1793 results about "Cloud storage" patented technology

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

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

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

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

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

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

The invention relates to the technical field of industrial internet data acquisition, in particular to a data acquisition method and system based on an industrial internet operating system, and the method comprises the following steps: S1, detecting and recognizing data sources of a plurality of industrial devices and sensors; s2, collecting original data from a data source in real time, and preprocessing the original data; s3, distributing part of calculation tasks to a plurality of edge nodes; s4, storing the data in a distributed database or cloud storage in a classified manner; s5, performing fragmentation processing on the data in the cache; s6, performing data synchronization and consistency verification; and S7, converting the data into a corresponding format. According to the invention, by implementing efficient data fragmentation processing, a dynamic storage strategy and secure data consistency verification, the processing speed and storage efficiency of the industrial internet data acquisition system and the security and integrity of data are remarkably improved, and the requirements of high efficiency and high reliability of modern industrial production are effectively supported.

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

The invention discloses an intelligent inspection system and method for a photovoltaic module of a photovoltaic power generation system, and relates to the technical field of intelligent inspection. The method comprises the steps that a high-definition camera and an infrared imager carried by an unmanned aerial vehicle automatically inspect a photovoltaic panel, real-time operation data are acquired and transmitted to a preprocessing module, the preprocessing module optimizes an image through denoising, graying, contrast enhancement and geometric correction, a Unet algorithm is utilized to accurately segment strings and assemblies in the image, and the photovoltaic panel is obtained. And a defect identification module analyzes the processed image, calculates the average temperature, compares the average temperature with a threshold value, identifies hot spot defects, and finally outputs specific information, fault types and maintenance suggestions of a defective component by the system, and generates an inspection report. According to the technical scheme, a fault source can be quickly positioned, and cloud storage and an AI analysis tool are used for managing data and predicting potential maintenance requirements.

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

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

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

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

The invention discloses a multi-cloud storage cluster management method, device and equipment and a storage medium, is applied to a preset management system, and relates to the technical field of creativity creativity, and the method comprises the steps: monitoring the business load condition, business data and database copy operation condition of each storage cluster in a multi-cloud storage cluster on a creativity creativity server; the current load information is obtained; performing index analysis based on the load information by using a current database copy storage resource state and a database copy storage resource use condition to obtain a corresponding index analysis result; and determining a corresponding target resource scheduling strategy based on a preset scheduling decision model, a preset intelligent learning mechanism, the load information and the index analysis result, and triggering a resource allocation operation corresponding to the multi-cloud storage cluster by using a preset interface and the target resource scheduling strategy. Intelligent dynamic allocation and efficient management of storage resources can be realized.

Cloud units in cloud storage include containers including data containers storing file segments, segment tree containers storing upper-level segments of segment trees representing the files, and cloud containers storing headers from the data and segment tree containers. A header for a data container includes fingerprints identifying the file segments. A header for a segment tree container includes fingerprints identifying the upper-level segments. A storage appliance stores a first level of metadata for each cloud unit in a read-write state, a second level of metadata for each cloud unit in a read-only state, and a third level of metadata for each cloud unit in an offline state. The third level requires less storage than the first and second levels. The second level requires less storage than the first level. The limited metadata space of the appliance is managed by maintaining at least a subset of cloud units in the read-only state.

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

The invention discloses a client information management method based on a cloud environment and a related device, and relates to the technical field of data processing, and the method comprises the steps: carrying out the information safety analysis based on a client information composition parameter generated through the information composition analysis of client information, and obtaining an information safety parameter; acquiring category identification information of the customer information based on the category logic chain, and acquiring corresponding local density based on the preset information field and the category identification information; classifying the customer information based on the local density and the information security parameters; the client information of each category is stored and encrypted; obtaining a mapping relationship between the storage performance information and the encrypted customer information of each category, and determining an information storage strategy based on the mapping relationship in combination with a node partitioning strategy; and storing the encrypted customer information of each category into a storage node corresponding to a cloud storage center. According to the invention, the reliability of classified storage of the customer information is improved, and the classified storage of the customer information achieves a more ideal effect.

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

The invention discloses a block chain-based data storage optimization method. The method comprises the following steps of 1, establishing a storage strategy and a continuous mechanism for optimizing storage; 2, according to a storage strategy, calculating storage cost caused by monitoring real-time change of a storage amount statistical value and storage resource allocation service calculation; 3, continuously optimizing data storage in the whole data life cycle by utilizing the object label and the life cycle strategy; step 4, synchronizing transfer optimization of the data storage layer based on the access frequency of the data storage block; the method is applied to a data storage optimization system based on the block chain, the system comprises a storage strategy mechanism module, a storage cost calculation module and a data storage optimization module, in cloud storage, an intermediate data set is used for carrying out storage layer conversion decision making on data through the change of access frequency, and the cloud storage cost is saved. The method has the characteristics of improving storage performance and reducing storage cost.

The invention discloses an unmanned aerial vehicle infrastructure construction supervision method and system based on 5G, and belongs to the field of unmanned aerial vehicle monitoring. The unmanned aerial vehicle infrastructure construction supervision system comprises an unmanned aerial vehicle module, a 5G communication module, a data processing module, a cloud storage module, a mobile terminal module, a system management module and a visual management module; the unmanned aerial vehicle module is provided with a high-definition camera, an infrared sensor and a laser range finder, and high-quality construction site data is obtained; the data processing module processes and analyzes the data acquired by the unmanned aerial vehicle; the visual management module compares data acquired by the unmanned aerial vehicle with a construction plan, can generate a real-time visual chart of the construction progress, and displays the construction progress to a supervisor in a visual manner; real-time image transmission is achieved through the system, supervisors can know the progress condition of a project in real time, discover problems in time and take corresponding measures, construction efficiency and quality are improved, and cost and risks are reduced.

The invention relates to the field of data processing systems or methods for supervision, and provides a full-automatic carbon emission monitoring system and method, and the method comprises the following steps: S1, collecting an image of a monitored device through a data collection module, and determining the type of the device; s2, using a data acquisition module to acquire equipment operation related data in real time; s3, the data processing module processes the related data; s4, the analysis module converts the vibration signal after noise reduction into an equipment power consumption value by using a similar energy state mapping model; s5, the analysis module detects and analyzes the vibration signals through fractal dimensions; s6, the analysis module performs time sequence analysis on the historical power data and the real-time monitoring data of the equipment through LSTM, predicts a future power consumption value Ppredicted (t + 1), and dynamically adjusts the prediction model by using a historical prediction error e (t); s7, the analysis module calculates the carbon emission based on the power consumption value; and S8, the communication module uploads the monitoring result to the cloud for storage, and feeds back the monitoring result to the user terminal.

The invention provides a cloud computer integrated storage method and device, electronic equipment, a chip and a medium, and the method comprises the steps: determining the prediction time when the usage amount of a local storage space exceeds a preset threshold value based on local storage data, and enabling the prediction time to be obtained through the prediction of a trained target prediction model; according to the storage information configuration file, a target cloud storage space is determined from at least one cloud storage space contained in the storage information configuration file, and the cloud storage space is used for the cloud application to store data; before the prediction time, mounting the target cloud storage space to a local mounting point; and uploading the file written into the local mounting point to the target cloud storage space. According to the invention, the cloud storage space of other cloud applications can be integrated to the local cloud computer client, the integrated storage of the cloud computer client and the cloud storage space is realized, the utilization rate of the storage space is improved, elastic capacity expansion can be realized according to requirements through automatic mounting, the user experience is improved, and the capacity expansion efficiency is improved.

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

The invention discloses a data integrity verification method and device, electronic equipment and a storage medium, and relates to the technical field of information security, and the method comprises the following steps: receiving a data verification request, and selecting an index code of a target encrypted data block; randomly generating a first prime number and a second prime number; on the basis of the first prime number, the second prime number, the index code and the system parameters, challenge information is constructed and sent to a cloud storage server, and the cloud storage server obtains N encrypted data blocks stored by the user side on the basis of the index code; and substituting the first verification parameter and the second verification parameter returned by the cloud storage server into a verification equation, and obtaining a data integrity verification result based on a calculation result of the verification equation. According to the method and the device, the technical problem that data of a user side has potential safety hazards due to the fact that the third-party auditing side is not completely credible when data integrity verification is carried out through interaction between the user side and the third-party auditing side in related technologies is solved.

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

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

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

The invention relates to the technical field of enterprise cloud management, and discloses an ERP enterprise cloud management system, which comprises a system main body deployed based on a cloud computing architecture, the system main body is integrated with a financial management module, an inventory control module, a purchase management module, a sales management module, a production plan module, a human resource management module and a customer relationship management module, and data interaction interfaces and cooperative work mechanisms are arranged among the modules; data are stored by the cloud storage unit, and a data processing task is executed by the cloud operation unit. The ERP enterprise cloud management system breaks data barriers among departments of an enterprise, integrates core modules of financial management, inventory control, purchase management, sales management, production planning, human resource management, customer relationship management and the like, ensures the accuracy and consistency of data, and realizes the cloud management of the enterprise through a data sharing function. All the modules can obtain needed data and information in real time, and seamless transmission and cooperative work of the information are achieved.

The invention relates to the technical field of archive management, in particular to a cloud-based archive information management system, which comprises a data association analysis module, an intelligent storage strategy module, a dynamic resource management module and a disaster recovery regulation and control module. According to the invention, by analyzing the access timestamp and the user tag of the archive data in the cloud environment, the accurate identification of the archive access mode and the access frequency is realized, the association degree between the archives is analyzed, the archive management efficiency is improved, and the archive access delay is reduced according to the intelligent identification of the data access hotspot and the optimization of the selection of the storage position. And by dynamically adjusting the storage capacity and bandwidth resources, the use efficiency of cloud storage resources is improved, quick response can be ensured in the peak demand, real-time monitoring of disaster recovery and a multi-site remote backup strategy are ensured to effectively deal with potential risks, the continuity and safety of data are ensured, and the reliability and disaster resistance of the whole system are improved.