Disaster Tolerance Backup Data Management System Based on Cloud Computing

Abstract

The present invention relates to the field of information security technology, and specifically discloses a disaster recovery backup data management system based on cloud computing, including: a data classification module, configured to classify data according to preset data characteristics and the transmission mode of existing data, and generate different disaster recovery backup strategies according to different types of data; a backup module, configured to determine whether the content of real-time backup data meets the backup requirements according to the disaster recovery backup strategy; a scheduling module, configured to accept service requests from the backup module and complete data transmission, verification, retrieval, recovery, and deletion processing; a storage module, configured to perform storage management of backup data on the data provided by the storage device; the storage module includes a data encryption unit, and the data encryption unit is configured to encrypt metadata; a recovery module, configured to perform recovery operations on the backup data and its application system after a disaster. The present invention ensures to improve the data backup efficiency and rate, and improve the automation degree of the disaster recovery backup data management system.

Description

Technical Field

[0001] The present invention relates to the technical field of information security, and particularly to a disaster recovery backup data management system based on cloud computing. Background Art

[0002] Data disaster recovery backup is the foundation of information security. Through a specific disaster recovery mechanism, all or part of the production data is copied from the hard disk or array of the application host to other storage media. After various disasters occur, it can prevent data loss to the greatest extent to ensure that the information system provides normal services.

[0003] In order to ensure that the original data is not lost or damaged after a disaster and ensure the security and efficiency of data backup, it is an important issue to be solved to ensure the secure backup of system data and accurate data recovery after a disaster by setting up a disaster recovery backup data management system; by setting up various servers and database application platforms, functions such as daily maintenance and processing of data operation and backup can be basically ensured for the data recovery and business operation system.

[0004] However, the existing disaster recovery backup data management system far fails to meet the requirements of an accurate and efficient data backup and recovery system only through data backup processing methods; the existing method of ensuring data recovery after a disaster by backing up all system data becomes more cumbersome when dealing with relatively complex data backup strategies; and due to the lack of a security detection process for data backup and adjustment of scientific data backup methods, a large amount of human management intervention and program operations are required during the data backup and recovery process, resulting in a low degree of automation of the disaster recovery backup data management system. Summary of the Invention

[0005] The purpose of the present invention is to provide a disaster recovery backup data management system based on cloud computing to solve the following technical problems:

[0006] How to improve data backup efficiency, increase the data backup rate, and improve the automation degree of the disaster recovery backup data management system.

[0007] The purpose of the present invention can be achieved through the following technical solutions:

[0008] A disaster recovery backup data management system based on cloud computing includes:

[0009] A data classification module, which is used to classify data according to preset data characteristics and the existing data transmission method, and generate different disaster recovery backup strategies according to different types of data;

[0010] A backup module, which is used to determine whether the content of real-time backup data meets the backup requirements according to the disaster recovery backup strategy:

[0011] If so, classify the data backup levels and manage the backup data;

[0012] If not, issue a warning notice and perform a security check on the data;

[0013] The scheduling module is used to receive service requests from the backup module and complete data transmission, verification, retrieval, recovery, and deletion processes; the scheduling module includes an access control unit and a monitoring and logging unit;

[0014] Among them, the access control unit is used to restrict user access from the client and record access information; the monitoring and logging unit is used to monitor scheduling information in real time and update the time, and record and save the scheduling information;

[0015] The storage module is used to manage the storage of backup data for the data provided by the storage device;

[0016] The storage module includes a data encryption unit, and the data encryption unit is used to encrypt the metadata;

[0017] The recovery module is used to perform recovery operations on the backup data and its application system after a disaster; the recovery module includes a metadata recovery unit and a decoding engine unit connected to the metadata recovery unit.

[0018] Preferably, the disaster recovery backup strategy in the backup module is:

[0019] Through the formula Calculate the data backup risk value Part;

[0020] Where N is the total number of disaster recovery data channels, i ∈ [1, N]; R a (t) is the real-time backup data range value; D v (t) is the real-time backup data volume; V t (t) is the real-time backup data transmission rate; Q si is the first conversion function; Q ki is the second conversion function; Q ui is the third conversion function; Δt is the preset time period; β i is the position coefficient of the i-th disaster recovery data channel; G is the judgment function.

[0021] Preferably, judge when Then G = B;

[0022] Otherwise, Among them, Q th (Δt) is the risk tolerance threshold corresponding to the Δt time period; B is the warning value.

[0023] Preferably, the data backup risk value P ar (t) is compared with the preset threshold (P A , P B ) to determine the size:

[0024] If P ar (t) ≤ P A , it is determined that the risk of the real-time backup data content is relatively small;

[0025] If P B >P ar (t) > P A , it is determined that the risk of the real-time backup data content is average and meets the backup requirements;

[0026] If P ar (t) ≥ P B , it is determined that the risk of the real-time backup data content is relatively large.

[0027] Preferably, the method for classifying the data backup level is as follows:

[0028] S1. Obtain the data backup index B da of the backup data module in real time;

[0029] S2. Compare the data backup index B da with the preset data threshold [B 1 , B 2 to determine the size:

[0030] If B da >B 2 , it is determined that the data backup index is relatively high, and the backup data is classified into the core-level backup management library;

[0031] If B 1 ≤B da ≤B 2 , it is determined that the data backup index is normal, and the backup data is classified into the central-level backup management library;

[0032] If B da <B 1 , it is determined that the data backup index is relatively low, and the backup data is classified into the general-level backup management library;

[0033] S3. Perform backup integration according to the activation scheduling module of each level of backup management library obtained, and determine the data backup method for each level.

[0034] Preferably, the method for obtaining the data backup index B da is as follows:

[0035] Calculate the data backup index B through the formula ​da ;

[0036] Among them, P im is the data importance parameter, R re is the data recovery degree ratio, S re is the data recovery speed; μ, σ, θ are weight coefficients; P 0 is the standard value of the data importance parameter; R 0 is the standard value of the data recovery degree ratio; S 0 is the standard value of the data recovery speed; ΔR is the deviation value of the data recovery degree ratio; ΔS is the deviation value of the data recovery speed.

[0037] Preferably, the backup method includes: full backup, differential backup and incremental backup;

[0038] Determine the data backup time point according to the user's needs, and perform backup fusion on the data through the scheduling module server. Combine the full backup and the incremental backup into the final full backup method, that is, the full backup on the first day + the incremental backup on the second day +... + the incremental backup on the nth day = the full backup on the nth day.

[0039] Preferably, the specific method of backup fusion is:

[0040] SS1. Select the full backup data job information before the predetermined time point;

[0041] SS2. Determine the differential backup data job information closest to the given time point among the full backup data job information;

[0042] SS3. Discover all incremental backup data job information existing between the differential backup data job information and the predetermined time point;

[0043] SS4. Obtain the set of all data job information and discover the corresponding data information archive files, and remove the older version of the same-name files according to the size of the field information;

[0044] SS5. Organize and send the obtained field information to the superior for backup in the specified order.

[0045] The beneficial effects of the present invention:

[0046] (1) The present invention ensures the automated management process of disaster recovery backup data by setting up five modules, namely, a data classification module, a backup module, a scheduling module, a storage module, and a recovery module, ensuring the accurate backup process of data, the complete storage of data, and the rapid recovery process of data, and comprehensively improving the management efficiency of actual backup by centralized management of data; the backup module is set to determine whether the content of real-time backup data meets the backup requirements according to the disaster recovery backup policy, and judge whether it meets the data backup requirements: if so, divide the data backup level and manage the backup data; if not, give a warning notice and perform a security detection on the data; and then analyze the backup data to ensure the smooth progress of the data automatic backup process.

[0047] (2) The present invention obtains the data backup risk value of the disaster recovery backup policy, and ensures the real-time detection and analysis of the data backup situation in different data channels by setting the data backup risk value, so as to accurately obtain the data backup information and improve the automation process of backup data transmission.

[0048] Of course, it is not necessary for any product implementing the present invention to achieve all the above advantages simultaneously. BRIEF DESCRIPTION OF THE DRAWINGS

[0049] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings required for describing the embodiments will be briefly introduced below. Obviously, the drawings in the following description are only some embodiments of the present invention, and those of ordinary skill in the art can also obtain other drawings based on these drawings without creative efforts.

[0050] Figure 1 It is a module diagram of the disaster recovery backup data management system based on cloud computing of the present invention;

[0051] Figure 2 It is a step diagram of the backup fusion method of the backup module of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0052] The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention. Obviously, the described embodiments are only some of the embodiments of the present invention, rather than all of them. All other embodiments obtained by those of ordinary skill in the art without creative efforts based on the embodiments of the present invention belong to the protection scope of the present invention.

[0053] Please refer to Figure 1 As shown, the present invention is a disaster recovery backup data management system based on cloud computing, including:

[0054] A data classification module, which is used to classify data according to preset data characteristics and the transmission methods of existing data, and generate different disaster recovery backup strategies for different types of data;

[0055] A backup module, which is used to determine whether the content of real-time backup data meets the backup requirements according to the disaster recovery backup strategy:

[0056] If so, divide the data backup level and manage the backup data;

[0057] If not, give a warning notice and perform a security check on the data;

[0058] A scheduling module, which is used to accept service requests from the backup module and complete data transmission, verification, retrieval, recovery, and deletion processing; the scheduling module includes an access control unit, a monitoring and logging unit;

[0059] Among them, the access control unit is used to restrict user access from the client and record access information; the monitoring and logging unit is used to monitor and update the scheduling information in real time, and record and save the scheduling information;

[0060] A storage module, which is used to manage the storage of backup data for the data provided by the storage device;

[0061] The storage module includes a data encryption unit, and the data encryption unit is used to encrypt the metadata;

[0062] A recovery module, which is used to perform recovery operations on the backup data and its application system after a disaster; the recovery module includes a metadata recovery unit and a decoding engine unit connected to the metadata recovery unit.

[0063] Through the above technical solutions, the existing disaster recovery backup data management system far fails to meet the requirements of an accurate and efficient data backup and recovery system only through data backup processing methods; the existing method of ensuring data system recovery after a disaster through system-wide data backup becomes more cumbersome when dealing with relatively complex data backup strategies; and due to the lack of a security check process for data backup and adjustment of scientific data backup methods, a large amount of human management intervention and program operations are required during the data backup and recovery process, resulting in a low degree of automation of the disaster recovery backup data management system.

[0064] To solve the above technical problems, the present invention designs a disaster recovery and backup data management system based on cloud computing. Specifically, the disaster recovery and backup data management system based on cloud computing includes five modules, namely: a data classification module, a backup module, a scheduling module, a storage module, and a recovery module. Through the above five modules, the automated management process of disaster recovery and backup data is ensured, the accurate backup process of data, the complete storage of data, and the rapid recovery process of data are guaranteed, and the management efficiency of actual backup is comprehensively improved through the centralized management of data.

[0065] Among them, first, by setting up a data classification module, the classification module is used to classify data according to preset data characteristics and the existing data transmission method, and generate different disaster recovery and backup strategies for different types of data; the classification module can ensure the distinction of different types of data, and the distinction basis is completed according to data characteristics and data transmission methods. Data characteristics include all existing known data information such as data resource information, user information, backup data association information, etc.; data transmission methods include optical transmission and IP transmission, that is, the way of IP fiber channel transmission, so as to ensure different transmission methods are adopted according to different data transmission distances and improve data transmission efficiency.

[0066] Then, set up a backup module. The backup module is used to determine whether the content of real-time backup data meets the backup requirements according to the disaster recovery and backup strategy. In order to ensure the smooth progress of the judgment process of the disaster recovery and backup strategy, the following judgments are made: judge whether it meets the data backup requirements: if so, divide the data backup level and manage the backup data; if not, issue a warning notice and perform a security detection on the data; and then analyze the backup data to ensure the smooth progress of the data automatic backup process.

[0067] Next, by setting up a scheduling module to accept the service request of the backup module, complete the transmission, verification, retrieval, recovery, and deletion processing of data; the scheduling module includes an access control unit, a monitoring and logging unit; by installing a scheduling server on the server host to manage the entire network backup system, the management process includes identifying the client identity information and responding to the service request, including completing the transmission, verification, retrieval, recovery, and deletion processing of data.

[0068] Moreover, in order to improve the automation process of backup data transmission, it also includes setting an access control unit, a monitoring and logging unit in the scheduling module to monitor and record the real-time collected data; among them, the set access control unit ensures the restriction of client user access and records access information; the monitoring and logging unit ensures the real-time monitoring and time update of scheduling information, and records and saves the scheduling information.

[0069] There is also a storage management for backing up data provided by a storage device through setting a storage module; ensuring centralized management of backup data, scheduling data, and storing the restored data in a storage server, and the stored data content includes information storage, file storage, and address storage; in order to ensure the distinctiveness and efficiency of data storage, a storage module is also set to include a data encryption unit, and the data encryption unit is used to perform encryption processing on metadata.

[0070] A recovery module is used to perform recovery operations on backup data and its application system after a disaster; the recovery module includes a metadata recovery unit and a decoding engine unit connected to the metadata recovery unit.

[0071] As an implementation manner of the present invention, the disaster tolerance backup strategy in the backup module is:

[0072] Through the formula calculate to obtain the data backup risk value Part;

[0073] where N is the total number of disaster tolerance data channels, i ∈ [1, N]; R a (t) is the real-time backup data range value; D v (t) is the real-time backup data volume; V t (t) is the real-time backup data transmission rate; Q si is the first conversion function; Q ki is the second conversion function; Q ui is the third conversion function; Δt is the preset time period; β i is the position coefficient of the i-th disaster tolerance data channel; G is the judgment function.

[0074] Through the above technical solution, a method for the disaster tolerance backup strategy in the backup module is provided in this embodiment, ensuring the detection of the data backup status before backing up data and calling data, and detecting and analyzing the data backup situation in different data channels by setting the data backup risk value; the analysis process is through the formula calculate to obtain the data backup risk value Part, and the risk value of the current data backup can be clearly judged whether it meets the backup requirements through the data backup risk value P ar (t); where N is the total number of disaster tolerance data channels, i ∈ [1, N]; R a (t) is the real-time backup data range value; D v (t) is the real-time backup data volume; V t (t) is the real-time backup data transmission rate; Q si is the first conversion function; Q ki is the second conversion function; Q uiis the third conversion function; Δt is a preset time period; β i is the position coefficient of the i-th disaster recovery data channel; G is a judgment function; and the first conversion function Q si , is the second conversion function Q ki , the third conversion function Q ui are all obtained by fitting the test data of the backup data detection device under the normal operation state in this system mode. Therefore, by substituting the real-time backup data range value R a (t), the real-time backup data volume D v (t), the real-time backup data transmission rate V t (t), and then the judgment function is used to give an early warning of the security risks existing in the backup.

[0075] It should be noted that the preset time period Δt is selected and set according to empirical data and can be adaptively adjusted according to the specific application backup server program; the position coefficient β of the disaster recovery data channel i is selected and set according to the states detected by different disaster recovery data channels and will not be elaborated here.

[0076] As an implementation manner of the present invention, it is judged that when then G = B;

[0077] Otherwise, wherein, Q th (Δt) is the risk tolerance threshold corresponding to the time period Δt; B is the warning value.

[0078] Through the above technical solution, G is a judgment function. When then G = B;

[0079] Otherwise, wherein, Q th (Δt) is the risk tolerance threshold corresponding to the time period Δt; B is the warning value.

[0080] As an implementation manner of the present invention, the data backup risk value P ar (t) is compared with the preset threshold (P A , P B ):

[0081] If P ar (t) ≤ P A , it is judged that the risk of the real-time backup data content is relatively small;

[0082] If P B > P ar (t) > P A , it is judged that the risk of the real-time backup data content is average and meets the backup requirements;

[0083] If P ar (t) ≥ P B , it is determined that the risk of the real-time backup data content is relatively high.

[0084] Through the above technical solution, the risk value P ar (t) of the real-time detected data backup is compared with the preset threshold (P A , P B ). By comparing the risk value P ar (t) of the real-time detected data backup with the preset threshold (P A , P B ), if P ar (t) ≤ P A , it is determined that the risk of the real-time backup data content is relatively low; if P B > P ar (t) > P A , it is determined that the risk of the real-time backup data content is average and meets the backup requirements; if P ar (t) ≥ P B , it is determined that the risk of the real-time backup data content is relatively high; in addition, when G = B, the size of the warning value B ensures that the risk value P ar (t) of the real-time detected data backup ≥ P B , and a warning notification is issued and the backup channel is repaired to ensure the security of the backup data and reduce the risk of data backup transmission.

[0085] As an implementation manner of the present invention, the data backup level division method is as follows:

[0086] S1. Obtain the data backup index B of the backup data module in real time da ;

[0087] S2. Compare the data backup index B da with the preset data threshold [B 1 , B 2 :

[0088] If B da > B 2 , it is determined that the data backup index is relatively high, and the backup data is classified into the core-level backup management library;

[0089] If B 1 ≤ B da ≤ B 2 , it is determined that the data backup index is normal, and the backup data is classified into the central-level backup management library;

[0090] If B da < B 1, it is determined that the data backup index is low, and the backup data is classified into the general-level backup management library;

[0091] S3. Perform backup fusion according to the enabled scheduling modules of the respective backup management libraries divided, and determine the data backup methods at all levels.

[0092] Through the above technical solution, in this embodiment, the batch and programmed management of the backup data is ensured through data backup level classification, improving the automated management process of the backup system; the specific data backup level classification method, as an implementation manner of the present invention, the data backup index B da is obtained as follows:

[0093] S1. Obtain the data backup index B of the backup data module in real time da ;

[0094] S2. Compare the data backup index B da with the preset data threshold [B 1 , B 2 to determine the size: if B da > B 2 , it is determined that the data backup index is high, and the backup data is classified into the core-level backup management library; if B 1 ≤B da ≤B 2 , it is determined that the data backup index is normal, and the backup data is classified into the central-level backup management library; if B da <B 1 , it is determined that the data backup index is low, and the backup data is classified into the general-level backup management library;

[0095] S3. Perform backup fusion according to the enabled scheduling modules of the respective backup management libraries divided, and determine the data backup methods at all levels.

[0096] As an implementation manner of the present invention, the data backup index B is calculated through the formula da ;

[0097] where P im is the data importance parameter, R re is the data recovery degree ratio, S re is the data recovery speed; μ, σ, θ are weight coefficients; P 0 is the standard value of the data importance parameter; R 0 is the standard value of the data recovery degree ratio; S 0 is the standard value of the data recovery speed; ΔR is the deviation value of the data recovery degree ratio; ΔS is the deviation value of the data recovery speed.

[0098] Through the above technical solution, in this embodiment, the data backup index is judged through the formula where P im is the data importance parameter, R re is the data recovery degree ratio, S re is the data recovery speed; μ, σ, θ are weight coefficients; P 0 is the standard value of the data importance parameter; R 0 is the standard value of the data recovery degree ratio; S 0 is the standard value of the data recovery speed; ΔR is the deviation value of the data recovery degree ratio; ΔS is the deviation value of the data recovery speed; P 0 and R 0 and S 0 are all obtained by analyzing the corresponding data values in the historical database, and ΔR and ΔS are also obtained by rational analysis and adjustment based on the detected numerical deviations, ensuring that the precise analysis of the data backup index can be achieved, improving the hierarchical adjustment of data backup, ensuring the delivery and selection of backup databases at different backup levels, and further refining the data backup method.

[0099] As an implementation manner of the present invention, the backup methods include: full backup, differential backup, and incremental backup;

[0100] The data backup time point is determined according to the user's needs, and the data is backup-fused through the scheduling module server. The full backup and the incremental are combined into the final full backup method, that is, the full backup on the first day + the incremental backup on the second day +... + the incremental backup on the nth day = the full backup on the nth day.

[0101] Please refer to Figure 2 As shown, as an implementation manner of the present invention, the specific backup fusion method is as follows:

[0102] SS1. Select the full backup data job information before the predetermined time point;

[0103] SS2. Determine the differential backup data job information closest to the given time point between the full backup data job information;

[0104] SS3. Discover all incremental backup data job information existing between the differential backup data job information and the predetermined time point;

[0105] SS4. Obtain the set of all data job information and discover the corresponding data information archive files, and remove the earlier time version of the same-name files according to the size of the field information;

[0106] SS5. Organize and send the obtained field information to the superior for backup in the specified order.

[0107] The above content is only an example and illustration of the concept of the present invention. Those skilled in the art of the present technology can make various modifications, supplements, or use similar methods of substitution to the specific embodiments described. As long as they do not deviate from the concept of the invention or exceed the scope defined by this claim book, they should all fall within the protection scope of the present invention.

Claims

1. A disaster recovery and backup data management system based on cloud computing, characterized in that, it includes: A data classification module, which is used to classify data according to preset data characteristics and the transmission methods of existing data, and generate different disaster recovery and backup strategies according to different types of data; A backup module, which is used to determine whether the content of real-time backup data meets the backup requirements according to the disaster recovery and backup strategy: If so, divide the data backup level and manage the backup data; If not, issue a warning notice and perform a security check on the data; A scheduling module, which is used to accept service requests from the backup module and complete the transmission, verification, retrieval, recovery, and deletion processing of data; the scheduling module includes an access control unit, a monitoring and logging unit; Among them, the access control unit is used to restrict the user access of the client and record access information; the monitoring and logging unit is used to monitor and update the scheduling information in real time, and record and save the scheduling information; A storage module, which is used to manage the storage of backup data for the data provided by the storage device; the storage module includes a data encryption unit, and the data encryption unit is used to encrypt the metadata; A recovery module, which is used to perform recovery operations on the backup data and its application system after a disaster; the recovery module includes a metadata recovery unit and a decoding engine unit connected to the metadata recovery unit; The disaster recovery and backup strategy in the backup module is: Obtained through the formula The data backup risk value P is calculated ar (t); Where N is the total number of disaster recovery data channels, and i ∈ [1, N]; R a (t) is the real-time backup data range value; D v (t) is the real-time backup data volume; V t (t) is the real-time backup data transfer rate; Q si is the first conversion function; Q ki is the second conversion function; Q ui is the third conversion function; Δt is the preset time period; β i is the position coefficient of the i-th disaster recovery data channel; G is the judgment function.

2. The disaster recovery and backup data management system based on cloud computing according to claim 1, characterized in that, Determine when Then G = B; Otherwise, where Q th (Δt) is the risk tolerance threshold corresponding to the Δt period; B is the warning value.

3. The disaster recovery and backup data management system based on cloud computing according to claim 1, characterized in that, Compare the data backup risk value P ar (t) with a preset threshold value (P A , P B ) to determine the size relationship: If P ar (t) ≤ PA, it is determined that the risk of the content of the real-time backup data is relatively small; If P B > P ar (t)> P A , it is determined that the content of the real-time backup data has a general risk and meets the backup requirements; If P ar (t) ≥ P B , it is determined that there is a relatively high risk in the content of the real-time backup data.

4. The disaster recovery and backup data management system based on cloud computing according to claim 1, characterized in that, The method for dividing the data backup level is: S1. Obtain the data backup index B of the backup data module in real time da ; S2. Compare the data backup index B da with the preset data threshold [B 1 , B 2 to determine the size relationship: If B da > B 2 , it is determined that the data backup index is relatively high, and the backup data is classified into the core-level backup management library; If B 1 ≤B da ≤B 2 , it is determined that the data backup index is normal, and the backup data is divided into the central-level backup management library; If B da <B 1 , it is determined that the data backup index is relatively low, and the backup data is classified into the general-level backup management library; S3. Perform backup fusion according to the enabled scheduling module of each level of backup management library divided, and determine the data backup method for each level.

5. The disaster recovery and backup data management system based on cloud computing according to claim 4, characterized in that, The data backup index B da is obtained as follows: Calculate the data backup index B through the formula da ;​ Among them, P im is the data importance parameter, R re is the data recovery degree ratio, S re is the data recovery speed; μ, σ, θ are weight coefficients; P 0 is the standard value of the data importance parameter; R 0 is the standard value of the data recovery degree ratio; S 0 is the standard value of the data recovery speed; ΔR is the deviation value of the data recovery degree ratio; ΔS is the deviation value of the data recovery speed.

6. The disaster recovery and backup data management system based on cloud computing according to claim 4, characterized in that, The backup methods include: full backup, differential backup, and incremental backup; Determine the data backup time point according to user requirements, and perform backup fusion on the data through the scheduling module server, and merge the full backup and the incremental backup into the final full backup method, that is, the full backup on the first day + the incremental backup on the second day +... + the incremental backup on the nth day = the full backup on the nth day.

7. The disaster recovery and backup data management system based on cloud computing according to claim 6, characterized in that, The specific method of backup fusion is: SS1. Select the full backup data job information before a predetermined time point; SS2. Determine the nearest differential backup data job information between the full backup data job information and a given time point; SS3. Find all incremental backup data job information existing between the differential backup data job information and the predetermined time point; SS4. Obtain the set of all data job information, discover the corresponding data information archive files, and remove the files with the same name in the earlier time versions according to the size of the field information; SS5. Organize and send the obtained field information to the superior for backup in the specified order.

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