188 results about "Data migration" patented technology

A storage device includes a memory device that includes a plurality of memory cells, and a memory controller that controls the memory device. The memory device includes a memory cell array that includes the plurality of memory cells, a control logic circuit that writes data to the memory cell array or reads data from the memory cell array in response to a command received from the memory controller, and a page buffer that stores sensing data sensed from the plurality of memory cells. The control logic circuit includes an error detecting circuit that detects an error of first data stored in first memory cells of the memory cell array, and the control logic circuit writes the first data to second memory cells of the memory cell array, based on an error comparison result obtained by comparing the error of the first data with a preset value.

This disclosure provides a data processing method and apparatus. The method, during the migration of a metadata table from a source cluster to a target cluster, detects a write operation on the metadata table, performs a checkpoint operation on the metadata table to back up the existing metadata information under the checkpoint operation, migrates the existing metadata information to the target cluster, and then replays the existing metadata information based on the corresponding log files after the write operation to obtain the target metadata information after the write operation. Finally, after the metadata table migration is complete, the corresponding data file is migrated to the target cluster. This technical solution avoids data location confusion during data migration and does not affect normal read / write operations for users.

Non-transitory, machine-readable storage medium containing instructions that, when executed, cause a management system to: A management interface to query first port information from ports in a first storage system and second port information from ports in a second storage system; to receive the first port information from ports in the first storage system from the first storage system, wherein the first storage system is to report the first port information to the management interface; to receive the second port information from ports in the second storage system from the second storage system, wherein the second storage system is to report the second port information to the management interface; to combine the first port information from the first storage system and the second port information from the second storage system into combined port information; and to receive a request from the first storage system for port information from ports in the second storage system; and to send the second port information to the first storage system for use in path selection from hosts to the first and second storage systems, where the request from the first storage system for port information of ports in the second storage system occurs during a data migration from the first storage system to the second storage system.

This invention provides a method, system, and medium for implementing a hybrid memory system based on HBM, including: initializing and establishing a hybrid memory controller, creating a heat monitoring table and an address translation table; the hybrid memory system receiving access requests; reallocating the storage location of data according to its heat, updating the address translation table, and redirecting access requests; migrating and caching data according to the address translation table, and completing the access request. This invention, taking advantage of the high bandwidth characteristics of HBM, divides HBM into c-HBM and m-HBM regions and performs detailed heat classification of data, significantly improving the access speed of hot data, reducing data migration overhead, and effectively reducing the overall access latency of the memory system. Compared to traditional memory systems that only add a small amount of HBM, this invention significantly improves the overall access speed of the memory system while providing more memory space, performing excellently under various workloads and showing good market prospects and application value.

Embodiments of the present application provide a data migration method and device, a storage medium and an electronic device. The method comprises: determining an OSS with a capacity utilization rate greater than a first threshold value in a distributed storage service set as a first OSS; obtaining a capacity utilization rate difference between the first capacity utilization rate of the first OSS and a second capacity utilization rate of at least one second OSS in the distributed storage service set; in the case that at least one target capacity utilization rate difference greater than or equal to a second threshold value is included in the at least one capacity utilization rate difference, determining the second OSS corresponding to the at least one target capacity utilization rate difference as a target OSS; and migrating object data stored in the first OSS to a storage space corresponding to the at least one target OSS according to a migration coefficient matched with the target OSS. The technical problem of low data migration efficiency in the related art is solved.

The present disclosure relates to the technical field of patch updating, and provides a patch updating method and device, electronic equipment, a storage medium and a program product. The method comprises: receiving a target patch for updating a first patch module in a host program; instantiating the target patch in a Wasm sandbox environment to obtain a second patch module; allocating memory space for the second patch module in the Wasm sandbox environment; migrating state data of the first patch module to the second patch module; processing, in parallel, service requests for the host program by using the first patch module and the second patch module; and in response to completion of processing of all service requests received before the migration starts, uninstalling the first patch module and taking the second patch module as an updated first patch module. The present disclosure solves the problems of service interruption and poor update security during patch updating, and can complete patch switching in a service interruption-free and security-isolated manner, thereby realizing zero-interruption patch updating.

This invention discloses a traceability code migration and status management method and system based on a relational database, belonging to the field of data migration management technology. The method includes: collecting migration request data and extracting and associating elements from the data to generate migration fingerprint request data; reconstructing parent-child connection paths from the pre-migration relationship snapshot using a path backtracking and rearrangement algorithm, and synchronously correcting hierarchical positions to generate relationship reconstruction data; employing a status linkage algorithm, writing the relationship reconstruction data back to the current state node by node in a bottom-up, parent-child hierarchical order, and synchronously updating the upper-level status identifier to generate status linkage data; performing version consistency verification on the status linkage data, obtaining consistency-passing data, writing the consistency-passing data into the status table and synchronously updating it to generate a migration management scheme. This invention improves the accuracy of relationship reconstruction and enhances the consistency of migration results.

The application discloses an electronic nose data generation method based on a self-encoder and a generative adversarial network, and comprises the following steps: constructing an electronic nose data set S; constructing a Gaussian mixture embedding generative adversarial network model GMEGAN; training the Gaussian mixture embedding generative adversarial network model through the electronic nose data set S; using the trained Gaussian mixture embedding generative adversarial network model to perform data augmentation based on a reconstruction sampling method or a prior sampling method; constructing a first electronic nose data set S'; retraining the trained Gaussian mixture embedding generative adversarial network model through the first electronic nose data set S'; and using the retrained Gaussian mixture embedding generative adversarial network model to perform data migration based on the prior sampling method. The application generates a large amount of generated data with similar category characteristics through a generative model, increases data distribution density, reduces overfitting of a classification model, and improves the classification effect on electronic nose data.

The application provides a cold and hot data identification method and a data migration method, and relates to the technical field of data management, and comprises the following steps: acquiring the access frequency of a data block in a past first preset time length, the access frequency of each access, the activity weighting factor of a thread corresponding to each access, and the time interval between each adjacent access; determining the short-term access heat of the data block according to the access frequency, the access frequency of each access, the activity weighting factor of each access, and the time interval between each adjacent access; inputting the short-term access heat into a machine learning model to obtain the long-term access heat of the data block output by the machine learning model; and determining the type of the data block according to the long-term access heat. The application can reduce the hysteresis and misjudgment rate of cold and hot data identification.

In a method of operating a storage device including a first storage device and a second storage device connected to each other via a host device, a data migration request for a first namespace included in the first storage device is received. A second namespace is generated in the second storage device. The second namespace corresponds to the first namespace. A pointer table for performing a data migration operation corresponding to the data migration request is set. First data and second data are migrated to the second namespace based on the pointer table. The first data is stored in the first namespace. The second data is stored in a first buffer memory included in the first storage device and corresponds to the first namespace. Based on the data migration operation being performed, the second data is directly sent from the first buffer memory to the second namespace.

This application relates to a distributed system processing method, apparatus, electronic device, and storage medium. The method includes: responding to a swimlane configuration request, generating a corresponding data processing instruction and sending it to a database management system, so that the database management system creates a swimlane shadow table with the same structure as the online table according to the data processing instruction, copies a specified range of target data from the corresponding online table to the swimlane shadow table, and returns; when a database access request carrying a swimlane identifier is received, rewriting the online table name in the SQL statement to the corresponding shadow table name according to the swimlane configuration rules, and routing the rewritten SQL request to the swimlane shadow table. This enables the database management system to create a swimlane shadow table with the same structure as the online table and copy a specified range of target data from the corresponding online table to the swimlane shadow table, without manual intervention in database table structure creation and data migration, greatly reducing operational complexity and improving configuration efficiency.

The application relates to an intelligent data classification storage method and system based on deep learning, which comprises the following steps: obtaining an original data set, and obtaining a multidimensional data set after preprocessing; obtaining a frequency statistical set of data, and fitting the frequency statistical set by using a least square method to obtain an access frequency prediction value of each type of data; generating high-frequency or low-frequency data identification for corresponding data according to the comparison between the access frequency prediction value and a preset frequency threshold; and constructing a high-frequency data list and a low-frequency data list accordingly; obtaining a storage cost weight and an access delay index of a storage node according to a storage location index; calculating a storage efficiency score of each storage node; calculating an allocation proportion of each storage node, adjusting the allocation proportion through the storage efficiency score, and generating a multi-level storage allocation scheme; and after generating corresponding migration instructions according to the multi-level storage allocation scheme, migrating data to realize hierarchical classification storage of the data. The application can improve the utilization rate of system storage resources.

The application discloses an AI driving-based structured data migration method and system, belongs to the technical field of big data migration, and aims to solve the technical problem of how to improve the efficiency of large-scale data migration and reduce resource waste. The method comprises the following steps: forming a private large language model with field adaptation capability; performing lexical analysis and syntax analysis on non-standard structured data in a source database based on an AST analysis method, generating an abstract syntax tree AST, traversing and optimizing AST nodes, and converting the AST nodes into standard structured data supported by a target database; calling the large language model, and converting unstructured data into structured data supported by the target database through instruction fine-tuning, thought chain reasoning and step-by-step analysis; generating a migration script by the large language model; monitoring the migration process in real time, recording abnormal events, automatically retrying or repairing the abnormal events by the large model, and optimizing the large language model configuration strategy and data conversion rules in combination with feedback data.

The application provides a kind of freight data pruning method, device and equipment, method includes: obtaining freight original data, delete the data of the flight owner pruning range outside of the freight original data;Delete the data in the freight original data that is outside the owner pruning range of freight;Remaining non-airline data is stored in release area;Remaining explicit airline data is stored in non-release area;Remaining implicit airline data is stored in non-release area;Iterate non-release data, migrate non-release data to release area or waiting area;Data in the release area is stored in file as pruned data file, and released to the data preprocessing module in the freight calculation system.The size of the freight data is reduced by deleting and arranging the freight original data, so that the pruned data does not lose and is not redundant.

The application discloses a data management method and system suitable for IMR, wherein when a bottom track is updated, data of an effective top track is actively migrated to other free tracks; in the MFML data update strategy, a zigzag four-stage data management scheme is adopted, in the first stage, data of the bottom track is updated; in the second stage, the MFML strategy is adopted to update data of the bottom track; in the third stage and the fourth stage, data distribution and bottom track data update are continuously performed according to the mode of the second stage, and data management is realized. The application can make the MFML update strategy better play the performance in the four-stage data management scheme, and can also relieve the performance decline problem caused by the too high top track occupation amount of the multi-stage distribution scheme.

The application discloses a kind of method and device of intelligent wearing equipment migration data, wherein method includes: when first and second wearing equipment network match successfully, first wearing equipment sends request migration signal to client, and receives feedback signal of client feedback;After first wearing equipment judges that feedback signal is agree migration feedback signal, continue to judge whether data migration instruction is received;When receiving the migration instruction of server data and local data, then first wearing equipment sends the migration instruction of server data to server, and local data is passed to second wearing equipment by network connection channel generated by network matching;Server data in the server storage area of first wearing equipment is written into the server storage area of second wearing equipment, and then written to second wearing equipment again.The application provides the way that first wearing equipment can conveniently and safely migrate local data and server data to second wearing equipment.

The application provides a large model training acceleration method and system based on CUDA shared memory and a PCIe expansion card, hardware configuration information is acquired to identify the PCIe expansion card and build an expansion storage space, and a unified management heterogeneous memory space is formed in combination with GPU shared memory; in the training process, according to a preset initialization strategy, model data blocks are loaded to different levels of the heterogeneous memory, data access frequency is monitored in real time, data migration instructions are dynamically generated based on multiple frequency thresholds, and intelligent scheduling of the data blocks among the levels of storage is realized; a transparent address remapping mechanism is used to enable CUDA kernel functions to seamlessly access the heterogeneous memory space; periodic data snapshots and persistent storage are simultaneously supported, and quick recovery after interruption of training is ensured; the application breaks through the GPU display memory capacity limit, improves the large model training efficiency, reduces the dependence on multi-card hardware and cost, and guarantees data security and training reliability.

The application discloses a kind of university student project education simulation method and system based on multi-agent.It includes: obtaining project creation request, creating project directory and initializing session manager and version controller, session manager maintains finite state machine, the state space of finite state machine includes three stages of system initialization, collaborative construction period and game defense period;In the collaborative construction period stage, each step is executed in turn, after completion, automatically promote state and carry out persistence, after completing review modification and auxiliary writing, enter the game defense period;In the game defense period stage, three experts parallel review and conflict detection, proposal revision based on expert opinion and final report chapter generation and cross-platform file export step are executed in turn, after proposal revision and audit pass, final report adopts the chapter independent generation strategy, and the PDF format file of Markdown and cross-platform adaptation is output.

This invention discloses a software data analysis method and system based on cloud computing, belonging to the field of cloud computing technology. The method includes the following steps: collecting access records of data shards in a distributed file system, constructing a business-related access log, and calculating the access heat index and periodic feature vector of the data shards; extracting the target data shard identifier and matching the target data shard identifier with the periodic feature vector; scheduling a background thread to stream the prefetched target shard from the cold storage medium to the hot storage buffer, and adjusting the corresponding I / O scheduling priority according to the preheating state. This invention solves the problem in existing technologies where static cold / hot layered architectures cannot perceive sudden changes in business access patterns, leading to delayed cold data determination, cross-layer queries triggering data migration storms, and consequently, performance fluctuations in hot data queries. This is achieved by constructing a business-aware prediction-prefetch-feedback closed-loop mechanism.

Embodiments of the present application provide a data migration method, device, storage medium and program product, and relate to the field of distribution. The method comprises: obtaining a data migration request, the data migration request being used to indicate migration of data tables in a source database to a target database; in response to the data migration request, performing data migration on each data table based on a system change number (SCN) of each data table; wherein the SCN of each data table obtained in the process of data migration is used to judge a data change range in subsequent incremental synchronization. The method of the present application increases the independent SCN control dimension for each data table, and improves the accuracy of incremental data synchronization and the overall migration efficiency.

The application belongs to the technical field of sensitive data management and control, and particularly relates to a business sensitive data management and control system based on a block chain, wherein the system comprises an architecture definition module, a ledger segmentation design module, a sub-ledger consensus construction module, a cross-business domain data interaction module, a sensitive data node allocation module and a ledger fault tolerance module. The scheme realizes local encrypted storage of sensitive data original text and operation logs of the sub-ledger by constructing a double-layer segmented architecture of the main ledger and the sub-ledger, combining cloud network fusion technology, completing sensitivity evaluation through feature selection and a machine learning model, preferentially allocating high-frequency data to near-main-ledger nodes according to data heat, preferentially allocating new data to low-load nodes according to node load, obtaining an optimal node load allocation scheme through a genetic algorithm when there is no low-load node, realizing dynamic expansion and merging of the sub-ledger, reducing data migration cost during business domain expansion, and guaranteeing uninterrupted sensitive data access services.

The application discloses a data migration method and device, electronic equipment and storage medium. The method comprises the following steps: constructing a hierarchical node graph according to demand elements of a data migration system under multiple demand types; different demand types are located in different levels; obtaining a standard data model matched with at least one data migration system respectively, the standard data model is used for storing the mapping relationship between multiple elements of the data migration system, and two elements in each element mapping relationship belong to different demand types; establishing the mapping relationship between demand elements in adjacent levels in the hierarchical node graph according to the standard data model, forming a migration mapping relationship graph; and migrating data in each data migration system to the data migration system according to the migration mapping relationship graph. Through the technical scheme, the data migration process based on data assets can be automated, the workload of manual data migration is reduced, and the data migration efficiency is improved.

Embodiments of the present application provide a garbage collection method, an electronic device and a storage medium. The method comprises determining a plurality of source blocks to be recycled and a corresponding destination block of each source block; performing secondary alternating programming on the plurality of source blocks to the plurality of destination blocks, and marking the source blocks that have participated in the first programming; and in response to one or more source blocks in the plurality of marked source blocks satisfying a preset condition, releasing the source blocks that satisfy the preset condition. By using secondary alternating programming and marking the source blocks that have participated in the first programming, the plurality of source blocks can interleave and program their respective valid data into the destination blocks, thereby delaying the release of each source block, and thus enabling the source block release step to be executed uniformly. Compared to recycling each source block one by one in the original manner, secondary alternating programming can reduce the additional data migration writing step, effectively suppressing the write amplification phenomenon, and thus improving the performance of the QLC storage device.

This invention provides an efficient expansion method and system for erasure coding-based deduplication systems, applicable to dynamic expansion scenarios of distributed storage systems based on object-specific encoding. By employing a direct data migration mechanism between nodes during system expansion, client-side data transmission is avoided, significantly reducing the amount of network data transmitted during expansion. When adding a new node, the original data block on the existing node is split into a retained portion and an incremental portion. Only the incremental portion is directly migrated to the new node for combination. An incremental verification update method is used, calculating the verification increment only based on the migrated incremental data and merging it into the original verification block, achieving synchronous updating of verification information. This avoids re-encoding and calculation of the entire data, improving expansion efficiency. By evenly distributing data, this invention ensures consistent I / O performance after expansion, effectively improving the system's expansion performance.

The application provides a kind of synchronous radiation light source data migration method, system and electronic equipment, relate to data storage technical field, the method can be based on the creation time, access time, access frequency and keyword search word frequency of data and other factors Quantification of the temperature of data, and according to this temperature, the future access mode of data is estimated, the accurate judgment of hot temperature cold data is realized, and the hot temperature cold data can be more reasonably stored in the corresponding hot temperature cold storage pool, solve the problem that the synchronous radiation light source data in the prior art exists in the process of data migration Data migration is not reasonable, the problem of low data migration efficiency.

This invention provides a cross-scenario data migration and adaptation method for intelligent agents in life services. The method includes extracting common dimensions across different scenarios to construct a shared feature vector space; converting unstructured labels from the source scenario into mathematical vectors; calculating the influence of source domain data on the target scenario and constructing a cross-domain attention network; based on an initial migration strategy obtained through weighted migration, adjusting the weights in reverse according to real user feedback data in the target scenario; defining a loss function and a comprehensive evaluation index, and updating parameters using gradient descent; when new and old data conflict, setting a time decay factor based on the principle of prioritizing real-time data, and if behavioral records exist in the target scenario within a specified time period, forcibly setting source domain weights and directly adopting the target domain profile. This invention achieves efficient migration and adaptation of user preference data from the source domain to the target domain through multiple stages, including constructing a common space, standardizing source data, weighted migration, closed-loop optimization, and conflict circuit breaking.

This invention discloses an AI-based data management system and method for production machinery operation, belonging to the field of industrial data management technology. The method includes: real-time acquisition of raw time-series data and derived data during the operation of production machinery, and extraction of data feature information; construction of a data value assessment model to score the total value of the data and predict access popularity, generating storage tags; construction of a data hierarchical decision model based on the storage tags, comprehensively analyzing real-time data access patterns, storage costs, and lifecycle stages, generating dynamic hierarchical instructions to dynamically allocate data to corresponding storage levels; real-time monitoring of data access patterns, dynamically adjusting storage tags, and triggering data migration operations to optimize storage performance and access efficiency. This invention improves the access speed of critical data and reduces the storage cost of low-value data through AI-driven dynamic data hierarchical storage.

The application discloses a data migration method and device, the method comprises the following steps: receiving migration instruction information; the migration instruction information at least comprises an established subtable, an original table storing data, and a migration data volume at each historical data migration time; based on the established subtable, an incremental data migration task is established, migration logic corresponding to the incremental data migration task is determined, and based on the migration logic, the incremental data migration task is executed to obtain a first subtable comprising incremental data; based on the data stored in the original table, the migration data volume at each historical data migration time, and a preset creation rule, at least one historical data migration task is created, and based on a preset timing scheduling task, the at least one historical data migration task is executed to obtain a second subtable comprising historical data to be migrated; and based on the first subtable and the second subtable, a target subtable is obtained. The above method realizes data migration when an application is running online without stopping, and improves the reliability of the application during the data migration process.

A method and a device for data migration of a blockchain. The method includes generating a state tree based on state data of accounts in the blockchain, in which the state tree is a B+ tree based on a copy-on-write technology, and each leaf node of the state tree is configured to store the state data; determining a target state tree to be migrated from the generated state tree when data migration is triggered; sending state data of the target state tree to an extended node of a master node; if migration completion information is received from the extended node, updating a data mapping table stored in the master node, in which the data mapping table is configured to represent a mapping relationship between state data stored in the extended node and the extended node; and deleting the target state tree in the master node.

The application discloses a data transmission method and a communication device. The method comprises the following steps: in the case of transmitting traffic data of a first service through a first channel, sending a first request to a second device, wherein the first request is used for requesting to switch the data transmission mode of the first service from a single-channel transmission mode to a double-channel transmission mode, and the first service is a real-time service; receiving first indication information from the second device, wherein the first indication information is used for indicating that the data transmission mode of the first service is allowed to be switched from the single-channel transmission mode to the double-channel transmission mode; and migrating part of the traffic data of the first service on the first channel to the second channel for transmission. Through the application, the data transmission rate is improved, the service delay is reduced, the carding phenomenon is avoided, the flexibility of data transmission is realized, and the user experience is improved.