990 results about "Datawarehouses" patented technology

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 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 relates to the technical field of data processing, and discloses a cross-border e-commerce commodity recommendation system and method based on multi-source data fusion. The system comprises an acquisition module for performing data acquisition to obtain a cross-border unified data warehouse and federal learning cooperation data; the classification module performs text classification processing to obtain a user preference analysis result and an interpretable attention mark; a quantization module carries out quantization processing to obtain a cross-border selection feature matrix; the fusion module carries out weighted fusion processing to obtain a basic comprehensive score and a weight convergence detection result; the evaluation module performs risk evaluation processing to obtain risk probability distribution and cross-border compliance evaluation results; and the sorting module carries out real-time processing through lightweight preprocessing and a flow-type calculation pipeline to obtain a comprehensive score sorting list of cross-border selected products. The problem that a traditional cross-border e-commerce product selection method cannot effectively integrate multi-source heterogeneous data and cannot analyze user feedback text semantic information is solved.

Optimizing data warehouse utilization by a data ingestion pipeline, including: gathering information describing usage of a plurality of data warehouses; receiving, from a data ingestion pipeline, a request for a data warehouse to receive data from the data ingestion pipeline; selecting, based on the information, the data warehouse from the plurality of data warehouses; and providing, to the data ingestion pipeline, an indication of the data warehouse.

The invention relates to a building module data interface intelligent monitoring system based on the Internet of Things and a deployment method, and belongs to the technical field of building information monitoring. The system is composed of a distributed sensing unit, an edge computing gateway, a cloud platform and a visual terminal, a self-adaptive filtering algorithm and an abnormal mode recognition model are built in the edge computing gateway, the cloud platform adopts a time sequence database to construct a multi-dimensional data warehouse, and structural health degree evaluation is carried out by fusing an LSTM neural network and a random forest algorithm. The deployment method comprises the steps of optimizing a sensor distribution strategy based on a BIM model, establishing a wireless Mesh ad hoc network communication architecture, and configuring a grading early warning mechanism and a fault tracing function. The innovation point is that a dynamic threshold adjustment algorithm and an interface performance degradation prediction model are provided, and real-time monitoring of the connection state of the building module and life prediction are realized. The system has the advantages of flexible deployment, high detection precision and low maintenance cost, and the intelligent level of building structure safety monitoring is effectively improved.

The invention relates to the technical field of data sharing, and discloses a multi-department data sharing method and device, equipment and a storage medium. The method comprises the following steps: carrying out acquisition and standardized preprocessing on multi-department data required by enterprise project application to generate an intermediate data packet; establishing an event trigger distributed data control mechanism based on the intermediate data packet, calculating a dynamic trigger threshold, monitoring data change, and generating a target control data stream; performing template-free adaptive learning to generate an optimal data processing flow definition file; executing data classification, self-adaptive compression and increment processing at the edge computing node to generate an optimized data packet and metadata thereof; and inputting the optimized data packet and the metadata thereof into a SaaS cloud server central data warehouse to generate a project declaration data packet, thereby realizing consistency coordination and conflict automatic resolution of data of multiple departments, and realizing unified access and standardized processing of heterogeneous data sources.

The invention belongs to the technical field of data management, and particularly relates to a block chain-based heterogeneous data source right confirmation method, which comprises the steps of multi-source heterogeneous data acquisition, digital fingerprint generation, block chain hash anchoring, data storage and circulation and ownership tracing and verification. According to the invention, original data is collected from various terminals and system platforms by adopting a multi-source heterogeneous data acquisition technology so as to ensure the diversity and universality of data sources, and the collected original data is processed and unique digital fingerprints are generated. The method comprises the following steps: processing a digital fingerprint through a hash algorithm to generate a corresponding block chain hash value, anchoring the block chain hash value on a block chain, ensuring authority and permanency of a data ownership record, storing original data in a safe data warehouse, recording the hash value and ownership information of the data on the block chain at the same time, and storing the data in the block chain. Through combination of multi-source heterogeneous data acquisition, digital fingerprint generation and block chain hash anchoring technologies, automatic registration and efficient management of data ownership are realized.

Provided in the present application is a code generation method, comprising: a code development platform receives input information by a user in a first code file, extracts context information of the input information according to a data warehouse to which the first code file belongs, and retrieves a business knowledge base corresponding to the data warehouse according to the input information so as to obtain target business knowledge; then the code development platform combines the input information, the context information and the target business knowledge according to a prompt template, so as to obtain prompt information; and then the code development platform inputs the prompt information into a large language model (LLM) for reasoning, and presents to the user a code segment reasoned by the LLM. Thus, using the prompt information from the same data source in different processes for prompting the LLM can achieve complete sharing and complete alignment of the prompt information, thereby improving the accuracy of generating code in single time. Furthermore, using a unified prompt template to combine the context information and the business knowledge can make a code segment generated by the LLM more accurate, thereby improving the code acceptance rate in professional domains.

The invention relates to the technical field of smart city traffic, and discloses a smart city traffic dynamic optimization system and method based on digital twinning. The system obtains urban traffic network multi-dimensional data from a plurality of heterogeneous data sources through a traffic multi-dimensional data acquisition module and integrates the urban traffic network multi-dimensional data into a traffic related data warehouse; a traffic digital twinning model construction module extracts features from the data warehouse to generate a traffic related feature matrix, and a digital twinning traffic dynamic model is constructed according to the traffic related feature matrix to output a theoretical traffic state value; the traffic flow map construction module determines a dimension link map of each dimension and constructs a traffic flow link map; the traffic core feature screening module screens a traffic core feature sequence based on the map; the traffic multi-dimensional optimization analysis module performs multi-dimensional difference analysis on the theoretical traffic state value and real-time actually measured traffic data, and generates a region-level difference coefficient matrix in combination with the core feature sequence; and the traffic event association positioning module can realize accurate management and dynamic optimization of urban traffic.

The invention relates to the technical field of transformer monitoring, and discloses a comprehensive online transformer monitoring system which comprises a sensor layer, an edge computing layer, a cloud platform layer and a communication module. The cloud platform layer comprises a data warehouse, a big data processing engine, a comprehensive diagnosis engine, a model training engine and a system management module, the edge calculation layer is in communication connection with the cloud platform layer through a communication module, the comprehensive diagnosis engine integrates a deep neural network and a knowledge graph inference engine, and cross validation of data driving and knowledge guiding is achieved; a model training engine utilizes historical data and online incremental data to continuously optimize a model, and a dynamic knowledge graph updates a fault rule through a real-time diagnosis result and expert feedback, so that the system can adapt to novel faults and complex working conditions, and the diagnosis accuracy and adaptability are greatly improved.

The invention belongs to the technical field of information technology and intelligent health service, and discloses a community old-age care service resource optimal configuration method based on edge computing, which comprises the following steps: collecting physiological data, environmental data and activity data of old people to form original heterogeneous data, uploading the original heterogeneous data to a data warehouse through an urban and rural community network adaptation mechanism, generating a space-time tagging multi-modal data set; constructing a dynamic digital twinborn model, generating a space thermodynamic diagram, identifying different level priority requirements, and generating a time period allocation scheme through a conflict priority scoring mechanism; an MILP model is constructed to dynamically optimize edge node computing power and functional area space resources, and a resource optimization report and a block chain transaction list are generated; desensitizing the original data of the old people, creating a digital identity card, generating an encrypted service log, and synchronously recording migration behaviors of the old people; and triggering network strategy switching and priority threshold self-adaption, and carrying out resource scheduling and strategy closed-loop optimization.

The invention discloses a Frechet distance-based abnormal inflow traceability method and system for a sewage treatment plant, and the method comprises the steps: carrying out the unit division of the inflow range of the sewage treatment plant through combining land parcel information, pipe network information, convergence conditions, monitoring stations and the like, and analyzing the correlation between total phosphorus and conductivity; arranging a pipe network water quality monitoring micro station, and carding a factory-network-land parcel-source pointer relation library; a sewage treatment plant big data warehouse is constructed, related data are cleaned and treated, and the data are accessed to a pollutant traceability visualization platform; a sewage plant water pollution traceability model is constructed, a water inlet total phosphorus standard exceeding event is selected to carry out traceability research, the traceability model is embedded into a visual platform, and sewage plant total phosphorus concentration standard exceeding traceability visualization is achieved. Compared with the abnormal inflow tracing method of the sewage treatment plant, the large data resource utilization rate can be greatly improved while a large amount of manpower and material resources are saved, and a basis is provided for locking secretly-discharged enterprises, predicting pollution diffusion and environment emergency.

The invention discloses a supply chain collaborative management method and system for a whole-industry link platform, and the method comprises the steps: constructing a distributed supply chain data warehouse through employing a block chain and lightweight encryption; generating a multi-dimensional matching degree score table to recommend suppliers; decrypting and storing the encrypted bidding file, and calculating a comprehensive score according to a quantitative index and an expert qualitative index; constructing a hierarchical prediction model, and outputting a three-dimensional prediction result; calculating a supply-demand gap according to a supply-demand elastic coefficient coupling algorithm, automatically generating an order, and synchronizing the order to each end in real time; building a supply chain digital twinborn model, and dynamically planning an optimal transportation route through an intelligent fusion road condition algorithm; early warning information is automatically sent to generate a coping strategy, and full-link operation data are integrated for visual display; the objective of the invention is to solve the problems of existing supply chain data splitting, low supplier matching efficiency, low demand prediction precision, rigid logistics planning, risk monitoring lagging and poor collaboration of all links.

The invention discloses a lithium battery full life cycle quality tracing system based on digital twinning, and relates to the technical field of lithium battery quality tracing, and the system comprises a physical entity layer which integrates multiple types of sensors and detection equipment to collect full-link data; the digital twinning modeling layer is used for constructing a multi-scale model to map a battery state; the data acquisition and transmission layer is used for realizing data real-time transmission preprocessing through an edge gateway and an encryption protocol; the data storage and management layer is used for establishing a data warehouse by using a distributed database and performing backup; the intelligent analysis and decision-making layer integrates an algorithm to realize anomaly detection, performance prediction and tracing; the visualization and interaction layer is used for providing a multi-terminal visualization and interaction function; and the block chain evidence storage layer constructs an alliance chain to ensure that the data cannot be tampered. According to the invention, full-life-cycle data connection and credible tracing of the lithium battery are realized, the quality problem positioning efficiency is improved, intelligent early warning and optimization capabilities are provided, the operation threshold is reduced, and industrial quality control and green development are facilitated.

The invention belongs to the field of big data, and relates to a financial data processing method, which comprises the following steps: acquiring enterprise end financial data and government end financial data of a target project; performing data integration on the enterprise end financial data and the government end financial data by adopting a preset data warehouse to obtain a comprehensive financial data set; receiving a report request of a user, determining an accounting criterion based on the report request, and loading accounting processing logic corresponding to the accounting criterion from a pre-established accounting module; and based on the accounting processing logic, performing financial analysis processing on the comprehensive financial data set, and generating a financial statement corresponding to the target project. The invention further provides a device, equipment and a medium. The financial data processing efficiency can be improved.

The invention discloses a data warehouse multi-hop relationship quick retrieval method and system based on a graph embedded index, and relates to the technical field of multi-hop relationship retrieval data processing. The quick retrieval method for the multi-hop relationship of the data warehouse based on the graph embedded index comprises the following steps of: monitoring data flow interference; monitoring index updating timeliness; monitoring the query accuracy of the multi-hop relationship; and timely monitoring of data query response. According to the method, whether to perform graph model modeling to obtain the graph model is determined by judging whether to trigger the interference suppression mechanism, then the multi-level index is constructed based on the graph model, and whether to trigger the index step length updating mechanism is determined based on the index updating timeliness judgment result; and finally, based on a multi-hop relationship result query accuracy verification result, determining whether to trigger a candidate node screening range shrinkage mechanism, thereby achieving the effect of improving the multi-hop relationship retrieval efficiency of the data warehouse, and solving the problem of low multi-hop relationship retrieval efficiency of the data warehouse caused by data flow interference in the prior art.

According to an embodiment of the present invention, a method for using information in conjunction with a data repository includes encrypting data associated with the information with an encryption key, sending at least the encrypted data to the data repository, and possibly deleting the information. The method also includes receiving a request for the information from a remote device, and sending a request for the encrypted data to the data repository. The method further includes receiving the encrypted data from the data repository, decrypting the encrypted data using the encryption key, and sending the information to the remote device.

An AI-powered data quality monitoring and automatic alerting system (100) for cloud-based data warehouses, comprising: a. an AI-driven data quality monitoring module configured to continuously assess data in the cloud-based data warehouse for accuracy, completeness, consistency, and timeliness; b. an anomaly detection and warning system configured to automatically detect data anomalies based on the assessment performed by the data quality monitoring module and to generate real-time alerts based on detected anomalies; c. an automated data cleansing and remediation workflow that triggers corrective actions in response to detected anomalies, including data imputation, record merging, and validation to maintain data integrity.

The present invention is directed to systems for data integration and analysis using artificial intelligence networks to generate predictive outputs based on one or more inputs. The present invention incorporates healthcare data from a variety of sources and databases into a single system for predictive analysis using artificial intelligence. The present invention incorporates multifaceted authorization to access data from a variety of databases and integrate the data into a single data warehouse. The system of the present invention is further configured to analyze the integrated data to identify predictors, and subsequently uses the predictors to determine a predictive output based on an input.

A generative artificial intelligence (AI) system includes a generative AI model configured to generate outputs based on a training data set. The AI system additionally includes a secure data vault system. The secure data vault system additionally includes a sandbox system storing the generative AI model and operatively coupled to the generative AI model to send inputs to generate the outputs from the generative AI model, wherein the sandbox system comprises an execution environment configured to restrict execution of the generative AI model to a predefined memory address range. The secure data vault system further includes a secure network service communicatively coupled to the sandbox system and configured to authenticate a connection to an external system and to download from the external system an update package for the generative AI model when the connection is authenticated.

The invention discloses a control method and system of automation equipment for industrial production, and particularly relates to the technical field of automation equipment control. The method comprises the following steps: extracting diversified samples of normal values, abnormal values and hidden defects from an existing data warehouse, labeling categories, and constructing a high-quality initial data set; evaluating the stability of the model by using a layered K-fold cross validation method, and testing and evaluating the consistency of the abnormal response capability and the historical mode through a high-load condition when the model is unstable; comprehensively analyzing the stability and the abnormal response capability of the model, and evaluating the capability of the AI for identifying error modes in historical data; for inaccurate identification, the dynamic time warping algorithm is used for detecting the deviation degree of the equipment operation parameters, an alarm is triggered in time, and the production process is paused to guarantee the safety of the equipment, so that the reliability and accuracy of the AI model in a dynamic environment are remarkably improved, and meanwhile, the risks of equipment damage and production safety accidents caused by optimization errors are effectively reduced.

The invention particularly relates to a nuclear power important parameter abnormity identification and alarm system. The system comprises a data warehouse and at least one group of parameter abnormity identification modules arranged in the data warehouse. And each parameter abnormity identification module receives the real-time operation data of the unit as input so as to carry out abnormity identification and alarm on specified nuclear power parameters. According to the invention, the operation state of the unit is monitored, and the abnormity of important parameters, equipment and systems can be identified timely and accurately.

In accordance with an embodiment, described herein is a system and method for providing support for extensibility and customization in an analytic applications environment. An extract, transform, load (ETL) or other data pipeline or process provided by the analytic applications environment, can operate in accordance with an analytic applications schema and / or a customer schema associated with a customer (tenant), to receive data from the customer's enterprise software application or data environment, for loading into a data warehouse instance. A semantic layer enables the use of custom semantic extensions to extend a semantic model, and provide custom content at a presentation layer. Extension wizards or development environments can guide users in using the custom semantic extensions to extend or customize the semantic model, through a definition of branches and steps, followed by promotion of the extended or customized semantic model to a production environment.

Mechanisms are provided for dynamically generating workspaces and provisioning them with datasets. The mechanisms store datasets in a data vault for provisioning to dynamically generated workspaces associated with users. The dynamically generated workspaces are computer environments through which the users can perform operations on the one or more datasets. The mechanisms receive a request, from a user, for access to a specified dataset, and retrieve a data usage agreement (DUA) corresponding to a pairing of the user with the specified dataset. The DUA specifies a level of obfuscation to be applied to the specified dataset when provisioning a workspace associated with the user, with the specified dataset. The mechanisms dynamically generate, on-demand, the workspace associated with the user based on the retrieved DUA. The mechanisms also automatically provision, on-demand, the dynamically generated workspace with a version of the specified dataset corresponding to the level of obfuscation specified in the DUA.

The invention provides a code generation method, which comprises the following steps that: a code development platform receives input information of a user in a first code file, extracts context information of the input information according to a data warehouse to which the first code file belongs, and retrieves a business knowledge base corresponding to the data warehouse according to the input information to obtain target business knowledge, and then the code development platform splices the input information, the context information and the target business knowledge according to a prompt template to obtain prompt information, then the code development platform inputs the prompt information into a large language model (LLM) for reasoning, and a code snippet generated by LLM reasoning is presented to a user. The LLM is prompted by using the prompt information of the same data source in different processes, complete sharing and complete alignment of the prompt information are realized, the accuracy of single code generation is improved, and context information and business knowledge are assembled through a unified prompt template, so that code snippets generated by the LLM are more accurate. Therefore, the code acceptance rate in the professional field is improved.

An example method for managing data configuration information across data repositories includes receiving, by a target data repository, data for replication from a source data repository; generating, by the target data repository and based on the receiving the data, target repository identifier information for the data, the target repository identifier information enabling clients to access the data on the target data repository and different from source repository identifier information; accessing, by the target data repository, the source repository identifier information that enables clients to access the data on the source data repository; providing, by the target data repository at a first time, the target repository identifier information for accessing the data on the target data repository; and providing, by the target data repository at a second time, the source repository identifier information for accessing the data on the target data repository.

The invention relates to the technical field of project cost consultation management, and discloses a project cost consultation management method and system, and the method comprises the steps: building a material price database which is dynamically updated in real time, obtaining market material price data from the Internet, and updating the material price database; adding the material price database into the data warehouse set, executing a cluster cache strategy, and optimizing the data warehouse set by using cache; executing a load balancing strategy on the server network of the transmission data warehouse set; acquiring data features of the cost project, executing an intelligent matching strategy, and acquiring an optimal cost model of the cost project; calculating the cost of the cost project by using the optimal cost model; if the cost is higher than the cost threshold, executing a budget project adjustment strategy, and adjusting budget distribution of sub-projects of the cost project; and executing the block chain recording strategy, and storing the operation record in the log record. And the reliability and accuracy of project cost management are improved.

The invention discloses an AI-driven product service recommendation method and system, relates to the field of wisdom medical treatment, and more accurately captures the relationship between user demands and product service characteristics by constructing personalized portraits and comprehensively mining product service data and a full-process automatic learning framework, thereby providing a more accurate recommendation result. Diversified demands of the user are deeply mined through the constructed personalized portrait, and personalized product service recommendation is provided according to the demands, so that the satisfaction degree of the user is improved; the full-process automatic learning framework extracts feature information from a user personalized portrait and product service data warehouse, model training and verification tuning are carried out, and a recommendation model is continuously learned and optimized through a multi-dimensional full-process automatic learning mode; the problem that an existing recommendation method cannot meet diversified requirements of users is solved, the diversified requirements of the users can be accurately met, and recommendation accuracy and user experience are improved.

A method includes receiving a text embedding generation query from a user requesting generation of a text embedding for one or more data elements stored at a data warehouse. In response, the method includes selecting, using the text embedding generation query, a text embedding model from a plurality of different text embedding models. The method includes generating, using the selected text embedding model, the text embedding for the one or more data elements and storing the text embeddings at the data warehouse. The method includes receiving a machine learning model training query from the user device requesting training of a machine learning model using the text embeddings. In response to receiving the machine learning model training query, the method includes training the machine learning model using the text embeddings. The method includes providing, to the user device, a notification indicating that training of the machine learning model is complete.

The invention provides a data blood relationship tracking method and device, computer equipment and a storage medium. The method comprises the following steps: respectively obtaining data source metadata, ETL task metadata and index system metadata; respectively constructing a data source node according to the data source metadata, constructing an ETL task node according to the ETL task metadata and constructing an index node according to the index system metadata in the graph database, and determining a node association relationship according to the data source metadata, the ETL task metadata and the index system metadata; constructing a data blood relationship chain taking the data source node as a starting point and the index node as an end point according to the node association relationship; and displaying the data blood relationship chain through the graph database. According to the method, a data warehouse table, an ETL scheduling task and an index system are integrated, and full-link blood relationship tracking and task monitoring operation and maintenance are achieved. The whole process of data from the data source to the index generation through the ETL task is visually presented, the workload of manually sorting the data relationship is greatly reduced, the data relationship can be changed along with the change of the data table structure, and the data blood relationship condition is flexibly and completely displayed.