Generation and dynamic optimization configuration method and terminal of multi-dimensional analysis data

Abstract

The present invention provides a method and terminal for generating and dynamically optimizing configuration of multi-dimensional analysis data, comprising: obtaining a backup task, determining a virtual machine to be backed up corresponding to the backup task; calculating the data to be backed up corresponding to the virtual machine to be backed up; obtaining multiple operation and maintenance modes of the backup strategy, and calculating first parameter information of the data to be backed up under each of the operation and maintenance modes according to the data to be backed up; obtaining multiple deduplication technologies, and calculating second parameter information of the data to be backed up after executing each of the deduplication technologies; generating an optimal backup strategy according to the first parameter information and the second parameter information; determining the most appropriate backup strategy by calculating and comparing the backup costs spent on the same backup data under multiple backup strategies, and using deduplication technology to clear unnecessary data in the backup data, thereby reducing the storage resources occupied by the backup data and thus reducing the total backup cost.

Description

Technical Field

[0001] The present invention relates to the field of data backup, and in particular to a method and a terminal for generating and dynamically optimizing configuration of multi-dimensional analysis data. Background Art

[0002] Data is a valuable asset for cloud computing companies, and various companies use virtual machines in various ways. A large amount of critical data is hosted on virtual machines, so it is very important to back them up. In the traditional virtual machine backup method, the backup data is stored in an off-site data center. If the main data center is affected by a natural disaster or technical failure, a copy of the data can be obtained. However, when virtual machine backup storage is required in multiple data centers, the cost of operating and maintaining the data center is very high. Therefore, many companies back up virtual machines to the cloud to avoid the expensive hardware and maintenance costs of traditional backup methods.

[0003] Enterprises back up virtual machines to the cloud so that clients can access data on demand, and the data in the cloud is not affected by any physical variables. Enterprises usually do not have their own cloud and need to rent cloud equipment for backup. Cloud equipment backs up files according to the backup strategy preset by the enterprise, but the current backup strategies are manually specified, and it is impossible to optimize the backup strategy in real time according to business needs, and it is impossible to achieve the optimal backup cost. Summary of the invention

[0004] The technical problem to be solved by the present invention is to provide a method and terminal for generating and dynamically optimizing configuration of multi-dimensional analysis data, automatically optimizing backup strategies, and reducing backup costs.

[0005] In order to solve the above technical problems, the technical solution adopted by the present invention is:

[0006] A method for generating and dynamically optimizing multi-dimensional analysis data, comprising:

[0007] S1. Obtain a backup task and determine the virtual machine to be backed up corresponding to the backup task;

[0008] S2. Calculate the data to be backed up corresponding to the virtual machine to be backed up;

[0009] S3, obtaining multiple operation and maintenance modes of the backup strategy, and calculating first parameter information of the data to be backed up under each of the operation and maintenance modes according to the data to be backed up;

[0010] S4, obtaining multiple data deduplication technologies, and calculating, according to the data to be backed up, second parameter information after the data to be backed up is executed with each of the data deduplication technologies;

[0011] S5. Generate an optimal backup strategy according to the first parameter information and the second parameter information.

[0012] In order to solve the above technical problems, another technical solution adopted by the present invention is:

[0013] A terminal for generating and dynamically optimizing multi-dimensional analysis data includes a memory, a processor, and a computer program stored in the memory and running on the processor. When the processor executes the computer program, the following steps are implemented:

[0014] S1. Obtain a backup task and determine the virtual machine to be backed up corresponding to the backup task;

[0015] S2. Calculate the data to be backed up corresponding to the virtual machine to be backed up;

[0016] S3, obtaining multiple operation and maintenance modes of the backup strategy, and calculating first parameter information of the data to be backed up under each of the operation and maintenance modes according to the data to be backed up;

[0017] S4, obtaining multiple data deduplication technologies, and calculating second parameter information after the to-be-backed up data is executed with each of the data deduplication technologies;

[0018] S5. Generate an optimal backup strategy according to the first parameter information and the second parameter information.

[0019] The beneficial effects of the present invention are as follows: by calculating the first parameter information generated under each operation and maintenance mode of the data to be backed up, it is analyzed which operation and maintenance mode the backup data can meet both business needs and consume less production costs; at the same time, a variety of deduplication technologies are used to perform deduplication operations on the data to be backed up respectively to remove unnecessary data in the backup data, thereby reducing the storage resources occupied by the backup data; and the optimal deduplication technology is determined through the second parameter information, thereby further reducing the cost of the backup data. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] Figure 1 A flowchart of the steps of a method for generating and dynamically optimizing configuration of multi-dimensional analysis data provided by an embodiment of the present invention;

[0021] Figure 2 A schematic diagram of the structure of a terminal for generating and dynamically optimizing configuration of multi-dimensional analysis data provided by an embodiment of the present invention;

[0022] Description of labels:

[0023] 1. A terminal for generating and dynamically optimizing configuration of multi-dimensional analysis data; 2. Memory; 3. Processor. DETAILED DESCRIPTION

[0024] In order to explain the technical content, achieved objectives and effects of the present invention in detail, the following is an explanation in combination with the implementation modes and the accompanying drawings.

[0025] Please refer to Figure 1 , a method for generating and dynamically optimizing multi-dimensional analysis data, comprising:

[0026] S1. Obtain a backup task and determine the virtual machine to be backed up corresponding to the backup task;

[0027] S2. Calculate the data to be backed up corresponding to the virtual machine to be backed up;

[0028] S3, obtaining multiple operation and maintenance modes of the backup data strategy, and calculating first parameter information of the data to be backed up under each of the operation and maintenance modes according to the data to be backed up;

[0029] S4, obtaining multiple data deduplication technologies, and calculating second parameter information after the to-be-backed up data is executed with each of the data deduplication technologies;

[0030] S5. Generate an optimal backup strategy according to the first parameter information and the second parameter information.

[0031] From the above description, it can be seen that the beneficial effects of the present invention are: by calculating the first parameter information generated under each operation and maintenance mode of the data to be backed up, it is analyzed which operation and maintenance mode of the backup data can both meet business needs and consume less production costs, and at the same time, multiple deduplication technologies are used to perform deduplication operations on the data to be backed up to remove unnecessary data in the backup data, reduce the storage resources occupied by the backup data, and determine the optimal deduplication technology through the second parameter information, thereby further reducing the cost of backup data.

[0032] Furthermore, the S2 is specifically:

[0033] Obtaining source data to be backed up corresponding to the virtual machine to be backed up;

[0034] Determine the exchange file in the source data to be backed up;

[0035] The source data to be backed up of the virtual machine to be backed up except the exchange file is calculated and marked as data to be backed up.

[0036] As can be seen from the above description, the memory of the virtual machine may not be enough to store application and operating system data. In this case, the operating system occupies a part of the hard disk to store additional data, which is called swap file or swap partition in the operating system. The swap file is usually 1.5 times the size of the memory, and the data in these files changes regularly, so these files are also backed up every time a backup is performed. However, this data is irrelevant to the business needs of the backup. Therefore, these files should be excluded from the data to be backed up to reduce the storage cost of the backup data.

[0037] Furthermore, the S3 is specifically:

[0038] The first parameter information includes a first data repetition rate and a first production consumption value; the backup strategy includes a plurality of operation and maintenance parameters;

[0039] The S3 is specifically:

[0040] Acquire the multiple operation and maintenance parameters, and determine multiple operation and maintenance modes of the backup strategy and an operation and maintenance cycle of the backup strategy according to the multiple operation and maintenance parameters;

[0041] According to the data to be backed up, a first data repetition rate and a first production consumption value of the data to be backed up associated with each of the operation and maintenance modes within the operation and maintenance cycle are calculated.

[0042] From the above description, we can see that virtual machines need to regularly back up a large amount of mission-critical data, but this data will inevitably grow over time, and users need to ensure that cloud storage backup and data transmission do not cost a lot of money. Therefore, by calculating the data duplication rate under multiple backup strategies, the valid data contained in the backup data can be screened according to the data duplication rate to ensure the proportion of valid data in the cloud storage space and avoid invalid storage costs.

[0043] Furthermore, the S4 is specifically:

[0044] Acquire multiple data deduplication technologies and the operation and maintenance cycle of the backup strategy, and execute each of the data deduplication technologies on the data to be backed up to obtain multiple target data to be backed up;

[0045] According to the target data to be backed up, a second data repetition rate and a second production consumption value of the target data to be backed up within the operation and maintenance cycle are calculated.

[0046] From the above description, it can be seen that in order to ensure that the virtual machine backup data occupies as little space as possible in the cloud storage, after the backup data is executed with multiple deduplication technologies respectively, the corresponding second data duplication rate of the backup data is calculated and compared with the second production consumption value, and the deduplication technology with the lowest second data duplication rate is selected, thereby reducing the invalid data in the backup data, reducing the storage space of the backup data, and thus reducing the backup cost.

[0047] Furthermore, the S5 is specifically:

[0048] Determining an optimal operation and maintenance method and an optimal data deduplication technology according to the sorting results of the first parameter information and the second parameter information;

[0049] An optimal backup strategy is determined according to the optimal operation and maintenance method and the optimal data deduplication technology.

[0050] From the above description, it can be seen that in the traditional way, users create different backup strategies according to actual conditions. This application pre-simulates and calculates the costs incurred after using a variety of different backup strategies for data backup, and can automatically provide users with reference data for backup strategy selection, thereby providing the optimal backup strategy according to user needs.

[0051] Please refer to Figure 2 , a terminal for generating and dynamically optimizing configuration of multi-dimensional analysis data, comprising a memory, a processor, and a computer program stored in the memory and running on the processor, wherein the processor implements the following steps when executing the computer program:

[0052] S1. Obtain a backup task and determine the virtual machine to be backed up corresponding to the backup task;

[0053] S2. Calculate the data to be backed up corresponding to the virtual machine to be backed up;

[0054] S3, obtaining multiple operation and maintenance modes of the backup strategy, and calculating first parameter information of the data to be backed up under each of the operation and maintenance modes according to the data to be backed up;

[0055] S4, obtaining multiple data deduplication technologies, and calculating second parameter information after the to-be-backed up data is executed with each of the data deduplication technologies;

[0056] S5. Generate an optimal backup strategy according to the first parameter information and the second parameter information.

[0057] From the above description, it can be seen that the beneficial effects of the present invention are: by calculating the first parameter information generated under each operation and maintenance mode of the data to be backed up, it is analyzed which operation and maintenance mode of the backup data can both meet business needs and consume less production costs, and at the same time, multiple deduplication technologies are used to perform deduplication operations on the data to be backed up to remove unnecessary data in the backup data, reduce the storage resources occupied by the backup data, and determine the optimal deduplication technology through the second parameter information, thereby further reducing the cost of backup data.

[0058] Furthermore, the S2 is specifically:

[0059] Obtaining source data to be backed up corresponding to the virtual machine to be backed up;

[0060] Determine the exchange file in the source data to be backed up;

[0061] The source data to be backed up of the virtual machine to be backed up except the exchange file is calculated and marked as data to be backed up.

[0062] As can be seen from the above description, the memory of the virtual machine may not be enough to store application and operating system data. In this case, the operating system occupies a part of the hard disk to store additional data, which is called swap file or swap partition in the operating system. The swap file is usually 1.5 times the size of the memory, and the data in these files changes regularly, so these files are also backed up every time a backup is performed. However, this data is irrelevant to the business needs of the backup. Therefore, these files should be excluded from the data to be backed up to reduce the storage cost of the backup data.

[0063] Furthermore, the S3 is specifically:

[0064] The first parameter information includes a first data repetition rate and a first production consumption value; the backup strategy includes a plurality of operation and maintenance parameters;

[0065] The S3 is specifically:

[0066] Acquire the multiple operation and maintenance parameters, and determine multiple operation and maintenance modes of the backup strategy and an operation and maintenance cycle of the backup strategy according to the multiple operation and maintenance parameters;

[0067] According to the data to be backed up, a first data repetition rate and a first production consumption value of the data to be backed up associated with each of the operation and maintenance modes within the operation and maintenance cycle are calculated.

[0068] From the above description, we can see that virtual machines need to regularly back up a large amount of mission-critical data, but this data will inevitably grow over time, and users need to ensure that cloud storage backup and data transmission do not cost a lot of money. Therefore, by calculating the data duplication rate under multiple backup strategies, the valid data contained in the backup data can be screened according to the data duplication rate to ensure the proportion of valid data in the cloud storage space and avoid invalid storage costs.

[0069] Furthermore, the S4 is specifically:

[0070] Acquire multiple data deduplication technologies and the operation and maintenance cycle of the backup strategy, and execute each of the data deduplication technologies on the data to be backed up to obtain multiple target data to be backed up;

[0071] According to the target data to be backed up, a second data repetition rate and a second production consumption value of the target data to be backed up within the operation and maintenance cycle are calculated.

[0072] From the above description, it can be seen that in order to ensure that the virtual machine backup data occupies as little space as possible in the cloud storage, after the backup data is executed with multiple deduplication technologies respectively, the corresponding second data duplication rate of the backup data is calculated and compared with the second production consumption value, and the deduplication technology with the lowest second data duplication rate is selected, thereby reducing the invalid data in the backup data, reducing the storage space of the backup data, and thus reducing the backup cost.

[0073] Furthermore, the S5 is specifically:

[0074] Determining an optimal operation and maintenance method and an optimal data deduplication technology according to the sorting results of the first parameter information and the second parameter information;

[0075] An optimal backup strategy is determined according to the optimal operation and maintenance method and the optimal data deduplication technology.

[0076] From the above description, it can be seen that in the traditional way, users create different backup strategies according to actual conditions. This application pre-simulates and calculates the costs incurred after using a variety of different backup strategies for data backup, and can automatically provide users with reference data for backup strategy selection, thereby providing the optimal backup strategy according to user needs.

[0077] A method for generating and dynamically optimizing multi-dimensional analysis data can customize the optimal backup strategy for virtual machine backup according to the actual needs of users, meet the backup needs and reduce the backup cost. The following is an explanation through a specific embodiment:

[0078] Please refer to Figure 1 , Embodiment 1 of the present invention is:

[0079] A method for generating and dynamically optimizing multi-dimensional analysis data, comprising:

[0080] S1. Obtain a backup task and determine a virtual machine to be backed up corresponding to the backup task.

[0081] Among them, the backup task is the backup requirement specified by the user, including the backup target and the corresponding backup data; the virtual machine refers to a complete computer system with complete hardware system functions simulated by software and running in a completely isolated environment.

[0082] In an optional implementation, the virtual machine is an instance deployed in a cloud server. The method for generating multi-dimensional analysis data and dynamically optimizing configuration in the present application can also be used to generate optimization strategies for backing up runtime data of instances in the cloud server, thereby helping users who rent cloud servers to reduce costs.

[0083] S2. Calculate the data to be backed up corresponding to the virtual machine to be backed up;

[0084] The S2 is specifically:

[0085] S21, obtaining the source data to be backed up corresponding to the virtual machine to be backed up;

[0086] S22, determining the exchange file in the source data to be backed up;

[0087] S23: Calculate the source data to be backed up of the virtual machine to be backed up except the exchange file, and mark it as data to be backed up.

[0088] In an optional implementation, when calculating the data to be backed up corresponding to the virtual machine to be backed up, invalid data in the backup data needs to be excluded; wherein the invalid data includes partition gaps, unused space within a partition, files that are not time-effective after restoration, and files specified for exclusion by the user.

[0089] S3, obtaining multiple operation and maintenance modes of the backup strategy, and calculating first parameter information of the data to be backed up under each of the operation and maintenance modes according to the data to be backed up;

[0090] The first parameter information includes a first data repetition rate and a first production consumption value; the backup strategy includes a plurality of operation and maintenance parameters;

[0091] The S3 is specifically:

[0092] S31, obtaining the multiple operation and maintenance parameters, and determining multiple operation and maintenance modes of the backup strategy and the operation and maintenance cycle of the backup strategy according to the multiple operation and maintenance parameters;

[0093] It should be noted that the operation and maintenance parameters of the backup strategy include a backup cycle set, a backup time set and a retention rule set, and backup cycle subsets, backup time subsets and retention rule subsets are selected from the backup cycle set, the backup time set and the retention rule set to form a plurality of the operation and maintenance methods; wherein, the backup cycle subsets, the backup time subsets and the retention rule subsets may be composed in a variety of ways, for example, all the operation and maintenance parameter subsets are randomly arranged and combined; or the subset intervals selected by the user according to the law of change of virtual machine data are randomly arranged and combined.

[0094] S32. Calculate, based on the data to be backed up, a first data repetition rate and a first production consumption value associated with each of the operation and maintenance modes of the data to be backed up during the operation and maintenance cycle.

[0095] The operation and maintenance cycle is the time required to complete a specified backup task; and the first production consumption value is the total backup cost spent on the specified backup task.

[0096] In an embodiment of the present application, a preset invalid data exclusion algorithm can be used to simultaneously calculate the first data repetition rate of the data to be backed up and parse the invalid data in the data to be backed up; wherein, the preset invalid data exclusion algorithm described in this embodiment can be pre-stored in the storage space of the virtual machine to facilitate retrieval and application.

[0097] Specifically, the first data repetition rate of the data to be backed up and the invalid data of the data to be backed up are calculated by using a preset invalid data exclusion algorithm:

[0098] S201, backup initialization, creating and reading backup configuration files, reading system time, reading virtual machine mother disk and parsing disk partition table, generating full disk bitmap file and duplicate data index file;

[0099] S202, scanning partition gap data blocks according to the full disk bitmap file, and scanning and determining valid data according to the partitions to obtain data to be backed up;

[0100] S301. Calculate the first data repetition rate according to the repetitive data index file.

[0101] In an embodiment of the present application, a preset backup cost calculation model can be obtained, the backup billing items executed during the execution of the backup task can be marked, and the first generated consumption value can be calculated based on the number of backup billing items actually used by the backup strategy and the execution method; wherein, the preset backup cost calculation model described in this embodiment can be pre-stored in the storage space of the virtual machine to facilitate retrieval and application.

[0102] It should be noted that the backup cycle: that is, the time interval between two backup operations, can be daily, weekly or monthly; the backup time: the backup can be performed at any specified time point on the backup day; the retention rule: the backup retention method generated by the backup strategy can be retained by time or by quantity, or even permanently; the operation and maintenance parameters contained in different operation and maintenance methods in the multiple operation and maintenance methods are not exactly the same.

[0103] S4, obtaining multiple data deduplication technologies, and calculating second parameter information after the to-be-backed up data is executed with each of the data deduplication technologies;

[0104] The second parameter information includes a second data repetition rate and a second production consumption value;

[0105] The S4 is specifically:

[0106] S41, obtaining multiple data deduplication technologies and operation and maintenance cycles of the backup strategy, executing each of the data deduplication technologies on the data to be backed up respectively, and obtaining multiple target data to be backed up;

[0107] S42: Calculate a second data repetition rate and a second production consumption value of the target data to be backed up within the operation and maintenance cycle according to the target data to be backed up.

[0108] In an optional embodiment, the preset invalid data exclusion algorithm can calculate the size of the data to be backed up during the parsing process; wherein, the first parameter information also includes the size of the first backup data; the second parameter information also includes the size of the second backup data; the data size of the data to be backed up before and after the deduplication technology and the compression technology can be compared to determine the best deduplication solution, thereby reducing the storage cost of the backup data.

[0109] S5. Generate an optimal backup strategy according to the first parameter information and the second parameter information.

[0110] The S5 is specifically:

[0111] S51, determining an optimal operation and maintenance method and an optimal data deduplication technology according to the sorting results of the first parameter information and the second parameter information;

[0112] S52: Determine an optimal backup strategy according to the optimal operation and maintenance method and the optimal data deduplication technology.

[0113] Among them, the corresponding operation and maintenance methods are sorted in ascending order according to the first data repetition rate and the first production consumption value; the corresponding deduplication technologies are sorted in ascending order according to the second data repetition rate and the second production consumption value; the operation and maintenance methods or deduplication technologies corresponding to lower data repetition rates and production consumption values ​​are ranked in front.

[0114] Among them, the backup strategy is formulated according to the operation and maintenance parameters included in the optimal operation and maintenance method and is coordinated with the corresponding optimal data deduplication technology.

[0115] In an optional implementation, each backup strategy may generate a corresponding backup data report.

[0116] Please refer to Figure 2 , Embodiment 2 of the present invention is:

[0117] A terminal 1 for generating and dynamically optimizing configuration of multi-dimensional analysis data includes a memory 2, a processor 3, and a computer program stored in the memory 2 and running on the processor 3. When the processor 3 executes the computer program, each step in the above-mentioned embodiment 1 is implemented.

[0118] In summary, the present invention provides a method and terminal for generating and dynamically optimizing configuration of multi-dimensional analysis data, which can obtain the backup data size and backup cost under different backup strategies according to the backup task requirements, compare and screen the backup strategies with excessively large backup data and excessively high costs, and select the optimal strategy; at the same time, considering the problem of duplicate data, the backup data size and backup cost after executing duplicate data deduplication technology or compression are obtained, and the optimal deduplication technology is selected, thereby reducing the storage resources occupied by the backup data, thereby optimizing the user's backup strategy and reducing the user's backup cost.

[0119] The above descriptions are merely embodiments of the present invention and are not intended to limit the patent scope of the present invention. Any equivalent transformations made using the contents of the present invention's specification and drawings, or directly or indirectly applied in related technical fields, are also included in the patent protection scope of the present invention.

Claims

1. A method for generating and dynamically optimizing multi-dimensional analysis data, characterized in that: include: S1. Obtain a backup task and determine the virtual machine to be backed up corresponding to the backup task; S2. Calculate the data to be backed up corresponding to the virtual machine to be backed up; S3, obtaining multiple operation and maintenance modes of the backup strategy, and calculating first parameter information of the data to be backed up under each of the operation and maintenance modes according to the data to be backed up; S4, obtaining multiple data deduplication technologies, and calculating second parameter information after the to-be-backed up data is executed with each of the data deduplication technologies; S5. Generate an optimal backup strategy according to the first parameter information and the second parameter information; The first parameter information includes a first data repetition rate and a first production consumption value; the backup strategy includes a plurality of operation and maintenance parameters; The S3 is specifically: Acquire the multiple operation and maintenance parameters, and determine multiple operation and maintenance modes of the backup strategy and an operation and maintenance cycle of the backup strategy according to the multiple operation and maintenance parameters; Calculating, according to the data to be backed up, a first data repetition rate and a first production consumption value associated with each of the operation and maintenance modes of the data to be backed up during the operation and maintenance cycle; The operation and maintenance parameters of the backup strategy include a backup cycle set, a backup time set and a retention rule set, and a backup cycle subset, a backup time subset and a retention rule subset are respectively selected from the backup cycle set, the backup time set and the retention rule set to form a plurality of the operation and maintenance modes; the operation and maintenance cycle is the time required to complete a specified backup task; The first production consumption value is the total backup cost spent on the specified backup task; The second parameter information includes a second data repetition rate and a second production consumption value; The S4 is specifically: Acquire multiple data deduplication technologies and the operation and maintenance cycle of the backup strategy, and execute each of the data deduplication technologies on the data to be backed up to obtain multiple target data to be backed up; According to the target data to be backed up, a second data repetition rate and a second production consumption value of the target data to be backed up within the operation and maintenance cycle are calculated.

2. The method for generating and dynamically optimizing multi-dimensional analysis data according to claim 1, characterized in that: The S2 is specifically: Obtaining source data to be backed up corresponding to the virtual machine to be backed up; Determine the exchange file in the source data to be backed up; The source data to be backed up of the virtual machine to be backed up except the exchange file is calculated and marked as data to be backed up.

3. The method for generating and dynamically optimizing multi-dimensional analysis data according to claim 1, characterized in that: The S5 is specifically: Determining an optimal operation and maintenance method and an optimal data deduplication technology according to the sorting results of the first parameter information and the second parameter information; An optimal backup strategy is determined according to the optimal operation and maintenance method and the optimal data deduplication technology.

4. A terminal for generating and dynamically optimizing multi-dimensional analysis data, comprising a memory, a processor, and a computer program stored in the memory and running on the processor, characterized in that: When the processor executes the computer program, the following steps are implemented: S1. Obtain a backup task and determine the virtual machine to be backed up corresponding to the backup task; S2. Calculate the data to be backed up corresponding to the virtual machine to be backed up; S3, obtaining multiple operation and maintenance modes of the backup strategy, and calculating first parameter information of the data to be backed up under each of the operation and maintenance modes according to the data to be backed up; S4, obtaining multiple data deduplication technologies, and calculating second parameter information after the to-be-backed up data is executed with each of the data deduplication technologies; S5. Generate an optimal backup strategy according to the first parameter information and the second parameter information; The first parameter information includes a first data repetition rate and a first production consumption value; the backup strategy includes a plurality of operation and maintenance parameters; The S3 is specifically: Acquire the multiple operation and maintenance parameters, and determine multiple operation and maintenance modes of the backup strategy and an operation and maintenance cycle of the backup strategy according to the multiple operation and maintenance parameters; Calculating, according to the data to be backed up, a first data repetition rate and a first production consumption value associated with each of the operation and maintenance modes of the data to be backed up during the operation and maintenance cycle; The operation and maintenance parameters of the backup strategy include a backup cycle set, a backup time set and a retention rule set, and a backup cycle subset, a backup time subset and a retention rule subset are respectively selected from the backup cycle set, the backup time set and the retention rule set to form a plurality of the operation and maintenance modes; the operation and maintenance cycle is the time required to complete a specified backup task; The first production consumption value is the total backup cost spent on the specified backup task; The second parameter information includes a second data repetition rate and a second production consumption value; The S4 is specifically: Acquire multiple data deduplication technologies and the operation and maintenance cycle of the backup strategy, and execute each of the data deduplication technologies on the data to be backed up to obtain multiple target data to be backed up; According to the target data to be backed up, a second data repetition rate and a second production consumption value of the target data to be backed up within the operation and maintenance cycle are calculated.

5. A terminal for generating and dynamically optimizing configuration of multi-dimensional analysis data according to claim 4, characterized in that: The S2 is specifically: Obtaining source data to be backed up corresponding to the virtual machine to be backed up; Determine the exchange file in the source data to be backed up; The source data to be backed up of the virtual machine to be backed up except the exchange file is calculated and marked as data to be backed up.

6. A terminal for generating and dynamically optimizing configuration of multi-dimensional analysis data according to claim 4, characterized in that: The S5 is specifically: Determining an optimal operation and maintenance method and an optimal data deduplication technology according to the sorting results of the first parameter information and the second parameter information; An optimal backup strategy is determined according to the optimal operation and maintenance method and the optimal data deduplication technology.

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