2972 results about "Data administration" patented technology

The invention discloses an intelligent monitoring management method and system based on archive digitization, and relates to the technical field of data management, and the method comprises the steps: collecting and preprocessing multi-source archive data, employing a multi-mode BERT model to carry out the feature fusion of different data sources, and generating a unified semantic representation; semantic labeling is performed on archive data through a multi-label classification model, a semantic graph of archive content is constructed by using a graph database, an association relationship between archives is represented, a semantic index tree is constructed based on the semantic graph, and rapid positioning and calling of the archive content are optimized; and recording the change of each file version, positioning the change position based on a semantic index tree, identifying the semantic change of the file through a semantic difference comparison algorithm, recording hash, carrying out granularity division on the file content through the semantic boundary of each level of node in the index tree, and generating a user access strategy. According to the invention, dynamic perception and risk early warning of user behaviors are realized, and the intellectualization and safety of the archive management system are effectively improved.

The invention relates to a medical information processing and intelligent data management technology, and discloses a hospital multi-source heterogeneous data integration management system and method based on an EMPI, and the system carries out the real-time subscription and batch extraction of the data of an HIS, an EMR, an LIS, a PACS, a DRG and a pre-hospital first-aid system through an interface adapter, and generates a unified main index identifier through the matching of the EMPI, and carries out the real-time subscription and batch extraction of the data of the HIS, the EMR, the LIS, the PACS, the DRG and the pre-hospital first-aid system. Field-level cleaning, standardization and semantic mapping are carried out on the free text and the structured data, an intermediate standardized record set is formed, an event graph is constructed, event cluster recognition is carried out in combination with time, clinical entities, doctor seeing scenes and department similarity, consistency judgment and EM iterative fusion are carried out on the basis of data source reliability, and event cluster recognition is carried out. And outputting a unified patient file set and a downstream service interface. According to the invention, high-precision integration, standardized management and traceable application of multi-source data are realized, and a reliable data basis is provided for clinical decision support, medical insurance management and scientific research.

A management server system may obtain data associated with a vehicle. The management server system may obtain the data from a plurality of data sources associated with the vehicle. The data may include a plurality of subsets of data that each correspond to correspond to a particular driver assignment category. Each subset of data may identify a particular persona as a driver of a vehicle. The management server system may determine a driver of the vehicle based on each subset of data and a hierarchical plurality of driver assignment categories. Based on the determination of the driver of the vehicle, the management server system can dynamically map the driver to a particular event. The management server system may generate a user interface identifying the driver and the event.

The invention belongs to the technical field of financial data management, and particularly discloses a financial multi-source protocol self-adaptive fusion system based on AI semantic understanding and a knowledge graph, which comprises the steps of avoiding fusion errors caused by semantic misunderstanding through deep semantic analysis and a conflict resolution decision based on the knowledge graph; when protocol version updating is detected, incremental learning and model adjustment are carried out based on a newly added sample and historical experience, and a fusion protocol standard is dynamically updated, so that the high adaptive capacity to dynamic change of a financial protocol is realized; an exception monitoring and repairing mechanism is introduced, data missing, format errors and other exceptions are found in time and repaired online based on historical data modes and business logic, and negative influences of abnormal data on downstream risk control, transaction decision making and other key business links are avoided; through deep collaboration and information feedback among the intelligent modules, an intelligent system capable of self-learning and evolution is constructed.

The invention discloses a distributed time sequence library data management method supporting cold and hot data level-to-level management, and relates to the technical field of computer databases, comprising: receiving a write-in request of time sequence data, and executing preliminary write-in data aggregation and sorting; dynamically identifying data cold and hot attributes based on a multi-dimensional data cold and hot degree calculation model in combination with a write-in behavior and an access behavior of time series data; according to the cold and hot attribute recognition result, in combination with the hierarchy boundary of self-adaptive division, automatic hierarchical storage of the time series data is executed; based on a cold and hot data dynamic migration and scheduling mechanism, according to the access frequency and time decay characteristics of the time series data, dynamically adjusting the storage hierarchy of the time series data, and executing a data migration task; and constructing a hierarchical index system adaptive to different cold and hot attribute time series data, and combining query frequency dynamic identification and index structure automatic upgrading. By adopting a cold and hot data automatic identification and hierarchical storage mechanism, the overall performance and the resource utilization rate of the system are improved.

The invention discloses a bus duct full life cycle health management system based on digital twinning, and relates to the technical field of health management. The multi-source sensor collects operation parameters and static information, the digital twin modeling and mapping module constructs a physical model and associates real-time data, and a thermal-electric coupling equation is used for simulating temperature; the state monitoring and fault diagnosis module compares data to judge states and diagnoses faults by means of methods such as a fault tree, the health assessment and decision making module constructs an index system to assess health and makes a maintenance decision, the data management and interaction module stores data and realizes visual interaction and system integration, and the intelligent optimization module performs intelligent optimization based on operation and maintenance data. And optimizing model parameters and a decision strategy. According to the invention, intelligent health management of the bus duct is realized, multi-source data acquisition is accurate and comprehensive, fault diagnosis is more accurate and prediction is more timely through combination of digital twinning and an algorithm, and intelligent assessment assists scientific maintenance decision; and the operation and maintenance efficiency and the power transmission stability are improved through system integration and edge calculation.

In some implementations, a device may identify a network node that supports a first radio access technology (RAT), wherein the network node is associated with sessions with user equipments (UEs). The device may calculate a percentage of traffic from the sessions that is associated with the first RAT and not a second RAT. The device may determine that the network node is associated with a score based on the percentage of traffic associated with the first RAT not satisfying a threshold. The device may collect, based on the network node being associated with the score, device data associated with the UEs. The device may determine a network issue associated with the network node based on the device data. The device may determine a root cause for the network issue. The server may transmit an indication of the root cause.

The invention discloses a medical data management method and system based on multi-modal fusion and privacy protection, and the method comprises the steps: receiving multi-modal medical data, and executing cross-modal embedding learning to generate a joint embedding vector; constructing a patient multi-modal graph based on the joint embedded vector, and detecting abnormal inconsistency between the cross-modal data by using a graph attention network to generate an abnormal detection result; a privacy protection strategy is dynamically adjusted according to the anomaly detection result and the access context information, and desensitization processing is conducted on the medical data; and constructing a global model through security aggregation based on the desensitized medical data to generate an optimal governance action and apply the optimal governance action to a data management process. Through organic cooperation of multi-modal fusion, anomaly detection, adaptive privacy protection, security aggregation and dynamic strategy optimization, a closed-loop medical data governance platform is successfully constructed, and the problems of multi-modal data fragmentation, privacy compliance conflict and dynamic governance deficiency are effectively solved.

The invention discloses a sewage data management system and method based on kitchen garbage treatment, and relates to the technical field of data management. The system comprises a data acquisition and historical database construction module, a correlation analysis and dynamic response rule construction module, a sewage data trend pre-judgment module and an operation regulation and data updating module. The data acquisition and historical database construction module is used for acquiring garbage treatment load and sewage data and constructing a historical database covering time period fluctuation and seasonal periods; the correlation analysis and dynamic response rule construction module constructs a dynamic response rule and determines the time delay duration; the sewage data trend pre-judging module is used for acquiring load data in real time and pre-judging a future sewage change trend; and the operation regulation and data updating module adjusts parameters of the sewage treatment system, collects effluent data to construct a new sample, supplements a database and updates a dynamic response rule. According to the invention, accurate sewage data management is realized, and the operation efficiency and stability of the sewage treatment system are improved.

The invention relates to the technical field of medical data management, and discloses a medical data classification management system based on big data. A data integration and classification module of the system receives a historical medical data set and a real-time medical data stream, performs feature fusion and classification prediction through a deep learning model, and generates a theoretical classification result. And the deviation quantitative analysis module performs multi-dimensional deviation calculation on the theoretical classification result and the clinical actual classification result to generate a patient-level multi-dimensional deviation tensor. And the graph network anomaly positioning module inputs the multi-dimensional deviation tensor into a graph neural network for anomaly propagation simulation based on a medical institution topological structure, and generates an anomaly probability distribution diagram to identify a physical region where an anomaly occurs. And the adaptive decision control module dynamically allocates monitoring resources according to the abnormal probability distribution diagram, including starting high-frequency data acquisition for a high-probability abnormal region and executing an intervention test on adjacent nodes.

Various embodiments include methods and systems for automatically evaluating data structures and / or data management documentation for compliance with relevant standards, requirements, and policies. Various embodiments provide a framework for automating compliance reviews of data structures and / or data management against the latest version of standards, requirements, and policies. Methods may include transforming input requests for analysis and data structures and / or data management documents into formats suitable for processing by a large language model (LLM), prompting the LLM to identify concepts and / or elements that are likely subject to a standard, requirement, or policy, submitting a query to a vector database to identify standards, requirements, or policies relevant to the requested analysis and documents, prompting the LLM to assess compliance the data structures and / or data management documents with standards, requirements, or policy, and producing a report on compliance of the data structures and / or data management documents with the relevant standards, requirements, or policies.

The invention relates to the technical field of data processing, and discloses an automatic control platform operation data management method. The method comprises the steps of collecting industrial equipment data through a field bus, storing the industrial equipment data into a database in a classified mode and establishing an index table, extracting a control loop to construct a DCS model, generating a process abnormality diagnosis report, calculating control parameters by using a self-tuning algorithm and outputting a control instruction, optimizing a process formula based on an implementation effect and constructing a recommendation knowledge base. According to the intelligent data management method, the process abnormality is accurately diagnosed, the control parameters are automatically optimized, and the process knowledge is continuously accumulated, so that the control precision, the diagnosis efficiency, the parameter optimization capability and the knowledge reuse level of the industrial production process are improved.

The invention discloses a power data management method based on a block chain, and relates to the field of block chains, and the method comprises the steps: initiating a data request; randomly selecting a preset number of oracle machine nodes; segmenting a private key of the request task into a plurality of parts by adopting a threshold key segmentation method; acquiring requested power data from an external data source; verifying the obtained power data; a Byzantine fault-tolerant consensus mechanism is adopted to achieve a consensus for the power data; signing the verified power data through a threshold signature method; the oracle machine management contract performs aggregation verification on the received signature data; the oracle machine management contract returns the power data passing the aggregation verification to the intelligent contract of the data requester; the intelligent contract of the data requester obtains the returned power data through an event monitoring mechanism, and executes corresponding business logic and state updating according to the power data; aiming at the low fault-tolerant capability of an oracle machine in power data management in the prior art, the fault-tolerant capability of the system is improved.

The invention relates to the technical field of enterprise data management, in particular to a lightweight-based enterprise data dynamic integrated management system, which is characterized in that an acquisition module is used for deploying edge computing nodes, receiving multi-source heterogeneous information streams from manufacturing execution systems and equipment logs, dynamically analyzing and standardizing the information streams, and adding metadata tags; uploading is carried out in a batch processing mode; the map construction module is used for constructing a semiconductor blood relationship map by taking the standardized key information as a blood relationship clue; a graph database is used for efficient storage, and a RESTful API interface is configured to support batch import, so that data storage and relevance expression are more flexible and efficient; the prediction module performs reasoning on the atlas by adopting a graph neural network to generate predictive risk distribution, and a correlation analysis set generated by the prediction module is stored back to the atlas in a structured manner; by introducing a multi-thread concurrent write-in and lock mechanism, the atlas supports complex combination query based on a Cypher query language, and supports multi-level and traceability query.

The invention relates to the technical field of Internet of Things data management, in particular to an intelligent terminal identity authentication and data security management method, system and device based on a block chain and a medium, and the method comprises the steps: generating a unique identity label and a public and private key pair of an intelligent terminal device, a fog node and a user, packaging a digital certificate, and writing the digital certificate into a block chain network; the intelligent terminal equipment initiates a registration request, and the blockchain network distributes a hosting fog node to the intelligent terminal equipment after verification is passed; the intelligent terminal device generates data, constructs a data fingerprint, sends the data fingerprint to the block chain network for storage, encrypts the data, and sends the data to the hosting fog node for storage; a user initiates an authentication request, and a temporary access token is generated after verification is passed; and establishing a secure communication channel by using the temporary access token, obtaining the required encrypted data stored by the fog node, decrypting to obtain the required data, and verifying the integrity of the required data by using the data fingerprint. According to the invention, the overall security and reliability of data management of the Internet of Things system can be significantly improved.

The invention discloses an intelligent accompanying robot system, and the system comprises the following modules: a multi-mode interaction module which is composed of a voice recognition unit, a voice synthesis unit, an emotion recognition unit, and a visual recognition unit, and is used for collecting the voice, facial expression, motion, and environment information of a user; the localized AI decision module is used for deploying a lightweight DeepSeekR1 large language model based on an ESP32-S3 edge computing chip, performing real-time processing on the multi-modal data and generating a social guidance strategy and an emotion intervention instruction; the data management module comprises a user behavior database and a privacy protection unit and is used for desensitizing the data and realizing sensitive data isolation through local storage; according to the method, the single-person intervention cost is reduced, and the time consumption of manual scene simulation is reduced.

Methods, systems, and computer storage media for providing a modular cybersecurity platform are described. The modular cybersecurity platform is implemented using a modular cybersecurity engine that operates based on an analytical framework for dynamic data analysis and data management in a data intelligence system. In particular, the analytical framework is based on complementary modular components that are designed to interoperate in the modular cybersecurity engine. The modular cybersecurity engine includes a modular distributed system, a credential detection system, and a credential semantic graph system. The modular cybersecurity engine supports cybersecurity and sensitive data management scenarios that can empower investigators in various investigations, and provide automated flows that are highly scalable and support different types of functionality (e.g., priority embedding pipeline, credential scanning, and credential semantic graph analysis). The utility of the modular cybersecurity engine is demonstrated by its wide-ranging application in addressing complex cybersecurity challenges and sensitive data management tasks.

The invention discloses a metadata prefetching method and device, a storage medium, electronic equipment and a program, and relates to the field of metadata management of a distributed storage system.The metadata prefetching method comprises the steps that a first information block corresponding to a metadata operation event in a target storage area of each node is obtained; updating the three-dimensional correlation degree between any two metadata in each node according to the first information group; determining a prefetching priority corresponding to any two pieces of metadata through the updated three-dimensional correlation degree; and generating a heat association list of the metadata in each node according to the prefetching priority, and respectively sending the heat association lists corresponding to the plurality of nodes to the client so as to indicate the client to prefetch the target metadata in the plurality of nodes according to the plurality of heat association lists. Through the method and the device, the problem of low pre-fetching accuracy caused by dependence on static path association of the metadata and poor cross-node adaptability of a pre-fetching method for the metadata in a distributed storage system is solved, and the pre-fetching accuracy is improved.

The invention relates to the technical field of block chain and smart home data management, discloses a smart home data contribution quantification and excitation method and system based on a block chain identifier, and aims to solve the data source problem of vertical large model training in the smart home industry and guarantee data security and personal privacy. The method comprises the steps of equipment identifier registration and binding, home local data processing and desensitization, data contribution degree quantitative evaluation, intelligent contract incentive execution and standardized data delivery and evidence storage. The system comprises a block chain identifier management module, a home edge data center, a data contribution quantitative evaluation module, an intelligent contract incentive engine and a large model training data delivery interface. According to the technical scheme, accurate tracing of data sources and clear definition of contribution subjects can be achieved, the family computing power is activated, the risk of data privacy disclosure is reduced, high-quality and scenarized credible data sources are provided, and marketization circulation and value conversion of smart home data elements are promoted.

The invention discloses a distributed database built-in monitoring system, belongs to the technical field of databases, and aims to solve the technical problem of how to overcome the defects of complex monitoring deployment, high data response delay, coarse information acquisition granularity and low system integration in the prior art. According to the technical scheme, a monitoring platform module is integrated with a database kernel, a front-end and rear-end separation framework is adopted, and visual display and centralized management and control of the database operation state are achieved; the data acquisition module is used for simultaneously starting with a database process through a built-in HTTP (Hyper Text Transport Protocol) server to realize native support of a monitoring function; the distributed state query module is used for discovering cluster node states through a Gossip protocol, supporting node health detection and remote calling state service, and supporting gRPC-based streaming communication and concurrent processing; and the time sequence data management module is used for storing, compressing and querying the collected time sequence monitoring data.

The invention discloses a traceability application-oriented block chain database data management system, particularly relates to the technical field of block chain database data management, and is used for solving the problems of easy data tampering and low verification efficiency caused by traceability data logic association and physical storage splitting. The method comprises the following steps: firstly, matching a parent physical sector offset through an association analysis module, determining a storage partition, and assembling a write-in instruction; then, a topology binding module executes a disk write operation, synchronously reads parent node hash and performs combinatorial operation in combination with a physical address to generate a topology binding check value bound with the physical position; the aggregation anchoring module uses a Merkel tree aggregation check value to generate a root fingerprint and perform uplink, and establishes an anchoring voucher; and finally, a path verification module positions a physical position according to the certificate, executes reverse recursive addressing and step-by-step Hash recalculation, and assembles a pedigree verification packet, so that a closed-loop trust system from physical storage to chain certificate storage is constructed, and the tamper-proof capability and verification efficiency of the traceability data are improved.

The invention discloses an operation and maintenance data management system based on cloud computing, particularly relates to the field of data management, and comprises a distributed data acquisition module, a cloud native data processing module, an intelligent analysis module, a visualization and decision module and a cloud resource management center module. According to the method, heterogeneous data are collected in real time through the lightweight agent, and high efficiency is guaranteed by adopting multi-thread and self-adaptive sampling; data cleaning, standardization and mixed storage are realized by using a stream batch integrated architecture; the intelligent analysis module fuses an improved Robust Z-Score, an LSTM-Attention model and a complex event processing engine, and supports dynamic anomaly detection and fault tracing; the visual assembly builds an instrument panel in a dragging mode, alarm strategies are aggregated in a grading mode, and storm is restrained; the cloud resource management center deeply integrates Kubernetes, predicts demands based on reinforcement learning, automatically expands and shrinks capacity and optimizes cost, and deals with burst traffic in combination with a burst capacity pool.

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 relates to the technical field of municipal comprehensive GIS data management, in particular to a municipal comprehensive GIS data integration and management system for hub airport construction. The system comprises a data acquisition unit which is used for acquiring spatial geographic parameters, construction parameters and traffic operation parameters to construct an original multi-source data set, preprocessing the original multi-source data set and outputting a standardized data set; the equipment scheduling and resource management unit generates a total conflict set based on the spatial fusion data set and the dynamic progress data set, and obtains a Pareto optimal solution set by constructing a multi-objective optimization function; and the multi-dimensional conflict early warning unit obtains a security conflict set based on the spatial fusion data set and the dynamic progress data set in combination with the total conflict set, and finally generates dynamic early warning information through risk grade division. The coordinated scheduling scheme can be automatically generated under the scene of progress conflict of multiple contractors, complete dependence on manual decision making is avoided, and the construction efficiency and collaboration are improved.

The invention discloses an image data management method and system for high-precision size visual inspection, and relates to the technical field of image data, the image data management method for high-precision size visual inspection comprises the following steps: S1, collecting a data set, and carrying out region segmentation processing; s2, correcting and enhancing the data set, and extracting contour feature parameters; s3, mapping association is carried out, and screening classification is carried out; s4, parameters are optimized and adjusted, and a standardized size detection feature set is generated; s5, constructing a size detection model to obtain a target size quantification detection result; and S6, performing accuracy verification, and establishing an image feature size parameter association database. According to the invention, through synchronous acquisition of a multi-view image data set and a physical size calibration data set, and in combination with calibration reference of a geometric quantity standard appliance, the problems of incomplete information and large physical size mapping deviation of a traditional single-view image are solved.

Methods, systems, and devices for data management are described. A data management system (DMS) may receive a request to migrate data from a source data storage entity to a target data storage entity managed by the DMS. The DMS may verify that the data from the source data storage entity is compatible with one or more constraints associated with the target data storage entity and may identify one or more properties of the source data storage entity. The DMS may create the target data storage entity in accordance with the one or more constraints and properties of the source data storage entity. The DMS may provide a user with access to the target data storage entity, and a migration tool may be used to migrate the data to the target data storage entity. On completion, the DMS may commit the migration and activate the target storage entity.

The invention discloses a multi-tenant management and control system and method for a big data platform, and relates to the technical field of data management, and the system comprises a data basic management and control module layer and a data processing collaborative management and control layer. The data basic management and control module layer comprises a big data component management module, a tenant management module, a unified login authentication module, a project management module, a data resource authority management module and a computing resource management module; the data processing collaborative management and control layer comprises a component health monitoring sub-module, a resource prediction distribution sub-module, a dynamic desensitization encryption sub-module, a task collaborative management sub-module and a data quality management and control sub-module; the problems of weak tenant basic management and control, low resource allocation efficiency and insufficient component reliability and data security management and control in the existing multi-tenant management and control technology are solved, and the defects of lack of cross-tenant cooperative capability, lack of task and data quality management and control and insufficient data security management and control caused by the problems are overcome.

The invention relates to the technical field of distributed architectures, and discloses a multi-screen cooperative control method based on an OpenHarmony distributed architecture and an all-in-one machine system.The method comprises the steps that a broadcast frame found by equipment is sent through an OpenHarmony distributed soft bus, security authentication is conducted on a plurality of display terminals responding to the broadcast frame, a virtual bus channel is established, and the virtual bus channel is sent to the display terminals; obtaining a virtual device group; creating a distributed industrial data object on the main control equipment of the virtual equipment group and synchronizing the distributed industrial data object to each slave display terminal to obtain a distributed data model; mapping the anonymous shared memory to an annular buffer area; and writing real-time data acquired by the industrial field equipment into an annular buffer area, and transmitting the real-time data to each slave display terminal of the virtual equipment group, in the method, each display terminal accesses an agent instance of an industrial data object through a distributed data management framework; and the requirements of high reliability, low delay and high consistency on multi-screen cooperative control in an industrial field are met.

Provide are a system and a method for data management based on an Industrial IIoT data center. The system includes an IIoT user platform, an IIoT service platform, an IIoT management platform, an IIoT sensor network platform, and an IIoT sensing and control platform. The IIoT management platform is configured to: determine a future acquisition parameter of a sensor network sub-platform based on a remaining storage space, a first retrieval feature, and a future retrieval feature; determine a pre-increment storage capacity based on the future acquisition parameter; determine a group to be deleted based on the pre-increment storage capacity, the remaining storage space, and a group retrieval feature and a group data volume corresponding to each of a plurality of data groups; generate a parameter update instruction based on the future acquisition parameter; and generate a data deletion instruction based on the group to be deleted.