1998 results about "Data value" patented technology

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 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.

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.

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.

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 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.

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 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.

The invention relates to the technical field of data resource management, in particular to an online evaluation method for data resource value, which comprises the following steps of: constructing a multi-dimensional evaluation index set, and acquiring metadata and market dynamic information of to-be-evaluated data in real time through a distributed interface; carrying out weight distribution on the index set based on a dynamic weighting algorithm, generating a weight coefficient set and constructing a value calculation model; performing standardized preprocessing on the collected original data, inputting the data into the model, and calculating an initial estimated value; and dynamically correcting the initial value estimation value based on the timeliness attenuation factor and the data scarcity factor, and generating a final value estimation value and an estimation report. Through multi-dimensional index construction, a dynamic weighting algorithm, standardized preprocessing and two-factor correction, automatic evaluation of the data value is realized, the problems of single dimension, static weight and neglect of timeliness scarcity of a traditional method are solved, the real-time performance and scientificity are improved, and a quantitative decision basis is provided for data capitalization.

The invention provides a computer big data storage control system, and relates to the technical field of computers, and the system comprises a data evaluation module which is used for comprehensively calculating the real-time popularity of data, and the real-time popularity is obtained through the comprehensive calculation of the data access frequency and the access duration; performing quantitative evaluation on the real-time popularity of the data in combination with a multi-dimensional factor to obtain a value evaluation value of the data; analyzing the system resource information of the storage device on the computer to obtain a resource analysis result; and the intelligent contract execution module is used for carrying out comprehensive analysis and judgment on the real-time popularity, the value evaluation value and the system resource analysis result of the data so as to trigger adjustment of a storage strategy. According to the method, the real-time popularity model is constructed, the analytic hierarchy process is combined to achieve multi-dimensional value evaluation, the intelligent contract dynamically adjusts the storage strategy according to the evaluation result and the resource state, the data value and the storage medium are accurately matched, the problems that a traditional system is extensive in evaluation and rigid in strategy are solved, and efficient and accurate scheduling of storage resources is achieved.

The invention discloses a PLC register point location address determination method and device, equipment, a medium and a product, and the method comprises the steps: obtaining a target data packet according with a monitoring rule from data packets sent by PLC equipment to output equipment; based on the filtering condition of the target parameter, filtering out target analysis content from the analysis content of each target data packet; wherein the analysis content of at least part of the target data packet comprises a data value and a register point location address for storing the data value; any target parameter is a parameter displayed by the output device; the register point location address in the target analysis content is the point location address of the target parameter. Real valuable information can be extracted from numerous devices and sensors, accurate acquisition and real-time feedback of data are ensured, a reliable basis is provided for deeply analyzing a production process, capturing key performance indexes and implementing fine control, and core information is also provided for realizing intelligentization, optimization and transparency of the production process.

Systems and methods perform data analysis on at least two separate datasets to identify any redundancies. The data analysis includes comparing first data values of a first dataset with second data values of a second dataset, the comparing including evaluating similarities of the first data values and second data values, identifying, from the comparing, that the first data values and the second data values include at least a portion of substantially similar data, and interpreting similarities of the portion of substantially similar data, the interpreting including determining that a dataset of the first dataset and second dataset is a subset of the other dataset. Further, control signal(s) are transmitted to a user device to initiate displaying, via a user interface of the user device, a prompt indicating that the at least one dataset is likely the subset of the other dataset.

The invention relates to the technical field of pipeline risk assessment, and discloses a multi-source heterogeneous data-driven heat distribution pipeline safety risk assessment method, which comprises the following steps of: performing heterogeneous feature alignment processing on multi-source heterogeneous data of a heat distribution pipeline; obtaining a fault event feature vector, a plurality of operation parameter time sequence segments and an environment feature vector corresponding to each pipeline node; based on the multi-modal feature fusion network, generating a comprehensive risk representation vector and inputting the comprehensive risk representation vector into a risk assessment model comprising a Weibull distribution layer, a neural network feature mapping layer and a joint output layer to obtain a pipeline failure condition probability; and generating a risk assessment result based on the comprehensive risk representation vector, the pipeline failure condition probability and the weight coefficient. According to the method, multi-source heterogeneous data can be effectively integrated, the risk assessment accuracy and comprehensiveness are improved, a scientific, detailed and dynamic risk assessment basis is provided for operation management of the heat distribution pipeline, the limitation of a traditional mode is broken through, and the data value is fully played.

The invention discloses a grotto temple cavern microenvironment monitoring data quality control and evaluation method, and particularly relates to the field of grotto temple cavern microenvironment monitoring data, and the method comprises the steps: obtaining grotto microenvironment monitoring data, carrying out the time-space reconstruction of the grotto microenvironment monitoring data, and building a data expression basis with a consistent attribute structure. An execution entry is provided for subsequent processing; potential abnormal fragments are identified from the cross relation between the time rhythm and the numerical value change, abnormal points are eliminated, and the validity of the cave microenvironment monitoring data value domain is guaranteed; the problem of disordered timestamp sequence is recognized, a correct sorting structure is recovered in combination with a trend rule, and time sequence consistency is guaranteed. By constructing a multi-stage closed-loop processing chain with rhythm change recognition, numerical mutation elimination, trend sequence recovery and structure quality fusion as the core, structure unification, anomaly recognition, time sequence correction and credibility output of monitoring data are achieved, and therefore effectiveness, consistency and application value of the monitoring data are systematically improved.

The invention provides an atmospheric temperature and humidity profile repairing method integrating a statistical method and a deep learning technology, belongs to the technical field of meteorological observation, and aims to solve the problem of large-area data missing caused by cloud pollution by taking an FY-4B satellite temperature and humidity profile product as a processing object. Data reconstruction is realized through a three-stage architecture of random cloud mask generation, statistical filling and pre-restoration, and deep learning and fine correction; according to the method, the problem of data truth value missing is solved, meanwhile, the advantages of a statistical method and a deep learning technology are combined, the repair precision in an extreme missing scene is remarkably improved while physical reasonability is guaranteed, and meanwhile the method has the advantages of being few in needed computing resources, high in computing speed and the like.

The invention relates to a data sharing and exchanging method and system based on privacy computing and a big data technology, and belongs to the technical field of data privacy protection and big data analysis. The method comprises the steps of platform initialization and user registration; preprocessing and encrypting data; authority control and task configuration; initiating and scheduling a calculation task; performing privacy calculation; performing result aggregation and decryption; accessing and feeding back a result; auditing and monitoring; the invention further comprises a module for executing the method. According to the platform, through decentralized architecture design and various privacy computing technologies, data availability and invisibility are achieved, the functions of big data cleaning, analysis and modeling are integrated, data privacy safety is protected, and the data value utilization efficiency is improved.

The invention discloses a multi-modal data and dynamic knowledge graph fusion method, relates to the technical field of knowledge graphs, and solves the technical problems that multi-modal data weight evaluation is single in dimension and lacks a data source, time and scene collaborative quantization mechanism. In combination with data source authority and time freshness, it is ensured that data value evaluation better meets service requirements, dimension weight coefficients can be flexibly adjusted according to scenes, different from fixed coefficients in the prior art, requirements of different fields are met, data full-link metadata is stored through a directed acyclic graph, conflict sources can be rapidly traced, and the data value evaluation efficiency is improved. And automatic classification and targeted correction of conflict types are realized based on a predefined rule, and the error rate of conflict correction is reduced.

The invention belongs to the technical field of data processing, and discloses a dynamic configurable data cleaning method and system based on big data, and the method comprises the steps: collecting a heterogeneous data source, and carrying out the access of the heterogeneous data source in combination with a data source adaptive interface; identifying an abnormal mode in the heterogeneous data source and encoding the abnormal mode into an antigen fingerprint by using a simulated antigen identification memory mechanism; constructing a data value density scoring function based on the antigen fingerprints to obtain a data value density score; constructing a three-dimensional storage topological space according to the data value density score, adopting a lattice migration algorithm, automatically identifying a cold and hot data critical point, triggering hierarchical migration when the temperature of the heterogeneous data source is reduced to a preset temperature threshold value, introducing a thermodynamic diffusion mechanism, and simulating the migration process of the heterogeneous data source in the three-dimensional storage topological space as a thermal diffusion path; and the operation efficiency of the cleaning task is obviously improved.

The invention relates to the technical field of gang drills, in particular to a numerical control gang drill device which comprises a machine tool body, a telescopic assembly is arranged in the machine tool body, the output end of the telescopic assembly faces downwards and is connected with an extrusion block, and a storage plate is arranged at the position, below the telescopic assembly, in the machine tool body. One side of the machine tool body is connected with a gang drill plate through an air cylinder, and the side, facing the machine tool body, of the gang drill plate is evenly connected with a plurality of through drill seats. According to the device, a second clamping block and a first laser distance measuring sensor are arranged, the first laser distance measuring sensor can detect the distance value between a drill bit and an extension block at different positions of the drill bit, and the distance value between the drill bit and the extension block can be detected in the mode that the drill bit rotates; the detected distance value is uploaded to a background control system to be compared with a standard data value, and if the detected data value exceeds a standard data value interval, it is indicated that the drill bit deforms.

The invention discloses a pumped storage power station building foundation dangerous point management and control system based on BIM, and the system comprises a data layer which enables linear engineering monitoring data to be organized into a high-order tensor of pile number * time * sensor type * spatial position through four-dimensional tensor modeling, and carries out the Tucker decomposition, strain rate abrupt change points are dynamically recognized in the compression process by combining sliding window anomaly detection, original data are directly stored, and then a hierarchical storage strategy is implemented on non-abrupt change data through a data value evaluation model driven by deep reinforcement learning; the model layer is used for fusing a physical information neural network, embedding a rock-soil mechanics constitutive equation into an LSTM network to construct a mixed loss function, pre-training a general geologic model through transfer learning, and then carrying out few-sample fine tuning in combination with project data; and the application layer is used for dynamically generating a hazard source list through a BIM model and triggering instant early warning based on the compressed and reconstructed data of the data layer and the output of the model layer, and simulating a disaster situation development path in combination with a digital twinborn technology.

The present disclosure involves systems, software, and computer implemented methods for transaction auditing. One example method includes receiving text submitted with a request. Token values are automatically extracted for selected token types from the text using a machine learning model. Tokens are identified in the text, and for each token, features and a token type of the token are determined, along with a score that indicates a likelihood that the token has the token type. A token value is extracted for the token from the text. Extracted token values are compared to request data values. A request value for the selected token type is identified in the data values for each selected token type. The extracted token value for each selected token type is compared to the request value. An audit alert is generated in response to determining that an extracted token value does not match a corresponding request value.

The invention relates to an intelligent server fault detection method based on a BMC, and the method comprises the steps: adjusting a to-be-detected server to a debug mode of the BMC to obtain original data, judging whether the data received by the BMC is sensor fault data or not, and when the BMC receives an alarm that a sensor is abnormal; constructing a fault model and carrying out training and optimization; acquiring specified data of a to-be-detected server in real time, preprocessing the specified data, inputting the preprocessed specified data into the optimized fault model, automatically classifying the preprocessed specified data by the fault model, judging whether the to-be-detected server has a fault or a fault tendency, if so, starting an automatic repair strategy, and if not, starting an automatic repair strategy. According to the automatic decision, which repair measure is taken is determined according to the severity and the influence range of the fault, and the repair operation is executed through the automatic script or tool of the automatic operation. Through deep mining of BMC underlying data value and fusion of an intelligent model and an automatic repair mechanism, full-process optimization of server fault detection and processing is realized.

Systems and methods are disclosed for real-time anomaly prediction using near real-time data. The invention addresses delays in telemetry data collection from infrastructure components, by collecting metrics and logging this data in real-time. Extracted logged data undergoes initial analysis to identify patterns and anomalies, followed by cleaning to remove noise and errors. Feature engineering enhances the data, creating or modifying features to improve machine learning model performance. The system calculates weighted means of previous data values and computes first and second-order differences to capture immediate changes and trends. These calculations adjust the extrapolated value to accurately reflect current conditions. The adjusted data is integrated into the dataset and validated. The validated data trains and tests a machine learning model, which is then finalized and deployed for real-time anomaly detection. This system ensures accurate and timely anomaly prediction, enabling automated incident response to maintain the reliability and performance of infrastructure components.

A method for prioritizing predictive model data streams includes receiving, by a first device, a plurality of predictive model data streams. Each predictive model data stream includes a set of model parameters for a corresponding predictive model. Each predictive model is trained to predict future data values of a data source. The method includes prioritizing, by the first device, priorities to each of the plurality of predictive model data streams. The method includes selecting at least one of the predictive model data streams based on a corresponding priority. The method includes parameterizing, by the first device, a predictive model using the set of model parameters included in the selected predictive model data stream. The method includes predicting, by the first device, future data values of the data source using the parameterized predictive model.

A device may include a policy acquisition circuit configured to interpret a vehicle policy data value including an external event trigger condition. A device may include a policy processing circuit configured to generate parsed policy data including the external event trigger condition. A device may include a policy execution circuit configured to collect vehicle data from one or more vehicle end points in response to the parsed policy data.