11945results 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.

Examples of file analytics systems are described that may obtain metadata data and events data from a virtualized file server. The file analytics systems may detect one or more events from the events data matching a criteria indicating malicious activity. The file analytics systems may validate the detection of malicious activity. The validation may be performed by comparing the file type, such as the MIME type, of sample files before and after the suspected malicious activity. The systems may recover a share of the distributed file server including the one or more affected files by replacing the one or more affected files with stored versions of the one or more affected files from a snapshot of the share taken prior to the detected malicious activity.

Techniques are described for partitioning B-tree keys of file systems into key ranges for parallel processing in delta generation during file storage replications between file systems in different cloud infrastructure regions. In certain embodiments, a delta generation processing for cross-region replication may utilize a key-range splitting mechanism involving a recursive algorithm that partitions B-tree keys of a source file system into roughly equal-size key ranges. All the partitioned key ranges may be processed in parallel and concurrently by different processing threads, one thread per key range, to improve the performance of the delta generation and achieve scalability.

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.

A recovery storage system asynchronously protecting a synchronously replicated dataset, where the asynchronous protection of the synchronously replicated dataset includes: receiving, by a recovery storage system, an identifier of a synchronously replicated dataset, wherein the synchronously replicated dataset is a dataset synchronously replicated across the plurality of storage systems; asynchronously replicating, on the recovery storage system, the synchronously replicated dataset from the plurality of storage systems; detecting that each dataset in the synchronously replicated dataset on the plurality of storage systems has become unavailable; and restoring the dataset on the recovery storage system.

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 relates to the technical field of information security, and discloses a data security management system and method in an intelligent power grid, and the system comprises a dynamic encryption and key management module which is used for carrying out the encryption and key management of the data of the intelligent power grid, determining an encryption algorithm according to the sensitivity of the data, and generating a corresponding key, the dynamic encryption and key management module transmits encrypted data and key information to the adaptive access control module so as to ensure the security of data access; and the self-adaptive access control module is used for managing access permissions of the user and the equipment and performing dynamic authorization according to the attribute label and the context information. Through dynamic encryption and adaptive key management, an encryption algorithm and a key length are adjusted in real time according to data sensitivity and a network environment, a quantum key distribution technology is combined, high security and quantum attack resistance of a key are ensured, and then security and real-time performance of data transmission are ensured.

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.

The invention belongs to the technical field of data detection, and particularly relates to an efficient data anomaly automatic detection and alarm system. The detection and alarm system comprises a sensor and data acquisition device, a data processing and analysis module, an alarm module, a monitoring center and visual interface, a communication and network module, a storage and backup module, an intelligent decision support module and a user feedback and adjustment module. According to the system, data can be monitored and analyzed in real time, abnormity can be quickly found, an alarm is given, manual monitoring requirements are reduced, data misjudgment is avoided, data accuracy is ensured through data cleaning and processing, an intelligent decision support module provides optimization suggestions based on AI, and a user can adjust rules to adapt to requirements. The system has the functions of data processing, anomaly recognition and trend prediction, the monitoring center visually displays the state, the storage and backup module ensures long-term storage of data, the automatic mechanism improves the response speed and the operation benefits, and the adaptability is high.

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The present invention relates to the field of energy operation technology, and discloses a data security protection system and a fragmented encryption method for an energy operation platform. The system includes data acquisition, processing and security protection modules. The data acquisition module collects multi-source data and assesses risks; the data processing module uses a combination of fragmented encryption and dynamic access control to ensure data security; and the security protection module builds a multi-level protection system. The fragmented encryption method encrypts data in blocks. The invention can accurately assess risks, provide early warnings and prevent them; strengthen data encryption and protect privacy; achieve multi-level protection and resist various threats; and can also improve system performance, adapt to complex scenarios, and effectively solve data security issues on energy operation platforms, ensuring stable operation of the platform and data security and reliability.

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.

The invention relates to the technical field of computers and the field of data testing, in particular to a multivariable real-time measurement and state testing method oriented to a data center. The method comprises the following steps: acquiring original data from a sensor, equipment and an application program in a data center, and filtering, cleaning and formatting the original data to generate preprocessed data; storing the preprocessed data to a distributed file system, and constructing a reverse index and B-tree index structure to realize efficient retrieval and redundant backup; intelligent analysis is carried out on the stored data by introducing a machine learning model and an automatic operation and maintenance strategy, the abnormal state of the key performance index is identified, and the resource use condition is predicted; and based on an intelligent analysis result, executing dynamic scheduling and optimal allocation operation of the resources. Through multi-variable real-time monitoring and intelligent scheduling, the resource utilization efficiency and the operation stability of the data center are improved.

The present invention relates to the field of environmental protection technology, and discloses a sewage treatment system based on intelligent perception and dynamic control, including: a multi-parameter detection module, which monitors the treatment process in real time by collecting and transmitting real-time data; an AI data processing engine module, which analyzes through data processing algorithms and generates process control schemes; a 5G communication module, which is used for data transmission between modules; an intelligent control platform module, which performs automatic adjustment of process parameters; a cloud storage service module, which is used to store system data in the cloud; an equipment automatic maintenance module, which monitors the equipment status through sensor data; an energy and resource recovery module, which is used to recover the heat and gas resources generated during the treatment process for reuse. In the present invention, the AI ​​engine adjusts the aeration amount, dosage and other parameters in the treatment system in real time through comprehensive analysis of historical data and real-time data, realizes fully automatic dynamic control, and greatly improves the efficiency of sewage treatment.

The invention provides a relational database backup recovery method and system based on big data, and relates to the technical field of database backup recovery, and the method comprises the following steps: obtaining total backup data and incremental backup data of a relational database, and obtaining merged backup data; based on the features and the access mode of the merged backup data, constructing a multi-dimensional index structure at the same time, and integrating a data dependency graph, a classification result and cold and hot layering information into a multi-dimensional index; decompressing the target backup data to obtain recovered backup data, and performing intelligent verification on the recovered backup data by using a data integrity constraint rule; if the intelligent verification is passed, the recovered backup data is imported into the relational database, and data recovery at a specified time point is completed; and if the intelligent verification is not passed, intelligent data restoration and complementation are carried out, and then verification, verification and importing are carried out again until the intelligent verification is passed.

The invention belongs to the technical field of vehicle-mounted upgrading control, and particularly relates to a vehicle-mounted entertainment system upgrading method and system based on Flash CAN, the method comprises the steps of hardware preparation, firmware updating file obtaining and upgrading, the upgrading process comprises the steps that current vehicle-mounted entertainment system firmware is backed up, the downloaded firmware updating file is verified, the next step is continued if verification succeeds, and the next step is continued if verification succeeds; and if the verification fails, the firmware update file is downloaded again, a specified Flash storage area is erased, the upgrade management module requests and transmits a data packet of the firmware update file to the embedded control unit, and if the request or the transmission fails, the upgrade management module rolls back to an original system, and the steps of data receiving and verification, restarting and verification are carried out, and verifying whether the functions of the vehicle-mounted entertainment system are normal. According to the invention, the risk of upgrade failure is reduced, the upgrade efficiency is improved, the security of the system is enhanced, and the system abnormity caused by improper upgrade is effectively avoided.

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.

Queries may be made against a non-relational database without impacting the capacities of the non-relational database by enabling performance of queries to a non-relational database via an external query management, backup management, and a non-relational database backup hosted in an external data storage. The query management and backup management of the non-relational database service may split transactional load to the non-relational database and analytical load, and furthermore allow selection of a desired data freshness for queries including various time ranges, such as seconds, minutes, or days.

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.