6139 results about "Backup" patented technology

The invention discloses a network line intelligent operation and maintenance monitoring management system and method. The method comprises the steps that an intelligent network monitoring and optimizing system regularly collects network equipment information through a protocol, and a monitoring agent collects server and machine room environment data; processing data in real time by adopting distributed storage and a stream processing framework; predicting a network fault by adopting threshold monitoring and a machine learning model, and identifying a bottleneck and scheduling resources based on flow analysis; in combination with deep packet inspection and an AI algorithm, network traffic is monitored in real time, security threats are identified, and IPS system protection is deployed; a repair operation is automatically triggered through a self-healing strategy, and backup resources are started; the AI engine analyzes historical and real-time data, generates network topology and link state visual content, and automatically generates reports, optimization and decision support; according to the intelligent operation and maintenance monitoring management system and method for the network line, a fault tree analysis and decision support system is introduced, and a plurality of fault reasons are intelligently analyzed and repair measures are automatically executed.

The invention relates to a safe backup and recovery method and system for medical data, and the method comprises the steps: obtaining the multi-dimensional attribute information of the medical data, and carrying out the dynamic classification of the multi-dimensional attribute information based on a preset classification rule, and obtaining an attribute information label; performing differential encryption on the medical data according to the attribute information tag to obtain encrypted medical data, writing the encrypted medical data into a multi-level storage node, and performing multi-level backup construction to obtain a cross-platform backup topology; performing traceability node extraction on the multi-level storage nodes according to the abnormal node data access behavior to obtain traceability node information, and performing data traceability verification on the traceability node information according to the backup topology configuration to obtain a trusted recovery chain; and performing recovery node selection on the multi-level storage nodes according to the trusted recovery chain, and performing multi-node collaborative decryption reconstruction to obtain a recovery verification report. According to the method, the redundancy storage cost can be reduced while the data recovery reliability is guaranteed.

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

The invention relates to the field of hard disk data protection, in particular to a hard disk data protection method and device based on artificial intelligence. The method comprises the following steps: acquiring a hard disk history monitoring log; performing multi-period hard disk load analysis on the historical hard disk monitoring log, and performing load peak fluctuation fitting so as to construct a hard disk load peak fluctuation curve; carrying out peak value trend evolution analysis on the hard disk load peak value fluctuation curve, and carrying out abnormal load peak value detection to obtain a time period where an abnormal peak value point is located; carrying out current time period redundant data calculation on the hard disk according to the time period of the abnormal peak point, and carrying out dynamic global data backup decision, so as to generate an abnormal peak global backup strategy; and performing abnormal write behavior detection and attack node tracing positioning on the hard disk historical monitoring log, and marking a hostile attack tracing positioning point. According to the invention, safe and reliable hard disk data protection is realized.

The invention relates to the technical field of big data, in particular to a cloud computing-based tumor early screening data sharing platform construction method and system. The sharing platform construction method comprises the following steps: carrying out association modeling on multi-dimensional early screening data by adopting a knowledge graph technology; encrypting the sensitive field of the metadata index database, and synchronously generating an irreversible anonymous identifier to replace the original identifier; partitioning and arranging the metadata index database in a distributed storage system corresponding to a cloud platform according to tumor types, and performing cross-region redundant backup by adopting an erasure code technology; and constructing an access control smart contract based on a block chain technology, verifying the qualification of a requester through a digital identity authentication module, and after verifying the validity of a digital certificate of the requester, dynamically decrypting and transmitting a data copy conforming to an authority level. Through organic fusion of the knowledge graph, the encryption technology, distributed storage and the block chain, a safe, efficient and reliable tumor early screening data sharing platform is constructed.

The invention relates to the field of data backup, in particular to an intelligent data backup method and system based on an AI large model. The method comprises the following steps: acquiring an enterprise global data list, performing intelligent data structure deconstruction and dynamic attribute mapping modeling, and constructing a holographic data semantic perception model; performing real-time transient risk mutation detection on the holographic data semantic perception model, and constructing an intelligent backup triggering mechanism; carrying out storage resource demand prediction based on an intelligent backup trigger mechanism, carrying out multi-storage cloud environment resource dynamic scheduling, and constructing an elastic backup storage resource pool; carrying out incremental backup analysis and self-adaptive compression coding to obtain an incremental backup coding packet; and performing dynamic backup sequence adjustment and intelligent incremental backup decision on the incremental backup coding packet based on the elastic backup storage resource pool, and constructing an intelligent incremental backup execution engine. According to the method, the reliability, the accuracy and the traceability of a backup result are improved through self-adaptive intelligent incremental backup.

The invention relates to the technical field of database disaster recovery backup, and discloses a multi-node database disaster recovery backup system. The system comprises a data fragmentation module, a real-time synchronization module, a fault detection module, a data recovery module and a strategy optimization module. The data fragmentation module performs logic fragmentation based on a consistent Hash algorithm to realize load balancing and redundant backup; the real-time synchronization module captures data change of the main node by using an incremental log synchronization technology; the fault detection module evaluates the health condition of the node through a self-adaptive heartbeat detection mechanism; the data recovery module recovers data by means of a multi-version snapshot recovery algorithm; and the strategy optimization module generates an optimal disaster recovery backup strategy by applying a dynamic weight adjustment algorithm. The system solves the problems of non-uniform data distribution, low synchronization efficiency, untimely fault detection, incomplete data recovery, lack of strategy optimization and the like in multi-node database disaster recovery backup, improves system performance, stability and data security, and meets diversified disaster recovery backup requirements.

The invention discloses a cooperative monitoring device and method for a large deep foundation pit complex supporting system, and relates to the technical field of foundation pit supporting. The device comprises a multi-dimensional sensor network module, a BIM-GIS digital twin platform module, an intelligent analysis and early warning module, a construction collaborative decision module, a data backup and recovery module and a remote monitoring and management module. The method comprises the following steps: step 1, carrying out multi-source data space-time registration; step 2, dynamically constructing a digital twinborn model; step 3, coupling risk assessment; step 4, construction collaborative optimization; according to the technical scheme, the method comprises the following steps of data processing, data quality control and system performance evaluation, through three innovations of deep coupling of multi-source data, dynamic model correction and intelligent collaborative decision making, a'monitoring-analysis-decision-execution 'full closed loop is constructed, technical breakthroughs are achieved in complex working condition adaptability, early warning real-time performance and construction safety, and the method has remarkable engineering application value.

The invention relates to the technical field of computers and the field of data testing, in particular to a multivariable real-time measurement and state testing method oriented to a data center. The method comprises the following steps: acquiring original data from a sensor, equipment and an application program in a data center, and filtering, cleaning and formatting the original data to generate preprocessed data; storing the preprocessed data to a distributed file system, and constructing a reverse index and B-tree index structure to realize efficient retrieval and redundant backup; intelligent analysis is carried out on the stored data by introducing a machine learning model and an automatic operation and maintenance strategy, the abnormal state of the key performance index is identified, and the resource use condition is predicted; and based on an intelligent analysis result, executing dynamic scheduling and optimal allocation operation of the resources. Through multi-variable real-time monitoring and intelligent scheduling, the resource utilization efficiency and the operation stability of the data center are improved.

A method may include, based on a recovery policy associated with the primary database, creating a first snapshot of the primary database at a primary computing system, replicating the first snapshot of the primary database to a secondary computing system, wherein a backup database of the primary database is hosted on the secondary cluster, in response to the primary database being unavailable, applying the first snapshot of the primary database to the backup database, creating, according to the recovery policy associated with the primary database, a snapshot of the backup database, in response to the primary database having restored availability, creating, according to the recovery policy associated with the primary database, a second snapshot of the primary database.

The invention relates to the technical field of intelligent power grids, in particular to an intelligent design method and system for electric power engineering cloud resources based on distributed computing. Comprising the following steps: constructing an electric power engineering multi-dimensional resource modeling system, dividing calculation nodes into three types of heterogeneous resource units including a real-time processing core, a data analysis core and a disaster recovery backup core, and establishing a dynamic attribute matrix containing time delay sensitivity, an energy consumption coefficient and a risk assessment value; the real-time processing core is configured with a hardware acceleration instruction set and supports microsecond response; a self-adaptive dynamic clustering algorithm is adopted, elastic resource clusters facing task requirements are generated according to the spatial topological relation of resource units and the load change trend, a quantum genetic optimization mechanism is introduced in the clustering process to dynamically adjust the inter-cluster coupling degree, and a cross-cluster communication link based on credibility evaluation is established; according to the design, the problems of low resource utilization efficiency and service quality degradation caused by a static allocation mode can be fundamentally solved.

The invention relates to the technical field of intelligent ship control, in particular to an intelligent ship moving control method and system based on a self-adaptive algorithm. The method comprises the following steps: acquiring hardware equipment data required by ship movement control; configuring a network topology structure of a computer network system, winch equipment and an encoder according to hardware equipment data, and constructing an electrical ring network; carrying out initialization setting and communication test on the electrical ring network, and establishing a data sharing mechanism between devices to obtain system initial configuration data; collecting the winding and unwinding length and tension parameters of a steel wire rope through a sensor of winch equipment based on initial configuration data of the system, and constructing a real-time monitoring model of the position and posture of the ship; and performing ship state monitoring by using the real-time monitoring model to generate ship real-time state data. According to the invention, the electrical ring network with a redundant backup mechanism is adopted, the communication real-time performance and reliability of the system are improved, and the stability of control signal transmission is ensured.

Methods and systems for managing a data processing system are disclosed. The data processing system may provide computer-implemented services and may include in-band hardware resources that operate as specified by operation data of the data processing system while providing the services. However, if the operation data becomes unavailable to the data processing system, then the data processing system may be unable to provide the desired services. Therefore, to increase the likelihood of providing the desired services, the operation data may be managed using distributed backup and recovery processes. To decrease security risk associated with operation data backup and recovery, the distributed processes may be performed using out-of-band components of the data processing system, bypassing in-band hardware components that may be more vulnerable to being compromised (e.g., by a malicious party). The out-of-band management of the operation data may reduce the likelihood of interruptions in providing the desired services.

The invention discloses a comprehensive algorithm for distribution and scheduling of AGV (Automatic Guided Vehicle) carrying tasks. S1: an SCADA system; equipment communication, data acquisition and storage, protocol analysis and logic processing; s11, the communication protocol, the address and the communication rhythm of the equipment are configured through the system, data acquisition data are stored in ES (ElasticSearch), the data acquisition data are stored for one year and backed up regularly, and the system achieves service logic processing through an LUA script. The carrying efficiency of the AGV is integrally improved, and the execution time of carrying a single task by the AGV is shortened by 10%; the production capacity of a workshop is improved, and the carrying rate exceeds 96% under the condition of no interference; the workshop logistics cost is reduced, and the overall number of AGVs is reduced by 10%; and the first pass yield of material carrying is improved by 15%. The deep fusion data analysis capability enables the whole system to predict demands more accurately, avoid conflicts, optimize the overall production efficiency, optimize the production process, reduce waiting and resource waste, and improve the flexibility of path planning and the reliability of task completion.

The invention discloses a permanent incremental backup method based on an S3 bucket-level virtual snapshot, and relates to the technical field of data storage and backup, the method comprises the following steps: initializing a backup system and starting a backup agent program, an index management service and an S3 object storage service; complete backup is executed, and a VID and S3 bucket-level virtual snapshoot are generated; efficiently managing a mapping relation between a file path and a snapshot identifier through a B + tree and an inverted index by utilizing a MongoDB index service; incremental backup is executed, and file changes are accurately recognized through Hash comparison and modification time comparison; a storage position is optimized by adopting an incremental storage and deduplication storage mechanism and an IncDSI optimization algorithm; executing backup chain integration and cleaning; and performing data recovery based on the VID of the file and the directory. According to the method, the backup efficiency is remarkably improved, the storage overhead is reduced, the data recovery process is optimized, and the method is suitable for data backup and management of a large-scale file system and has important application value and prospect.

The invention discloses a terminal data automatic backup and recovery method and system, and relates to the technical field of data backup and recovery, and the method comprises the steps: dynamically adjusting the priority, and generating a personalized backup strategy; capturing data change in real time and executing incremental backup; and encrypting the backup data for version recovery. According to the terminal data automatic backup and recovery method provided by the invention, accurate analysis and priority division are performed on the terminal data through the classification model, so that the backup plan can be dynamically adjusted according to the importance of the data in the backup process. Data change is captured in real time, incremental backup is executed, and the real-time performance and efficiency of backup are effectively improved. An incremental backup algorithm is adopted, only a file change part is recorded, and a change area of a data block is quickly positioned by utilizing hash value comparison, so that the requirement of a storage space is reduced, and meanwhile, the transmission overhead of backup data is reduced. According to the method, better effects are achieved in the aspects of backup real-time performance, backup accuracy, backup pertinence and backup efficiency.

The invention relates to a financial bill management system and an implementation method thereof. The financial bill management system comprises a data acquisition module, an AI identification module, an auditing module, a cost reimbursement module, a report generation module and a data storage and backup module. According to the financial bill management system and the implementation method thereof, by introducing the AI recognition module, automatic recognition and classification of bill types are achieved, the requirement for manual operation is reduced, the working efficiency and accuracy are improved, the AI recognition module can automatically process bill information, the whole process from image preprocessing to final key information extraction is highly automatic, and the efficiency is high. The system is not limited to bill identification and classification, but also covers a plurality of functional modules such as data acquisition, auditing, expense reimbursement processing, report generation, data storage and backup and the like, and provides a one-stop solution for users. The image preprocessing unit can effectively remove image noise, correct inclined images, cut redundant parts and enhance image contrast and brightness.

A video gateway system at a worksite is coupled to multiple cameras on a network, and backs-up video streams generated by the cameras to a backend cloud backup video storage system and a frontend (cache) video storage system. The video gateway system generates an aggregated video asset from a plurality of streams of video from the multiple cameras, and generates metadata and a backup report associated with the video asset. The video asset, metadata, and backup report are stored on the backend cloud backup video storage system in an file system, and are also stored on the frontend (cache) video storage system such that for at least a period of time, the video asset and the associated the metadata and backup report are stored on both the cloud backup video storage system, facilitating quick access and retrieval to the stored video for retrieval for streaming, activity detection, and other uses.

The invention discloses a wireless network high-reliability transmission method based on double-link redundancy, which relates to the technical field of wireless communication, and comprises the following steps: establishing double communication links, and collecting link quality data of the double communication links; performing weighting operation on the link quality data according to a preset weighting coefficient to obtain a first link score and a second link score, and judging the first link score and the second link score; issuing an original data packet and a backup data packet of the original data packet to the dual communication link according to a judgment result, and collecting a data transmission state in a receiving time window; and when it is monitored that the original data packet is lost in the receiving time window, extracting a backup data packet of the original data packet for data recovery, and recalculating the link score of the original data packet. Through organic combination of multi-dimensional link quality evaluation, link selection of weighted calculation, double-link parallel transmission and rapid data recovery, the network resource utilization efficiency is improved while the data transmission reliability is guaranteed.

The invention relates to a method and a system for storing and backing up electric power data of a data-in-data station based on use popularity, and belongs to the technical field of data-in-data stations, and the method comprises the following steps: classifying electric power business data into hot data, temperature data and cold data based on preset access frequency, time window and data volume index; according to a classification result and a business importance degree, storing hot data in an SSD cloud disk or a memory database, storing temperature data in a distributed column database, and compressing cold data and then archiving the cold data to an object storage or a tape library; dynamically configuring differentiated backup strategies for different types of data; automatic scheduling of storage and backup is realized through a cloud platform component, and hot switching of master and slave databases, OSS archived data recovery by a DataWorks tool and a remote multi-copy recovery mechanism are supported; and migrating data in advance in combination with a historical access prediction popularity trend, and triggering cold data compression and archiving when storage resources exceed 80%. The method is suitable for power industry scenes, the storage cost is optimized, and the data calling efficiency is improved.

The invention relates to the field of data storage and backup, and discloses a method and a system for transmitting disaster recovery backup capability of an emergency recovery unit. The method comprises the steps of establishing a quantum encryption transmission channel, monitoring a network index in real time and predicting transmission quality to start preventive optimization, calculating a link quality score to carry out link switching, verifying integrity and consistency of received data and executing retransmission, carrying out multi-mode data backup, and switching to an emergency recovery unit when a main data center fails. The system comprises a quantum encryption transmission module, a transmission monitoring and switching module, a data verification and retransmission module, a multi-mode backup module and an emergency switching and synchronization module. Through the technical scheme, the problems of data transmission security and high efficiency can be effectively solved, the disaster recovery backup capability of the emergency recovery unit is improved, the service continuity is ensured, and the method and the device are suitable for scenes with high requirements on data security and service continuity.

The invention discloses a meter reading system and a meter reading method based on dual-mode communication, and relates to the technical field of meter reading systems and methods, the meter reading system comprises a dual-mode communication module, intelligent data demand switching, strong NB-IoT penetration and far coverage; the BLE is short-distance, high-speed, low-power-consumption and fast in switching, the intelligent electric meter module and the high-precision metering chip measure electric energy accurately, historical data is stored in a large-capacity mode, the shell protection is high, the intelligent electric meter module adapts to severe environments, the data processing and storage module and the high-performance processor compress and process data, the SSD stores in a large-capacity mode and backups redundancy, and the user interaction module and the large-screen LCD clearly display power utilization information. And a button or a touch screen is convenient to operate, and a remote management and monitoring module is provided. Innovative technologies such as dual-mode communication are fused, high-precision metering, intelligent switching communication and convenient interaction are achieved, power line carrier standby and fault early warning diagnosis are achieved, meter reading efficiency, precision and reliability are improved, and development of an intelligent power grid is promoted.

The invention belongs to the technical field of data processing. The data security and privacy protection method comprises the following steps: determining an encryption algorithm of data according to the sensitivity and access frequency of the data; determining the authority of the user by using the behavior prediction model; determining an optimal desensitization method by using a context-aware desensitization technology; analyzing security threats and abnormal behaviors of the current data operation by using a predictive security audit model to obtain a precautionary measure method; obtaining backup time and frequency of the data by utilizing the sensitivity and the change frequency of the data; and processing the data according to the encryption algorithm of the data, the authority of the user, the optimal desensitization method, the precautionary measure method and the backup time and frequency of the data to obtain a data security and privacy protection method. By utilizing the method, the effects of encrypting the data in multiple aspects, dynamically adjusting the user permission and thoroughly desensitizing are achieved.

According to the data backup method and device provided by the specification, description information corresponding to target data is obtained, and the description information comprises at least one of data change frequency, health state information of a storage medium stored in the target data or table association information; the table association information comprises a plurality of tables used for storing the target data and a dependency relationship among the plurality of tables, and a target backup strategy is intelligently generated through a pre-trained backup strategy model, so that backup of the target data according to the target backup strategy is more intelligent. The target backup strategy is intelligently obtained according to the data change frequency of the target data, the health state information of the storage medium and / or the table association information instead of a fixed backup strategy, so that the data can be effectively protected by performing data backup through the target backup strategy generated by the method in the specification.

The invention relates to the technical field of space ranging, in particular to a smart agriculture big data cloud service supervision platform fusing aerial remote sensing and space ranging information. The method comprises the steps that a data acquisition unit acquires agricultural data based on a multi-source acquisition module; the data processing unit performs cleaning, correction and matching processing based on the agricultural data acquired by the data acquisition unit; the data analysis unit performs multi-aspect evaluation analysis through a multi-party analysis technology based on the agricultural data processed by the data processing unit; and the storage management unit manages and protects agricultural data through a cloud computing distributed storage technology based on an analysis result of the data analysis unit. According to the design of the invention, multi-dimensional evaluation and prediction are carried out on agricultural data by adopting a multi-party analysis technology, and a scientific basis and intelligent decision support are provided for agricultural production; through a cloud computing distributed storage technology, rapid access, backup and sharing of large-scale agricultural data are realized, and meanwhile, the security and privacy of the data are ensured.

The present invention discloses a communication data backup method and system based on Internet of Things security service, which relates to the technical field of communication data backup. The backup period is set for each type of communication data according to the importance evaluation of the communication data; the backup request of each type of communication data is triggered according to the backup period of each type of communication data, and the communication data triggering the backup request is preliminarily screened; the preliminarily screened communication data is processed in blocks, and then the backup is performed in sequence according to the backup requirement information of each data block; when the backup of the last data block is triggered, the backup data is integrity checked and risk predicted; the communication data is fully backed up according to the integrity check result; the risk of attack and the possibility of loss of the backup data are evaluated according to the risk prediction result, and then the backup strategy is adjusted. Efficient and accurate backup operation is achieved, and data security is enhanced through integrity check and risk prediction.

The invention relates to the technical field of power distribution station supervision, in particular to an intelligent auxiliary and artificial intelligence visual gateway control method and system for a station building, which monitors network state parameters in real time, calculates a network quality score and dynamically selects an optimal transmission path based on a reinforcement learning algorithm. Comprising a high bandwidth mode, a low delay mode and a disaster recovery backup mode, stage processing and dynamic compression are performed on transmission data according to data types and network quality scores, link switching and a local caching mechanism are automatically triggered when a network is abnormal, and finally, multi-gateway load balancing and visual monitoring are realized through a software defined network architecture. The problem of transmission delay or interruption caused by the fact that a static data transmission framework cannot adapt to network state changes in the prior art is effectively solved, and the reliability and the real-time performance of data transmission are remarkably improved through dynamic path optimization, intelligent hierarchical transmission and a fault self-healing mechanism.

The invention discloses a remote sensing large model construction method fusing edge semantic knowledge, and the method comprises the following steps: S1, carrying out the data preprocessing and enhancement of a strategy for later use; s2, feature extraction and fusion network design based on boundary enhancement; s3, training and evaluating the data set processed in the step S1 through the model constructed in the step S2; according to the method, a double-branch architecture of semantic branches and boundary branches is adopted, global semantic information and local boundary information are fully integrated, and a segmentation framework with global guidance, boundary refinement and semantic alignment capabilities is constructed. By introducing a boundary generation module, a multi-scale boundary refining module and a boundary semantic fusion module, the recognition precision and segmentation continuity of the model to a boundary region are enhanced.

The invention provides a data processing method and system based on cloud computing, belongs to the technical field of data processing, and realizes high concurrent processing and automatic load balancing by dynamically scheduling computing power through elastic resource allocation and a distributed computing framework and combining a storage and computing separation framework. The system supports cross-node disaster recovery backup and multi-layer encryption, and data security is guaranteed; and an on-demand payment mode is adopted, so that the hardware input cost is reduced, the resource utilization rate is improved, and the large-scale data processing efficiency and the system expandability are remarkably improved.