622 results about "Relational database" patented technology

The invention discloses a multi-source field-level blood relationship tracking method and system. The method comprises the following steps: extracting features from a multi-source heterogeneous system, matching templates, triggering a corresponding acquisition strategy, and outputting an original blood relationship log; reading an original blood relationship log to generate an abstract syntax tree, extracting a source / target field set to output a field-level mapping relation, further writing the field-level mapping relation into a graph database to form a blood relationship edge, writing metadata into a relational database, establishing a two-level index, and outputting a blood relationship graph capable of being quickly positioned; and taking the blood relationship map as input, returning a target path with the maximum sum of confidence coefficients by adopting a weighted shortest path algorithm, outputting a link-level traceability result, and displaying the link-level traceability result. According to the method, complete tracking of the field-level blood relationship is realized, the query response time is shortened to a millisecond level, rapid access to multiple types of data sources is supported, and the problems of insufficient blood relationship analysis precision, low query efficiency and weak multi-source adaptability in the prior art are solved.

A system and method for querying, processing, and ranking governmental data across heterogeneous databases may include integrating large language models (LLMs) and multi-agent orchestration to enhance accuracy and mitigate bias. The system receives a natural language query, processes the query using an agent orchestration LLM to generate search query instructions, and executes specialized data record processing agents to retrieve data from diverse sources, including relational, vector, and graph databases. A plurality of candidate natural language responses is generated and validated by data verification LLMs based on relevance and accuracy metrics to reduce hallucinations. The system further employs cross-source validation, statistical and linguistic consistency analyses, and weighted scoring to refine responses, which may include textual summaries, tables, or downloadable files presented via a graphical user interface. Accordingly, the approach ensures precise and contextually relevant results, particularly for applications involving sensitive or regulated data such as legislative and governmental records.

Methods, systems, and devices for data management are described. A method may include obtaining a new snapshot of a relational database of a source computing environment, comparing the new snapshot of the relational database to a prior snapshot of the relational database, updating, in response to the comparison indicating that data of a row of a relational database table changed between the prior snapshot and the new snapshot, a field of a second versioning control column of a corresponding row of a backup database table of the set of backup database tables to indicate a version of the new snapshot, and adding, in response to the comparison indicating that the data of the row of the relational database table changed, an additional row to the backup database table, where a field of a first versioning control column of the additional row includes an indication of the version of the new snapshot.

The invention provides an intelligent agent-based automatic verification and rule matching system for insurance-waiting evaluation reports. The intelligent agent-based automatic verification and rule matching system comprises a report acquisition module which pulls to-be-verified and historical insurance-waiting reports; the auditing processing module calls an attention Bi-LSTM model which is subjected to reinforcement learning fine adjustment and is fused with knowledge graph embedding, and scores the report sentence by sentence; the rule management module is used for storing and analyzing an equal-guarantee 2.0 structured machine rule and carrying out secondary accurate matching on low-confidence sentences; the knowledge graph module is used for maintaining a multi-dimensional association graph of a standard family, a control measure and an evaluation item and providing graph reasoning; the agent decision-making module generates actions of passing, returning, manual rechecking or automatic correction according to the auditing state; the feedback learning module collects artificial recheck differences, an incremental training model, a strategy network and atlas vectors; and the storage module stores reports, models, rules, maps and logs in a multi-modal manner through a relation library, a map library and a vector library, and implements data governance and access control. According to the invention, the audit efficiency, accuracy and standard consistency of the evaluation report can be improved.

The invention discloses an electric power field credible question-answering system based on a knowledge graph and multiple agents, and belongs to the technical field of intelligent power grids. The electric power field credible question-answering system comprises the steps that an existing knowledge base is stored through a vector knowledge base, the knowledge graph and a relational database, and then a multi-agent system is constructed based on a large language model; a complex problem is decomposed into a plurality of sub-problems by adopting a logic form language in real-time dialogue, solving is carried out based on operators such as planning, reasoning and retrieval, symbolization and structuring of a problem solving process are realized, knowledge is aligned and integrated by using a semantic reasoning technology, a semantic relationship between the knowledge is identified and established, and a real-time dialogue result is obtained. And realizing credible questions and answers in a manner of recalling related knowledge. According to the method, the deep fusion of the unstructured text, the structured data and the semantic relationship is realized by constructing the multi-modal knowledge storage system, the knowledge coverage rate and the semantic consistency are remarkably improved, and the understanding and response capabilities of the system to compound problems are improved.

The invention relates to the technical field of data processing, in particular to a relational database intelligent query method based on machine learning, which comprises the following steps of: processing multi-modal flow data through time sequence alignment, generating a unified semantic representation vector, constructing a dynamic psychological state map, and modeling a psychological state evolution track by utilizing a neural common differential equation mechanism. After user query is received, historical dialogue nodes are retrieved from the graph, enhanced query intention representation is generated, the enhanced query intention representation is converted into an execution plan through a neural symbol inference engine, and a graph neural network is adopted to predict execution cost. And finally, a personalized analysis report is generated by combining a causal discovery algorithm, and system adaptive optimization is realized through feedback signals. According to the method, the problems of inconsistent time sequence semantics and strong context dependency of the multi-modal psychological data are effectively solved, and the query accuracy and the personalized level in a psychological dialogue scene are improved.

The invention discloses a structured data storage method based on natural language transformation, which comprises the following steps of: a system initialization configuration stage: deploying a protocol adapter in a local memory of a PC (Personal Computer) client, and loading natural language processing pipeline configuration parameters; a heterogeneous data acquisition stage: capturing a multi-source text data stream through the protocol adapter, uniformly converting the multi-source text data stream into a standardized data packet, and sending the standardized data packet to a message queue theme; a text cleaning stage: a named entity recognition stage: inputting the pure text data into an NER module deployed with a language model loader; in the conditional feature extraction stage, feature vectors are generated for texts meeting preset conditions on the basis of entity type tags in the entity recognition result; and a consistent storage stage: inserting the entity identification results into a relational database in batches, and updating the entity mapping relationship in the cache. According to the method, the intelligent level of cache management is remarkably improved, and the access fluency of the key data of the user is guaranteed.

The invention discloses a meteorological operation and maintenance intelligent question-answering method based on multi-mode GraphRAG. The method comprises the following steps: S1, a meteorological operation and maintenance intelligent question-answering system divides input data into unstructured data and structured data and processes the unstructured data and the structured data respectively; s2, constructing a knowledge base system composed of a vector database, a relational database and a graph database; s3, analyzing the user query text and dividing question types; s4, selecting a corresponding retrieval strategy according to the question type and starting a processing flow, and finally processing a retrieval result through a fusion and rearrangement module to obtain a knowledge evidence set; and S5, the meteorological operation and maintenance intelligent question-answering system organizes the knowledge evidence set and the user questions into structured cues and submits the structured cues to the large language model for processing to generate structured answers. According to the method, optimization is carried out according to characteristics of the meteorological operation and maintenance field, professional terms and field specific knowledge can be effectively processed, and high-quality field professional services are provided.

The invention discloses a method and system for generating an engineering design specification based on a large language model, and the method comprises the steps: converting a plurality of formats of engineering documents into structural data through a non-structural document analysis module, and carrying out the multi-modal storage through a vector database and a relational database; a document recall module is combined with an expert predefined generation strategy and user input information to intelligently retrieve related materials from a vector database; and finally, calling a large language model through a document generation module to rewrite, expand or independently generate recalled contents, and outputting a highly customizable design specification which conforms to engineering specifications. According to the method, the efficiency, accuracy and specialty of engineering design document generation are remarkably improved, multi-section concurrent generation and manual fine adjustment are supported, and the method is suitable for multiple engineering stages such as project proposal, feasibility research report and construction drawing design.

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

The invention discloses a multi-source heterogeneous information extraction and structured processing method based on natural gas business data, and relates to the technical field of artificial intelligence application in the energy industry, and the method comprises the steps: analyzing a multi-modal document: carrying out the content analysis of natural gas sales documents in various formats, extracting key information, and obtaining the analyzed original data; data preprocessing: cleaning, recombining and standardizing the data to obtain preprocessed data; the mixed information extraction comprises key business index extraction, field rule base establishment, mixed extraction model establishment and context reasoning, and missing items in data are complemented by analyzing overall information and local content of a document, so that the integrity and accuracy of the information are improved; performing intelligent post-processing and constructing a relational database; according to the processing method, the natural gas service data can be accurately and efficiently extracted from the documents in various formats, and a basis is provided for subsequent data analysis and decision support.

The invention discloses an acceleration tube full-process automatic reliability test system and device, and relates to the technical field of high-power microwave device testing, and the system specifically comprises the following modules: a control test module, a hardware control module, an acquisition processing module, a business logic module, a data persistence module, and a user performance module. Writing and executing an automatic reliability test script of the acceleration tube; receiving and executing a hardware control instruction, and uploading an original analog / digital signal of the equipment; the communication module communicates with equipment in the hardware control module, converts an original analog / digital signal into a digital signal which can be processed by a C # program, and carries out filtering, unit conversion and validity verification on the digital signal; an over-limit alarm function is provided, and the structured test data is sent to the data persistence layer; storing all test related data through a relational database; test parameter configuration, real-time monitoring, alarm states, data records and dose rate curves are provided based on a graphical user interface.

The invention relates to the technical field of chip design, and provides a clock constraint generation method, electronic equipment and a storage medium, and the method comprises the steps: building a relational database based on hierarchical design information of a chip, the relational database comprising an instantiated module relationship in the chip and a cross-module port connection relationship; determining a clock source from the relational database, and traversing a propagation path of the clock source in the relational database by taking the clock source as a starting point; and generating a clock constraint file of the chip based on the propagation path of the clock source. According to the method, the electronic equipment and the storage medium provided by the invention, the hierarchical design information is converted into the dynamic, queriable and traversal relational database, so that the clock propagation path is reconstructed, the clock constraint file is driven to be automatically generated, and on the premise that the complete hierarchical design information is reserved, the automatic generation of the clock constraint file is realized. The efficient, accurate and complete propagation path reconstruction is realized, and the clock constraint generation efficiency and accuracy are improved.

Techniques for processing sensor data are provided. Sensor data, such as individual messages or data points from devices having one or more hardware sensors, can be annotated with one or more metadata elements to facilitate sensor data processing. An annotation rule for sensor data can be determined and sensor data annotated according to the annotation rule. Sensor data can be written to a relational database table, where the table has a schema that provides columns for storing data for particular indicators of an indicator group having a plurality of indicators.

The invention belongs to the technical field of intelligent manufacturing of plastic packaging containers, and particularly discloses an intelligent blow molding and wall thickness closed-loop control system for PET plastic bottles, which integrates feedforward correction and closed-loop feedback, pre-corrects process parameters by acquiring characteristic parameters of raw materials, and generates point cloud data in combination with full-circumferential scanning of a non-contact three-dimensional optical measurement device. A partition three-dimensional model is constructed, and the wall thickness is accurately calculated; and based on the wall thickness deviation data, blow molding process parameters are dynamically adjusted through the mapping relation database, and a grading quality control strategy, a multivariable self-adaptive feedback mechanism and an online incremental learning mechanism are configured. The system realizes self-adaptive compensation under working conditions of raw material fluctuation, environmental disturbance and the like, improves wall thickness control precision and product consistency, reduces manual intervention, ensures high-speed continuous production, has self-learning and long-term stable regulation and control capabilities, and provides technical support for light-weight and high-precision forming of PET bottles.

The invention provides a BIM-based electromechanical pipeline intelligent collision detection and automatic optimization adjustment method, which comprises the steps of analyzing a BIM model database, and extracting component information of electromechanical components; establishing a spatial index and an adjacency relationship between the electromechanical components, constructing a component spatial relationship database, and storing the component information, the spatial index and the adjacency relationship in the component spatial relationship database; calling data in the component space relation database to carry out multi-level collision detection; performing collision optimization adjustment on the electromechanical component based on a result after multi-level collision detection, and generating an optimized BIM model to be fed back to constructors; according to the method, the optimized BIM model is subjected to 3D display, meanwhile, a figure roaming scene is constructed, the moving view angle of a constructor in a building space is simulated, collision points are secondarily checked in the roaming process, the collision recognition precision can be improved through multi-level collision detection, collision conflicts are eliminated through optimization adjustment, design errors are reduced, and the engineering efficiency is improved.

The invention discloses a storage and management method suitable for automatic scoring data of the Internet of Things, and the method comprises the steps: constructing a three-layer heterogeneous storage collaborative architecture fusing a relational database, a time sequence database and a graph database, and achieving the dynamic distribution and collaborative updating of data through an intelligent routing and cross-database transaction consistency mechanism; establishing a self-adaptive index system based on reinforcement learning, dynamically optimizing a multi-class index strategy and supporting low-efficiency index reconstruction; a storage strategy based on data value dynamic adjustment and AI-driven data quality closed-loop management are implemented, and rapid positioning and repairing of problem data are achieved; and elastic scheduling of storage resources is realized by utilizing containerization and Kubernetes technologies. According to the method, the comprehensive performance of the automatic scoring data of the Internet of Things in the aspects of storage efficiency, query performance, consistency, reliability and cost control is remarkably improved, and the method has important practicability and popularization value.

In one embodiment, a method comprises generating a backup of a table of a relational database by partitioning a plurality of records of the table among a plurality of Parquet files; and extracting metadata of the table into one or more metadata files; and requesting access of at least one of the plurality of Parquet files of the backup of the relational database responsive to an SQL query entered by a user into a user interface.

The invention discloses a self-adaptive table structure generation and index optimization method for JSON data driven by a real-time mode, and relates to the field of computer information management application. According to the self-adaptive table structure generation and index optimization method for the JSON data driven by the real-time mode, efficient and automatic persistence from the JSON data to a relational database is achieved through a four-layer technical architecture including dynamic analysis, intelligent mapping, self-healing expansion and index optimization. Comprising a dynamic mode analysis engine which analyzes a JSON nested structure in real time and generates a mode feature tree with weight; the self-adaptive storage engine is used for dynamically generating or expanding a table structure according to the mode feature tree and executing data type mapping; the index optimizer is used for automatically creating / cleaning indexes based on query log analysis; and the uniform interface layer is compatible with multi-database dialects and shields bottom layer differences. Dynamic table structure generation, intelligent type inference and storage optimization, self-healing Schema extension, query-driven index self-optimization and cross-database compatibility are realized.

The invention provides a data serialization processing method, a data deserialization processing method, a medium and equipment, and the serialization processing method comprises the steps: receiving a target object, and reading a previous-level field path under an initial condition; obtaining the type of the target object, and determining whether the target object is empty or not; after determining that the target object is not empty, processing the target object according to the type of the target object to generate a corresponding key value pair, and putting the key value pair into an initialized key value pair list to generate a target key value pair list; wherein the key value pair comprises a key value and a value value; according to the method, a complex object is converted into a simple multi-row and two-column key value pair, so that a structure is conveniently and quickly read, the method is suitable for storage of a relational database, search and analysis can be realized by directly utilizing functional characteristics of the relational database, and the data search and analysis efficiency is improved.

The invention discloses an Internet of Things equipment data analysis method and system supporting multi-protocol custom splicing, and relates to the technical field of communication protocols, and the method comprises the steps: constructing a multi-protocol equipment access link; in response to an original data message reported by the Internet of Things equipment through the multi-protocol equipment access link, determining a product model of the Internet of Things equipment based on the original data message; splitting the original data message into a plurality of functional paragraphs based on the attribute of the product model; analyzing each function paragraph to obtain function paragraph data, and mapping the function paragraph data to a corresponding attribute of the product model; and storing the mapped functional paragraph data into a time sequence database and a relational database. According to the method, the external data interface can be customized and created, the service system can conveniently obtain the equipment data, the operation data and the freezing data, the method has the functions of real-time reading control and the like, the data transmission safety can be guaranteed, equipment access under a large number of protocol concurrency scenes can be effectively handled, and the access efficiency is improved.

The invention relates to the technical field of special equipment, in particular to an intelligent auditing system and method for a special equipment report, and the system comprises an infrastructure layer, a data layer, a service layer and a display layer. A server platform and an operating system are deployed in the infrastructure layer so as to provide hardware resources, the operating system, middleware and a network environment for system operation, the data layer is connected with the infrastructure layer, a relational database is adopted to store service data, and a memory database is utilized to perform data caching; the service data comprises a test report template, a parameter variable and a rule set; the service layer is connected with the data layer and is used for bearing an auditing rule engine and a business processing module; the display layer is connected with the service layer and comprises a management end and a user end, the management end provides rule configuration and system management functions, and the user end is integrated in an existing service system in a browser plug-in mode. Therefore, the detection efficiency and accuracy of the special equipment report are improved.

A method for execution by a database system to improve testing, the method includes creating a synchronization database object (SDO) indicating a set of conditions for suspension of execution of queries. The method further includes establishing a first and second session, where the first session applies the SDO to a query expression and the second session monitors the SDO. The method further includes initiating, via the first session, execution of queries, and suspending the execution of queries when detecting a condition is met. The method further includes updating, via the first session, at least one relational database table to include monitoring data indicating suspension of the queries. The method further includes the second session verifying the suspension and instructing the SDO to resume suspended queries. The method further includes resuming, via the first session, the queries until a next condition is met or execution of the queries is finished.

The invention discloses an enterprise financial reimbursement automatic auditing and exception intercepting system based on RPA and AI, and relates to the technical field of process intelligent processing, and the system comprises a data acquisition and preprocessing module, an intelligent auditing engine module, an RPA process execution module, an exception intercepting and risk control module, and a data storage and interface module. The data acquisition and preprocessing module comprises an OCR (Optical Character Recognition) unit, a natural language processing unit and a data access adapter; the intelligent auditing engine module comprises a rule engine, a multi-mode AI learning model and an intelligent decision-making center; the RPA process execution module comprises an RPA task scheduler and an RPA robot cluster; the data storage and interface module comprises a relational database, a knowledge graph database and a vector database; according to the method, the OCR and NLP technologies are fused, various unstructured reimbursement data can be automatically and accurately extracted and understood, the time consumption and error rate of manual input and checking are greatly reduced, full-process automation is achieved, and the reimbursement period is remarkably shortened.

The invention relates to the technical field of artificial intelligence and industrial chain analysis, and discloses an automatic chain building and falling method and system based on agent collaboration, and the method comprises the steps: receiving an automatic chain building demand of a user, and carrying out the first-level chain building operation on an industrial chain directed acyclic graph through an industrial chain collaboration system based on the automatic chain building demand, and obtaining a first-level node; performing subdivision link establishment operation on the first-level nodes by using an industrial chain collaboration system to obtain second-level nodes, third-level nodes and leaf nodes; the industrial chain collaboration system is used for executing industrial chain labeling operation on the first-level nodes, the second-level nodes, the third-level nodes and the leaf nodes, and an industrial chain labeling result is obtained; and based on an industrial chain labeling result, performing enterprise chain falling operation on the leaf nodes by using an industrial chain collaboration system to obtain a chain falling relation database and an industrial chain panorama. According to the method, the problems of difficulty in dynamic link establishment, lack of multi-stage collaboration and low enterprise chain falling precision can be solved.

The application discloses a kind of generator stator winding partial discharge quantity's inversion method and system, belong to large-scale rotating electrical machine insulation state diagnostic technical field, method includes: obtaining or estimating the key distributed capacitance parameter in insulation structure multi-capacitance equivalent circuit model, mapping relationship database is established;Partial discharge detection is carried out to the generator stator winding of applied test voltage, and the characteristic information of discharge pulse is collected;Based on the key distributed capacitance parameter in mapping relationship database, pre-generated pulse waveform-defect capacitance calibration curve, the characteristic information of discharge pulse collected is executed nonlinear iterative inversion calculation;Quantitative evaluation, type identification and health diagnosis are carried out to the insulation defect of generator stator winding.The application introduces pulse waveform feature into partial discharge physical quantity inversion, improves the accuracy, quantitative level and defect identification ability of generator stator insulation state evaluation, provides technical support for generator life extension and condition maintenance.

The application discloses a water conservancy knowledge differentiation retrieval and query decomposition method based on a large language model, and the method comprises the following steps: constructing a standardized retrieval channel for four types of heterogeneous data sources, namely a graph database, a relational database, a document library and a time series database; secondly, establishing a sub-query type classification system for the water conservancy field and forming a template library; thirdly, collecting training data from historical queries and expert samples, and adopting a multi-round labeling mechanism to label query decomposition results, channel mapping relationships and dependency relationships; finally, adopting a low-rank adaptation method to fine-tune the pre-trained large language model, so that the model can intelligently decompose a user natural language query into a sub-query sequence, automatically route to the corresponding retrieval channel and plan an execution sequence. The application realizes intelligent processing of a composite query in the water conservancy field, significantly improves the efficiency and reliability of cross-data source knowledge retrieval, and is suitable for decision support scenes such as flood control scheduling and reservoir group joint scheduling.

The application relates to a software package installation method and a software package installation device. The method comprises the following steps: obtaining attribute information of a software package to be installed based on a software package installation request input in a software package management tool, and then querying the attribute information of the software package to be installed in a software package index tree; then, determining a list of dependent software packages of the software package to be installed from a dependency database in the case that the attribute information of the software package to be installed exists in the software package index tree; and finally, executing an installation step of the software to be installed based on the list of dependent software packages. The leaf node of the software package index tree is attribute information of a plurality of software packages; the dependency database stores key-value pairs of each software package; and the key-value pair of each software package comprises attribute information of the software package and a list of dependent data packages. The method can improve the software package installation efficiency.

The present application relates to the technical field of data classified storage, in particular to a data integration method and system for multi-classification sports events and a medium. First, the time sequence collection frequency of each professional data is subjected to cluster analysis to identify the multi-modal frequency characteristics thereof. Second, the collaborative index between the frequency cluster of different professional data is calculated to divide the high-correlation data cluster and reduce the mixed storage of low-correlation data and the null value rate of the relational database. Further, the write demand of each cluster is dynamically calculated in combination with the frequency matching strength and storage space occupied in the data cluster, so that the professional data is integrated and stored. In summary, the present application clusters the different types of professional data according to the collaboration of the frequency variation, reduces the null value problem in the storage process of the relational database, analyzes the storage space occupied by the professional data, and reduces the write pressure of the server.

Techniques for generating SQL queries from natural language requests are described. In some examples, a method for generating a SQL query from a natural language request includes performing entity extraction on the natural language query to extract entities and predict domains; determining required tables of the relational database to answer the natural language query; generating a SQL generation ready entity relationship graph based on the determined required tables; and generating a SQL query from at least the SQL generation ready entity relationship graph.