6702results about "Redundant operation error correction" patented technology

The invention discloses a server cooperative control method, a storage medium and electronic equipment, and relates to the technical field of firmware, and the method comprises the steps: employing a programmable logic device to actively take over the control authority of target equipment based on the operation parameters of a sensing device in a server starting stage, stable operation of equipment before initialization of the substrate management control device is not completed is ensured, a mechanism that a preset communication link and the substrate management control device are subjected to interactive verification is adopted, and the functional integrity and communication reliability of the substrate management control device during permission transfer are ensured through bidirectional communication verification. A dynamic permission recovery strategy based on real-time anomaly detection is adopted, control right reverse switching is triggered through a sensing device and communication link state dual monitoring mechanism, and the purpose of actively taking over equipment management responsibilities when a substrate management control device runs abnormally is achieved. Therefore, double improvement of system fault self-healing capability and equipment control continuity is realized, and the problem of difficulty in cooperative control among heterogeneous units is solved.

The invention discloses a multi-level energy management system based on multi-dimensional data, and relates to the technical field of energy intelligent management, and the system comprises a multi-source data collection module which collects power utilization, environment and equipment state data in real time; the data fusion processing module is used for processing abnormal values through an algorithm and fusing multi-scale data features; the energy state evaluation module is used for realizing equipment state evaluation and early warning by using a fusion algorithm and a prediction model; the multi-level energy scheduling module adopts an optimization algorithm to balance the energy cost, the production efficiency and the carbon emission, and dynamically adjusts the strategy; the energy performance analysis module is used for developing an analysis tool and an evaluation model; and the decision support module is used for configuring an expert knowledge base and developing a fault diagnosis system and a knowledge graph. Through multi-dimensional data acquisition and multi-level management, the energy data error is greatly reduced, the comprehensive energy cost and carbon emission are remarkably reduced, the cost of participating in enterprise operation is reduced, and the accuracy, safety and sustainability of energy management are improved.

The invention relates to a safe backup and recovery method and system for medical data, and the method comprises the steps: obtaining the multi-dimensional attribute information of the medical data, and carrying out the dynamic classification of the multi-dimensional attribute information based on a preset classification rule, and obtaining an attribute information label; performing differential encryption on the medical data according to the attribute information tag to obtain encrypted medical data, writing the encrypted medical data into a multi-level storage node, and performing multi-level backup construction to obtain a cross-platform backup topology; performing traceability node extraction on the multi-level storage nodes according to the abnormal node data access behavior to obtain traceability node information, and performing data traceability verification on the traceability node information according to the backup topology configuration to obtain a trusted recovery chain; and performing recovery node selection on the multi-level storage nodes according to the trusted recovery chain, and performing multi-node collaborative decryption reconstruction to obtain a recovery verification report. According to the method, the redundancy storage cost can be reduced while the data recovery reliability is guaranteed.

The invention relates to the technical field of data processing, and discloses a data processing method and system for cloud computing and a storage medium. The method comprises the following steps: acquiring preprocessed data through a distributed acquisition node to obtain a structured data packet; performing classification analysis through mixed feature extraction to obtain a safety disaster recovery data set; analyzing the incidence relation through a heterogeneous fusion network to generate a knowledge base; detecting abnormity through a self-adaptive threshold algorithm to form a risk library; determining a recovery scheme through a multi-objective decision to generate an execution log; and a protection strategy is formed through multi-angle comparison and optimization of parameters. According to the method, the data association relationship is mined and analyzed by constructing the multi-level heterogeneous fusion network, the optimal disaster recovery scheme is automatically determined through the multi-target decision process, and meanwhile, a continuous learning optimization mechanism is introduced; the intelligent level of data processing in the cloud computing environment, the timeliness of disaster recovery response and the stability of system operation are remarkably improved.

The invention discloses a multi-modal data real-time identification and cooperative processing system based on edge computing and federated learning. The multi-modal data real-time identification and cooperative processing system comprises a cloud center coordination node, a plurality of edge computing nodes, a cross-modal encryption engine, a federated learning controller and a model updating verification module. The cloud center coordination node executes federated learning model aggregation and dynamic task allocation, and generates a cross-modal encryption strategy; and the edge computing node is configured with a multi-modal data acquisition module, a local model training unit and a co-processing gateway to realize multi-modal data acquisition and local processing. The system encrypts vision, acoustics and text data by using differentiated algorithms such as spatial confusion, frequency domain permutation and homomorphic encryption; the federated learning controller carries out multi-modal feature fusion, hierarchical encryption and dynamic networking at the edge node; and the model updating verification module performs aggregation updating after ensuring parameter consistency by using secure multi-party calculation. According to the method, real-time processing and privacy protection of multi-modal data are realized, and the data co-processing efficiency is improved.

The invention relates to the technical field of data processing, in particular to a cloud backup platform data deduplication method based on distributed storage. Comprising the following steps of: performing self-adaptive blocking on data by utilizing a dynamic blocking algorithm, generating a unique identification code and extracting multi-dimensional feature information; establishing a global index through a distributed hash table, mapping a data position and maintaining index consistency; performing redundancy analysis and similarity matching based on a deep learning algorithm, and executing a duplicate removal strategy; according to the access frequency and the load condition, distributed storage of data blocks is realized by adopting a hierarchical graph division algorithm; and a partitioning strategy, index frequency and storage layout are dynamically optimized by using a reinforcement learning algorithm, and global optimization of performance and resources is realized. According to the invention, high efficiency, accuracy and dynamic optimization of data deduplication are realized, and the data storage and management efficiency of the cloud backup platform is improved.

The invention provides an electronic certificate management and supply chain quality tracing method and system based on a block chain, and belongs to the technical field of information. According to the method, encryption storage and tamper-proof protection are carried out on the electronic certificate information through the distributed account book of the block chain and the Hash algorithm, and verification, approval and data updating operations are automatically executed based on the intelligent contract. Fine-grained authority management and abnormal access detection of all parties of the supply chain are realized through a user access control module; and through a tracing query module, performing multi-condition combination query on the electronic certificate information stored in the block chain, and generating a quality tracing report meeting supervision requirements. According to the method, through tamper-proofing, distributed storage and intelligent contracts of the block chain, trusted storage, automatic verification and full-chain quality tracing of the electronic certification are realized, data security and supply chain transparency are improved, trust cost is reduced, and supervision and tracing efficiency is improved.

The invention discloses a self-adaptive cloud management platform system based on intelligent resource scheduling and container arrangement, and relates to the field of computer information management. The system comprises a refined resource scheduling and adaptive optimization module, a containerized application life cycle management and dynamic container arrangement module, a high-precision operation and maintenance monitoring and self-healing mechanism module based on big data analysis, and a dynamic resource allocation and elastic scaling strategy module of an intelligent scheduling engine. The system takes a containerization technology as a core, realizes centralized management and monitoring of cloud computing resources, can realize dynamic intelligent resource scheduling and optimization, accelerates application deployment, improves system flexibility, strengthens operation and maintenance monitoring and platform safety guarantee, and comprehensively improves resource optimization and cost effectiveness. The resource scheduling efficiency is improved, the application deployment is simplified, the operation and maintenance monitoring is enhanced, and efficient resource management is realized through an adaptive optimization technology.

The invention relates to the technical field of big data, in particular to a cloud computing-based tumor early screening data sharing platform construction method and system. The sharing platform construction method comprises the following steps: carrying out association modeling on multi-dimensional early screening data by adopting a knowledge graph technology; encrypting the sensitive field of the metadata index database, and synchronously generating an irreversible anonymous identifier to replace the original identifier; partitioning and arranging the metadata index database in a distributed storage system corresponding to a cloud platform according to tumor types, and performing cross-region redundant backup by adopting an erasure code technology; and constructing an access control smart contract based on a block chain technology, verifying the qualification of a requester through a digital identity authentication module, and after verifying the validity of a digital certificate of the requester, dynamically decrypting and transmitting a data copy conforming to an authority level. Through organic fusion of the knowledge graph, the encryption technology, distributed storage and the block chain, a safe, efficient and reliable tumor early screening data sharing platform is constructed.

The invention relates to the field of data governance, and discloses a data governance risk early warning method based on big data mining, which constructs a three-dimensional coupling model through a big data mining technology, and realizes global perception, dynamic prediction and closed-loop disposal of risks. The big data mining-based data governance risk early warning method comprises the steps of obtaining a three-dimensional super-network topological structure, obtaining a phase change critical region coordinate set, generating a cross-layer risk conduction path map, dynamically triggering graded early warning and outputting a disposal instruction set. According to the three-dimensional super-network modeling technology, the meteorological equipment topology, the data product dependency chain and the cross-border access behavior are fused, the composite energy field model with meteorological ocean domain characteristics is constructed, the cross-regional data chain fracture risk can be warned in advance, and the data backup failure probability during the typhoon passing period is reduced.

The invention relates to the field of data backup, in particular to an intelligent data backup method and system based on an AI large model. The method comprises the following steps: acquiring an enterprise global data list, performing intelligent data structure deconstruction and dynamic attribute mapping modeling, and constructing a holographic data semantic perception model; performing real-time transient risk mutation detection on the holographic data semantic perception model, and constructing an intelligent backup triggering mechanism; carrying out storage resource demand prediction based on an intelligent backup trigger mechanism, carrying out multi-storage cloud environment resource dynamic scheduling, and constructing an elastic backup storage resource pool; carrying out incremental backup analysis and self-adaptive compression coding to obtain an incremental backup coding packet; and performing dynamic backup sequence adjustment and intelligent incremental backup decision on the incremental backup coding packet based on the elastic backup storage resource pool, and constructing an intelligent incremental backup execution engine. According to the method, the reliability, the accuracy and the traceability of a backup result are improved through self-adaptive intelligent incremental backup.

The embodiment of the invention provides a BIM-based hydraulic engineering digital management method and system. The method comprises the following steps: performing multi-dimensional monitoring system deployment on a to-be-monitored area to obtain multi-dimensional hydrological data; constructing a first BIM based on the topographic data of the to-be-monitored area, the real-time work area image, the hydraulic engineering construction information of the design stage and the equipment deployment information; fusing the multi-dimensional hydrological data, the water area change data, the construction progress data and the operation and maintenance monitoring data into the first BIM, and constructing a second BIM including a full life cycle; and through the equipment characteristic curve and the water conservancy project physical model, in combination with the multi-dimensional hydrological data and the water area change data, predicting a water conservancy project structure change trend and potential risk factors in the second BIM, fusing the water conservancy project structure change trend and the potential risk factors into the second BIM, and displaying an implementation effect and improvement suggestions in real time in the second BIM. A user is assisted to realize hydraulic engineering digital management, and the engineering management efficiency is improved.

The invention relates to the technical field of database disaster recovery backup, and discloses a multi-node database disaster recovery backup system. The system comprises a data fragmentation module, a real-time synchronization module, a fault detection module, a data recovery module and a strategy optimization module. The data fragmentation module performs logic fragmentation based on a consistent Hash algorithm to realize load balancing and redundant backup; the real-time synchronization module captures data change of the main node by using an incremental log synchronization technology; the fault detection module evaluates the health condition of the node through a self-adaptive heartbeat detection mechanism; the data recovery module recovers data by means of a multi-version snapshot recovery algorithm; and the strategy optimization module generates an optimal disaster recovery backup strategy by applying a dynamic weight adjustment algorithm. The system solves the problems of non-uniform data distribution, low synchronization efficiency, untimely fault detection, incomplete data recovery, lack of strategy optimization and the like in multi-node database disaster recovery backup, improves system performance, stability and data security, and meets diversified disaster recovery backup requirements.

The invention discloses a cost optimization method for resource scheduling management of a cloud data center, and relates to the technical field of cloud computing, and the method comprises the following steps: S1, collecting and modeling a multi-dimensional resource state of the cloud data center, and generating a resource change trend based on a sliding time window and a prediction model; and S2, constructing a multi-target game scheduling model taking calculation, storage, bandwidth and energy consumption as participants, outputting a scheduling game solution in combination with task modal adaptability parameters, and forming task-resource optimal matching. According to the method, through multi-dimensional resource state collection, a sliding time window and an advanced prediction model, resource dynamic changes and future trends can be captured more accurately, more reliable input is provided for scheduling decisions, resource waste or performance bottlenecks caused by information lag are avoided, calculation, storage, bandwidth and energy consumption are modeled as multi-party game participants, and the game efficiency is improved. Nash equilibrium is solved in combination with task modal adaptability parameters, and an optimal scheduling scheme giving consideration to resource utilization rate, performance and cost can be found.

A content editor or a plugin thereto automatically generates authorship tokens that identify content authored by a human author or an artificial author. The authorship tokens are applied to the work while the work is being produced. Thus, subsequent review of the work can identify regions produced by a human author and other regions produced by an artificial intelligence.

In one embodiment, a computer-implemented method executed using one or more processors of an incident management system comprises receiving a notification of an incident associated with a computer system, and in response to receiving the notification: extracting an error message from the notification; reading a set of computer program code changes that have been implemented in the computer system; matching the error message to the set of computer program code changes and outputting a set of one or more candidate code changes that may correspond to the error message; based on the set of candidate code changes, generating an automatic remediation for the incident; and using the one or more processors of the incident management system, executing the automatic remediation on the computer system.

The invention discloses a cooperative monitoring device and method for a large deep foundation pit complex supporting system, and relates to the technical field of foundation pit supporting. The device comprises a multi-dimensional sensor network module, a BIM-GIS digital twin platform module, an intelligent analysis and early warning module, a construction collaborative decision module, a data backup and recovery module and a remote monitoring and management module. The method comprises the following steps: step 1, carrying out multi-source data space-time registration; step 2, dynamically constructing a digital twinborn model; step 3, coupling risk assessment; step 4, construction collaborative optimization; according to the technical scheme, the method comprises the following steps of data processing, data quality control and system performance evaluation, through three innovations of deep coupling of multi-source data, dynamic model correction and intelligent collaborative decision making, a'monitoring-analysis-decision-execution 'full closed loop is constructed, technical breakthroughs are achieved in complex working condition adaptability, early warning real-time performance and construction safety, and the method has remarkable engineering application value.

A method may include, based on a recovery policy associated with the primary database, creating a first snapshot of the primary database at a primary computing system, replicating the first snapshot of the primary database to a secondary computing system, wherein a backup database of the primary database is hosted on the secondary cluster, in response to the primary database being unavailable, applying the first snapshot of the primary database to the backup database, creating, according to the recovery policy associated with the primary database, a snapshot of the backup database, in response to the primary database having restored availability, creating, according to the recovery policy associated with the primary database, a second snapshot of the primary database.

The invention relates to the technical field of intelligent power grids, in particular to an intelligent design method and system for electric power engineering cloud resources based on distributed computing. Comprising the following steps: constructing an electric power engineering multi-dimensional resource modeling system, dividing calculation nodes into three types of heterogeneous resource units including a real-time processing core, a data analysis core and a disaster recovery backup core, and establishing a dynamic attribute matrix containing time delay sensitivity, an energy consumption coefficient and a risk assessment value; the real-time processing core is configured with a hardware acceleration instruction set and supports microsecond response; a self-adaptive dynamic clustering algorithm is adopted, elastic resource clusters facing task requirements are generated according to the spatial topological relation of resource units and the load change trend, a quantum genetic optimization mechanism is introduced in the clustering process to dynamically adjust the inter-cluster coupling degree, and a cross-cluster communication link based on credibility evaluation is established; according to the design, the problems of low resource utilization efficiency and service quality degradation caused by a static allocation mode can be fundamentally solved.

The invention relates to the technical field of data encryption transmission, and particularly discloses a data security encryption transmission system and method in a cloud computing environment, and the system comprises an identity authentication module which is used for verifying the identity of each transmission party through at least two authentication modes, and authorizing a legal user to carry out data transmission; the data encryption module is used for encrypting to-be-transmitted data by adopting a mode of combining symmetric encryption and asymmetric encryption, and automatically adjusting an encryption strategy based on a dynamic encryption algorithm selection mechanism and a quantum encryption technology; the key management module is used for dynamically generating and managing a key in an encryption transmission process, performing multi-level key storage and access control by adopting a distributed key management scheme, and tracking the life cycle of the key by utilizing a block chain technology; not only is the security of data transmission in the cloud computing environment enhanced, but also the flexibility and the strain capacity of data transmission are improved, and the challenge of network attacks is fully dealt with.

The invention discloses a permanent incremental backup method based on an S3 bucket-level virtual snapshot, and relates to the technical field of data storage and backup, the method comprises the following steps: initializing a backup system and starting a backup agent program, an index management service and an S3 object storage service; complete backup is executed, and a VID and S3 bucket-level virtual snapshoot are generated; efficiently managing a mapping relation between a file path and a snapshot identifier through a B + tree and an inverted index by utilizing a MongoDB index service; incremental backup is executed, and file changes are accurately recognized through Hash comparison and modification time comparison; a storage position is optimized by adopting an incremental storage and deduplication storage mechanism and an IncDSI optimization algorithm; executing backup chain integration and cleaning; and performing data recovery based on the VID of the file and the directory. According to the method, the backup efficiency is remarkably improved, the storage overhead is reduced, the data recovery process is optimized, and the method is suitable for data backup and management of a large-scale file system and has important application value and prospect.

A video gateway system at a worksite is coupled to multiple cameras on a network, and backs-up video streams generated by the cameras to a backend cloud backup video storage system and a frontend (cache) video storage system. The video gateway system generates an aggregated video asset from a plurality of streams of video from the multiple cameras, and generates metadata and a backup report associated with the video asset. The video asset, metadata, and backup report are stored on the backend cloud backup video storage system in an file system, and are also stored on the frontend (cache) video storage system such that for at least a period of time, the video asset and the associated the metadata and backup report are stored on both the cloud backup video storage system, facilitating quick access and retrieval to the stored video for retrieval for streaming, activity detection, and other uses.

The invention provides an online document processing method and system based on a document processing model, and the method comprises the steps: obtaining an editing request of a user for a target data unit, generating a temporary locking identifier according to a user authority identifier and a request timestamp, limiting the modification operation of other users for the target data unit, and obtaining a conflict isolation result; constructing a data consistency module according to a conflict isolation result, extracting editing state information of the current data unit from the temporary locking identifier, and performing sequential recording on concurrent operation by adopting a distributed transaction processing technology; if it is detected that version conflicts exist in the sequenced records, a rollback mechanism is triggered, operation priorities are redistributed, and data versions after consistency verification are determined; and designing a hierarchical cache structure according to the data version after consistency verification, storing the high-frequency access data into a memory cache region, and storing the low-frequency access data into a disk cache region to obtain a hierarchical storage structure. The data consistency and security are good, and the storage and access efficiency is improved.

The invention relates to the field of water quality monitoring analysis, in particular to a multi-modal water quality monitoring data intelligent processing method and system. The method comprises the following steps: obtaining a water quality monitoring parameter stream and a water quality monitoring image, and carrying out adaptive processing to obtain a filtering optimization parameter stream and an optimization monitoring image; performing water quality state evolution perception according to the adaptive filtering optimization parameter flow, and constructing a water quality state evolution graph; and performing biological index response analysis and water quality ecological influence evaluation based on the water quality state trend evolution graph to obtain a water quality evaluation report. And carrying out pollutant spatial distribution change analysis based on the optimized monitoring image, and executing final water quality monitoring evaluation according to a water quality evaluation report. The invention provides efficient and accurate water quality status analysis.

The invention provides a disaster recovery data recovery method and system of a disaster recovery system, and the method comprises the steps: adjusting a network bandwidth allocation proportion according to a priority, and if the target timeliness is higher than a preset recovery time threshold, increasing a real-time power grid alarm data bandwidth proportion, and generating a bandwidth allocation scheme; dynamically scheduling computing resource allocation according to a bandwidth allocation scheme, and if the key evaluation characteristic exceeds a set upper limit, increasing a computing resource share of a service key level, and generating a resource allocation decision; and after the priority updating scheme is obtained, monitoring a system load in concurrent request processing through a disaster recovery system, and if the system load exceeds a preset threshold value, triggering rebalance of network bandwidth allocation in combination with timeliness deviation to obtain a resource optimization result.

The invention relates to a method and a system for storing and backing up electric power data of a data-in-data station based on use popularity, and belongs to the technical field of data-in-data stations, and the method comprises the following steps: classifying electric power business data into hot data, temperature data and cold data based on preset access frequency, time window and data volume index; according to a classification result and a business importance degree, storing hot data in an SSD cloud disk or a memory database, storing temperature data in a distributed column database, and compressing cold data and then archiving the cold data to an object storage or a tape library; dynamically configuring differentiated backup strategies for different types of data; automatic scheduling of storage and backup is realized through a cloud platform component, and hot switching of master and slave databases, OSS archived data recovery by a DataWorks tool and a remote multi-copy recovery mechanism are supported; and migrating data in advance in combination with a historical access prediction popularity trend, and triggering cold data compression and archiving when storage resources exceed 80%. The method is suitable for power industry scenes, the storage cost is optimized, and the data calling efficiency is improved.

The invention discloses an engineering change management and risk control intelligent decision support system, and relates to the technical field of engineering management, the system comprises the following components: a data acquisition module, a quantum computing simulation module, a risk assessment module, a decision support module and a knowledge base module; according to the invention, through integrating a plurality of modules of data acquisition, quantum calculation simulation, risk assessment and decision support, comprehensive intelligent support of engineering change management is realized, especially the quantum calculation simulation module can rapidly traverse massive change possibility combinations by using superposition and entanglement characteristics of quantum bits, and the change probability is improved. And simulation analysis is carried out in different risk scenes for each combination, so that the decision-making efficiency is greatly improved, a more accurate and reliable change strategy is provided for a decision maker through accurate simulation and analysis, and the risk of decision-making errors is reduced.

Techniques are described for efficient replication and maintaining snapshot data consistency during file storage replication between file systems in different cloud infrastructure regions. In certain embodiments, provenance IDs are used to efficiently identify a starting point (e.g., a base snapshot) for a cross-region replication process, conserve cloud resources while reducing network and IO traffic.

Techniques are described for efficient replication and maintaining snapshot data consistency during file storage replication between file systems in different cloud infrastructure regions. In certain embodiments, snapshot creation and deletion requests that occur during cross-region replications may be temporarily withheld until appropriate times to execute such requests safely, depending on the timing relationship between such requests and cross-region replication cycles.

The invention relates to the technical field of data processing, and discloses a real-time data stream processing method and device, electronic equipment and a storage medium. The method comprises the following steps: distributing a received to-be-processed data stream to a first distributed node through a preset load balancer; fragmenting the received distribution data stream at the first distributed node according to a preset fragmentation rule, then writing the fragmented data stream into a message queue, and performing parallel processing on fragmented data in the message queue through multiple threads; storing the intermediate state data in the fragmented data processing process, generating snapshot data according to the fragmented data, and synchronizing the snapshot data to the distributed storage system; acquiring load data of the first distributed node, and dynamically updating a data flow distribution strategy of the load balancer according to the load data through the resource use prediction model; and writing a processing result of the to-be-processed data stream into persistent storage through an asynchronous batch mechanism, and returning the processing result through a preset real-time communication protocol. The method improves the data stream processing efficiency and stability.