2228 results about "Distributed database" patented technology

The invention provides a standard document automatic generation and multi-dimensional auditing method and system based on a large model, and relates to the technical field of artificial intelligence, and the method comprises the steps: 1, building a distributed database of a multi-source document, and analyzing a heterogeneous text through natural language processing to obtain a standardized knowledge network; step 2, extracting index elements based on the standardized knowledge network, and forming a structured parameter library through verification and verification; and step 3, based on the structured parameter library, constructing a template library, analyzing user demands in combination with semantic matching, and automatically generating a standard document outline. The document generation efficiency and quality are improved, the manual auditing cost is reduced, and the auditing comprehensiveness and accuracy are enhanced.

The invention provides a security management and retrieval method based on Internet of Vehicles data, and the method comprises the steps: carrying out the cooperative training of a global key management model through a federated learning technology on the premise of not leaking the original data of all parties, and achieving a security key management scheme under the participation of multiple parties through a model parameter aggregation and updating mechanism; when data retrieval is needed, a user submits a retrieval request and identity authentication information, and after a system verifies the identity of the user, a ciphertext data identifier meeting conditions is matched in an attribute index according to a keyword and an authorization attribute of the request; the system acquires corresponding ciphertext data from a distributed database according to the ciphertext data identifier, and directly performs keyword matching calculation on the ciphertext data through a secure multi-party computing protocol under the condition of not decrypting the data; and according to the mined security threats and abnormal modes, a data encryption strategy and an access control strategy are dynamically adjusted, authority limitation or access blocking is performed on suspicious users, and the security protection capability of the system is improved.

The invention discloses a charging station operation maintenance method based on a multi-dimensional data system, and relates to the field of intelligent optimization, and the method comprises the steps: collecting the operation, environment, user and power grid data of a charging pile, preprocessing the data into JSON, carrying out the real-time analysis and classification through Kafka-Flume, storing the data into a distributed database, building a composite index, and carrying out the retrieval through Presto; after cleaning, combining GraphQL and a graph query language to construct a space-time atlas, and extracting a multi-dimensional feature vector; a digital twinborn model is generated, multi-source data are fused through virtual-real mapping, and residual correction and reinforcement learning optimization are combined; fusing multi-modal data by using a hypergraph neural network, and optimizing topology in combination with an entropy regularization multi-target strategy and meta-learning; power is dynamically distributed, LSTM-GAN predicts and positions faults, and PID regulates and controls output; and the block chain contract automatically triggers equipment freezing and power limiting, and on-chain data feedback optimizes scheduling and load balancing. According to the invention, through multi-dimensional data fusion and a dynamic optimization mechanism, the operation and maintenance accuracy and response efficiency of the charging station are significantly improved.

The embodiments disclose a file management and database system including a database system server configured to track user asset transactions and asset-related activities, at least one digital electronic device coupled to the database system server configured to allow the user to access the asset transactions and asset-related activities, a plurality of distributed databases coupled to the database system server configured to collectively manage and interlink data of plural operational components, a file management system coupled to the database system server configured to maintain and identify the plural operational components data, at least one processor coupled to the database system server configured to identify and match the plural operational components in the file management system with the plurality of distributed databases and data of the plural operational components, at least one communication device wirelessly coupled to the database system server and a digital device application wirelessly coupled to the at least one communication device.

System and methods are provided for real-time data integration, analysis, and notification within a Mobile App system. Embodiments include initializing a data layer and preprocessing phase, leveraging distributed database strategies to store structured and unstructured data. The Real-Time Data Mesh (RTDM) continuously draws data from various platforms, employing signal processing methods and machine learning techniques for noise removal and data feature extraction. The Advanced Analytics and Machine Learning (AAML) engine processes data, while decision constructs derive suitable actions. The Push Notification Service activates based on an Event-Driven Architecture (EDA) or a Publish-Subscribe (Pub-Sub) system, delivering customized notifications. Technical precision ensures timely and pertinent information reaches end-users. The system optimizes operations through adaptive feedback mechanisms and secures data through encryption.

The automatic tax declaration and recheck system comprises a character recognition module, a process automation module, a dynamic rule engine module, a block chain evidence storage module and a distributed database. The character recognition module forms a tax field signal, a structured signal and a semantic tag signal. And the process automation module forms a tax declaration form according to the tax field signal. And the dynamic rule engine module receives the structured signal, performs multi-dimensional verification through a machine learning model, and forms a verification signal. The block chain evidence storage module comprises a distributed database, and the block chain evidence storage module receives the semantic tag signal and forms a unique hash value. And the distributed database performs feature fusion on the unique hash value, the tax declaration form and the verification signal to form a declaration signal and stores the declaration signal in the distributed database. According to the automatic tax declaration and rechecking system, the problem that structural analysis of multi-format tax documents is difficult can be solved.

The invention discloses an industrial odor online monitoring system based on big data. The system comprises a data acquisition module, a preprocessing module, a feature fusion module, a multi-source sensing weight analysis module, a dynamic risk assessment module, a self-adaptive correction module and a real-time feedback module. Data interaction among the modules adopts a block chain encryption transmission protocol, and a feature evaluation mapping chain with a timestamp is established in a distributed database. Through the innovative design of heterogeneous sensor network collaborative perception, cross-modal feature organic fusion, pollution diffusion three-dimensional dynamic deduction and a credible data governance architecture, a new industrial bad smell monitoring normal form with environment intelligent adaptability is constructed. The four technical effects form complementary support from four dimensions of data acquisition credibility, feature modeling scientificity, risk assessment accuracy and system decision robustness.

The invention relates to the technical field of industrial internet data acquisition, in particular to a data acquisition method and system based on an industrial internet operating system, and the method comprises the following steps: S1, detecting and recognizing data sources of a plurality of industrial devices and sensors; s2, collecting original data from a data source in real time, and preprocessing the original data; s3, distributing part of calculation tasks to a plurality of edge nodes; s4, storing the data in a distributed database or cloud storage in a classified manner; s5, performing fragmentation processing on the data in the cache; s6, performing data synchronization and consistency verification; and S7, converting the data into a corresponding format. According to the invention, by implementing efficient data fragmentation processing, a dynamic storage strategy and secure data consistency verification, the processing speed and storage efficiency of the industrial internet data acquisition system and the security and integrity of data are remarkably improved, and the requirements of high efficiency and high reliability of modern industrial production are effectively supported.

A method for processing a query for data stored in a distributed database includes receiving, at an edge device, the query for data stored in the distributed database from a query device. The method includes causing, by the edge device, the query to be stored on a dynamic ledger maintained by the distributed database. The method includes detecting, by the edge device, that summary data has been stored on the dynamic ledger. The method includes generating, by the edge device, an approximate response to the query based on the summary data stored on the dynamic ledger. The method includes transmitting, to the query device, the approximate response.

The invention provides an intelligent data retrieval optimization method based on a large model and deep learning. The method comprises the following steps: performing data cleaning, labeling and index construction on original data; performing semantic analysis on user query by using a large-scale pre-training language model to generate deep semantic representation of the query; performing semantic matching in a distributed database according to deep semantic representation queried by a user, and retrieving related high-dimensional feature vectors; classifying and sorting the matched high-dimensional feature vectors through a deep learning network; dynamically adjusting parameters of the pre-training language model and the deep learning network in combination with online learning and a real-time feedback mechanism; and a cache strategy and an index optimization technology are adopted, and a retrieval result most relevant to user query is output. According to the method, by combining an advanced language model and a deep learning technology, efficient and accurate intelligent data retrieval is realized, and the retrieval experience of a user is remarkably improved.

The invention relates to the technical field of e-commerce intelligent recommendation, in particular to an e-commerce intelligent recommendation system based on user behavior big data, which comprises a data acquisition and storage unit, a data preprocessing unit, a model construction training unit and a recommendation generation and output unit. The method comprises the steps that data such as user behaviors, equipment, geographic positions and access time are collected and stored in a distributed database, a data preprocessing unit extracts the data, constructs a user behavior sequence and encodes commodity and behavior characteristics, and a model construction training unit obtains user interest vectors by using a long and short-term memory network in combination with an attention mechanism; model parameters are updated with the help of a deep Q network, commodities are screened, sorted and displayed by a recommendation generation and output unit according to interest vectors, meanwhile, a feedback optimization system is collected, recommendation accuracy is improved, user shopping experience is enhanced, and an e-commerce platform is assisted to improve operation efficiency and commodity sales conversion rate.

The invention relates to the technical field of clothing manufacturing, in particular to an AI scheduling optimization method for clothing flexible production and an ERP integrated system. Comprising the following steps: multi-source heterogeneous data acquisition and fusion: acquiring real-time equipment state data of a clothing production line through Internet of Things equipment, wherein the real-time equipment state data comprises a sewing machine rotating speed, a cutting bed load rate and a quality inspection station abnormal signal; obtaining an order delivery period, a material inventory and a BOM process path through an ERP system; the equipment data and the order data are stored in a distributed database after being aligned through timestamps; dynamic priority modeling: constructing a dynamic priority calculation model based on the order emergency degree, the production line bottleneck process load threshold and the material neat rate, and generating an order-insertable process-level priority queue; according to the scheme, the limitation of a traditional method can be broken through, and the modern production requirements of flexibility and high aging are met.

The invention provides a railway signal monitoring data generation method and system, and relates to the technical field of railways, and the method comprises the steps: carrying out the time-space correlation fusion processing of obtained carrier frequency characteristics, response coding information, the receiving time of transponder signal data, train operation speed data and train positioning data, and generating a monitoring data record under a unified coordinate system; adopting a rule-based data compression algorithm to adaptively select a compression rule and a compression rate to perform data compression on the monitoring data record; packaging the compressed monitoring data record into a monitoring data file in a standard format; and the data management server decompresses and decodes the monitoring data file, cleans, classifies and indexes the data in the monitoring data file by adopting a MapReduce parallel computing framework, stores the structured monitoring data into a distributed database, and performs fragmentation and redundant backup management on the database.

The present application discloses a distributed database-oriented compensating transaction implementation method. The method specifically comprises: a client initiates a transaction request, and determines, by means of an execution directory, whether a transaction SQL exceeds a limit value (S1); a coordination node allocates a timestamp to a transaction initiated by the client in step S1, and simultaneously sends a related operation of the transaction to a data node (S2); upon receiving the transaction request, the data node determines whether to commit or roll back the transaction, and returns an execution result to the coordination node for confirmation and verification, and if the verification fails, the coordination node notifies the data node and performs a compensation operation (S3); and when the operation is completed, the coordination node commits the transaction, performs regular cleaning, and returns the execution result to the client (S4).

The invention discloses a multi-platform store normalizing method, which relates to the technical field of computers, and comprises the following steps: extracting a standardized data set through a multi-modal feature extraction technology to generate a multi-platform feature vector; the feature vectors of the multiple platforms are input into a multi-algorithm fusion engine, parallel matching calculation is executed, meanwhile, a dynamic weight distribution mechanism is designed, normalized platform IDs and a cross-platform mapping relation table are output, and a matching confidence report is generated; a normalization result is stored in a distributed database based on a block chain, an incremental synchronization mechanism is designed, data are divided and stored according to geographic areas, node loads are dynamically distributed through a consistent Hash algorithm, and updated normalized platform data are obtained; through a multi-modal feature extraction technology and a multi-algorithm fusion engine, deep mining of a standardized data set is realized, an accurate feature vector set is generated, and the accuracy of cross-platform data matching is remarkably improved.

The invention is suitable for the technical field of database management, and provides a distributed database management method and system based on artificial intelligence. The system comprises a database content analysis module, a storage layout adjustment module, an anomaly detection module, an unmanned aerial vehicle storage communication module, an access request guide module and an unmanned aerial vehicle node recovery module. The scheme that an unmanned aerial vehicle is adopted as a storage and communication node brings remarkable innovation and multiple advantages to a distributed database management method. Firstly, the mobility of the unmanned aerial vehicle enables the unmanned aerial vehicle to quickly take over a data storage task when a network is interrupted or a node fails, so that the integrity and availability of data are ensured, and the response capability in a traditional architecture is relatively limited. In complex scenes such as disaster recovery or remote area deployment, the unmanned aerial vehicle can break through network limitation through off-line transmission, and data safety and storage continuity are effectively guaranteed.

The invention belongs to the technical field of distributed databases, and particularly relates to a decentralized copy control method of a distributed database, which comprises the following steps: step 1, copy management: determining the number and storage positions of copies according to data volume and system load requirements, and reasonably distributing data copies to different nodes; 2, replica consistency: selecting a consistency model to balance system performance and data consistency requirements; 3, synchronizing the copies: determining a data synchronization strategy between the copies, and adjusting according to the real-time requirement of the system; 4, conflict detection and resolution: detecting data conflict by using a timestamp, a version number or a logic clock mechanism, and designing a conflict resolution strategy; and 5, replica recovery: monitoring the state of each node, quickly detecting replica faults, and recovering data from other replicas. The stability, reliability and performance of the distributed database system can be effectively improved, and the distributed database system adapts to constantly changing service requirements.

The invention relates to a Yellow River upstream main stream reservoir dam group section multi-disaster big data early warning system and method. A geological disaster monitoring module obtains surface displacement and deformation data, low-altitude remote sensing data and high-resolution remote sensing image data in real time; the data processing and storage module performs cleaning, space-time alignment and semantic mapping on the multi-dimensional monitoring data based on an ETL technology to form a standardized data set, and stores the standardized data set in a distributed database; the multi-disaster early warning calculation module triggers a specific disaster model according to spatial-temporal characteristics of input data, and performs single-disaster risk probability calculation and cross-disaster risk superposition probability analysis; the early warning issuing module can convert the calculation result into early warning information and send the early warning information to an early warning department in real time; and the real scene display module can convert a disaster risk early warning result into a visual three-dimensional scene. According to the invention, the early warning timeliness of the sudden disaster chain can be effectively improved, and the whole-process intelligent management from data acquisition to emergency response and the accurate prediction of the disaster chain are realized.

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.

Systems and methods disclosed herein automatically authorize, audit, and manage usage of protected digital content via agentic artificial intelligence (AI) models. A data access / usage request is received (e.g., from a graphical user interface) that is associated with digital assets licensed from third parties. The system uses a first AI agent set to identify the digital content and retrieve corresponding access policies from a distributed database. The system uses a second AI agent set (same as or different from the first AI agent set) to evaluate the request against the retrieved policy to generate a permission set and / or settlement instructions. The system uses a third AI agent set (same as or different from the first and / or second AI agent sets) to embed digital watermarks and / or cryptographic signatures into the accessed content, and to record an audit trail of access, authorization, and / or settlement events in a distributed ledger or database.

The invention relates to the technical field of big data processing, in particular to a data acquisition and analysis system for network big data information analysis, which comprises a data acquisition module, a data preprocessing module, a data storage module, a data analysis module and a data visualization module, wherein the data acquisition module is used for acquiring original data from various data sources in real time through a network interface; the data preprocessing module is used for preprocessing the original data; the data storage module is used for carrying out storage and management by adopting a distributed database; and the data analysis module is used for performing deep analysis by applying a machine learning algorithm and a data mining technology to generate a trend prediction result. According to the method, real-time acquisition, automatic preprocessing, distributed storage and multi-algorithm fusion analysis of the multi-source data are realized, so that the problems of low data quality, poor processing efficiency, model isolation and the like in the prior art are solved, and the precision, efficiency and decision support capability of big data analysis are remarkably improved.

The invention discloses a big-data-driven network operation state monitoring management method and system, and relates to the technical field of digital information monitoring, and the method comprises the steps: building a distributed database, storing multi-modal data from network equipment, link states, user behaviors and security logs, carrying out the time synchronization and semantic association of the multi-modal data, and storing the multi-modal data in the distributed database; constructing an abnormal state multi-dimensional association graph; a multi-level collaborative network is used for collaborative recognition, an additional abnormal result is generated by combining abnormal positioning and time sequence prediction, finally, a recognition result is compensated, and network state early warning is generated. According to the invention, the technical problems of insufficient network anomaly detection precision and early warning lag caused by incapability of effectively integrating multi-modal heterogeneous data and adapting to a dynamically changing network environment in real time in the prior art are solved, and multi-modal data fusion and multi-level collaborative analysis driven by big data are achieved. The technical effects of accurate network anomaly detection and prospective early warning are achieved.

The invention belongs to the technical field of product inventory management, and discloses an e-commerce product inventory management method based on machine vision, which realizes a full-automatic process of product information from acquisition, recognition to statistics by means of a machine vision technology, acquires images in real time from multiple angles by a high-definition industrial camera, inputs the images into a CNN model to extract features after preprocessing, and realizes product inventory management. Through accurate identification classification and quantity statistics, manual intervention of inventory and data entry is not needed; through the automatic inventory management process, the manpower cost expenditure of manual checking, data processing and the like is greatly reduced, and the personnel employment and training cost is reduced. And on the other hand, historical data is deeply mined by using algorithms such as time sequence analysis, the sales trend and the inventory demand are accurately predicted, the replenishment strategy is optimized, capital and storage space occupation caused by inventory overstock is avoided, meanwhile, order loss caused by stockout is prevented, and in addition, a distributed database storage technology ensures data reliability, and the efficiency is improved. And extra cost caused by data errors is reduced.

The invention discloses an intelligent networking distributed storage interaction system based on multi-cabin cooperation and a method thereof, and belongs to the technical field of camping intelligent cabin interaction, and the method comprises the following steps: taking each intelligent cabin as an independent node, initializing and accessing an intelligent networking distributed system, and enabling the nodes of the intelligent cabins to establish initial connection with other nodes in the system; each intelligent cabin collects user data, environment data and equipment state data through a built-in sensor and stores the collected data in a local storage unit in a classified mode. According to the method, the data synchronization among the nodes of the intelligent cabin is realized through a distributed database consistency algorithm, the data consistency under the condition of network delay or interruption is ensured, the data conflict and damage are prevented, and the system reliability is improved. Meanwhile, resource allocation is dynamically optimized based on an intelligent scheduling algorithm of machine learning, different load requirements are met, resources are saved, and the system efficiency and the intelligent level are improved.

The invention discloses a lithium battery full life cycle quality tracing system based on digital twinning, and relates to the technical field of lithium battery quality tracing, and the system comprises a physical entity layer which integrates multiple types of sensors and detection equipment to collect full-link data; the digital twinning modeling layer is used for constructing a multi-scale model to map a battery state; the data acquisition and transmission layer is used for realizing data real-time transmission preprocessing through an edge gateway and an encryption protocol; the data storage and management layer is used for establishing a data warehouse by using a distributed database and performing backup; the intelligent analysis and decision-making layer integrates an algorithm to realize anomaly detection, performance prediction and tracing; the visualization and interaction layer is used for providing a multi-terminal visualization and interaction function; and the block chain evidence storage layer constructs an alliance chain to ensure that the data cannot be tampered. According to the invention, full-life-cycle data connection and credible tracing of the lithium battery are realized, the quality problem positioning efficiency is improved, intelligent early warning and optimization capabilities are provided, the operation threshold is reduced, and industrial quality control and green development are facilitated.

The invention relates to the field of technical data acquisition, and particularly discloses a data acquisition method and device for an industrial Internet of Things and a storage medium, and the method comprises the following steps: S1, constructing a dynamic sensor network, and collecting the operation data of industrial equipment in real time through a multi-modal sensor; s2, dynamically adjusting a data acquisition frequency # imgabs2 # based on an environment noise coefficient # imgabs0 # and an equipment health index # imgabs1 #; s3, performing hierarchical compression and encryption on the acquired data, and allocating a compression rate # imgabs4 # and encryption strength # imgabs5 # according to data type importance # imgabs3 #; s4, executing load balancing through an edge computing node, and distributing a computing task to an optimal node; and S5, storing the processed data into a distributed database according to spatial-temporal correlation, and generating a multi-copy disaster recovery index. Through dynamic acquisition, intelligent processing, optimized storage and adaptive strategy adjustment, accurate and efficient data acquisition is realized, data security and processing performance are flexibly guaranteed, storage query efficiency and disaster tolerance capability are improved, and storage resources are utilized to the maximum extent.

The invention discloses a distributed database node online upgrading system and method, and relates to the technical field of distributed databases, the system comprises a distributed database cluster comprising a plurality of database nodes, and each node is responsible for operating database basic service, connecting the cluster to which the node belongs, and obtaining node state information in the cluster; the cluster management module is responsible for monitoring node states in a cluster, receiving an upgrading command and triggering a corresponding process; the distributed consistency management module is responsible for generating a state view and implementing a change operation; the database execution module is responsible for executing statements; the database storage engine is responsible for regularly reporting a copy state; the node upgrading strategy module is responsible for judging whether the node supports online upgrading or not, informing to disable DDL statements and determining the migration direction of a lease holder and a data copy; and the version verification and rollback module is responsible for storage format compatibility verification, sending a rollback notification and notifying recovery of a node health state. According to the invention, the node can be simply and efficiently upgraded.

The invention discloses a mechatronics product detection system based on an artificial intelligence image recognition technology, and relates to the technical field of artificial intelligence and mechatronics, and the mechatronics product detection system comprises an image acquisition module, a preprocessing module, a feature extraction module, a defect recognition module, a data management module, a result output module and a system control module, the preprocessing module has the functions of adaptive zooming and intelligent obstacle avoidance, and processes images by using a deep learning algorithm and a quantum filtering technology; a deep convolutional neural network is used for feature extraction, SVM and CNN are combined for defect recognition, a meta-learning mechanism is introduced, a distributed database and federated learning are adopted for data management, AR navigation and gesture interaction is used for result output, and a block chain intelligent contract is used for system control to deploy resources. The method has obvious advantages and high detection precision, and can accurately identify tiny and hidden defects; efficiency is greatly improved, and edge and cloud computing are combined to reduce processing time; operation is convenient, VR, AR and voice interaction are used, and the maintenance efficiency is improved.

The invention provides a dynamic tool integration method and system for an intention-driven AI dialogue system, and the method comprises the steps: S5, extracting resource occupation information from a transaction log according to a state persistence record, carrying out the resource release and temporary context cleaning of each heterogeneous component, and obtaining a released resource list; s6, searching a breakpoint state snapshot and a transaction log from the distributed database according to the released resource list, and reconstructing a tool chain execution flow historical state to obtain a fault context analysis result; and S8, according to the context recovery state and the semantic feature vector, updating a tool chain execution stream, and reallocating computing resources through a heterogeneous component coordination mechanism to obtain a task continuity confirmation result. According to the method, the recovery efficiency and the resource utilization rate of the tool chain in an abnormal scene are remarkably improved, and the task continuity and the system robustness in a high-concurrency environment are guaranteed.

The invention discloses a flexible automatic production management and control system for a new energy high-power high-frequency transformer, and relates to the field of production management and control, the flexible automatic production management and control system comprises a production demand receiving module, a production resource management module, a production planning module, a production execution module and a quality control module, an order is received through an API or EDI, and a work order is generated; collecting equipment and material information in real time, and constructing a production resource association network; carrying out production scheduling and process matching by adopting an improved deep learning model; scheduling materials, procedures and equipment based on a multi-level analysis control model; the quality is evaluated by using a two-dimensional cloud model, and data recording is performed through a distributed database, so that the traceability of the quality problem is ensured. According to the system, the production efficiency and the quality management level are effectively improved, the problem that existing rigid automatic equipment lacks flexibility is solved, and efficient and accurate production of the new energy high-power high-frequency transformer is ensured through cooperative work of a plurality of modules so as to meet different production requirements.