439 results about "Data restoration" patented technology

The embodiment of the invention provides a computer communication method and system based on the Internet of Things, and the method comprises the steps: constructing a multi-level system architecture, carrying out the preprocessing and feature extraction of an original data flow, recognizing the data characteristics through a time sequence analysis method, and constructing a dynamic data model; deploying a monitoring agent at a transmission node to collect network performance indexes in real time, and constructing a network quality evaluation model; for an incomplete sensor data flow, prior probability distribution is constructed based on a dynamic data model and a network quality grade, and an optimal estimation value of missing data is calculated by adopting a Bayesian reasoning framework and an iterative algorithm; establishing a mapping relation between a network state and an optimal parameter through reinforcement learning to realize self-adaptive adjustment and optimization; and grading the data according to reliability, extracting high-reliability data points as anchor points, designing an iterative refinement algorithm to realize information propagation, and fusing to obtain a complete sensor data stream. According to the method, the problems of poor data recovery accuracy, static parameter configuration and insufficient adaptability in a complex network environment are solved.

The invention discloses a bulk cargo supply chain data transmission method and system based on an intelligent transportation box, and relates to the technical field of logistics data transmission, and the method comprises the steps: collecting multi-modal sensing data in real time through a multi-source data collection interface of the intelligent transportation box; the intelligent transportation box edge calculation module performs cleaning, compression aggregation and key event intelligent identification on the data; a transmission strategy is adaptively selected according to the data type and the network state, a key event alarm message is immediately sent, data are locally cached when the network is interrupted, and subsequent transmission is recovered; transmitting the data to a cloud by adopting a secure communication protocol; the cloud receives management data and provides remote configuration, alarm notification and data visualization functions, the system comprises an intelligent transportation box terminal unit and a cloud service platform unit, data transmission reliability and efficiency are improved, bandwidth and cloud cost are reduced, cargo transportation safety is guaranteed, and the system is suitable for bulk cargo cross-regional transportation scenes.

The invention relates to the field of ocean data buoy data quality control, and discloses a buoy meteorological data restoration method based on a space-time double-attention neural network, and the method comprises the following steps: data collection and preprocessing; establishing a space-time double-attention neural network model, wherein the model comprises a time coding link, a space coding link and a feature fusion layer; model training: inputting the time features containing the target elements to be predicted into a time coding link, inputting the meteorological elements into a space coding link, and outputting the target elements at the moment to be predicted by the model; after model training, evaluating the model by using the test set; and predicting a vacancy value or an abnormal value in the multi-dimensional meteorological element data acquired in real time by using the model which is qualified in evaluation, so as to realize data restoration. According to the method disclosed by the invention, time and space constraints are effectively combined, and abnormal values and vacant values in the meteorological data are predicted and replaced, so that the meteorological data are repaired.

The invention relates to the technical field of electric leakage detection, in particular to an underground coal mine low-voltage remote electric leakage test system and method, and the system comprises a task allocation module, an off-state discrimination module, a signal transmission module, a state recognition module and a path receipt module. According to the method, the loop load level and the channel response condition are accurately evaluated, the task channel is dynamically matched, the task distribution blind area and error are reduced, the impedance difference structure and the continuous feedback trend are integrated to recognize the abnormal branch segment, and the driving and decoding mode is intelligently adjusted by using the signal amplitude and the interference proportion relation; signal transmission stability and data reduction accuracy are improved, a waveform drift trend and a load state combination are calculated in real time to accurately detect an electric leakage position, task initiation and response nodes are compared to ensure path synchronization legality, efficient linkage between load state change and signal drift is realized, electric leakage detection response speed and precision are improved, and reliability of electric leakage detection is improved. And the line abnormal state discrimination accuracy is enhanced.

The invention discloses a distribution network dynamic topology identification and self-healing control method and device, and the method comprises the steps: collecting operation data of a distribution network, carrying out the time synchronization processing, data quality evaluation and data restoration, and obtaining a high-quality network state data set; based on the data set, performing dynamic topology identification by adopting a path improved by a relaxation method and a forest enhancement algorithm to obtain a dynamic network topology model; constructing a backpack network model by adopting a cyclic data generation method, and evaluating distributed photovoltaic output and load conditions; network abnormity is monitored in real time, and a machine learning algorithm is adopted to assist in judging fault types, positions and influence ranges; power supply reliability and economy are comprehensively considered, and a multi-objective optimization algorithm is adopted to generate a switch operation sequence; switching operation is executed, a temporary micro-grid is formed or a power supply path is adjusted, and the execution effect is monitored and evaluated in real time. According to the method, the dynamic topology of the high-proportion distributed photovoltaic power distribution network can be accurately identified, and fault positioning and efficient self-healing control are realized.

Embodiments of the present disclosure are directed to a cloud-based vehicle monitoring platform (CVMP) configured to manage events impacting the operation of a plurality of vehicles. An onboard vehicle monitoring system associated with a particular vehicle is communicably coupled to CVMP centralized services and can determine corrective actions to employ to mitigate the events. The corrective actions in some contexts are generated by a rules engine based on a rule set generated by a federated learning model associated with the CVMP centralized services. If the onboard vehicle monitoring system determines that a first set of corrective actions generated based on a first set of rules is unsuccessful at mitigating the events, the onboard vehicle monitoring system can request a secondary set of rules from the CVMP centralized services. Various data recovery operations in some contexts are employed to revert the vehicle from a problem state back into a steady state.

The invention relates to the technical field of data backup, and discloses an iOS device data backup and recovery method and system, and the method comprises the steps: carrying out the SSL certificate verification and EscrowBag key authentication of an iOS device, and obtaining a data path list; according to the data path list, parallel scanning of APFS file system B-Tree traversal, NAND flash memory page reading and SQLite WAL log analysis is carried out on the iOS device storage partition, and an original data set is obtained; performing integrity evaluation based on the original data set to obtain an effective data set and constructing a virtual file index; the data selection list of the user is confirmed based on the virtual file index, safety recovery is executed, and a data recovery result is returned. The SQLite page Cell reconstruction and fragment file signature matching technology is combined, intelligent repair and quality prediction can be conducted on damaged data, the user experience and operation efficiency are improved, and the user experience is improved. And the security and the reliability in the data recovery process are ensured.

The invention discloses a laser wind-finding radar missing measurement data filling method based on a DFA scale index, and the method comprises the following steps: (1) carrying out the standardization processing and quality control of the original data of a laser wind-finding radar, and obtaining the wind speed standardization data; step (2), performing detrending fluctuation analysis on the wind speed standardized data and calculating a DFA scale index; combining the wind speed standardized data with the DFA scale index to construct wind speed data containing the DFA index; (3) dividing the wind speed data containing the DFA index into a training set and a test set; performing machine learning model training by using the training set to generate a missing data filling model; and step (4), filling the missing measurement data of the laser wind finding radar by using the evaluated missing measurement data filling model. The method can solve the problem that the existing laser wind-finding radar missing measurement data filling method depends on artificial experience and cannot fully utilize the internal physical mechanism of the wind speed, so that the data restoration precision is not ideal.

The invention provides an offshore wind turbine abnormal data recovery and risk early warning method and system, and the method comprises the steps: receiving the multi-modal monitoring data of an offshore wind turbine in real time, the multi-modal monitoring data comprising time domain sensor data, frequency spectrum images and environmental parameters; inputting the multi-modal monitoring data into a pre-trained visual language large model, and inputting the frequency spectrum image into a visual encoder of the visual language large model to extract frequency domain features; time domain sensor data and environmental parameters are input into a language encoder of the visual language large model to generate time sequence features and semantic features; and performing cross-modal fusion on the frequency domain features, the time sequence features and the semantic features through a cross-modal fusion module of the visual language large model to obtain cross-modal fusion features, and synchronously executing abnormal data recovery and risk early warning based on the cross-modal fusion features to obtain recovered complete monitoring data and a risk early warning result. According to the method, time domain and frequency domain data are combined, and the multi-mode large model is utilized to realize high-precision data restoration and intelligent risk early warning.

The invention relates to the technical field of big data, and provides a data verification method and device in big data cluster migration, equipment and a medium, and the method comprises the steps: obtaining a source data cluster and a target data cluster; dividing the source data cluster and the target data cluster into a plurality of data layers according to a standard data warehouse layering strategy, and binding different data comparison strategies for each data layer; establishing a fingerprint baseline table and constructing a sampling model feature library; performing data fragmentation processing on each data layer, and controlling each data layer to execute a data comparison task in parallel; and if any data layer of the target data cluster and any data layer of the source data cluster are detected to have difference, recording difference data and performing data restoration. According to the method, the source data cluster and the target data cluster are subjected to hierarchical parallel fragmentation comparison, a plurality of fragments of each layer are subjected to data comparison tasks in parallel at the same time, different comparison strategies are bound to the data layers, the comparison process can be achieved, and the overall data processing efficiency is improved.

The invention relates to the technical field of electric energy metering anomaly detection and diagnosis analysis, in particular to a dynamic return and compensation method and system based on an electric energy metering data restoration model, and the method designs a distortion traceability algorithm of multi-dimensional feature fusion and improved SVM (Support Vector Machine), and extracts the characteristics of load features, environment features, packet loss rate, sampling delay distortion and the like. And load fluctuation-dominated distortion, environment interference-dominated distortion and coupling-dominated distortion can be accurately positioned. For multi-source distortion restoration, a load self-adaption-environment compensation-coupling correction three-level restoration model is provided, and through differential compensation of different load types, dynamic calibration of environment parameters such as temperature and humidity, electromagnetic interference and the like and coupling coefficient matrix optimization, the measurement data restoration precision is stably maintained to be 98% or above. According to the invention, through the research on the metering data distortion traceability-coupling distortion data reduction-dynamic return and supplement calculation-return and supplement result closed-loop optimization method, the precise repair of metering errors and the scientific calculation of electric quantity return and supplement in a complex scene are realized.

The invention discloses a distributed file backup method for a sonar database system, which belongs to the technical field of sonar data processing and comprises the following steps: acquiring file data containing underwater acoustic signal data from the sonar database system; fragmenting the file data to generate data blocks and dynamically adjusting the sizes of the data blocks; generating a unique identifier for the data block based on the content hash value and the metadata; selecting distributed storage nodes based on availability, storage capacity, network delay and geographical distribution factors; the transmission data block uses an encryption protocol; updating the storage position and state of the backup metadata record; periodically verifying the integrity and availability of the backup data; executing a data recovery process to recover the original file data; the dynamically adjusted backup policy is based on real-time operation requirements and data importance levels. According to the method, through distributed storage and dynamic optimization, the backup efficiency, the data security and the system reliability are improved, and the method is suitable for sonar data management in an underwater detection system.

The invention discloses a wireless network data reliability transmission system, which is used in the technical field of data transmission and comprises a slave gateway deployment and data aggregation module. A scheduling protocol and priority management module; a hierarchical caching and breakpoint resuming module; and a trend prediction optimization decision module. According to the invention, in combination with priority-based scheduling, link quality evaluation, data caching and retransmission mechanisms, reliable transmission of data with different priorities in a wireless network can be ensured, and normal transmission can be recovered through an efficient data recovery mechanism when the network is unstable; through real-time environment state estimation and prediction, communication parameters and resource scheduling can be dynamically optimized, network performance is effectively improved, and communication delay is reduced. The hidden state of a communication environment is estimated in real time through comprehensive analysis of link quality, event records and cache state data, future hidden state changes are predicted through a time sequence convolutional network, and potential performance degradation risks are recognized in advance.

The invention discloses a data recovery method, device and equipment, a medium and a program product, and relates to the technical field of computers. The data recovery method comprises the following steps: analyzing a pre-write log WAL file in a database to obtain a log frame in the WAL file; the log frame comprises modification information, a page number, transaction metadata and page mapping information for a database page; according to the transaction metadata in the log frame, performing boundary identification on the modification information of the database page to obtain at least one item of transaction information; and according to the transaction information, the page number, the page mapping information and a data structure of the database, performing data recovery in the database to obtain a recovered database. According to the technical scheme provided by the embodiment of the invention, the problem of data loss easily occurring during data recovery can be solved by utilizing the characteristic that the WAL file is independently stored and is not easy to lose.

The invention provides a data backup and recovery method and electronic equipment, which can be applied to the technical field of information. The data backup and recovery method comprises the following steps: in response to a detected backup trigger event, performing multiple times of backup on target data based on a preset backup strategy corresponding to the backup trigger event to obtain multiple pieces of backup data and backup metadata for respectively indexing the multiple pieces of backup data; storing the plurality of backup data in at least one of a local storage medium and a remote backup server, and storing the plurality of backup metadata in the remote backup server; in response to the detected backup recovery event, generating multiple pieces of candidate backup version information based on the multiple pieces of backup metadata; and in response to a recovery instruction for target backup version information in the multiple pieces of candidate backup version information, recovering target backup data corresponding to the target backup version information based on target backup metadata corresponding to the target backup version information.

The invention relates to a local-global association learning-based multi-modal power data restoration method, which comprises the steps of obtaining initial data and performing preprocessing to obtain a power data abnormal value, the initial data comprising power operation data, external association data and multi-modal data; based on the preprocessed power operation data and the power data abnormal value, local association learning is carried out, local data association is mined, and a local restoration scheme of the power data abnormal value is obtained; performing global association learning based on the preprocessed initial data and the abnormal value of the power data, mining a cross-time and cross-source global association rule, and obtaining a reasonable correction range of the abnormal value of the power data; and generating a power operation data restoration result in combination with the local restoration scheme and the reasonable correction range. Compared with the prior art, the method has the advantages that the data exception can be accurately recognized and repaired, and the data repair accuracy is greatly improved.

Data recovery in a virtual storage system, including: detecting, within storage provided by a first tier of storage of the virtual storage system, data loss within a dataset, wherein recovery data for the dataset is stored in a second tier of storage; determining a recovery point for the dataset up to which a consistent version of the dataset is recoverable from the recovery data stored in the second tier of storage; and restoring, within the storage provided by the first tier of storage of the virtual storage system, the consistent version of the dataset.

Mechanisms are provided for identifying clean backups of a monitored computing system. A backup of a state of a computing system is generated. In response, these operations are performed: alert data associated with the monitored computing system is retrieved, where alert data is stored in response to patterns of data accesses indicating a possible cyber-attack on the computing system; a classification engine executes a classification operation on the alert data to determine if it indicates that the state of the monitored computing system was the target of a cyber-attack within a predetermined period of time prior to a time the backup was generated; if so, a first tag is generated for the backup indicating the backup to be corrupted; if not, a second tag for the backup is generated indicating the backup to be clean; and the first or second tag is stored in association with the backup.

The invention provides a cloud platform data backup method and system based on an organizational structure, and relates to the technical field of data backup, and the method comprises the steps: obtaining a hierarchical relationship of the organizational structure, generating an organizational structure tree, and constructing a backup label set; receiving a data backup request of an organization node, obtaining a corresponding backup strategy set, and performing dynamic load balancing backup; and establishing a multi-dimensional data backup security mechanism based on the security policy, performing hierarchical encryption processing on the to-be-backed-up data, and performing data recovery in combination with the block chain distributed storage account book. According to the invention, efficient dynamic load balancing and multi-level security assurance of data backup are realized.

The invention discloses a data exception processing platform, a data exception processing method, equipment and a storage medium, and relates to the technical field of data processing, and the data exception processing platform comprises a data collection module which is used for collecting atomic operation information and concurrent transaction information of incremental data and performance indexes of business processing nodes; the data writing and synchronizing module is used for writing the incremental data into a target database according to the data type of the incremental data and carrying out data synchronization and consistency verification operation; the monitoring and alarm module is used for deploying a monitoring and log recording mechanism on each business processing node, generating a log record corresponding to the written incremental data, and sending an alarm push when abnormal data is detected; and the data recovery module is used for carrying out data recovery on the abnormal data according to the atomic operation information, the concurrent transaction information, the performance indexes and the log records so as to realize abnormal data processing.

The invention relates to the field of data access, and discloses a document security access method and device based on a block chain, equipment and a storage medium. The method comprises the following steps: storing identification information and encrypted data characteristics of a document in the block chain; setting a permission configuration strategy of the document through a preset configuration logic of the block chain; performing identity and permission verification on the user based on the permission configuration strategy, and storing verification-related interaction information in the block chain; and performing data recovery or integrity verification on the document based on the encrypted data features and the identification information stored in the block chain. According to the document security access method based on the block chain provided by the invention, the security and reliability of document storage are improved and auditing and recovery processes are simplified by utilizing the characteristics of decentralization, tampering resistance and transparency of the block chain.

The invention relates to the technical field of automatic data processing of integrated circuit design, in particular to an automatic parasitic parameter result analysis system based on an FUZZ test. The fuzzy test engine module adopts syntax tree variation to generate a test case library comprising illegal characters, extreme values and logic loops, the exception response module captures illegal operations in an analysis and simulation stage through the probe detection unit, and the correction decision unit executes data restoration or generates an exception report. And the incremental updating unit synchronizes the abnormal features to a model self-optimization module rule base. And the dynamic adaptation module converts the multi-process parasitic parameters into a standardized simulation model through the process parameter abstraction layer. All the modules form a closed-loop system for test case generation, anomaly detection and repair and rule dynamic optimization, automatic processing of an unexpected input scene is achieved, and robustness and multi-process compatibility of parasitic parameter analysis are improved.

The invention discloses a computer multi-cloud data security backup method and system based on scene security, and the method comprises the steps: carrying out the risk assessment based on scene parameters, and formulating a security backup strategy; the method comprises the following steps: processing cloud data through adaptive fragmentation encryption, and dividing the cloud data into core data and non-core data; based on the relevance between the fragmented data, adding an identifier; performing evaluation based on the security index data of the multiple cloud storage nodes of each cloud storage platform to obtain the cloud storage platform meeting the storage requirement; through intelligent routing distribution, the encrypted data fragments are distributed and stored in the heterogeneous cloud storage platform, and it is ensured that the fragments stored in any single cloud platform are insufficient to reconstruct complete data; and constructing a multi-factor data recovery mechanism based on a data exception condition, and implementing dynamic migration on the fragments of the high-risk region. The method has the advantages that the flexibility and recovery capability of data backup are effectively improved through adaptive fragmentation encryption and intelligent routing distribution, and potential security threats are avoided.

The application is suitable for the technical field of data storage, and provides a data disaster recovery method and device, comprising the following steps: determining a fault type in a distributed cluster system and a range of data to be recovered; acquiring state parameters of multiple normal nodes in the distributed cluster system, and service sensitivity features and data block access features of the data to be recovered; performing data preprocessing operations; respectively adopting a service sensitivity evaluation model and a node state evaluation model to determine a data recovery priority list and a node priority list; determining a fault processing strategy according to the data recovery priority list and the node priority list; and executing the fault processing strategy. Through the above method, the real-time evaluation algorithm of the heterogeneous big data node is increased on the basis of dynamically updating the data recovery priority based on the service sensitivity evaluation model, and compared with the traditional technology, the fault perception of the business system can be reduced while the data disaster recovery efficiency is improved.

The invention relates to a geological disaster abnormal data cleaning method and system, and belongs to the technical field of geological disaster monitoring data processing. According to the method, the data stream is locally divided by setting the sliding window, and the abnormal data are effectively identified by comprehensively utilizing the relevance and the time sequence characteristics among the data. According to the method, firstly, data distribution is dynamically monitored, abnormal points are accurately detected based on abnormal probability calculation and joint abnormality judgment, and abnormal data are further classified and analyzed to identify potential types and causes of abnormality. By improving a dynamic threshold value selection strategy and a dependency relationship representation method, the detection accuracy and applicability of the algorithm are remarkably improved. Finally, efficient cleaning of abnormal data is realized, and the quality and reliability of geological disaster data are improved. According to the method, the accuracy and scientificity of the geological disaster early warning system are enhanced, important technical support is provided for subsequent data restoration and risk assessment, and the method has a wide application prospect.

The invention provides a MySQL database rapid recovery method based on a Bin log, and relates to the technical field of database management and data recovery, and the method comprises the following steps: screening and extracting log content in a corresponding time period from the Bin log of a MySQL database according to a time interval of occurrence of misoperation specified by a user; the extracted log content is converted into a text format through an analysis tool, and the log content recorded in the ROW format is selected as a processing object; processing a DELETE operation record in the log content, converting a DELETE statement into an INSERT statement, and generating an SQL script used for recovering the deleted data; processing the UPDATE operation record in the log content, adjusting the sequence of a WHERE clause and an SET clause of a UPDATE statement, and generating an SQL script used for recovering updated data; and importing the generated SQL script for recovering the deleted data and the SQL script for recovering the updated data into a target database, and executing the scripts through a database client tool to complete data recovery operation.

Embodiments of the present description provide data backup, restoration and query methods and apparatuses for a distributed database. The data backup method comprises: writing, into a backup medium, a plurality of first data blocks to be backed up of database nodes of a distributed database, wherein the plurality of first data blocks come from a plurality of SST files corresponding to a plurality of data shards that the database nodes are responsible for managing; for each target SST file among the plurality of SST files, constructing a first index tree on the basis of first metadata of each first data block of the target SST file and a first address on the backup medium, writing the first index tree into the backup medium, and writing second metadata of the target SST file into the backup medium, wherein the second metadata comprises a root node address of the first index tree; and constructing a second index tree on the basis of a second address of each second metadata on the backup medium, and writing the second index tree into the backup medium for data restoration.

The invention relates to the technical field of intelligent financial accounting, and discloses an intelligent financial accounting system based on multi-dimensional data verification, comprising: S1, a multi-dimensional data verification module for performing real-time multi-dimensional data verification on a financial accounting system, comprising a business rule dimension verification unit, a time sequence continuity dimension verification unit, a multi-source association dimension verification unit and a risk scanning dimension verification unit. According to the intelligent financial accounting system based on multi-dimensional data verification, real-time risk scanning is realized through the multi-dimensional data verification module, threats can be rapidly isolated and resource utilization can be optimized in combination with hierarchical response control and a dynamic resource allocation mechanism when risks occur, and meanwhile, through trap data generation and a multi-dimensional two-way verification technology, the risk can be effectively eliminated. The method effectively improves the emergency disposal capability and data recovery reliability of the system in a complex risk scene, and has the advantage of dual improvement of financial data safety protection and system operation efficiency.

A method for restoring data in a flash memory-based file system according to an aspect of the present disclosure includes receiving a file system image file; analyzing a metadata structure of the image file; and generating a target file to be restored based on the analysis of the metadata structure.

The invention discloses a local data updating method and system in a local database, relates to the technical field of data management and security, and realizes user identity authentication by acquiring electrocardiogram and environment data through an edge computing node to generate a dynamic permission token. And then, the system verifies and authorizes a database updating request, and performs data fusion analysis by adopting federated learning and a deep neural network to ensure the security and accuracy of data updating. In the data updating process, the system uses chaotic encryption and a trusted execution environment to guarantee the confidentiality and integrity of data. And after updating, broadcasting an operation record through a block chain network, and performing auditing and rollback control by using zero-knowledge proof and a smart contract. And finally, ensuring the integrity and non-tampering property of data recovery by adopting a hash chain technology. Through multi-technology fusion, the security, reliability and efficiency of the data updating process are improved.