1910 results about "Relational database" patented technology

A Generative Artificial Intelligence (Gen. AI) based chatbot apparatus implements classical AI models and Generative AI models to answer user queries with results retrieved from relational databases and unstructured knowledge bases. When a user query is received, it is determined if the intent of the user query can be determined with a confidence greater than a configured confidence limit. If yes, a structured query mapped to the intent is employed to answer the user query. If the intent determination confidence is less than the configured confidence limit then Gen. AI-based techniques using a plurality of LLMs are used to respond to the query. A Gen. AI switch is also implemented to switch between the plurality of LLMs to answer user queries with greater accuracy.

A database system may include one or more relational databases storing information for a plurality of tenants in accordance with database object definitions. The database system may also include a communication interface providing the plurality of tenants with access to web applications through which to access the information and configured to receive an indication of one or more of the database object definitions from a tenant. The database system may also include a storage device storing a prompt template specific to the tenant and that includes one or more natural language instructions for generating text and a reference to the one or more database object definitions. The database system may also include a processor configured to retrieve a database record associated with the tenant and corresponding to the one or more database object definitions and to determine a text generation prompt based on the database record and the prompt template.

The invention relates to the technical field of food storage management, in particular to a food storage management system, which comprises a data acquisition layer, is composed of a plurality of devices and is used for acquiring food, environment and user operation data; the data transmission layer transmits data by using wired and wireless network technologies; the data storage layer stores various types of data in combination with a relational database and a non-relational database; the data processing layer comprises a plurality of functional modules, such as an intelligent warehousing management module, and is used for realizing automatic input verification and batch management of food information; the intelligent ex-warehouse management module is used for optimizing ex-warehouse sequence and tracking food directions; the expiration and waste management module is used for carrying out expiration reminding and surplus value evaluation; the data analysis and purchase optimization module is used for predicting demands and analyzing loss; and the dish and inventory association module is used for matching the dish formula and the inventory and predicting the sales allocation inventory. The system improves the management efficiency, reduces the waste, optimizes the purchase decision, enhances the dish and inventory association management, guarantees the system safety, and provides data support to assist the decision.

The invention relates to the technical field of image processing, and discloses a stranded wire production quality monitoring method and system based on machine vision. The method comprises the following steps: acquiring the surface of a stranded wire at multiple angles through a high-speed camera array to obtain an original image; filtering high reflection and noise to obtain a feature map; performing edge detection, segmenting and identifying defects, and generating a position mapping table; quantitatively calculating defect parameters to form a quality score; performing correlation analysis to adjust process parameters; and constructing a defect-process database to realize quality tracing early warning. According to the invention, full-process automatic control from defect identification, quantitative evaluation to process parameter optimization is realized, the defect-process relational database is constructed, and the tracing and early warning functions of production quality are supported, so that the quality stability and the intelligent level of stranded wire production are improved.

The invention relates to a multi-modal data and RAG technology fused power design knowledge base construction method, and belongs to the technical field of power software development. The method comprises the following steps: carrying out collection and information extraction on multi-source heterogeneous original data; the method comprises the following steps of: constructing a multi-dimensional knowledge element structure containing parameters, specifications and case relationships by carrying out classification, specialized and precise processing and cross-modal association on data; based on a vector, graph and relational database mixed storage architecture, semantic vector efficient retrieval, knowledge graph relation management and business data synchronization are achieved respectively; and a dynamic optimization result is subjected to hybrid retrieval, a dual-drive reasoning mechanism outputs compliance conclusions and bases, and a retrieval enhancement generation service ensures that output contents conform to specifications. And systematic management and intelligent application of the electric power design knowledge are realized.

The invention discloses an agent long-term memory modeling method based on a memory network, and relates to the field of agents, and the method comprises the steps: storing vector data to a vector database, and storing structured metadata to a relational database; receiving an input request of a user, and executing vector similarity retrieval in the vector database to obtain semantic similar fragments; executing structured data query in the relational database to obtain structured metadata; the memory abstract is retrieved; forming a candidate data set; taking a filtered result as memory information, inputting the memory information into a large language model through a cue word project, and generating reply content; the input request of the user and the generated reply content are combined to form a new interaction record; processing the new interaction record and historical interaction records stored in a vector database and a relational database through a large language model to generate a memory abstract; aiming at the insufficient long-term memory emotion interaction coherence of the intelligent agent, the emotion interaction coherence is improved.

An intelligent monitoring, inversion and prediction system for a transient process of pumped storage units is provided, including: an equipment layer including sensors for obtaining hydraulic, mechanical and electrical operation data of the pumped storage units in real time, a data layer including a real-time database, a relational database, an expert knowledge base and a file system and used for storing hydraulic, mechanical and electrical operation parameters collected by the equipment layer in different levels, a twin layer including a modeling and an application of a digital twin model and used for a three-dimensional modeling and a key information visual display of a pumped storage system with BIM technology and a service layer displaying one-dimensional time domain parameters and designing and visualizing front-end interfaces for six functions: operation optimization, online monitoring, inversion, dynamic prediction, health assessment and fault warning.

The invention discloses a large model knowledge base construction method based on multi-granularity retrieval. The method comprises the steps of obtaining a target text and analyzing a document structure, splitting and reorganizing text data according to three levels of titles, fragments and sentences, and constructing three levels of knowledge of the titles, the fragments and the sentences; using a large language model to construct a generative index as question-level knowledge according to the sentence-level knowledge; using a vector model to generate a first vector representing index keys of fragment-level knowledge and / or sentence-level knowledge, and a second vector representing index keys of title-level knowledge and / or question-level knowledge; and respectively storing the first vector and the second vector into a vector database, and storing the index values and the index types corresponding to the first vector and the second vector into a relational database. According to the method, multi-granularity document knowledge base construction is achieved, the expression modes of knowledge under different granularities are expanded, the accuracy of user query and knowledge matching is improved, and the problem that information is lost or incomplete in an existing method is solved.

The invention relates to the field of abrasion morphology determination and abrasion loss calculation, and discloses an abrasion morphology and contact stress analysis and prediction method and system based on a rough contact surface, a medium and equipment, and the method comprises the steps: constructing a dynamic contact model, and obtaining an accurate movement rule and a stress analysis result of a moving mechanical component; constructing a three-dimensional rough surface digital twinborn model conforming to an actual contact surface; establishing a contact stress field multi-physical field analysis model to obtain elastic deformation and a friction heat effect; based on the contact stress distribution and the local wear rate which are obtained in real time, carrying out dynamic reconstruction on the wear morphology, and establishing a nonlinear iteration relation between the wear loss and the contact stress distribution; determining an accurate boundary condition based on an accurate movement rule and a stress analysis result of the moving mechanical component, and constructing a failure criterion based on contact stress gradient mutation and surface roughness evolution; key parameters are calibrated through experiments, and a mapping relation database of wear rate-stress state-morphological characteristics is established so as to carry out friction wear analysis.

The invention provides a relational database backup recovery method and system based on big data, and relates to the technical field of database backup recovery, and the method comprises the following steps: obtaining total backup data and incremental backup data of a relational database, and obtaining merged backup data; based on the features and the access mode of the merged backup data, constructing a multi-dimensional index structure at the same time, and integrating a data dependency graph, a classification result and cold and hot layering information into a multi-dimensional index; decompressing the target backup data to obtain recovered backup data, and performing intelligent verification on the recovered backup data by using a data integrity constraint rule; if the intelligent verification is passed, the recovered backup data is imported into the relational database, and data recovery at a specified time point is completed; and if the intelligent verification is not passed, intelligent data restoration and complementation are carried out, and then verification, verification and importing are carried out again until the intelligent verification is passed.

Queries may be made against a non-relational database without impacting the capacities of the non-relational database by enabling performance of queries to a non-relational database via an external query management, backup management, and a non-relational database backup hosted in an external data storage. The query management and backup management of the non-relational database service may split transactional load to the non-relational database and analytical load, and furthermore allow selection of a desired data freshness for queries including various time ranges, such as seconds, minutes, or days.

The invention discloses a multi-platform Internet of Things equipment unified access management method, equipment and a storage medium, and relates to the technical field of Internet of Things integration. The method comprises the step of solving the fragmentation problem of cross-platform equipment access through dynamic configuration adaptation. The method comprises the following steps: starting from receiving a front-end equipment operation request, indexing a pre-configured mapping relation database through an equipment unique identifier, and obtaining a target platform type, an API address and an encryption certificate; dynamically calling a protocol adapter based on a platform type, and injecting an account certificate into a request header or a message body according to a platform specification; converting the original parameters into a format supported by a target platform through a protocol conversion engine, and combining the original parameters with the authentication information to generate a complete request; and realizing high-reliability communication by using an asynchronous message queue, and finally returning a platform response to a front end. According to the invention, through mapping management of the configuration driver and dynamic adapter scheduling, equipment access unification across the Internet of Things platform is realized.

Methods, systems, and computer-readable storage media for receiving, from probes, probe data indicative of vulnerabilities of one or more devices to cybersecurity threats. The devices are connected over a network. Aggregated probe data is generated by mapping the probe data using data relationships obtained from a relational database, The data relationships define vulnerability types of cybersecurity threats. A cybersecurity compliance score of each of the one or more devices is determined using a correlation of the aggregated probe data to cybersecurity status scenarios defining consequences related to the cybersecurity threats. A cybersecurity assessment report including the compliance score of the one or more devices and an action plan preventing the consequences related to the cybersecurity threats are provided.

A method for AI-driven natural language search includes receiving a user query for one or more items from a user, processing the user query by searching against at least one relational database associated with the query, where the relational database is generated by extracting features from electronic documents of a plurality of items associated with the one or more items and by identifying specifications or respective values corresponding to the extracted features of the plurality of items, generating one or more query results based on the processing of the user query, where the one or more results include at least one item identified from the plurality of items and a justification for explaining an irrelevance of the at least one item, and transmitting the one or more query results to a user device for presentation to the user.

Systems, computer program products, and methods are described herein for application anomaly detection using advanced computational AI machine learning modeling. In this way, performance metric data is extracted and the variances are derived by comparing the performance metrics of the application workload for the current time period against the same for a previous period. The AI model is trained at regular intervals by using the derived performance metrics data to identify only the candidate workloads which are degrading or underperforming at an early state, while avoiding reporting workloads that are not causing impact to performance stability of applications across an entity database network. The system monitors application workload across a relational database of an entity for degraded application performance and identifies changes in application workload performance and applies the anomaly detection artificial intelligence machine learning model.

The present invention discloses a multi-module coordinated fault detection and early warning method for hydrogen-electric electric vehicles, which relates to the technical field related to electric vehicle fault detection. The method includes: determining multiple functional modules of hydrogen-electric electric vehicles; collecting multiple functional modules through an on-board sensor network to obtain multiple sensor data sets; performing pattern recognition on multiple sensor data sets based on a pattern classification algorithm, and outputting a real-time matching working mode; constructing a module coordination relationship database; calling the module coordination relationship database to identify the module coordination relationship diagram corresponding to the real-time matching working mode; performing coordinated fault detection on multiple sensor data sets according to the module coordination relationship diagram, and outputting a fault early warning signal corresponding to the real-time matching working mode. The method solves the technical problem that the comprehensive fault detection means lacking the coordinated relationship of multiple functional modules in the prior art, which leads to untimely and incomplete fault identification of electric vehicles, and achieves the technical effect of improving the comprehensiveness and accuracy of fault identification.

The large model knowledge base retrieval method based on multi-granularity retrieval comprises the steps that a knowledge base based on three levels of knowledge of titles, fragments and sentences is constructed, and the knowledge base comprises a vector database storing first vectors and second vectors and a relational database storing index values and index types corresponding to the first vectors and the second vectors; generating a vector value queried by a user by using a vector model, respectively querying a plurality of query index vectors with the highest similarity with the query vector value in the first vector and the second vector, and obtaining query index values and query index types corresponding to the query index vectors from the relational database as candidate retrieval results; and jointly constructing a query prompt word by using a plurality of candidate retrieval results and user query, and inputting the query prompt word into the large model to obtain a final query reply. According to the method, the precision, the efficiency and the intelligent level of a retrieval system are remarkably improved, and the problems of low precision, poor query adaptability, slow knowledge base updating and the like in the prior art are solved.

The invention discloses a natural language query method and system for a relational database, and the method comprises the steps: extracting a list structure from a local connection database, and generating a sub-database based on a natural language problem; inputting the sub-database mode and the natural language question into a screening cue word template to obtain a complete cue word, inputting the complete cue word into a large language model, and generating a simplified database mode after obtaining a table name and a column name; putting a natural language question into a prediction cue word template, obtaining a predicted SQL keyword skeleton, then converting the predicted SQL keyword skeleton into a prediction vector, screening out an example database, extracting question and answer pairs in the example database, extracting a keyword skeleton from an SQL statement of the question and answer pairs, and after extracting the keyword skeleton, converting the extracted keyword skeleton into a question and answer pair vector; calculating the similarity to obtain a similarity result and a corresponding prediction vector; the content is input into a cue word template, cue words are input into a large language model, the large language model generates SQL statements through a thinking chain, and natural language query is completed.

The invention relates to a body weight measuring method and device with automatic calibration and temperature compensation functions, and the method comprises the steps: synchronously obtaining the surface temperature and environment temperature data of a bearing structure through a temperature sensor array; calculating a current temperature difference value between the surface temperature and the environment temperature, generating a control signal when the current temperature difference value exceeds a material thermal deformation threshold value, and calculating a surface temperature change rate; selecting a compensation mode according to the compensation control signal and the surface temperature change rate, starting a compensation process based on a closed loop verification mechanism, and correcting the original weighing value according to a temperature-drift relational database to generate preliminary compensation data; when the calculated accumulated temperature impact value reaches a set threshold value, triggering a parameter self-updating operation to obtain final weighing data; and outputting final weighing data with temperature credibility rating to an interactive interface so as to solve the problems of error accumulation and manual intervention dependence caused by temperature fluctuation in a traditional scheme.

The invention discloses an enterprise data link management and value management method and system, and the method comprises the steps: carrying out the real-time capturing of multi-source heterogeneous data of all business systems in an enterprise through a distributed data collection engine, and recognizing the types of original data in different formats through a preset data source adapter; if structured data is detected, a relational database connector is adopted for extraction, and if the structured data is recognized as unstructured data, a document analysis module is started for content extraction, and an initial data set containing metadata tags is obtained; performing format conversion and field mapping on the initial data set according to a pre-established data standardization rule base, eliminating duplicate records and abnormal values through a data cleaning algorithm, performing automatic evaluation on a data quality grade by adopting a naive Bayes classifier, if a data quality score is lower than a preset threshold value, triggering a data recovery process, and if the data quality score is lower than the preset threshold value, performing data recovery. And standardized data meeting a unified standard is obtained. The normativity and value utilization efficiency of data management are effectively improved.

The invention provides a digital twin hydrological station system which is characterized in that the system comprises a layered architecture system and a multi-dimensional safety protection mechanism, the layered architecture system is composed of a sensing layer, a transmission layer, a data layer, a model layer, an application layer and a display layer from bottom to top, the sensing layer comprises a heterogeneous hydrological monitoring equipment cluster, and the transmission layer comprises a heterogeneous hydrological monitoring equipment cluster. The equipment cluster is provided with a water level sensor array, a Doppler flowmeter, a spectrum water quality analyzer and an environment monitoring terminal, and total element acquisition of hydrological element data, equipment operation state data and environment data is realized; the transmission layer adopts a hybrid communication protocol stack, integrates an ultra-short wave communication module, an NB-IoT narrowband communication module and an optical fiber communication module, and constructs a multi-path redundant transmission channel, and a key data transmission channel is configured with a dynamic link selection algorithm; the data layer comprises a distributed storage architecture, adopts a mixed storage mode of a relational database cluster and an unstructured database, and is internally provided with a data cleaning engine and a spatio-temporal data fusion module.

Systems and methods for processing search queries are provided. A method, according to one implementation, includes a step of receiving a query from a user interface, the query including one or more questions or commands pertaining to datasets stored in a relational database. The method also includes a step of generating a prompt having instructions related to how a Large Language Model (LLM) is to handle a complex query having one or more cascading dependencies. Also, the method includes a step of providing the prompt, datasets, and query to an LLM with instructions to convert the query into Structure Query Language (SQL) code.

The invention relates to the technical field of artificial intelligence, and provides an enhanced RAG-based automatic test case generation method, which comprises the following steps of: extracting characters from a system document, segmenting the extracted characters into text fragments, forming text vectors, and storing the text vectors into a vector database; a local first large language model generates a corresponding triple, and the triple is stored in a knowledge graph database; directly delivering the system document to a local first large language model, extracting an article outline, and storing the article outline into a relational database; according to a test case demand input by the user, obtaining a text fragment, a triad and an article outline related to test case generation, and sending the text fragment, the triad and the article outline to a local second large language model; and the second large language model automatically generates a specific test case and a detailed test case document according to the information. According to the method, the test case book can be automatically generated, so that the working content is reduced, and the working efficiency is improved.

The invention relates to the technical field of intelligent inspection, and discloses an intelligent inspection method and system based on AI multi-source fusion, and the method comprises the steps: collecting multi-mode perception data when an expert carries out the touch inspection operation; analyzing the multi-modal perception data, and constructing a biomechanical model and a tactile knowledge mapping model; establishing a standard tactile operation model and an acoustic feature mapping relation database; converting the standard tactile operation model into animation guidance and tactile feedback in an augmented reality interface; actual operation data of the inspectors are collected and compared with the standard touch operation model, and operation improvement suggestions are generated; fusing the tactile inspection result, the acoustic detection result and the visual inspection result to generate a comprehensive evaluation report; according to the invention, digital inheritance and standardized application of the tactile skills are realized, and the technical problem that the tactile skills are difficult to transmit in traditional inspection is solved.

The invention provides a database knowledge body construction method and a structured query language generation method, a relational database knowledge body comprises a plurality of knowledge body units, and the plurality of knowledge body units are mutually connected through associated fields to form a knowledge network; each knowledge body unit corresponds to one data table, each knowledge body unit comprises multi-layer node data, and the multi-layer node data comprises table information, field information, field vectors, field enumeration value information and field enumeration value vector representation of the data tables. The construction method comprises the steps of obtaining first data table information based on data table metadata; reasoning and extracting the text code pairing data set through a large language model to obtain second data table information; and fusing the first data table information and the second data table information to construct a relational database knowledge body. Refined, low-redundancy, accurate, comprehensive and rich dependency data is provided for the TEXT2SQL task of the large language model, and the TEXT2SQL generation capacity of the large language model is improved.

The invention relates to the technical field of power system fault diagnosis, and discloses a distribution network line fault detection data processing method, system and device and a medium, and the method comprises the steps: obtaining load data of a target distribution line detection point, and carrying out the first preprocessing of the load data, and obtaining a fusion feature vector; performing second optimization operation on the fusion feature vector to obtain an optimized fusion feature vector; calculating a potential feature matrix according to the optimized fusion feature vector, and mapping the potential feature matrix to a low-dimensional space to obtain a low-dimensional sample set; presetting an adaptive label propagation algorithm, and performing fault category judgment on the low-dimensional sample set based on the adaptive label propagation algorithm; and storing a judgment result in a relational database. The problems that feature extraction is not accurate in a high-noise environment, and a classification model is insufficient in new fault expansion capacity are effectively solved, and fault signal processing robustness is improved.

A database system may include one or more relational databases storing information for a plurality of tenants in accordance with database object definitions. The database system may also include a communication interface providing the plurality of tenants with access to web applications through which to access the information and configured to receive an indication of one or more of the database object definitions from a tenant. The database system may also include a storage device storing a prompt template specific to the tenant and that includes one or more natural language instructions for generating text and a reference to the one or more database object definitions. The database system may also include a processor configured to retrieve a database record associated with the tenant and corresponding to the one or more database object definitions and to determine a text generation prompt based on the database record and the prompt template.

The invention provides an intelligent form processing method based on multi-modal verification and large model feedback, and relates to the technical field of artificial intelligence and data processing, and the method comprises the steps: obtaining a corresponding business rule from a relational database through a rule extraction and matching request based on user input data; error verification, service logic verification and cross-field and cross-method verification are carried out on the user input data and the corresponding service rules, and verification results are obtained; based on the verification result, natural language feedback is generated through the large model to provide error prompts and improvement suggestions.

The invention provides a knowledge graph construction method based on a low-code development platform, and belongs to the technical field of knowledge graph construction. The method comprises the following steps: counting and carding metadata and business entity structures in a low-code development platform, and formulating a mapping specification for mapping from a relational database to semantic data; obtaining a target relational database in the configuration file, extracting table data and column data in the target relational database, and converting the table data and the column data into entity class parameters and attribute class parameters in the knowledge graph based on a mapping specification; extracting an unstructured data source related to the target business from the low-code development platform, and converting the unstructured data source into structured data; and associating the structured data with the entity class parameters and the attribute class parameters, and storing the associated data in the knowledge graph. Automatic mapping and storage from the relational database to the knowledge graph are realized, so that the data utilization rate and the business intelligence level are improved.

The invention discloses a relational database-oriented data retrieval enhancement generation method and system, and relates to the technical field of internet data retrieval services. The relational database-oriented data retrieval enhancement generation method comprises the following steps: S1, collecting original structure data, meta-information data and semantic interference data, and constructing a standardized multi-granularity expression set after preprocessing; s2, analyzing a semantic structure queried by a user, and dynamically adjusting an information extraction range according to the semantic structure; s3, comprehensively evaluating field matching strength and time coverage suitability, and switching a context construction strategy; s4, evaluating the semantic integrating degree and the information coverage degree of the candidate segments, and constructing a multi-level prompt context structure; and S5, tracking the semantic granularity and the context adaptation degree, and performing response quality diagnosis and dynamic correction. The problems that in a relational database, natural language query semantic granularity is difficult to switch in a self-adaptive mode, structure fragment matching is not accurate, and context prompt construction is redundant and scattered are solved.