819 results about "Access time" patented technology

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.

The invention provides a data all-flash storage optimization method and system based on cloud computing, and the method comprises the steps: receiving a storage operation request stream of a user terminal through collecting a multi-dimensional performance data set of each node of an all-flash storage cluster in real time, wherein the multi-dimensional performance data set comprises a storage space fragmentation rate, an input / output request queue depth and a network link bandwidth utilization rate; analyzing a to-be-stored data block identifier set and an operation mode label, traversing a historical access record library based on to-be-stored data block identifiers, extracting historical access time sequence characteristics and physical storage position proximity of associated data blocks, and combining the multi-dimensional performance data and the historical access time sequence characteristics to obtain a data block identifier set; according to the method, a dynamic access popularity prediction model in a preset time period is constructed, and finally, a cross-node fragmentation storage topological graph and a cache preloading strategy matrix are generated according to the proximity of the dynamic access popularity prediction model and a physical storage position, so that the optimization of all-flash storage is realized, and the data storage performance in a cloud computing environment is improved.

To provide a data storage device comprising a processing device, and a method.SOLUTION: Provided is a method implemented or performed when a read / write channel circuit of a control circuit of a disk drive controls operation of the disk drive. The method includes selecting a seek time model from a plurality of seek time models at least partially based on operation characteristics of an access command. The operation characteristics is related to the off-track sensitivity of executing the access command. The method also uses the selected seek time model to determine the access time for the access command and selects the next access command for execution based on the determined access time for the access command and the determined access time for the other access command among the plurality of access commands.SELECTED DRAWING: Figure 2C

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 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 field of industrial internet intrusion prevention, in particular to a computer network big data security protection method and system, and the method comprises the following steps: monitoring network flow, collecting user identity, equipment security state, access time and network behavior data, and generating a network behavior record. In the invention, a dynamic security assessment system is constructed through network flow monitoring, identity verification and behavior analysis, and compared with static strategy or fixed rule matching, the dynamic security assessment system can adapt to a continuously changing network attack environment. Authority dynamic adjustment is performed based on the user identity verification state, flexible management and control of the access authority are ensured, and the problem of long-term high authority abuse is avoided. Threat detection not only depends on historical mode matching, but also is combined with real-time behavior analysis, and accurate isolation is executed for potential threats, so that protection measures are changed from passive response to active intervention, and the delay of manual intervention is reduced.

The invention discloses a CXL-based training time consumption prediction method and device in heterogeneous computing power, and relates to the technical field of heterogeneous computing, and the method comprises the steps: obtaining task description information of a distributed training task; determining a neural layer calculation sequence of a neural network in the distributed training task and each heterogeneous computing power corresponding to the distributed training task according to the task description information; according to the neural layer calculation sequence, initial calculation process diagrams corresponding to the heterogeneous calculation power are constructed; adding a CXL-based parameter synchronization process to a backward transmission calculation node in the initial calculation process diagram to obtain each target calculation process diagram; respectively obtaining neural layer calculation time consumption and memory access time consumption of each node in the target calculation process graph; and according to the calculation time consumption of each neural layer and the access time consumption of each memory, predicting the total time consumption of training the distributed training task. The method solves the problems of long time consumption of parameter synchronization, low time consumption prediction accuracy and the like, reduces the time consumption of model parameter synchronization, and improves the accuracy of training time consumption prediction.

The invention relates to the technical field of data management, in particular to an international standardization organization system file electronic management method and system, and the method comprises the following steps: based on a recorded international standardization organization system file, collecting the access time, size and frequency information of the file, analyzing the information and the current access mode of the file, and obtaining file access data; according to the invention, the system can adjust the storage position in real time according to the use frequency and the size of the file, so that the data retrieval time is shortened, and the access path is optimized. And the file position is updated in real time, so that the frequently accessed file is closer to a data processing core, and the delay of data transmission is effectively reduced. By analyzing the future access trend of the file and adjusting the expected access strategy according to the future access trend, not only is the adaptability of the system to future requirements improved, but also the backup plan and resource allocation are optimized. A dynamic storage adjustment mode can ensure the efficiency of resource allocation, and resource pre-allocation is performed during a prediction high-load period, so that system overload is effectively prevented.

The invention discloses a cache processing method of heterogeneous equipment, a heterogeneous system, a product, a device and a medium, and relates to the technical field of data storage. And according to the target virtual address access times corresponding to each device in the heterogeneous system, realizing a global data caching mechanism. And determining a file page corresponding to the target heterogeneous device based on the target virtual address access frequency, so that the file system layer has the capability of managing the memory cache of the heterogeneous device. And the predicted file page of the target heterogeneous device is verified, and the file page is moved under the condition that the verification is passed, so that the distribution accuracy of the target heterogeneous device is improved. The file page of the target heterogeneous device is mapped and moved to the memory of the host side, the target heterogeneous device does not need to be accessed, the file page stored in the memory of the processor is directly accessed, the increased access rate of the processor accessing the target heterogeneous device is avoided, the delay of accessing the memory of the target heterogeneous device is reduced, and the access efficiency is improved. And the data transmission rate and the system efficiency of the heterogeneous system are improved.

The invention belongs to the field of data storage, and discloses a frequency access data storage migration method and device for big data, and the method comprises the steps: obtaining a data access log, carrying out the preprocessing of the data access log, and obtaining a time window statistical matrix; carrying out timeliness attenuation weighting calculation on a plurality of data access times in the time window statistical matrix to obtain a weighted access frequency vector; obtaining a multi-dimensional feature matrix based on the weighted access frequency vector; clustering the feature vector of each time window slice in the multi-dimensional feature matrix through a Gaussian mixture model to obtain a clustering tag vector; inputting the clustering label vector into an access frequency prediction model to obtain an access frequency prediction value of the data in a future preset time step; the migration decision matrix is determined based on the access frequency predicted value and the data storage cost parameter, data migration is carried out based on the migration decision matrix, the data migration complexity can be reduced, and the access speed and the storage cost are balanced.

The invention relates to the technical field of financial data processing, in particular to a financial marketing platform data storage system and method, and the system comprises a data prediction regulation and control module, a hotspot recognition module, a storage medium adjustment module, a sensitive encryption module, a data backup module and a performance evaluation module. According to the method, by collecting and calculating the average value of the data traffic and the access time points of the financial marketing platform, the time period with high access frequency can be accurately predicted, the data traffic can be effectively managed, the probability of system overload can be reduced, and by matching suitable storage media such as SSD and HDD for different types of data, optimization is carried out according to the access speed requirement, the access speed of the financial marketing platform can be greatly improved. The data processing speed and cost efficiency are remarkably improved, differentiated encryption processing and backup are performed on the data, and a security policy is designed according to the sensitivity level of the data, so that the operation efficiency of data storage is effectively monitored and improved, and the efficiency and security of a data storage system are improved.

The invention discloses a dynamic access control strategy system based on behaviors, comprising: A, a behavior acquisition module for acquiring user behavior data in real time, the behavior data comprising login information, operation records, access resources, access time, access frequency and equipment information; b, the behavior analysis module is used for carrying out preprocessing, behavior pattern recognition and anomaly detection on the collected behavior data to generate a behavior analysis result; c, an access decision module which dynamically generates an access control decision in combination with user identity information, a behavior analysis result and a resource authority requirement; and D, a strategy updating module which updates the access control strategy in real time according to the security audit result, the user feedback and the new threat information. Compared with the prior art, the method has the advantages that a dynamic access control strategy based on behaviors is provided, the access authority of the user is dynamically adjusted by collecting and analyzing the user behavior data in real time, and finer, more flexible and safer access control is achieved.

The invention relates to the technical field of network security, in particular to a method and device for detecting illegal external connection of a terminal and a storage medium, and the method comprises the steps: obtaining all terminal equipment connected with a local area network, distinguishing authorized terminal equipment and unauthorized terminal equipment through authorization detection, and carrying out the risk grading of the authorized terminal equipment; the method comprises the following steps: generating an external connection behavior data set by combining client monitoring data, network layer monitoring data and intranet detection data, extracting key information such as an external connection target, access time, a protocol type and a port number, and dividing into pure intranet connection equipment, normal external connection equipment and unknown external connection equipment according to external connection permissions; inputting the log data of the unknown external connection equipment into the log anomaly detection model based on the Transform architecture, identifying an abnormal event, and calculating a log risk score; and if the log risk score exceeds a preset threshold, determining that the device is an illegal external connection device. According to the method, accurate detection of illegal external connection behaviors is realized, and the safety performance of a monitoring system is remarkably improved.

The invention provides a meta-universe scene distributed two-stage cache optimization method combining MADDPG and GCN, and the method comprises the steps: dividing a meta-universe scene in the horizontal and vertical directions, obtaining a preset number of sub-scenes, and carrying out the integration of resources needed by the rendering of each sub-scene; the sub-scenes are compressed through an edge network, and the similarity of user tracks is calculated and obtained through a fast dynamic time warping algorithm; based on the similarity of different track fragments, sub-scene popularity ranking is obtained by weighting, summarizing and sorting the number of access times of a GPU server, sub-scenes predicted to be frequently accessed are requested and cached in advance according to the popularity ranking of the sub-scenes, and a two-stage cache replacement framework combining GCN and MADDPG is adopted to adjust a cache strategy of the server. According to the method, intelligent cache distribution and adjustment can be realized according to sub-scene popularity prediction, the cache hit rate is improved, and the delay response of a user request is reduced.

The invention relates to the technical field of distributed storage, in particular to a high-speed distributed storage system and method, and the system comprises a file index characterization module which carries out the statistics of the access frequency and the recent access timestamp of each data block in the system, and obtains the popularity magnitude of the file data blocks; according to the method, the popularity magnitude concept is introduced on the basis of linkage statistics of the data block access frequency and the recent access time, the data blocks with remarkable service influence in the current system can be recognized, the priority processing sequence of different data blocks in the repair process is divided, and the decision-making efficiency during fault recovery is improved. After the to-be-repaired data blocks are identified, the comprehensive weight values are formed through correlation calculation with the popularity magnitude, and all the weight values are arranged in a descending order, so that dynamic grading and sequential scheduling of repair tasks are realized, and important data blocks are guaranteed to preferentially obtain recovery resource support.

The invention relates to the technical field of database cache management, in particular to an LSM engine data cache optimization method, which comprises the following steps of: based on cache region data of an LSM storage engine, recording access times of implementation detailed rules, counting distribution density of implementation detailed rules, arranging the data into a priority sequence, and generating an implementation detailed rule access distribution information set. According to the method, accurate mastering of the implementation detailed rule distribution density is achieved by recording the implementation detailed rule access times and distribution positions, the implementation detailed rule distribution density is grouped and sorted into the priority sequence, the cache data reading efficiency is improved, and the cache data reading efficiency is improved by adjusting and optimizing the data storage layout and reducing the high-frequency data retrieval delay. The method has the advantages that the problem of uneven storage allocation is solved, the resource utilization rate is increased, data distribution is optimized through high-frequency implementation rule comparison, new implementation rule matching is dynamically adjusted, the hit rate and the response speed are increased, the system load is reduced, and the storage performance and the resource allocation efficiency are remarkably improved.

The invention relates to the technical field of distributed storage, and discloses a data caching method, computer equipment, a storage medium and a program product, and the method comprises the following steps: obtaining a name of a to-be-read object and a to-be-read data range of the to-be-read object; judging whether a key value which is the same as the name of the to-be-read object exists in a preset hash data structure or not; if the preset Hash data structure does not exist in the to-be-read data range, reading corresponding data from the system main memory based on the to-be-read data range, returning the data to the client, and updating the preset Hash data structure and the window cache; judging whether the ratio of the used capacity of the window cache to the total capacity exceeds a first ratio threshold value or not; if so, acquiring a first eliminated cache object in the window cache; obtaining the access frequency of the first eliminated cache object, and screening out a target cache object of which the access frequency is greater than the access frequency of a tail cache object of a first linked list in the main cache from the first eliminated cache object; and caching the target cache object to a main cache. The service life of the solid state disk is prolonged.

The invention relates to the technical field of computer storage, in particular to an intention perception data prefetching method and device of solid-state storage equipment and a storage medium. The method comprises the following steps: collecting user behavior data, wherein the user behavior data comprises an application use frequency, file access time, a system call log, a hard disk reading track and network flow information; classifying, filtering and preprocessing the collected user behavior data to generate a time sequence and a behavior sequence, and mapping the time sequence and the behavior sequence into multi-dimensional feature vectors; according to the intention perception data prefetching method of the solid-state storage device, dynamic and efficient data prefetching is achieved in combination with real-time analysis of the user operation intention, and the data access speed and the overall performance of the solid-state storage device are improved.

The invention discloses a Chiplet-based matrix chain multiplication accelerator and an acceleration method. The matrix chain multiplication accelerator comprises a first matrix multiplication module, a first external memory interface and a nonlinear processing module, and a second matrix multiplication module and a second external memory interface which are connected with each other through Chiplet. The method comprises the following steps: acquiring operator data of a Transform network and preprocessing the operator data to obtain a matrix chain data stream; based on the matrix chain data stream, performing initialization processing on a matrix chain multiplication accelerator to obtain an initialized matrix chain multiplication accelerator; and inputting the matrix chain data stream into the initialized matrix chain multiplication accelerator for matrix multiplication operation to obtain attention matrix data. According to the embodiment of the invention, the utilization rate of the processing element can be improved, so that the memory access times and the data transmission delay are reduced, and the calculation efficiency is improved. The method can be widely applied to the technical field of computer hardware acceleration.

A hotspot data elimination and maintenance method in a distributed cache system comprises the steps that the access frequency, the time locality score, the service weight and the real-time data popularity value of data are acquired, and the dynamic priority score of the data is generated; determining failure data based on the dynamic priority score; dividing the data into hot spot area data, temperature spot area data and cold spot area data, when the cache space is insufficient, preferentially eliminating the data with the lowest dynamic priority score and the retention benefit value lower than a preset threshold value in the cold spot area data, and if the cache space is still insufficient, performing data elimination on the temperature spot area data according to the latest access time of the temperature spot area data; packaging the cold point area elimination data, the warm point area elimination data and the failure data into a final transaction message; and broadcasting the final transaction message through a preset protocol, and performing distributed consistency maintenance operation on the final transaction message through the receiving node. According to the invention, intelligent and efficient cache data elimination and consistency maintenance management can be realized.

The invention discloses a memory scheduling method, system and product based on a separated memory system, and relates to the technical field of memory scheduling. The method comprises the following steps: determining target memory equipment accessed by a current task according to demand parameters of the current task and actual operation parameters of a separated memory system, and before actually deploying the memory equipment, executing the target memory equipment according to the demand parameters of execution of the current task; the target memory device to be accessed by the current task is preliminarily determined corresponding to the demand parameters of different tasks and the actual operation parameters of the separated memory system. In order to reduce the access time delay corresponding to the current task, the access cost of the current target memory device is estimated through the historical call times and the access time delay of the target calculation accelerator corresponding to the current task, and the scheduling memory device of the current task is determined according to the access cost. Therefore, the access cost of the scheduling memory device accessed by the target computing accelerator corresponding to each task is low, and the access execution efficiency of the target computing accelerator of the current task is improved.

The invention belongs to the technical field of computers, and particularly discloses a data migration method based on a heterogeneous network memory pool and a data center, the method is applied to the data center, the data center comprises a management node and a plurality of cabinets, each cabinet comprises a plurality of nodes, CXL is adopted between the nodes in the same cabinet, RDMA is adopted between the nodes of different cabinets, and the management node is connected with the management node. The method comprises the following steps: monitoring access popularity of a node to a memory page; under the condition that the access popularity of the first node to the target memory page is greater than a popularity threshold value, migrating the target memory page from a second node to which the target memory page belongs to the first node through the management node, and initializing the access popularity of the first node to the target memory page; according to the method and the device, the access times and the access frequency of each cross-cabinet page are tracked, so that the high-heat target memory page with relatively high access times and access frequency can be migrated from the node to which the target memory page belongs to the CXL memory of the node of the cabinet to accelerate access.

The invention relates to the technical field of data storage, and provides an object storage intelligent layering method and device based on a decision tree, and the method comprises the steps: collecting object data, carrying out the preprocessing of the collected object data, and setting an access time window and a storage label for the obtained standardized object data; performing feature extraction on the stored standardized data, and performing dimension reduction processing on the extracted features to obtain feature data; constructing a training model, training and testing the training model by adopting feature data, and selecting a decision tree model from the training model for production deployment; and performing classified storage prediction on the uploaded object data by adopting the deployed decision tree model, and storing the object data to a corresponding storage pool according to a classified storage prediction result. According to the method, the future access frequency of the object can be predicted and directly allocated to different storage medium layers, so that the data migration time and space loss are saved, the storage system efficiency is improved, and the user access performance is improved.

The invention provides a resource optimization method and system for a router, a gateway and a camera in the technical field of edge computing. The method comprises the following steps: S1, training a network state prediction model and deploying the network state prediction model to edge computing equipment; s2, the edge computing device collects network index data based on a dynamic weight data collection mechanism and caches the network index data to a time sequence database; s3, inputting the network index data into the network state prediction model to obtain a network state prediction result; s4, generating a network resource optimization strategy based on the network state prediction result, verifying the network resource optimization strategy based on the digital twin technology, and executing the network resource optimization strategy; and S5, performing rolling updating on the network index data cached in the time sequence database based on the access times and the final access time. The method has the advantages that the flexibility and rationality of network resource configuration are greatly improved, and then the system performance and efficiency are greatly improved.

The invention discloses a device access method and an electronic device, relates to the technical field of artificial intelligence, and can dynamically adjust the access priority of the device and reduce the perception of a user on the access time delay of the whole device in the environment. In the scheme, when the plurality of intelligent devices are to be accessed, the first device can acquire the state information of the user, and when the state information acquired by the first device at the first time is the first state information, the intelligent device corresponding to the first state information in the plurality of intelligent devices can be preferentially controlled to be accessed. And when the state information acquired by the first equipment at the second time is changed into the second state information, the access of the intelligent equipment corresponding to the second state information in the plurality of intelligent equipment can be preferentially controlled.

The invention discloses a cloud storage acceleration method and system based on edge computing, and relates to the field of electrical digital data process.The method comprises the steps that a file access track ring is constructed; extracting historical access files as a first associated file set; calculating the access time difference of each historical access file; determining a plurality of association clusters and two association file sets according to the access time difference; performing cyclic displacement in the second associated file set to obtain a displacement associated cluster; extracting a third associated file set; determining a prefetching file; setting an annular cache queue, and determining a prefetching position according to the position information; when the space of the annular cache queue is smaller than a preset value, the prefetched file corresponding to the prefetched position in the preset interval of the annular cache queue is reserved, and a final prefetched file is obtained; and when it is detected that the user accesses the target file, loading the final prefetched file from the cloud storage server to the edge server. The method and device are used for solving the problem that the cache hit rate of the associated files is reduced, and the cloud storage acceleration speed is increased.

The invention relates to the technical field of security monitoring for preventing confidential files from being leaked, and discloses a security monitoring method for preventing confidential files from being leaked. By setting the user permission matrix and the dynamic access condition set, the system finely manages user permissions, it is ensured that the user only accesses related files, and the risk of unauthorized access is reduced. The dynamic condition set includes access time, device identification, and geographic location, preventing access by unsecure devices or locations. And the system verifies the permission matching degree in real time and rejects access which does not conform to conditions in time, so that the risk of permission abuse and leakage is reduced. A detailed log is generated in each access, an operation result and rejection information are recorded, and an administrator is notified when an abnormal behavior occurs. In combination with file sensitivity evaluation and a multi-layer encryption strategy, the system takes different protection measures according to file sensitivity. Through real-time authority management and auditing, file operation compliance is monitored, and potential violation behaviors are found in time.

The invention provides a hierarchical storage read cache management method and system, a storage medium and a product, and relates to the field of data storage, the method comprises the following steps: dividing all data blocks into three data hierarchies of hot data, warm data and cold data for respective storage according to access popularity, the data storage positions, the storage data proportions and the information recording precision of the three data levels are different; when the cold data is accessed, the accessed target cold data is upgraded to the temperature data, and the temperature data is updated; when the hot data is accessed, the complete metadata information of the accessed target hot data is cached in the memory, and the hot data is sorted and updated again; and when the temperature data is accessed, determining whether to replace the target hot data with the target temperature data or not according to the size relationship between the first historical access time of the accessed target temperature data and the second historical access time of the target hot data with the lowest access heat. By implementing the method, the occupation of memory resources can be reduced when the popularity of mass data is tracked.

The application discloses a method and device for realizing high-speed operation of an algorithm based on a multi-level field cache, which comprises the following steps: a configuration management CPU sends algorithm parameters to an FPGA chip and saves the algorithm parameters in a RAM; a service processing CPU creates a handle and sends the handle to the configuration management CPU; the configuration management CPU generates a key number according to the handle; the configuration management CPU sends a key library to the FPGA chip; the service processing CPU sends a to-be-operated message to the FPGA chip; an algorithm scheduling module performs fragmentation judgment, and performs subsequent processing according to a sequence judgment result; a password operation module takes the received key, an initialization vector or an intermediate chain variable and the to-be-operated message as input, performs password operation, and outputs a calculation result and an operated intermediate chain variable; whether the to-be-operated message is the last data fragment is judged according to a tail fragment identifier, and subsequent processing is performed according to a tail fragment judgment result. The application can reduce the communication interface and CPU access times, realize high-speed operation of a password algorithm, and improve the overall performance of a password service system.

The invention relates to the technical field of station data acquisition, in particular to an intelligent station data acquisition method and system based on digital twinning. The method comprises the following steps: firstly, obtaining the flow attention of a flow link according to the access times and the access duration of all accounts to the flow link in each operation record and the reference weight of the accounts; obtaining the preorder influence degree of the target link according to the abnormal condition of the monitoring time sequence data corresponding to each process link before the target link and the time interval between the process link and the target link; according to the preorder influence degree and the process attention degree of the target link, obtaining the process monitoring degree of the target link; and adjusting the preset sampling frequency according to the flow monitoring degree of the target link, and obtaining the adjusted sampling frequency of the monitoring equipment in the target link. According to the invention, by deeply mining the degree of the process link needing to be monitored, reasonable setting of the sampling frequency is realized, so that monitoring requirements are met and effective utilization of monitoring resources is guaranteed.