825 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 relates to the technical field of tea leaf detection, and discloses a tea leaf quality grade detection method and system.The method comprises the steps that a tea leaf sample is collected and pretreated; extracting chemical component indexes and physical properties of the pretreated tea leaves, and performing fusion and analysis; and establishing a tea quality grade classification model, inputting characteristics of a tea sample to be detected, outputting a detection result, and generating a tea quality grade report. Through combination of the support vector machine, the gradient boosting decision tree and the improved logistic regression algorithm, the precision of the tea quality classification model is optimized. The dynamic threshold function and the segmented excitation function are introduced to further solve the boundary fuzzy problem of the high-grade tea leaves and the low-grade tea leaves, and accurate judgment of tea leaf grade classification is achieved. And data restoration is carried out through weighted compensation correction and a recheck process, so that the output tea grade report is ensured to have high accuracy and reliability.

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 invention discloses an omnibearing risk early warning monitoring method and system based on intelligent wearable equipment, and relates to the technical field of health management, and the method comprises the steps: collecting and compressing multi-modal data through the intelligent wearable equipment, transmitting the compressed data to a central database, and carrying out the data restoration; multi-modal data analysis is carried out on the restored multi-modal data, comprehensive features are extracted, and a logic regression model is constructed to evaluate the health score of the user. According to the invention, the intelligent wearable device collects and compresses the multi-modal data, and transmits the compressed data to the central database for data restoration; performing multi-modal data analysis on the restored multi-modal data, extracting comprehensive features, and constructing a logic regression model to evaluate health scores of the users; the data processing and transmission efficiency is improved, the accuracy and response speed of health monitoring are improved, and the accuracy of data analysis is enhanced.

The invention discloses an anomaly detection and repair method for non-Gaussian multi-mode sensor data, and belongs to the technical field of unmanned system multi-source autonomous navigation. The method solves the problems that an existing method is poor in performance when facing non-Gaussian data, anomaly detection is difficult to carry out on high-dimensional unstructured data, the robustness of a static anomaly detection model is insufficient, and redundancy compensation is lacked when abnormal data of a sensor is repaired. The method specifically comprises the following steps: step 1, acquiring IMU, GNSS, point cloud and image data of an unmanned system, and then carrying out preprocessing and time synchronization on the acquired data; step 2, respectively extracting features of IMU, GNSS, point cloud and image data at each acquisition moment after time synchronization; step 3, fusing the data features at each acquisition moment; and 4, performing anomaly detection on the data at each acquisition moment according to a fusion result, and further performing data restoration if the data is detected to be abnormal. The method can be applied to data anomaly detection and repair.

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 data backup recovery method and device based on data merging, and the method comprises the steps: obtaining a first data snapshot of initial full backup data; the first data snapshot is mounted to a local database, so that the initial full-amount backup data is backed up in the local database in a full-amount mode; when incremental backup data is obtained, merging the incremental backup data to the first data snapshot to obtain a second data snapshot; uninstalling a mounting point of the local database, and backing up the synthesized total backup data corresponding to the second data snapshot in the local database; and when a data recovery instruction is obtained, recovering data from the local database based on the data recovery instruction to obtain target recovery data. According to the method and the device, relatively high backup efficiency and relatively low recovery complexity can be realized at the same time.

The invention discloses a one-way optical shutter video stream transmission method based on weak supervision semantic segmentation, and belongs to the technical field of one-way optical shutter information transmission. Comprising the following steps: carrying out key region and background region classification and marking on all video data received by an optical shutter outer end through a weak supervision semantic segmentation model; performing priority division on a key region and a background region in the video frame; dividing the data packets according to time windows, and packaging the data packets; performing dynamic data compression and segmented coding on the packaged data packet; reed-Solomon redundant coding is carried out on all the data packets of the key area after compression coding is carried out on the data packets of the key area; dynamically adjusting the transmission sequence of the data packets to perform link scheduling; and a receiving end carries out de-encapsulation and data restoration, and reconstructs the restored video frame. According to the method, redundancy and compression of data transmission contents are accurately controlled, so that the transmission efficiency and reliability are remarkably improved, the bandwidth utilization is optimized, and the transmission reliability and stability are enhanced.

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 technical field of block chains, and discloses a block chain node fault recovery method and a block chain system.The block chain node fault recovery method comprises the following steps that S1, the fault state of a block chain node is detected through a fault detection and notification module, whether a target node needs to be recovered or not is judged, and a recovery request is broadcasted; s2, through a recovery strategy generation module, generating initial probability distribution based on the recovery demand of the target node and the bandwidth and load state of the normal node, and determining a data distribution strategy of the normal node; and S3, according to the response state of the normal node, the probability distribution is adjusted through a dynamic adjustment and optimization module. According to the method, the problems of low node fault recovery efficiency, non-uniform task distribution, poor transmission reliability and long verification time consumption in the prior art are solved by dynamically adjusting probability distribution optimization, data blocking and multi-path parallel transmission, redundant coding combined consistency verification and a hierarchical priority strategy; and the efficiency and adaptability of data recovery are greatly improved.

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 provides an abnormal data detection and recovery method and system for reports of a water plant management and control platform, and relates to the technical field of water plant data detection.The abnormal data detection and recovery method comprises the steps that report data of the water plant management and control platform are collected, then the threshold range of the report data is obtained through threshold detection, and the report data exceeding the threshold range are marked as potential abnormal data; performing exception scoring on each potential exception data in the report data by adopting an exception detection model, and then marking the potential exception data with an exceptional exception scoring result as burr data points; and deleting each burr data point in the report data, and then estimating missing data points by utilizing a linear relationship between adjacent data points of the deleted burr data points to carry out linear interpolation so as to carry out data recovery on the report data. The method has the beneficial effects that the accuracy and stability of data analysis of the water plant management and control platform are improved through multi-stage anomaly detection, and efficient detection and recovery of abnormal data are realized through combination of statistical analysis and machine learning technologies.

The invention discloses a table data recovery method based on an OCR model, and the method comprises the steps: S1, recognizing table types through employing a lightweight model, and enabling the table types to comprise a wired table and a wireless table; s2, respectively acquiring a logic coordinate and a physical coordinate of a detection frame of the wireless table and a cell physical coordinate of the wired table; s3, recovering a logical structure of the table according to the detected table coordinates, and determining a row-column relationship of the table; s4, performing character recognition on the image by using a text recognition model, obtaining the position and content of a character box, performing accurate matching of characters and cells by calculating the coordinate overlap ratio of the OCR recognition character box and the cell box, and determining the character content in each cell; and S5, for the cells which are not matched with the characters, cutting the cell areas, carrying out secondary OCR identification on the image areas of the cells, and supplementing missing character contents. According to the method, the automatic identification and analysis capability of the complex table data is improved.

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.

Systems and methods are described for performing an instant recovery of data associated with a locked snapshot. In various examples, the amount of time for performing a recovery of data associated with a locked snapshot is significantly reduced by making use of enhanced volume cloning functionality instead of making an actual copy of the data to be recovered. In one embodiment, the resulting volume clone representing the recovery volume is cleared of all data protection information (e.g., WORM flags and / or lock metafiles) that was previously used to protect the content from being changed when stored on the data protection volume so as allow the recovery volume to be used in read-write mode.

Systems and methods include reception of a request to recover data of a first tenant to a point in time, determination of backups of first and second database table shards corresponding to the point in time, and generation of metadata associating a second tenant with the first and second shards and the backups. In response to a request to access the first second shards, it is determined based on the metadata that the first and second shards are not stored in a storage layer and, in response, the first shard is recovered to a first storage node from the backup of the first shard, the second shard is recovered to a second storage node from the backup of the second shard, and identifiers of the first storage node and the second storage node are returned.

The invention discloses a data recovery method and a related device, the method can be applied to a server running a database system, and the method comprises the steps that in response to modification of a first data page, a first log is generated, modification of the first data page is recorded in the first log, and a first identifier indicates the version of the first data page; under the condition that the database system fails, the version of a second data page is determined, the second data page is the data page of the data to be recovered, and the page identifier of the second data page is the same as the page identifier of the first data page; and under the condition that the version of the first log is greater than that of the second data page and the version indicated by the first identifier is the same as that of the second data page, performing data recovery on the second data page by using the first log. Through the first identifier, data recovery on the second data page by using a log with a wrong version is avoided, and the accuracy of data recovery is ensured.

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 cluster backup recovery system based on multiple data centers, and belongs to the technical field of cluster backup recovery, and the system comprises a plurality of data centers with dispersed geographic positions, which are used for deploying and operating the same business application and database service at the same time; the global synchronous optimization module is used for dynamically selecting an optimal data transmission channel to transmit the data fragments to a target data center in parallel according to a scheduling instruction of the system and network conditions among the data centers; the distribution consistency guarantee module is used for enabling each data center to backup and write correct data in parallel according to a Multi-Paxos consistency protocol, log synchronization and conflict detection; and the remote parallel recovery module is used for extracting the decompressed target backup data from each data center in parallel according to the task number, and transmitting the decompressed target backup data to the to-be-recovered data center for data recovery. The problems that the backup system recovery task recognition efficiency is low, and scheduling mechanism optimization is insufficient are solved.

The present application relates to the technical field of storage, and provides a data recovery method, apparatus, and system, which can achieve resource saving in a data recovery process and ensure smooth operation of database services. The method comprises: receiving a data recovery instruction, wherein the data recovery instruction comprises a specified time point, and the data recovery instruction is used for instructing to recover data of a database to the specified time point; on the basis of backup data in a backup storage device, obtaining full backup data of which the backup time is earlier than the specified time point; on the basis of an archive log of the database stored in the backup storage device, obtaining the archive log, wherein the archive log comprises a plurality of logs generated before the specified time point and after the backup time of the full backup data, and the serial numbers of the plurality of logs are consecutive; performing data recovery on the database on the basis of the full backup data and the archive log to obtain data of the database at the specified time point; and storing the data at the specified time point.

The invention relates to the technical field of embedded systems, in particular to a data backup and recovery method applied to an embedded device, an electronic device and a storage medium, comprising: in response to creation of a user partition and a backup partition, storing data in the user partition, and backing up the data stored in the user partition in the backup partition; wherein the user partition comprises a plurality of user sub-partitions, the backup partition comprises a plurality of backup sub-partitions, and each user sub-partition and each backup sub-partition store verification files; when it is confirmed that data consistency verification is started, whether data stored in the user sub-partition is consistent with data stored in the corresponding backup sub-partition or not is verified through the verification file; and if the user sub-partitions and the backup sub-partitions are not consistent, data recovery operation is performed, so that the data stored in the corresponding user sub-partitions and the backup sub-partitions are kept consistent, and the defects of an existing data backup and recovery mechanism of the embedded system are effectively overcome.

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.

The invention provides a backup method, device and equipment for a distributed database and a storage medium, and the backup method for the distributed database comprises the steps: determining a backup cluster object of the distributed database corresponding to a business system, generating a backup task in the backup cluster object in response to a backup request, and executing the backup task through the backup cluster object. Determining target cache data of the distributed database, copying the target cache data into a plurality of backup storage nodes of a backup storage pool, and realizing unified backup by adopting a mode of creating a backup cluster object of the distributed database to meet a data backup requirement of the distributed database; meanwhile, the backup cluster is not a single database which is independent of each other, but is a new distributed cluster formed by a plurality of backup storage nodes, so that the overall clustering response capability is achieved, and the data recovery requirement of the distributed database can be met.

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.