11575results about "Relational databases" patented technology

The invention discloses an interactive question intelligent agent system based on a large language model, and relates to the technical field of dialogue interaction systems. Aiming at the technical defects of the traditional BI tool, the technical scheme is as follows: a heterogeneous data management engine realizes unified access and secure access of cross-source data; the NLP semantic recognition engine converts a natural language into structured semantics through multiple steps, drives the hybrid SQL generation engine and provides visual parameters; the generation engine realizes precise generation from semantics to SQL through a two-stage architecture based on metadata and authority rules; and the visual management console is combined with multi-party configuration and parameters to visually build a number-asking agent. The method is used for realizing intelligent conversion from a natural language to a structured query language (SQL) and a data service interface, and automatically generating an interactive data visualization result.

The invention provides an HBase client main-standby switching method and system based on fault perception, and relates to the technical field of database management, and the method comprises the steps: collecting system operation data through a distributed fault monitoring module, building an anomaly detection baseline model, carrying out fault diagnosis based on a system health assessment distribution matrix, and generating a fault characteristic spectrum; and switching is triggered according to a risk level evaluation result, and the optimal standby node is selected for sequential switching by using a deep strategy learning algorithm, so that rapid and accurate fault detection, diagnosis and intelligent switching can be realized, and the reliability and performance of the system are improved.

The invention discloses a method for enhancing dynamic knowledge retrieval based on a large language model, belongs to the field of knowledge retrieval, and aims to solve the problems of knowledge solidification, insufficient timeliness and illusion of a traditional LLM (Logistics Language Model). A multi-granularity knowledge base is dynamically constructed, and a rule and semantic partitioning technology is combined, so that a text is converted into a normalized vector, and a hybrid index is established; a two-channel retrieval triggering mechanism is adopted, keyword matching scores and BERT semantic probability analysis are fused, and retrieval requirements are intelligently judged; vectorization retrieval is realized through a BGE-M3 model, and candidate results are reordered in combination with a cross encoder to improve the precision. The system supports multi-language adaptive processing, dynamic switching of word segmentation strategies and cross-language retrieval, and introduces real-time knowledge updating and version control. According to the method, the answer timeliness and accuracy are remarkably improved, the context coherence of multiple rounds of dialogues is optimized, the method can be widely applied to the fields of intelligent customer service, professional questions and answers and the like, the LLM illusion risk is effectively reduced, and the knowledge traceability is enhanced.

Aspects of the present disclosure provide systems, methods, and computer-readable storage media that support generative artificial intelligence (AI)-assisted analytics of structured data sets. For example, a system may receive a prompt that includes information associated with a structured data set which includes at least some numerical data. The system may provide the prompt as input to an agent orchestrator to select one or more generative AI agents to perform analytics tasks corresponding to the information. The agent orchestrator includes a trained AI classifier configured to select the one or more generative AI agents from a plurality of generative AI agents. The system may execute an ensemble model to generate a response to the prompt based on the structured data set. The ensemble model includes the one or more generative AI agents. The system may output a graphical user interface (GUI) that includes one or more elements based on the response.

The invention discloses a knowledge base system construction method and device for multi-source heterogeneous data and a medium, and relates to the technical field of artificial intelligence and natural language processing. The method comprises the following steps: integrating a dynamic graph convolutional network and a hierarchical attention mechanism to construct a multi-modal document analysis engine; performing semantic structure analysis on the original heterogeneous document on the basis of a multi-modal document analysis engine to extract document structure features and content semantic features, and constructing an original document relationship model on the basis of the document structure features and the content semantic features; based on the original document relationship model, performing classification fusion on heterogeneous data in the original heterogeneous document to obtain a to-be-stored heterogeneous data corpus, and processing the to-be-stored heterogeneous data corpus by using a graph neural network to establish a cross-modal semantic association index; and based on the cross-modal semantic association index, performing classified storage on the to-be-stored heterogeneous data corpora by utilizing a preset heterogeneous database so as to complete knowledge base system construction of the multi-source heterogeneous data.

The systems and methods described herein relate to improvements to generative artificial intelligence systems through the use of generative artificial intelligence pipelines to supply external information to pre-trained large language models for use in answering queries. To improve the efficiency and accuracy of large language models in responding to user queries, according to various aspects described herein, such queries may be modified and augmented with additional relevant information from decentralized data sources. The retrieval-augmented generation pipeline may interface with one or more decentralized data sources and retrieve the external information, including unstructured text and / or structured data.

The implementations herein disclose advanced systems and methods for integrating, analyzing, and reasoning over heterogeneous data at scale. In some implementations, the system comprises a synergistic data processing infrastructure featuring: a graph database core for unified data representation; specialized loaders for concurrent ingestion and processing of structured, unstructured, and time series data; a natural language reasoning engine leveraging large language models; and a multi-modal user interface.

The invention relates to the technical field of knowledge maps, and discloses a semantic association and logic rule inference method based on a knowledge map, and the method comprises the steps: obtaining a cross-functional department first data set, and constructing a cross-department knowledge map; extracting index nodes from the cross-department knowledge graph, identifying semantic conflict indexes, generating an index semantic conflict list, and constructing the cross-department knowledge graph after semantic mapping based on the index semantic conflict list; obtaining a second data set of a cross-functional department, and obtaining a fused cross-department knowledge graph and a semantic association degree matrix based on the second data set and the cross-department knowledge graph after semantic mapping; dynamically adjusting the weight of each node and edge of the fused cross-department knowledge graph according to the semantic association degree matrix, and generating a cross-department collaborative knowledge graph; generating a cross-department collaborative decision suggestion based on the cross-department collaborative knowledge graph; according to the invention, the efficiency and quality of cross-department collaborative decision-making are significantly improved.

The invention relates to an intelligent question-answering system optimization method and device based on a knowledge graph, and the method comprises the steps: obtaining original knowledge data of a target knowledge domain, and constructing a knowledge graph structure model; extracting term information of entity nodes in the knowledge graph structure model, and constructing an entity term set; receiving a natural language question input by a user, executing a semantic understanding operation based on the standardized expression set to obtain a structured question semantic representation, and matching the question semantic representation with the case training set to obtain context semantic features; constructing a cue word template, and executing a query instruction generation operation to obtain a target query statement of the graph database; submitting the target query statement to a graph database to execute data retrieval operation, and obtaining query result data corresponding to the question semantic representation; and performing personalized rendering processing on the query result data based on the user portrait information to generate final question and answer return content. The method has the effect of improving the query accuracy.

A system, device and method are provided for assessing actions of authenticated persons within an enterprise system. The illustrative method includes extracting metadata comprising a plurality of categories from a plurality of data sources, and applying an unsupervised machine learning process to the extracted metadata. A plurality of clusters of the plurality of categories of the extracted metadata is generated, and thereafter one or more review criteria are applied thereto to generate curated clusters. The method includes training a supervised machine learning model with the curated clusters. The method includes, in response to receiving a new metadata input, processing the new metadata input with the trained supervised machine learning model. Data associated with the new metadata input is ingested based on respective clusters output by the trained supervised machine learning model for categories of the new metadata input.

Methods and systems for interpreting inputted information are described herein. In some embodiments, a method comprises processing inputted information wherein processing inputted information uses one or more intelligence modules using one or more intelligence models to process the inputted information; making, by the one or more intelligence modules, one or more decisions about inputted information based on the one or more intelligence models; learning, by the one or more intelligence modules, to update the one or more intelligence models; and interpreting inputted information based on the one or more decisions.

The present invention relates to the technical field of energy saving and carbon reduction of iron and steel enterprises. Disclosed are an iron and steel product carbon footprint management method and management system based on life cycle assessment. Aiming at an iron and steel enterprise with the characteristic of a blast furnace-converter long process, the present invention establishes an iron and steel product carbon footprint calculation model, an iron and steel product carbon footprint prediction model and an iron and steel product carbon footprint optimization model at a production device level by relying on the conversion relationship and the flow relationship of a substance flow and an energy flow in an iron and steel product production process during carbon footprint management. A plurality of carbon footprint management methods such as carbon footprint calculation, analysis, prediction and optimization are realized in view of the characteristic of path diversity in the iron and steel product production process, and a carbon footprint management system is formed on this basis. The carbon footprint management method overcomes the defect whereby existing carbon footprint management methods only analyze carbon footprints from the perspective of result comparison, and realizes targeted, interlocked and systematic carbon footprint management during the life cycle of an iron and steel product.

A system, device and method are provided for managing data ingested into remote computing environments. The illustrative method includes ingesting a first set of data into a first container of a remote computing environment (RCE), the ingesting resulting in a data set structured with structural formatting of the RCE. The method includes determining a functional data model applicable to the ingested data set, and constructing, in the RCE, a relational dataset by at least in part applying the functional data model. The method includes generating a base data set from the ingested data set and the relational data set and persisting the base data set in a second container, the relational data set contributing via one or more tools for accessing relational databases. The method includes returning at least some of the base data set to in response to user requests to access enterprise data.

The present application provides a digital twin utility tunnel system based on a reduced-order simulation model and a real-time calibration algorithm. The system includes a big data aggregation unit and a real-time simulation deduction unit. The big data aggregation unit is configured to collect static attribute data and real-time dynamic data. The real-time dynamic data includes fixed monitoring data and mobile monitoring data. The fixed monitoring data is collected by gas sensors fixedly installed in the utility tunnel, and the mobile monitoring data is collected by mobile sensors in the utility tunnel. The real-time simulation deduction unit includes a forward prediction module and an inversion calibration module. The forward prediction module is configured to perform dimension reduction simplification and rapid prediction, and the inversion calibration module is configured to perform real-time calibration on a predicted physical field, correct the predicted physical field, and perform inversion on hazard sources.

The invention relates to the technical field of financial data processing, in particular to a multi-source heterogeneous fund data processing method and system, and the method comprises the steps: recording source information through building a data source registry, and marking a unique identifier for data; performing differential analysis on the fund data in different formats to generate standardized column type storage data; traversing column type storage data to extract statistical characteristics, performing field classification through metadata analysis and financial dictionary matching, analyzing business connotations of fields difficult to classify in combination with a localized large language model, and mapping the business connotations to a header fusion knowledge graph; generating a mapping rule from the source field to the enterprise-level data model by applying a rule engine template on the basis of field classification and semantic recognition results; converting the data structure according to the mapping rule and executing standardization processing; the quality is further optimized through data cleaning; and finally, the data are verified, standardized fund data supporting data traceability are output, and the strict requirements of financial supervision application are met.

The invention discloses a data sharing system and method for realizing multi-agent interaction, and belongs to the technical field of agents. According to the method, production command instructions are labeled, a large language model is trained, and task types are identified; disassembling the production command instruction into a subtask sequence; constructing a knowledge relationship graph through the graph database; establishing a unified data interface, and constructing a data sharing center; dividing agent types according to the power transmission and distribution service scene, and determining a function boundary of each agent; a multi-agent simulation environment is used for testing the cooperation efficiency of the subtasks, and the execution sequence of the subtask sequence is optimized; sub-tasks are dynamically allocated according to the intelligent agent load and the skill matching degree; the intelligent agent accesses the knowledge relation graph through a unified data interface, and real-time pushing of data change is achieved through a message queue; the decision of the large language model is optimized through cue word engineering, specific operation is executed by utilizing a tool library component, and the intelligent agent executes operation according to a subtask sequence.

A system and method are provided for examining data from a source. The method is executed by a device having a processor and includes receiving a set of historical data and a set of current data to be examined, from the source. The method also includes generating multiple statistical models based on the historical data and a forecast for each model. The method also includes selecting one of the multiple statistical models based on at least one criterion, and generating a new forecast using the selected model. The method also includes comparing the set of current data against the new forecast to identify any data points in the set of current data with unexpected values. The method also includes outputting a result of the comparison, the result comprising any data points with unexpected values.

The invention provides a multi-source heterogeneous data integration and governance-oriented data weaving method, which comprises the following steps of: performing data acquisition from an accessed multi-source heterogeneous data source to generate an original multi-source heterogeneous data stream; performing standardization processing on the original multi-source heterogeneous data stream to generate a standardized multi-source heterogeneous data set; performing active content scanning processing on the standardized multi-source heterogeneous data set to determine business metadata, and performing consanguinity tracking processing on the business metadata to generate enhanced business metadata; calling a domain ontology framework to carry out standardized constraint on the enhanced service metadata to obtain standardized service metadata without cross-data source semantic ambiguity, and carrying out implicit association mining processing on the standardized service metadata based on a graph neural network to generate a semantic knowledge graph containing core entities and relationships; and performing logic abstraction processing on the distributed data resources according to the semantic knowledge graph to generate a unified data access interface.

In an embodiment, a workflow automation computer system comprises one or more hardware processors; one or more network interfaces that are communicatively coupled to one or more internetworks and capable of network communication with a browser extension hosted on an agent computer, a relational database system, and a support ticket system; and one or more non-transitory computer-readable storage media coupled to the one or more hardware processors and storing one or more trained machine learning models having been trained to output predictions of actions of web-based applications based on input specifying a plurality of browser events from interactions with the web-based applications; and one or more sequences of instructions which, when executed using one or more processors, cause the one or more processors to execute receiving, from the browser extension, one or more browser event objects corresponding to user input signals arising from interactions of the agent computer with the web-based applications, extracting attribute values from the one or more browser event objects, and storing, in the relational database system, the one or more browser event objects and attribute values as a plurality of events of user action time series records; clustering the plurality of events and performing action analysis on the plurality of events by executing a first inference stage of the one or more trained machine learning models over the one or more events to output suggestions of associations of user actions corresponding to the one or more events; forming one or more training data records based on the associations of user actions corresponding to the one or more events; re-training the one or more trained machine learning models based on the associations of user actions to produce one or more re-trained machine learning models.

The invention discloses an automatic instrument fault prediction system and method based on big data analysis, and belongs to the technical field of fault detection. The system comprises the following modules: an intelligent data processing and normalizing module which collects multi-source heterogeneous data of an instrument and an environment sensor in real time and performs data cleaning, standardization and quality evaluation; the working condition environment characteristic analysis module is used for identifying the current working condition state and quantitatively evaluating the influence degree of environmental factors on instrument operation; the multi-monitoring-parameter coupling analysis module is used for calculating and analyzing the mutual influence relationship among the monitoring parameters of the instrument and evaluating the coupling strength and influence links among the monitoring parameters in real time; the dynamic threshold calculation module is used for dynamically calculating and adjusting an early warning threshold system of each monitoring parameter; the fault prediction decision module is used for comprehensively evaluating various monitoring parameters and calculating a fault risk probability; and the early warning output and feedback module optimizes early warning output through an intelligent filtering mechanism, and collects early warning effect feedback for continuous optimization.

The invention belongs to the technical field of fishery resource informatization management and ecological prediction, and particularly relates to a dynamic prediction method of a resource habitat dynamic prediction system based on multi-source heterogeneous data fusion, and the method comprises the steps: a multi-source heterogeneous data collaborative collection and standardization processing module synchronously collects cross-regional data, and generates a time-space aligned standardized data set; the multi-modal habitat adaptability evaluation and prediction model is used for receiving the standardized data set as input, constructing environmental, biological and social modalities based on multi-source fusion data, and generating a habitat adaptability prediction result; the three-dimensional dynamic visualization and decision support platform is used for receiving the habitat suitability prediction result and generating a habitat thermodynamic diagram, a resource abundance gradient and an environmental parameter dynamic visualization display and decision under multiple spatial and temporal scales; according to the method, an international data collaboration mechanism, an ecological niche model optimization algorithm and a lightweight visualization engine are subjected to system-level integration, and an engineering solution is provided for biological resource protection of a sea area.

An apparatus for generating obfuscated data within a computing environment, comprising a processor and a memory containing instructions configuring the processor to access a database containing a plurality of private data elements belonging to at least a private record, generate a set of obfuscated data elements, representative of the at least a private record, as a function of the plurality of private data elements using an generative model, determine a first distance measure between at least an obfuscated data element within the set of obfuscated data elements and at least a private data element of the plurality of private data elements within the database, and verify the first distance measure is within a distance range, wherein a minimum threshold of the distance range is determined as a function of a deidentification parameter and a maximum threshold of the distance range is determined as a function of an obfuscation parameter.

The invention discloses a full-life-cycle-based medical equipment monitoring analysis system and method, and relates to the technical field of medical equipment monitoring, and the method comprises the steps: collecting medical equipment data in real time, and dynamically constructing a full-life-cycle digital twin model of medical equipment; constructing a medical equipment knowledge graph based on the equipment type, the function association and the spatial distribution; when the medical equipment node detects abnormal data, an early warning signal is sent to a full-life-cycle digital twin model associated with the medical equipment in the medical equipment knowledge graph in combination with the medical equipment knowledge graph; preliminarily judging fault causes and fault location, and generating an analysis report; performing multi-dimensional verification on the diagnosis result in the digital twin environment, comprehensively evaluating the risk coefficient of the scheme, and outputting an optimal maintenance strategy; in the maintenance process, the maintenance process is recorded in real time, and maintenance data and equipment state updating are synchronously fed back to the equipment full-life-cycle digital twin model.

The embodiment of the invention provides a method for constructing a large language model knowledge management system. The method comprises a knowledge construction step, a knowledge retrieval step and an answer production step. According to the scheme, the artificial intelligence large language model technology can be utilized. External knowledge is obtained from multiple data sources, a search technology and a cue word function of a large language model are combined, RAG proposes a problem to the large model, knowledge obtained by searching the problem in the multiple data sources is used as a background context, and information of the problem and the background context is integrated into cue words of the large language model. And therefore, the large language model can make a final accurate answer.

The invention relates to a method convenient for data consanguinity collection and analysis, which comprises the following steps of: obtaining original consanguinity data comprising a task execution log, application metadata and a cross-system dependency relationship, and carrying out standardization processing on the original consanguinity data to generate structured consanguinity information comprising an asset unique identifier, an upstream and downstream association relationship and a data operation type; structured consanguinity information is synchronously written into a graph database and a distributed data warehouse, the graph database stores real-time association topology, the distributed data warehouse stores full-amount historical versions, and a transaction consistency algorithm is adopted to ensure the atomicity of double-write operation, so that the data storage efficiency is improved. Single-asset-level consanguinity tracking is performed based on real-time topology of a graph database, global consanguinity analysis is performed based on batch computing power of a distributed data warehouse, a direct dependence path, a deep association network and a closed-loop link detection result are generated, and a closed-loop management mechanism from data acquisition, analysis to optimization is formed. And the problem that an analysis result is disjointed from an acquisition end in a traditional scheme is solved.

The invention discloses a multi-source heterogeneous knowledge graph data fusion method and system, and the method specifically comprises the following steps: S1, collecting data from a plurality of heterogeneous data sources, cleaning the collected data, removing noise data and repeated data, and converting the data in different formats into a unified intermediate format; s2, a semantic model is established, and semantic heterogeneous concepts and relations in different data sources are recognized and aligned through a natural language processing technology and domain ontology knowledge. The invention relates to the technical field of knowledge graph data processing, and the multi-source heterogeneous knowledge graph data fusion method and system effectively reduce the problems of noise, repetition and semantic inconsistency in data through data cleaning, semantic alignment and conflict resolution, and ensure the accuracy and consistency of fused data, thereby improving the quality of a knowledge graph and improving the reliability of the knowledge graph. Reliable knowledge support is provided for application in the fields of medical treatment and the like.

Systems and methods are described for a managed multidimensional search based on an application query and management policies. The application can receive a pipeline endpoint. The query can be sent to the pipeline endpoint. The pipeline can vectorize the query for comparison against a vector database of an identified dataset. The closest vectors can be converted back to content chunks. The system can generate prompts related to the content chunks and send those prompts to an AI model. The AI model can then output a response that includes the most relevant content, citations, and hyperlinks. These can be displayed in the application.

The invention discloses a multi-modal visual arrangement recommendation method, belongs to the technical field of artificial intelligence and data visualization crossing, and realizes visual arrangement recommendation based on multi-modal input analysis, a dynamic mixed recommendation model and an intelligent optimization algorithm. Comprising the following steps: multi-modal intention analysis: realizing intelligent analysis of multi-modal input through combined use of a base model and a fine tuning model, realizing high-precision intention classification in combination with a pre-training language model and a domain adaptation fine tuning technology, and triggering dynamic prompt word recommendation; performing intelligent layout generation: performing global optimization of component space allocation by adopting a genetic algorithm, performing business rule adaptation by combining a constraint solver, and modeling an interaction relationship between components by utilizing a graph neural network; and dynamic mixed recommendation: constructing a three-level recommendation architecture including collaborative filtering, content matching and reinforcement learning. According to the method, a closed-loop recommendation process of user intention-intelligent recommendation-feedback optimization is realized, and the intelligent level of visual arrangement and the user experience are remarkably improved.

The invention discloses a DDGS three-dimensional storage device based on PLC control and a management method of the DDGS three-dimensional storage device, relates to the technical field of intelligent storage management, and solves the problems that in traditional DDGS feed storage, the storage space utilization rate is low, the storage turning cost is high, risk control is not timely, and a quick response strategy is lacked. According to the method, the dynamic perception metadata warehouse is constructed, and DDGS material characteristic key data is monitored in real time to support anomaly detection; a storage control topology model is generated by using a graph theory modeling and dynamic attribute injection method to map an exception type; periodically collecting equipment data and fusing and updating a storage state; state diagram nodes are enhanced, risk response priorities are calculated, and grading operation is triggered; pLC control logic is dynamically adjusted based on historical data, and real-time monitoring and multi-device linkage control are achieved; the intelligent level of DDGS three-dimensional warehouse management is remarkably improved, the warehouse space utilization rate is increased, and the risk control capability is enhanced.

The invention provides a sewage treatment data optimization method and system based on cloud computing, and relates to the technical field of data processing.The method comprises the steps that real-time key information, including water quality indexes, equipment operation parameters and energy consumption data, collected by monitoring equipment in real time is received, the key information is preprocessed, and a processed data set is obtained; constructing a cloud database according to the processed data set, and performing data analysis including trend prediction and anomaly detection by using the cloud database to obtain an analysis result; and according to the analysis result, constructing a sewage treatment data optimization model, and inputting the water quality standard, the equipment operation efficiency and the energy consumption cost parameters into the sewage treatment data optimization model to generate an optimization scheme. By collecting and analyzing sewage treatment key information in real time and constructing and optimizing the data model, intelligent adjustment and optimization of the sewage treatment process are realized, the water quality is improved, the energy consumption is reduced, and the equipment operation efficiency and the overall treatment effect are improved.