105 results about "Cloud storage system" patented technology

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.

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.

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.

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 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.

The invention discloses a secure data sharing caching system and method based on a block chain and a named data network. The system comprises five core components, namely a terminal node, an edge node, a credible authorization node, a cloud storage system and a block chain platform, and safe and credible data sharing is realized by fusing a decentralized trust mechanism of a block chain and an efficient data distribution capability of a named data network. The system adopts a terminal-edge-cloud storage system three-layer architecture, and ensures that data transaction is real and traceable in combination with multi-level identity authentication and a reputation mechanism; automatic management is realized through an intelligent contract, and data privacy is protected by utilizing multi-level authority management; and the system performance is improved by a node weight-based clustering method and a collaborative caching strategy. According to the method, the problems that in an existing data sharing system, the data authenticity is difficult to guarantee, a privacy protection mechanism is imperfect, and the system expandability is limited are solved, and the safety, efficiency and expandability of data sharing are remarkably improved.

A system and method for inspecting virtual instances in a cloud computing environment for cybersecurity threats utilizing disk cloning is presented. 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.

Systems and methods include pre-processing documents in cloud storage using query embeddings, providing personalized prompts to users based on documents in cloud storage, real-time anticipation of user interest in information contained in documents in cloud storage, and providing generative answers including citation to source documents in cloud storage. The system and methods generate generative machine learning model (MLM) prompts based on document portions of documents in a cloud-based content management platform. The systems and methods use the generative MLM to generate responses to prompts, and the responses include citations to the document portions used to generate the responses in order for users to verify the responses.

To streamline volume restoration. [Solution] The device for managing the storage system includes a processor and a storage device for storing a backup management model, the backup management model includes information on the snapshot generation of a volume stored in the restore destination storage system and information on the snapshot generation of the volume stored in the cloud storage system, and the processor refers to the backup management model and selects the restore source for each snapshot generation to be used for restoring a specified generation in the restore destination storage system from the restore destination storage system and the cloud storage system.

A content distribution facilitation system is described comprising configured servers and a network interface configured to interface with a plurality of terminals in a client server relationship and optionally with a cloud-based storage system. A request from a first source for content comprising content criteria is received, the content criteria comprising content subject matter. At least a portion of the content request content criteria is transmitted to a selected content contributor. If recorded content is received from the first content contributor, the first source is provided with access to the received recorded content. The recorded content may be transmitted via one or more networks to one or more destination devices. Optionally, a voice analysis and / or facial recognition engine are utilized to determine if the recorded content is from the first content contributor.

Provided in the embodiments of the present disclosure are a data storage method, a data reading method, a device, a system, a storage medium and a program product. In the embodiments of the present disclosure, introducing a second buffer pool between a first buffer pool and a persistent storage medium of a storage node allows more data objects to be located in the buffer pools, fully exerting the read-write performance advantages that the buffer pools bring to a cloud storage system. Further, by means of compressing data and then storing same in the second buffer pool, more data can be stored with limited second buffer pool resources, thereby further improving the hit rate when searching for data in the buffer pools, further exerting the effect of the buffer pools, improving the performance of the cloud storage system, and reducing storage cost.

A system includes a key service, that is configured to authenticate requests from an external manufacturing system (“EMS”), receive a request for generating a keyset, generate the keyset, send the keyset to the EMS, and send the keyset to a persistent cloud storage system, wherein an external cloud system accesses the keyset for use in secure communication with a hardware device.

The embodiment of the invention provides a data storage and reading method, device and system, a storage medium and a program product. In the embodiment of the invention, the second cache pool is introduced between the first cache pool and the persistent storage medium of the storage node, so that more data objects can be located in the cache pool, and the read-write performance advantage brought to the cloud storage system by the cache pool is fully exerted; furthermore, the data is compressed and then stored in the second cache pool, so that more data can be stored by using limited resources of the second cache pool, the hit rate when the data is searched from the cache pool is further improved, the effect of the cache pool is further played, the performance of the cloud storage system is improved, and the storage cost is saved.

The application discloses a data storage method, system and device, a storage medium and a program product, and belongs to the field of computer data processing. The method comprises the following steps: receiving distributed storage data sent by an execution node, wherein the distributed storage data is data stored by the execution node in a distributed storage manner; transferring the distributed storage data to a local disk for storage, wherein the local disk is a local storage area corresponding to the current device; in response to the local disk meeting data uploading conditions, uploading local storage data in the local disk to a cloud storage system according to a preset uploading strategy, wherein the preset uploading strategy is a strategy determined based on the average file size in the local disk; and in response to the uploading of the local storage data being completed, cleaning the local storage data from the local disk. The method achieves the effect of cooperation between local storage and the cloud storage system, and solves the problem that the large data workload cannot be effectively deployed in an offline hybrid cluster due to the fact that storage and calculation cannot be separated.

The invention provides a data integrity verification method and system oriented to an industrial cloud storage system, and the method comprises the steps: a plurality of cloud computing servers receive a secret sharing share from a fog node, and filter the secret sharing share based on additional commitment and proof; the secret sharing share is calculated based on a randomly selected signature secret key; a plurality of cloud computing servers receive the data file and execute blocking, calculate the data labels of the data blocks based on the secret sharing share, and integrate the data labels of all the data blocks after the data file is blocked to obtain a label set of the data file; the main cloud computing server sends the data file and the label set thereof to a cloud storage provider for storage; the verification comprises the following steps: the third-party auditor sends random challenge information to the cloud storage provider, and then the cloud storage provider generates an integrity proof of the data file; and the third-party auditor receives the integrity proof of the data file and checks the validity of the integrity proof to obtain an integrity verification result of the data file.

The application relates to the technical field of artificial intelligence, and discloses a double-recording examination auxiliary method and device based on artificial intelligence, equipment and a medium, which comprises the following steps: receiving a double-recording key and a double-recording abstract sent by a double-recording device, and acquiring double-recording information from a cloud storage system according to the double-recording key; cutting the double-recording information according to the double-recording abstract to obtain at least one double-recording segment, and extracting audio information in the double-recording segment; converting the audio information to obtain text information, and associating the double-recording abstract with the text information; receiving a quality inspection request sent by a control end, and sending the text information to the control end according to the quality inspection request. The application guarantees the simplification and accuracy of the text information, improves the efficiency of quality inspection personnel in checking the double-recording information and monitoring whether the on-site personnel business operation is standardized, and reduces the fatigue strength of the quality inspection personnel.

The invention discloses a system optimization method and device, electronic equipment, a storage medium and a program, and relates to the technical field of big data, and the method comprises the steps: inputting a plurality of original data in a cloud storage system in an operation and maintenance state into a preset fusion data model, and determining a target knowledge ontology based on a model output result and an output dimension requirement, the preset fusion data model is a model which is adjusted based on a diamond model template and is used for constructing an association relationship among the multiple pieces of original data; processing the target knowledge ontology according to a preset pruning strategy to obtain a plurality of operation behaviors, the preset pruning strategy including at least one of a white list pruning strategy and a divide-and-conquer pruning strategy; carrying out discrimination processing on the plurality of operation behaviors to obtain an operation and maintenance analysis result of the cloud storage system; and performing operation optimization on the cloud storage system according to the operation and maintenance analysis result. The problems of low system operation and maintenance efficiency and low accuracy caused by single observability technology implementation means in related technologies are solved.

The present application discloses a video surveillance system and a method for disaster recovery processing thereof, comprising: after detecting that a first video cloud storage system has an abnormality, sending the recording plan of the first video cloud storage system to a second video cloud storage system, causing the second video cloud storage system to save the surveillance video of the front-end surveillance device specified by the recording plan of the first video cloud storage system in a NAS server at the same site as the front-end surveillance device, and recording the recording index of the surveillance video; after detecting that the first video cloud storage system has returned to normal, instructing the second video cloud storage system to synchronize the recording index with the first video cloud storage system, causing the first video cloud storage system to write the recording index into the database of the first video cloud storage system, and drift-mounting the NAS volume indicated by the NAS volume identifier to the first video cloud storage system. Application of the present application can greatly reduce the hardware equipment cost for disaster recovery processing and reduce the occupation of the system network bandwidth.

The invention discloses an alarm automatic grading method and device of an intelligent cloud storage system, a server and a computer readable storage medium. According to the method, after an alarm data sample stored in an intelligent cloud is obtained, alarm data at each moment is extracted and input into a deep learning model for feature extraction, manual feature design is replaced, an implicit mode is automatically learned from time sequence data, dependence on experience of operation and maintenance personnel is reduced, a multi-stage evolutionary complex fault mode is accurately identified, and the fault diagnosis efficiency is improved. The feature vectors generated by the deep learning model are mapped to alarm levels such as high / medium / low risks by using the classifier, subjectivity of manual classification is avoided, levels can be generated in real time and corresponding responses can be triggered, and compared with manual classification, consumed time is shortened by more than 90%, so that the operation and maintenance efficiency of the system is improved.

A resource scheduling method based on a cloud storage system, an electronic device and a storage medium are provided. The method includes: obtaining a traffic characteristic of a target cloud disk and a traffic characteristic of each of a plurality of candidate storage clusters in response to a scheduling instruction of the target cloud disk; matching a target scheduling strategy from a preset scheduling strategy set according to the traffic characteristic of the target cloud disk and the traffic characteristic of each of the plurality of candidate storage clusters; in which the scheduling strategy set is used for maintaining a plurality of scheduling strategies configured based on different traffic characteristics; matching at least one candidate storage cluster from the plurality of candidate storage clusters as a target storage cluster according to the target scheduling strategy, and controlling the target cloud disk to be scheduled to the target storage cluster.

A system and method for balancing database workload in a cloud database system employing multiple database instances, wherein intermediate results are shared between database instances via object store or cloud storage system accessible by the multiple database instances. During sharing of intermediate results between database instances having different node sizes or processing unit sizes, load balancing is achieved by evaluating the target and source database instance sizes to select an appropriate load balancing strategy. Data and metadata contained within the intermediate results shared between source and target database instances are organized to provide optimal load balancing on the target database instance.

The embodiment of the invention provides a cloud storage system, a data storage and reading method, equipment, a storage medium and a program product. The cloud storage system comprises at least one computing node and a plurality of cloud storage nodes with different read-write performance, and the computing node can persist a data object needing to be stored persistently to the cloud storage node with the read-write performance matched with the cold and hot degree of the data object. Therefore, each cloud storage node can be flexibly used according to the cold and hot degree of the data, the utilization rate of each cloud storage node is improved, the application requirement of a user can be met, the storage cost can be reduced, and the overall access performance of the cloud storage system is effectively improved.

The embodiment of the invention discloses a log data query method and device, equipment, a storage medium and a program product, and the method comprises the steps: determining a target folder from a plurality of folders of a cloud storage system according to a query time range contained in a query request, and positioning to a range of a specified folder according to the query time range. Unnecessary data reading is reduced, and the query efficiency of the log data can be improved; the log data in the target folder is counted to obtain the query data volume, the corresponding target computing platform is determined according to the query data volume, the target computing platform matched with the query data volume can be determined in a targeted mode, and then computing resources of the target computing platform are called. In addition, the log data query is performed in the target folder according to the query request, the log data query does not need to be performed by writing related scripts, and the query efficiency of the log data can be further improved.

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 embodiments of the present disclosure relate to a container management method and apparatus, and a device and a medium. The method comprises: when a target container is started, mounting a cloud storage system designated by a storage volume to a read-write layer in a root file system of the target container; and in response to a deletion request for the target container, executing a retention operation on incremental data in the read-write layer and the cloud storage system, and after the target container is restarted, re-mounting the cloud storage system to the read-write layer in the root file system of the restarted target container.

The application discloses an optimization method and device of a system, electronic equipment, a storage medium and a program, relates to the technical field of big data, and comprises the following steps: inputting a plurality of original data in a cloud storage system in an operation and maintenance state into a preset fusion data model, determining a target knowledge ontology based on a model output result and an output dimension requirement, wherein the preset fusion data model is a model for constructing the association relationship between the plurality of original data and is adjusted based on a diamond model template; processing the target knowledge ontology according to a preset pruning strategy to obtain a plurality of operation behaviors, wherein the preset pruning strategy comprises at least one of the following: a white list pruning strategy and a divide-and-conquer pruning strategy; performing discrimination processing on the plurality of operation behaviors to obtain an operation and maintenance analysis result of the cloud storage system; and performing operation optimization on the cloud storage system according to the operation and maintenance analysis result. The problems that the observability technical implementation means is single in the related art, the system operation and maintenance efficiency is low, and the accuracy is not high are solved.

The invention provides a data processing method and system based on a cloud storage system, which can be applied to the technical field of artificial intelligence, and the method comprises the following steps: receiving a cloud storage system access request of a target user, and obtaining equipment environment information and login information of the target user according to the cloud storage system access request; determining a login risk category of the target user according to the login information and an abnormal login detection model, and if the login risk category conforms to a login risk category range, determining a terminal equipment environment risk category of the target user according to the login risk category, the equipment environment information and an equipment environment risk assessment model; if the equipment environment risk category conforms to the equipment environment risk category range, authorizing the target user to access the cloud storage system; and receiving a data processing request of the target user, and completing data processing of the cloud storage system according to the data processing request. According to the invention, the security of accessing the cloud storage system by the user can be improved, and the data security of the cloud storage system can be ensured.

The invention discloses a data storage method, a cloud storage system and a storage medium, and the data storage method comprises the steps: receiving to-be-stored data, and storing the to-be-stored data to a cache layer; obtaining the data size of each piece of to-be-stored data in the cache layer, determining the to-be-stored data of which the data size is lower than a preset data threshold in the cache layer to obtain small data, and determining the to-be-stored data of which the data size is not lower than the preset data threshold in the cache layer to obtain big data; aggregating the plurality of small data in the cache layer to obtain a small data aggregation file; and storing the small data aggregation file to an acceleration layer, and storing the big data to a storage layer. The read-write number of the acceleration layer can be reduced, the service life of the storage medium is prolonged, the overall read-write efficiency is improved, the characteristics of different storage media are fully utilized for classified storage of data, and storage resources are reasonably utilized.

According to one embodiment, a method for performing domain-wide authentication and authorization in a cloud-based environment can comprise receiving, from a third-party service of the cloud-based environment, a request to perform authentication and authorization of a user of the third-party service for accessing a cloud-based storage system of the cloud-based environment. A mapping of the user of the third-party service to a user account of the cloud-based storage system can be generated and one or more tokens for the user of the third-party service can in turn be generated based on the mapping of the user of the third-party service to the user account of the cloud-based storage system. The one or more tokens can provide access to services of the cloud-based storage system. The one or more tokens can be provided to the third-party service.