2528 results about "Data value" patented technology

The invention discloses a data security sharing method and system, and relates to the technical field of data security, and the method comprises the steps: receiving to-be-shared data uploaded by a data provider; fragmenting the data by adopting a biologically inspired adaptive fragmentation algorithm; generating a digital fingerprint for each fragment by using a multi-dimensional dynamic Hash algorithm; encrypting data fragments by adopting a post-quantum encryption algorithm and storing the data fragments in a decentralized network; recording the access authority, the digital fingerprint and the storage position information to a block chain security account book; when a data request is received, the access permission is verified; carrying out identity verification by adopting multi-mode biological feature recognition; after the verification is passed, obtaining a digital fingerprint and a storage position from the block chain; acquiring the encrypted fragments from the decentralized network and decrypting the encrypted fragments; verifying the integrity by using the digital fingerprint; and recombining the verified fragments to obtain complete data and returning the complete data to the requester. According to the method, efficient data sharing is realized while the data security is guaranteed, and the data value and privacy protection are effectively balanced.

The invention discloses an enterprise data asset intelligent analysis method and system based on AI recognition, and the method comprises the steps: receiving an enterprise multi-source heterogeneous data stream, carrying out the joint feature extraction and semantic alignment through a pre-trained multi-modal fusion recognition model, and generating a structured data asset recognition result; constructing a dynamic enterprise data asset atlas according to the structured data asset identification result in combination with the data access trajectory and authority metadata collected in real time; performing spatio-temporal evolution analysis on the dynamic enterprise data asset map, and extracting potential data value density features and risk exposure features; inputting the data value density features and the risk exposure features into a self-organizing mapping network to generate a data asset grading topological graph; and based on the data asset grading topological graph, through strategy constraint reinforcement learning, generating an executable data governance action sequence. According to the embodiment of the invention, the identification precision and real-time analysis capability of special assets of enterprises can be improved.

The invention discloses a medical and educational informatization comprehensive supervision and diagnosis platform system based on multi-modal data fusion. A data acquisition module acquires multi-modal data from a medical and educational information system and preprocesses the multi-modal data; the data storage module constructs a hybrid architecture to classify and store data and establishes an index. The multi-modal data fusion module deeply fuses medical records, images and physiological monitoring data of medical treatment and teaching resources, learning behaviors and teaching evaluation data features of education by means of a feature extraction technology and a fusion algorithm. The supervision and diagnosis module constructs models of medical service quality evaluation, disease diagnosis assistance, teaching quality monitoring and the like based on fusion features to realize accurate analysis. The user interaction module provides interaction interfaces for different roles and supports operations such as data query and uploading. The platform system breaks the data barrier, fully mines the data value, provides powerful support for informatization management, decision making and service optimization in the medical and education fields, and promotes the innovative development of the two fields.

The invention belongs to the technical field of dam safety early warning, provides a dam safety monitoring method based on digital twinning, and aims at solving the problems that an existing monitoring method is weak in prediction capability, lags in early warning and the like. The method comprises the following steps: deploying multiple types of sensors to collect multi-dimensional physical state data; establishing a digital twinborn model containing a multi-physics field coupling simulation sub-model and data driving correction based on the BIM, the Internet of Things and finite elements; inputting a historical data driving model to predict an output state trend sequence and a state data value at a certain moment; performing difference analysis to construct an error vector, and calculating and correcting a deviation index; and calculating a structure health score, and if a threshold value is exceeded, early warning. The method has the advantages of improving prediction capability, enhancing adaptability, realizing comprehensive evaluation and providing systematic advanced decision basis for safety management.

Systems and techniques are provided for automatically analyzing and processing domain-specific image artifacts and document images. A process can include obtaining a plurality of document images comprising visual representations of structured text. An OCR-free machine learning model can be trained to automatically extract text data values from different types or classes of document image, based on using a corresponding region of interest (ROI) template corresponding to the structure of the document image type for at least initial rounds of annotations and training. The extracted information included in an inference prediction of the trained OCR-free machine learning model can be reviewed and validated or corrected correspondingly before being written to a database for use by one or more downstream analytical tasks.

The invention relates to the field of data asset management, and discloses a DCMM-based data asset active management method and system, and the method comprises the following steps: collecting data assets from all business systems, extracting multi-dimensional features, and generating a data attribute matrix; constructing a data value quantification model, and calculating the value of each data asset; based on a multi-objective optimization model, data assets are subjected to classification management, and optimization balance of data value, security, access efficiency and storage cost is realized; dynamically adjusting the weight of an optimization target according to different levels of the DCMM to enable a data management strategy to adapt to the data management maturity requirement of an enterprise; and performing resource allocation and priority control on the data assets according to an optimization result to realize active management. According to the method and the system, the accuracy, the flexibility and the resource utilization rate of data asset management are improved, and optimal configuration and efficient management of data assets in different development stages of enterprises are facilitated.

The invention relates to the technical field of data fusion, in particular to a multi-source data fusion law enforcement record generation system and method, and the system comprises a data collection module which normalizes and eliminates repeated data, an anomaly detection module which carries out the data verification and cleaning, and an information source evaluation module which carries out the quantitative scoring of a data source. And the weight adjustment module dynamically adjusts the weight based on the score, and the data fusion module executes weight fusion and field verification and outputs a collaborative law enforcement record. According to the method, the data quality is further ensured through the verification of the data value range and the calculation of the deviation value, and the reliability of the data set is enhanced by removing the abnormal data. And through quantitative scoring and dynamic weight adjustment, the quality evaluation of the data source and the fairness of input are ensured, so that the law enforcement record is more comprehensive and authoritative. The refined data processing not only improves the accuracy of law enforcement records, but also enhances the security and integrity of information, thereby significantly improving the response speed and decision quality of a law enforcement agency.

A method generates data analysis templates. A computing device receives, via a graphical user interface, a natural language input, which includes multiple terms. The terms include one or more data fields and / or data values from a data source. The device designates the natural language input as trigger text for a data analysis template. The trigger text includes the multiple terms. A user defines one or more rules for a subset of the terms, each rule specifying respective criteria to match a respective term in the subset. A user specifies multiple data visualizations corresponding to the trigger text. Each of the data visualizations (i) uses a respective one or more data fields, from the data source, corresponding to terms from the trigger text and (ii) has a respective data visualization type. The device generates the data analysis template according to the trigger text, the user definition, and the user specification.

The invention relates to an unmanned aerial vehicle fault traceability analysis method and device, equipment and a storage medium. The method comprises the steps of defining entity types and relationship types among entities based on a predefined fault ontology model to construct a mode layer of an unmanned aerial vehicle fault knowledge graph; based on the mode layer, extracting a fault triple from the multi-source operation data of the unmanned aerial vehicle by using a mixed extraction model, and constructing a fault knowledge graph containing instance data; endowing a dynamic weight probability representing confidence to a relation edge in the fault knowledge graph, and generating a probabilistic fault knowledge graph; and mapping to-be-analyzed fault information to the probabilistic fault knowledge graph, performing traceability analysis by using a hybrid inference engine, and outputting a fault reason and a transmission path. According to the method, structured deep fusion of domain knowledge and data value is realized, and the traceability conclusion is improved from qualitative judgment to quantitative decision support with confidence measurement.

The invention provides a power transmission line mountain fire disaster risk assessment method and system based on space-time event driving, and the method comprises the steps: obtaining mountain fire monitoring data of a to-be-assessed region, and generating a space-time event according to the mountain fire monitoring data; according to an event association rule, the space-time event is associated with a power transmission line tower through a directional edge, and then a power transmission line mountain fire disaster hidden danger map is constructed; performing similarity matching on the time-space events generated in real time and the time-space events in the power transmission line mountain fire disaster hidden danger map, and searching the corresponding time-space events in the power transmission line mountain fire disaster hidden danger map by adopting a map traversal algorithm; and for the searched towers influenced by the space-time events, quantitatively calculating the risk score of each tower based on the data values of the real-time space-time events, and dividing the risk grades according to the risk scores. According to the method, the association precision of power transmission line risk assessment and the precision and efficiency of power transmission line forest fire prevention and control can be improved.

A system and method for root cause analysis in incident processing, including root cause change detection, is presented. The method includes processing a plurality of event records, including a plurality of alert records and a plurality of change records, each event record generated based on an event in a computing environment; parsing each alert record based on a predetermined data field; extracting from each predetermined data field a data value; correlating a group of alert records of the plurality of alert records based on at least an extracted data value; generating an incident data record based on the extracted data values of the correlated group of alert records; detecting a change record of the plurality of change records related to the incident data record; determining that the change record is a root cause change of the incident data record; and initiating a mitigation action based on the root cause change.

The invention discloses an intelligent data management system and method based on multi-source data collection, and relates to the technical field of data processing. Multi-source data from multiple data sources are obtained, an original data set with credibility marks is generated, data records in the original data set are classified according to data types, and for time series data, data records in the original data set are classified according to credibility marks. Extracting trend features by applying a segmented multi-model time sequence feature extraction framework; for the data of the unstructured data, extraction of unstructured features is carried out through a feature extraction model based on a multi-layer perceptron; for structured data, a knowledge graph technology is adopted to establish an adaptive association relationship, association relationship data is generated, trend features, unstructured features and the association relationship data are used as input of a data value evaluation model, and data asset performance evaluation of dynamic weight is generated; and intelligent management and value evaluation of the multi-source heterogeneous data are realized.

The invention belongs to the technical field of power grid big data, and particularly relates to a power grid regulation and control data asset value evaluation method and device, equipment and a medium. According to the method, key factors (data integrity, data diversity, data timeliness, data scale and data relevance) influencing the value of the power grid regulation and control data assets are determined, the weight coefficient of each factor is calculated by using an analytic hierarchy process, and then the value of the power grid regulation and control data assets is calculated. According to the scheme, a unified data value evaluation system is established, and a unified value measurement standard is provided for power grid regulation and control data assets by determining the key influence factors and the weight coefficients. Through data value evaluation, the value and distribution condition of each data asset can be clearly known, and efficient management and reasonable utilization of the data assets can be realized. Meanwhile, through data value evaluation, the potential value of the data assets can be found, and possibility is provided for value increment and achievement of the data.

The invention discloses a sky-ground network integrated high-standard farmland intelligent monitoring management system, and belongs to the field of farmland monitoring management systems. The system is provided with a space monitoring data acquisition unit, an air monitoring data acquisition unit and a ground monitoring data acquisition unit; the system can be combined with satellite remote sensing equipment, unmanned aerial vehicle equipment and a plurality of sensor nodes in a multi-level manner to jointly form a sky-ground integrated data acquisition network for target farmland data acquisition, so that the comprehensiveness and accuracy of data acquisition are greatly improved; meanwhile, mapping layer three-dimensional modeling is carried out on data values acquired and converted by various sensors which are uniformly distributed at certain intervals, so that the display intuition, convenience and layering sense of different data on the target farmland are greatly improved; a user can more intuitively master the health states of crops at different positions of the target farmland and the advantages and disadvantages of the crop growth environment.

A system for data collection related to a fluid conveyance environment includes a data acquisition circuit comprising inputs and outputs; input sensors to provide sensor data values, coupled to a component in the fluid conveyance environment; and a processor comprising the data acquisition circuit. The processor is configured to determine a data storage profile; responsive to the data storage profile, configure the data acquisition circuit to selectively couple at least one of the inputs to at least one of the outputs; interpret the at least one of the sensor data values; store at least a portion of the at least one of the sensor data values in response to the data storage profile; analyze a set of the sensor data values and determine a data quality parameter; and adjust at least one of the data storage profile and a data collection routine in response to the data quality parameter.

The invention discloses a multi-scene application data acquisition method based on atomization design. The method comprises the following steps: analyzing reported data into an atomization data structure; performing semantic recognition on the data labels to generate a label semantic mapping result; constructing a layered cache structure; generating a compressed semantic mapping rule by applying a multi-level semantic difference coding algorithm; identifying an optimal query path from the tag affinity matrix; and executing historical data cleaning based on the data value. Intelligent mapping of data labels is achieved through semantic recognition and version control, the storage efficiency is improved through predictive lazy loading and compression coding, the query path is optimized based on the affinity matrix, and the technical problem of multi-scene application data collection is effectively solved.

An artificial intelligence digital assistant that retrieves and consolidates data from diverse sources is described. After a system initiates a response to an information request received from a user, a language model generates a high-dimensional vector that represents the information request from the user. The language model identities data access tools that access corresponding data types identified for the information request. The system executes the identified data access tools that retrieve, from corresponding data sources, data values from corresponding data records represented by corresponding unique high-dimensional vectors that have similarities, which exceed a confidence level, to the high-dimensional vector that represents the information request. The system sends a consolidation of the data values in the response to the user.

The invention discloses an abnormity monitoring method of Internet of Things equipment, a storage medium and a computer program product, and relates to the technical field of Internet of Things, and the method comprises the steps: obtaining the current equipment state data of the Internet of Things equipment at the current moment; determining a data value range based on the current equipment state data, and detecting whether the current equipment state data is within the data value range; if the current equipment state data is not in the data value range, determining that the Internet of Things equipment is in an abnormal operation state at the current moment; and determining the current abnormal degree of the Internet of Things equipment at the current moment, determining a target strategy corresponding to the current abnormal degree from preset response strategies corresponding to different abnormal degrees, and controlling the Internet of Things equipment to operate according to the target strategy. According to the invention, the anomaly detection accuracy of the Internet of Things equipment and the overall reliability and efficiency of the system are improved.

The invention discloses a mine digital asset storage management method, which comprises the following steps of: extracting characteristics of mine data, dividing the mine data into hot data, warm data and cold data according to the characteristics, selecting different storage devices for the data, and dynamically adjusting migration of the data among the different storage devices based on access frequency and business requirements; future data storage requirements are predicted through a machine learning algorithm, and storage resources are scheduled; performing full life cycle management on the data, performing business value evaluation on the data, and adjusting storage priority and resource allocation based on the data value; carrying out full-life-cycle encryption on the data, and recording data operation through a block chain technology; energy consumption of the storage equipment is monitored in real time, and storage energy efficiency and carbon footprint management are adjusted. The problems of low storage efficiency, high storage cost and insufficient energy consumption optimization caused by data type diversity, access frequency difference and insufficient storage resource utilization in mine data management can be solved.

The invention particularly relates to a data governance method and system based on a large model. According to the data governance method based on the large model, data are automatically collected, and the data are preliminarily cleaned and preprocessed; carrying out deep analysis, mining and prediction on the data by utilizing the large model, and carrying out data conversion and formatting according to business requirements; a data quality standard, a data security policy and a data management process are formulated and executed according to service requirements, so that the accuracy, integrity and security of data are ensured; the data quality and the data flow are monitored in real time, and if a data quality problem is found, the data quality problem is fed back to the large model so that the large model can solve the data quality problem in time. According to the data governance method and system based on the large model, intelligentization and automation of data governance are achieved, the burden of manual operation is relieved, the data governance efficiency is improved, the data security and compliance are optimized, intelligent decision support is achieved, and the data value mining capacity is improved.

The invention relates to the technical field of logistics storage management, and discloses a logistics storage warehousing management method based on big data, which comprises the following steps: step 1, acquiring order data in an order management system through Internet of Things equipment, acquiring cargo information by using an RF ID tag and bar code scanning equipment, acquiring storage environment parameters by using a temperature and humidity sensor and a light sensor, and storing the storage environment parameters in a database; a built-in sensor of a forklift and a built-in sensor of an automatic guide vehicle are used for collecting equipment state data, real-time image data of goods are collected through a high-definition camera, and the collected data comprise timestamps, data source identifiers and initial data values. According to the method, modeling is carried out on historical order quantity, warehousing frequency, cargo types and time sequence characteristics by utilizing a long-short-term memory network model, so that time dependence and seasonal change trend of data are accurately captured, and accurate prediction of future warehousing demands is realized; and the effects of improving the prediction accuracy and providing a reliable basis for goods allocation and resource scheduling are achieved.

A request to update a database system based on a natural language input document may be received. An input metadata extraction prompt may be determined based on the natural language input document and a prompt template including a fillable portion. The input metadata extraction prompt may be determined at least in part by filling the fillable portion with natural language input text selected from the natural language input document. The prompt template and the input metadata extraction prompt may each include natural language instructions to generate novel text identifying data values characterizing the natural language input document and corresponding to one or more database fields in the database system. A completed metadata extraction prompt may be determined by executing the natural language instructions via a generative language model to generate the novel text. A database query updating the database system may be determined based on the novel text.

A system for data collection related to a fluid conveyance environment includes a data acquisition circuit comprising inputs and outputs; input sensors to provide sensor data values, coupled to a component in the fluid conveyance environment; and a processor comprising the data acquisition circuit. The processor is configured to determine a data storage profile; responsive to the data storage profile, configure the data acquisition circuit to selectively couple at least one of the inputs to at least one of the outputs; interpret the at least one of the sensor data values; store at least a portion of the at least one of the sensor data values in response to the data storage profile; analyze a set of the sensor data values and determine a data quality parameter; and adjust at least one of the data storage profile and a data collection routine in response to the data quality parameter.

A method for managing a data stream (DS) includes: receiving a first request from an administrator; obtaining the DS from a streaming storage system (SSS); performing a first analysis of the DS to extract relevant data; making a first determination that the relevant data is worth indexing; generating a set of indexes corresponding at least a portion of the DS; receiving a query about a data item in the DS from a user; sending a second request to a database engine comprising the query; performing a second analysis of the query; making a second determination that a re-indexing of the portion of the DS is not required; identifying an index from the set of indexes that is associated with the query; sending position information of the data item to an API service; retrieving the data item from the SSS; and initiating displaying of the data item to the user.

A system and method for reducing data record processing in incident report generation is provided. The method includes: accessing a plurality of event records, each event record generated based on an event in a computing environment; parsing each event record based on a predetermined data field; extracting from each predetermined data field a data value; correlating a group of event records of the plurality of event records based on at least an extracted data value; generating an incident data record based on the extracted data values of the correlated group of event records; generating a prompt based on the incident data record; and generating an incident report by configuring a large language model (LLM) to execute the generated prompt.

A network adapter includes a port and circuitry. The port is to connect to a communication network. The circuitry is to send to a remote network adapter, via the communication network, a remote memory-set instruction that instructs the remote network adapter to fill one or more address ranges in a memory with multiple copies of one or more fill-data values.

The invention relates to the technical field of energy management and intelligent scheduling, provides a photovoltaic energy storage motor collaborative scheduling optimization system based on multi-modal data, and constructs a collaborative scheduling system integrating data intelligent processing, high-precision prediction and multi-objective optimization by fusing photovoltaic module, energy storage equipment, motor and power grid side multi-modal data. Data quality is improved through Z-score cleaning and Kalman filtering, accurate prediction of photovoltaic power, load demand and energy storage aging is realized by using a long-short-term memory neural network and an Arrhenius equation, and a multi-target collaborative scheduling strategy including economic, reliable and environmental benefits is constructed based on a particle swarm optimization algorithm. Dynamically adjusting photovoltaic output distribution, energy storage charging and discharging and a motor speed regulation ratio; systematic innovation of data value deep mining, multi-target dynamic balance, equipment full-life-cycle management and renewable energy source efficient consumption is realized, and the intelligent level and comprehensive operation efficiency of the micro-grid are remarkably improved.

The invention belongs to the technical field of intelligent information, and particularly discloses a government affair intelligent safety protection and efficient cooperation system which comprises core modules of intelligent safety protection, cross-department cooperative office and the like. By means of deep learning multi-mode detection, block chain credibility verification, reinforcement learning process optimization and other algorithms, security threat accurate identification, data security sharing and efficient operation of a business process are realized. And meanwhile, embodiment schemes such as federated learning and differential privacy cross-regional data collaboration, space-time attention emergency resource scheduling and the like are integrated, so that security threats can be effectively resisted, and the cross-department collaboration efficiency is improved. Through intelligent decision support and data value mining, powerful support is provided for government affair decision, safety and stability of a government affair system are guaranteed comprehensively, and government affair service quality and management efficiency are remarkably improved.

The embodiment of the invention discloses a landslide displacement monitoring method, system and device, and a storage medium, and aims at minimizing the total mean square error, and can adaptively find an optimal weighting factor corresponding to each piece of landslide displacement data, so that the fused data value is optimal. According to the optimization method, the error difference between the landslide displacement data can be fully considered, so that a more accurate and reliable data fusion result is obtained, and therefore, the first weighting factor of each landslide displacement data is utilized to carry out weighted fusion on all the landslide displacement data to obtain the landslide displacement deformation data of the landslide displacement monitoring point; the landslide displacement deformation data can comprehensively and effectively judge the landslide deformation stage, and the landslide deformation stage is closer to the real condition of landslide deformation.

The invention discloses an intelligent hierarchical storage method for unstructured data, and relates to the technical field of unstructured data processing, and the method specifically comprises the steps: determining an initial value through data feature analysis; establishing an attenuation model to output a dynamic value; generating a preliminary storage hierarchy according to the value attenuation degree; using the access record to train a prediction model, and evaluating a future access probability; adjusting a data value weight according to the access probability; and determining an optimal storage hierarchy based on the adjusted comprehensive value index. According to the method, dynamic matching of storage resources and access popularity is realized through a dual value evaluation mechanism; various reference factors are fused, real-time dynamic quantification of data values is realized, and limitation of static evaluation is broken through; through collaborative optimization of a value attenuation model and machine learning prediction, the core pain points of storage strategy lag and low resource utilization rate in a traditional method are solved.