Disaster backup method, device and equipment for data, medium and product

Abstract

The invention provides a disaster backup method, device and equipment for data, a medium and a product, and relates to the field of data management. Comprising the steps of obtaining source data to be backed up and corresponding multi-dimensional features; constructing a multi-dimensional priority judgment matrix according to the multi-dimensional features; determining a comprehensive score of the source data to be backed up according to the multi-dimensional priority judgment matrix; determining a priority interval of the source data to be backed up according to the comprehensive score; determining a target backup storage pool according to the priority interval; and backing up the source data to be backed up to the target backup storage pool. According to the method and the device, the technical problem that the disaster backup efficiency of the data is relatively low due to low utilization rate of storage resources due to the fact that the prior art mostly depends on a single device or a centralized storage medium to carry out regular copying and remote storage of the data is solved.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of data management, and in particular to a data disaster backup method, device, equipment, medium and product. BACKGROUND

[0002] With the rapid development of information technology, the amount of data generated and relied on by enterprises, institutions and various organizations in their daily operations has grown explosively, and data has gradually evolved into a key resource driving business operation and decision-making. In industries such as finance, healthcare, government, and energy, which have extremely high requirements for data reliability, data disaster backup has become a core requirement to ensure business continuity. Therefore, how to achieve rapid recovery when data is damaged or lost becomes a core problem in the data management system.

[0003] In traditional disaster backup methods, data is mostly copied and saved in different places by a single device or centralized storage medium. The core logic is to periodically copy data from the production environment to the backup environment through physical devices or fixed storage nodes to achieve data redundancy.

[0004] However, existing technologies mostly rely on a single device or centralized storage medium for periodic replication and off-site storage of data, resulting in low utilization of storage resources and low efficiency of data disaster backup. SUMMARY

[0005] The present application provides a data disaster backup method, device, equipment, medium and product to solve the problem of low utilization of storage resources and low efficiency of data disaster backup in existing technologies that mostly rely on a single device or centralized storage medium for periodic replication and off-site storage of data.

[0006] In a first aspect, the present application provides a data disaster backup method, comprising:

[0007] Obtaining source data to be backed up and corresponding multi-dimensional features;

[0008] According to the multi-dimensional features, a multi-dimensional priority judgment matrix is constructed;

[0009] According to the multi-dimensional priority judgment matrix, the comprehensive score of the source data to be backed up is determined;

[0010] According to the comprehensive score, the priority interval of the source data to be backed up is determined;

[0011] According to the priority interval, a target backup storage pool is determined; wherein the priority interval and the backup storage pool have a preset mapping relationship;

[0012] Backup the source data to be backed up to the target backup storage pool.

[0013] In a possible design, the multi-dimensional features include multiple of the access frequency, the historical fault, the redundancy state and the risk label.

[0014] In a possible design, the comprehensive score of the source data to be backed up is determined according to the multi-dimensional priority judgment matrix, including:

[0015] The weighted parameter corresponding to each multi-dimensional feature is determined according to the multi-dimensional priority judgment matrix;

[0016] The comprehensive score of the source data to be backed up is determined according to the weighted parameter and the multi-dimensional feature.

[0017] In a possible design, the priority interval of the source data to be backed up is determined according to the comprehensive score, including:

[0018] The comprehensive scores of all the source data to be backed up are obtained;

[0019] The standard distance value division processing is performed according to the comprehensive scores of all the source data to be backed up, to obtain multiple priority intervals and the comprehensive score range value corresponding to each priority interval;

[0020] The priority interval of the source data to be backed up is determined according to the comprehensive score range value corresponding to the priority interval and the comprehensive score of the source data to be backed up.

[0021] In a possible design, after the source data to be backed up is backed up to the target backup storage pool, including:

[0022] In response to the source data access request for the backup storage pool, the source data access request is subjected to the hash consistency check, to determine the triggering mode of the source data access request; wherein the triggering mode includes the user active access and the system automatic access;

[0023] According to the triggering mode, the source data access request is subjected to the access behavior classification processing, to determine the access category corresponding to the access behavior; wherein the access category includes the normal access and the abnormal access;

[0024] According to the access category, the corresponding access behavior adjustment measure is triggered.

[0025] In a possible design, according to the triggering mode, the source data access request is subjected to the access behavior classification processing, to determine the access category corresponding to the access behavior, including:

[0026] According to the triggering mode, the access frequency threshold value corresponding to the source data access request is determined;

[0027] The access frequency of the source data access request is obtained;

[0028] According to the triggering mode, the access frequency threshold, and the access frequency, a corresponding access category of the access behavior is determined.

[0029] In a possible design, according to the access category, a corresponding access behavior adjustment measure is triggered, including:

[0030] If the triggering mode is user-initiated access and the access category is abnormal access, a firewall policy is triggered to limit the access source.

[0031] If the triggering mode is system automatic access and the access category is abnormal access, a reset access policy or an update system decoding adapter is triggered.

[0032] In a possible design, the method further includes:

[0033] A storage pool log of the metadata to be backed up in the backup storage pool is obtained, wherein the storage pool log includes a historical erasing frequency, a historical error correction frequency, and a storage pool capacity utilization rate;

[0034] According to the storage pool capacity utilization rate, the historical erasing frequency, and the historical error correction frequency, a health status of the backup storage pool is jointly determined.

[0035] If the determination result of the health status is that the backup storage pool is abnormal, a data migration operation is triggered.

[0036] In a possible design, the method further includes:

[0037] A backup transmission path of the source data to be backed up is obtained.

[0038] The backup transmission path is abstracted as a link topology structure, and each node except an endpoint is marked as an intermediate node.

[0039] A connectivity detection is performed on each intermediate node by using a detection tool to locate a faulty link.

[0040] When the faulty link is located, a backup path is switched to.

[0041] In a second aspect, the present application provides a data disaster backup device, including:

[0042] An obtaining module is configured to obtain source data to be backed up and corresponding multi-dimensional features.

[0043] A constructing module is configured to construct a multi-dimensional priority judgment matrix according to the multi-dimensional features.

[0044] A first determining module is configured to determine a comprehensive score of the source data to be backed up according to the multi-dimensional priority judgment matrix.

[0045] A second determining module is configured to determine a priority interval of the source data to be backed up according to the comprehensive score.

[0046] The third determining module is configured to determine the target backup storage pool according to the priority interval, wherein the priority interval has a preset mapping relationship with the backup storage pool.

[0047] The backup module is configured to backup the source data to be backed up to the target backup storage pool.

[0048] In a third aspect, the present application provides a disaster backup device for data, comprising a memory and a processor.

[0049] The memory stores computer execution instructions.

[0050] The processor executes the computer execution instructions stored in the memory, so that the processor executes the disaster backup method for data as the summary of the first aspect.

[0051] In a fourth aspect, the present application provides a computer readable storage medium, wherein the computer readable storage medium stores computer execution instructions, and the computer execution instructions are executed by the processor to implement the disaster backup method for data as the summary of the first aspect.

[0052] In a fifth aspect, the present application provides a computer program product, comprising a computer program, wherein the computer program is executed by the processor to implement the disaster backup method for data as the summary of the first aspect.

[0053] The present application provides a disaster backup method, device, equipment, medium and product for data, comprising: obtaining source data to be backed up and corresponding multi-dimensional features; constructing a multi-dimensional priority judgment matrix according to the multi-dimensional features; determining a comprehensive score of the source data to be backed up according to the multi-dimensional priority judgment matrix; determining a priority interval of the source data to be backed up according to the comprehensive score; determining a target backup storage pool according to the priority interval; and backing up the source data to be backed up to the target backup storage pool. Compared with the prior art which mostly relies on a single device or a centralized storage medium for periodic copying and off-site storage of data, the utilization rate of storage resources is low, thereby resulting in low disaster backup efficiency of data. The present application dynamically adjusts the backup strategy by constructing a matrix through multi-dimensional data classification, avoids insufficient protection of key data due to priority misjudgment, improves the utilization rate of storage resources, and thus improves the disaster backup efficiency of data. BRIEF DESCRIPTION OF DRAWINGS

[0054] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the following will briefly introduce the drawings needed to be used in the embodiments or the prior art description. Obviously, the drawings in the following description are some embodiments of the present application, and for those skilled in the art, other drawings can also be obtained without creative labor.

[0055] Figure 1 A schematic diagram of a data disaster backup method provided in this application embodiment;

[0056] Figure 2 A schematic flowchart of a data disaster backup method provided in this application embodiment. Figure 1 ;

[0057] Figure 3 A schematic flowchart of a data disaster backup method provided in this application embodiment. Figure 2 ;

[0058] Figure 4 This is a schematic diagram of the source data access behavior judgment process provided in the embodiments of this application;

[0059] Figure 5 A schematic flowchart of a data disaster backup method provided in this application embodiment. Figure 3 ;

[0060] Figure 6 A schematic diagram of the structure of the disaster recovery device for data provided in the embodiments of this application;

[0061] Figure 7 This is a schematic diagram of a data disaster backup device provided in an embodiment of this application. Detailed Implementation

[0062] Exemplary embodiments will now be described in detail, examples of which are illustrated in the accompanying drawings. When the following description relates to the drawings, unless otherwise indicated, the same numbers in different drawings denote the same or similar elements. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with this application. Rather, they are merely examples of apparatuses and methods consistent with some aspects of this application as detailed in the appended claims.

[0063] In the embodiments of this application, the terms "first" and "second" are used to distinguish identical or similar items with substantially the same function and effect. Those skilled in the art will understand that the terms "first" and "second" do not limit the quantity or execution order, nor do they necessarily imply difference. It should be noted that in the embodiments of this application, words such as "exemplary" or "for example" are used to indicate examples, illustrations, or explanations. Any embodiment or design scheme described as "exemplary" or "for example" in this application should not be construed as being more preferred or advantageous than other embodiments or design schemes. Specifically, the use of words such as "exemplary" or "for example" is intended to present the relevant concepts in a concrete manner. In the embodiments of this application, "at least one" refers to one or more, and "more than one" refers to two or more.

[0064] It should be noted that the "at" in the embodiments of the present application can be at the moment when a certain condition occurs, or in a certain period of time after a certain condition occurs, and the embodiments of the present application do not make specific limitations. In addition, the data disaster backup method provided by the embodiments of the present application is only as an example, and the data disaster backup method can also include more or less content.

[0065] With the rapid development of information technology, enterprises, institutions and various organizations generate data in daily operations at an exponential rate. Data has become a key resource driving business operations, decision-making and core competitiveness. However, in a complex IT infrastructure environment, data storage, transmission and processing processes face multiple potential risks, such as hardware failure, software defects, power outages, cyber attacks, natural disasters and human operation errors, which can cause data loss or system crashes, thereby posing a significant threat to business continuity, security compliance and economic benefits.

[0066] In industries such as finance, healthcare, government and energy, where data reliability is extremely high, data disaster backup has become a core requirement to ensure business continuity. For example, financial institutions need to ensure real-time backup of transaction data to deal with system failures; the healthcare industry needs to protect patient privacy data from ransomware attacks; government agencies need to quickly restore public service systems after natural disasters. Traditional backup solutions have shown significant technical limitations in dealing with dynamically growing data volumes, complex and variable failure scenarios, and high-precision recovery requirements. Therefore, how to achieve rapid recovery when data is damaged or lost has become a core problem in data management systems.

[0067] In recent years, to improve data protection capabilities and disaster recovery efficiency, a "storage pool" architecture based on storage virtualization technology has emerged in recent years. This technology integrates and abstracts multiple physical storage resources to build a logically unified, dynamically scalable resource pool, thereby achieving flexible scheduling, automatic load balancing and centralized management of storage space. The data disaster backup solution based on the storage pool can effectively improve resource utilization, simplify operation and maintenance processes, enhance system redundancy, and support cross-regional multi-active deployment and rapid recovery mechanisms, becoming an important direction for building a new generation of data disaster recovery system.

[0068] Optionally, traditional disaster backup methods mostly rely on a single device or centralized storage media for regular data replication and off-site storage, and data backup is achieved through physical devices or fixed storage nodes. However, such methods have many defects such as low resource utilization efficiency, poor scalability, weak disaster recovery capability, and complex maintenance, making it difficult to meet the growing data protection needs, and also facing long response delays and high manual intervention costs in actual recovery processes.

[0069] Optionally, the traditional disaster backup method also establishes a communication connection between the cluster storage device and the disaster recovery center, divides the backup pool according to the number of storage pools, and replicates the data to the corresponding backup pool in units of storage pools.

[0070] Specifically, the existing network slice data disaster backup method has the following main deficiencies:

[0071] On the one hand, the resource utilization efficiency is low: the storage resources are allocated statically, and the backup strategy cannot be dynamically adjusted according to the data priority, resulting in waste of storage space or insufficient protection of critical data.

[0072] On the one hand, the scalability is poor: it is difficult to cope with dynamic growth of data volume, and expansion requires manual intervention and high cost.

[0073] On the one hand, the disaster recovery capability is weak: the centralized architecture has the risk of single point of failure, and the response delay is long during disaster recovery, and the business interruption time is long.

[0074] On the one hand, the maintenance is complex: manual inspection of backup status is required, fault location depends on experience, and the degree of automation is low.

[0075] On the one hand, it depends on the cluster environment: it needs to preinstall a cluster architecture, and lacks flexibility in adapting to edge nodes or hybrid cloud scenarios.

[0076] On the one hand, the degree of intelligence is low: data classification and dynamic priority adjustment are not considered, and the backup strategy is rigid; there is a lack of data consistency verification (such as hash detection) and automatic fault diagnosis mechanism.

[0077] On the one hand, the disaster recovery switching mechanism is single: only fixed backup paths are supported, and backup paths or local link faults cannot be dynamically switched; storage pool health state analysis (such as write times and error correction times threshold monitoring) is not designed.

[0078] On the other hand, the recovery accuracy is insufficient: the best pre-backup point is not selected in combination with the disaster timestamp, and outdated or redundant data may be used.

[0079] In view of the above problems, the inventor found in the process of researching the low efficiency of data disaster backup that the existing technology mostly relies on a single device or centralized storage medium for regular data replication and off-site storage, and the utilization rate of storage resources is low. Accordingly, the inventor considers dynamic priority backup, constructs a matrix through multi-dimensional data classification, dynamically adjusts the backup strategy, avoids insufficient protection of critical data due to priority misjudgment, and improves the utilization rate of storage resources. Based on this, the embodiments of the present application provide a data disaster backup method, device, equipment, medium and product, which can be used in the field of data management, and aims to solve the problem of low efficiency of data disaster backup in the prior art.

[0080] The technical solutions of the present application and how the technical solutions of the present application solve the above technical problems will be described in detail below with specific examples. The following specific examples can be combined with each other, and the same or similar concepts or processes can not be described again in some examples. The embodiments of the present application will be described below with reference to the accompanying drawings.

[0081] Figure 1 A system architecture schematic diagram of a data disaster backup method provided by an embodiment of the present application is provided, and the data disaster backup system is for a computer device. Figure 1 In the above architecture, the above architecture includes at least one of a data acquisition device 101, a processing device 102, and a display device 103.

[0082] It can be understood that the structure illustrated in the embodiment of the present application does not constitute a specific limitation on the processing system architecture of the data disaster backup method. In another possible implementation of the present application, the above architecture can include more or fewer components than the schematic diagram, or combine certain components, or split certain components, or different component arrangement, which can be determined according to the actual application scenario, and is not limited herein. Figure 1 The components shown can be implemented in hardware, software, or a combination of software and hardware.

[0083] In the specific implementation process, the data acquisition device 101 can include an input / output interface and can also include a communication interface. The data acquisition device 101 can be connected with the processing device through the input / output interface or the communication interface to obtain the source data to be backed up and the corresponding multi-dimensional features.

[0084] The processing device 102 can backup the source data to be backed up to the target backup storage pool according to the source data to be backed up and the corresponding multi-dimensional features.

[0085] The display device 103 can also be a touch display screen or a screen of a terminal device, which is used to receive user instructions while displaying the above content to realize interaction with the user.

[0086] It should be understood that the above processing device can be implemented by a processor reading and executing instructions in a memory, or can be implemented by a chip circuit.

[0087] In addition, the network architecture and business scenarios described in the embodiments of the present application are used to more clearly illustrate the technical solutions of the embodiments of the present application, and do not constitute a limitation on the technical solutions provided by the embodiments of the present application. It can be known by those skilled in the art that with the evolution of network architecture and the appearance of new business scenarios, the technical solutions provided by the embodiments of the present application are also applicable to similar technical problems.

[0088] The technical solutions of the present application will be described in detail below with reference to specific examples:

[0089] Figure 2 A data disaster backup method flow provided by an embodiment of the present application Figure 1 As shown in Figure 2 The method comprises the following steps.

[0090] S201, acquiring source data to be backed up and corresponding multi-dimensional features.

[0091] The multi-dimensional features include multiple of access frequency, historical failure, redundancy state and risk label.

[0092] S202, constructing a multi-dimensional priority judgment matrix according to the multi-dimensional features.

[0093] In this embodiment, the multi-dimensional priority judgment matrix is constructed through four-dimensional data classification in the multi-dimensional features, i.e. access frequency, historical failure, redundancy state and risk label.

[0094] S203, determining a comprehensive score of the source data to be backed up according to the multi-dimensional priority judgment matrix.

[0095] Specifically, the weighted parameters corresponding to each multi-dimensional feature are determined according to the multi-dimensional priority judgment matrix.

[0096] Specifically, the comprehensive score of the source data to be backed up is determined according to the weighted parameters and the multi-dimensional features.

[0097] In this embodiment, the weighted parameters corresponding to the scores of each item in the multi-dimensional priority judgment matrix except the risk label category are acquired, and the comprehensive score of the source data to be backed up is determined based on the weighted parameters.

[0098] S204, determining a priority interval of the source data to be backed up according to the comprehensive score.

[0099] Specifically, the comprehensive scores of all the source data to be backed up are acquired.

[0100] Specifically, the standard distance value division processing is performed according to the comprehensive scores of all the source data to be backed up, so as to obtain multiple priority intervals and the comprehensive score range values corresponding to each priority interval.

[0101] Specifically, the priority interval of the source data to be backed up is determined according to the comprehensive score range values corresponding to the priority interval and the comprehensive score of the source data to be backed up.

[0102] For example:

[0103] First, the comprehensive scores of the multi-dimensional priority judgment matrix corresponding to all the source data to be backed up are acquired, and a comprehensive score set is constructed based on the comprehensive scores.

[0104] Secondly, the maximum comprehensive score and the minimum comprehensive score in the comprehensive score set are obtained, and the maximum comprehensive score and the minimum comprehensive score are subjected to floor operations respectively.

[0105] Thirdly, the priority interval to be divided under the floor operation is obtained, and the priority interval to be divided is subjected to standard distance value division processing: the priority interval to be divided is equally divided by the standard distance value to obtain a plurality of comprehensive score level intervals, i.e., a plurality of priority intervals of comprehensive scores, and each comprehensive score is mapped according to the comprehensive score level interval to which the comprehensive score belongs.

[0106] Then, the comprehensive score level interval is reversely and one-to-one corresponding to the priority, the priority of each comprehensive score level interval is obtained, and the priority corresponding to the source data is upgraded by one level when the risk level label category result is the first risk level label category.

[0107] Finally, a backup mode corresponding to the priority corresponding to the source data in the priority backup database is called, and the source data is backed up based on the backup mode to obtain backup data.

[0108] S205, determining a target backup storage pool according to the priority interval.

[0109] The priority interval and the backup storage pool have a preset mapping relationship.

[0110] S206, backing up the source data to be backed up to the target backup storage pool.

[0111] The data disaster backup method provided in the embodiment includes: obtaining source data to be backed up and corresponding multi-dimensional features; constructing a multi-dimensional priority judgment matrix according to the multi-dimensional features; determining a comprehensive score of the source data to be backed up according to the multi-dimensional priority judgment matrix; determining a priority interval of the source data to be backed up according to the comprehensive score; determining a target backup storage pool according to the priority interval; and backing up the source data to be backed up to the target backup storage pool. Compared with the prior art which mostly relies on a single device or a centralized storage medium for periodic copying and off-site storage of data, the utilization rate of storage resources is low, thereby resulting in low data disaster backup efficiency. The dynamic priority backup of the present application constructs a matrix through multi-dimensional data classification, dynamically adjusts the backup strategy, avoids insufficient protection of key data due to priority misjudgment, improves the utilization rate of storage resources, and thereby improves the data disaster backup efficiency.

[0112] Figure 3 A data disaster backup method flow process provided in the embodiment of the present application Figure 2 As shown in the above step S206, the method further includes: Figure 3

[0113] ​S301, in response to a source data access request for a backup storage pool, performing hash consistency check on the source data access request to determine a triggering manner of the source data access request.

[0114] The triggering manner includes user active access and system automatic access.

[0115] Specifically, in the access behavior judgment process, the user access behavior and the system automatic behavior are respectively set as judgment paths, and the difference recognition ability for different access sources is enhanced.

[0116] S302, according to the triggering manner, performing access behavior classification processing on the source data access request to determine an access category corresponding to the access behavior.

[0117] The access category includes normal access and abnormal access.

[0118] Specifically, according to the triggering manner, the access frequency threshold corresponding to the source data access request is determined.

[0119] Specifically, the access frequency of the source data access request is obtained.

[0120] Specifically, according to the triggering manner, the access frequency threshold and the access frequency, the access category corresponding to the access behavior is determined.

[0121] S303, according to the access category, triggering corresponding access behavior adjustment measures.

[0122] Optionally, if the triggering manner is user active access and the access category is abnormal access, a firewall policy is triggered to limit the access source.

[0123] Optionally, if the triggering manner is system automatic access and the access category is abnormal access, the access strategy is reset or the system decoding adapter is updated.

[0124] For example, when the triggering manner is user active access, the user active access manner is the data access caused by actual user operation, such as clicking, querying and other operations.

[0125] Specifically, the category to which the access ID belongs is obtained.

[0126] Optionally, if the access ID is a single access ID, it is the first source data access behavior, and the first source data access behavior adjustment measure is executed.

[0127] The first source data access behavior adjustment measure is to use an encoding recognition tool to reparse the source data, and when the parsing fails, the source user reuploads the source data in a standard format.

[0128] Optionally, if the access ID is a non-single access ID, the first access frequency is compared with a user standard access frequency threshold.

[0129] The first access frequency is the access frequency of the non-single access ID, and the access frequency of the non-single access ID is the sum of the access frequencies of the single access IDs in the non-single access ID.

[0130] The single access ID is a single user, and the non-single access ID is a multi-user.

[0131] The user standard access frequency threshold is the upper limit of the data allowed to be accessed by the user in a unit of time, and in this embodiment, it is 50 times / minute.

[0132] Specifically, setting the threshold for the access frequency of the non-single access ID can prevent misjudgment caused by repeated access or cluster access, reduce the false positive probability, and improve the system stability.

[0133] Further optionally, if the first access frequency is greater than the user standard access frequency threshold, the second source data access behavior is executed, and a second source data access behavior adjustment measure is executed.

[0134] The second source data access behavior is an external attack or malicious scanning behavior, which further causes file garbled code or parsing failure behavior.

[0135] The second source data access behavior adjustment measure is to configure a firewall policy.

[0136] Further optionally, if the first access frequency is less than or equal to the user standard access frequency threshold, a source data content judgment step is executed.

[0137] For example, the triggering mode is system automatic access, and the system automatic access mode is a data access behavior caused by a program task, such as a timing task, a synchronization service, etc.

[0138] Specifically, the category to which the access ID belongs is obtained.

[0139] Optionally, if the system automatic access mode is obtained, a second access frequency is obtained, and a comparison with a system standard access frequency threshold is performed.

[0140] The system standard access frequency threshold is the upper limit of the data allowed to be accessed by the system in a unit of time, and in this embodiment, it is 20 times / minute.

[0141] Further optionally, if the second access frequency is greater than the system standard access frequency threshold, a third source data access behavior is executed, and a third source data access behavior adjustment measure is executed.

[0142] Among them, the third source data access behavior is an abnormal system access, the monitoring program gets stuck in an infinite loop, and the log analysis program is repeatedly called.

[0143] Among them, the measures to adjust the access behavior of the third source data are to reset the access policy or update the decoding adapter.

[0144] Optionally, if the second access count is less than or equal to the system's standard access count threshold, it is considered a fourth source data access behavior, and adjustment measures for the fourth source data access behavior are implemented.

[0145] The fourth source data access behavior is garbled characters caused by abnormal source file entry.

[0146] Among them, the fourth source data access behavior adjustment measure is to prompt the user that the initial structure is abnormal, such as the presence of erroneous logic or erroneous characters.

[0147] In one possible embodiment, Figure 4 This is a schematic diagram of the source data access behavior judgment process provided in the embodiments of this application, such as... Figure 4 As shown, the source data access behavior judgment process starts with "determining whether it belongs to the user's active access method" as the key node. If "yes", it enters the user's active access path and sequentially judges whether the access ID is a single access ID: if yes, the first source data access behavior and adjustment measures are executed; otherwise, the first access count is compared with the user's standard threshold. If the threshold is exceeded, the source data content judgment is triggered and the second source data access behavior and adjustment measures are executed; if "no", it enters the system's automatic access path. The second access count is compared with the system's standard threshold. If the threshold is exceeded, the third source data access behavior and adjustment measures are executed. If the threshold is not exceeded, the fourth source data access behavior and adjustment measures are executed, forming a closed-loop process of dual-path, multi-level judgment.

[0148] This process achieves precise diversion of source data access behavior through a dual-path (user-initiated / system-automatic) classification and judgment mechanism; by dynamically comparing the uniqueness of access ID and access frequency thresholds, it effectively controls abnormal access behavior and triggers targeted adjustment measures, which not only ensures the efficiency of normal access, but also strengthens the interception and optimization capabilities of abnormal access, ultimately achieving a combination of improved access efficiency, reasonable allocation of system resources and reduced security risks.

[0149] In this embodiment, hash consistency verification is used to distinguish between user-initiated access and system-triggered automatic access. By comparing access frequency thresholds, access behavior is accurately classified (normal / abnormal). Finally, targeted adjustment measures such as firewall restrictions, policy resets, or decoding adapter updates are triggered. This achieves full-process security control over access requests to the backup storage pool source data, ensuring the stability of legitimate access and effectively defending against the risk of abnormal access, thereby improving the security protection capabilities and access behavior management efficiency of the storage system.

[0150] Figure 5 A schematic flowchart of a data disaster backup method provided in this application embodiment. Figure 3 ,like Figure 5 As shown, after step S206 above, the following is also included:

[0151] It should be noted that a storage pool health analysis is performed after step S206 above.

[0152] S501. Obtain the storage pool logs of the metadata to be backed up in the backup storage pool.

[0153] The storage pool logs include historical write / erase counts, error correction counts, and storage pool capacity utilization.

[0154] Among them, the storage pool capacity utilization rate is the ratio of the currently used storage capacity in the storage pool to the total capacity of the storage pool.

[0155] Among them, the historical write count and error correction count are the total number of write events and error correction events that occurred between the time the storage pool was enabled and the latest disaster event, respectively.

[0156] S502. Based on the storage pool capacity utilization, historical write / erase counts, and historical error correction counts, jointly determine the health status of the backup storage pool.

[0157] First, a preliminary screening of storage pool health is performed during storage pool health analysis:

[0158] Specifically, the storage pool capacity utilization rate is compared with the standard storage pool capacity utilization rate threshold:

[0159] Optionally, if the storage pool capacity utilization rate is greater than the standard storage pool capacity utilization rate threshold, a preliminary screening result of the storage pool health under load is obtained, and a storage pool fault alert is issued.

[0160] Optionally, if the storage pool capacity utilization rate is less than or equal to the standard storage pool capacity utilization rate threshold, obtain the historical write / erase counts in the storage pool log and perform storage pool point fault diagnosis steps.

[0161] In the embodiment, the standard storage pool capacity utilization rate threshold is 92% based on historical data after a disaster event.

[0162] Specifically, by analyzing the storage pool capacity utilization rate and combining historical erase data for preliminary screening, hardware risks caused by high load operation can be detected early. This step provides early warning of storage failures without affecting normal services, prolongs the service life of the storage pool, and avoids the spread of major damage.

[0163] Secondly, storage pool point fault diagnosis is performed in the storage pool health analysis:

[0164] The historical erase word number is compared with the standard historical erase word threshold, and the error correction number is compared with the standard error correction number threshold.

[0165] Optionally, if the historical erase word number is greater than the historical erase word threshold and the error correction number is greater than the standard error correction number threshold, the erase damage result is obtained, and the corresponding storage data reconstruction adjustment measure is executed.

[0166] The historical erase word threshold refers to the upper limit of the total number of erases that the storage medium can tolerate within a certain usage period. Exceeding this threshold is considered to be at risk of write damage. In the embodiment, the historical erase word threshold is 100,000 times.

[0167] Optionally, if the historical erase word number is less than or equal to the historical erase word threshold and the error correction number is greater than the standard error correction number threshold, the storage pool medium aging result is obtained, and the corresponding storage data reconstruction adjustment measure is executed.

[0168] The standard error correction number threshold is the acceptable upper limit value of automatic error correction processing of storage data by the system within a unit usage period.

[0169] According to the number of error correction times per year, the value corresponding to the time from the time stamp of the source data input to the latest disaster event time stamp is taken as the standard error correction number threshold.

[0170] Optionally, if the historical erase word number is greater than the historical erase word threshold and the error correction number is less than or equal to the standard error correction number threshold, the storage pool has no fault, and the path abnormality analysis step is executed.

[0171] Optionally, if the historical erase word number is less than or equal to the historical erase word threshold and the error correction number is less than or equal to the standard error correction number threshold, the storage pool has no fault, and the path abnormality analysis step is executed.

[0172] Further optionally, if the judgment result is the erase damage result, the corresponding storage data reconstruction adjustment measure is to migrate the damaged data to a new storage pool and replace the original storage pool.

[0173] Further alternatively, if the judgment result is the storage pool medium aging result, the corresponding storage data reconstruction adjustment measure executed is to disperse and migrate the data in the aging medium to multiple healthy storage pools.

[0174] Specifically, the historical erase count and the error correction count are combined to judge, which can distinguish between storage pool hardware wear and temporary failure, is helpful to more accurately judge the medium health status, has more reliability of judgment logic compared with the strategy of monitoring a single indicator, is convenient for implementing on-demand reconstruction or maintenance measures, and saves resource cost.

[0175] S503, if the judgment result of the health status is that the backup storage pool has an exception, triggering a data migration operation.

[0176] It should be noted that if the judgment result of the health status is that the backup storage pool has no failure, triggering an execution path exception analysis, and the implementation steps of the execution path exception analysis specifically include:

[0177] First, the backup transmission path of the source data to be backed up is obtained.

[0178] Second, the backup transmission path is abstracted as a link topology structure, and each node except the endpoint is marked as an intermediate node.

[0179] Third, the connectivity of each intermediate node is detected by using a detection tool to locate the failure link.

[0180] Finally, when the failure link is located, switch to the backup path.

[0181] In this embodiment, the health status is judged by obtaining the historical erase count, the error correction count and the capacity utilization rate in the storage pool log, the data migration is triggered when there is an exception to ensure data safety, and when it is normal, the link topology abstraction of the backup transmission path, the intermediate node connectivity detection and the failure link positioning to switch the backup path are used to realize the precise monitoring and abnormal response of the health status of the backup storage pool, improve the reliability and fault tolerance of the backup transmission path, and ensure the safe and stable and efficient execution of the backup process. Through the flow design of the link topology abstraction of the backup transmission path, the intermediate node connectivity detection and the failure link positioning to switch the backup path, the active exception detection and dynamic fault tolerance switching of the backup transmission path are realized, the stability and continuity of data transmission in the backup process are effectively improved, the risk of backup interruption caused by single path failure is avoided, and the efficient and reliable execution of the backup task under the healthy storage pool state is ensured.

[0182] Figure 6 The structural schematic diagram of the data disaster backup device provided by the embodiment of the application is as follows: Figure 6As shown, the apparatus comprises: an acquisition module 61, a construction module 62, a first determination module 63, a second determination module 64, a third determination module 65, and a backup module 66.

[0183] The acquisition module 61 is configured to acquire source data to be backed up and corresponding multi-dimensional features.

[0184] The construction module 62 is configured to construct a multi-dimensional priority judgment matrix according to the multi-dimensional features.

[0185] The first determination module 63 is configured to determine a comprehensive score of the source data to be backed up according to the multi-dimensional priority judgment matrix.

[0186] The second determination module 64 is configured to determine a priority interval of the source data to be backed up according to the comprehensive score.

[0187] The third determination module 65 is configured to determine a target backup storage pool according to the priority interval, wherein the priority interval and the backup storage pool have a preset mapping relationship.

[0188] The backup module 66 is configured to backup the source data to be backed up to the target backup storage pool.

[0189] In a possible design, the multi-dimensional features include multiple of the following: access frequency, historical fault, redundancy state, and risk label.

[0190] In a possible design, the determination of the comprehensive score of the source data to be backed up according to the multi-dimensional priority judgment matrix comprises:

[0191] The first determination module 63 is further configured to determine a weighting parameter corresponding to each multi-dimensional feature according to the multi-dimensional priority judgment matrix.

[0192] The comprehensive score of the source data to be backed up is determined according to the weighting parameter and the multi-dimensional feature.

[0193] In a possible design, the determination of the priority interval of the source data to be backed up according to the comprehensive score comprises:

[0194] The second determination module 64 is further configured to acquire comprehensive scores of all source data to be backed up.

[0195] The standard distance value division processing is performed according to the comprehensive scores of all source data to be backed up, so as to obtain multiple priority intervals and a comprehensive score range value corresponding to each priority interval.

[0196] The priority interval of the source data to be backed up is determined according to the comprehensive score range value corresponding to the priority interval and the comprehensive score of the source data to be backed up.

[0197] In a possible design, after backing up the source data to be backed up to a target backup storage pool, the method comprises the following steps:

[0198] In response to a source data access request for the backup storage pool, the source data access request is subjected to a hash consistency check to determine a triggering manner of the source data access request; wherein the triggering manner comprises user-initiated access and system-automatic access;

[0199] According to the triggering manner, the source data access request is subjected to access behavior classification processing to determine an access category corresponding to the access behavior; wherein the access category comprises normal access and abnormal access;

[0200] According to the access category, a corresponding access behavior adjustment measure is triggered.

[0201] In a possible design, according to the triggering manner, the source data access request is subjected to access behavior classification processing to determine an access category corresponding to the access behavior, comprising:

[0202] According to the triggering manner, an access frequency threshold value corresponding to the source data access request is determined;

[0203] An access frequency of the source data access request is obtained;

[0204] According to the triggering manner, the access frequency threshold value and the access frequency, an access category corresponding to the access behavior is determined.

[0205] In a possible design, according to the access category, a corresponding access behavior adjustment measure is triggered, comprising:

[0206] If the triggering manner is user-initiated access and the access category is abnormal access, a firewall policy is triggered to limit access to the source;

[0207] If the triggering manner is system-automatic access and the access category is abnormal access, a reset access policy or an update system decoding adapter is triggered.

[0208] In a possible design, the method further comprises:

[0209] A storage pool log of the metadata to be backed up in the backup storage pool is obtained; wherein the storage pool log comprises a historical erasing frequency, a correction frequency and a storage pool capacity utilization rate;

[0210] According to the storage pool capacity utilization rate, the historical erasing frequency and the historical correction frequency, a health state of the backup storage pool is jointly determined;

[0211] If the determination result of the health state is that the backup storage pool is abnormal, a data migration operation is triggered.

[0212] In a possible design, the method further comprises:

[0213] acquiring a backup transmission path of source data to be backed up;

[0214] abstracting the backup transmission path as a link topology structure, and marking each node other than an endpoint as an intermediate node;

[0215] detecting connectivity of each intermediate node by using a detection tool to locate a faulty link;

[0216] switching to a backup path when the faulty link is located.

[0217] The data disaster backup device provided in the embodiment can execute the data disaster backup method described above, and has similar implementation principles and technical effects, which will not be described here again.

[0218] In the specific implementation of the data disaster backup method, each module can be implemented as a processor, and the processor can execute computer execution instructions stored in the memory, so that the processor executes the data disaster backup method.

[0219] Figure 7 A structural schematic diagram of a data disaster backup device provided in the embodiment is shown in FIG. 7. Figure 7 As shown in the figure, the data disaster backup device 70 includes at least one processor 71 and a memory 72. The data disaster backup device 70 also includes a communication component 73. The processor 71, the memory 72, and the communication component 73 are connected through a bus 74.

[0220] In the specific implementation process, the at least one processor 71 executes computer execution instructions stored in the memory 72, so that the at least one processor 71 executes a data management method performed by the data disaster backup device side.

[0221] The specific implementation process of the processor 71 can refer to the method embodiments described above, and has similar implementation principles and technical effects, which will not be described here again.

[0222] In the above embodiments, it should be understood that the processor can be a central processing unit (CPU), and can also be other general-purpose processors, digital signal processors (DSP), application specific integrated circuits (ASIC), etc. The general-purpose processor can be a microprocessor or any conventional processor, etc. The steps of the method disclosed in combination with the application can be directly embodied as hardware processor execution, or executed by a combination of hardware and software modules in the processor.

[0223] The memory can include a high-speed RAM memory, and can also include a non-volatile storage NVM, such as at least one disk memory.

[0224] The bus can be an industry standard architecture (ISA) bus, a peripheral component (PCI) bus, or an extended industry standard architecture (EISA) bus, etc. The bus can be divided into an address bus, a data bus, a control bus, etc. For the sake of representation, the bus in the drawings of the present application does not limit to only one bus or one type of bus.

[0225] The functions realized by the data disaster backup device and the master control device described above are introduced for the scheme provided by the embodiments of the present application. It can be understood that the data disaster backup device or the master control device includes the corresponding hardware structure and / or software modules for executing each function in order to realize the above functions. In combination with the units and algorithm steps of each example described in the embodiments disclosed in the embodiments of the present application, the embodiments of the present application can be realized in the form of hardware or a combination of hardware and computer software. Whether a certain function is executed by hardware or computer software driven hardware depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of the technical solution of the embodiments of the present application.

[0226] The present application also provides a computer readable storage medium, and the computer readable storage medium stores computer execution instructions, when the processor executes the computer execution instructions, for realizing the above method in the field of data management.

[0227] The readable storage medium described above can be realized by any type of volatile or nonvolatile storage devices or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic storage, flash memory, magnetic disk or optical disk. The readable storage medium can be any available medium that can be accessed by a general or special purpose computer.

[0228] An exemplary readable storage medium is coupled to the processor, so that the processor can read information from the readable storage medium and write information to the readable storage medium. Of course, the readable storage medium can also be an integral part of the processor. The processor and the readable storage medium can be located in an application specific integrated circuit (ASIC). Of course, the processor and the readable storage medium can also exist as discrete components in the data disaster backup device or the host device.

[0229] The present application also provides a computer program product, which comprises a computer program stored in a readable storage medium, at least one processor of the data disaster backup device can read the computer program from the readable storage medium, and the at least one processor executes the computer program to enable the data disaster backup device to perform the scheme provided in any of the above embodiments.

[0230] Those skilled in the art can understand that all or part of the steps of the above-mentioned method embodiments can be completed by program instruction related hardware. The foregoing program can be stored in a computer readable storage medium. The program executes to perform the steps of the above-mentioned method embodiments; and the foregoing storage medium includes ROM, RAM, magnetic disk or optical disk and various storage medium that can store program codes.

[0231] So far, the technical scheme of the present application has been described in combination with the preferred embodiments shown in the drawings, but those skilled in the art can easily understand that the protection scope of the present application is obviously not limited to these specific embodiments, and the above embodiments are only used to illustrate the technical scheme of the present application, but not to limit it; although the present application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that they can still modify the technical scheme recorded in the foregoing embodiments, or make equivalent replacement for part or all of the technical features; and these modifications or replacements do not make the essence of the corresponding technical scheme deviate from the scope of the technical scheme of the embodiments of the present application.

Claims

1. A disaster recovery method for data, characterized in that, include: Obtain the source data to be backed up and its corresponding multidimensional features; Based on the aforementioned multidimensional features, a multidimensional priority judgment matrix is ​​constructed; Based on the multidimensional priority judgment matrix, determine the comprehensive score of the source data to be backed up; Based on the comprehensive score, the priority range of the source data to be backed up is determined; The target backup storage pool is determined based on the priority range; wherein the priority range and the backup storage pool have a preset mapping relationship. The source data to be backed up is backed up to the target backup storage pool.

2. The method according to claim 1, characterized in that, The multidimensional features include multiple features such as access frequency, historical failures, redundancy status, and risk labels.

3. The method according to claim 2, characterized in that, The step of determining the comprehensive score of the source data to be backed up based on the multi-dimensional priority judgment matrix includes: Based on the multidimensional priority judgment matrix, determine the weighting parameters corresponding to each multidimensional feature; The overall score of the source data to be backed up is determined based on the weighting parameters and the multidimensional features.

4. The method according to claim 3, characterized in that, The step of determining the priority range of the source data to be backed up based on the comprehensive score includes: Obtain the combined score of all source data to be backed up; Based on the comprehensive score of all source data to be backed up, standard distance values ​​are used to divide the data into multiple priority intervals and the comprehensive score range value corresponding to each priority interval. The priority range of the source data to be backed up is determined based on the comprehensive score range corresponding to the priority range and the comprehensive score of the source data to be backed up.

5. The method according to any one of claims 1 to 4, characterized in that, After backing up the source data to be backed up to the target backup storage pool, the process includes: In response to a source data access request for the backup storage pool, a hash consistency check is performed on the source data access request to determine the triggering method of the source data access request; wherein, the triggering method includes user-initiated access and system-automatic access; Based on the triggering method, the source data access request is classified into access behaviors to determine the access category corresponding to the access behavior; wherein, the access category includes normal access and abnormal access; Based on the access category, corresponding access behavior adjustment measures will be triggered.

6. The method according to claim 5, characterized in that, The step of classifying the source data access request according to the triggering method to determine the access category corresponding to the access behavior includes: Based on the triggering method, determine the access frequency threshold corresponding to the source data access request; Obtain the access frequency of the source data access requests; The access category corresponding to the access behavior is determined based on the triggering method, the access frequency threshold, and the access frequency.

7. The method according to claim 6, characterized in that, The step of triggering corresponding access behavior adjustment measures based on the access category includes: If the triggering method is user-initiated access and the access category is abnormal access, then the firewall policy will be configured to restrict the access source. If the triggering method is automatic system access and the access category is abnormal access, then the access policy will be reset or the system decoding adapter will be updated.

8. The method according to any one of claims 1 to 4, characterized in that, Also includes: Obtain the storage pool logs of the metadata to be backed up in the backup storage pool; wherein, the storage pool logs include historical write / erase counts, error correction counts, and storage pool capacity utilization. The health status of the backup storage pool is determined by combining the storage pool capacity utilization rate, the historical write / erase count, and the historical error correction count. If the health status determination result indicates that the backup storage pool is abnormal, a data migration operation is triggered.

9. The method according to any one of claims 1 to 4, characterized in that, Also includes: Obtain the backup transmission path of the source data to be backed up; The backup transmission path is abstracted into a link topology, and each node other than the endpoints is marked as an intermediate node; Connectivity detection tools are used to examine the intermediate nodes in order to locate faulty links; When a faulty link is located, switch to the backup path.

10. A data disaster recovery device, characterized in that, include: The acquisition module is used to acquire the source data to be backed up and its corresponding multidimensional features; A construction module is used to construct a multidimensional priority judgment matrix based on the multidimensional features; The first determining module is used to determine the comprehensive score of the source data to be backed up based on the multi-dimensional priority judgment matrix. The second determining module is used to determine the priority range of the source data to be backed up based on the comprehensive score; The third determining module is used to determine the target backup storage pool based on the priority range; wherein the priority range and the backup storage pool have a preset mapping relationship; The backup module is used to back up the source data to be backed up to the target backup storage pool.

11. A data disaster recovery device, characterized in that, include: Memory, processor; The memory stores computer-executed instructions; The processor executes computer execution instructions stored in the memory, causing the processor to perform the method as described in any one of claims 1-9.

12. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer-executable instructions, which, when executed by a processor, are used to implement the method as described in any one of claims 1-9.

13. A computer program product, characterized in that, Includes a computer program that, when executed by a processor, implements the method described in any one of claims 1-9.

Images