CN112732487A - Data recovery method and device - Google Patents
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Abstract
The invention discloses a data recovery method and a device, wherein the method comprises the following steps: the first monitoring center acquires characteristic data of first data to be recovered; the feature data is obtained by extracting the first to-be-recovered data according to at least one preset feature extraction algorithm; the first data to be recovered is monitoring data in a preset time period before the second monitoring center is abnormal; the first monitoring center obtains second data to be recovered according to the characteristic data and at least one preset data recovery algorithm; the at least one preset data recovery algorithm is a preset data recovery algorithm corresponding to the at least one preset feature extraction algorithm.
Description
Technical Field
The present invention relates to the field of data recovery technologies, and in particular, to a data recovery method and apparatus.
Background
Data centers are required to process large amounts of data every day, and the operating conditions of the data centers are particularly critical. At present, a data center is usually monitored by a monitoring center, so that the operation condition of the data center can be reflected in time. However, the monitoring center can also be abnormal, and the monitoring can be continued by switching different monitoring centers, so that the monitoring is not interrupted.
However, when one monitoring center is abnormal, after the original monitoring node is switched to a new monitoring center, the problem of loss of original monitoring data exists. The current way of monitoring data recovery is remote backup. However, the remote backup needs to be transmitted across the network, the data volume of the monitoring data is large, and the transmission overhead is large, so that the recovery time of the original monitoring data is long.
Disclosure of Invention
The invention provides a data recovery method and a data recovery device, which solve the problem that the recovery time of original monitoring data in the prior art is longer.
In a first aspect, the present invention provides a data recovery method, including: the first monitoring center acquires characteristic data of first data to be recovered; the feature data is obtained by extracting the first to-be-recovered data according to at least one preset feature extraction algorithm; the first data to be recovered is monitoring data in a preset time period before the second monitoring center is abnormal;
the first monitoring center obtains second data to be recovered according to the characteristic data and at least one preset data recovery algorithm; the at least one preset data recovery algorithm is a preset data recovery algorithm corresponding to the at least one preset feature extraction algorithm.
In the above manner, the feature data is obtained by extracting the first to-be-recovered data according to at least one preset feature extraction algorithm, so that the feature data represents characteristics of the first to-be-recovered data, and therefore, the first monitoring center can also obtain second to-be-recovered data similar to the first to-be-recovered data according to the feature data and at least one preset data recovery algorithm, and the feature data has a smaller data size than the first to-be-recovered data of the original data, so that the transmission overhead is smaller, and the recovery time can be greatly prolonged on the basis of ensuring certain accuracy.
Optionally, the first data to be recovered includes a plurality of first sub data to be recovered of a first type; the at least one preset feature extraction algorithm comprises a plurality of preset feature extraction algorithm combinations of a second type; the plurality of first types and the plurality of second types have a first preset mapping relation; the first preset mapping relation is determined according to data characteristics of the first sub data to be recovered of the plurality of first types;
the characteristic data is obtained specifically in the following manner:
aiming at first sub-data to be recovered of any one first type in the multiple first types, obtaining sub-feature data of a third type according to the first sub-data to be recovered of the first type and a preset feature extraction algorithm combination of a second type corresponding to the first type;
and taking a plurality of sub-feature data of a third type obtained by the plurality of first sub-data to be recovered of the first type as the feature data.
According to the method, the corresponding feature extraction algorithm is set according to the data characteristics of different sub-data to be recovered, and the feature data of the corresponding type is obtained, so that the feature data which is more similar to the original data can be obtained according to the data characteristics.
Optionally, the obtaining sub-feature data of a third type according to the combination of the first sub-data to be restored of the first type and the preset feature extraction algorithm of the second type corresponding to the first type includes:
determining a baseline value of the first type of first sub data to be recovered and a special value of the first type of first sub data to be recovered according to the first type of first sub data to be recovered and a preset feature extraction algorithm combination of a second type mapped by the first type;
and taking the baseline value of the first type of the first sub data to be recovered and the special value of the first type of the first sub data to be recovered as the third type of sub characteristic data.
In the above method, the characteristics of each type of sub-data are further accurately characterized by the baseline value and the special value of the sub-data.
Optionally, the second data to be recovered includes a plurality of second sub data to be recovered of the first type; the at least one preset data recovery algorithm comprises a plurality of preset data recovery algorithm combinations of a fourth type; the plurality of third types and the plurality of fourth types have a second preset mapping relation; the second preset mapping relation is determined according to the data characteristics of the plurality of sub-feature data of the third type;
the first monitoring center obtains second data to be recovered according to the characteristic data and at least one preset data recovery algorithm, and the method comprises the following steps:
aiming at the sub-feature data of any one of the third types, the first monitoring center obtains second sub-data to be recovered of the first type according to the sub-feature data of the third type and a preset data recovery algorithm combination of a fourth type mapped by the third type;
and the first monitoring center takes a plurality of second sub data to be recovered of the first type obtained by the plurality of sub characteristic data of the third type as the second data to be recovered.
In the above manner, according to the sub-feature data of the third type, a data recovery algorithm combination is preset to obtain second sub-data to be recovered of the corresponding first type, so that the second data to be recovered, which is more similar to the original data, can be obtained according to the data characteristics.
Optionally, the first data to be recovered is synchronized to the cache center by the second monitoring center; the first monitoring center obtains characteristic data of first data to be recovered, and the characteristic data comprises the following steps:
and the first monitoring center acquires the first data to be recovered from the cache center.
In the above manner, the first data to be recovered is synchronized to the cache center by the second monitoring center, and then is acquired from the cache center by the first monitoring center, so that unified management is performed by the monitoring center without coordination between the first monitoring center and the second monitoring center, and data transmission efficiency is saved.
Optionally, the first data to be recovered includes a plurality of pieces of data to be recovered; the data to be recovered are all synchronized to the cache center block by the second monitoring center; the first monitoring center obtains the first data to be recovered from the cache center, and the method includes:
for any block of data to be recovered in the plurality of blocks of data to be recovered, when the data to be recovered in the cache center meets a preset condition, the first monitoring center takes the data to be recovered in the cache center as a block of data to be recovered, and stores the block of data to be recovered to a persistent memory space of the first monitoring center;
the preset conditions are as follows: the data volume of the data to be restored in the cache center is greater than a preset data volume threshold value, or the data to be restored in the cache center is the data to be restored, the duration of which is greater than the preset duration.
In the above manner, when the data to be restored in the cache center meets the preset condition, the first monitoring center takes the data to be restored in the cache center as a block of data to be restored, and the first monitoring center stores the block of data to be restored to the persistent memory space of the first monitoring center, so that the data to be restored is persisted block by block, and the reliability of the data is ensured.
In a second aspect, the present invention provides a data recovery apparatus, comprising: the acquisition module is used for acquiring the characteristic data of the first data to be recovered; the feature data is obtained by extracting the first to-be-recovered data according to at least one preset feature extraction algorithm; the first data to be recovered is monitoring data in a preset time period before the second monitoring center is abnormal;
the processing module is used for obtaining second data to be recovered according to the characteristic data and at least one preset data recovery algorithm; the at least one preset data recovery algorithm is a preset data recovery algorithm corresponding to the at least one preset feature extraction algorithm.
Optionally, the first data to be recovered includes a plurality of first sub data to be recovered of a first type; the at least one preset feature extraction algorithm comprises a plurality of preset feature extraction algorithm combinations of a second type; the plurality of first types and the plurality of second types have a first preset mapping relation; the first preset mapping relation is determined according to data characteristics of the first sub data to be recovered of the plurality of first types;
the acquisition module is further configured to:
aiming at first sub-data to be recovered of any one first type in the multiple first types, obtaining sub-feature data of a third type according to the first sub-data to be recovered of the first type and a preset feature extraction algorithm combination of a second type corresponding to the first type;
and taking a plurality of sub-feature data of a third type obtained by the plurality of first sub-data to be recovered of the first type as the feature data.
Optionally, the obtaining module is specifically configured to: determining a baseline value of the first type of first sub data to be recovered and a special value of the first type of first sub data to be recovered according to the first type of first sub data to be recovered and a preset feature extraction algorithm combination of a second type mapped by the first type;
and taking the baseline value of the first type of the first sub data to be recovered and the special value of the first type of the first sub data to be recovered as the third type of sub characteristic data.
Optionally, the obtaining module is specifically configured to: determining a baseline value of the first type of first sub data to be recovered and a special value of the first type of first sub data to be recovered according to the first type of first sub data to be recovered and a preset feature extraction algorithm combination of a second type mapped by the first type;
and taking the baseline value of the first type of the first sub data to be recovered and the special value of the first type of the first sub data to be recovered as the third type of sub characteristic data.
Optionally, the second data to be recovered includes a plurality of second sub data to be recovered of the first type; the at least one preset data recovery algorithm comprises a plurality of preset data recovery algorithm combinations of a fourth type; the plurality of third types and the plurality of fourth types have a second preset mapping relation; the second preset mapping relation is determined according to the data characteristics of the plurality of sub-feature data of the third type; the processing module is specifically configured to:
aiming at any one of the sub-feature data of the third type in the plurality of third types, obtaining second sub-data to be recovered of the first type according to the sub-feature data of the third type and a preset data recovery algorithm combination of a fourth type mapped by the third type;
and taking a plurality of second sub data to be recovered of the first type obtained by the plurality of sub feature data of the third type as the second data to be recovered.
Optionally, the first data to be recovered is synchronized to the cache center by the second monitoring center; the acquisition module is further configured to: and acquiring the first data to be recovered from the cache center.
Optionally, the first data to be recovered includes a plurality of pieces of data to be recovered; the data to be recovered are all synchronized to the cache center block by the second monitoring center; the acquisition module is specifically configured to: regarding any piece of data to be recovered in the plurality of pieces of data to be recovered, when the data to be recovered in the cache center meets a preset condition, taking the data to be recovered in the cache center as a piece of data to be recovered, and storing the piece of data to be recovered to a persistent memory space of a first monitoring center;
the preset conditions are as follows: the data volume of the data to be restored in the cache center is greater than a preset data volume threshold value, or the data to be restored in the cache center is the data to be restored, the duration of which is greater than the preset duration.
The advantageous effects of the second aspect and the various optional apparatuses of the second aspect may refer to the advantageous effects of the first aspect and the various optional methods of the first aspect, and are not described herein again.
In a third aspect, the present invention provides a computer device comprising a program or instructions for performing the method of the first aspect and the alternatives of the first aspect when the program or instructions are executed.
In a fourth aspect, the present invention provides a storage medium comprising a program or instructions which, when executed, is adapted to perform the method of the first aspect and the alternatives of the first aspect.
These and other aspects of the invention are apparent from and will be elucidated with reference to the embodiments described hereinafter.
Drawings
In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the drawings needed to be used in the description of the embodiments will be briefly introduced below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and it is obvious for those skilled in the art to obtain other drawings based on these drawings without inventive exercise.
Fig. 1 is a schematic flowchart corresponding to a data recovery method according to an embodiment of the present invention;
fig. 2 is a schematic diagram of an extraction process of feature data corresponding to a data recovery method according to an embodiment of the present invention;
fig. 3 is a schematic diagram of a specific recovery process corresponding to a data recovery method according to an embodiment of the present invention;
fig. 4 is a schematic diagram of a system architecture of a data recovery system according to an embodiment of the present invention;
fig. 5 is a schematic diagram of an implementation process in a data recovery system according to an embodiment of the present invention;
fig. 6 is a schematic diagram illustrating implementation of data recovery corresponding to a data recovery method according to an embodiment of the present invention;
fig. 7 is a schematic structural diagram of a data recovery apparatus according to an embodiment of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention clearer, the present invention will be described in further detail with reference to the accompanying drawings, and it is apparent that the described embodiments are only a part of the embodiments of the present invention, not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
As shown in fig. 1, an embodiment of the present application provides a data recovery method.
Step 101: the first monitoring center obtains characteristic data of the first data to be recovered.
The feature data is obtained by extracting the first to-be-recovered data according to at least one preset feature extraction algorithm; the first data to be recovered is monitoring data in a preset time period before the second monitoring center is abnormal.
Step 102: and the first monitoring center obtains second data to be recovered according to the characteristic data and at least one preset data recovery algorithm.
The at least one preset data recovery algorithm is a preset data recovery algorithm corresponding to the at least one preset feature extraction algorithm.
In an optional embodiment, the first data to be recovered is synchronized to a cache center by the second monitoring center; step 101 may specifically be:
and the first monitoring center acquires the first data to be recovered from the cache center.
Further, in one possible scenario, the first to-be-recovered data includes a plurality of blocks of to-be-recovered data; the multiple pieces of data to be recovered are all synchronized to the cache center block by the second monitoring center, and the steps may be as follows:
for any block of data to be recovered in the plurality of blocks of data to be recovered, when the data to be recovered in the cache center meets a preset condition, the first monitoring center takes the data to be recovered in the cache center as a block of data to be recovered, and stores the block of data to be recovered to a persistent memory space of the first monitoring center;
the preset conditions are as follows: the data volume of the data to be restored in the cache center is greater than a preset data volume threshold value, or the data to be restored in the cache center is the data to be restored, the duration of which is greater than the preset duration.
In the method from step 101 to step 102, the specific process may be as follows:
and processing mass time sequence type monitoring data by utilizing a flexibly combined data feature extraction algorithm to generate a small amount of feature data, and storing the feature data to other monitoring centers through a centralized cache. When the original data (first data to be recovered) is abnormal, the characteristic data is rapidly recovered by using a baseline restoration algorithm, a data recovery algorithm and a deep recovery algorithm, so that monitoring data (second data to be recovered) with the same effect as the original data is obtained. As shown in fig. 2, the detailed process is as follows:
step 1-1: and (4) a flexible combination data characteristic value algorithm.
Step 1-2: and processing the original mass monitoring data to generate characteristic data.
Step 1-3: the characteristic data is stored in a cache.
Step 1-4: and writing the characteristic data in the cache into other monitoring centers.
Further, a detailed process of data fast recovery may be, as shown in fig. 3, when a single monitoring center data is abnormal, the following steps may be performed:
step 2-1: and performing baseline algorithm reduction on the characteristic data of the first recovery data to obtain intermediate data 1.
Step 2-2: and performing data calibration on the intermediate data 1 to obtain intermediate data 2.
Step 2-3: and performing deep repair and calibration on the intermediate data 3 to obtain second recovery data.
Step 2-4: providing the data service through the second recovery data.
In an alternative embodiment (hereinafter referred to as the type-based embodiment), the first data to be restored includes a plurality of first sub data to be restored of a first type; the at least one preset feature extraction algorithm comprises a plurality of preset feature extraction algorithm combinations of a second type; the plurality of first types and the plurality of second types have a first preset mapping relation; the first preset mapping relation is determined according to data characteristics of the first sub-data to be restored of the plurality of first types.
The characteristic data is obtained specifically in the following manner:
step (1): and aiming at the first sub-to-be-recovered data of any one first type in the plurality of first types, obtaining sub-feature data of a third type according to the first sub-to-be-recovered data of the first type and a preset feature extraction algorithm combination of a second type corresponding to the first type.
Step (2): and taking a plurality of sub-feature data of a third type obtained by the plurality of first sub-data to be recovered of the first type as the feature data.
In the type-based embodiment, the possible situations in step (1) are as follows:
determining a baseline value of the first type of first sub data to be recovered and a special value of the first type of first sub data to be recovered according to the first type of first sub data to be recovered and a preset feature extraction algorithm combination of a second type mapped by the first type;
and taking the baseline value of the first type of the first sub data to be recovered and the special value of the first type of the first sub data to be recovered as the third type of sub characteristic data.
It should be noted that, for the steps (1) to (2), the most obvious characteristic of the data is time sequence and detail for the raw monitoring data.
Such as:
| TimeStamp | Index 1 | Index 2 | Index 3 |
| 2020/10/18/12:00:00 | 32％ | 20％ | 65％ |
| 2020/10/18/12:00:30 | 32％ | 20％ | 65％ |
| 2020/10/18/12:01:00 | 42％ | 20％ | 65％ |
| … | … | … | … |
| 2020/10/19/12:00:00 | 32％ | 20％ | 65％ |
Based on this feature of the raw data, a feature value composed of a baseline value and a feature value for a predetermined time length is calculated by a feature extraction algorithm, for example, a feature value calculated from the raw data:
when the data is recovered, the characteristic value is used to rapidly recover the original monitoring appearance (namely the second recovered data) through a data baseline recovery algorithm, a data recovery algorithm and a deep recovery algorithm.
In the type-based embodiment, in one possible case, the second data to be recovered includes a plurality of second sub data to be recovered of the first type; the at least one preset data recovery algorithm comprises a plurality of preset data recovery algorithm combinations of a fourth type; the plurality of third types and the plurality of fourth types have a second preset mapping relation; the second preset mapping relation is determined according to the data characteristics of the plurality of sub-feature data of the third type; step 102 may be as follows:
and aiming at the sub-feature data of any one of the third types, the first monitoring center obtains the second sub-data to be recovered of the first type according to the sub-feature data of the third type and the preset data recovery algorithm combination of the fourth type mapped by the third type.
And the first monitoring center takes a plurality of second sub data to be recovered of the first type obtained by the plurality of sub characteristic data of the third type as the second data to be recovered.
The method of the steps 101 to 102 is based on the characteristics of the monitoring data, different algorithms are flexibly selected for combination according to the characteristics of different monitoring indexes, and the optimal characteristic value of the monitoring data is calculated. The original mass monitoring data is not easy to transmit and store, the original mass monitoring data is changed into a small amount of characteristic data which is easy to transmit and store after being calculated by a characteristic value algorithm, the characteristic data is stored in other monitoring centers, and the monitoring data can be quickly restored through the characteristic data subsequently. The data recovery accuracy rate reaches more than 95%, and the data recovery time is monitored within 1min within 24 hours.
The method of steps 101 to 102, which converts the original monitoring data (first recovery data) into the characteristic value standard data, has the following advantages:
and (3) data lightweight: the feature data converted by the feature extraction algorithm can be reduced to one thousandth or one ten thousandth of the original monitoring data (first recovery data).
The data reduction degree is high: the feature data converted by using the feature extraction algorithm can at least reach the 95% reduction degree of the original data. The trend of the restored monitoring curve is consistent with that of the original monitoring curve, and only a little difference may exist in individual details. Monitoring data generally focuses on the overall monitoring trend, so that the restored data completely meets the monitoring requirement.
The data recovery is quick: the processing time for restoring the monitoring data through the characteristic data is within one minute, and the normal use of a user is not influenced.
The stored historical data is more: since the data volume of the characteristic data is much smaller than that of the original data, the storage of the characteristic data can enable the storage of longer historical data on the basis of the existing monitoring resources.
A data recovery method provided in an embodiment of the present invention is described in detail below with reference to fig. 4.
As shown in fig. 4: the unified management center consists of a data query interface, a routing module and a cache center:
each local monitoring center consists of two storage units and an algorithm center
Further, with reference to fig. 5, the specific implementation process is as follows:
the monitoring data persistence can be realized by the following 6 processes with reference to the above figure:
step (a), calculating characteristic value:
the description is given by a center a (a monitoring center, such as a first monitoring center) performance class index Cpu and capacity class index Filesystem: selecting Cpu algorithms 1,4,5 and 7 for combination, and calculating to obtain 4 types of characteristic values; and (3) selecting algorithms 2,3 and 5 for combination by the Filesystem, and calculating to obtain 3 types of characteristic values. And writing the characteristic value into the cache center in real time.
Step (b), data persistence:
after the characteristic value of the A center of the cache center reaches a set threshold value (the threshold value can be controlled from two dimensions of time and data volume), data is written into the local B center, and data persistence is carried out.
Assuming a center a fails, an example of a data recovery process is shown in fig. 6.
Step (c), center switching:
and controlling the data stream originally pointing to the center A by the routing module, pointing to the center B, writing the new data into the center B, and triggering the center B to restore the historical data of the center A.
Step (d), baseline reduction:
algorithms a, d, e passing algorithm centers at center B use the Cpu eigenvalues 1,2,3 to recover the Cpu baseline monitoring data, and algorithms B, c use the Fs eigenvalues 1,2 to recover the Filesystem baseline monitoring data.
Step (e), data repair:
on the basis of the restored baseline data, Cpu monitoring data is further restored by algorithm g using Cpu eigenvalue 4, and Filesystem monitoring data is further restored by algorithm f using Fs eigenvalue 3.
And (f) restoring the monitoring data:
and optimizing the monitoring curves recovered in the two steps by a deep repair algorithm of an algorithm center, and finally recovering the monitoring curves with the similarity of more than 95% to the original monitoring curves.
As shown in fig. 7, the present invention provides a data recovery apparatus, comprising: an obtaining module 701, configured to obtain feature data of first data to be recovered; the feature data is obtained by extracting the first to-be-recovered data according to at least one preset feature extraction algorithm; the first data to be recovered is monitoring data in a preset time period before the second monitoring center is abnormal;
a processing module 702, configured to obtain second data to be recovered according to at least one preset data recovery algorithm according to the feature data; the at least one preset data recovery algorithm is a preset data recovery algorithm corresponding to the at least one preset feature extraction algorithm.
Optionally, the first data to be recovered includes a plurality of first sub data to be recovered of a first type; the at least one preset feature extraction algorithm comprises a plurality of preset feature extraction algorithm combinations of a second type; the plurality of first types and the plurality of second types have a first preset mapping relation; the first preset mapping relation is determined according to data characteristics of the first sub data to be recovered of the plurality of first types;
the obtaining module 701 is further configured to:
aiming at first sub-data to be recovered of any one first type in the multiple first types, obtaining sub-feature data of a third type according to the first sub-data to be recovered of the first type and a preset feature extraction algorithm combination of a second type corresponding to the first type;
and taking a plurality of sub-feature data of a third type obtained by the plurality of first sub-data to be recovered of the first type as the feature data.
Optionally, the obtaining module 701 is specifically configured to: determining a baseline value of the first type of first sub data to be recovered and a special value of the first type of first sub data to be recovered according to the first type of first sub data to be recovered and a preset feature extraction algorithm combination of a second type mapped by the first type;
and taking the baseline value of the first type of the first sub data to be recovered and the special value of the first type of the first sub data to be recovered as the third type of sub characteristic data.
Optionally, the obtaining module 701 is specifically configured to: determining a baseline value of the first type of first sub data to be recovered and a special value of the first type of first sub data to be recovered according to the first type of first sub data to be recovered and a preset feature extraction algorithm combination of a second type mapped by the first type;
and taking the baseline value of the first type of the first sub data to be recovered and the special value of the first type of the first sub data to be recovered as the third type of sub characteristic data.
Optionally, the second data to be recovered includes a plurality of second sub data to be recovered of the first type; the at least one preset data recovery algorithm comprises a plurality of preset data recovery algorithm combinations of a fourth type; the plurality of third types and the plurality of fourth types have a second preset mapping relation; the second preset mapping relation is determined according to the data characteristics of the plurality of sub-feature data of the third type; the processing module 702 is specifically configured to:
aiming at any one of the sub-feature data of the third type in the plurality of third types, obtaining second sub-data to be recovered of the first type according to the sub-feature data of the third type and a preset data recovery algorithm combination of a fourth type mapped by the third type;
and taking a plurality of second sub data to be recovered of the first type obtained by the plurality of sub feature data of the third type as the second data to be recovered.
Optionally, the first data to be recovered is synchronized to the cache center by the second monitoring center; the obtaining module 701 is further configured to: and acquiring the first data to be recovered from the cache center.
Optionally, the first data to be recovered includes a plurality of pieces of data to be recovered; the data to be recovered are all synchronized to the cache center block by the second monitoring center; the obtaining module 701 is specifically configured to: regarding any piece of data to be recovered in the plurality of pieces of data to be recovered, when the data to be recovered in the cache center meets a preset condition, taking the data to be recovered in the cache center as a piece of data to be recovered, and storing the piece of data to be recovered to a persistent memory space of a first monitoring center;
the preset conditions are as follows: the data volume of the data to be restored in the cache center is greater than a preset data volume threshold value, or the data to be restored in the cache center is the data to be restored, the duration of which is greater than the preset duration.
Based on the same inventive concept, embodiments of the present invention also provide a computer device, which includes a program or instructions, and when the program or instructions are executed, the data recovery method and any optional method provided by the embodiments of the present invention are executed.
Based on the same inventive concept, embodiments of the present invention also provide a computer-readable storage medium, which includes a program or instructions, and when the program or instructions are executed, the data recovery method and any optional method provided by the embodiments of the present invention are executed.
While preferred embodiments of the present invention have been described, additional variations and modifications in those embodiments may occur to those skilled in the art once they learn of the basic inventive concepts. Therefore, it is intended that the appended claims be interpreted as including preferred embodiments and all such alterations and modifications as fall within the scope of the invention.
It will be apparent to those skilled in the art that various changes and modifications may be made in the present invention without departing from the spirit and scope of the invention. Thus, if such modifications and variations of the present invention fall within the scope of the claims of the present invention and their equivalents, the present invention is also intended to include such modifications and variations.
Claims (10)
1. A method for data recovery, comprising:
the first monitoring center acquires characteristic data of first data to be recovered; the feature data is obtained by extracting the first to-be-recovered data according to at least one preset feature extraction algorithm; the first data to be recovered is monitoring data in a preset time period before the second monitoring center is abnormal;
the first monitoring center obtains second data to be recovered according to the characteristic data and at least one preset data recovery algorithm; the at least one preset data recovery algorithm is a preset data recovery algorithm corresponding to the at least one preset feature extraction algorithm.
2. The method of claim 1, wherein the first data to be recovered comprises a plurality of first sub data to be recovered of a first type; the at least one preset feature extraction algorithm comprises a plurality of preset feature extraction algorithm combinations of a second type; the plurality of first types and the plurality of second types have a first preset mapping relation; the first preset mapping relation is determined according to data characteristics of the first sub data to be recovered of the plurality of first types;
the characteristic data is obtained specifically in the following manner:
aiming at first sub-data to be recovered of any one first type in the multiple first types, obtaining sub-feature data of a third type according to the first sub-data to be recovered of the first type and a preset feature extraction algorithm combination of a second type corresponding to the first type;
and taking a plurality of sub-feature data of a third type obtained by the plurality of first sub-data to be recovered of the first type as the feature data.
3. The method according to claim 2, wherein the obtaining sub-feature data of a third type according to the combination of the first sub-data to be restored of the first type and the preset feature extraction algorithm of the second type corresponding to the first type comprises:
determining a baseline value of the first type of first sub data to be recovered and a special value of the first type of first sub data to be recovered according to the first type of first sub data to be recovered and a preset feature extraction algorithm combination of a second type mapped by the first type;
and taking the baseline value of the first type of the first sub data to be recovered and the special value of the first type of the first sub data to be recovered as the third type of sub characteristic data.
4. The method of claim 2, wherein the second data to be recovered includes a plurality of second sub data to be recovered of the first type; the at least one preset data recovery algorithm comprises a plurality of preset data recovery algorithm combinations of a fourth type; the plurality of third types and the plurality of fourth types have a second preset mapping relation; the second preset mapping relation is determined according to the data characteristics of the plurality of sub-feature data of the third type;
the first monitoring center obtains second data to be recovered according to the characteristic data and at least one preset data recovery algorithm, and the method comprises the following steps:
aiming at the sub-feature data of any one of the third types, the first monitoring center obtains second sub-data to be recovered of the first type according to the sub-feature data of the third type and a preset data recovery algorithm combination of a fourth type mapped by the third type;
and the first monitoring center takes a plurality of second sub data to be recovered of the first type obtained by the plurality of sub characteristic data of the third type as the second data to be recovered.
5. The method of any of claims 1 to 4, wherein the first data to be recovered is synchronized by the second monitoring center to a caching center; the first monitoring center obtains characteristic data of first data to be recovered, and the characteristic data comprises the following steps:
and the first monitoring center acquires the first data to be recovered from the cache center.
6. The method of claim 5, wherein the first data to be recovered comprises a plurality of blocks of data to be recovered; the data to be recovered are all synchronized to the cache center block by the second monitoring center; the first monitoring center obtains the first data to be recovered from the cache center, and the method includes:
for any block of data to be recovered in the plurality of blocks of data to be recovered, when the data to be recovered in the cache center meets a preset condition, the first monitoring center takes the data to be recovered in the cache center as a block of data to be recovered, and stores the block of data to be recovered to a persistent memory space of the first monitoring center;
the preset conditions are as follows: the data volume of the data to be restored in the cache center is greater than a preset data volume threshold value, or the data to be restored in the cache center is the data to be restored, the duration of which is greater than the preset duration.
7. A data recovery apparatus, comprising:
the acquisition module is used for acquiring the characteristic data of the first data to be recovered; the feature data is obtained by extracting the first to-be-recovered data according to at least one preset feature extraction algorithm; the first data to be recovered is monitoring data in a preset time period before the second monitoring center is abnormal;
the processing module is used for obtaining second data to be recovered according to the characteristic data and at least one preset data recovery algorithm; the at least one preset data recovery algorithm is a preset data recovery algorithm corresponding to the at least one preset feature extraction algorithm.
8. The apparatus of claim 7, wherein the first data to be recovered comprises a plurality of first sub data to be recovered of a first type; the at least one preset feature extraction algorithm comprises a plurality of preset feature extraction algorithm combinations of a second type; the plurality of first types and the plurality of second types have a first preset mapping relation; the first preset mapping relation is determined according to data characteristics of the first sub data to be recovered of the plurality of first types;
the acquisition module is further configured to:
aiming at first sub-data to be recovered of any one first type in the multiple first types, obtaining sub-feature data of a third type according to the first sub-data to be recovered of the first type and a preset feature extraction algorithm combination of a second type corresponding to the first type;
and taking a plurality of sub-feature data of a third type obtained by the plurality of first sub-data to be recovered of the first type as the feature data.
9. A computer device comprising a program or instructions that, when executed, perform the method of any of claims 1 to 6.
10. A computer-readable storage medium comprising a program or instructions which, when executed, perform the method of any of claims 1 to 6.
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