190 results about "Cloud storage system" patented technology

The invention provides a cloud storage method and system based on data grading and blocking encryption, and belongs to the technical field of file cloud storage. The method comprises the following steps: carrying out grading and block encryption on files in the cloud storage system; the method specifically comprises the following steps: carrying out classification and risk assessment on files in the cloud storage system, and carrying out file encryption by adopting different encryption algorithms for files with different risk levels under the same category; aiming at the file which is divided into a plurality of file blocks, independently encrypting each data block, and matching a corresponding encryption algorithm according to the data characteristics of the data blocks; after file grading and block encryption are completed, keys are managed in various modes of a key derivation structure, a hash tree and a threshold cryptographic algorithm. The method is used for remarkably improving the security of the stored data, and meanwhile, the efficient operation of the system is kept.

A method and system for handling a user query are provided. The method includes setting up a symbol or link in a user interface, the symbol or link representing a dataset in a cloud environment, receiving a user query initiated by a user through the user interface, parsing the dataset to identify a data file associated with the user query, generating a sequence of queries based on the user query and content parsed from the data file, and searching against the data file using the sequence of queries to obtain a set of query results.

The invention particularly relates to an object storage data encryption method based on a large model. According to the object storage data encryption method based on the large model, user data is partitioned and compressed, and a deep learning large model is trained by using historical data accumulated by a cloud storage provider and known security threats; according to a large model prediction result, selecting an optimal encryption algorithm and a key management strategy; encrypting the preprocessed data, and storing the encrypted data and metadata in a cloud storage system; and when the user needs to access the data, reading the metadata, obtaining the decryption key, and recovering the original data. The object storage data encryption method based on the large model is efficient, safe and flexible, the optimal encryption strategy can be intelligently selected according to changes of data characteristics and the safety environment, dynamic encryption is achieved, therefore, the safety and privacy of data are remarkably improved, and the requirements of a modern object storage system can be met.

The invention belongs to the technical field of cloud storage, and particularly relates to an enterprise service management cloud storage system which comprises a system initialization module, a distributed data storage module, a data encryption module, an authority control module, a multi-level cache module, a data backup and recovery module, a log monitoring module and a task scheduling and dynamic extension module. The distributed data storage module is responsible for dividing data in enterprise service management into a plurality of data blocks; the data encryption module is used for encrypting the enterprise service data stored in the cloud; the authority control module is responsible for managing the access authority of the user to the data; the multi-level cache module accelerates data access by configuring a local cache and a global cache; the data backup and recovery module is responsible for backing up the stored data regularly; the log monitoring module records various operation logs of the system; and the task scheduling and dynamic extension module is used for dynamically distributing calculation and storage resources according to the priority of the task and the utilization rate of the system resources.

According to the agriculture-like cultivated land and land humidity monitoring robot and the monitoring method, structurally, a soil turning device, a soil extraction device and a driving wheel device are connected with a driving motor, the driving motor, a camera device and the soil extraction device are in signal connection with a remote control terminal, and the soil turning device is arranged in a front-back telescopic and up-down movable mode relative to a machine body; the soil extraction device is movably arranged up and down relative to the machine body, the camera device is rotationally arranged relative to the machine body and used for monitoring the movement of the robot in real time, and by optimizing robot hardware configuration and sensor layout and integrating multiple sensors and a core control unit, comprehensive and accurate collection of farmland soil key parameters is achieved. And a wireless data transmission and cloud storage system is deployed to ensure real-time and accurate transmission and analysis of data. Artificial intelligence and computer vision technologies are integrated, soil images are processed, features are extracted and classified, and accurate evaluation and prediction of soil quality are achieved.

The invention discloses a cloud storage image privacy protection encryption method based on cross-plane dynamic grouping, and belongs to the technical field of information security and cloud storage. The method comprises the following steps: acquiring an image in a cloud storage environment, and preprocessing the image; encrypting the image by adopting a dynamic grouping cross-plane encryption method; constructing a ciphertext image structure containing permission labels and visual adjustment information and a data packet used for realizing access control and semantic hierarchical display; and uploading the ciphertext information to a cloud storage system through a secure transmission protocol. According to the method, dynamic grouping cross-plane encryption is adopted, so that the security of the encrypted image is improved, and security vulnerabilities caused by a fixed grouping mode are avoided; in combination with cloud storage, data access and authority control are adaptively optimized, so that different users can access corresponding encrypted data according to authorization levels, and the adaptability of the encryption method is improved; the effectiveness of local disturbance is improved through dynamic grouping, large information leakage is effectively avoided, and the data security is improved.

The invention discloses a metadata version control and consistency verification method and system, and belongs to the technical field of distributed system metadata management, the method comprises the steps of version tree construction, version query and backtracking, version merging and conflict processing, the version tree construction takes an initial timestamp associated with an initial version of metadata as a root node of the version tree; whenever the metadata is changed, a unique timestamp is generated for the changed metadata; carrying out associated storage on the generated timestamp and the changed metadata; and creating a new node according to the newly generated timestamp, establishing a father-son relationship between the new node and the node of the previous version to form a branch structure of the version tree, and arranging the nodes in the version tree according to the sequence of the timestamp. According to the method, the management efficiency of multi-version metadata can be remarkably improved, the data integrity and consistency are effectively guaranteed, and the method is suitable for cloud storage system metadata management, cross-platform metadata migration and other scenes.

A central node can: receive a query comprising at least one parameter comprising a time range of a dataset stored in a cloud storage system; transmit one or more of the query parameters comprising the time range to a metadata service; receive from the metadata service a list of files related to the query; and assign to each processing node of a plurality of processing nodes a subset of the files. Each processing node can: determine that the subset is not stored on a cache; retrieving the subset not stored on the cache from the cloud storage system; store the retrieved subset in a local memory; scan the subset stored in the local memory for data matching the at least one parameter to generate a subset of query results; and concurrently copy using a separate thread from the scanning, the subset stored in the local memory to the cache.

The invention is applicable to the technical field of cloud document management, and provides a cloud document management method and platform based on a block chain technology, after a user uploads a target document to a specified distributed cloud storage node driven by a block chain, an initial operation priority index of the target document is determined, and a file volume of the target document is acquired; acquiring a historical query log of a user for the specified distributed cloud storage node, determining an access heat interval of the user according to the historical query log, and determining a target node state parameter of the specified distributed cloud storage node for the access heat interval; the finally generated priority index comprehensively considers document characteristics, user behaviors and node performance, so that document scheduling is more intelligent and precise, the resource allocation efficiency and the node service response capability are remarkably improved, and the overall performance and the user experience of the distributed cloud storage system are enhanced.

The invention discloses a reinforcement learning-based cloud storage access permission intelligent management method. The method comprises the following steps of S1, constructing a hierarchical reinforcement learning model; s2, collecting historical access data of a user in the cloud storage system; s3, configuring an initial permission and resource allocation according to a user group permission strategy, and applying a configuration result to a low-layer operation module; s4, feeding back to a hierarchical reinforcement learning model; s5, preferentially retaining the files or data blocks accessed at high frequency in the cache; s6, the hierarchical reinforcement learning model optimizes an authority strategy through a real-time reward mechanism; s7, dynamically recovering the data blocks or files accessed at low frequency; s8, performing synchronous optimization of the access authority and the cache strategy; s9, temporarily freezing the high-risk authority; and S10, generating a new permission strategy and a cache strategy. According to the invention, the response speed and the resource utilization efficiency of the system are improved, and the security and the fault-tolerant capability of the system are enhanced.

The invention discloses a data processing method for a full-life-cycle resident health record. The method comprises the following steps: S1, integrating data from various devices and systems through a standardized interface; s2, preprocessing the collected health data, and storing the health data in a distributed database and a cloud storage system; s3, performing consistency processing on data from different sources by using data cleaning, duplicate removal and standardization technologies; s4, performing deep analysis on the health data by applying a long-short-term memory network; s5, carrying out data encryption through AES and RSA technologies, and guaranteeing the privacy of the user through role-based access control and a differential privacy technology; and S6, integrating the mobile health application and the intelligent wearable device into a health file management system, and supporting real-time health monitoring and remote medical services. The system has the advantages of comprehensive coverage, real-time processing, efficient storage, intelligent analysis, data security and privacy protection, cross-platform sharing and remote medical integration.

A method for providing access to objects associated with a particular client in a cloud storage system is disclosed. The method includes the steps of establishing a connection with a user, providing a client namespace associated with the client to the user, where the client namespace represents objects stored on the cloud storage system and objects stored on a private storage system apart from the cloud storage system, receiving a request from the user to access an object stored on the private storage system, and providing information to the user to facilitate access to the object stored on the private storage system by said user. Other systems and methods are also disclosed. Important advantages of the present invention are facilitated by separating the logic for user access (control plane) from the actual storage (Storage plane). Private file system access can still be managed from the cloud, while keeping the client data private.

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

The embodiment of the invention provides a data processing method, a cloud storage system and electronic equipment. The method is applied to a first network device, and the first network device comprises a first application layer, a first user mode protocol stack and a first network card. The first application layer comprises a first application program; the method comprises the following steps: distributing a first target memory block for to-be-sent data of a first application program from a first registration memory space of a first network card by utilizing a first application layer; writing the to-be-sent data into the first target memory block by using the first application layer; calling a first user mode protocol stack by using the first application layer so as to carry out encapsulation processing on the to-be-sent data in the first target memory block; and sending the packaged data to be sent in the first target memory block by using the first network card. The method provided by the embodiment of the invention can shorten the time delay, is friendly to CPU scheduling, and further improves the system processing performance.

The disclosure relates to a method for streamlining baggage screening across modes of transportation. The method includes scanning a baggage item at an airport and uploading scanned baggage imagery of the contents and license plate identification to a secure cloud storage system. A screening manifest is generated which lists an additional mode of transportation requiring different screening rules. The contents of the stored baggage imagery are reanalyzed according to these different rules and regulations, to determine compliance, and creates a baggage authorization record. Based on the compliance result, represented by an indicium affixed to the baggage, a trusted custody handler can directly deliver the baggage to the next mode of transportation without additional screening, or direct it for further security screening if non-compliant. This system improves efficiency and security in multi-modal transportation networks.

A system and method for inspecting virtual instances in a cloud computing environment for cybersecurity threats utilizing disk cloning. The method includes: selecting a virtual instance in a cloud computing environment, wherein the virtual instance includes a disk having a disk descriptor with an address in a cloud storage system; generating an instruction to clone the disk of the virtual instance, the instruction when executed causes generation of a cloned disk descriptor, the cloned disk descriptor having a data field including the address of the disk of the virtual instance; inspecting the cloned disk for a cybersecurity threat; and releasing the cloned disk in response to completing the inspection of the cloned disk.

The invention relates to the technical field of cloud storage, and discloses a block chain enabled cloud storage data integrity verification method, which comprises the following steps of performing hash value calculation on a plurality of data blocks in cloud storage, and constructing a Merkle tree based on the hash values to generate a root node hash value for data integrity verification; after the root node hash value is generated, sparse sampling is carried out on the hash value set by using a compressed sensing technology, and sampled data is restored through a sparse recovery algorithm; the invention also discloses a block chain enabled cloud storage data integrity verification system. The system comprises a hash processing module; a sampling reconstruction module; an optimization decision module; and a verification execution module. According to the method, the compressed sensing technology, the Pareto optimal strategy and the block chain technology are combined, efficient, low-resource-consumption and intelligent data integrity verification is achieved, the delay and calculation burden of the verification process are optimized, and the performance and reliability of the cloud storage system are improved.

Embodiments of the present disclosure provide a cache type cloud storage system, a data reading and writing method, a device, a storage medium, and a program product. The embodiments of the present disclosure provide a high-performance and highly elastic cache type storage disk, allowing a user to mount and use the cache type storage disk on any computing node, and supporting a storage-compute separation architecture, such that elastic resource scaling can be realized, reducing the idle waste of computing and storage resources. In addition, using a one-hop data reading and writing protocol to directly read and write at storage nodes helps to reduce the delay of data reading and writing. Meanwhile, data is stored in shards, and each data shard is stored on at least two storage nodes to achieve redundant storage of replicas. Compared with a local disk, the cache type storage disk has higher data reliability and availability, making data calculation and analysis more cost-effective and providing better user experience.

Provided in the embodiments of the present disclosure are a cloud storage system, a data read / write method, a device, a storage medium, and a program product. In the embodiments of the present disclosure, provided is a highly available cross-availability-zone cloud storage disk. On the basis of cross-availability-zone disaster recovery requirement information, data storage layout information for the cloud storage disk is generated, and on the basis of the data storage layout information, a data read / write operation is performed on persistent storage nodes across availability zones. The data storage layout information can embody a multi-copy and erasure coding fused redundant storage method, which is used by the cloud storage disk, and by means of which data storage is highly available across availability zones, so as to reduce a data redundancy ratio; furthermore, a better read / write performance is provided and elastic requirements for availability zones are met, a cross-availability-zone network bandwidth is reduced, and the storage cost is reduced.

The invention provides an industrial machine platform production material cloud synchronization method and system, and the method comprises the steps: firstly collecting a local production material set in a current production period of an industrial machine platform, obtaining a generation timestamp and an associated process identifier of each material unit, combing the relevance through a local processing module, and generating a structured production material unit; the structured production data unit is sent to a cloud coordination node, historical production data version sequences are called and compared, data version evolution characteristics are generated, and a dynamic synchronous execution scheme is determined based on the data version evolution characteristics and network transmission state parameters; and finally, transmitting the structured production data unit to a cloud storage system for incremental updating according to the dynamic synchronous execution scheme, generating a synchronization result, and feeding back the synchronization result to the industrial machine, thereby realizing real-time and efficient cloud synchronization of the production data of the industrial machine.

A cloud-integrated, AI-powered smart speaker for language learning, which includes the following: A microphone array for speech capture; ◯ an on-device AI module with a Neural Processing Unit (NPU) or Tensor Processing Unit (TPU) for real-time speech analysis; • Wi-Fi and 5G connectivity for secure data synchronization with cloud servers; ◯ a cloud-based AI engine that uses serverless computing services to trigger AI models for advanced language assessment and adaptive learning; ◯ a cloud storage system with relational databases for structured data, such as user profiles and progress metrics, and object storage for unstructured data, such as audio recordings, secured with AES-256 encryption and RBAC for data protection; ◯ a true-to-life speaker for AI-generated speech output; ◯ a screen (OLED / E-Ink) for text corrections and real-time lesson guidance; ◯ Touch and gesture sensors for user interaction; ◯ A battery module with USB-C charging function for portability.

Provided in the embodiments of the present disclosure are a cloud storage system, a data read-write method, a device, and a storage medium. Provided in the embodiments of the present disclosure is a general-purpose hierarchical encryption solution concerning cloud storage, which solution can uniformly meet the requirements in terms of data encryption and decryption and key management. By means of layering a cloud storage system, data encryption and decryption and key management processes are coordinated and unified; on a customer interaction layer and a storage service layer, a customer master key and a storage service key are used to cooperate with each other to perform key management, and on the storage service layer and a storage persistence layer, the storage service key and a data encryption key are used to cooperate with each other to control the data encryption and decryption process; and a key management function is decoupled from a data encryption and decryption function, thereby improving the flexibility and expandability. Furthermore, the storage service layer can directly reuse an encryption capability provided by a data persistence layer, such that it is not necessary to encrypt data multiple times on each layer, thereby reducing the encryption cost.

The invention relates to the technical field of computers, and discloses an ecological compensation data value-oriented hierarchical security policy and hierarchical cloud storage system.The method comprises the steps that firstly, security isolation of a virtual network is ensured, technologies such as a virtual network security group and a virtual firewall are adopted, and communication between virtual machines and communication between the virtual machines and an external network are limited; reducing potential attack surfaces, performing security detection on virtual machine images in a resource pool, including verifying integrity and credibility of the images, scanning malicious software, ensuring that image files do not contain known viruses, Trojan horses or malicious software, monitoring behaviors of virtual machines in the resource pool, and using an intrusion detection system (IDS) or a behavior analysis technology. Abnormal behavior, malicious activity or potential security threats are detected. According to the method, the particularity and the security requirement of a virtualization environment are considered specially for the construction requirement of the computing power network security resource pool. According to the method, security risks related to the computing power network can be detected and evaluated more accurately, and the comprehensiveness is high.

The present invention discloses a vehicle speed monitoring and prompting method in a vehicle networking scenario, which relates to the technical field of vehicle speed monitoring, and comprises the following steps: integrating multi-dimensional data collected by sensors or cameras on traffic roads through multi-source data fusion technology to form a comprehensive traffic status view; uploading the integrated multi-dimensional data to a cloud storage system; using a machine learning algorithm in artificial intelligence to conduct an in-depth analysis of traffic flow and vehicle speed; using virtual simulation technology to establish a dynamic simulation model of urban traffic; generating real-time traffic information based on multi-source data fusion, artificial intelligence and dynamic simulation models, and a traffic management center performing real-time signal control based on the real-time traffic information; designing a smart contract for traffic violation detection based on real-time traffic information and blockchain technology; the present invention realizes efficient monitoring of vehicle speed and timely prompting.

A central node can: receive a query comprising at least one parameter comprising a time range of a dataset stored in a cloud storage system; transmit one or more of the query parameters comprising the time range to a metadata service; receive from the metadata service a list of files related to the query; and assign to each processing node of a plurality of processing nodes a subset of the files. Each processing node can: determine that the subset is not stored on a cache; retrieving the subset not stored on the cache from the cloud storage system; store the retrieved subset in a local memory; scan the subset stored in the local memory for data matching the at least one parameter to generate a subset of query results; and concurrently copy using a separate thread from the scanning, the subset stored in the local memory to the cache.

Systems and methods are described for a cross-site high availability distributed storage system. According to one embodiment, a computer implemented method includes providing a remote direct memory access (RDMA) request for a RDMA stream, and generating, with an interconnect (IC) layer of the first storage node, multiple IC channels and associated IC requests for the RDMA request. The method further includes mapping an IC channel to a group of multiple transport layer sessions to split data traffic of the IC channel into multiple packets for the group of multiple transport layer sessions using an IC transport layer of the first storage node and assigning, with the IC transport layer, a unique transaction identification (ID) to each IC request and assigning a different data offset to each packet of a transport layer session.

The invention discloses a camera potential safety hazard patrol identification method based on video frame image segmentation, and belongs to the technical field of image processing. The method comprises the following steps: acquiring data through a video cloud storage system, classifying the data to form a basic data pool, and selecting a target video source set by combining a daily safety patrol mechanism and a special safety patrol mechanism; a region growing algorithm and a U-Net network are adopted to carry out image segmentation and feature extraction, and judgment and recognition operations are executed based on different selection mechanisms. And meanwhile, dynamically evaluating and updating a potential safety hazard list item and an easy-to-occur time period, generating a potential hazard report record and optimizing a patrol strategy. According to the invention, potential safety hazards in the construction environment of the construction site can be comprehensively and automatically identified, and the early warning accuracy and efficiency are improved.

The invention provides a data management method and device and a data management platform. The method comprises the following steps: distributing podno for Pod of a Kubernetes cluster; a storage bucket and a corresponding object key are created in the cloud storage system by adopting a CSI plug-in, the object key is a directory structure of a target path-podno-application data directory, the application data directory is index information of the storage bucket in the cloud storage system, and the target path is a path of a Kubernetes node of a Kubernetes cluster; the storage bucket is mounted on the target path, and the target path is mounted on the Pod; according to the method and the device, the application data of the Pod is stored through the storage bucket corresponding to the Pod, so that the container of the Pod can access the application data stored in the storage bucket corresponding to the Pod according to the object key corresponding to the podno, and the problem of data chaos between the Pods of the Kubernetes cluster in the prior art is solved.

The invention discloses a medical image data processing system, an information processing method, equipment and a medium, and relates to the technical field of data processing, and the system comprises a cloud server which is used for carrying out the model optimization training of an initial fracture region recognition model through the medical image training data in a cloud storage system, and obtaining a pre-trained fracture region recognition model, deploying the pre-trained fracture region recognition model to an edge computing device; the medical scanning imaging equipment is used for collecting a to-be-detected image of a to-be-detected part of the target object; and the edge computing device is used for receiving the to-be-detected image sent by the medical scanning imaging device through a locally deployed pre-trained fracture region recognition model, performing reasoning detection on the fracture region in the to-be-detected image based on local computing power resources, and outputting a target detection result containing fracture region position information. Model training is achieved through the cloud layer, deployment and use of a lightweight model are achieved through the edge layer, and accurate processing of medical image data is completed through a small number of local resources.

The invention discloses a distributed cloud storage system for credible storage of bulk transaction data, which comprises a three-layer hybrid architecture and a core function module for cooperative work, and has the functions of credible storage, privacy protection and cross-mechanism verification of the full life cycle of the bulk transaction data. According to the system, through innovative hybrid architecture design and core technology research and development, the core goal of storing hash on a chain and storing original under the chain is achieved, it is guaranteed that data cannot be tampered in the whole life cycle, meanwhile, it is guaranteed that data sovereignty belongs to enterprises, the cross-mechanism verification efficiency is improved, the operation cost is reduced, and solid technical support is provided for bulk commodity transaction digitization.