1224 results about "Access frequency" 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 intelligent security operation and maintenance, in particular to an artificial intelligence network security system based on multi-modal large model training, which comprises a server fault diagnosis module, a network attack detection module, an endpoint security monitoring module, a key management optimization module and a threat analysis feedback module. According to the method, the fault prediction accuracy is improved through multi-dimensional data analysis, service interruption caused by sudden hardware faults is reduced, the network access frequency, source and instruction features are evaluated based on the server abnormality, the attack detection accuracy is improved, the misjudgment risk is reduced, the endpoint equipment execution behavior, resource calling and behavior sequence are extracted, and the service performance of the terminal equipment is improved. Fine-grained security monitoring is realized, attack traceability is enhanced, a key strategy is dynamically adjusted, security adaptability is improved, strategy lag risk is reduced, multi-level data is integrated to calculate threat behavior and attack fitting degree, threat assessment fineness and response speed are enhanced, and global security situation awareness is improved.

The invention discloses a big data stream-oriented memory cache storage system, relates to the technical field of memory cache storage, and is used for solving the problem of poor memory cache storage management. According to the method, the access frequency, the timeliness and the service importance of the data are evaluated, the priority ranking model is constructed, the cache strategy is optimized based on the ranking result, the data storage and scheduling efficiency is improved, the write-in and read operation of the memory cache is adjusted in real time in combination with the flow prediction model, memory overload is effectively avoided, and the data storage and scheduling efficiency is improved. According to the method, non-emergency data writing is delayed by adopting a delayed writing and lazy loading strategy, data is loaded as required, meanwhile, a cache elimination mechanism is designed according to data priority, access frequency, survival time and size, data migration is carried out when a cache space is insufficient, reasonable resource allocation is ensured, and through a multi-level and dynamic cache management scheme, the cache management efficiency is improved. Therefore, the system performance, the resource utilization rate and the response speed are remarkably improved, the data processing efficiency is optimized, and cache conflicts are reduced.

The invention relates to the technical field of data processing, in particular to a cloud backup platform data deduplication method based on distributed storage. Comprising the following steps of: performing self-adaptive blocking on data by utilizing a dynamic blocking algorithm, generating a unique identification code and extracting multi-dimensional feature information; establishing a global index through a distributed hash table, mapping a data position and maintaining index consistency; performing redundancy analysis and similarity matching based on a deep learning algorithm, and executing a duplicate removal strategy; according to the access frequency and the load condition, distributed storage of data blocks is realized by adopting a hierarchical graph division algorithm; and a partitioning strategy, index frequency and storage layout are dynamically optimized by using a reinforcement learning algorithm, and global optimization of performance and resources is realized. According to the invention, high efficiency, accuracy and dynamic optimization of data deduplication are realized, and the data storage and management efficiency of the cloud backup platform is improved.

The invention provides an AI agent memory management method and system based on cold and hot stratification, and relates to the technical field of artificial intelligence, and the method comprises the steps: obtaining and preprocessing the historical information of an AI agent, and carrying out the vectorization storage of the historical information; calculating a score based on the access frequency and the semantic association; constructing a dual-threshold triggering mechanism to realize data cold and hot layered migration; establishing index mapping and dynamically monitoring data popularity for returning; and a storage water level dynamic adjustment migration strategy is monitored in real time. According to the invention, the problem of low memory management efficiency of the AI agent is solved, efficient access of memory content is realized, and the storage cost is reduced.

The present invention relates to a data interaction method, device, equipment and storage medium of a multi-layer stacked memory, comprising the following steps: based on the access frequency clustering result, the target transmission data is mapped to a storage location to obtain a multi-layer storage allocation strategy table; based on the multi-layer storage allocation strategy table, the target transmission data is interactively planned to obtain an optimal interactive path plan; based on the optimal interactive path plan, the target transmission data is hierarchically migrated and scheduled to obtain a data migration execution queue; and the data migration execution queue is transmitted in parallel through an asynchronous pipeline transmission mechanism, which solves the technical problem that the existing data transmission strategies often lack in-depth analysis of the historical access characteristics of the storage hierarchy, resulting in unreasonable data distribution, and frequent cross-level data migration increases system delay and energy consumption.

The invention relates to the technical field of information security, in particular to an archive electronic information security system which comprises an archive operation behavior prediction module, an archive data encryption and security storage module, an archive security protection strategy execution module and an archive resource scheduling and security monitoring module. According to the method, through time series data analysis, potential risk behaviors can be recognized in advance, illegal access and abnormal operation can be prevented, losses caused by human errors or malicious behaviors are reduced, data integrity and confidentiality are guaranteed, archive data are stored in blocks, encryption is carried out according to sensitivity and access frequency, and the security of the archive data is improved. Multi-factor identity verification is executed, the flexibility and accuracy of protection measures are enhanced, security vulnerabilities are reduced, priority protection of sensitive archives is ensured through real-time monitoring and resource scheduling optimization, resource waste and risk exposure are avoided, and the security of the sensitive archives is improved. And the overall safety protection capability of archive management in a complex threat environment is improved by collaborative measures.

The invention relates to the technical field of data storage, in particular to a dynamic data distribution storage method and system for an RAID (redundant array of independent disks), and the method comprises the following steps: based on the access frequency of data blocks in a disk, recording the access frequency and timestamp of each data block, carrying out heat rating on the data blocks, and determining thresholds of hot data and cold data. According to the method, the dynamic distribution of data storage is effectively optimized by analyzing the access frequency of the data blocks, and the performance and the economic efficiency of a storage system are remarkably improved by migrating the frequently accessed data to the cache disk and migrating the less accessed data to the low-cost storage; according to the method, unnecessary occupation of high-performance storage resources is reduced, the operation cost is also reduced, heat rating is performed on the data blocks, and the storage positions of the data blocks are correspondingly adjusted, so that the data processing speed is higher, meanwhile, the data security is improved, when a hard disk fails, data recovery is faster, and the service continuity is ensured.

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 data security, in particular to a data asset classification and grading and authority dynamic management system, which comprises an asset parameter acquisition module, a classification feature judgment module, a grading weight calculation module and an authority dynamic adaptation module. According to the method, metadata and path parameters are automatically collected through a distributed crawler in combination with a regular expression, naming parameters and content feature parameters are separated to generate a structured parameter set, field naming similarity is quantized through a Levenshtein distance, content character distribution features are verified through chi-square verification, and a naming similarity and distribution feature dual verification mechanism is constructed. According to the method, sensitivity, access frequency and data volume weight are dynamically distributed through an entropy weight method, grading parameters are generated through linear superposition, an RBAC model is combined with a Dijkstra algorithm to verify access path topology legality, unauthorized access is blocked, and the heterogeneous data classification grading and authority control dynamic adaptive capacity is improved.

The invention discloses a large model data distributed management method and device, equipment and a storage medium, and relates to the technical field of data processing, and the method comprises the steps: segmenting large model training data to obtain a plurality of data blocks, and determining a predicted access frequency based on a long short-term memory network model and the historical access frequency of the data blocks; caching the large model training data corresponding to the data block to a corresponding data cache layer by utilizing the predicted access frequency; constructing a resource portrait by using the static attribute and the dynamic index of the GPU node, and allocating the large model training task to a target GPU node by using a preset hybrid strategy and the resource portrait based on the predicted access frequency and the storage position corresponding to the data block in the data cache layer; when it is monitored that the large model training task on the target GPU node is executed, periodic snapshot is conducted on the large model training task through a distributed snapshot algorithm, and the obtained complete data state is stored in a distributed storage center. And the resource vacancy rate is reduced.

The invention relates to a machine room environment intelligent regulation and control method fusing multi-source sensing information, and the method comprises the steps: collecting the temperature and humidity data of different regions in a machine room through a temperature and humidity sensor, and collecting the movement track and stay time data of a person in the machine room through a person positioning device, transmitting the collected machine room environment data and personnel activity data to a data fusion module; in the data fusion module, machine room environment data and personnel activity data are aligned according to a time sequence, wrong data and outliers are eliminated, and a data set in a unified format is obtained; a correlation coefficient matrix among the characteristic parameters of the computer room is analyzed through correlation, the characteristic parameters with correlation higher than a preset correlation threshold value are screened out, and the characteristic parameters comprise temperature, humidity, personnel access frequency and staying duration parameters.

The invention relates to the technical field of big data processing, and provides a mass data storage optimization method which comprises the following steps: analyzing target data according to access frequency and / or access time of the data, and dividing the target data into hot data, warm data and cold data; aiming at the hot data, the temperature data and the cold data, performing compression processing by adopting corresponding compression algorithms; before the target data is stored, performing duplicate removal on the target data according to the fingerprint of the target data; monitoring the use condition of the memory, the access frequency of the target data and the performance of the compression algorithm in real time, and deploying system resources according to a monitoring result; and setting a multi-level cache, and caching different types of target data to the multi-level cache according to the divided types of the target data. According to the technical scheme, mass data can be efficiently stored and rapidly accessed, and the storage space utilization rate and the overall system performance are remarkably improved.

The invention relates to the technical field of data storage, can be applied to business scenes such as medical health and financial science and technology, and discloses a multi-Pool storage resource adaptive writing method which comprises the following steps: monitoring available storage space, read-write input / output rate, average response time and data access frequency of each Pool; dynamically adjusting the current write-in weight of the Pool according to the monitoring result; converting the current write-in weights of all Pool into probability values, and constructing a discrete probability distribution table; selecting a target Pool through a pseudo-random number generator according to the probability distribution table; and writing the to-be-stored data into the target Pool. According to the method, the running state of each Pool is continuously monitored, and the writing weight is adjusted in real time, so that the problem of resource allocation rigidity under a fixed weight strategy is avoided; in combination with a discrete probability distribution table constructed based on normalized weight and a pseudo-random selection mechanism, selection of the target Pool has certain load balancing elasticity, and data can be more effectively guided to be written into the Pool with light load and excellent performance.

The invention relates to the technical field of data processing, in particular to a block chain-based data sharing system, which comprises a field path hash index module, a permission entry sorting module, a path compliance judgment module, a certificate state updating and verification module and a joint permission confirmation module. According to the method and the device, the multilevel structure information of the field path chain is extracted from the data sharing calling request, salt coding is performed in combination with the calling party address, and hash processing is performed, so that unique mapping of the field path is realized, field identification fuzziness and path conflict are effectively avoided, and accurate positioning of sharing operation is ensured. By means of path structure analysis and access frequency normalization processing, a field entry weight sequence is established in combination with a scoring mechanism, field access entries can be sorted according to actual use frequency and structure hierarchy, and authorization judgment efficiency is improved. A task responsibility number and a timestamp are extracted from an access behavior track, a responsibility chain segment is established, and abnormal changes in a responsibility path can be effectively recognized.

The invention relates to the technical field of big data and the field of financial science and technology, and discloses a database load optimization method based on dynamic fragmentation, which comprises the following steps: establishing an association relationship between fragments and storage nodes by configuring basic logic fragments and dynamic logic fragments; monitoring a data access record, identifying hotspot data and marking the hotspot data as a to-be-migrated data block; when the fragment load exceeds a threshold value, migrating the high-load data block to the dynamic logic fragment; and when the access frequency is reduced, moving the low-load data block back to the basic logic fragment. According to the method, through configuration and management of the dynamic logic fragments, in combination with real-time monitored access records and load state analysis, the high-load data blocks are recognized and migrated to the dynamic logic fragments for storage, and the overload problem of the basic logic fragments is relieved. Meanwhile, the low-load data blocks are moved back to the basic logic fragments, dynamic adjustment and balance of storage resources are further achieved, the load balancing capacity of the system is improved, and the requirements for high concurrency and real-time load changes in a financial scene are met.

The invention provides a safety monitoring method and system for a financial information service platform. Wherein a collaborative encryption transmission channel between a budgeting unit and an auditing department is constructed, a joint key is generated based on a multi-party computing protocol, and the ciphertext operation authority of a sensitive field is dynamically configured; performing ciphertext variance calculation and regression analysis on the encrypted budget execution record through the security operation node, generating a verifiable statistical result, binding the authority, and synchronously updating the sensitive field access frequency; a hierarchical security threat baseline is generated in combination with permission, frequency and historical query records, and cross-project high-frequency query and data operation deviation are associated; and when the query deviates from the baseline, calculating the risk confidence coefficient according to the deviation, triggering multi-party verification path adjustment and collaborative alarm, and dynamically limiting the ciphertext permission of the abnormal query. According to the technical scheme provided by the invention, cross-department data security sharing and dynamic risk collaborative prevention and control are realized.

The invention discloses a data storage control method and device, a storage medium and electronic equipment, and relates to the technical field of computers, and the data storage control method comprises the steps that the access frequency of multiple pieces of data accessed in a future time period is predicted according to historical access characteristics of the multiple pieces of data in a storage system; detecting whether the data access types of the data are changed or not according to the frequency range in which the access frequency falls; under the condition that it is detected that the data access type of the target data is changed, a storage strategy of the target data is adjusted according to the target data access type obtained after the target data is changed, and a target storage strategy is obtained; and executing the target change operation on the target data, the target change operation being used for changing the storage strategy of the target data to the target storage strategy, thereby solving the technical problem that it is difficult for a storage system to consider both storage overhead and storage performance during data storage. The technical effect that the storage cost and the storage performance are both considered when the storage system stores the data is achieved.

The invention relates to the technical field of storage optimization, in particular to a data storage management system and method based on big data, and the system comprises an efficiency monitoring module, a medium evaluation module, a decision generation module, a strategy execution module and an effect feedback module. According to the method, input and output delay, a medium health attenuation curve and a bandwidth fluctuation map are collected, a dynamic feature space is constructed, the stability of the system is judged in combination with a Lyapunov index, early warning of performance abnormity is achieved, health indexes such as an erasing cycle, a seeking error rate and a charge retention rate are fused, and the access frequency and the load pressure are combined. Calculating a compensation coefficient, improving heterogeneous medium cooperation efficiency, pre-migration evaluation cost and topology adjustment income, improving resource configuration accuracy, dynamically adjusting hot and cold data distribution and node load, enhancing system scheduling flexibility, comparing throughput, medium wear rate and synchronous delay, and quantifying stability gain brought by configuration change; and a closed-loop process from identification to feedback is constructed.

The invention relates to the technical field of power management, in particular to an intelligent computing resource energy-saving scheduling system based on load prediction, which comprises a cache access monitoring module, a burst load judgment module, a frequency fluctuation analysis module, a load classification and identification module and an intelligent regulation and control strategy module. According to the method, the access frequency fluctuation is analyzed by monitoring the cache access data, the access mode change is identified, the load judgment precision is improved, the burst load identification is enhanced in combination with the hit rate reduction range, the resource scheduling is more accurate, the CPU / GPU operation frequency and the fluctuation rate are monitored, the abnormal fluctuation is identified, the calculation resource allocation is optimized, and the energy consumption waste of frequency adjustment is reduced; load classification, resource matching degree optimization, dynamic adjustment of CPU / GPU computing power and memory allocation, coordination of computing and data transmission, optimization of energy consumption management, guarantee of task stability and reduction of idle and unbalanced resource problems are carried out based on a time proportion and task characteristics.

The invention relates to the technical field of cloud computing and distributed storage, and discloses a distributed metadata management method and storage system based on cloud computing, and the method comprises the following steps: S1, collecting the access frequency of a plurality of metadata objects in a set time period; s2, performing weighting processing on the access frequency, and constructing access heat distribution under multiple time scales; s3, obtaining the available cache capacity of a plurality of cache nodes, and constructing cache resource weight distribution; s4, assessing the access cost and migration cost between each metadata object and each cache node; and S5, generating a scheduling strategy between the metadata object and the cache node based on a preset optimization target, wherein the optimization target is access cost minimization. According to the method, a heat, resource and cost joint modeling mechanism is constructed, and optimal scheduling and feedback adaptive control are combined, so that accurate decision, dynamic adjustment and migration cost controllability of copy deployment are realized, and the performance and stability of distributed metadata management are effectively improved.

The invention relates to the technical field of computer data backup, in particular to a cloud-based data backup system and method, and the system comprises a data load monitoring and partitioning module which constructs a feature set of computer backup data based on storage nodes in a cloud environment, recognizes a data set with similar features, and carries out data backup according to the difference degree between feature parameters; and judging whether the feature set of the computer backup data needs to be re-divided or not to obtain partition data of the computer backup data. According to the invention, through real-time monitoring and clustering analysis of the data features, the backup process is more intelligent and efficient. The dynamic adjustment of the feature set enables data with different access frequencies and importance to automatically obtain corresponding priorities. According to the method, the backup of the key data can be preferentially completed in the peak period, the risk that the key data is delayed to be processed in the case of resource shortage is reduced, meanwhile, the idle period is fully utilized to process the low-priority data, and the resource utilization efficiency is optimized.

The invention relates to the technical field of data security, in particular to a financial data security management system and method, and the system comprises a data monitoring module, a vulnerability prediction module, a dynamic isolation module, a behavior analysis module and a security response module. According to the invention, through deep monitoring and analysis of financial data, the capability of identifying and processing abnormal operation is improved, so that the data security is enhanced, and through real-time collection of financial transaction and account behavior data and detailed analysis of fund flow frequency and account access frequency, risks are effectively identified and potential security vulnerabilities are predicted. By dynamically analyzing the association between the transaction node and the risk event, evaluating the risk level and automatically adjusting the data storage position and the access authority according to the risk evaluation, the security of the data in the transmission and storage process is remarkably improved, in addition, by monitoring the behavior mode change of the isolated account, the abnormal activity is further identified and corrected, and the safety of the data transmission and storage process is improved. And the overall safety and stability of the financial data environment are ensured.

The invention discloses an information security management method and system based on sensitive data, relates to the technical field of data management, and comprises the following steps: realizing quantum encryption transmission of remote high-sensitive data by combining a quantum key distribution link and a dynamic chaotic key stream; the partitioning and synchronization priority is dynamically adjusted according to the node load, the data integrity is verified through CRC, the distributed storage disaster recovery system with extremely high safety and usability is achieved, it is ensured that data can still be recovered even under extreme disasters, a zero-trust data channel based on an IPSec tunnel is constructed in the data transmission process, and the reliability of data transmission is improved. The tamper-proof, anti-counterfeiting and anti-leakage capabilities of a data access link are greatly improved, the access frequency abnormity is detected through the isolated forest model, the behavior trend is detected through the LSTM time sequence prediction model, the baseline deviation analysis is performed through user behavior portrait comparison, and the real-time, multi-modal and chained security monitoring of the user data operation behavior is realized.

The invention relates to the technical field of data supervision, in particular to a data security protection supervision system, which comprises an access monitoring module, a behavior abnormity monitoring module, a security configuration module and a data risk management module, and is characterized in that based on user access behavior data, access frequency and security level information are collected, and user behavior characteristics are automatically extracted; an access control snapshot is obtained. According to the method, through dynamic permission configuration and real-time abnormal activity monitoring, data access safety is remarkably improved, user behavior characteristics are automatically extracted, behavior deviation is analyzed, quick response to potential risks is ensured, and through the continuously-updated safety strategy and risk assessment, the safety of data access is greatly improved. The security measures are allowed to timely adapt to the constantly changing threat environment, and through detailed risk management, the processing speed and accuracy are improved, so that the data supervision is more efficient, the possibility of data leakage and abuse is reduced, and the flexibility and reliability of the system in the aspect of protecting data privacy and integrity are ensured.

The invention relates to the technical field of data processing, in particular to an artificial intelligence data aggregation method based on big data, and the method comprises the following steps: collecting traffic monitoring original data, removing repeated data and correcting wrong data through data cleaning and preprocessing, and generating preliminary cleaning data; dynamic index adjustment is applied to the preliminarily cleaned data, the depth and branches of an index tree are dynamically adjusted according to the access frequency and types of the data items, and optimized index data are obtained. In the dynamic index adjusting process, the index tree structure is dynamically optimized according to the data access frequency and type, the retrieval efficiency of high-frequency data is improved, and the storage overhead of low-frequency data is reduced. The hierarchical data compression performs hierarchical processing on the data, the use of storage space is greatly reduced through a mode of combining lightweight compression and an advanced compression algorithm, and a foundation is laid for large-scale data processing.

The invention provides an unstructured data storage optimization method and system based on information entropy and a block chain, and relates to the technical field of data storage, and the method comprises the steps: building a block chain storage network to obtain data, recognizing redundant data through dimension reduction, mapping and hyperplane construction, and removing the redundant data based on fuzzy similarity. The to-be-stored data is partitioned, the access frequency is calculated, the consistent Hash algorithm is executed to determine the storage position, the access index and the verification sequence are established, and the data integrity verification is executed, so that the data access efficiency can be improved, and the storage security can be enhanced.

A method for managing log retention period of an application includes: inferring dependencies and connectivity among applications; generating an application chain (AC) based on the inferred dependencies and connectivity; analyzing distributed logs and AC to extract health parameters; deriving a failure score of the AC based on the health parameters and inferred dependencies; inferring a user access frequency (UAF) and remaining assigned storage space (RASS) for an application log (AL); analyzing the failure score, UAF, and RASS for the AL; making a first determination that the failure score is greater than a failure score; making a second determination that the AC comprises a high priority application (HPA); making a third determination that the RASS for the HPA's AL is less than a storage space limit; notifying an administrator to initiate obtaining of extra storage space; and extending the current AL retention period of the HPA's AL.

The invention discloses a distributed time sequence library data management method supporting cold and hot data level-to-level management, and relates to the technical field of computer databases, comprising: receiving a write-in request of time sequence data, and executing preliminary write-in data aggregation and sorting; dynamically identifying data cold and hot attributes based on a multi-dimensional data cold and hot degree calculation model in combination with a write-in behavior and an access behavior of time series data; according to the cold and hot attribute recognition result, in combination with the hierarchy boundary of self-adaptive division, automatic hierarchical storage of the time series data is executed; based on a cold and hot data dynamic migration and scheduling mechanism, according to the access frequency and time decay characteristics of the time series data, dynamically adjusting the storage hierarchy of the time series data, and executing a data migration task; and constructing a hierarchical index system adaptive to different cold and hot attribute time series data, and combining query frequency dynamic identification and index structure automatic upgrading. By adopting a cold and hot data automatic identification and hierarchical storage mechanism, the overall performance and the resource utilization rate of the system are improved.

The invention relates to the technical field of data processing, and discloses a big data-based file analysis storage division mode and a distributed storage system.The method comprises the following steps: S1, file analysis: analyzing an input big data file, and extracting a logic structure, field content and an association relationship of the big data file; s2, data block generation: dividing an analysis result into data blocks, and generating size and relevance information of the data blocks; s3, association weight generation: establishing a weight relationship between the similarity between the data blocks and the joint access frequency; and S4, storage division optimization: based on the association weight, optimizing the distribution of the data blocks among the storage nodes. According to the method, distribution of the data blocks is optimized through the synergistic effect of the association weight generation module and the dynamic decision-making module, so that the data blocks with high association degree are preferentially distributed to the same storage node, cross-node data transmission is greatly reduced, a storage strategy can be adjusted in real time, and the storage efficiency is improved. Therefore, communication delay and network resource consumption caused by cross-node access are reduced.