1151 results about "Data recovery" patented technology

A system and methods for implementing adversarial-robust compression and reconstruction using a vector quantized variational autoencoder (VQ-VAE) with secure latent space management. The system provides comprehensive protection against adversarial attacks through multi-channel threat detection, adaptive defensive parameters, and coordinated response mechanisms. Input data is continuously monitored for potential threats, and defensive parameters are dynamically adjusted based on detected threat levels. The system implements bounded constraints and hierarchical projections to maintain latent space security while preserving compression efficiency. Multi-stage reconstruction with progressive validation ensures reliable data recovery even under adversarial conditions. The system coordinates defensive responses across all compression and reconstruction processes, implementing various recovery mechanisms when security violations are detected. This approach enables robust compression and reconstruction of time-series data while maintaining protection against various forms of adversarial manipulation.

The present application discloses a data management method, apparatus and system for a distributed storage system, and an electronic device. The method comprises: traversing all storage paths in a target distributed storage cluster, and acquiring file storage information of the storage paths during traversal; on the basis of the file storage information, calculating a redundancy policy score of each storage path, and on the basis of the redundancy policy score and a first redundancy policy score threshold, determining a storage policy adapted to the storage path; when it is determined that the storage policy adapted to the storage path is an erasure code storage policy, determining a data recovery mode of the storage path on the basis of the architecture information of the storage path; and when it is determined that the storage policy adapted to the storage path is a multi-copy storage policy, determining the number of copies of the storage path on the basis of a service importance degree of the storage path and the redundancy policy score.

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

The invention relates to the technical field of network and information security, in particular to a zero-trust multi-party security data exchange method, which comprises the following steps of: generating a zero-trust token and a capability authorization bill and constructing context description data; authentication encryption is performed on the plaintext fragment, attribute-based encryption is performed on the data one-time key, and a policy bearing ciphertext container is assembled; in the trusted execution environment, authentication decryption is completed through a one-time secret key of data recovery through joint challenge and threshold deblocking, or strategy migration and transfer are achieved through proxy re-encryption; and aggregating the decryption event and the forwarding event by vector commitment, generating a time anchor, and mapping the time anchor into a next round of random base. According to the invention, fine-grained authorization of the key layer, no plaintext transfer, event-level verifiable auditing and rapid revocation are realized.

The invention discloses a solid-state storage hard disk data recovery method and system, and relates to the technical field of data processing, the method comprises the following steps: carrying out difference analysis on historical log state characteristics and health state characteristics stored in a solid-state hard disk, and identifying a data loss event of the solid-state hard disk based on a difference analysis result; constructing a data recovery mechanism based on the data loss abnormal event, and performing recovery processing on lost data of the solid state disk based on the data recovery mechanism; and verifying and evaluating the recovered data, and optimizing the data recovery mechanism according to a verification and evaluation result. According to the method, differentiated technical means such as metadata restoration, backtracking reasoning and data engraving are combined, classification positioning, cause recognition and structured restoration of lost data are achieved, and the pertinence, accuracy and automation degree of data restoration are effectively improved.

The invention discloses an intelligent traffic data storage method, system and device based on the Internet of Things and a medium, and relates to the technical field of electric digital data processing, and the method comprises the steps that an edge node packages received data and inserts the data into a local directed acyclic graph account book; dividing the verified data into Level-0 fragments according to a fixed duration window, and performing variable-length fragmentation based on a Rabin fingerprint algorithm; calculating Hash values of the variable-length fragments, and performing duplicate removal through a Bloom filter and a Hash index table in sequence; generating a local erasure block and a global erasure block for the deduplicated fragments, and storing complete copies of the key fragments on a plurality of storage nodes; and periodically checking a local erasure block and a complete copy, and triggering self-healing reconstruction when detecting that the fragment is lost. According to the method, the technical problems of high delay of distributed write-in consistency, high storage redundancy, complex cross-node time sequence correction, slow data recovery, poor system expansibility and the like are solved.

The embodiment of the invention provides a data processing method and device, a storage medium and electronic equipment, and relates to the technical field of computers.The method comprises the steps that in response to a wrong disk recovery request of a storage system, a recovery method corresponding to a wrong disk is determined; the number of the staggered disks is at least one; performing data recovery on the wrong disk by utilizing a recovery method to obtain a data recovery result; each stripe in the storage system is divided into a first local check group, a second local check group and a global check group; the global check group comprises a global check disk, a first data disk part and a second data disk part; the first local check group comprises a first local check disk, a first data disk part and a global check disk; and the second local check group comprises a second local check disk, a second data disk part and a global check disk. Therefore, a grouping mode of the first local check group, the second local check group and the global check group is provided, at least one wrong disk can be recovered by using the recovery method corresponding to the wrong disk, and the fault-tolerant capability is improved.

The invention provides an information resource multi-level data recovery method and system and a medium, and the method comprises the steps: generating a snapshot header for each snapshot, which comprises a snapshot ID, a previous snapshot hash value, a current snapshot hash value and an optional digital signature; meanwhile, hash calculation is carried out on all the blocks in the snapshot, and a Merkel tree is constructed to obtain a global hash value; when a new or updated block is detected, carrying out Hash calculation on the new or updated block, adding the new or updated block into a Merkel tree, generating a new snapshot Hash value, and writing the new snapshot Hash value into a snapshot head; and taking the hash value of the previous snapshot as a predecessor reference of the new snapshot to form chained association. In a multi-copy environment, when data recovery needs to be carried out, firstly, related snapshots are screened and determined through a snapshot index and a filter; then verifying the snapshot chain to ensure that the front and back references of each snapshot are correctly matched with the Merkel tree hash value; for block-level data, if copies are inconsistent, a correct version is discriminated through weighted consensus; and finally reconstructing the damaged block.

The embodiment of the invention provides a storage node upgrading method, system and device, a storage medium and electronic equipment, and relates to the field of computers.The method comprises the steps that node upgrading is conducted on a storage node in a first key value state in a storage cluster, and the first key value state is used for indicating that upgrading preparation of the storage node is completed; in the node upgrading process, under the condition that it is detected that the key value state of at least one storage node is changed from the first key value state to the second key value state, the at least one storage node is isolated from the storage cluster, and data recovery is conducted on the at least one storage node, the second key value state is used for indicating that upgrading of at least one storage node fails; and adding the at least one storage node after data recovery into the storage cluster again. According to the embodiment of the invention, the problem of relatively low upgrading efficiency of the storage node in related technologies is solved.

The invention provides a sparse array direction-of-arrival estimation method based on a local and global self-supervised network, and the method comprises the steps: 1, carrying out the signal processing: constructing any sparse array receiving signal, and carrying out the difference set virtual array extension, and obtaining the difference set virtual array measurement data; constructing a neural network training data set according to the measurement data; 2, establishing a local and global feature extraction network; step 3, establishing an ANM (Atomic norm Minimization) self-supervised network; and step 4, performing meshless signal recovery and DOA (direction of arrival) estimation, realizing label-free self-supervision data recovery, and finally performing DOA estimation by using the recovered meshless data. The new scheme provided by the invention has meshless recovery performance, solves the problem of base mismatching of a traditional neural network method, and has more accurate recovery and estimation performance.

The invention discloses a row-level data recovery method and device for a damaged file of a GoldenDB database, and the method comprises the following steps: S1, scanning a to-be-recovered GoldenDB data file, and recognizing and caching all data pages; s2, on the basis of metadata of the data pages, cluster index leaf node pages containing user data are screened out from the cached data pages; s3, for each screened leaf node page, analyzing an internal record organization structure of the leaf node page, and performing row-level data recovery; the row-level data recovery comprises the steps of traversing user records according to a virtual record pointer, and performing cross check and recovery on a traversal path in combination with a page directory slot so as to extract and mark the user records one by one; and S4, outputting all the recovered row-level data.

The invention discloses an archive electronic information data recovery method, which relates to the technical field of archive management and data recovery, and comprises the following steps: obtaining original archive data, extracting first data and generating second data according to format characteristics, generating a version sequence of the data object based on the structured metadata, matching the third data and the encrypted data block which is not extracted in the first data as fourth data according to a preset strategy, performing decryption, generating fifth data according to a format feature, if the first data has the unextracted non-encrypted damaged data block, generating supplementary repair data, and if the first data has the unextracted non-encrypted damaged data block, generating the supplementary repair data. The first data, the third data, the fifth data and the repair data are integrated according to the hierarchical identifier, the association between the catalogue and the metadata is reconstructed, a complete file is generated, and a three-in-one system is formed by constructing a structured metadata version sequence, repairing heterogeneous data in a graded manner and fusing block chain dual verification and is suitable for file recovery.

The invention relates to the technical field of data processing, in particular to a cloud database data compression method based on an adaptive quantization algorithm. The method comprises the following steps of: 1, regarding to-be-compressed original data in a cloud database as a matrix, dividing the data into a plurality of non-overlapped data blocks according to predefined statistical characteristics, and calculating a mean value and a variance of each data block; 2, defining the complexity of each data block, and calculating the self-adaptive quantization step length of each data block according to the mean value and variance of each data block; step 3, performing non-uniform adaptive quantization on each data item in each data block to obtain a quantized value; and 4, establishing a probability model for a quantization result of each data block, calculating a coding length, obtaining a compression ratio, and carrying out entropy coding. The quantization step size of the data block is dynamically adjusted to be matched with the local characteristic of the data, so that the quantization error is reduced, and the data recovery precision is improved.

Implementations of the present specification provide a data writing, data recovery, and data reading method, and a corresponding secure disk apparatus. The data writing method includes following: User data blocks are first written into a write cache, and a plurality of user data blocks identified by LBAs are read from the write cache under a certain condition. For each user data block, an HBA is allocated to the user data block, authenticated encryption is performed on the user data block to generate an encrypted data block and authentication information, and first metadata corresponding to the user data block is generated, where the first metadata is organized in a form of a KV pair and includes the LBA, the HBA, and the authentication information. A plurality of write commands for the plurality of user data blocks are submitted to a host disk for the disk to store the encrypted data block based on the corresponding HBA. In addition, the first metadata corresponding to each user data block is further written into a first metadata table maintained by using an LSM tree. Then, a synchronization operation command is sent to the LSM tree and the host disk for the LSM tree and the host disk to complete data write persistence.

The invention relates to the technical field of fire alarm, and discloses a self-adaptive threshold fire-fighting buzzer alarm method and system based on fuzzy logic, and the method comprises the following steps: S1, environment data collection; s2, data preprocessing; s3, performing fuzzy processing; s4, performing fuzzy reasoning and alarm threshold calculation; s5, optimizing an alarm threshold value; s6, real-time feedback and dynamic adjustment: according to the real-time fire suppression result and the environment change data, adjusting an alarm threshold to form a dynamic feedback mechanism; s7, applying a fire propagation model; and S8, alarm execution. By adopting distributed data storage, redundant backup, encryption technology and automatic data management, efficient and safe data processing, reliable data recovery and accurate alarm decision are realized, and the response speed and stability of the system are improved.

The invention discloses a power distribution network construction method, system and device based on digital twinning and a storage medium, and relates to the technical field of power systems, and the method comprises the steps: collecting the operation link data of a power distribution network; constructing a power distribution network digital twinborn model based on the power distribution network operation link data; optimizing the prediction and decision-making capability of the digital twin model of the power distribution network, and performing evaluation and verification; the optimized power distribution network digital twinborn model is used for carrying out full-life-cycle dynamic simulation on the power distribution network; in the dynamic simulation process of the whole life cycle of the power distribution network, the operation state of the power grid is analyzed in real time, an optimal scheduling scheme is provided, the state of power grid equipment is monitored in real time, fault hidden dangers are found, fault causes are diagnosed, and a repair scheme is provided. The method further comprises the steps of encrypting sensitive data, backing up important data regularly, establishing a perfect data recovery mechanism and implementing strict access authority management. According to the invention, the operation efficiency, reliability, safety and intelligent level of the power distribution network can be improved.

The invention provides a fast recovery method for hadoop cluster data, which relates to the technical field of erasure codes, and comprises the following steps of: calculating an importance score of a data block to be recovered, determining a target copy number based on a nonlinear copy number adjustment function, calculating a load weight in combination with network topology information, and selecting candidate nodes; and predicting a node fault probability by using a long short-term memory network, calculating a migration priority based on the importance score and the fault probability, and selecting an optimal target node to execute data recovery. According to the invention, the intelligence and differentiation of the data recovery process are realized, and the data recovery efficiency and reliability are improved.

The present application relates to the technical field of storage, and provides a method and apparatus for determining an optimal read voltage parameter of a flash memory, and a flash memory device. The method comprises: on the basis of a linear relationship model between a read retry voltage offset value and a bit flip count, acquiring a new read retry voltage offset value corresponding to a minimized predicted bit flip count of the flash memory; determining whether data can be successfully read under the new read retry voltage offset value; if yes, adding the new read retry voltage offset value to the header of a read retry offset table to form a new read retry offset table; and on the basis of the new read retry offset table, updating the linear relationship model by means of an adaptive learning model. By means of the efficient and adaptive voltage parameter determination method, the present application can improve the success rate of data recovery and reduce performance loss.

The invention discloses an intelligent network card management and control method, device and system and electronic equipment, and relates to the technical field of computers, the method comprises the steps that temporary data of an intelligent network card is reserved and cached in a preset persistent cache region, when a system of target computing equipment is restarted or shut down, the data in the preset persistent cache region cannot be cleared, and the data in the target computing equipment cannot be cleared; when the intelligent network card enters the initialization stage after the system is started, data recovery is performed on the intelligent network card based on the reserved data cached in the preset persistent cache region, so that rapid initialization of the intelligent network card is realized, the initialization time of the intelligent network card is shortened, and important data of the intelligent network card is prevented from being lost.

The invention relates to the technical field of data recovery, and discloses a data recovery method and system based on a multi-cloud platform, and the method comprises the following steps: S1, constructing an intelligent data acquisition layer: carrying out the dynamic adaption of four data collection channels based on a multi-modal data recognition engine; s2, constructing an adaptive transmission protocol stack: dynamically selecting based on a network quality real-time evaluation model; s3, heterogeneous system architecture recovery: realizing architecture recovery through a virtual environment abstraction layer; s4, establishing a space-time dimension data management model, which is realized based on a cloud native storage architecture; and S5, automatically recovering an arrangement engine through a workflow engine. According to the method, the reliability and the stability of the system are improved while the labor cost is saved, high-frequency copy synchronization is supported, the resource utilization rate is improved, and the data reading efficiency can be improved through block-level data synchronization.

The invention discloses a financial data security regulation and control management method and system, and relates to the technical field of commercial financial management, and the method comprises the following steps: 1, data encryption regulation and control: employing an encryption protocol to carry out encryption transmission, and carrying out encryption storage; step 2, access permission regulation and control: distributing corresponding access permissions; step 3, backup and recovery regulation and control: making a backup, and performing a data recovery test; 4, authenticating the identity of the regulator, wherein a multi-factor identity authentication and digital certificate authentication mode is adopted; 5, approval optimization regulation and control: refining the approval authority, and introducing an approval risk assessment mechanism and an abnormity early warning mechanism; and step 6, monitoring and auditing regulation and control: monitoring access and operation of data, and regularly performing internal and external auditing. According to the method, transmission encryption and storage encryption are utilized, man-in-the-middle attack is effectively prevented, permissions are allocated according to responsibilities, unauthorized operation is avoided, the internal abuse risk is reduced, and the security of financial data regulation and control management is improved.

The invention relates to the technical field of data processing, in particular to a traffic data completion method and device based on a time autoregression low-rank tensor, and the method comprises the following steps: obtaining incomplete traffic data; constructing a three-dimensional tensor to represent incomplete traffic data, and obtaining an observation index set and a tensor matrix; taking a low-rank tensor completion model based on rank minimization as a framework, and introducing time autoregressive regularization and truncation weighted nuclear norms to obtain a low-rank tensor completion model; performing problem decomposition solving on the low-rank tensor completion model on the basis of an ADMM framework to obtain four variable parameters, namely, a variable parameter, a variable parameter and a variable parameter; and iteratively updating the variables according to the sequence until a preset convergence standard is reached, and outputting the complemented complete traffic data. According to the method, the time change is introduced into the completion of the three-order tensor as a new regularization item, so that the historical information can be learned during data recovery, the low-rank performance of the completed tensor can be ensured, and a complex missing scene can be better processed.

The invention discloses a spacecraft sensor abnormal data recovery method based on spatio-temporal information fusion, and relates to a spacecraft sensor abnormal data recovery method. The method aims at solving the problems that in an existing spacecraft sensor abnormal data recovery technology, the signal-to-noise ratio is deteriorated due to environment interference, and multi-dimensional feature modeling has limitation and is insufficient in self-adaptive capacity. The method comprises the following steps: step 1, preprocessing original sensor data through a sliding mean filter; 2, constructing a time-space dual-scale feature extraction network based on Transform, mining a sensor time sequence dependency relationship from a time dimension, and analyzing multi-sensor association features from a space dimension; 3, introducing a particle filter algorithm to carry out optimal fusion on space-time dual-channel output, and quantifying the uncertainty of a data recovery result; and step 4, by comparing the actually measured data with the uncertainty confidence interval, abnormal detection and data recovery of sensor anomalies are realized. The invention belongs to the technical field of satellite sensor abnormal data recovery.

The invention discloses a data recovery method, electronic equipment and a computer readable storage medium, and the method is applied to a cloud storage system, and comprises the steps: obtaining data traffic information of the cloud storage system in a current time period, predicting data traffic in a future time period based on the data traffic information, obtaining a data traffic prediction result, and outputting the data traffic prediction result. Determining data recovery time matched with the data traffic prediction result; determining a data recovery bandwidth based on the data traffic prediction result and load information corresponding to the cloud storage system; and in response to data loss of the cloud storage system, performing data recovery on the cloud storage system based on the data recovery time, the data recovery bandwidth and the load information corresponding to the cloud storage system. According to the scheme, the reasonability of data recovery can be improved.

A method performed by a generative adversarial network, GAN, based system for outputting an estimated adversarial data, EAD, of an attack on an artificial intelligence, AI, model is provided. The method includes classifying a data point from an input data as (i) a real data point, or (ii) a manipulated data point. The method further includes, when the classification is a manipulated data point, outputting the estimated adversarial data including a difference between the manipulated data point and the data point from the input data. The method may further include using the EAD to build a data recovery module.

Linear unretimed interfaces lack clock and data recovery and retiming circuits implemented on a sophisticated digital signal processor. When the interface is not a retimed interface, it can be especially useful to extract some information about the link to allow for debugging and optimization of system deployment. An efficient solution can be implemented in unretimed interfaces to compute metrics such as the impulse response and signal histogram. Having the metrics allows for the solution to check and monitor the quality and the status of the link. Based on the link quality and status information, it is possible to address non-idealities and optimize performance of the unretimed interfaces and the link.

The invention relates to a low-delay error-correctable high-speed serial transmission circuit which comprises a transmitting end circuit and a receiving end circuit, the transmitting end circuit and the receiving end circuit are divided into a PCS layer and a PMA layer, the PCS layer of the transmitting end circuit completes scrambling and coding of service data, the PMA layer is responsible for converting parallel low-speed data streams output by the PCS layer into high-speed serial data streams, and the high-speed serial data streams are transmitted to the receiving end circuit. The signal is output through the driving circuit; a receiving end circuit PMA layer extracts a clock from an input high-speed serial data hopping edge, completes clock data recovery and then converts a high-speed serial data stream into a parallel low-speed data stream, and a PCS layer decodes parallel data output by the PMA layer, descrambles an effective data part and recovers original service data. According to the invention, the detection and correction of 2-bit error codes in each frame of data can be realized, and the requirements on transmission efficiency, reliability, delay performance, resource use and the like of a coding protocol are well balanced.

The invention relates to the technical field of data processing, and provides a cloud data archiving method and system, and the method comprises the steps: obtaining a source data set of to-be-archived data, carrying out the grade division, obtaining a data grade label, dividing the source data set into a plurality of data blocks, carrying out the data encryption of different grades, obtaining a plurality of encrypted data blocks, and storing the encrypted data blocks in a database; analyzing and coding to obtain a multi-dimensional data storage structure, uploading the multi-dimensional data storage structure to a preset cloud storage node, and generating a storage index information set based on multiple data blocks in the uploading process; and when a data recovery request is received, acquiring the data block from the cloud storage node according to the storage index information set, and performing decryption and data recombination. Through the scheme, classification, hierarchical encryption and structured archiving of the source data set are realized, the security and management efficiency of the data in a cloud storage process are improved, and the problem that the encryption level cannot be dynamically adjusted by data sensitivity or content features when a large-scale heterogeneous data source is used is solved.

The invention relates to a multi-rate wireless frame adaptive architecture method and device, equipment and a storage medium, and the method comprises the steps: receiving channel state information, and determining pilot frequency configuration according to the channel state information; receiving service data to be transmitted, selecting a corresponding modulation mode based on the channel state information, and performing symbol modulation on the service data according to the modulation mode to obtain a service modulation symbol; carrying out frequency hopping distribution on the service modulation symbols based on the pilot frequency configuration, and carrying out frequency hopping transmission on the service modulation symbols according to the frequency hopping distribution; and receiving the frequency hopping data after the frequency hopping transmission, and performing data recovery on the frequency hopping data to obtain decoded service data. The method and the device have the effect of improving the adaptability of the multi-rate architecture.

The invention provides a database data processing method and device, equipment and a storage medium. The method can be applied to the cloud computing field. The method comprises the following steps: performing incremental change detection on a main database to identify incremental data needing to be backed up; predicting a backup starting moment of the incremental data based on the historical transaction processing data of the main database and the data volume of the incremental data; and performing backup on the incremental data according to the predicted backup starting moment so as to recover the backed-up incremental data to the target delay reading moment by utilizing the delay database under the condition that the backup is completed.