimization. The method comprises the following steps: analyzing all indexes in a database, and dividing the indexes into atomic indexes and composite indexes; a blood relationship dependency tree is constructed according to the mathematical relationship between the indexes, each node represents one index, and each directed edge represents the dependency relationship; receiving a to-be-queried index set, and recursively searching all dependent target atomic indexes based on the graph structure; grouping the target atomic indexes according to data sources, and generating corresponding SQL query statements; and executing the SQL statement, and calculating a final query index result after obtaining an atomic index value. According to the invention, through unified management of the index dependence structure, repeated calculation across indexes is eliminated, the query performance is improved, the resource consumption is reduced, and the index development and maintenance efficiency is improved.

Systems and methods automatically optimize database queries for a distributed database management platform of an enterprise. A server system of a distributed database management platform receives data queries from users associated with the enterprise for data stored in the distributed database management platform. The server system assigns a score to each of the data queries, with the score for a data query being indicative of a quality of the data query. The server system generates, based on the scores for the data queries, a general score for at least one user group. The server system automatically undertakes a computing query optimization action for the at least one user group based on the general score for the at least one user group.

The present application provides a query optimization method and system for a distributed database. The method includes: determining a high-frequency data unit set in a distributed database, and generating a sub-query task division scheme for each data unit in the high-frequency data unit set; determining the interaction frequency between each data unit according to the dependency relationship between sub-query tasks represented by the sub-query task division scheme, and determining a data distribution scheme based on the interaction frequency; determining an index set for each node to query the stored data units according to the storage relationship indicated by the data distribution scheme; for each index set, calculating the execution cost of different sub-query tasks according to the access mode supported by the data units supported by the index set, and generating a sub-query task parallel scheme based on the execution cost; generating a node scheduling scheme that satisfies the execution of the sub-query task parallel scheme. The present application can improve the query speed of a distributed database and the overall performance of the system.

Techniques and solutions are provided for implementing query constraints. A keyword in a query language is provided that indicates the presence of a constraint. During query execution, if the constraint is not satisfied, the query can be terminated / caused to fail. In some cases, the keyword is introduced into a query by a query optimizer. In a specific example, the keyword is introduced as part of optimizing a query where at least some query operations are performed using a federated database system. The keyword indicating the constraint can be included in a query language statement and sent to the federated database system for execution. If the constraint is not satisfied, the federated database system can send a failure notification to the main database system.

A method of incorporating a bloom filter (BF) into cost-based bottom-up query optimization includes: receiving a query including a request to join at least two data tables that include a first data table and a second data table; generating, in response to the request, at least two query plans that includes a first query plan including subplans to: create the BF based on the second data table, apply the BF to the first data table for filtering data of the first data table before the first data table is joined with another data table, and join the first data table with the another data table; submitting the at least two query plans to a cost-based bottom-up optimizer to obtain a target query plan; and providing, as a response to the received query, a set of data that is retrieved from a data retrieval system by executing the target query plan.

In some examples, a seed query is received. A set of candidate queries is retrieved from a database based on matching the seed query with each candidate query. Based on the seed query, a ranking model is used to assign a relevance score to each candidate query. The relevance score indicates suitability of the candidate query as a follow-up query to the seed query (“next query” suitability). At least one candidate query is outputted based on the relevance scores. For example, candidate queries may be ordered or filtered based on their relevancy scores. In some implementations, an action is instigated automatically or semi-automatically based on a model-generated response to a candidate query. In some implementations, generative model calls are instigated on the seed query and a next query selected from the candidate next queries.

The invention discloses an unstructured data connection query optimization system and method based on a twin network, and belongs to the field of unstructured data processing. Comprising a feature extraction module for extracting feature vectors of different types of unstructured data by using a deep learning model; the twin network module is used for converting the feature vector into a low-dimensional embedding space; the judgment layer module is used for outputting a connection matching probability through a full-connection network after the feature difference and the original features are spliced; the threshold decision module is used for determining an optimal upper and lower threshold pair through adaptive mapping and grid search and dividing a connection matching probability into an automatic decision area and a recheck area, and the recheck area is used for further judging a connection relationship through a special reasoning model; and the query optimization module is used for optimizing a query execution plan of the unstructured data based on the connection matching result. According to the method, efficient unstructured database connection operation can be realized, an automatic decision-making and accurate rechecking mechanism is supported, and the accuracy and efficiency of database query connection operation are remarkably improved.

The invention provides a self-adaptive cardinality estimation method, system and device and a storage medium, and belongs to the field of database query optimization, and the method comprises the steps: constructing original connection sequence data and an optimal table sequence of a table based on a disclosed benchmark test data set as a data set, and converting the data in the data set into an original feature vector; the original feature vectors are input into a plurality of Transform models with different hyper-parameter settings for model training, a linear transformation module is included in front of each Transform model, and a plurality of cardinal number prediction results are obtained through training; and selecting a model with a minimum error as an optimal model according to a cardinality prediction result to obtain all original query feature vectors and corresponding optimal models, and learning a mapping relationship between the query features and the optimal models by utilizing a classifier. Obtaining a new SQL query input classifier, and distributing an optimal model based on the index data set; and processing the feature vector by using the optimal model to obtain a cardinality estimation result. The defect of single model query is changed, and the accuracy of a query result is improved based on feature classification query.