622 results about "Data restoration" patented technology

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 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 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.

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 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 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 discloses a squeeze casting forming quality detection device and system, and belongs to the field of quality detection.According to the shape and size of a squeeze casting, ultrasonic emission signal parameters are adjusted, and self-adaptive preprocessing is conducted on received ultrasonic echo signals; according to the method, data fusion and feature extraction are carried out on ultrasonic echo signals received by multiple channels, abnormal grid units are screened, the abnormal types of the abnormal grid units are identified through a deep learning model, then abnormal areas are divided, and the molding quality score of the extrusion casting part is obtained through comprehensive evaluation on the abnormal areas. Carrying out qualification judgment on the extrusion casting part; detection data is stored, managed and maintained, the detection data is classified, filed and indexed by constructing a product database, a squeeze casting quality tracing path is generated, and data backup and data recovery are regularly performed on the product database; the objective of the invention is to solve the problems of accurate and efficient quality detection and effective data management of extrusion castings.

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 discloses a land utilization dynamic change monitoring method and system based on multi-modal remote sensing data, and relates to the technical field of multi-modal remote sensing data analysis. The system comprises a data acquisition and preprocessing module, a consistency baseline construction module, a real-time data analysis module and an abnormality diagnosis and processing module. The data acquisition and preprocessing module realizes space-time alignment of multi-source data; the consistency baseline construction module divides land utilization types based on geographic features and establishes a baseline database; the real-time data analysis module screens suspected abnormal areas through difference indexes; the abnormity diagnosis and processing module distinguishes data quality abnormity and emergency abnormity based on the characteristic association relation, the data quality abnormity triggers data restoration and verification, and the emergency abnormity marks a high-priority area and outputs position information. According to the system, through multi-modal data fusion and correlation analysis, high-precision monitoring and abnormal intelligent classification of land utilization changes are realized.

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 data backup method and device, a storage medium and a program product, and relates to the technical field of cluster storage, and the data backup method comprises the following steps: in response to a snapshot creation request, sending a snapshot generation instruction to a first cluster; the snapshot generation instruction is used for indicating the first cluster to determine a target volume and creating a first bitmap for the target volume; the first bitmap is used for identifying the copy state of the data in the target volume; creating a second bitmap for the source volume; wherein the second bitmap is used for identifying the first write-in state of the data after the snapshot is created in the source volume; executing a snapshot creation operation on the source volume to obtain a snapshot corresponding to the source volume; according to the snapshot and the second bitmap, data backup is realized, and the first bitmap and the second bitmap are updated, the source volume and the target volume of the snapshot are isolated on different clusters, when a cluster-level fault occurs, data recovery of the clusters can be realized, availability of partial functions of the clusters is ensured, and disaster recovery capability of the clusters is improved.

The invention relates to the technical field of health data processing, in particular to a cross-device health data fusion method, which comprises the following steps of monitoring a device connection state, identifying data missing and resending, calibrating multi-device time and performing data difference, calculating a device stability score to adjust confidence fusion data, extracting periodic health parameters and analyzing a change trend. And detecting the factor activation state and combining the sensitivity to obtain a risk score, and outputting a health processing result. According to the method, through combination of equipment connection state identification and data caching progress calculation, the precision of breakpoint data recovery is enhanced, a timestamp alignment and interpolation correction mechanism is adopted, continuous compensation of multi-source data is realized, the data weight is adjusted by using a stability score, the credibility of fused data is improved, and the reliability of the fused data is improved. In combination with behavior characteristic trend extraction and risk factor sensitivity determination, the accuracy and individualized distinguishing ability of chronic disease risk identification are optimized, and the individualized health management and disease prevention ability is enhanced.

The invention discloses a 4HPU-based natural language character and numerical value general solution system and method and application thereof, and aims to solve the technical problem of realizing unification, universality and reasonability of natural language character and numerical value codes. A main coding path for realizing coding generation and structure nesting is arranged; the method comprises the following steps of: constructing a basic coding unit, judging the types and codes of numerical values, letters, Chinese characters or symbols; compared with the prior art, the method has the advantages that natural numbers, letters, Chinese characters and symbols are subjected to structured coding in a right-to-left nesting mode, complete coded word strings are obtained, structural expression and path reasoning are clear, dynamic expansion and compression can be achieved, and the coding efficiency is improved. And a more excellent basic coding method is provided for application scenes of data recovery and reverse recognition, natural language processing, intelligent communication and low-power-consumption equipment.

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.

A system and method for compressing and restoring data using a hierarchical multi-level autoencoder architecture and correlation network. The system compresses data using a cascade of autoencoders, each focusing on different scales or features, allowing for efficient representation across various resolutions. Data restoration employs a corresponding hierarchical decoder structure and a correlation network, trained on cross-correlated data sets. This approach leverages inter-data relationships at multiple scales, potentially recovering more lost information than traditional single-scale methods. The hierarchical structure adapts to diverse data types, achieving higher compression ratios while maintaining data quality, applicable to fields such as remote sensing, IoT data processing, and multimedia compression.

The invention discloses a quantum resistance data encryption system and method based on biological characteristics, and the method comprises the steps: a data collection module obtains various biological characteristics and environment data of a user in real time, carries out the filtering of a preprocessing module, achieves the optimal fusion of multi-dimensional biological information through a multi-modal fusion module, and carries out the encryption of the multi-dimensional biological information; and the feature extraction module dynamically adjusts an extraction strategy to generate a biological feature template. And the key generation module generates a dynamic key in real time according to the template, and performs quantum resistance encryption fragmentation on the original data through the data encryption module. And the fragment recombination module completes data restoration after verifying the consistency of the biological characteristics and the secret key. According to the method, multi-source biological and environmental information is fully fused, highly personalized and dynamic data encryption and secure recombination are realized, the data confidentiality and the anti-attack ability under the background of quantum computing are effectively improved, and the method is suitable for a data protection scene with a high security requirement.

A data restore system is provided. The data restore system includes a backup data storage configured to store data for a client and a data restore module configured to receive a restore trigger from the client and to initiate restore operation for selected data from the backup data storage in response to the received trigger. The data restore module is further configured to receive information regarding the selected data to be restored and access a metadata store to receive metadata information for the selected data and provide the metadata information and the downloaded data blocks to a controller to facilitate sorting of the downloaded data blocks based on the files they belong to and store the downloaded restored data to a target data storage. The data restore module is further configured to interact with the checkpointing module to track the progress of restore operation in persistent storage and to minimize rework when restore operation is restarted from interrupt.

The invention discloses a data backup and recovery method, which relates to the technical field of data backup and recovery, and comprises the following steps: S1, pre-analyzing data to be backed up; s2, connecting a storage medium; s3, data transmission backup; s4, data backup recording; and S5, data recovery. According to the data backup recovery method, a plurality of independent storage media are adopted, only one medium is connected with a computer at the same time, the media are physically isolated, data are not intercommunicated, even if a certain medium is damaged, backup data of other media are still intact, and a single-point fault is avoided; the physical movement requirement is reduced to prevent the storage medium from being lost, all data blocks of a single file can be ensured to be completely stored in the same medium by analyzing the file structure and the incidence relation in advance, it is ensured that the data logic relation can be completely restored during concentrated storage and recovery of related files, and the incidence failure caused by scattered storage is avoided.

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 Internet of Things security, in particular to an Internet of Things vulnerability data restoration system, which is used for solving the problems that in the prior art, multi-dimensional vulnerability features cannot be accurately extracted and propagated, potential vulnerabilities cannot be efficiently identified, high-risk vulnerabilities cannot be preferentially processed according to a dynamic resource adjustment strategy, and the vulnerability data restoration efficiency cannot be improved. And the accuracy, the automation level and the response efficiency of vulnerability management are reduced. According to the method, a heterogeneous relation graph between equipment and components is constructed through the vulnerability identification and positioning module, multi-dimensional vulnerability features are accurately extracted and propagated, efficient identification of potential vulnerabilities is realized, priority ranking is performed in combination with CVSS scores and equipment importance, high-risk vulnerabilities are preferentially processed through scientific scores and a dynamic resource adjustment strategy, and the efficiency of vulnerability identification is improved. And the to-be-fixed vulnerability list containing multi-aspect information is generated, so that the vulnerability management accuracy, the automation level and the response efficiency are remarkably improved.

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 relates to a data recovery method and device, computer equipment, a storage medium and a computer program product. The method comprises the following steps: receiving a data reading command, and determining protection information of target data corresponding to the data reading command; the APP domain information in the protection information comprises bad sector mark BST information corresponding to a sector where the target data is located; under the condition that the BST information is damaged, verification information corresponding to the APP domain information is determined in the APP domain stripe; verifying the APP domain information according to the verification information to obtain recovered APP domain information; the recovered APP domain information contains the recovered BST information. By adopting the method, the data recovery efficiency can be improved.

The invention discloses a data rescue and archiving system based on a cloud platform, and relates to the technical field of data management, the system is composed of a plurality of functional modules, and the system comprises a data sensing and acquisition module which acquires operation data in real time by deploying an edge computing node, the operation data comprises sensor data, business logs and historical fault data, and the data sensing and acquisition module is used for acquiring the operation data; a transmission link is encrypted by a hybrid encryption technology and is transmitted to a cloud end in a standardized manner through a unified data interface; the digital twinborn simulation module is used for generating a multi-dimensional data state vector model based on a lightweight modeling engine, integrating a spatio-temporal data fusion algorithm to dynamically update a twinborn state, introducing a preposed disaster condition of a virtual-real interaction test, simulating an execution effect of a data recovery plan under different disaster conditions through a Monte Carlo method, and performing disaster spreading simulation; and the reinforcement learning optimization module adopts a deep reinforcement learning model.

The invention discloses a distributed new energy data scheduling processing method based on cloud side-end cooperation, and relates to the technical field of distributed new energy data analysis. The method specifically comprises the following steps that a cloud computing center outputs a task scheduling strategy adaptive to task processing capacity of each edge node to terminal equipment based on edge node information, terminal equipment information and an edge node task scheduling model; the terminal device uploads the distributed new energy operation data to the corresponding edge node according to the task scheduling strategy and the encryption strategy; and the edge node performs abnormal data identification and correction on the distributed new energy data based on the abnormal data detection model and the abnormal data restoration model. The method has the beneficial effects that the transmission time is shortened, the data processing efficiency is improved, and the timeliness and accuracy of data output are improved.

A system and method for compressing and restoring data using a hierarchical multi-level autoencoder architecture and correlation network. The system compresses data using a cascade of autoencoders, each focusing on different scales or features, allowing for efficient representation across various resolutions. Data restoration employs a corresponding hierarchical decoder structure and a correlation network, trained on cross-correlated data sets. This approach leverages inter-data relationships at multiple scales, potentially recovering more lost information than traditional single-scale methods. The hierarchical structure adapts to diverse data types, achieving higher compression ratios while maintaining data quality, applicable to fields such as remote sensing, IoT data processing, and multimedia compression.