1506 results about "Data application" patented technology

The invention relates to the technical field of data security, in particular to a data security sharing method and system. The method comprises the following steps: acquiring original shared data, carrying out reverse mapping and block marking, generating block marking data, carrying out inter-block random arrangement on the block marking data, carrying out differential disturbance simulation based on a random sequence, forming disturbance block data, and encrypting the disturbance block data by applying a progressive encryption technology. The method comprises the following steps: carrying out fragmentation storage, generating storage shared data, carrying out access monitoring on the storage shared data, collecting shared access data, and constructing an evidence chain and generating an access traceability data chain according to the data, thereby realizing the security and traceability of data sharing. According to the invention, a safer and more credible data security sharing method is realized.

An application server or other processing entity may receive, via a cloud-based platform, user input that may include at least one request for data. The application server may classify the user input into a first of a plurality of deterministic-stochastic spectrum classifications based on the user input and a probability of mapping the at least one request for data to at least one data location. The application server may retrieve the data from the at least one data location and based on the first deterministic-stochastic spectrum classification. The application server may transmit, based on the first deterministic-stochastic spectrum classification and the user input, an input to a large language model. The application server may present a response to the user input, where the response is based on a combination of an output of the large language model and the data retrieved from the at least one data location.

The invention relates to the technical field of data processing and privacy protection, in particular to a data desensitization and integrity verification method and system based on a differential privacy algorithm, and aims to realize collaborative optimization of data privacy protection and integrity guarantee. Determining a total privacy budget by calculating global sensitivity, a data leakage risk coefficient and independent and joint data privacy information amount, dynamically allocating the privacy budget for a single-task or multi-task scene, and injecting noise based on an allocation result to realize differential privacy desensitization; meanwhile, a verification voucher is generated by using a hash function, a digital signature and an alliance chain, and the data integrity is ensured through signature verification, data consistency verification and block chain verification. The system comprises a data application processing module, a global sensitivity calculation module, a desensitization core processing module, a verification voucher generation module and an integrity verification module, is suitable for government affairs, enterprises and other scenes with high requirements for data privacy and integrity, and effectively improves the safety and reliability of data processing.

The invention relates to the technical field of data processing, in particular to an artificial intelligence data aggregation method based on big data, and the method comprises the following steps: collecting traffic monitoring original data, removing repeated data and correcting wrong data through data cleaning and preprocessing, and generating preliminary cleaning data; dynamic index adjustment is applied to the preliminarily cleaned data, the depth and branches of an index tree are dynamically adjusted according to the access frequency and types of the data items, and optimized index data are obtained. In the dynamic index adjusting process, the index tree structure is dynamically optimized according to the data access frequency and type, the retrieval efficiency of high-frequency data is improved, and the storage overhead of low-frequency data is reduced. The hierarchical data compression performs hierarchical processing on the data, the use of storage space is greatly reduced through a mode of combining lightweight compression and an advanced compression algorithm, and a foundation is laid for large-scale data processing.

The invention relates to the technical field of power grids, in particular to a metering box remote monitoring method, system and device based on artificial intelligence and a medium. Acquiring electrical parameter data and environmental data of the metering box; performing time domain and frequency domain feature extraction on the data to obtain key features; inputting the processed data into a preset abnormal state detection model; identifying an abnormal state of the metering box based on a model output result; and when the abnormity is identified, alarm information is generated and sent to a monitoring terminal through a remote communication network. Multi-dimensional monitoring data are collected in real time through multiple sensors, feature extraction and anomaly detection are performed by applying an artificial intelligence technology, a model is continuously optimized in combination with an online learning mechanism, and comprehensive sensing of the running state of the metering box and accurate recognition of the abnormal state are achieved. Compared with a traditional monitoring system, the method improves the detection accuracy, reduces the false alarm rate and the missing report rate, improves the fault processing efficiency through the automatic generation of the maintenance suggestions, and provides a powerful guarantee for the operation of a power system.

The invention discloses a security protection method and system for an information management system and a storage medium, and relates to the technical field of security protection.During operation of the system, when a user logs in or carries out sensitive operation, multiple identity verifications are carried out, an identity verification coefficient AC is obtained through calculation, various security parameters in the system are collected and updated in real time, and the security protection efficiency is improved. A high-strength encryption algorithm is applied to data to be transmitted, an encrypted data packet is generated, a transmission encryption coefficient EC is obtained through calculation, corresponding access permissions are set for different data based on user roles, identities and access content, an abnormal monitoring coefficient ADC is obtained, and a comprehensive safety coefficient SEC is obtained through calculation according to an identity verification coefficient AC, the transmission encryption coefficient EC and the abnormal monitoring coefficient ADC. The overall safety of the system is dynamically evaluated, abnormal behaviors are recognized and logs are recorded by comparing the comprehensive safety coefficient SEC with a preset threshold value, and when the system detects an abnormal access mode, a safety alarm is triggered and countermeasures are taken.

The invention discloses an energy industry intelligent algorithm optimization method based on federal learning. The method comprises the following steps: S1, collecting and preprocessing local energy data; s2, acquiring and preprocessing expert example data; s3, constructing a comprehensive data set by applying a multi-modal data fusion technology; s4, each participant performs local model training on the comprehensive data set by using a federal adaptive optimization algorithm to generate local model update; s5, the central server aggregates local model updating to generate a global model; s6, applying a soft actor-commentator algorithm in the global model, and combining an inverse reinforcement learning technology to optimize an energy distribution strategy; s7, creating a virtual simulation environment by using a digital twinning technology, and testing and optimizing the performance of the global model; and S8, providing a user-friendly interface and a natural language processing interface, and realizing personalized energy management suggestions. According to the method, federated learning, a soft actor-commentator algorithm and an inverse reinforcement learning technology are combined, and an energy distribution strategy is optimized.

The invention relates to the technical field of natural language processing, and discloses a knowledge base question and answer platform construction method based on a large language model, which comprises a knowledge acquisition module, a data preprocessing module, a text processing module, a vectorization module, a question understanding module, a mixed retrieval module, a prompt generation module, an answer generation module and an answer quality analysis module. A secondary inquiry processing module and a feedback learning module; according to the method, a semantic segmentation algorithm is combined with semantic retrieval and keyword retrieval, so that the flexibility is high; normalized prompts are constructed, input is performed according to correlation sorting, and the accuracy of answers is improved; multi-dimensional confidence evaluation is introduced, strict multi-layer security and compliance filtering is set, and the reliability of the system is ensured; the relevance of multiple rounds of dialogues is judged and complemented, so that interaction is more natural and efficient; knowledge is collected and updated in real time, a knowledge base and a retrieval strategy are continuously optimized, and a closed loop of data-application-feedback-tracing-optimization is formed.

A method includes: obtaining an original image designated as a background screen; obtaining, based on time information and weather information, a predefined prompt; obtaining display area information of a user interface (UI) object; generating a modified image corresponding to the time information and the weather information by applying the original image, the predefined prompt, and the display area information as input data to a generative model that generates the modified image, wherein in the modified image, the predefined prompt is reflected in a display area where the UI object is displayed in accordance with a first degree, and the predefined prompt is reflected in a remaining area other than the display area in accordance with a second degree that is different from the first degree; and displaying the modified image and the UI object as the background screen by overlaying the UI object on the modified image.

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 monitoring system can generate compressed storage system monitoring data segments using monitoring data obtained from a storage system. The monitoring system can obtain storage system monitoring data and generate a segment by applying the storage system monitoring data to a machine learning model trained to segment the storage system monitoring data. The monitoring system can generate a compressed segment by applying a specified compression technique to the segment. In response to user query, the user query specifying a portion of the storage system monitoring data; the monitoring system can perform at least one of: reconstructing and providing the portion using the compressed segment; or providing the compressed segment for reconstruction of the portion.

The invention discloses a small-probability aviation unsafe incident cause analysis and online monitoring method and system based on flight data, and belongs to the field of aviation safety technology and flight data application. The method comprises the following steps: S0, determining an input parameter set through an aircraft flight quality monitoring standard, and constructing a risk identification architecture I based on weak supervised learning; s1, based on the risk identification architecture I, obtaining a risk identification network I and a quantitative risk value, and obtaining a high-risk time point and an inducement by using statistical analysis; and S2, inheriting the ability of the risk identification network I to identify the risk of the unsafe event to a risk identification network II by using a knowledge distillation method, obtaining the risk identification network II for online monitoring, and carrying out online risk detection based on the risk identification network II for online monitoring.

The invention discloses a full-link monitoring method and system for state application in an edge distribution scene, and the method comprises the steps: S1, deploying a monitoring agent at an edge calculation node, and collecting the state data and performance indexes of edge equipment; s2, sending the collected data to a central monitoring system in real time through an efficient data transmission protocol; s3, storing and managing synchronous data between the edge nodes and the center node by using a distributed database; s4, preprocessing the collected data by applying a real-time data stream processing technology; s5, performing anomaly detection and mode recognition on the preprocessed data, and generating intelligent early warning; s6, adopting a full-link tracking tool to monitor and record a full-link path requested in the system, and identifying and analyzing a performance bottleneck and a fault point; and S7, optimizing a data transmission process by using a message queue system, and implementing a data encryption and access control strategy. The method has the advantages of low delay, high data security and high system response speed.

The invention provides a coal preparation plant production data full-process management system and method, and the method comprises the steps: a data collection subsystem collects equipment data and production data of different data sources of each plant through a preset data collection interface; the data transmission subsystem encrypts and transmits equipment data and production data acquired by the data acquisition subsystem to the data cleaning subsystem for cleaning, and the data storage subsystem stores the data cleaned by the data cleaning subsystem to a plurality of databases of different types in a mixed storage mode. And the data application subsystem obtains the target data from the data storage subsystem and performs production plan optimization, equipment fault early warning and quality control and optimization based on the target data and preset data. By the adoption of the method, efficient processing and deep application of the whole process of production data of the coal preparation plant are achieved, powerful support is provided for intelligent and refined production management of the coal preparation plant, and the overall operation level and economic benefits of the coal preparation plant can be effectively improved.

The invention discloses a vehicle electromagnetic compatibility test method and device based on a big data platform, and relates to the technical field of vehicle testing, and the method comprises the steps: creating a vehicle electromagnetic compatibility detection item in a cloud platform, and generating a detection task; detecting equipment and a to-be-detected piece are connected to the cloud platform through the Internet of Things technology, and standardized parameters and a hardware connection template of a detection task are configured; physical connection of the detection system is completed based on the hardware connection template; calling an automatic test module to execute a standardized test process, and collecting test data in real time; performing multi-dimensional analysis and intelligent evaluation on the test data to generate a detection report and authentication data; synchronizing the detection report and the authentication data to a service platform for secondary data application; visualization and collaborative management of the test process are carried out through the remote monitoring module.

The invention discloses an industrial park big data center construction method and a big data center system, and belongs to the technical field of edge computing and artificial intelligence, and the method comprises the steps: data collection, obtaining of data required by a big data platform from multiple data sources, data access, and visual configuration and task scheduling management. Managing and controlling the process of aggregation and access of various types of data; data calculation: extracting data from a data source, processing the data, and storing the data, so as to complete data reconstruction; the data service comprises data calling, data persistence, security management, basic data service, data collision service, holographic query service, relationship-based service and location-based service; and data application: unified convergence access is carried out on structured and unstructured data of all departments and the Internet by providing a standard data unified access mechanism. The problem that data cannot be efficiently circulated and shared in the operation process of the professional park of the oral health industry is solved.

The application provides a pumped storage power station data fusion intelligent analysis platform, comprising: a data source comprising a plurality of original data; the data center is configured to collect original data and convert the original data to obtain unified data; the data application comprises an algorithm middle table module, and the algorithm middle table module is configured to construct an operator library, associate corresponding measurement attributes with measurement point identifiers of unified data and form a matching table; obtaining an algorithm construction instruction; according to the algorithm construction instruction, the operator library and the measurement attributes, performing algorithm modeling through a drag-and-drop operation process to obtain a power station data analysis algorithm; instantiating the power station data analysis algorithm to obtain an instantiated algorithm; and processing the corresponding unified data according to the instantiation algorithm and the matching table to generate an analysis result data set. The pumped storage power station data fusion intelligent analysis platform provided by the invention can realize multi-source heterogeneous data integration and analysis, and is high in efficiency, low in cost and good in real-time performance and accuracy.

Disclosed herein are systems, methods, and media for emissions data analysis. The disclosed embodiments include accessing emissions activity data from at least one emissions activity data source. The emissions activity data may correspond to an entity and the at least one emissions activity data source corresponds to an activity region. The disclosed embodiments include extracting structured emissions data from the emissions activity data by applying the emissions activity data to a machine learning model configured to standardize data. The machine learning model may be trained with emissions training data. The disclosed embodiments include accessing an emissions factor database containing a plurality of emissions factors. The disclosed embodiments include selecting, from the emissions factor database, at least one emissions factor corresponding to the activity region. The disclosed embodiments include generating an emissions line item based on the structured emissions data and the at least one selected emissions factor.

The invention provides a water conservancy equipment remote control method and system for water conservancy informatization, and the method comprises the following steps: connecting water conservancy equipment to the Internet of Things, collecting equipment operation data through a sensor, and uploading the data to a cloud server; establishing a state monitoring model of the water conservancy equipment by applying a machine learning algorithm based on the collected equipment operation data; the water conservancy equipment is remotely controlled through a mobile application or a Web interface; in combination with real-time weather data, water demand prediction and equipment state information, an operation strategy of the water conservancy equipment is automatically adjusted; the information is presented to a manager through a data visualization technology; and feedback information of remote control and scheduling is collected regularly, and the system is optimized and adjusted. According to the invention, through real-time monitoring, accurate prediction, intelligent scheduling, data fusion, visual display and continuous optimization, efficient, stable and intelligent water conservancy equipment management is realized, and the operation efficiency and safety of a water conservancy project are greatly improved.

A computerized method for predicting a failure of a component based on environmental conditions is described. Environmental conditions proximate a component in a server are monitored. When the current environmental conditions exceed a threshold level, the current environmental conditions are applied against historical data to predict when the component will fail. Mitigating actions are performed in response to, and prior to, the predicted failure of the component.

A data management arrangement that comprises system(s) enabling monitoring and interconnectivity system resources and configuration parameters to be dynamically adapted to ensure re-defined data prioritization and associated essential minimal data interconnectivity is maintained during high-dependence or critical data applications with arrangement includes the capability to combine and adapt / adjust online network application services connectivity parameters and configurations, mobile or remote monitoring and / or information and communication technology in accordance to required monitoring criteria, monitoring and interconnectivity conditions, status of monitored individual or object, and / or available resources and conditions associated with said NAS connectivity and / or mobile or remote monitoring and / or ICT systems, in accordance to the application and applicable risk mitigation and high-dependence connectivity monitoring aspect and associated requirements, and the capability to combine mobile monitoring or computing location details with current and / or forecast and / or normal conditions in order to determine travel, health, and other alerts, advice and recommendations.

A device assembly having a syndicated data service interface that is communicatively coupled to a vendor-specific syndicated data application platform and having an installed vendor specific syndicated data application, with the interface receiving point-to-multipoint non-addressed transport bearer channels of different syndicated data channel transport networks initially only including the monitoring of the syndicated data channels and not decoding the syndicated data messages or syndicated data informational data contained therein, identifying from the transport bearer channels a received syndicated data channel having an syndicated data channel identifier of the specific syndicated data device, and reading the syndicated data messages within the received syndicated data channel and extracting the syndicated data supplier-independent informational data from the syndicated data message and providing the extracted syndicated data informational data to at least one of the installed vendor specific syndicated data applications of the syndicated data device.

An astronavigation material database system belongs to the technical field of astronavigation material databases and comprises a basic platform layer, an application service layer and a presentation layer. The public service layer comprises a public service plate; the application function layer comprises but is not limited to a material management plate, a material data application plate and a material big data analysis plate. The invention discloses an astronavigation material data system, which comprises a word standard specification, establishes an astronavigation material database, can perform big data analysis according to a computer software technology, a network technology and a digital technology, and can realize intelligent recommendation and intelligent knowledge management of astronavigation materials.

Described are methods, platforms, systems, and media, for enhancing well-being by using methodology including receiving media comprising an unstructured user-generated narrative; applying one or more algorithms to the unstructured user-generated narrative to extract semi-structured user context data; applying a first machine learning model to classify one or more of sentiment, intent, habits, patterns, beliefs, and motivations from at least the user context data, wherein the first machine learning model comprises an unsupervised machine learning model; applying a second machine learning model to identify one or more recommended next actions from at least the user context data, wherein the second machine learning model comprises a supervised machine learning model; and generating one or more well-being-related insights for the user based at least on the user context data, and one or more of the sentiment, intent, habits, patterns, beliefs, motivations, and one or more recommended next actions to encourage a healthy lifestyle.

Systems and methods method include establishing, a connection between the first computing system and an application of a second computing system, the connection associated with an entity having one or more first accounts with the first computing system and a second account with the application of the second computing system, receiving a dataset corresponding to the second account, the dataset comprising a plurality of data entries corresponding to respective invoices, retrieving account data corresponding to the one or more first accounts with the first computing system, applying, the dataset and the account data as inputs to a machine learning model trained to generate optimized orders, and generating, by the one or more processors, a first user interface including data of a list of the plurality of entries, the plurality of entries being ordered according to the optimized order for the dataset.

The invention discloses a petroleum industry personnel behavior safety video monitoring system based on artificial intelligence, and the system comprises a front-end equipment layer which is used for collecting physical environment data; the edge computing layer is deployed on a local server of a factory and is used for preprocessing and preliminarily analyzing the data acquired by the front-end equipment layer; the cloud analysis layer is used for carrying out deep analysis on the data uploaded by the edge calculation layer by utilizing deep learning and natural language processing technologies; the cloud analysis layer can integrate data from the front-end equipment layer, the edge computing layer and the cloud analysis layer; and the application service layer is used for presenting the result of the cloud analysis layer to a user in a user-friendly manner. According to the method, video spatio-temporal features are extracted through the CNN-BiLSTM fusion model, risk behaviors such as no helmet wearing and illegal operation are accurately identified, and the false alarm rate is reduced; the NLP analyzes the work order to construct the knowledge graph, the knowledge graph is matched with the video behavior event in a space-time manner, the risk score is dynamically evaluated, and the comprehensive early warning accuracy is improved.

The invention relates to the technical field of big data intelligent decision making, and provides a household appliance commodity dynamic pricing system and method based on multi-source data fusion, and the system comprises a data collection server, a feature extraction engine, a model training cluster, and a strategy execution terminal. The feature extraction engine comprises a time sequence feature constructor used for generating sliding window statistics; the semantic analysis unit is used for analyzing a commodity detail page text description to generate a brand premium coefficient; and the image recognition module is used for predicting the willingness to pay by the user through the commodity main image quality score. Through collaborative design of the feature extraction engine and the model training cluster, the dynamic response capability and the data application depth which are deficient in a traditional pricing mechanism are realized, traditional household appliance merchants are assisted to adapt to market changes, the customer satisfaction and the market share are improved, and the survival and development of the merchants are guaranteed.

The invention belongs to the technical field of data processing, and particularly relates to a medical big data processing method, device and equipment based on lake and warehouse integration and a medium, the method comprises the following steps: collecting original medical data, and uniformly accessing the original medical data to a data access layer of a lake and warehouse integrated architecture in an original format and full-amount information to form a source pasting library; preprocessing the original medical data in the source library, performing unified coding and format conversion according to the medical data standard set, generating a standardized data set, and storing the standardized data set in a normalization library; on the basis of data in the normalization library and the source pasting library, multi-modal medical data are associated and integrated through cross-source federated query and computational flow technologies according to business scene requirements of clinical diagnosis and treatment, scientific research analysis or management decision, a special topic library oriented to a specific business topic is constructed, and a special topic library view is generated; and providing a query interface and an analysis service for the data application platform through the thematic database. The data processing speed and efficiency are improved, delay is reduced, and the data throughput efficiency is ensured.

The invention discloses a data collection method and device based on an unmanned aerial vehicle, a medium and a product, and relates to the field of unmanned aerial vehicle data collection, and the method is realized based on an instruction-level data service bus. The method comprises the following steps: automatically adapting to a lightweight communication protocol based on a multi-source heterogeneous system access control module, and carrying out real-time adaptive access of an unmanned aerial vehicle system; performing full-duplex communication monitoring on the unmanned aerial vehicle system; management and control are carried out according to the monitoring result; based on the received communication information type of the unmanned aerial vehicle system, instruction / data transmission logic channels are adopted, and different logic channels are automatically distributed; according to the received communication information, a data processing and analyzing module is adopted to carry out data processing; and according to the data application service gateway, the data packet set is packaged and published as an application service for the unmanned aerial vehicle to collect data. According to the invention, control management of data collection by using the unmanned aerial vehicle is realized, and the unmanned aerial vehicle can effectively, safely and reliably collect data in real time.

The invention is suitable for the field of data processing, and discloses a data treatment method, terminal equipment and a storage medium. The method is applied to a data facility, the data facility comprises a data platform, a data system and a data tool, and the method comprises the steps of accessing or collecting to-be-governed data; executing a preset operation corresponding to the full life cycle stage on the to-be-treated data, wherein the full life cycle stage comprises a data production stage, a data processing stage, a data circulation stage, a data application stage, a data maintenance stage, a data display stage and a data destruction stage; multi-dimensional data assessment corresponding to the full life cycle stage is executed on the to-be-governed data, assessment results are obtained, and the assessment results comprise a data value assessment report, a data quality assessment report, a data standardization assessment report and a data safety assessment report; and constructing a matrix type responsibility system according to an evaluation result, wherein the responsibility system maps a data engineer, a data analyst, a data manager, a data security teacher and a data quality evaluator to a corresponding life cycle stage in the full life cycle stage. According to the method, the problems of full-flow fault, evaluation subjectivity, role fuzzification and facility islanding in datamation treatment are solved.