1122 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 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 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 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.

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 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 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 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.

The invention provides an AI-based distributed database fault diagnosis and self-healing method and system, and relates to the technical field of AI. The method comprises the steps of obtaining real-time monitoring data, determining potential fault features by using an attention mechanism, constructing a fault association map to judge fault types, generating a self-healing strategy, and executing the strategy after parameters are optimized and repaired through a deep Q network. According to the method, fault root causes can be accurately identified, automatic fault repair is realized, the stability and availability of a database system are improved, manual intervention is reduced, and the operation and maintenance cost is reduced.

The invention discloses a general cargo transportation background business management system and method and electronic equipment, the system comprises a data acquisition module, an information management module, a position tracking module and a scheduling management module, and automatic acquisition of cargo position, attribute and safety inspection information is realized through an integrated sensor, an RFID tag technology and an image identification device; the GPS positioning and local wireless network complementary correction technology is utilized to break through the positioning precision limitation in the indoor and outdoor complex environment; based on a distributed database and a real-time data processing engine, high-concurrency query and dynamic information updating are supported; in combination with cargo state and position data, an optimization scheduling instruction is generated through an algorithm, empty driving waiting of loading and unloading equipment is reduced, the method covers intelligent scheduling processes of multi-source data acquisition, standardized processing, real-time tracking and cloud driving, full-life-cycle automatic management of cargos is realized, and electronic equipment realizes full-life-cycle automatic management through a software and hardware collaborative architecture. An existing port management system is compatible, and deployment cost is reduced.

The invention discloses an intelligent propolis component identification and traceability system and method, and relates to the technical field of computer vision and mode recognition, and the system comprises a data collection module which collects spectrum and chromatographic data and traceability information of a production place, processing and the like; the data preprocessing unit is used for eliminating overlapping peaks, normalizing data and synchronizing timestamps through filtering and baseline correction denoising; the component feature extraction module is used for screening 10 spectral feature peaks, calculating chromatographic features, and reducing the dimensions to 32 dimensions through principal component analysis after splicing; the intelligent identification module is used for judging the purity level and the confidence degree by using a CNN-LSTM model; according to the traceability management module, a block chain stores full-link information, and a distributed database manages query permissions; and the result output module is used for visually displaying the result and triggering an alarm when the confidence degree or the traceability integrity does not reach the standard. The multi-source fusion improves the identification precision, the model can be updated online to adapt to a new scene, and the propolis quality and the market order are efficiently guaranteed.

The invention discloses a water pollution monitoring method and device based on an intelligent sensor, and the method comprises the steps: collecting the real-time water temperature, pollutant concentration and environmental parameter data of a water body through a sensor network, and storing the data into a distributed database to form a multi-dimensional time series data set; a feature extraction and dimension reduction module is used for obtaining key feature vectors, a potential pollution event marking module is used for screening out a preliminary event candidate set, and an event detection model is trained by means of a random forest algorithm. And when the model judges that a pollution event occurs and the confidence reaches the standard, generating an early warning signal. The water temperature abnormal trend is predicted through a long and short term memory network, Kalman filtering smoothing processing is carried out, finally, a pollution event early warning report is generated according to abnormal points in a stable water temperature change trend sequence, and efficient and accurate monitoring and early warning of water pollution are achieved.

The invention discloses a rice field irrigation control method, and belongs to the field of rice field irrigation, and the method comprises the steps: obtaining soil humidity, water level and meteorological data from a rice field through a sensor network, storing the data in a pre-established distributed database, and obtaining a multi-source data set; according to the data set in the unified format, key features of soil humidity, water level and meteorological data are extracted by adopting a principal component analysis method, and a feature vector set is obtained; if the water consumption of the predicted irrigation scheme exceeds a preset threshold value, the scheme is optimized and adjusted through a linear programming algorithm, and an optimized irrigation scheme is obtained; according to the optimized irrigation scheme, the water consumption in the irrigation process is monitored in real time through flow meter equipment, and accurate measurement data are obtained; and the accurate measurement data is distributed through a quota management module, a regional water resource allocation scheme is generated by adopting a dynamic quota algorithm, and a quota management result is obtained.

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 relates to the technical field of distributed data storage, and discloses a database cluster partition operation method and device under a network partition scene and a medium, and the method comprises the steps that a multi-master distributed database cluster is constructed, and each node continuously captures a database native transaction log; obtaining change event data, and performing unified data storage; analyzing the original log into structured data, and executing uniform fragmentation and parallelization processing; processing the block log change record, and arranging change events of the primary key according to a rule; executing conflict processing and data synchronization, and synchronizing all nodes after the conflict is solved; through exception handling and fault tolerance, self-healing is realized and data consistency is maintained. According to the method, millisecond conflict identification and data consistency convergence after network disconnection can be realized, continuous and stable operation of a cluster in a partition state is ensured, and self-healing and continuous availability of a system are realized.

The invention relates to the technical field of model construction, in particular to a construction method of a disease feature recognition and evaluation model based on the Internet of Things. The method comprises the following steps that multi-modal physiological data of a patient are collected in real time through Internet of Things medical equipment, data preprocessing is conducted on the multi-modal physiological data of the patient, standard patient physiological data are generated, and the standard patient physiological data are stored in a distributed database; extracting gene sequencing data based on the biological sample library; performing global health risk factor perception on the standard patient physiological data to generate user level health risk perception data; and carrying out feature fusion on the user level health hazard perception data and the gene sequencing data to construct a disease feature matrix containing a space-time dimension. Through multi-modal data fusion, spatio-temporal evolution modeling, deep learning architecture and personalized risk assessment, the precision and timeliness of disease feature recognition assessment model construction are improved.

Systems and methods disclosed herein automatically generate and executing computer programs using artificial intelligence (AI) models. The system obtains an output generation request (e.g., from a graphical user interface (GUI)) to generate an output using an unstructured dataset. The system determines a feature set of the unstructured dataset using a first AI model set, and dynamically generates a programmatic workflow set using a second AI model set (same as or different from the first AI model set) based on the feature set to be stored in a distributed database. The system automatically executes the programmatic workflow set using a third AI model set (same as or different from the first and / or second AI model sets) to generate an artifact responsive to the output generation request. The system displays (e.g., on the GUI) representations of the output generation request, programmatic workflow set, and / or generated artifact.

The invention discloses a marine meteorological observation data millisecond query system based on time sequence block indexing. The marine meteorological observation data millisecond query system comprises a data input module, a data preprocessing module, a data hierarchical modeling module, a data storage cluster, a data analysis engine, a data query engine and a data operation interface module. Based on data modeling, data thinning and data partitioning technologies, data preprocessing is carried out for different meteorological ocean space weather data, and distributed storage and access of mass data are achieved; the method comprises the following steps: inputting ocean actual observation data, forecast data or historical data, carrying out model construction and index design according to meteorological ocean space weather data characteristics, completing distributed storage, and applying a data query engine and a data analysis engine to realize rapid aggregation query of the meteorological ocean space weather data; storage and query of lattice point data are realized by using a distributed database, and the problems of scale and query performance of meteorological ocean space weather data are effectively solved.

The invention relates to a multi-master election method and device based on an OpenHarmony soft bus, and the method comprises the steps: constructing six efficiency indexes according to the networking characteristics of the soft bus; operating system kernel state extraction: increasing a subsystem in an operating system layer, expanding system capability, obtaining index data of each device in a network in real time, storing the index data in a distributed database, and extracting required data from the distributed database by a multi-main election component to calculate and lift the device; performing analytic hierarchy process index weight construction and election formula derivation: establishing an efficiency index score system through an analytic hierarchy process, and calculating the weight of each index at the level to which the index belongs; and after the weights are determined, performing corresponding fuzzy judgment on each index by using a fuzzy comprehensive judgment method from the bottom-layer index, and finally obtaining a judgment score. According to the invention, the problem that the traditional open-source soft bus solves the problem that the single-center networking survivability of the traditional soft bus is insufficient is solved.

Methods and apparatus for biometric data maintenance, access and distribution across two or more experiential and / or network domains. In one embodiment, a 5G NR-based network architecture is provided which allows ultra-low latency and effectively user-imperceptible biometric data use for e.g., authentication and maintenance of user state across multiple domains via multiple constituent user devices (e.g., UEs). The network architecture includes both (i) a distributed biometric database (BDB) model wherein relevant biometric data for individuals / UEs is intelligently cached in various portions of the distributed database, and (ii) centralized and local BAEs (biometric analytics entities) which manage the aforementioned intelligent caching, as well as network configuration using one or both of 5G NR network “slicing” and CU / DU split options to optimize end-user biometric-related applications such as those providing identification / authentication, AR functions, VR functions or yet others.

The invention relates to the technical field of cloud computing, in particular to a cross-domain PaaS cloud platform distributed architecture scheduling method and system. The method comprises the following steps: carrying out architecture design and deployment, deploying a main node, a standby node and an arbitration node in different data centers, and configuring network connection; carrying out load balancing configuration, setting a load balancer and a health check mechanism, and regularly detecting the state of a service node; designing a scheduling strategy, and designing a scheduling algorithm according to the node load condition, the resource utilization rate and the response time; and carrying out distributed database deployment and formulating an availability strategy, including configuring an arbitration node and designing a database availability strategy. High availability of the system is achieved through multi-data center deployment, the overall performance of the system is improved through intelligent load balancing and dynamic resource scheduling, data consistency and service continuity are ensured through an arbitration mechanism, the technical problems of a traditional PaaS architecture in the aspects of cross-region deployment, resource scheduling and disaster recovery are solved, and the system is suitable for large-scale popularization and application. And cross-domain multi-activity, intelligent scheduling and automatic disaster recovery are realized.

The invention discloses a distributed database built-in monitoring system, belongs to the technical field of databases, and aims to solve the technical problem of how to overcome the defects of complex monitoring deployment, high data response delay, coarse information acquisition granularity and low system integration in the prior art. According to the technical scheme, a monitoring platform module is integrated with a database kernel, a front-end and rear-end separation framework is adopted, and visual display and centralized management and control of the database operation state are achieved; the data acquisition module is used for simultaneously starting with a database process through a built-in HTTP (Hyper Text Transport Protocol) server to realize native support of a monitoring function; the distributed state query module is used for discovering cluster node states through a Gossip protocol, supporting node health detection and remote calling state service, and supporting gRPC-based streaming communication and concurrent processing; and the time sequence data management module is used for storing, compressing and querying the collected time sequence monitoring data.

The invention discloses a communication network multi-source big data analysis method based on distributed database collaboration, and relates to the technical field of communication data analysis, and the method comprises the steps: 1, generating a session contract number, a table snapshot number, a flow site set and a transaction time point at the start of a session, registering a collaborative metadata table, judging semantic compatibility, and setting a state; step 2, pushing down three anchors to a lake-warehouse, a message bus and a transaction library according to the serial number, performing freezing reading according to the anchors to obtain a lake-warehouse segment, a streaming segment and a transaction segment, performing alignment extraction on a joint feature set in the step 3, generating output through a deterministic operator chain, and storing the extracted joint feature set in a database; the method comprises the following steps: step 1, setting a session contract number, a field source and an operator relationship, reading an anchor code as a blood relationship code and writing the blood relationship code into a result table, and step 2, resetting three anchors and multiplexing a query plan during reproduction verification and cross-domain migration, and integrating blood relationship equivalence, result equivalence, freezing consistency, registration difference and migration fingerprints to realize session-level consistent reading, recalculation and auditing.

The embodiment of the invention discloses a multi-level caching method and device for a distributed database and a medium, relates to the technical field of database management, and is used for solving the problem that the current dynamic collaborative management effect is poor. The method comprises the following steps: monitoring a configuration change event of a configuration center based on a long polling mechanism to obtain configuration update data; issuing a configuration rule parameter corresponding to the configuration updating data to determine a current caching strategy, and performing caching hierarchy switching based on the current caching strategy to realize caching of the current changed data; monitoring the cache to determine whether the current micro-service cluster receives a cache failure instruction or not; if yes, operation indexes of the distributed relational database are received in real time, and whether database exception exists or not is determined, so that the current caching strategy is backtracked and updated; and asynchronously synchronizing the current changed data to the distributed cache database based on the backtracking updated cache strategy.

The invention provides a full-link supply chain data management method and system based on digital twinning, and the method comprises the steps: carrying out the real-time data collection from a plurality of supply chain data sources, inputting the data into a digital twinning model, and storing the data in a distributed database; performing local data preprocessing based on edge computing nodes on the digital twin model; a comprehensive objective function is designed, the comprehensive objective function is used for measuring the scheduling effect, the weight of each optimization objective is dynamically adjusted according to real-time environment changes and task requirements, and it is ensured that resources are reasonably allocated to each task according to the priority of the task; and designing a three-dimensional visualization system to display an optimization task and a resource state in a three-dimensional space, and displaying each link state, an optimization effect and a potential problem of the supply chain in real time through a three-dimensional visualization interface. According to the invention, a digital twinning technology, edge calculation, an adaptive optimization algorithm, cloud edge collaboration and other frontier technologies are adopted, and many pain points in traditional medical supply chain management are solved.

The invention discloses a jewelry transaction data security and privacy protection method based on a 5G fusion application. The method comprises the following steps: establishing a distributed database; generating a first transaction public key and a first transaction private key of two transaction parties through a pre-established first asymmetric encryption algorithm; carrying out random multivariate function calculation on the first transaction public key and the first transaction private key of the two transaction parties, and desensitizing the random multivariate function through an encryption algorithm and storing the random multivariate function in a distributed database; solving a random seed of the random multivariate function as a second transaction private key of the two transaction parties through a key derivation function of a second Hash algorithm; in the encryption period, the transaction data of the two transaction parties are encrypted through an encryption algorithm, and meanwhile, the encrypted data are desensitized and stored; in the decryption period, the two transaction parties obtain plaintext data through the second transaction private key; on the basis of an existing encryption algorithm, the neural network algorithm is introduced to generate the purchaser key, and meanwhile, desensitization processing is performed on the ciphertext data, so that jewelry transaction data is safer.

The invention provides a substation cable trench inspection robot adaptive networking system and method. The system comprises a channel quality monitoring module, a multi-band communication module, a dynamic topology reconstruction module, an electromagnetic compatibility optimization module, a novel antenna array module, a data storage and management module and a man-machine interaction module which are in communication connection with one another. The channel quality monitoring module is used for providing real-time communication environment perception for the system; the multi-band communication module is used for carrying out adaptive cooperative communication through multiple bands; the dynamic topology reconstruction module is used for optimizing a routing table in real time; the electromagnetic compatibility optimization module is used for optimizing the strong electromagnetic interference resistance of the system; the novel antenna array module is used for monitoring geometrical characteristics and material attributes of the metal wall of the cable trench and dynamically adjusting the transmitting angle and polarization mode of an antenna; the data storage and management module is used for recording a running log through a distributed database; the man-machine interaction module is used for providing an operation interface and an interaction interface.