1218 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.

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.

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 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.

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 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 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.

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.

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.

The invention discloses a data integrity verification method based on a block chain, and particularly relates to the field of data verifier.The method comprises the steps that firstly, a mixed framework of a relational database and a non-relational database is constructed, various kinds of data and statistical information are stored, and then image and text data are subjected to multi-dimensional segmentation; a specific algorithm is adopted to calculate a data unit hash value, a data block hash value and a top level hash value are obtained through hash aggregation, an alliance chain architecture and an intelligent contract are utilized to realize data uploading and storage, access permissions of different block chains are set, and a consensus algorithm is dynamically adjusted by monitoring a network load and a node performance state in real time, so that the network load and the node performance state are monitored in real time. According to the method, the block chain network is guaranteed to run stably, in the data integrity verification link, through preliminary verification and detailed verification, comparison of hash values of all levels and accurate judgment of data integrity, complex data can be effectively processed, tampering positions can be quickly positioned, and verification efficiency and safety are improved.

The invention relates to the technical field of data processing, and provides a high-dimensional data library falling method, device and equipment based on a hybrid index and a medium, a configuration interface is called to convert multi-modal high-dimensional data into a high-dimensional vector in a configuration format, and efficient conversion and interaction among different modal data can be realized through a unified vectorization interface; the high-dimensional vector is compressed and dimensionally reduced, so that the storage overhead can be effectively reduced; an HNSW index and an IVFFLAT index of a vector to be subjected to library falling are constructed based on a hybrid index strategy, a hybrid index of the vector to be subjected to library falling is obtained, and a hybrid index structure can support rapid indexing of large-scale high-dimensional vector data; according to the method, the mixed index and the vector to be subjected to database falling are subjected to database falling to the centralized relational database, high-dimensional data are subjected to compression and dimension reduction before database falling, so that the database falling overhead is reduced, the database falling efficiency is improved, and meanwhile, the mixed index structure is combined, and the data retrieval efficiency can be improved after database falling.

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.

Automatic data transfer between a source and a target using semantic artificial intelligence (AI) for robotic process automation (RPA) is disclosed. A user may be provided with the option of selecting a source and a target and indicating through an intuitive user interface that he or she would like to copy data from the source to the destination, regardless of format. This may be done at design time or at run time. For instance, the source and / or target may be a web page, a graphical user interface (GUI) of an application, an image, a file explorer, a spreadsheet, a relational database, a flat file source, any other suitable format, or any combination thereof. The source and the target may have different formats. The source, target, or both may not necessarily be visible to the user.

The invention relates to the field of large models, in particular to a mixed information retrieval generation method, system and device and a medium. The method comprises the steps that a user retrieval request is input into a preset special large model, and whether a structured retrieval intention is recognized or not is judged; if the structured retrieval intention is recognized, querying a relational database, judging whether a query result exists or not, and if the query result exists, taking the query result as a generation result; if the non-structured retrieval intention is recognized or no query result exists, querying a vectorization database; and if the similarity of the queried recall content exceeds a threshold value, inputting the recall content and the user retrieval request into a preset general large model to obtain a generation result. According to the technical scheme, the appropriate data source can be automatically selected for query according to the retrieval intention of the user, and both structured query needing accurate data and unstructured query needing semantic understanding can be met, so that the applicability and the flexibility of the system are improved.

Relational database systems are disclosed that are enabled to operate with versioned metadata. The relational database system includes a lock manager, a transaction manager and a version aware metadata storage and cache configured to store to store and manage versions of metadata, to determine which of such versions should be visible at any given point in time, and to enable creation of the proper versions of metadata. In an aspect, the transaction manager manages transaction identifiers and their associated start times, abort times and / or commit times. Such data enables determination of transaction visibility, and consequently the metadata version visibility, for any point in time. In an aspect, such metadata versioning support enables snapshot isolation of metadata transactions.

The disclosed techniques provide improved methods of operating system (OS) search. Users are enabled to search for documents, emails, presentations, content they entered into a web form, meetings they participated in, and other interactions they had with their computing device. To accomplish this, screenshots are periodically captured and indexed. Machine learning models are used to infer embeddings for visual elements of the screenshots and / or text extracted from the screenshots. A full text index of a relational database may also be populated with text extracted from the screenshots. The embeddings and full text index may then be used to retrieve screenshots in response to a user history query. For example, screenshots of embeddings within a defined distance of an embedding of the user history query may be selected. Query results from different embedding indices and relational databases may be ordered by applying different weights to different kinds of search scores.

The invention discloses an intelligent auxiliary acquisition method and system for manufacturing equipment design requirements, and belongs to the technical field of industrial design. The method comprises the following steps: establishing a hierarchical relationship of manufacturing equipment design requirements, and further constructing a manufacturing equipment historical design requirement hierarchical relationship database; constructing a design demand intelligent auxiliary acquisition model, and training the design demand intelligent auxiliary acquisition model based on the manufacturing equipment historical design demand hierarchical relationship database; original design demand information of the new manufacturing equipment is collected, the design demand intelligent auxiliary acquisition model is utilized to acquire design demand information, and the design demand information comprises a design demand information hierarchical relationship and labeled abnormal demands; and modifying, confirming and outputting the calculated design demand information. According to the intelligent auxiliary acquisition method and system for the manufacturing equipment design demand, the accuracy and integrity of equipment design demand acquisition are improved, the acquisition efficiency is greatly improved, and the acquisition cost is reduced.

The invention belongs to the technical field of log monitoring, and particularly relates to a real-time log monitoring alarm method, system and equipment based on event driving, and the method comprises the steps: dynamically obtaining a log file path needing to be monitored, monitoring a log file on a server, and sending incremental log data to a specified Topic of Kafka in real time; through an event-driven architecture, using a Kafka consumer group to consume log data in Kafka in real time, and using a kset data type of Redis to temporarily store the log data; performing real-time processing on the log data in the Redis; comparing the statistical result with a preset threshold value, and if the statistical result reaches the preset threshold value, sending alarm information according to a configured notification mode and a notifier; and storing the monitoring result in a relational database, and displaying the monitoring big disk through a visual page. And an event-driven architecture is adopted, so that the data processing flow is more efficient, and the overall performance and expandability of the system are improved.

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 discloses a social blog management system and method based on Vue and SpringBoot, belongs to the technical field of social networks, and solves the problems that an existing system only focuses on user information collection, activity registration and basic data storage and cannot meet the complex requirements of a blog platform for content creation, personalized recommendation and multi-dimensional social interaction, and the social blog management efficiency is improved. The system comprises a front-end module, a rear-end module and a database module, and the front-end module and the rear-end module carry out data interaction through AJAX and RESTful API (Application Program Interface); according to the system, a front-end and back-end separation framework of Vue.js and SpringBoot is adopted, and a relational database MySQL, a memory database Redis and OSS storage are combined, so that efficient storage and cache management of data are realized, the system performance is optimized, a back-end module can generate personalized recommendation information by analyzing user blog information, the user experience is improved, and the user experience is improved. Rich user management and social contact functions are provided, and interaction between users is enhanced.