627 results about "Data application" patented technology

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.

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.

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 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.

Systems, computer program products, and methods are described herein for an integrated multimodal artificial intelligence framework for automated provisioning systems. The present disclosure is configured to aggregate and process data from multiple sources, apply advanced machine learning techniques for data normalization, feature extraction, and pattern recognition, and integrate these capabilities into an automated workflow for application provisioning. The system utilizes a processing device and non-transitory storage containing instructions which, when executed, enable the handling of complex workflows, decision-making processes, and real-time error management. Incorporating voice recognition, the system allows for natural language user interactions, enhancing accessibility and efficiency. The AI-driven framework adapts to evolving operational needs, ensuring precise and resilient application deployment within dynamic environments.

The invention relates to the technical field of data processing, and discloses an Internet of Things gateway data grading processing method and system based on edge computing, and the method comprises the steps: collecting original operation data and a data traffic value received by an Internet of Things gateway; performing feature extraction and clustering grouping according to the original operation data, calculating an average influence coefficient of samples in a group in combination with a preset service weight, and determining an importance level corresponding to the group; important data is screened out for distribution immediate evaluation; performing aging characteristic analysis according to the important data, and predicting a data processing priority; analyzing resource demand allocation according to the priority sequence and the data flow value, and constructing a resource allocation proportion; according to a resource allocation proportion and an allocation proportion lower limit, identifying nodes with insufficient proportions and optimizing the priority sequence to obtain a final scheduling scheme; according to the final scheduling scheme, the processing sequence is applied to the original operation data, and data grading output is obtained. According to the method, efficient and accurate data processing is realized.

Methods and apparatus for: i) providing a user equipment (UE) access to a core network, e.g., a 3GPP 5G core network, via a wireless local area network access point (WLAN AP) (e.g., a non-integrated non-3GPP access network), without the use of a non-3GPP Interworking Function (N3IWF) or a Trusted non-3GPPP Gateway Function (TNGF) and ii) allowing the transfer of application data via the core network are described. The transfer of application data is, i.e., in both directions, e.g. UE to User Plane Function (UPF) to data network (DN) and DN to UPF to UE. The access obtained by the UE does not require the presence of a 3GPP access network, e.g., a 3GPP radio access network (RAN) such as a gNB.

The invention discloses an equipment monitoring method and system based on big data, and belongs to the technical field of industrial internet and big data analysis, and the method comprises the steps: multi-source data collection: deploying sensors at key parts of tobacco equipment, collecting time sequence data, and obtaining image data through an industrial camera, synchronously recording production work orders and maintaining log text data; edge end preprocessing: an edge computing node performs abnormal point filtering on time sequence data by adopting a sliding window algorithm, and vibration signal spectrum features are extracted through Fourier transform; a YOLO algorithm is applied to the image data to detect equipment part abrasion or material blockage conditions; packaging the processed data into a feature vector in a fixed format; cloud intelligent analysis; and performing dynamic early warning and decision making. According to the method, cross-equipment and cross-time-period fault mode correlation analysis can be realized, multi-source data are fused by using big data analysis, intelligent equipment monitoring is realized, and the fault diagnosis and prediction capability is improved.

The invention discloses an audio processing method and device, a storage medium and an electronic device, and relates to the technical field of data processing, and the audio processing method comprises the steps: obtaining voice feature data and semantic feature data of original audio data; the original audio data is audio data extracted from the target audio and video data; generating segmentation position data according to the voice feature data and the semantic feature data; and generating audio paragraph data and paragraph visualization data according to the segmentation position data, and providing the paragraph visualization data to a user interaction interface. According to the technical scheme of the embodiment of the invention, the utilization rate of the audio information can be improved, the data processing cost in an application scene is remarkably reduced, and the data application efficiency and scene adaptability are improved.

The embodiment of the invention discloses an assembly type building construction management system based on BIM and GNN models, the system comprises a data collection module, a data processing module, a data application module and a data visualization module, the data collection module is used for collecting multi-source heterogeneous data of the whole period of building construction, the multi-source heterogeneous data comprises a building information model, a data processing module, a data application module and a data visualization module, and the data processing module is used for processing the multi-source heterogeneous data in the whole period of building construction. BIM (Building Information Modeling) data, sensor data, video stream data and external data; the data processing module is used for processing the multi-source heterogeneous data to obtain a data processing result which comprises a fused data set, standardized graph structure data and structured decision suggestions; the data application module is used for adjusting resource allocation according to the data processing result and generating an optimization instruction which comprises a structured instruction set and a structured scheme set; and the data visualization module is used for converting the optimization instruction and the structured decision suggestion and displaying a conversion result in a visual interface. According to the invention, the integration level, the intelligence and the cost-benefit balance degree of fabricated building construction management can be improved.

The invention discloses an indoor and outdoor fine three-dimensional modeling method based on multi-modal data fusion. The method comprises the steps that unmanned aerial vehicle close-range photography, airborne laser scanning and ground supplementary photography are carried out on a building in a low-altitude area, and a first outdoor photo data set, a first laser point cloud data set and a POS data set of the building are obtained; continuously collecting at an indoor entrance through a laser SLAM mobile scanning system and a panorama camera according to a pre-planned route to obtain a second indoor photo data set and a second laser point cloud data set of the building; aerial triangulation adjustment calculation of a multi-source data set is carried out under the assistance of a POS data set, and an indoor and outdoor integrated fine surface three-dimensional model is automatically constructed. According to the method, the indoor and outdoor integrated surface three-dimensional model of the large building can be duplicated, the element cosmic space base scene is realized, and data application is provided for the industries of element cosmic scenes, indoor navigation, surveying and mapping and the like.

The invention discloses an intelligent agricultural big data system, and belongs to the field of agricultural big data, and the system comprises an environment monitoring module, a sensing layer deploys a sensor to collect original data, and a transmission layer is provided with an edge gateway which is connected with the sensor and carries out the transmission of the original data; the equipment layer is used for regulating and controlling irrigation, ventilation and light supplement schemes, and the control layer is used for optimizing MQTT message middleware to control the agricultural Internet of Things system; according to the data management module, a data collection layer obtains original data, a data processing layer processes the original data according to a three-layer data cleaning mechanism, and a data storage layer stores the processed data; according to the system management module, the data application layer constructs an interface covering Web, a mobile terminal and AR equipment, the data display layer generates a soil moisture thermodynamic diagram, a crop growth distribution diagram and an equipment operation state diagram, and intelligent management of agricultural production is ensured. The advanced Internet of Things technology is adopted, and the accuracy and the real-time performance of data acquisition are improved.

The invention discloses a server health examination method and device, equipment and a medium. The method comprises the following steps: acquiring hardware layer data, system layer data, application layer data and physical environment layer data of a target server through a data acquisition component; determining a current value and a predicted change range of each health index regularly, and determining a current health level and a predicted health level range of the target server according to the current value and the predicted change range of each health index; when the current health level belongs to an abnormal level, executing a self-healing operation corresponding to the current health level; and when the predicted health level range contains an abnormal level, executing an early warning operation. According to the embodiment of the invention, real-time quantitative evaluation, abnormity prediction, early warning and automatic repair of the health state of the server can be realized, health evaluation and health pre-judgment can be comprehensively and accurately carried out on the server, and the operation stability of the server is improved.

The invention belongs to the technical field of sensor measurement and recognition, and particularly relates to a human body motion recognition system based on LSTM (Long Short Term Memory), which comprises a data acquisition module, a data calculation module and a data application module, and utilizes a neural network model and a wearable electronic sensor to detect a human body walking state. A new long short term memory (LSTM) recurrent neural network model, namely an LSTM-STRM model, is established, the model combines multi-task learning, an attention mechanism and spatio-temporal feature fusion to accurately classify the walking state of the human body, and the walking state is compared with the original LSTM model. The result shows that the LSTM-STRM model can be used for classifying the time sequence data collected by the measuring unit, the states of the five targets can be classified with high precision, and the recognition accuracy is higher than that of an original LSTM model. The method improves the recognition precision, is high in adaptability, is suitable for the fields of medical treatment, human-computer interaction, exercise training and the like, and has a wide application prospect.

The application provides a security management system and method applied to a smart education platform, wherein the system comprises: a platform application security management module, which is used for performing security management on a data application layer of the smart education platform; a data security management module, which is used for performing security management on a data storage layer of the smart education platform; and a big data component security management module, which is used for performing security management on a big data basic component layer of the smart education platform. The security management system applied to the smart education platform of the application performs security management on the data application layer, the data storage layer and the big data basic component layer of the smart education platform through the platform application security management module, the data security management module and the big data component security management module, so that the security of the big data platform is ensured.

The invention relates to an interactive three-dimensional rendering system and method, and the system is characterized in that a three-dimensional rendering server carries out the signaling exchange according to a connection request, builds a webpage real-time communication channel, compresses a rendered image frame into a video stream through the webpage real-time communication channel, and transmits the video stream to a client for display; when the client detects an interaction operation of a user, the generated instruction interaction data is transmitted to the three-dimensional rendering server through a webpage real-time communication channel; the three-dimensional rendering server applies the instruction interaction data to the rendering process of the image frame according to the three-dimensional map service unit and the preset instruction information, so that the client updates the displayed video stream; unified processing of video stream transmission and instruction interaction is realized by establishing a standardized webpage real-time communication channel, different map engine interfaces are compatible by adopting a preset instruction protocol specification, the system integration complexity is reduced while the real-time rendering performance is ensured, and the method has the advantages of improving cross-platform interaction compatibility and reducing instruction analysis delay.

The invention relates to a streaming data and mass batch data storage method, which belongs to the technical field of data storage, and comprises the following steps: classifying mine data into streaming data and batch data; according to the method, a mixed storage architecture is constructed, the mixed storage architecture comprises a data acquisition layer, a batch processing layer, a speed layer, a service layer and an application layer, so that streaming data and batch data are processed and stored by adopting different rules, and the method comprises the following steps of: acquiring the streaming data by adopting an acquisition adaptation gateway and a real-time message queue Kafka; collecting the batch data by adopting a relational database DM8 and a timing scheduling Airflow mode; the batch processing layer is used for dividing the data into cold and hot temperature data and performing classified storage on the data; the speed layer is used for processing and storing the real-time streaming data; the service layer is used for carrying out data integration on the temporary stream processing data and the batch processing result data and providing data query interface service; and the application layer is used for setting a business application module and accessing each database to realize data application.

A method for providing personalized non-profit venue visit recommendations to a visitor at a non-profit venue, comprising: providing a management interface for management of a set of multimedia assets; receiving a mapping of multimedia assets to a display in at least one site plan; receiving metadata for the set of multimedia assets; providing an interface for receiving data indicating personal interests of a visitor, including visitor-provided preference data and passively-collected visitor interaction data; receiving personal interest data for a plurality of non-profit venue visitors; receiving preference data relating to a prospective visitor; applying a machine learning system to analyze the metadata, the data indicating personal interests of the visitor, the personal interest data for the plurality of non-profit venue visitors, and the preference data relating to the prospective visitor; and generating a selection or sequence of non-profit venue location recommendations for the prospective visitor.

The embodiment of the invention provides a memory management method, electronic equipment, a memory management device and a storage medium, and relates to the technical field of computers, the method comprises the following steps: obtaining a memory allocation request initiated by an application program; the migration type of target data aimed at the memory allocation request is determined as a target migration type, and the migration type comprises a migratable type and a non-migratable type; determining whether a free space capable of accommodating target data exists in the page block of which the migration type is the target migration type or not, wherein the migration type of the page block is the migration type of data capable of being stored in the page block; if yes, storing the target data into the page block; if the page blocks do not exist, searching the page blocks of which the migration types are idle types, storing the target data in the searched page blocks, updating the migration types of the page blocks storing the target data to the target migration types, and not storing the data in the page blocks of the idle types. By applying the scheme provided by the embodiment of the invention, the problem of memory fragmentation can be solved.

The invention belongs to the technical field of nuclear power data processing, and particularly relates to a multi-level data cleaning method for the nuclear power industry. Comprising a first hierarchy data importing and preprocessing layer, and the first hierarchy is a basic unit for nuclear power multi-hierarchy data cleaning; the second level carries out local processing layer data processing, and the data subjected to secondary cleaning is recompressed, feature codes are extracted, and then the data are transmitted to the third level; or directly forwarding the unprocessed original compressed data; the third hierarchy carries out data processing of the platform layer in the data; and the fourth level performs data processing of the data application layer, and is responsible for performing persistent storage on the data according to a standardized format after the data is subjected to full-process cleaning, compression, feature extraction and desensitization processing. The method has the beneficial effects that the method is specially customized for the nuclear power industry: special requirements of high confidentiality, network isolation, large data volume and the like of data in the nuclear power industry are fully considered, and a special cleaning and desensitization process is designed.

This invention discloses a construction project archive digitization and information management system and method, comprising: an engineering data integration layer, an engineering data management layer, an engineering data application layer, and an engineering data display layer. The engineering data integration layer acquires engineering data, implements cloud computing, and uploads it to the engineering data management layer. The engineering data management layer is used for data extraction, transformation, storage, and the generation of several digitized engineering archives. The engineering data application layer includes several management models, which are used to associate several digitized engineering archives and apply the associated digitized engineering archives to construction project management operations. The engineering data display layer includes a web interface and web controls, whereby the web controls are used to call the associated digitized engineering archives to perform construction project management operations and display the operation results on the web interface. This invention achieves the goal of improving the accuracy of digitized engineering archives and facilitating maintenance and control.

Methods, apparatuses, system, devices, and computer program products for AI-driven data aggregation and analysis for entity scoring and assessment are disclosed. In a particular embodiment, a controller generates an entity profile for an entity by iteratively retrieving data related to an entity, the data including structured data and unstructured data, determining a structure for the unstructured data by applying the retrieved data to the AI model, the structure including a plurality of fields, and populating one or more fields of the entity profile with a respective value that is extracted from the retrieved data using the AI model. The controller stores the entity profile in a database comprising a plurality of entity profiles corresponding to different entities. The controller generates, using the AI model, a score for the entity in relation to an attribute based on an AI-driven analysis of the plurality of entity profiles.

The invention discloses a data asset management system and method, and relates to the field of data management, and the system comprises a data collection layer which is used for collecting multi-source heterogeneous data; the data integration layer is used for integrating the multi-source heterogeneous data and constructing a data warehouse; the data storage layer is used for carrying out structured storage on the data in the data warehouse; the data governance layer is used for performing standardization processing and quality management on the data stored in the data storage layer to obtain target data; and the data application layer is used for performing dynamic tracking inventory and classification level-to-level management on the data assets based on the target data. Through cooperative work of five layers of architectures, full-process closed-loop management of the multi-source heterogeneous data from collection to application is realized, and the problems of difficulty in integration of the multi-source data, low management efficiency, uncontrollable quality and the like are effectively solved by the overall scheme.

The invention discloses a power distribution network electric energy quality comprehensive evaluation method based on a multi-dimensional index system, and the method comprises the steps: firstly constructing the multi-dimensional index system, and taking three dimensions as a basic technical index, a user satisfaction index and other key indexes; secondly, performing standardization preprocessing on the collected index data by adopting an extreme value standardization method, and mapping a standardization value to [0, 1]; then, carrying out weight determination on each index by adopting a combined weighting method, wherein the combined weighting method comprises the steps of determining a subjective weight by adopting an analytic hierarchy process, determining an objective weight by adopting an entropy weight method and a combined weight of the subjective weight and the objective weight; and finally, establishing an excellent evaluation grade, a good evaluation grade, a middle evaluation grade and a poor evaluation grade, and carrying out single-dimensional fuzzy evaluation and comprehensive evaluation to obtain a final score so as to determine the grade of the power distribution network. The method has the advantages that the evaluation range is more comprehensive and effective, and the evaluation result is more professional and reasonable, the electric power service quality can be improved, the sustainable development of an electric power system is promoted, and the requirements of intelligent power grid development and power distribution network big data application technology development are met.

Techniques for geospatial-temporal pathogen tracing in zero knowledge include: generating, by a first user device, a first proximity token for contact tracing; receiving, by the first user device, a second proximity token from a second user device; generating, by the first user device, a hash based on the first proximity token and the second proximity token; generating, by the first user device using a prover function of a preprocessing zero knowledge succinct non-interactive argument of knowledge (pp-zk-SNARK), a cryptographic proof attesting that an individual associated with the first user device tested positive for a pathogen; transmitting, by the first user device, first publicly verifiable exposure data including at least the cryptographic proof and the hash to a public registry; and applying at least the first publicly verifiable exposure data and second publicly verifiable exposure data to a machine learning model, to obtain actionable intelligence associated with the pathogen.

The invention relates to the technical field of data management, in particular to a supply chain-based manufacturing industry big data management system, which comprises an event attribution module, a data integration module, an inventory trend module, a batch judgment module and a parameter tracing module. According to the method, automatic association analysis of multi-dimensional parameters such as process, logistics, inventory and environment is realized through time sequence synchronous acquisition of multi-source data and dynamic linkage of business events, and accurate circulation of whole-process data is promoted by adopting an event-level feature recognition and parameter screening mechanism; furthermore, positioning and traceability of an abnormal fluctuation process are supported in a process traceability and trend discrimination mode, information interconnection and parameter relation chain establishment of each manufacturing link are enhanced, synchronous processing and hierarchical clustering of multi-parameter heterogeneous data are realized, and the real-time decision support capability and traceability precision of the data are improved; and the response speed of an abnormal process and inventory fluctuation in a manufacturing scene and the intelligent level of data application are effectively enhanced.

The invention provides a credit repair certificate automatic issuing method and device and electronic equipment, and belongs to the technical field of credit management. Comprising the steps of obtaining a credit repair certificate issuing application submitted by an enterprise, and dividing the issuing application into a local data application and a remote data application; based on the operation behavior in the whole auditing process, generating a block chain evidence storage record containing a timestamp, an operator identifier and an auditing opinion; and uploading the application state data to the credit service platform, and generating a corresponding credit repair proof file for the passed application. According to the method, efficient, safe and traceable whole-process online issuing of the credit repair certificate is realized by intelligently distinguishing local and remote dishonesty data, implementing a grading processing mechanism combining automatic verification certificate issuing and manual auditing and combining block chain certificate storage and electronic signature technologies.

The invention discloses a supply chain collaborative optimization method and device based on multi-modal data fusion and a medium, and belongs to the technical field of supply chain intelligent management. The method comprises the following steps: acquiring multi-modal data in a supply chain environment; preprocessing the multi-modal data to generate standardized data; performing space-time alignment on the point cloud and the image data to generate time alignment data; based on uncertainty federated learning, edge node credibility is quantified, conflict evidence is processed, and credibility data is generated; processing the point cloud and the image data by using cross-modal attention fusion to generate fusion features; generating an inventory prediction or path planning decision based on the fused features; and outputting the decision to a supply chain management system to execute automatic operation. According to the method, high-precision fusion and credible collaborative decision-making of multi-modal data are realized, and meanwhile, the technical effects of privacy security and real-time performance are ensured.

The present disclosure discloses a method for federated learning, including: requesting, by a target application, a user to provide a federated learning data access right to access privacy data; calling, by the target application, a unified federated learning service (UnifiedFED) application to perform horizontal federated learning, acquiring, by the UnifiedFED application, the privacy data isolated from the target application according to the federated learning data access right, receiving, by the UnifiedFED application, non-privacy data from the target application, and providing, by the UnifiedFED application, the privacy data and the non-privacy data to an artificial intelligence (AI) model for federated learning training.

The invention discloses a whole vehicle data storage system based on a block chain, and relates to the field of data storage, and the system comprises a data collection module which is used for collecting key data of a vehicle; the data processing module is used for carrying out desensitization processing on the vehicle key data, carrying out format conversion and encryption on the vehicle key data and compressing redundant data; the block chain evidence storage module is integrated in a block chain node in the T-Box and is used for converting the processed key data into a hash value and uploading the hash value to a block chain network; the intelligent contract module is used for performing logic consistency verification on the uploaded data according to intelligent contract logic and a preset multi-dimensional association rule; the data application module is used for service application driven by vehicle data and feeding back application information to the feedback optimization module; and the feedback optimization module is used for receiving the verification result and the feedback application information, and dynamically adjusting the collected data and the intelligent contract logic according to the verification result and the feedback application information.