1331 results about "Cloud storage" patented technology

The invention relates to the technical field of archive management, in particular to an archive full life cycle management system and method based on cloud computing. The method specifically comprises an intelligent classification module based on a format adaptation degree function and semantic density analysis, a five-stage life cycle management module, and an authority scheduling module fusing RBAC / ABAC authority control and block chain auditing, and based on a matrix decomposition archiving static coding mechanism, document security storage and analysis are realized through an orthogonal matrix and pseudo-inverse operation, and the file security is realized. The data security in a long-term archiving scene is improved; cloud storage resources are dynamically optimized according to the document life cycle state and the access frequency, and the storage cost is reduced in combination with a cold and hot layering strategy; and an AI algorithm is introduced, the archive retention value is automatically evaluated according to laws and regulations and semantic analysis, and a compliant destruction decision is realized. According to the method, through multi-model fusion metadata extraction, dynamic strategy binding and automatic state transition, the whole-process management efficiency and safety are remarkably improved.

The invention relates to the technical field of data processing, and discloses a multilevel cloud storage cluster construction and data storage method. The method comprises the steps that S1, multiple levels of storage layers are constructed and comprise a cache layer, a performance layer and a capacity layer, and the storage layers are provided with different storage media which are used for storing hot data, warm data and cold data respectively; s2, determining data popularity by monitoring data access frequency, access time interval and data importance; and S3, dynamically adjusting the storage hierarchy of the data according to the data popularity, and triggering hierarchical migration of the data. By constructing a multi-level storage layer and adopting a hierarchical architecture that the cache layer stores high-frequency data, the performance layer stores intermediate-frequency data and the capacity layer stores low-frequency data, high-heat data is preferentially stored in a high-performance medium, access delay is reduced, and data reading and writing efficiency is improved. Meanwhile, in combination with a dynamic data migration strategy, the storage position can be adjusted in real time according to the data access popularity, and the overall performance of the system is further optimized.

A video streaming system including a video cache system having a cache database and a video storage device, and including a video server configured to communicate with a gateway system configured to store multiple video streams, communicate with a cloud backup system configured to store video in a file system, and communicate with a cloud server to receive a stream request, the stream request indicating requested video that comprises multiple video segments. The video server is further configured to receive the stream request and determine a playlist that indicates where on the video cache system, the cloud backup system, and the gateway system the video segments that are needed to fulfill the stream request are stored, and to communicate needed video segments to the video cache system in preparation for providing streaming video to fulfill the stream request.

The invention discloses a cloud-edge collaborative intelligent storage node dynamic deployment method and system. The method comprises the following steps: monitoring performance indexes such as edge node data traffic and storage resource state in real time; a deep learning algorithm combining time sequence analysis and an LSTM neural network is adopted to analyze traffic information, and a data access demand is predicted; edge nodes and cloud storage resource configuration are dynamically adjusted based on a prediction result, and an intelligent scheduling algorithm, a data cold and hot separation strategy and a self-adaptive fragmentation technology are introduced to allocate resources; the storage node layout is optimized in real time, and an optimal data distribution path is selected through a reinforcement learning strategy; data security and access control are realized by adopting end-to-end encryption, multi-level access control and block chain technologies. The system comprises a monitoring acquisition module, a prediction analysis module, a resource scheduling module, a path optimization module and a security control module. According to the method, the utilization efficiency of storage resources is improved, data delay is reduced, system stability and data security are enhanced, and the method is suitable for a cloud edge collaborative storage scene.

The invention discloses a cloud edge collaborative multi-source data fusion safety management and control system for intelligent power distribution equipment, relates to the technical field of intelligent power grids and power distribution automation, and is used for managing and controlling 10-35 kV power distribution equipment. The system comprises an edge data acquisition module, a preprocessing module, a cloud storage management module, a cloud edge collaborative scheduling module, a multi-source data fusion module, an intelligent risk assessment module, a safety control execution module, a safety protection module and a man-machine interaction module. The modules are interacted through a 5G / industrial Ethernet; the acquisition module obtains multiple parameters, the preprocessing module cleans standardized data, the cloud side performs hierarchical storage, the scheduling module allocates tasks, the fusion module integrates data, the evaluation module performs grading risk, the protection module guarantees safety, and the interaction module performs visual alarm. The method improves the power distribution data quality and risk assessment precision, optimizes the cloud edge cooperation efficiency, enhances the safety protection capability, achieves the preventive operation and maintenance of equipment, reduces the fault and power failure time, reduces the operation and maintenance cost, and provides support for the safe and efficient operation of a power distribution network.

The invention discloses a data management system and method based on distributed cloud storage, and relates to the technical field of distributed cloud storage, and the system comprises an access log collection module, a node state monitoring module, a data thermal evaluation module, a redundancy strategy decision module and a storage execution scheduling module. A fragmentation access behavior is recorded through an access log acquisition module, a node state monitoring module acquires a node resource state in real time, and a data thermal evaluation module constructs a three-dimensional thermal model based on an access frequency, a network hop count and a storage cost to calculate a thermal value and supports dynamic weight adjustment; the redundancy strategy decision module divides the fragments into high frequency, medium frequency and low frequency according to the thermodynamic value, and single copy + local cache, erasure code storage and dynamic adjustment strategies are adopted respectively; the storage execution scheduling module executes fragmentation operation and optimizes reading node selection, and data access efficiency is improved through load awareness and a bandwidth-delay joint model; according to the invention, dynamic balance between data access efficiency and storage cost is realized.

The invention relates to the technical field of cloud computing storage, in particular to a cloud storage dynamic optimization method and system, and the method comprises the following steps: based on a cloud storage data access record, recognizing an access time interval sequence, analyzing the variation amplitude of adjacent time intervals, and calculating the standard deviation of the access time intervals; and determining the access period stability according to the standard deviation and the change amplitude, and classifying the data objects to obtain a data access period stability classification value. According to the method, task scheduling is more accurate by identifying a data access time interval sequence and changes thereof and refining data classification and storage resource allocation, a high-throughput and low-delay storage path is matched, the health condition of a storage node is evaluated, a fault node is identified in advance, data stability is guaranteed, and data copy distribution is adjusted; the storage reliability and stability are improved, the storage path for accessing the intensive data is optimized, the dynamic load balance of the storage resources is realized, and the storage efficiency is improved.

The invention discloses a human resource archive filing management system, and relates to the technical field of data management, the system comprises an acquisition module, a processing module and a management module, the acquisition module acquires original archive data of employees, transmits the data to the processing module, classifies the original archive data according to a preset first rule, and stores the classified data to the management module; the first archive data is obtained, controlled and managed, a calling permission and a distribution strategy are generated, the first archive data is recognized through a preset second rule, second archive data is generated, corresponding processing is conducted according to the processing priority, a processing log is reserved, third archive data are obtained and transmitted to the management module, and the management module is used for managing the first archive data. A distributed cloud storage cluster and a local storage node are constructed, an overall storage architecture and a retrieval path of third archive data are optimized, archive management is refined through three-dimensional classification, data integrity is guaranteed through field-level verification and automatic completion, scene-based dynamic authority control safety is achieved, compliance risks are intelligently cleared and avoided, and efficiency is improved through mixed storage. And active management of archive management is promoted.

The invention discloses a forestry ecological environment real-time monitoring and management method based on big data, and belongs to the technical field of forestry ecological environment management, and the method comprises the following steps: S1, multi-source heterogeneous data collection and sensor network deployment; s2, multi-modal data fusion and intelligent transmission: developing an adaptive communication protocol dynamic switching module, and automatically switching to a satellite communication link in a 4G / 5G network blind area; s3, mass data storage and distributed calculation: constructing a hybrid cloud storage architecture, and storing real-time monitoring data into a Redis cache queue; s4, intelligently extracting ecological environment indexes; s5, dynamically evaluating the ecological bearing capacity; s6, performing multi-level early warning and emergency response; s7, making a precise forestry management decision; and S8, carrying out system self-optimization and closed-loop management. According to the method, the air-space-ground integrated monitoring network is constructed, so that the comprehensiveness and the real-time performance of forestry ecological environment perception are remarkably improved; multi-source data of the unmanned aerial vehicle, the satellite remote sensing and the ground sensor are complementary.

The invention discloses a refrigerator refrigerating capacity adjusting method which comprises the following steps that S1, environment data are collected in real time through a plurality of sensors, and future environment changes are accurately predicted through a machine learning algorithm; s2, constructing an environment change prediction model by using a machine learning algorithm; s3, the refrigerator intelligent refrigeration adjusting system analyzes and models use habits of the user, and predicts future refrigeration requirements; s4, according to the prediction data obtained in the step S1 and the step S3, the refrigerant flow and the working mode of the compressor are dynamically adjusted; s5, the refrigerator intelligent refrigeration adjusting system dynamically adjusts a refrigeration strategy according to comprehensive analysis of the real-time temperature data and the prediction result, and the operation mode of the refrigerator is continuously optimized; and S6, regularly storing the use data and the environment data of the user in a local or cloud storage. According to the method, future refrigeration requirements can be accurately predicted, the refrigeration strategy is dynamically adjusted, and the energy efficiency is improved to the maximum extent.

The invention discloses an archive retrieval method and system based on cloud computing, and the method comprises the steps: receiving retrieval request information inputted by a user, and analyzing a retrieval intention according to the retrieval content in the retrieval request information, the historical retrieval behavior of the user and a retrieval field database by using a context perception semantic analysis model, generating a user retrieval semantic vector in combination with the retrieval intention; the method comprises the following steps: constructing an archive data index map for archive data stored in a distributed manner in a cloud storage environment; according to the user retrieval semantic vector, a search domain related to retrieval semantics is dynamically positioned in the archive data index map in a multi-layer projection calculation mode; and in the search domain, screening a candidate archive set according with the query intention by using a matching algorithm of multi-modal semantic fusion, and sorting and displaying the candidate archive set. By utilizing the embodiment of the invention, the accuracy and efficiency of file retrieval and the user experience can be improved.

The invention provides an intelligent electronic assembly quality control system based on AOI (automatic optical inspection) and SPI (solder paste printing inspection) integration, and belongs to the field of electronic information equipment flexible production lines. Comprising an AOI module, an SPI module, a data integration module, a feedback control module and a data storage and transmission module. All the modules carry out high-frequency data transmission through an industrial Ethernet; according to the system provided by the invention, the precision of quality detection is improved, and the accurate recognition of defects is realized through the data association of the AOI and the SPI module and the anomaly detection of the SVM and the CNN algorithm. Real-time process optimization is achieved, the feedback control module automatically adjusts printing and mounting process parameters according to the quality data model, and the product consistency is remarkably improved. The method improves the traceability and safety of the data, achieves the efficient storage and safety access of the detection data through distributed cloud storage and data encryption transmission, and supports the quality problem traceability.

The invention provides a local cache resource optimization method and system combined with a node state, and relates to the technical field of cloud storage optimizing.The method comprises the steps that firstly, a state index set, including indexes such as resource use, security levels, survivability and transmission cost, of multiple nodes in a distributed network is obtained; calculating a target cache value of the current node to the target information resource according to the target cache value, generating a local cache decision parameter after correction of a dynamic adjustment factor, if the local cache decision parameter exceeds a node cache trigger threshold value, storing the target information resource into a local cache of the current node and generating a cache resource distribution update instruction, and updating the cache resource distribution update instruction. And adjusting the adjacent node cache synchronization strategy according to the cache resource distribution updating instruction, thereby realizing the cooperative operation of the cache resources, and improving the resource utilization rate and access efficiency of the distributed network.

The invention relates to the technical field of land parcel management, in particular to a land parcel full life cycle compliance management and operation monitoring system, which comprises a multi-source data acquisition module for acquiring multi-dimensional land parcel data such as spatial geographic ownership planning and approval from a plurality of heterogeneous data sources in real time; the data fusion and storage module performs data cleaning and standardized fusion, adopts a distributed cloud storage architecture and introduces data version management to support historical tracing; the intelligent compliance judgment engine generates a compliance state judgment result and a compliance risk report based on the dynamic compliance rule base and machine learning; the interactive visual monitoring module is used for dynamically displaying the compliance state of the land parcel space information life cycle stage and the key index trend; and the automatic early warning and intervention module is used for performing graded early warning and triggering a rectification or emergency intervention process. According to the invention, the problems of difficulty in compliance judgment, low efficiency, non-visual monitoring and slow risk response of existing land parcel management data integration are solved, and intelligent compliance management and operation monitoring are realized.

Systems, methods, and apparatuses are described for enhancing a user experience in event live streaming applications such as video conferencing systems. In an example, first and second virtual meetings are scheduled, each meeting scheduled to include a presentation of the same document. The document is stored in a cloud storage and is associated with both the first meeting and the second meeting. During the first meeting and the second meeting, respective virtual meeting communication data is captured and stored in the cloud storage as metadata associated with the document, and associated with the respective first or second meeting during which it was captured. After the meetings conclude, a user accesses the document and is presented with an option to view the metadata associated with the first or second meeting.

The invention discloses an intelligent facial skin care monitoring management system which is composed of a hardware layer, a data layer, an algorithm layer and an application layer, the hardware layer collects facial skin images and external environment data through an RGB camera, a UV light sensor, a depth sensor and an environment sensor, and the data layer achieves encryption transmission and cloud storage of the collected data. The algorithm layer performs skin state detection, classification and trend prediction based on multi-modal data fusion, a deep convolutional neural network and a time sequence model, and the application layer provides skin care scheme suggestions and virtual trial functions through a personalized recommendation algorithm. The system can detect the multi-dimensional features of the skin at high precision, generate a dynamic skin care scheme, predict the change trend of the skin state, and improve the user experience through a visual interface and an interaction function. The method has the advantages of being high in detection precision, scientific in recommendation scheme, accurate in trend prediction, safe in user privacy and the like, and can be widely applied to skin care product research and development, personalized skin care management and beauty industries.

The invention relates to the technical field of agricultural environment monitoring, and discloses a cultivated land quality monitoring method. The method comprises the following steps: acquiring soil multi-parameter data, and carrying out sensor network deployment and anti-interference processing; performing exception filtering, standardization and missing value interpolation on the data; cloud storage data are generated through sliding window segmentation and encryption compression; generating a monitoring report based on the multi-dimensional analysis and the dynamic chart; adopting a plurality of intelligent algorithms to identify abnormal modes and constructing an early warning strategy library; and finally, real-time monitoring and early warning grade division are realized. According to the invention, full-process automatic monitoring and intelligent early warning of the cultivated land quality are realized, and the monitoring precision and the decision-making efficiency are obviously improved.

The invention discloses a cache data security protection system based on cloud computing, particularly relates to the field of security protection, and comprises a data acquisition module, a cloud storage module, a verification execution module, a security processing module and a protection early warning module. The data acquisition module acquires cache data in the platform in real time, and the cloud storage module adopts a distributed architecture to safely store various types of data, and performs classified preprocessing and encrypted storage; the verification execution module identifies the safety signal, generates an execution signaling, and transmits the execution signaling to the execution node through the encryption channel; the security processing module is used for cleaning, desensitizing and encrypting input and recorded information and converting the input and recorded information into calculation processing data, and the protection early warning module is used for integrating a data source, constructing an analysis data set, detecting abnormal behaviors through an algorithm and a rule engine and generating a security signal; in the security comparison stage, the to-be-approved data is compared in real time, the operation risk is evaluated, the signal data is transmitted through an encryption channel, and a full-link log is stored in the block chain.

The invention relates to the technical field of storage systems, in particular to an electrolytic cell test data cloud storage method, which comprises the following steps: performing timestamp marking and partition size calculation on electrolytic cell test data, aggregating the data into partitions with fixed sizes according to timestamps, calculating a data density value and a write-in rate value of each partition, obtaining a partition data set, and storing the partition data set into a database; and comparing the write-in rate value in the partition data set with a preset partition threshold value. According to the method, through a dynamic partition management and multi-path data flow processing mechanism, in the storage process of the test data of the electrolytic cell, the data is subjected to partition aggregation through timestamp marking, a data density value and a write-in rate value reflect the dynamic change of the partition, and the size of the partition is adjusted in real time to cope with the fluctuation of a data write-in load. Multi-channel data stream distribution and merge sorting improve concurrent processing performance and eliminate single-channel bottleneck restriction. A double-buffer-area framework is matched with data access frequency analysis to support intelligent migration of cold and hot data, and IO resource configuration is optimized.

The invention relates to the technical field of video processing, and discloses a panoramic monitoring camera video processing method and device based on cloud storage, and the method comprises the steps: carrying out the coding compression and layering processing of original video data collected by a panoramic monitoring camera, obtaining an initial data set, and executing the calibration frame parameter optimization calculation, camera calibration parameters are obtained; generating hierarchically stored processing data according to the initial data set and the camera calibration parameters; performing multiband fusion and triangulation calculation on the calibrated panoramic video data to obtain a three-dimensional scene digital model; analyzing the hierarchically stored processing data and the three-dimensional scene digital model to obtain a monitoring object behavior prediction result; according to the method, differential modeling and behavior prediction of panoramic monitoring on different areas are realized, smooth user experience is provided, and video access delay and operation complexity are reduced.

The invention relates to the technical field of government affair information management, in particular to a cloud-based government affair information management method, which comprises the following steps of: S1, adding a sensitivity label for each piece of government affair data in a cloud storage layer; s2, extracting data from the government affair database and generating a dynamic data view with an authority mark; s3, establishing a mapping relation table between routing keys and data source addresses; s4, acquiring a requester department function code, a business scene code and a target data identifier; s5, matching a target data source, and based on the authority mark in the dynamic data view, executing a desensitization operation on the sensitive field; and S6, if the genetic marker bit of the original data is true, superposing the data security classification in the original tag and the current security classification to form a new tag. According to the method, by constructing the sensitivity label and the dynamic authority control mechanism, safe inheritance and accurate access of government affair data in cross-department circulation are achieved, and the safety and compliance of data management are remarkably improved.

The invention relates to an engineering quality evaluation archive management method based on digital twinning, which belongs to the technical field of engineering quality management, and comprises the following steps of: integrating a BIM (Building Information Modeling) (modeling each component and coding to construct a digital twinning mapping relation), an AR (Augmented Reality) technology (realizing double superposition of the model and display) and cloud storage; according to the invention, intelligentization of constructional engineering quality evaluation and digitalization and convenience of quality acceptance data management are realized, the efficiency and level of engineering quality management are effectively improved, engineering problems can be early warned in time in the construction process, detection and repair can be carried out in time, and the reliability and traceability of engineering quality are guaranteed; in addition, according to the scheme, potential hazards can be warned in advance according to data in the project quality verification and evaluation file management method.

The invention relates to the technical field of concrete transportation monitoring, and discloses a concrete transportation monitoring method and system, and the method comprises the following steps: S1, data collection: obtaining concrete state data and environment data in real time through a data collection module, the data acquisition module comprises a temperature sensor, a humidity sensor, a vibration sensor, a light sensor and a high-definition camera device; s2, data fusion and preprocessing; s3, data transmission and cloud storage; s4, cloud data analysis; and S5, performing anomaly detection and early warning response. By means of various sensors and high-definition camera devices in the data acquisition module, state data and environment data of concrete are obtained in real time, multi-dimensional information monitoring is achieved, the edge calculation module conducts data preprocessing, data transmission delay is remarkably reduced, the real-time performance of the system is improved, whole-course non-blind-area monitoring of the concrete transportation process is achieved, and real-time monitoring of the concrete transportation process is achieved. And data accuracy and reliability are ensured.

The invention discloses a dynamic log collection method and system and a server. According to the method, a flexible strategy configuration management system is introduced, user-defined configuration is supported, a plurality of target servers, different log files and different collection frequencies can be configured in one log collection strategy, and flexible collection strategies can be configured for different collection objects; in addition, in the method, strategies are automatically distributed, and the complexity of deployment and management is simplified; after the change of the file is monitored, the log file in the appointed path with the change is automatically collected, the automatic log collection and storage process reduces the requirement of manual operation, reduces the possibility of error occurrence, and improves the integrity and reliability of log data; in addition, the method supports the simultaneous storage of the logs in a plurality of target storage systems, such as a local file system, a cloud storage, a database and the like, so that the flexible configuration of log storage is realized.

This application describes an AI-powered Cloud-native Network Video Recorder (NVR) apparatus. This apparatus features multiple Power over Ethernet (PoE) ports, processors, computer-readable memories, and firmware implementing a camera-integration module, an Artificial Intelligence (AI) module, and a cloud-access module. The camera-integration module enables efficient connection and video data reception from heterogeneous cameras. The AI module processes the video data, identifying and generating clips of interest, optimizing surveillance efficiency. Finally, the cloud-access module facilitates the uploading of these clips to cloud storage, ensuring accessible and secure data management. This innovative NVR apparatus enables users to deploy a cloud-native video surveillance system using existing camera devices, significantly lowering the cost of adoption.

The invention discloses a cloud storage service implementation method and system for unified management of multiple types of storage, and belongs to the technical field of cloud storage, and the method specifically comprises the steps: building a unified abstract model oriented to heterogeneous storage media, mapping distributed files, object storage and block storage devices into standardized storage units, deploying a cloud block chain, and carrying out uplink, a storage index table under a chain is constructed, an index state is dynamically updated through a loose coupling consensus mechanism, the loose coupling consensus mechanism comprises a weighted voting algorithm based on a storage node reputation value and an asynchronous verification mechanism based on space-time certification, and data is divided into data pieces and verification pieces; fragmentation information is written into a storage metadata registration chain and a service quality proof chain at the same time, cross-chain storage is carried out on data, and cloud storage resources are dynamically distributed; according to the method, the storage management function is decoupled through the block chain, so that the retrieval accuracy is improved, the retrieval response time is effectively shortened, and the data transmission cost is reduced.

The invention discloses a verifiable data sharing auditing method supporting ownership transfer. The method comprises the following steps: 1) a system initializes common parameters; 2) a data owner encrypts the data based on a block-level message locking encryption algorithm; 3) the data owner calculates a data related label and uploads the data related label to the cloud server; 4) the user side can interact with the cloud server to carry out ownership one-to-many transfer; 5) after the transfer is completed, the transfer party inherits data access and auditing authority; and 6) the new data owner entrusts a third party to verify the integrity of the encrypted data of the target cloud. And 7) the new owner can dynamically update the data. In order to solve the problem that user data in cloud storage is managed by a cloud server so as to lose ownership of the data, an improved encryption algorithm based on block-level message locking encryption is designed to provide block-level ciphertext auditing and realize one-to-many transfer of the ownership of the data at the same time; and the utilization rate of the cloud server and the security of cloud data storage and use are improved.

The invention provides an intelligent document management method and system, and the method comprises the steps: obtaining an uploaded file set, processing each file through a text analysis algorithm, an image analysis algorithm and a structured data analysis algorithm, generating a first feature set, and storing the first feature set in a temporary database; obtaining a distribution record from the first permission configuration file, writing the distribution record into an audit database through a log audit module, and generating a first operation traceability chain; according to the first operation tracing chain, a storage management module is called to perform fragmentation processing on the file and upload the file to cloud storage, a first storage index is generated, and the metadata table and the archive position are associated; and according to the second storage index, calling a disaster recovery backup algorithm to perform multi-region copying on the file, generating a first backup confirmation signal, and recording the first backup confirmation signal into an operation and maintenance log. According to the system and the method, intelligent classification, sensitive information protection and whole-course traceable management of the files are realized, the automation level and the safety of document management are improved, and a comprehensive solution is provided for document management.

The invention provides a college course teaching archive electronic auxiliary arrangement system and method, and the system comprises a multi-source data collection terminal which is used for collecting teaching data and environment data; the behavior fusion model module is used for generating multi-dimensional behavior characteristics through the teaching data, constructing a space-time behavior graph and generating a behavior dependency relationship of teacher-student interaction in a classroom through the space-time behavior graph; the dynamic cloud storage module is used for synchronizing the teaching data and the environment data in real time and allocating storage resources according to presetting; the automatic teaching evaluation module is used for generating behavior diversity evaluation, comprehensive learning trend prediction and teaching abnormity feedback through the spatio-temporal behavior graphs of different time nodes, multi-source data acquisition, behavior analysis, dynamic storage and teaching evaluation are integrated, college teaching management is promoted to be upgraded from informatization to intellectualization, and the teaching efficiency is improved. The method has important academic value and application value.

The invention relates to the technical field of cloud storage, can be applied to business scenes of financial science and technology, medical health and the like, and discloses a load and message queue regulation-based migration method, device, equipment and medium. Monitoring message backlog amount and consumption rate to generate message queue state information, generating migration regulation and control parameters based on the two types of state information, determining concurrency level and issuing rhythm by combining object size classification information, reducing issuing frequency and preferentially scheduling small objects when the message backlog amount is increased and oversize objects exist, and distributing the tasks to the working nodes according to the concurrency level and the issuing rhythm to execute migration, and adjusting migration regulation and control parameters based on execution state updating information. According to the method, concurrency and rhythm are regulated and controlled in real time, object size classification is combined, the problems that traffic is uncontrollable and throughput is affected by large files are solved, and efficient and stable data migration is achieved.