736 results about "Data value" patented technology

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-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.

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.

Automatic volumetric quantification can be performed for various parameters of an object by providing volumetric data, such as three-dimensional image data, to at least one neural network. A network can extract features from the data that can be used to infer a point cloud representative of the surface of the object. One or more loss functions can be used to adjust the relevant network parameters. The network can also attempt to infer a segmentation mask for the object, indicating which data values correspond to the object of interest. Since the network performs the segmentation and point cloud generation in parallel, updates to the network parameters can impact the segmentation process, effectively constraining the segmentation based on the inferred shape of the object. Ensuring that the segmentation mask corresponds closely to the surface of the object can cause the segmentation process to be more accurate than conventional segmentation processes alone.

The invention discloses a real-time cleaning and aligning method for multi-source heterogeneous data, and particularly relates to the technical field of source heterogeneous data, a self-adaptive interface adapter is compatible with multiple types of data, and a semantic index atlas is generated through four-dimensional classification labeling; constructing a cloud-edge collaborative streaming processing framework, preprocessing edge nodes, and performing accurate cloud alignment; a dynamic rule cleaning and semantic-dimension-entity three-layer progressive alignment mechanism is adopted, and a four-dimensional quality evaluation system is combined for real-time monitoring; and through closed-loop optimization, homomorphic encryption, a block chain and other security mechanisms, data security and traceability are ensured. According to the real-time cleaning and aligning method for the multi-source heterogeneous data, the real-time performance and accuracy of data processing are effectively improved, the method is adaptive to multiple service scenes, and high-quality data support is provided for data value mining.

The invention relates to the field of industrial Internet of Things, and discloses an industrial scene-oriented multi-protocol compatible IoT acquisition and intelligent operation and maintenance system, which comprises a configuration management module used for analyzing an encrypted configuration file and dynamically loading a communication protocol driving program by using a reflection mechanism; the data acquisition module is used for establishing connection with industrial field equipment by utilizing the loaded communication protocol driving program and acquiring original data; the data processing module is used for calculating a semantic entropy value of the original data in the time window and marking a data value grade according to the semantic entropy value; the data storage module is used for executing differentiated storage operation on the data according to the data value grade; a memory management module and a data forwarding module. By constructing a data acquisition module comprising a software and hardware dual-mode interface and combining a self-adaptive driving correction mechanism based on physical layer signal feature analysis, the effect of still maintaining communication connection when standard protocol driving fails or equipment aging parameters drift is achieved.

The embodiment of the invention provides a key value data processing method, a database platform, equipment, a medium and a product, and the method comprises the steps that in response to a disk refreshing instruction of a target storage unit in a target partition, a target key value pair in a memory buffer area of the target storage unit is obtained, the target key value pair is any key value pair in the memory buffer area, and the target storage unit in the target partition is stored in the memory buffer area; the target key value pair comprises a target index key and a corresponding target data value; if the target data value is greater than the set size threshold value, writing the target data value into a data file, and writing a target index key and a target file path of the target data value in the data file into a disk storage file of a target storage unit, the structure of the data file is the same as that of the disk storage file, the data file and the disk storage file are stored in the same path, the recovery mechanisms of the data file and the disk storage file are different, and the data file and the disk storage file are distinguished through the set file mark added in the metadata area. The invasion to the database is small, and the write amplification is reduced.

The invention provides a harbor district oil depot control system, belongs to the technical field of harbor district oil depot management and control, and particularly relates to a harbor district oil depot control system which comprises a multi-source data acquisition module, a data processing module, an index generation module, a production scheduling module, a safety management module, an equipment protection module and a control module. Universe data collection of the storage tank area and the loading and unloading area is achieved through the multi-source monitoring equipment, a scheduling, safety and equipment dynamic index system is formed in combination with the AI algorithm, and the data value density is improved; loading and unloading task allocation, four-level safety early warning and equipment health intervention measures are dynamically generated based on a threshold value, and automatic closed-loop management of'monitoring-analysis-execution 'is achieved; through a multi-stage early warning mechanism, full-process plan triggering from daily maintenance to emergency response is supported, and the accident prevention efficiency is improved; a three-dimensional visual billboard and a data cockpit are provided through the integrated management and control platform, cross-department collaborative decision making is achieved, and the emergency response speed is increased.

The systems and methods disclosed herein embed and store traceable watermarks across multiple data modalities including text, image, audio, video, and structured data. The systems and methods disclosed herein receive an input dataset and generate unique watermark patterns for each data modality using cryptographic hash functions applied to dataset identifiers. The watermark patterns include parameters that provide instructions to watermark the input dataset. Watermarking includes modifying discrete data values, relationships, or arrangements within the original dataset at predetermined locations. The watermarked dataset is used to generate a hierarchical fingerprint structure including multiple levels of cryptographic hash identifiers that represent different portions of the data (e.g., at varying granularities). Both the watermark patterns and hierarchical fingerprints are stored in a distributed database accessible through a decentralized network, thus enabling authorized computing devices to verify data provenance and detect potential misappropriation across heterogeneous digital content types.

An apparatus may include a policy acquisition circuit configured to interpret a vehicle policy data value including an external event trigger condition. An apparatus may include a policy processing circuit configured to generate parsed policy data including the external event trigger condition. An apparatus 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.

Methods, systems, and apparatus, including computer programs encoded on computer storage media, for controlling data access. One of the methods includes receiving, from a user device associated with an account, a request for data; accessing data from one or more data sources that includes, for each of a plurality of data types, corresponding data values; selecting, from the plurality of data types and using permissions data for the account, one or more data types that the account has permission to access; and providing, to the user device and for each of the one or more data types that the account has permission to access, the corresponding data values.

The invention relates to an electric power data asset value evaluation method and system based on a data value chain, and the method comprises the following steps: obtaining multi-source electric power data asset information, carrying out the data preprocessing, and obtaining a unified data asset record; performing multi-dimensional feature extraction on the data asset record to obtain multi-dimensional feature information; constructing a power data asset value map based on the multi-dimensional feature information; community detection and key node identification are carried out on the power data asset value map, association information and value transmission paths among different data assets are determined, and candidate data asset paths are obtained; according to the node attributes and the associated information, performing path value scoring on the candidate data asset paths, and generating a path sorting result; and screening out a data asset combination with a high comprehensive value from a path sorting result to obtain an electric power data asset value evaluation result. Compared with the prior art, the method has the advantages that the systematicness and the globality of evaluation and the accuracy and the operability of an evaluation result are remarkably improved.

The invention discloses a heterogeneous computing power resource allocation method and system, and belongs to the technical field of computing power scheduling. The method comprises the following steps: analyzing an edge task through a content value analysis model, and generating a task label containing real-time, accuracy and exploratory demand scores; based on the task labels, a distributed computing power distribution protocol is utilized to match and distribute computing power resources for the tasks, and primary distribution and execution are completed; in the task execution process, the data value is evaluated through the lightweight evaluation model; if the evaluation value exceeds a dynamic threshold value, a computing power scheduling model agent is triggered to perform centralized secondary distribution, and the agent decides an optimal uploading path based on deep reinforcement learning and adjusts the threshold value so as to efficiently upload high-value data to a headquarter cloud and drive a large model to autonomously evolve. Through the double-track parallel architecture, the resource conflict between the real-time guarantee of the edge task and the evolution of the cloud model is solved, and the efficient and self-adaptive allocation of the computing power resource is realized.

The invention discloses a unified data access method and system for multi-protocol industrial equipment, and the method comprises the steps: constructing a unified equipment information model, and respectively establishing communication connection with at least two different types of industrial control protocol equipment through at least two different protocol adapters, receiving an original data message from the industrial equipment; starting a protocol adapter to parse the original data message so as to extract protocol specific data containing a device logic address, a data value and state information; mapping the protocol specific data to the unified equipment information model to generate a standard chemical control data object conforming to the specification of the unified equipment information model; and issuing the standard chemical control data object to an internal data communication bus so as to realize unified data supply for upper-layer application services. Therefore, an application developer can access and process all equipment data in a consistent and standard manner, the system integration complexity is greatly reduced, and the data fusion efficiency and stability are improved.

Techniques are disclosed relating to feature selection based on feedback-assisted optimization models. In various embodiments, for example, the disclosed techniques include accessing a training dataset that includes a plurality of data samples that include data values for a plurality of features, and a set of labels corresponding to the plurality of data samples. In some embodiments, a computer system performs feature-selection operations to select, from the plurality of features, a subset of features to include in a reduced feature set. For example, in some embodiments the feature-selection operations include processing the training dataset based on an optimization model, where an objective function utilized in the optimization model utilizes performance feedback information corresponding to machine learning models that are trained based on candidate feature sets. Based on the feature-selection operation, the computer system may generate an output value that indicates the subset of features to include in the reduced feature set.

The invention discloses a numerical control machine tool part machining data storage method and system. The numerical control machine tool part machining data storage method comprises the steps that multi-source data in the machining process is collected and synchronized in real time; performing process-entropy-based process section segmentation on the data stream, extracting multi-modal features, evaluating data values through a classifier fusing rules and machine learning, and generating feature data packets according to a hierarchical storage strategy; a data gene code is bound for each part, a processing process knowledge graph is constructed, and full-chain data association and accurate tracing are achieved; a sample is generated based on the feature data packet, a graph neural network and Bayesian optimization are utilized to train a process optimization model at a cloud end, optimization parameters are fed back to a machine tool to be executed, and a closed loop is formed. Process entropy segmentation, knowledge graph and GNN closed loop optimization are adopted, the problems that data storage cost is high, tracing is difficult, and data values are not mined are solved, and the accuracy of data tracing is improved. And intelligent data compression, full-life-cycle tracing and process adaptive optimization are realized.

Methods that provide a blockchain network for entities to securely collaborate with respect to rate card data records and data transactions triggering various events. In these methods, a computing device obtains, from a first blockchain node of an enterprise, via a blockchain channel, a rate card data record that includes at least one unique identifier, primary information about the rate card data record, and a plurality of data values that change over time. The rate card data record is in a form of a distributed ledger blockchain. The computing device generates, using a set of rules in a smart contract, a data transaction based on one or more values in the rate card data record. The data transaction is in the form of the distributed ledger blockchain. The computing device further provides, to a second blockchain node, via the blockchain channel, the data transaction that triggers an executable event.

The invention provides a data value evaluation method and system based on a knowledge mining large model and an analogue simulation agent. The method comprises the following steps: constructing a corpus containing consumption cases, domain knowledge and evaluation methodology, and completing acquisition and vectorization management; according to the design, a general value dimension is manually preset to form an evaluation framework, and FA-GRPO auto-reflection reinforcement learning is carried out on a large language model by using methodological data, so that the reasoning consistency is improved; constructing a data supplier, a data demander and a third-party evaluator agent, simulating bargaining and an attached condition protocol under information asymmetry based on a Rubinstein game, and generating a consensus usage scheme in combination with external retrieval; and finally, according to a consensus scheme, reversely deducing quantization parameters, and automatically outputting a structured evaluation report containing number paths and value estimation, thereby realizing low-subjectivity, high-consistency and verifiable data value evaluation.

The invention belongs to the technical field of database management technologies, and particularly relates to a dynamic allocation method and system for multi-level storage resources of a database, and the method comprises the following steps: S1, data feature and storage state collection: collecting multi-dimensional feature parameters of target data and state parameters of each storage level in real time; s2, multi-dimensional data value evaluation: constructing a data value evaluation model based on an analytic hierarchy process, determining each index weight in combination with expert scoring and an entropy weight method, and calculating a comprehensive value coefficient of target data; s3, a storage hierarchy matching decision is made, a value adaptation interval of each storage hierarchy is preset, and the optimal storage hierarchy of the target data is decided according to the comprehensive value coefficient of the target data and the real-time state parameters of each storage hierarchy; according to the method, reliable acquisition of data features, accurate evaluation of multi-dimensional data values, intelligent matching of storage resources and advanced pre-migration of data can be realized, and finally, the goal of'sharing high-performance storage by high-value data and controlling storage cost by low-value data 'is achieved.

The invention discloses a test control method and system for a direct-current power supply, and relates to the field of simulation testing, and the method comprises the steps: constructing a three-dimensional adaptive model to complete the matching calibration of system software and hardware, and achieving the load calibration through the comparison of dynamic load simulation and actual measurement data; in the testing process, a three-stage starting mechanism is adopted to power on step by step, fault protection is configured, and parameter scanning and real-time recording are executed according to a preset mode and a testing point; a double-closed-loop feedback mechanism is introduced, an inner loop adjusts output parameters, and an outer loop predicts power grid disturbance and performs advanced compensation; a safety threshold value is dynamically generated in combination with real-time environment data, linkage protection of approaching early warning and overrun protection is achieved, and a reliable effective data set is formed through fusion processing and double verification of multi-source test data. The system has the advantages that the precision and intelligence of the whole test process are realized through dynamic load simulation, a multi-level safety protection system is constructed through a three-level starting mechanism, and the precision, safety and data value of the test are comprehensively improved.

The invention belongs to the technical field of the Internet of Things and energy management, and particularly relates to an energy consumption data collector based on the Internet of Things and a collection method, the collector comprises a data processing module, a network state evaluation module, a data value evaluation module and a communication and control module, real-time protocol analysis and data conversion are carried out on original data, and the energy consumption data is obtained. Generating a standard energy consumption data packet with a unified timestamp and a device identifier, storing the standard energy consumption data packet according to a receiving sequence, and eliminating a time reference deviation caused by an instrument clock difference; communication signal strength and round-trip time with a remote Internet of Things platform are collected in real time, through multiple times of detection and normalization processing, a maximum value is selected as a network state factor, a dynamic change reference and a self-adaptive fluctuation interval are calculated based on a historical power data sequence, an instantaneous deviation rate and a continuous deviation duration are determined, and the network state factor is determined. A data value factor is generated through processing, and the network state factor and the data value factor are matched with a preset period adjustment mode to realize collaborative optimization.

The invention relates to the technical field of communication, and discloses a log sending method, a log receiving method, a log sending end and a log receiving end. The log sending method comprises the steps that an original log is converted into a compressed log based on a target log template, the target log template is predefined by a log sending end and a log receiving end and comprises structured data keys in the original log, the original log comprises structured data key value pairs, and the compressed log comprises a target template ID of the target log template; the structured data values are in one-to-one correspondence with the structured data keys in the target log template; the at least one compressed log is aggregated and packaged, a packaged compressed log is generated, and the packaged compressed log comprises a unique identifier of the packaged compressed log; and sending the packaged and compressed log to a log receiving end through a connectionless transmission protocol. According to the technical scheme, the network bandwidth in the log transmission process is saved, the waste of storage resources is avoided, and the system performance overhead is reduced.

An apparatus may include a policy acquisition circuit configured to interpret a vehicle policy data value comprising a data collection description. A device may include a policy processing circuit configured to generate parsed policy data corresponding to the data collection description, and to determine a memory allocation value in response to the parsed policy data. 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, and to store at least a portion of the collected vehicle data in response to the memory allocation value.

The invention discloses a fan fault analysis and operation and maintenance decision support method based on a new energy monitoring system, belongs to the field of new energy power generation monitoring, and aims at solving the problems that an existing system is low in fault recognition precision, depends on artificial experience and is not fully mined in data value. According to the method, a wind turbine generator SCADA system, meteorological data and operation log multi-source data are integrated, and a five-dimensional fault feature tag set of electrical and mechanical faults is constructed; after module pre-classification, a DBSCAN clustering algorithm is adopted for analysis, and samples with the similarity matching degree smaller than 60% of a new clustering center and an original clustering center serve as newly-added typical cases to update a fault processing knowledge manual; when the matching between the unit parameters and typical faults is greater than or equal to 80%, automatic early warning is carried out and a processing scheme is pushed; carrying out preventive maintenance in combination with a time sequence model, optimizing a spare part inventory according to a weighted model, and pushing a case when the matching between a fault and a historical case is greater than 90%; the method improves the fault diagnosis precision, shortens the processing time, reduces the operation and maintenance cost, and is suitable for various manufacturer models.

Systems and methods for document transformation and recordation are provided. Documents for a transaction of real property are identified based on a jurisdiction in which the real property is located. Each document includes a plurality of data fields and each data field is associated with a data field identifier. A data value is received in at least one of the data fields of one or more of the documents. The data fields in the documents associated with the data field identifiers that match the data field identifiers of the data fields in which the data values are received are identified. The identified data fields are automatically populated with the data values based on the data field identifiers. Completeness rules are applied to the documents by verifying all required documents for completing the transaction in the jurisdiction are satisfied. Documents or information pertaining the documents can be stored in a blockchain.

The invention discloses a data middle table design method and system suitable for a power system, and belongs to the field of power system data management. Comprising the following steps: constructing a quantitative mapping model through multi-source data acquisition, and outputting a standardized data value index; establishing a dynamic asset catalog and realizing accurate mapping from values to resources by adopting a multi-objective optimization algorithm; constructing an event-resource-carbon emission combined prediction model to generate an optimal regulation and control strategy; event regression is identified based on a value index, and stable recovery of the system is realized through a value recovery curve; a standardized value voucher is generated by using a block chain technology, and value conversion is completed by means of a cross-chain protocol. According to the method, the problems of data value evaluation deficiency, low resource scheduling efficiency, insufficient prediction capability and the like in the prior art are solved, accurate quantification of data values, intelligent resource scheduling, operation situation prediction, stable system recovery and value closed-loop conversion are realized, and the intelligent level of data management of the power system is remarkably improved.

Systems and methods are provided for processing a natural language question using structured query language (SQL). A computing system receives natural language questions, obtains access levels and database schema, generates prompts, identifies relevant tables, and generates SQL code using a SQL large language model (LLM). It executes the code on the database and generates a result message with obfuscated data values based on access level. The result message is provided in response to the natural language question.