150 results about "Distributed data store" patented technology

The invention provides a thermal power plant environment monitoring and early warning method and system based on the Internet of Things, and relates to the technical field of the Internet of Things, and the method comprises the steps: forming a preprocessing data stream for collected environment monitoring data through an edge calculation node; and carrying out real-time data stream processing by utilizing a distributed message queue, constructing a hierarchical distributed data storage structure, and carrying out homomorphic encryption and desensitization processing on data to form an encrypted data set. Based on an encrypted data set, a long-short term memory network, a random forest algorithm and a limit gradient boosting tree are adopted to construct a layered anomaly detection engine, environmental parameter spatial-temporal characteristics are extracted through a multi-head attention mechanism and residual connection, an early warning threshold value is iteratively calculated based on a Bayesian optimization algorithm, and an early warning level judgment standard is dynamically adjusted. And generating early warning information. And finally, through a three-dimensional visualization module and a knowledge inference engine based on a graph neural network and a deep reinforcement learning algorithm, visualization of early warning information and generation of a self-adaptive emergency processing strategy are realized respectively.

The invention relates to the technical field of project management, in particular to an enterprise project management system and method oriented to multi-project collaboration. A resource scheduling module; a progress control module; a cost quantification management module; and a risk management module. According to the scheme, through a micro-service architecture and a distributed data storage technology, multi-project full-period collaborative management is realized. And each module can operate and expand independently, so that the flexibility and expandability of the system are remarkably improved. The distributed data storage can ensure the real-time synchronization and quick access of the data even under the condition that a plurality of projects run in parallel. By constructing the incidence relation matrix, the system can clearly present the relation between each task and the resource of the project, and solid data support is provided for subsequent resource allocation and task management. The optimal configuration of resources and the smooth promotion of projects are realized, and the management efficiency of enterprises and the project success rate are remarkably improved.

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 building design, and discloses an Internet-based building design dynamic process management system, which comprises a multi-factor identity verification module, a distributed data storage module, a process dynamic optimization engine, a behavior evaluation and intervention module and an intelligent audit tracking system. According to the building design dynamic process management system based on the Internet, through cooperative work of the process dynamic optimization engine and the behavior evaluation and intervention module, the problem that task optimization of a traditional process management system depends on a preset rule is solved; the flow dynamic optimization engine dynamically adjusts the task priority and resource allocation by using an improved ant colony algorithm based on the real-time task progress and the executor behavior data; meanwhile, the behavior evaluation and intervention module quantitatively evaluates the behavior of the executor according to a weighted scoring model, triggers an automatic intervention process when necessary, and can quickly adapt to sudden changes, so that the resource allocation efficiency is improved, and human errors are reduced.

A robotic surgical system network includes a plurality of robotic surgical systems. Each system includes robotic arms, sensors, a surgeon console, and a control system with an integrated AI module. A network interface is associated with robotic surgical system and provides secure data communication. A central or distributed data repository securely stores surgical data aggregated from the robotic surgical systems. The surgical data includes at least one of procedural data, sensor readings, imaging data, AI decision logs, surgical outcomes, or user interaction data. A training module utilizes aggregated surgical data to train or update AI models for the robotic surgical systems using unsupervised learning, transfer learning, or federated learning techniques. A cybersecurity module implements security measures for data transmission and system access, the measures comprising at least one of encryption, multi-factor authentication, or real-time threat detection.

The invention relates to the technical field of data storage, in particular to a distributed data storage and access processing method. The method comprises the following steps: presetting a guide configuration file in a distributed storage cluster to carry out identity authentication and communication link establishment on each storage node and a cluster management center so as to generate a security data interaction channel; the data sending end equipment sends a data storage request to the cluster management center through the secure data interaction channel and issues data fragments to proper storage nodes; the data receiving end equipment sends a data access request to the cluster management center through the security data interaction channel, generates an access path and feeds back the access path to the data receiving end equipment; the corresponding resource use condition in each storage node is monitored in real time, and when a preset threshold value is reached, the resource is reallocated to other storage nodes according to a preset data migration strategy and a load balancing algorithm based on a resource use insufficiency result. According to the invention, efficient storage and rapid access of data in a distributed environment can be realized.

The invention relates to the technical field of fire alarm, and discloses a self-adaptive threshold fire-fighting buzzer alarm method and system based on fuzzy logic, and the method comprises the following steps: S1, environment data collection; s2, data preprocessing; s3, performing fuzzy processing; s4, performing fuzzy reasoning and alarm threshold calculation; s5, optimizing an alarm threshold value; s6, real-time feedback and dynamic adjustment: according to the real-time fire suppression result and the environment change data, adjusting an alarm threshold to form a dynamic feedback mechanism; s7, applying a fire propagation model; and S8, alarm execution. By adopting distributed data storage, redundant backup, encryption technology and automatic data management, efficient and safe data processing, reliable data recovery and accurate alarm decision are realized, and the response speed and stability of the system are improved.

The invention relates to the technical field of radio transmission systems, in particular to a dual-module redundant satellite communication and internet-of-things terminal system, which comprises a data processing module, a management module, a frequency band allocation module, a self-adaptive switching decision module, a scheduling module and an optimization module. The data processing module collects satellite signal parameters and terminal state data, and generates dynamic decision context information including conflict frequency band feature vectors, terminal dynamic position tags and channel load indexes; the system iteratively optimizes a frequency band allocation strategy through a closed-loop data stream, constructs a conflict spatio-temporal topology network by adopting a graph database to realize competitive traceability, manages historical parameters in combination with a distributed data storage module, finally reduces channel competition conflict and data retransmission rate, optimizes terminal energy consumption and communication reliability, and improves the system performance. The method is suitable for Internet of Things terminal communication in complex environments such as satellite signal shielding and electromagnetic interference.

A method and system for updating a topology on router nodes in a distributed storage system are described. The method can include obtaining, by a first service of a control plane service, topology information from an inventory data store by querying a second service of the control plane service that manages the inventory data store for the topology information. The method can further include generating, by the first service of the control plane service, a topology payload based on the obtained topology information. The method can also include sending, by the first service of the control plane service, a request comprising the topology payload to a router node.

The invention discloses a data consistency processing method, a distributed storage system and electronic equipment, and belongs to the field of distributed data storage systems, and the data consistency processing method comprises the following steps: when any storage node in the distributed storage system receives to-be-updated data sent by a client, obtaining a corresponding data updating path by taking the storage node as a first storage node; according to the data updating path, each storage node carries out data persistence on itself after receiving the data to be updated, if persistence succeeds, the data to be updated is transmitted to the next storage node, if persistence fails, data rollback is carried out, and feedback information of path updating failure is sent to the upstream of the data updating path; and after receiving the information, each upstream storage node carries out data rollback on the current updated data. According to the scheme, even if the update message processing flow fails, the consistency of the data between the survival copies / fragments can be ensured in real time.

A distributed data storage includes data stored in different data shards on nodes of the distributed data storage. A memory stores a dynamic heatmap indicative of a number of data requests for data stored on each of the nodes. A processor receives a request for retrieving the data stored in the distributed data storage. The request includes data ranges and index criteria of the data and location of the nodes in the distributed data storage. In response, the processor determines the nodes of the distributed data storage that include the data using the dynamic heatmap, retrieves the data as determined from the dynamic heatmap; and routes the data to a user associated with the request. When a data retrieval performance is below a desired threshold value, the processor performs dynamic data sharding to reallocate the data shards across the nodes of the distributed data storage.

Record-level transaction conflicts may be detected at storage nodes of a database back-end storage service. When a prepare statement is received, a storage node can determine whether another prepare statement acting on at least one of the same records has succeeded. If so, then the storage node can indicate to the database access application that there is a conflict, allowing the database access application to wait to try again or abort the transaction.

The invention is suitable for the technical field of intelligent property management, and provides an intelligent property management system based on multi-technology fusion, which comprises an infrastructure module, a user access module, a data fusion module, a data support module, an intelligent property module and an intelligent decision module. The infrastructure module collects data through equipment such as a sensor and performs edge calculation preprocessing; the user access module provides a multi-port entrance and adopts unified identity authentication and authorization; the data fusion module integrates multi-source heterogeneous data and constructs a user portrait; the data support module realizes distributed data storage and standardized management; the intelligent property management module integrates multiple management functions and realizes automation of a business process; and the intelligent decision-making module performs fault prediction, demand analysis and the like by using data analysis and machine learning. According to the invention, information islands are broken, the equipment monitoring capability and the service individuation level are improved, data sharing and business collaboration are realized, the management efficiency is improved, the cost is reduced, and intelligent decision support is provided for property management.

Storage-side filtering may be implemented for performing hash join operations with respect to data stored in distributed data storage. When a query to a database stored in a distributed data store is received, a hash join operation may be identified for performing the query. As part of performing the hash join operation, a query engine may cause storage nodes in the distributed data storing data for the database to filter data before sending the data to the query engine according to a join predicate for the hash join operation. A result of the query may then be provided to a user, using the filtered data provided from the storage nodes.

The invention provides an emergency command rescue data distributed storage method and system based on artificial intelligence, and the method comprises the steps: carrying out the feature identification processing through obtaining a rescue scene perception data set collected by multiple types of emergency terminal equipment in real time, and generating an emergency data feature set containing scene context features and storage demand features; and calling a pre-constructed distributed storage strategy model to carry out storage node adaptation analysis processing on the emergency data feature set, generating a distributed storage mapping scheme, distributing data in the rescue scene sensing data set to corresponding target storage nodes according to the distributed storage mapping scheme, obtaining a distributed data storage cluster, and sending the distributed data storage cluster to the rescue scene sensing data set. And collecting real-time operation state information of the distributed data storage cluster, and carrying out dynamic optimization adjustment processing on the distributed storage mapping scheme to generate an updated distributed storage mapping scheme. According to the invention, the adaptability, reliability and dynamic response capability of emergency command rescue data storage can be effectively improved.

The invention discloses a disease and pest control information management system based on big data, and the system comprises a multi-source data collection module which is used for obtaining multi-source data, carrying out the anomaly detection of all data, and processing the abnormal data; the distributed data storage module is used for constructing a plurality of databases and respectively storing the detected and processed data by utilizing the plurality of databases; the big data analysis and scheme generation module is used for generating a prevention and treatment scheme and outputting the prevention and treatment scheme; and the business application module is used for managing user information and presenting a prevention and treatment scheme. According to the method, data sources are increased, data comprehensiveness and accuracy are improved, multi-class databases are constructed, data are classified and managed, management efficiency is improved, during retrieval, the databases are positioned through keywords requested by a user firstly, then the positioned databases are retrieved, retrieval complexity and information processing amount are reduced, and retrieval efficiency is improved. The retrieval efficiency is improved, so that the prevention and treatment scheme generation efficiency is improved.

The invention relates to the field of enterprise application integration, in particular to a supply chain finance-oriented multi-protocol conversion and semantic verification system and method, which comprises a protocol adaptation layer, a core processing layer, a management support layer and a distributed data storage layer, the protocol adaptation layer adopts a plug-in and metadata driving design, and a protocol plug-in is an independent JAR packet and supports multi-protocol encoding and decoding and hot updating; the core processing layer realizes data standardization and financial-level semantic verification through a general message model, a dynamic routing engine, a visual data conversion module and a rule engine; and the management support layer provides a low-code portal and supports rule configuration, simulation testing and full-link monitoring. According to the technical scheme, through the seven steps of configuration, decoding, routing, conversion, verification, coding and auditing, the method adapts to a multi-specification heterogeneous system, data intercommunication accuracy and compliance are improved, docking cost is reduced, and supply chain financial service requirements are met.

The invention discloses a distributed data storage and access control method based on a block chain, and relates to the technical field of data security. The method comprises the following steps: fragmenting user data, encrypting the fragmented user data by using an encryption algorithm, and storing the encrypted user data in a plurality of distributed nodes; taking the hash value and the storage position of the data fragment as metadata to be recorded in a block chain, and performing integrity verification through a smart contract; when a user accesses, user attributes are verified based on an attribute-based encryption technology, and an access control strategy is dynamically executed through an intelligent contract; and an erasure code technology is adopted to carry out redundancy storage so as to realize data recovery and fault tolerance. The method effectively solves the problems of single-point failure, privacy disclosure and inflexible access control in traditional centralized storage, has the beneficial effects of improving data security, guaranteeing integrity, realizing fine-grained dynamic access control and enhancing system reliability, and is suitable for data security management in cloud computing, Internet of Things and big data environments.

For database high availability and for accelerated recovery of a failed replica of a database, a storage computer is dynamically allocated and temporarily persists database content modifications until the database replica is ready to receive the modifications. The storage computer is not allocated storage that stores the database. The storage computer persists a recent portion of the database and later receives a request to synchronize the recovering replica. During recovery, the storage computer responsively sends the portion of the database to the recovering replica. For acceleration, recovery herein does not entail content interpretation such as replay of a redo log. For horizontally scaled acceleration involving two distinct storage computers per recovering replica, multiple replicas are concurrently recovered by respective storage computers that each receives recovered database content only from a respective distinct other storage computer.

The invention discloses a configurable building system and method for an energy management system. The system comprises an infrastructure layer, a configuration management layer, an atomic function library layer, an atomic function combination layer and an application scene layer. The infrastructure layer provides a containerization facility and a distributed data storage facility of the atomic function model; the configuration management layer is used for visualized interaction, gateway and protocol configuration and atomic function model instantiation; the atomic function library layer is used for creating and storing an atomic function model; the atomic function combination layer is used for polymerizing atomic function model examples to obtain atomic function combinations; and the application scene layer is used for calling the atomic function combination and finally forming an energy management system suitable for a corresponding scene. The invention provides a highly flexible, configurable and extensible energy management system development solution.

The invention relates to the technical field of data management, and particularly discloses an efficient management method and system for a data directory in a data space, and the method comprises the steps: carrying out the incremental updating of metadata information, data product registration information and cross-node data directory information related to the data directory to a distributed data storage system; constructing a full-text index and a vector index of metadata in the distributed data storage system, and matching a vector generated based on the request content with the full-text index or the vector index to obtain a retrieval response result; marking a semantic equivalence data set based on the semantic vectors of all the data sets, constructing a data set node graph based on all the semantic entity lists, and outputting a semantic equivalence field list based on the semantic equivalence data set and the data set node graph; determining an associated recommendation result of the retrieval response result based on the semantic equivalence field list, and synchronously returning the retrieval response result and the associated recommendation result; data directory management and efficient utilization in a distributed heterogeneous cross-domain data space are supported.

The invention provides a big data processing method and system based on smart medical treatment, and relates to the technical field of smart medical treatment, and the method comprises the following steps: collecting health data of a patient through a medical device and an information system, and carrying out the data preprocessing of the health data of the patient through distributed data storage, and obtaining the preprocessed health data of the patient; analyzing the preprocessed health data of the patient by using a deep learning model to obtain a health analysis result of the patient; and performing disease prediction and risk assessment based on the health analysis result of the patient. The intelligent medical big data processing system has the beneficial effects that the safety, the real-time performance, the accuracy, the low power consumption, the fault tolerance and the like of the intelligent medical big data processing system are remarkably improved, so that the whole system can more efficiently support disease risk prediction, clinical early warning and remote diagnosis and treatment; and the intelligent and refined development of intelligent medical treatment is greatly promoted.

The invention relates to the technical field of intelligent voice data processing, and discloses an intelligent voice interface data calling method. The method comprises the following steps: receiving a voice instruction stream input by a user, and converting the voice instruction stream into a structured semantic unit set through a multi-level semantic analysis engine; and dynamically layering the instruction intention according to the semantic association degree in the structured semantic unit set to generate an intention sequence with priority marks. And based on the priority mark of the intention sequence, extracting a candidate data block set matched with the intention of each level from a distributed data storage cluster. And performing cross-modal feature alignment on the candidate data block set, and calculating the semantic coverage degree of each data block and the corresponding intention hierarchy. And screening out target data blocks according to a semantic coverage threshold, and recombining the data blocks according to a priority sequence of the intention sequence. And loading the recombined target data block set to a real-time processing channel of the voice interface to generate an interactive voice response data stream.

The present invention belongs to the field of distributed data storage and synchronization in information engineering, and specifically relates to a distributed experience feedback system data offline synchronization system and method. It mainly includes the following steps: application of system eigenvalue algorithm; data set export process based on JSON format; data encryption and decryption process based on eigenvalue algorithm. Its beneficial effect is: adopting a JSON-based data set mode without using SQL mode, supplemented by a data recovery algorithm, each data in the JSON data set is restored and compared with the target system data, so as to achieve the effect of securely merging data and avoid accidental deletion and overwriting of data. The data validity period is also set when the file is exported, and the system also generates a check code based on the validity period and the target system feature code. When importing, the check code can be used to judge the validity period. If the file exceeds the validity period, it cannot be imported, which improves the fault tolerance during a certain import.

The invention discloses a block chain decentralized data security storage method and system, and relates to the technical field related to data processing, the method comprises the following steps: performing fragmentation processing on to-be-stored data to generate N data fragments; obtaining N encrypted data fragments; calling application scene information of the to-be-stored data to evaluate the block chain network, and determining M nodes of the block chain network according to an evaluation result; performing node selection, and making a data distribution strategy according to a selection result; and distributing the N encrypted data fragments to a block chain network according to a data distribution strategy for data storage. The technical problems that in existing distributed data storage, it cannot be ensured that the data can be stored in safe, reliable and efficient nodes, and the safety, privacy and reliability of the data in the transmission and storage process are difficult to guarantee are solved, and the technical effect of improving the data safety and privacy and the data storage efficiency and reliability is achieved.

Various techniques for data deletion on a distributed data storage (DDS) protection network are disclosed. Some techniques can include receiving, by a first DDS protection network node of a plurality of DDS protection network nodes, a deletion request corresponding to a digital asset. The deletion request can encode a retroactive data protection object. Additionally, the techniques can include performing a deletion by deleting at least one of a plurality of component files of the digital asset based at least on at least one of a first plurality of identifiers encoded by the retroactive data protection object.

The invention relates to a multi-copy distributed data storage system and method based on SDS and Ceph, and the system architecture comprises an RADOS layer which is used for segmenting to-be-stored original data into a plurality of objects and storing the plurality of objects; the storage pool layer comprises a plurality of storage pools, each storage pool comprises a plurality of PG collating groups, the PG collating groups are used for carrying out organization management and position mapping on storage of the objects, and the PG collating groups and the objects are in a one-to-many mapping relationship; the OSD layer comprises a plurality of object storage devices OSDs, the OSDs are used for storing objects on the physical disks, the Ceph system manages the physical disks by managing the OSDs, and the OSDs and the physical disks are in one-to-one mapping relation. By means of the train data storage method and device, the problems that in the related technology, a centralized storage mode is usually adopted for train data, and reliability and expandability are poor are solved, and the effect of improving the reliability, expandability and redundancy of the train data is achieved.

In some examples, a data management and storage (DMS) platform comprises peer DMS nodes in a node cluster, a distributed data store comprising local and cloud storage, and at least one processor configured to perform operations in a method of creating a local consolidated patch file from a patch file chain stored in the cloud storage. Example operations comprise, in a first dry-run phase, creating a patch file image of data blocks in one or more cloud patch files stored in the cloud storage; in a second data-transfer phase, downloading at least some of the data blocks from the cloud patch files identified by the patch file image; and creating and storing, in the local storage, the local consolidated patch file using the downloaded data blocks.

The application relates to the technical field of distributed data storage, and discloses a database cluster partition running method in a network partition scenario, equipment and a medium, wherein the method comprises the following steps: constructing a multi-master distributed database cluster, continuously capturing database native transaction logs by each node; obtaining change event data and uniformly storing the data; analyzing the original logs into structured data, performing uniform sharding and parallel processing; processing block log change records, arranging the change events of primary keys according to rules; performing conflict processing and data synchronization, synchronizing all nodes after solving the conflicts; and through abnormal processing and fault tolerance, realizing self-healing and maintaining data consistency. The application can realize millisecond-level conflict identification and data consistency convergence after network disconnection, guarantees the continuous and stable operation of the cluster in a partition state, realizes self-healing and continuous availability of the system.

The present application relates to the technical field of data storage, and particularly relates to a distributed data storage and access processing method. The method comprises the following steps: presetting a guide configuration file in a distributed storage cluster to perform identity authentication and communication link establishment on each storage node and a cluster management center, thereby generating a secure data interaction channel; a data sending end device sends a data storage request to the cluster management center through the secure data interaction channel and distributes data shards to appropriate storage nodes; a data receiving end device sends a data access request to the cluster management center through the secure data interaction channel and generates an access path and feeds back the access path to the data receiving end device; by monitoring the corresponding resource usage in each storage node in real time, when a preset threshold is reached, based on the result of insufficient resources, according to a preset data migration strategy and a load balancing algorithm, the data is redistributed to other storage nodes. The present application can realize efficient storage and rapid access of data in a distributed environment.