4627 results about "Backup" patented technology

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 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 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 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.

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 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.

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 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.

The invention discloses a backup disaster recovery optimization method, and relates to the technical field of IT operation and maintenance and disaster recovery, and the method comprises the steps: analyzing a statistical dependency relationship between basic performance indexes through employing a causal discovery algorithm based on a standardized monitoring data flow, and constructing a directed weighted graph; when a basic performance index in the directed weighted graph deviates from a normal performance baseline, triggering a reverse traceability positioning root cause component, identifying a service influence path through forward path analysis, and generating a backup enhancement instruction; in a digital twin environment, according to historical drill records and a strategy library, multiple potential disaster recovery switching schemes are generated, parallel deduction is carried out, the performance influence of each potential disaster recovery switching scheme on a virtual service link is quantitatively evaluated in the deduction process, and the optimal disaster recovery switching scheme is selected by comparing evaluation results. According to the invention, the prospective optimization of the disaster recovery decision and the minimization of the business influence are realized, and the automation, intelligence and business adaptation capabilities of the backup disaster recovery system are enhanced.

The invention discloses a digital archive online management system based on big data. The digital archive online management system comprises a digital acquisition layer which is used for carrying out semantic perception and structured extraction on heterogeneous archives; and the intelligent classification layer is used for mapping the collected archive entities, attributes and relationships to a dynamically expanded domain knowledge graph based on a knowledge graph construction technology and a graph neural network, realizing association mining and dynamic classification of cross-modal archives through node embedding and link prediction, adapting to evolution requirements of archive themes in combination with a weak supervised learning mechanism, and realizing dynamic classification of the cross-modal archives. A classification system with causal interpretation is formed; the storage retrieval layer is used for encoding the generation time, the space trajectory and the event causal chain of the archive into space-time causal metadata; the archive utilization layer is used for actively pushing associated archives and generating a personalized analysis report by analyzing user behavior preferences and scene requirements; and the backup layer is used for dynamically sensing the threat type and calling an evolutionary algorithm to adjust the backup frequency, the storage position and the recovery path.

The invention discloses an encryption protection method for network data backup and recovery, which relates to the technical field of data protection, and comprises the following steps: respectively implementing differential backup strategies by adopting a local, cloud and remote three-terminal collaborative backup architecture; and performing local data backup by a combined scheme of complete backup and incremental backup to generate a chained hash value sequence. Three-terminal key isolation and dynamic derivation are adopted to guarantee data security, an independent key system is adopted locally, cloud and remotely, a chain type key is generated locally based on a hash chain, version independent keys are generated at the cloud and remotely, and data security of other terminals is not affected by key leakage of any storage terminal. The recovery priority is automatically switched from local to cloud to remote, the local decrypts incremental data layer by layer through a hash chain, the cloud directly calls an independent key of a target version and uses a synthetic backup complete copy in remote, and the disaster recovery efficiency is improved through layered recovery and synthetic backup.

The invention relates to the technical field of data backup, in particular to a data backup strategy automatic generation method and system. The method comprises the following steps: acquiring and quantifying strategy generation input information; evaluating strategy-related attributes of the data assets, and estimating resource input costs meeting different preset protection levels according to strategy generation input information; based on the strategy generation input information, the strategy related attributes and the resource input cost, constructing and updating a dynamic model reflecting the strategy tradeoff relation in real time; performing strategy decision based on the dynamic model, wherein the strategy decision selects a backup strategy based on compliance laws and regulations in combination with real-time resource conditions; and deploying a backup strategy, continuously monitoring an execution effect of the backup strategy, and taking actual operation data of the execution effect as feedback for adjusting parameters of the dynamic model. The limitation of an existing data backup strategy automatic generation method in complex conditions is solved, and the ability of actual recovery result feedback optimization strategies is improved.

The invention discloses a flexible control method for new energy grid connection, and relates to the technical field of new energy grid connection control. The method comprises the steps of obtaining grid-connected point electrical parameters through a self-measurement mode or a communication interface mode, performing harmonic feature extraction and compensation based on a deep learning network, and adopting a state estimation method to fuse multi-source data to estimate power grid parameters; and constructing a multi-objective optimization function including voltage quality, a power factor and harmonic suppression, dynamically adjusting a weight coefficient of the optimization function by using a swarm intelligence algorithm, and realizing inverter switching sequence optimization through multi-objective model prediction control. Through a dual-mode communication redundancy mechanism and local intelligent control, while a communication function of a traditional main link is reserved, a standby wireless communication link is introduced to realize real-time backup transmission of a control instruction, and through a local autonomous decision-making capability, the problem of control failure caused by a communication problem in a traditional scheme is fundamentally solved.

The invention provides a user travel data safety management system and method based on cloud computing, and the system comprises a multi-source data collection and adaption module which is used for collecting user travel data from various types of travel equipment; the hybrid encryption transmission channel module is used for establishing a safe transmission link between the data acquisition terminal and the cloud computing platform; the privacy screening and desensitization module is used for identifying and processing privacy sensitive contents in the data received by the cloud computing platform; the distributed storage architecture module is used for reliably storing user travel data on the cloud computing platform; the authority control and auditing module is used for controlling the calling authority of the external main body to the user travel data and auditing the data access condition; the data backup and recovery scheduling module recovers data when data risks exist; and the data visualization and analysis auxiliary module is used for presenting the data in a visualization mode. According to the invention, confidentiality, integrity and availability of travel data transmission and transparency and traceability of data access are ensured.

The invention discloses a method for comparing data integrity and consistency after database migration backup, which relates to the technical field of database management and comprises the following steps: S1, verifying metadata consistency; s2, quickly comparing statistical characteristics; s3, content accurate comparison; and S4, difference analysis and reporting. According to the data integrity and consistency comparison method after database migration and backup, a hierarchical verification strategy is adopted, and through a hierarchical verification process of four steps of metadata consistency verification, statistical feature rapid comparison, content accurate comparison and difference analysis reporting, the comparison efficiency is greatly improved, the total comparison time is shortened, and the data integrity and consistency comparison efficiency is improved. And the layered progressive method enables the comparison process to be more efficient and accurate. Meanwhile, automatic comparison between the heterogeneous databases is supported, automatic comparison between the heterogeneous databases can be supported in a cross-platform mode, the technical threshold is lowered, and migration and backup verification between different databases become easier and more feasible.

The invention relates to the technical field of embedded data storage, and discloses a method and a system for prolonging the service life of a FLASH simulation EEPROM (Electrically Erasable Programmable Read-Only Memory) in a single chip microcomputer, and the method comprises the following steps: firstly, initializing the FLASH, dividing the FLASH into a main data area, a mapping table area, a backup area and a transaction log area, selecting a physical block through a dynamic wear leveling algorithm when data is written, and writing the data into a database; the method comprises the following steps of: starting a backup area or a transaction log area, safely updating a mapping table by using a transaction mechanism, performing cyclic redundancy check when reading data, if the cyclic redundancy check fails, starting cascade recovery from the backup area or the transaction log area, and finally, updating a transaction log to record operation so as to form a closed-loop management process. According to the invention, through a set of dynamic wear leveling and data classification management mechanism, global and uniform distribution of the write load in the storage array is realized, the physical block with the least erasing times is selected during writing, the access frequency of the data can be identified, and cold data which is not changed frequently can be actively migrated.

The invention discloses an autonomous fault-tolerant system and method for dual-mode hot switching, and relates to the technical field of edge computing, and the method comprises the steps: switching a main communication link to a standby communication link when the main communication link has a fault, and generating a first hardware equipment migration instruction when any one of a plurality of hardware equipment has a fault risk, meanwhile, a second hardware equipment migration instruction is generated when any hardware equipment is in a fault state, the fault hardware equipment in the fault risk and the fault state is migrated to a target node based on the first hardware equipment migration instruction and the second hardware equipment migration instruction, and when the target node recovers execution of a service task, the target node is migrated to the target node. A service task is executed based on a communication link of the autonomous fault-tolerant system, the technical problems of long response time, delay and high cost generated by manual operation and maintenance and cloud redundant backup are solved, a dual-mode communication design idea is adopted, and all functional modules work cooperatively, so that a complete closed loop from fault monitoring to autonomous recovery is realized.

The invention provides a service system disaster recovery system based on cloud computing, and relates to the technical field of disaster recovery backup recovery and cloud computing, and the system comprises a software application registration and arrangement module which is used for registering an entity object of a service application to a software warehouse, arranging a topological structure of the service application according to a dependency relationship, and configuring a disaster recovery strategy; the software application deployment disaster recovery module is used for automatically deploying a pull-up service application and backing up the pull-up service application according to a disaster recovery strategy; the fault monitoring and damage decision-making module is used for performing fault monitoring and damage decision-making on the application instance, and sending out an alarm notification when a fault or a disaster is monitored; and the system recovery disaster recovery switching module is used for executing system recovery operation when receiving the alarm notification, executing disaster recovery switching operation such as upstream data flow and service access entry redirection, and realizing system recombination recovery and service replacement. The invention further provides a service system disaster recovery method based on cloud computing, electronic equipment and a storage medium.

Ransomware detection accuracy is improved by analyzing patterns of changes in filename extension counts, relative to each other, that occur in a file system over time. The disclosed approach is malware-agnostic and does not rely on recognizing malware extensions or on real-time monitoring of the target file system. Instead, during each successive backup job of the target file system, the disclosed technology counts different types of filename extensions and compares the counts to each other and to corresponding counts taken in earlier backup jobs. Preferably, the anomaly detection analysis uses machine learning to discern a behavior pattern of the file system, which indicates how filename extensions are distributed and how much they change between backup jobs over time.

The invention relates to the field of hardware encryption, and discloses a geological disaster monitoring scene-based low-power-consumption hardware encryption system, which comprises a layered architecture module for constructing a terminal sensing layer and a cloud platform layer, generating a three-dimensional risk thermodynamic diagram, and carrying out short-term early warning model and emergency deduction; the encryption and decryption module is used for carrying out multi-dimensional identity verification based on protocol layering and national secret algorithm fusion, carrying out dynamic management and eavesdropping monitoring on a secret key, optimizing a data processing flow, and establishing a secure channel to dynamically negotiate whether encryption or decryption is needed; the key management module is used for implementing a triple binding mechanism, carrying out processing and risk assessment on the monitoring data, and carrying out key backup and recovery by adopting a dynamic key period according to a generated risk level; and the safety protection module is used for performing intelligent management on the power consumption of the system based on the active state of the system, selecting a compression strategy according to the data type by adopting a dynamic compression algorithm, performing safety monitoring on hardware and firmware, performing intrusion monitoring on the system and performing automatic repair.

The invention discloses a cultural heritage file multi-dimensional storage system, which relates to the field of cultural heritage digital protection and intelligent storage and comprises a data acquisition and processing module, a double-library management module, a multi-dimensional index retrieval engine, a security and convergence module and a backup and disaster recovery module. According to the method, structured texts and unstructured files are uniformly accessed through various interfaces, a special tool chain is adopted for format standardization, audios and videos are transcoded into an H.264 / AAC format through FFmpeg, images are subjected to resolution adjustment and compression through ImageMagick, plain texts are extracted from texts through ApacheTika and are converted into UTF-8 codes, standardized data are uniformly converted into an intermediate format, and the format of the intermediate format is converted into the intermediate format. According to the technical scheme, the method comprises the following steps of: standardizing input data by adopting a three-layer duplicate removal strategy, facilitating other modules to receive data, performing refined redundancy processing on the standardized input data by adopting a three-layer duplicate removal strategy, remarkably reducing storage redundancy, partitioning a cultural heritage data main library according to three layers of ''project type-medium type-age'', and partitioning an inheritor information library according to two levels of ''province-inheritor number prefix''.

The invention discloses a double-layer BootLoader method for supporting a UDS service of a new energy automobile, and the method comprises the steps: S1, starting a first layer BootLoader after a system is powered on, executing hardware initialization, receiving an external diagnosis instruction according to a UDS protocol, and judging whether a legal firmware updating request exists or not; if the updating request exists, entering a downloading mode; s2, new firmware data are received in a segmented mode through the block transmission function of the UDS service, signature is conducted after data integrity is verified in real time in the receiving process, then the first layer BootLoader temporarily stores new firmware in a backup storage area, version information is updated, and then system reset is triggered; s3, after the system is reset, activating the second layer BootLoader, reading the new firmware written into the backup storage area, verifying the integrity and function logic of the firmware again, and if verification fails, rolling back to the original version and recording an error log; and if the verification is successful, the second layer BootLoader skips to the updated application program area for execution. The firmware integrity is guaranteed, and system paralysis caused by single-point failure is avoided.

Backup and recovery of multi-party computation (MPC) security data utilized to secure digital assets and transactions thereof can enhance user confidence and user experience in digital asset transactions. Example MPC security data can include cryptographic keys and key shares utilized with a N×M MPC signature and validation framework. A computing device participating in generation of MPC secure data can retain a segment of the MPC secure data and can encrypt and store an encrypted segment at a second device. A recovery service or recovery application at the second device can facilitate recovery of the MPC secure data segment at the computing device, or at an additional device not involved in generation of the MPC secure data. The recovery service or application can facilitate recovery of the MPC secure data segment even in the event the computing device is lost.

The invention discloses an intelligent electronic medical record generation management system and method, a terminal and a medium. The generation management system comprises a data acquisition module, a template generation module, an intelligent prompt module, an editing and saving module, a data security protection module and a permission retrieval recording module. The generation management method comprises the following steps: performing identity permission verification; integrating to form multi-modal structured data, and screening and matching to generate a template framework; a diagnosis suggestion sorting table is generated, and data is stored and backed up; transmitting and storing data, and recording operation behavior data. According to the invention, real-time automatic acquisition of basic information and examination results of patients is realized; a medical record template is automatically matched and optimized based on disease characteristics of a patient and an examination result, so that the workload of a doctor is reduced, and the medical record structure conforms to clinical specifications; transmission and storage data are encrypted in a layered mode, current account information operation behavior data are recorded based on a timestamp, and safe storage and real-time management of medical record data are effectively ensured.

The invention provides a power centralized control center data verification and backup method and device, and the method comprises the steps: obtaining hardware resource information corresponding to an operating system, and constructing a thread resource pool; the method comprises the following steps: connecting data sources of an electric power centralized control center, classifying and rating the data sources, and carrying out real-time demand analysis by adopting a parameterized mixed time algorithm to obtain a data task queue with a priority mark; monitoring the load state of an operating system and the length of a task queue, performing resource evaluation by applying an online distributed queue length estimation algorithm, dynamically creating a working thread and allocating resources; based on a thread resource allocation scheme, executing parallel data verification by adopting a sub-exponential algorithm on an independent set; and evaluating a backup demand according to the data verification result set, determining a backup strategy, and executing parallel data backup. According to the method, the characteristics of a domestic operating system are fully utilized, efficient verification and backup of the data of the electric power centralized control center are achieved, and the system resource utilization rate and the data safety are improved.

The invention discloses a network space security intelligent monitoring and analysis system, and relates to the technical field of network security monitoring and analysis, and the system comprises a multi-source data collection module which collects multi-dimensional data in a full-link manner, and the collection frequency is dynamically adjusted along with a network load; the data preprocessing module cleans the fused data and generates a standardized analysis data set; the AI intelligent risk identification module identifies various safety risks in real time through a mixed deep learning model; the real-time response processing module starts differential processing according to a three-level mechanism; the threat traceability analysis module traces an attack link and generates a report; the security situation visualization module displays the security state in multiple dimensions; the data encryption storage module encrypts and protects data and performs double backup; and the system self-optimization module dynamically optimizes the strategy through incremental learning. The method is accurate in risk identification, timely in response processing, reliable in traceability and evidence storage, and efficient in cross-domain cooperation; terminal protection and third-party access control are enhanced, and network space security and stable service operation are comprehensively guaranteed.

The invention belongs to the technical field of air traffic management, and discloses a control tower and flight guarantee command room decision instruction transmission method based on edge nodes, priorities are distributed for instructions through an intelligent grading model, the high-priority instructions can reserve resources in advance and are transmitted preferentially, the resources can be preempted in emergency, and the control tower and flight guarantee command room decision instruction transmission efficiency is improved. A multi-path transmission strategy is automatically switched when a certain path fails, data integrity is ensured in combination with fragment verification and block chain recording, instruction loss caused by a single point of failure is avoided through edge-cloud redundant backup, and the problems that instruction transmission is prone to interference and insufficient in safety in a traditional method are solved; through intelligent priority grading, resources are inclined to high-priority instructions, resource waste is avoided, a collaborative scheduling mechanism of an edge node cluster can dynamically adjust resource allocation according to a real-time load, and a transmission quality feedback and adaptive optimization strategy is continuously optimized, so that the system can still efficiently process a large number of instructions in a flight take-off and landing intensive period.

The invention discloses a video monitoring system based on multi-link redundant transmission and intelligent supplementary transmission, relates to the technical field of communication monitoring, and solves the problems that key frames are lost due to insufficient network link stability, transmission delay is uncontrollable due to bandwidth resource allocation rigidness, and the dependency relationship between complex systems is difficult to optimize in real time. The video monitoring system comprises a link transmission management module, an edge intelligent cache module, a dynamic supplementary transmission control module and an intelligent storage management module, the problem of network disconnection is solved through a camera device in combination with redundant backup of a cache time model, and meanwhile, automatic extraction and complementation are achieved through a protocol, and seamless connection is achieved; through memory redundancy two-stage storage, the hard disk writing speed is improved, and the blocking probability of video data is reduced.