3334 results about "Backup" patented technology

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 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 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 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.

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 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''.

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 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 power system operation reserve quantification method, system and device based on photovoltaic probability prediction and a medium. The method comprises the following steps: calculating an Euclidean distance between a photovoltaic predicted value of a point to be decided and a historical photovoltaic predicted value, and searching a photovoltaic power generation historical data set similar to the point to be decided; performing quantile regression based on the similar historical data set to obtain quantiles corresponding to a plurality of tail end quantile levels, fitting a probability density function of photovoltaic prediction deviation by using a Gaussian mixture model, and further calculating a risk loss expectation of a point to be decided; and constructing a standby cost function, considering the reliability constraint of the standby, taking the sum of the minimum standby reserved cost and the risk loss expected cost as a target function, and finally optimizing to obtain the standby capacity of the power system. According to the method, the change of the reserve price along with the capacity is considered, the photovoltaic probability prediction information can be fully utilized, and a more economical and reliable power system reserve quantification result is obtained.

The invention discloses a backup task scheduling method based on load prediction and an improved SAC algorithm, and relates to the technical field of data backup and intelligent scheduling. Aiming at the problems of response lagging, insufficient key task guarantee and low strategy exploration efficiency of a traditional scheduling method under a dynamic load, a task weight monitoring matrix is constructed, server performance, a network state and data importance are fused, and a task risk coefficient is calculated in combination with an exponential smoothing algorithm; carrying out sequence decomposition on a service load by adopting an Autoformer model, identifying a periodic mode through a weighted autocorrelation mechanism, and predicting a future load change trend; on this basis, an improved SAC scheduling framework is designed, a task weight guidance and temperature coefficient adaptive adjustment mechanism is introduced, and the task starting opportunity and execution duration are optimized in combination with an elastic time window. The invention provides an intelligent scheduling method with strong adaptive capability, which can autonomously optimize the task scheduling strategy according to the dynamic change of the system and effectively reduce the peak load of the server and the task backlog risk.

The invention belongs to the field of distributed storage, and provides a collaborative data replica control method and system for cloud real-time machine learning, and the method comprises the steps: evaluating each candidate edge node through a comprehensive cost function, taking the minimum comprehensive cost function as a target, determining an optimal replica position, and selecting a plurality of adjacent nodes as backup positions, synchronizing data copies between the cloud and the edge nodes by utilizing a federated learning technology, detecting the data copies by utilizing a Byzantine fault-tolerant mechanism, and eliminating the edge nodes with abnormal information synchronization; a data copy scheduling problem in a dynamic environment is abstracted into a Markov decision process, optimization solution is carried out, and closed-loop control is carried out according to a result after optimization solution. According to the method, cost-aware copy placement, safe and reliable cooperation in a cloud side end environment and dynamic closed-loop control can be realized, the delay is reduced, the reliability is improved, and the storage and transmission overhead is reduced.

Methods, systems, and devices for data management are described. A method may include obtaining a new snapshot of a relational database of a source computing environment, comparing the new snapshot of the relational database to a prior snapshot of the relational database, updating, in response to the comparison indicating that data of a row of a relational database table changed between the prior snapshot and the new snapshot, a field of a second versioning control column of a corresponding row of a backup database table of the set of backup database tables to indicate a version of the new snapshot, and adding, in response to the comparison indicating that the data of the row of the relational database table changed, an additional row to the backup database table, where a field of a first versioning control column of the additional row includes an indication of the version of the new snapshot.

A check point remote backup method based on large model parallel strategy perception comprises the following steps: S1, constructing a Checkpoint backup scheme: S11, constructing different cross-node parameter backup mapping relationships according to different parallel modes; s12, constructing a model parameter fragment distribution scheme according to the parameter load capacity of each card and the redundant copy number; s13, grouping the training nodes according to the number of backup copies set by a user, and generating a plurality of full mutual backup groups with equal sizes; the training nodes in the full mutual backup group are mutually backed up; s2, managing memory allocation in a Checkpoint backup process on the basis of a memory multiplexing mechanism; scheduling of CPU resources in the Checkpoint data transmission process is managed according to the asynchronous transmission process; s3, realizing punctual writing of the Checkpoint through a round and time threshold combined triggering method; and S4, controlling the startup and scheduling of the Checkpoint backup. According to the method, when the node breaks down, the system can quickly recover the model state from other nodes according to a set backup strategy, and the continuity of a training task and the fault-tolerant capability of the system are ensured.

The invention provides a Hadoop cluster incremental backup recovery method based on data block optimization, and relates to the technical field of data management, and the method comprises the following steps: obtaining distribution state information and historical change records of data blocks in a distributed file system, extracting space-time correlation characteristics, and constructing a data block dependency graph containing dependency direction and dependency intensity; identifying the changed data blocks, performing topological sorting, and identifying key path data blocks and independent data block clusters; calculating a distribution entropy value according to a copy distribution position, and determining a backup granularity type; a backup execution sequence is generated according to a topological sorting result, key path data blocks are backed up preferentially, independent data block clusters are backed up in parallel, and backup metadata containing dependency relationship identifiers and distribution entropy values are generated; and performing dependency consistency verification after recovery based on the dependency relationship identifier. According to the method, the backup recovery efficiency is improved, the data consistency is guaranteed, and the storage overhead is reduced.

The invention relates to the technical field of message recovery, and discloses an AI-based iOS device message recovery method and system, and the method comprises the steps: connecting an iOS device, positioning a database file of a message application, and obtaining a database file path list; reading an equipment storage sector, creating a physical mirror image, and analyzing a free block position mark in an APFS container super block; binary classification identification is carried out through an AI data block classification model, an SQLite page type label and a page mapping relation are obtained, deleted messages of a recombined database and a device end are determined, differential comparison is carried out on all historical backup snapshot records of an iCloud account, and cloud end deleted messages are obtained; according to the method and the system, the reorganization success rate of the database and the extraction integrity of the deleted messages are improved, and a technical solution is provided for comprehensive, accurate and efficient recovery of the iOS device messages.

The invention discloses an industrial control safety target range construction method and system based on a digital twinborn technology. The method comprises the following steps: constructing a digital twinborn simulation environment for an actual industrial scene, wherein the digital twinborn simulation environment comprises physical system modeling, communication network modeling and co-simulation modeling; defining an interactive interface between the simulation environment and an external tool, including selecting a communication mode according to a data exchange demand, defining a logic communication channel based on the selected communication mode and standardizing a data exchange format; converting an attack instruction initiated by an external tool into a simulation operation instruction in the simulation environment through the interactive interface, and feeding back a physical state change caused by the simulation operation instruction to the external tool; and an automatic target range resetting mechanism is established, and the state of the simulation environment is backed up and recovered through a snapshot mechanism. According to the invention, the management efficiency of the industrial control safety target range is improved, and the test reliability is improved.

The invention relates to the technical field of intelligent power grids, and provides a remote software upgrading method for an intelligent electric energy meter, which comprises the following steps: S1, a server generates an upgrading package containing a version identifier, and transmits the upgrading package to the intelligent electric energy meter in a fragmented manner through a TLS encryption channel; s2, the intelligent electric energy meter doubly verifies the integrity of the upgrade package and the legality of the source through an SHA-256 check code and a digital signature, the upgrade package and the source legality are written into a backup storage area of the Flash memory after verification is passed, and a CRC write-in check value is recorded; s3, performing secondary verification on the upgrade package of the backup storage area, triggering restarting after verification is passed, and writing data of the backup area into the main partition in the restarting process; s4, if the main partition system is not started or the core function is abnormal within the set time, returning to an old version and generating a fault log containing a timestamp; through a dynamic subpackage strategy, differential upgrading, SHA256 verification, a watchdog mechanism and a fault log record, the upgrading efficiency and safety of the intelligent electric energy meter are remarkably improved, and the dynamic subpackage strategy dynamically adjusts the size of a data packet according to the network condition.

The invention relates to the technical field of intelligent construction, and discloses an intelligent construction collaborative management platform and method based on digital twinning, and the method comprises the steps: firstly obtaining the practical operation data of a target engineering machine, analyzing the data safety level of the practical operation data to generate a distributed storage path, extracting storage node identifiers in the path, and deploying the storage node identifiers to a digital twinning platform; analyzing a node storage state index, querying a data backup demand and constructing a storage backup framework; then identifying an access request of the storage security framework, verifying a user identity, calculating an authority level index, analyzing an access range, and determining a platform multi-layer access regulation; then monitoring a permission change event, collecting a log, identifying a risk access behavior and calculating an access risk index; and finally, adjusting node encryption configuration according to the risk index, identifying an authority control key, and generating a self-adaptive security policy of the target engineering machinery on the platform. According to the invention, the integrity and safety of construction management can be improved.

The invention provides an intelligent leveling and ground pressure self-adaption system of a modularized movable laboratory, and relates to the technical field of laboratory leveling, the system realizes high-precision leveling through six-degree-of-freedom hydraulic support legs, is equipped with pressure and displacement sensors, and is combined with spherical hinge universal joints to compensate ground inclination. And the reliability and the safety of the system are improved through the backup design of the double hydraulic pumps and the energy accumulator. The system further has a ground pressure self-adaption function, the pressure of the supporting legs is dynamically adjusted based on the ground type and a bearing capacity model, local overload is prevented, and an anti-sedimentation strategy is set. In addition, the system integrates technologies of Beidou / GNSS positioning assistance, multi-modal data fusion, block chain traceability and the like, and the leveling efficiency, stability and traceability are enhanced. The system is suitable for mobile laboratory deployment under complex terrains, and has the characteristics of high efficiency, accuracy and reliability.

The invention provides an MCU function security implementation system based on a domain controller, and relates to the technical field of domain controllers, the system realizes dual backup storage and on-site execution of program codes by combining a security MCU without a built-in program memory with at least two redundant plug-in nonvolatile storage units, and the security of the MCU is improved. Consistency verification is carried out on key codes or data in the running process, and system failure caused by single-point storage faults or data tampering is effectively prevented; meanwhile, read-write operation without losing data during power failure is isolated through the independent data storage unit, and resource conflict and interference between program execution and data storage are avoided; the ASIL D-level power management unit supporting two-way fault monitoring is matched, resetting can be triggered in time or the MCU enters a safe state when a power supply is abnormal or an MCU internal fault occurs, and therefore a high-reliability and high-safety domain controller MCU system is constructed on the hardware architecture level, and the capacity of the MCU for meeting the ASIL D function safety level in a plug-in storage scene is improved.

The invention discloses an archive data security backup method and system based on cloud computing, and the method comprises the steps: obtaining archive data and a cloud backup pool, obtaining an edge verification package based on to-be-backed-up data, carrying out the grouping encryption of the edge verification package through a national password, and enabling the cloud backup pool to carry out the splitting and storage of the backup data according to a regeneration code strategy, the method comprises the following steps: acquiring data fragments and fragment roots, writing Hash of the fragment roots into an alliance chain channel, hosting a decryption key of a confidential envelope according to a storage period, storing fragments of the decryption key into a cloud HSM, and if a cloud backup pool monitors a disaster trigger event, pushing decryption key sub-fragments to a disaster recovery undertaking pool according to a disaster level, the subset of the readable file data is recovered to the edge node, and a recovery log is written back to the alliance chain. According to the method, data backup, safety and reliability are improved through desensitization grading, differential safety protection, regeneration code strategy splitting and multi-node disaster recovery.

The invention relates to the technical field of data processing, in particular to a mobile terminal data intelligent backup method and device, a mobile terminal and a storage medium. The mobile terminal data intelligent backup method comprises the steps that when a preset triggering condition is monitored, such as an NFC label approaching event and / or at least one additional triggering condition, safety verification is executed, and a backup task is triggered if verification is passed. Wherein the NFC label is only used for waking up the backup application program of the mobile terminal. And after a backup task triggering instruction is received, writing the to-be-backed-up data into the external storage device successfully connected with the mobile terminal according to the intelligent backup strategy. According to the data backup method and device, intelligent triggering is achieved through the NFC near field sensing characteristic, the expansion requirement of the external storage device is considered, backup can be conducted according to the intelligent backup strategy, the personalized backup requirement under the complex scene is met, and the convenience and applicability of data backup are improved.

The invention relates to the technical field of data backup, and discloses an iOS device data backup and recovery method and system, and the method comprises the steps: carrying out the SSL certificate verification and EscrowBag key authentication of an iOS device, and obtaining a data path list; according to the data path list, parallel scanning of APFS file system B-Tree traversal, NAND flash memory page reading and SQLite WAL log analysis is carried out on the iOS device storage partition, and an original data set is obtained; performing integrity evaluation based on the original data set to obtain an effective data set and constructing a virtual file index; the data selection list of the user is confirmed based on the virtual file index, safety recovery is executed, and a data recovery result is returned. The SQLite page Cell reconstruction and fragment file signature matching technology is combined, intelligent repair and quality prediction can be conducted on damaged data, the user experience and operation efficiency are improved, and the user experience is improved. And the security and the reliability in the data recovery process are ensured.

The invention discloses a BMC firmware starting method and device, equipment, a medium and a program product, and relates to the technical field of server management.The BMC firmware starting method comprises the steps that when a main mirror image file is damaged, BMC firmware is restarted on the basis of a standby mirror image file, if starting fails, the BMC firmware is restarted on the basis of a latest mirror image file of a remote server, and if starting fails, the BMC firmware is restarted on the basis of the latest mirror image file of the remote server; if the BMC firmware is started abnormally, the BMC firmware is restarted based on the local minimum mirror image file, so that the problem that a double-mirror image backup mechanism still faces system paralysis can be solved, a multi-stage mirror image file recovery mechanism is constructed through the multiple pre-deployed mirror image files, when the BMC firmware is started abnormally, the system is recovered step by step according to the multi-stage mirror image file recovery mechanism, and the system recovery efficiency is improved. The technical effects of ensuring the minimum availability of the system and improving the reliability and safety of the system are achieved.

The invention relates to the technical field of power internet of things, in particular to a method and a system for switching link access modes of sensing equipment of the power internet of things. The method comprises the following steps: calculating a score of each heterogeneous link based on a weight corresponding to each evaluation index and an evaluation index value of each heterogeneous link in a network layer of the electric power Internet of Things; the average value of the scores of all the heterogeneous links in the network layer of the electric power Internet of Things serves as a multi-link binding mode score; taking the maximum value in the scores of all the heterogeneous links in the network layer of the electric power internet of things as a multi-path switching mode score; taking the difference between the maximum value and the minimum value of the scores of all the heterogeneous links in the network layer of the electric power Internet of Things as a multi-path backup mode score; and taking the mode with the highest score as a target heterogeneous link access mode. According to the invention, the problem that channel congestion often occurs under the condition of high concurrency of the existing power internet of things is solved, and high-efficiency transmission of signals of the power internet of things is realized.

The invention discloses a data zero-copy processing method and system based on an eBPF zero-trust gateway, and belongs to the technical field of computer network security. According to the method, an XDP program is loaded on a network card driving layer, and an AFXDP technology is combined to construct a kernel mode and user mode shared memory pool UMEM; when an original message arrives, an eBPF unit mounted by an XDP program intercepts the message in real time, a tunnel packaging space is reserved in situ in an original memory, and if the head space is insufficient, a new memory page is logically mapped from a UMEM standby buffer pool to achieve extension. Identity fingerprints are generated by extracting message feature vectors, load in-situ encryption is completed in cooperation with a kernel kTLS module, and data are prevented from being returned to a user mode for processing. According to the method, multiple copying and context switching of a kernel-user mode of a traditional SSL VPN are eliminated, the throughput is improved by 2-3 times, the delay is reduced to the microsecond level, the CPU utilization rate is reduced by 40% or above, application layer DDoS attacks are effectively defended, and the method is suitable for 5G edge computing and cloud native zero-trust gateway scenes.

The invention relates to the field of electric vehicle charging control, and particularly provides a system, method, device and equipment for safe communication between a charging pile and a BMS and a storage medium, the system comprises a bidirectional authentication module, a communication encryption module, a verification module and an optimization module; the bidirectional authentication module is used for constructing a bidirectional authentication protocol between the charging pile and a battery management system (BMS) based on the hardware security module and authenticating the identity; the communication encryption module is used for customizing an integrated transport layer security protocol line through an open source tool and encrypting security communication data; the verification module is used for designing a three-level verification mechanism, blocking a malicious firmware injection path and verifying a secure communication process; and the optimization module is used for optimizing the key process by adopting a redundant backup scheme deployed in a containerization manner. Through the system, the effect of a safe communication process between the charging pile and the BMS can be realized.

The invention provides a private cloud-based multi-data center disaster recovery backup and rapid recovery method and system, and the method comprises the steps: enabling each data center to be connected with a disaster recovery control platform, and carrying out the deployment management; arranging a backup strategy according to the disaster recovery control platform; monitoring the health state of each data center on the basis of a disaster recovery control platform according to the arrangement result and the deployment result, analyzing the monitoring data on the basis of an AI model, and judging whether disaster recovery switching is triggered or not; and when it is judged that disaster recovery switching is triggered, service recovery and resource redistribution are carried out based on the disaster recovery control platform. Through automatic fault sensing and scheduling, the fault response time is effectively improved, multi-center backup and configuration recovery can be effectively realized through a unified disaster recovery control platform, and the management cost is further reduced.