771 results about "Distributed database" patented technology

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 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 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 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.

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 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 provides a power encryption database fine-grained access control method based on a quantum key, and relates to the technical field of quantum communication. The access control method comprises the following steps: performing identity authentication on a data user applying for data access; after the data user passes the identity verification, verifying the user attribute of the data user, and issuing a dynamic access token; after identity verification and attribute verification, establishing a QKD channel between the data center and the data user, and generating a quantum key; the data center defines an access strategy according to the user attribute corresponding to the plaintext; encrypting a plaintext and an access strategy through the generated quantum key to obtain a ciphertext; storing the ciphertext in a distributed database of the data center; verifying whether the user attribute of the data user is consistent with the access strategy in the ciphertext according to the access token; under the condition of consistency, the data center distributes the secret key to the data user through the QKD channel; and the data user decrypts the ciphertext according to the distributed key to obtain a plaintext.

The invention discloses a mineral resource metallogenic law simulation and target prediction system based on big data, and relates to the technical field of geological exploration. The multi-source data acquisition and standardization module supports acquisition of five types of data, and the data are preprocessed and then transmitted to the distributed database; mining key elements by using an improved Apriori algorithm, and constructing a correlation graph; building a model based on Unity 3D, and integrating three types of dynamic simulation; performing three-level prediction by using a CNN-LSTM model; three-dimensional rendering and multifunctional display are supported; and automatically updating data, models and rules to form an optimized closed loop. According to the method, multi-source data is integrated to improve ore-forming element recognition comprehensiveness, dynamic simulation fits geological reality, AI prediction is accurate, uncertainty analysis is included, visual interaction is practical, iterative optimization continuously improves efficiency, invalid exploration cost is reduced, and mineral exploration is efficiently guided.

The invention discloses a large-space assembly type geothermal station design method based on a BIM technology. The method comprises the following steps: constructing a geological foundation model and a geothermal well three-dimensional geological model by actually measured three-dimensional coordinate data, splitting assembly type modules according to functional areas and transportation rules, modeling and storing in a distributed database, and calling the modules according to a construction process sequence to complete the remodeling of the whole model. Meanwhile, a constraint detection mechanism is constructed through a collaborative operation log, whether an operation chain violates design constraints such as geometric conflicts or energy consumption constraints or not is monitored in real time, and blocking and optimization are achieved. Besides, technologies such as multi-source geological data visualization, parameterized interface generation, module attribute dynamic control, hoisting path optimization and geometric conflict automatic identification are integrated, the problems of low collaborative efficiency, insufficient construction precision, rough energy consumption calculation and the like of traditional design are solved, and the overall efficiency of design, construction and management of the geothermal station is improved.

The invention relates to the technical field of medical treatment, and discloses a knee joint knee protection treatment personalized planning system based on multi-modal data fusion. A data acquisition and preprocessing module of the system acquires a multi-modal data stream of the knee joint of a patient in real time and generates a standardized data packet; the treatment event identification module is used for automatically identifying and verifying key treatment events from the standardized data packet and generating a treatment event sequence; the historical treatment path query module queries a distributed database and extracts treatment modes of similar patients; the graph matching and path generation module is used for calculating the similarity between the current treatment event sequence and a historical treatment mode, generating a fusion treatment path when the similarity exceeds a threshold value and storing the fusion treatment path to the block chain network; the abnormity monitoring and analysis module monitors abnormal event points in the fusion treatment path in real time, triggers a path decomposition mechanism and performs multi-dimensional performance analysis; and the personalized scheme generation module is used for dynamically adjusting treatment parameters and generating a personalized treatment planning scheme.

The embodiment of the invention provides an elastic fragment routing and cold and hot data optimization method based on multi-dimensional feature prediction and related equipment, and belongs to the technical field of distributed database storage optimization. The method comprises the following steps: receiving a data stream from Internet of Things equipment; performing analysis and feature extraction on the data stream to obtain multi-dimensional feature data; according to the multi-dimensional feature data and the time sequence prediction model, performing prediction analysis on a fragmentation strategy to obtain an optimal fragmentation strategy; analyzing the routing decision according to the data priority of the data stream and a dual-mode routing engine to obtain routing decision result data; generating a target index name and creating a target index according to the optimal fragmentation strategy and the routing decision result data; according to the target index name, the data stream is routed to the target index according to the fragmentation key for batch writing; the fragmentation keys are from multi-dimensional feature data. According to the embodiment of the invention, intelligent prediction of the fragment number and automatic hierarchical storage of data can be realized, and the problems of resource elastic scaling and cost efficiency optimization are fundamentally solved.

The invention relates to an edge computing network-oriented micro-service and distributed database collaborative deployment system. The system comprises a database copy number dynamic optimization unit which obtains a dynamic optimization strategy of the copy number of a database based on a queuing gradient projection elastic scaling algorithm; a deployment strategy dynamic adjustment unit calculates end-to-end time delay based on the micro-service routing path and the database routing path; calculating a data inconsistency measurement index based on the number of each copy of the database; dynamically adjusting the deployment strategy based on the resource constraint between the end-to-end time delay and the data inconsistency measurement index and the dynamic optimization strategy of the number of copies of the database; the deployment strategy comprises micro-service instance deployment, database copy deployment and optimal routing path selection; according to the MEC scene-oriented distributed database design and fine-grained collaborative deployment optimization method, a message queue, a queuing theory and a Canal database incremental updating mechanism are integrated, and the micro-service application performance and the data query efficiency / reliability are improved.

Systems and methods are provided for implementing updatable private set intersection in distributed database architectures (e.g., the well-known MongoDB). The systems and methods can include a new specification language referred to for convenience as “MPPL,” for specifying general multi-party computation protocols. This architecture incorporates private set intersection, accessible via command line operators or other operators formatted according to a native query language. Other embodiments detail systems and methods for integrating multi-party database operations via new server node(s) added to a distributed database system (e.g., MongoDB cluster) that manage communication between parties holding private data.

The invention relates to a service data distributed storage method and device, computer equipment, a medium and a product. Comprising the steps of generating a corresponding target data fragment identifier according to data fragment information of business data under the condition that a write-in request of the business data is detected; first mapping information and second mapping information are extracted from a preset storage routing mapping table, the first mapping information represents the mapping relation between the data fragment identifier and the storage group of the distributed database, and the second mapping information represents the mapping relation between the storage group and the database node of the distributed database; querying a target storage group identified by the target data fragment from a distributed database according to the first mapping information; querying a target database node corresponding to the target storage group according to the second mapping information; and performing distributed storage on the business data through the target database node. By adopting the method, the limitation of distributed storage of the service data is reduced.

The invention discloses an intelligent charging management system of a new energy automobile, and relates to the technical field of charging control of the new energy automobile, and the system comprises a user identification and authentication module which supports three authentication modes, account association related information and authentication encryption transmission; the charging demand prediction module collects multiple types of data, and predicts charging related parameters through a fusion algorithm; the charging pile and battery state monitoring module collects various operation data in real time; the power grid load sensing module collects power grid information and synchronizes peak and valley periods; the charging strategy optimization module dynamically adjusts a charging scheme in combination with multi-source data; the safety protection module has six protection functions and a fault diagnosis capability; the data storage module adopts a distributed database and an encryption technology; the man-machine interaction module supports multiple operation modes and displays key information; and the communication coordination module is responsible for multi-terminal data interaction and protocol conversion. According to the invention, intelligence and accuracy of charging management are improved, safety protection and data safety are enhanced, and multi-scene charging requirements are met.

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

Support for distributed database backtracking, in which the database is reverted or iteratively reverted to a previous state, is provided. Log sequence number ranges and storage offset ranges for use in reconstructing the previous state are determined and used in the reconstruction. Clock sampling records can be generated indicating a feasible backtracking window with time points indicating allowable times to which backtracking can be performed. Appropriate log records, snapshots and checkpoints for database states are identified and organized with backtracking information record and timeline. A minimal and necessary set of log records, snapshots and checkpoints enabling database reconstruction within a time window is maintained. Measures are taken to maintain the necessary data to support backtracking even after prior backtrackings have occurred in the backtracking time window.

The invention relates to the technical field of physical education management, and particularly discloses a digital informatization physical education comprehensive management system, which comprises a user interaction module, a data acquisition module, a data processing module, a system optimization module and a distributed database, the data acquisition module acquires teaching content, motion completion degree and user physiological data through a camera, a motion capture sensor and wearable equipment; the data processing module cleans and converts the data, and quantitatively evaluates whether the learning results of the students are qualified or not by using a constructed education evaluation coefficient mathematical model; the system optimization module calculates a scene optimization coefficient by analyzing a user favorable comment trend and multimedia loading performance, and automatically identifies a teaching scene needing to be optimized; the distributed database adopts a master-slave replication and fragmentation technology to improve data access efficiency and fault tolerance, digital management of physical education is achieved, and therefore the quality and efficiency of physical education are improved.

The invention discloses a data retrieval method and system for a distributed database, and relates to the technical field of data storage and data retrieval, and the method comprises the steps: obtaining the operation state data periodically sent by each node through a heartbeat signal collection interface of the nodes of the distributed database, carrying out the time sequence analysis for the signal interval fluctuation, and obtaining the operation state data; if it is detected that the node signal interval exceeds a preset threshold value, marking the node as an abnormal node, determining an abnormal node list, and recording final task metadata analysis information of the abnormal node; according to the abnormal node list, screening out a node with the lowest current load from a standby node pool; according to the data retrieval method and system for the distributed database, quick self-healing, stable operation and continuous response of a distributed database retrieval service in a complex network environment are achieved, and the reliability of the system and the retrieval experience of a user are fundamentally improved.

The invention relates to the technical field of archive digitization, in particular to an archive digitization management method and system based on a large model, and the method comprises the steps: collecting archive data; the archive data comprises images obtained by scanning paper archives, digitally input audio and video archives, and directly imported electronic archives; inputting the archive data into a pre-trained large model for multi-modal identification and information extraction, and obtaining structured data; based on the structured data, automatically classifying and labeling the archives through a large model, and generating structured metadata, semantic tags and a knowledge graph; based on the structured data, the structured metadata, the semantic tag and the knowledge graph, an embedded vector is formed and then stored in a distributed database, an intelligent retrieval interface based on a large model is formed, and the method has the advantages of high efficiency, high accuracy, generalization and the like.

The invention discloses a manufacturing industry process parameter optimization method based on a knowledge graph, and belongs to the field of manufacturing industry intellectualization. The technical problems that in the prior art, parameter optimization depends on artificial experience, systematic management is lacked, intelligent reasoning is difficult to conduct, and knowledge retrieval precision is low are solved. The method comprises the steps of firstly collecting and preprocessing manufacturing industry process data; then, constructing a semantic vector embedding model, and converting the process description into vector representation by adopting a pre-trained bidirectional encoder; defining a process knowledge ontology model, and establishing a parameter association relationship through semantic coding to form a knowledge graph; storing the coded knowledge vector into a distributed database and establishing a retrieval index; user requirements are received, and parameter matching is carried out through cosine similarity calculation; optimizing the matching parameters based on the knowledge graph association relationship; and finally collecting feedback to continuously update the knowledge graph. Through continuous optimization of the feedback learning mechanism, the precision and efficiency of technological parameter optimization in the manufacturing industry are improved.

A collection of data is maintained by at least first and second nodes of a distributed database. The first node maintains a first subset of the collection of data in a first random-access memory, and the second node maintains a second subset of the collection of data in a second random access memory. The first node sends an HTTP-based request to obtain updates to the first subset of the collection of data. The request comprises a unique sortable identifier. The first node receives data from the second node, and updates the first subset of the collection of data based on the receive data.

Provided herein are database structures, systems, and methods for an anonymous, information exchange platform. The information exchange platform described herein may comprise blockchain structures, decentralized networks, peer-to-peer technology, cryptographic techniques, and / or a combination thereof. Past and present actors associated with a topic of information (e.g., a product) may flexibly exchange information on the information exchange platform by implementing question and answer protocols.

Systems and methods for enabling availability of backup data during restoration of a distributed database system are provided. An external storage system stores a plurality of backup files each comprising backup data and one or more backup metadata files. A plurality of ranges are generated and stored among a plurality of computing devices based on the backup files and metadata files. A plurality of virtual data files are generated and stored in the ranges, where each range of the ranges comprises one or more of the virtual data files, where the virtual data files for a respective range identify physical data configured to be stored by the respective range comprising at least a portion of the backup data of one or more of the backup files. The ranges are configured to serve, based on the virtual data files, a request directed to reading or writing the physical data.

The embodiment of the invention discloses a water resource dynamic evolution simulation analysis method and system based on digital twinning, and the method comprises the steps: calling a target drainage basin water resource monitoring data set from a preset distributed database cluster after obtaining the authorization authentication of a digital twinning server; performing space-time correlation processing on the data set and the meteorological environment data of the target watershed to obtain a conjoint analysis data set with a unified space-time reference system; performing time-space coupled water resource cyclic evolution simulation on the conjoint analysis data set through a preset MIKESHE model to obtain a target drainage basin water resource dynamic evolution process simulation result; according to the result, a quantitative influence simulation result of the climate change on the agricultural irrigation water resource of the target watershed is generated, the result comprises an irrigation water demand change trend, water resource supply and demand balance and agricultural water efficiency fluctuation characteristics, and a scientific basis is provided for agricultural irrigation water resource management.

The invention provides a distributed futures transaction service system of a stateless micro-service architecture based on RestfulApi. The system comprises a front routing service, a relay transaction service, a market information acquisition service, a market information service, a tidb distributed database and a codis memory distributed database. The front routing service provides a stateless interface based on Restful Api, and supports multi-node parallel expansion and dynamic load adjustment; the relay transaction service is integrated with mainstream futures sdk and interacts with the counter system through an asynchronous communication mechanism; the market information acquisition service acquires dynamic market information data and writes the dynamic market information data into the codis memory distributed database; the market information service reads the dynamic market information data and externally provides an api interface; the tidb distributed database is used for storing user account information, and the codis memory distributed database is used for storing entrustment return, transaction return and dynamic market data. All the modules cooperate to realize high-concurrency, low-delay, high-availability and multi-mode futures transaction request processing and return information management.

The invention provides a distributed database data migration method which can be applied to the technical field of big data and the field of distributed technologies. The distributed database data migration method comprises the steps that a data migration plan is obtained, wherein the data migration plan comprises distribution information that each source data fragment in a source distributed database cluster is distributed to a target node of a target distributed database cluster and parallel migration degree representing the number of source data fragments of concurrent migration; executing the data migration plan, including: controlling m source data fragments to migrate in parallel, m being a positive integer and m not greater than the parallel migration degree; and migrating each source data fragment to the allocated target node based on the allocation information, and obtaining a target data fragment corresponding to the source data fragment at the target node after migration is completed. The invention further provides a distributed database data migration device and equipment, a storage medium and a program product.

The invention relates to the technical field of virtual reality, and discloses a touch display screen panoramic interaction method based on virtual reality. The method comprises the following steps: deploying a tactile sensing network on the surface of a touch display screen, collecting space coordinates and pressure data of a touch point in real time, and synchronously obtaining a computing resource utilization rate and network bandwidth data of a virtual reality processing node; classifying the touch data to generate behavior mode labels, and determining an optimal touch point distribution set by using a dynamic load balancing algorithm in combination with resource data; performing normalization and entropy coding compression on the original interaction data stream to generate a standardized interaction data packet; transmitting to a temporary buffer area of the distributed database, activating a persistent trigger, acquiring a storage node state through a log synchronization module, and determining an optimal path for fragmentation storage. According to the method, multi-dimensional data are integrated, resource allocation and data processing storage are optimized, and the real-time performance and stability of virtual reality interaction are enhanced.

The invention provides a construction data analysis processing method and system, and relates to the technical field of data analysis, and the method comprises the steps: obtaining the target construction data of a current construction project, dividing a construction progress plan into a plurality of construction links, and dividing a time window according to the planned duration; constructing a hierarchical storage structure according to data types in each time window, and adding index tags containing project coordinates, construction time and construction link codes; and storing the data blocks into a distributed database based on the index tags, and generating a data blood relationship graph for recording the target construction data flow path. The query efficiency of the construction data is improved.