Big data backup method based on complex network environment

Abstract

The invention discloses a big data backup method based on a complex network environment, and particularly relates to the field of big data processing, and the method comprises the steps of S1, data backup feature information acquisition, S2, data backup information mining, S3, data standardization processing, S4, data backup effect analysis, S5, data backup effect judgment, and S6, judgment result screening. According to the method, the actual effect of the big data backup method can be accurately judged, so that a scientific decision basis is provided for the big data backup method, iterative optimization of the big data backup method is powerfully promoted, resource bottlenecks are found in time according to an evaluation result, and a backup strategy is specifically adjusted to improve the resource utilization rate; if potential risks exist in a data recovery link, a recovery mechanism can be rapidly improved to enhance the recovery capability, and the continuous optimization mechanism based on precise evaluation can significantly improve the overall efficiency of the big data backup method and ensure that the big data backup method always keeps efficient, stable and safe under the complex and changeable network environment and the mass data processing requirement.

Description

Technical Field

[0001] The present invention relates to the field of big data processing technology, and more specifically, to a big data backup method based on a complex network environment. Background Art

[0002] In the field of big data backup in complex network environments, traditional technologies have many defects. They are extremely unreasonable in network bandwidth utilization, unable to perceive and adapt to dynamic changes in bandwidth, often causing network congestion and low backup efficiency; resource consumption is huge and seriously affects system performance. Full backup not only occupies a large amount of storage resources, but also excessively occupies server computing and disk I / O resources during backup, interfering with normal business operations; data security measures are weak, encryption mechanisms are simple and easy to crack, and access control is rough and difficult to prevent abuse of authority and external attacks; backup and recovery strategies are rigid and lack flexibility, making it difficult to process according to data characteristics and business needs, and it takes a long time to locate and extract data during recovery. Therefore, an innovative big data backup method is proposed. This method has intelligent network perception and adaptive adjustment capabilities, dynamically optimizes backup strategies based on network parameters, and ensures stable and reliable backup. The existing technology uses bandwidth-free backup technology, and effectively responds to the instability of network bandwidth through asynchronous backup, data caching, data compression and deduplication. Asynchronous backup enables data to be transmitted in the background, making full use of idle network bandwidth. Data caching temporarily stores backup data when bandwidth is insufficient, and transmits it again when network conditions allow. Data compression and deduplication greatly reduce the amount of data transmitted. In this way, when network bandwidth fluctuates, backup tasks can continue without interruption or failure due to insufficient bandwidth, greatly improving the adaptability and reliability of backup, while reducing the additional network cost investment that may be incurred due to guaranteeing backup bandwidth. In terms of network communication, existing technologies use technology that does not require two-way network port opening, and use special network protocols such as HTTP / HTTPS or intranet penetration technology to achieve secure data transmission. Based on the HTTP / HTTPS protocol, the default open port can be used to avoid complex port configuration and security risks. The intranet penetration technology can forward intranet data to the external network target storage without the need for cumbersome port mapping on network devices, greatly simplifying the complexity of network configuration, reducing the security risks caused by port opening, and improving the feasibility and operation and maintenance efficiency of data backup solutions. It is especially suitable for scenarios with high network security requirements and complex network environments.

[0003] However, under the current technical landscape, there are still deficiencies in the existing big data backup technology system. Although there have been innovative breakthroughs in network bandwidth utilization and network port configuration, there is a lack of an effective strategy for multi-dimensional comprehensive evaluation of data backup methods. Due to the inherent limitations of their own technologies and methods, traditional big data backup effect monitoring methods can only obtain one-sided monitoring data, and it is difficult to achieve a comprehensive, accurate and in-depth evaluation of the big data backup effect. The lack of this evaluation capability has largely restricted the user's ability to make scientific judgments on the actual effects of big data backup methods, resulting in the user's inability to implement a more systematic and accurate comprehensive evaluation of the big data backup effect, which in turn has an adverse impact on the continuous optimization and iteration process of big data backup methods. Based on this, focusing on building a set of monitoring indicator systems that can accurately map the big data backup effect has extremely critical strategic significance and far-reaching value for comprehensively improving the overall effectiveness of big data backup methods and promoting the steady development of technologies in this field. Summary of the invention

[0004] In order to overcome the above-mentioned defects of the prior art, an embodiment of the present invention provides a big data backup method based on a complex network environment, and solves the problems raised in the above-mentioned background technology through the following scheme.

[0005] To achieve the above-mentioned purpose, the present invention provides the following technical solutions: a big data backup method based on a complex network environment, comprising: S1: acquisition of data backup feature information, S2: data backup information mining, S3: data standardization processing, S4: data backup effect analysis, S5: data backup effect judgment, S6: judgment result screening; The S1 data backup characteristic information acquisition: numbering the data blocks backed up by different data backup methods and recording them as 1, 2, 3...i...n in sequence, filtering the data backup characteristic text according to the data block number, and obtaining the data backup characteristic text data corresponding to the data block number; The S2 data backup information mining includes a data backup time information mining step, a data backup resource requirement information mining step, a data security assessment information mining step, and a backup strategy effectiveness information mining step, and is used to select key texts from the data backup feature text data to obtain a first key text; The S3 data standardization process is used to perform standardization processing and analysis on the first key text data to obtain the second key text; The S4 data backup effect analysis: importing the second key text data into the mathematical model of the backup time evaluation coefficient, the mathematical model of the resource demand evaluation coefficient, the mathematical model of the data security evaluation coefficient, the mathematical model of the backup strategy effectiveness coefficient and the mathematical model of the big data backup multi-factor fusion performance coefficient to obtain a text output value based on the second key text; The S5 data backup effect judgment: compare the backup time assessment coefficient, resource demand assessment coefficient, data security assessment coefficient, backup strategy effectiveness coefficient and big data backup multi-factor fusion performance coefficient with the backup time assessment coefficient preset value, resource demand assessment coefficient preset value, data security assessment coefficient preset value, backup strategy effectiveness coefficient preset value and big data backup multi-factor fusion performance coefficient preset value respectively, and screen out data blocks with big data backup multi-factor fusion performance coefficient values ​​lower than the preset values; S6: Output filtering information: used to import the corresponding information of the filtered data blocks into the user information terminal.

[0006] Preferably, the available bandwidth parameter refers to the available bandwidth B; the backup data volume parameter refers to the backup data volume D; the network delay parameter refers to the network delay L; the packet loss rate parameter refers to the packet loss rate P; the CPU usage parameter refers to the CPU usage C; the memory usage parameter refers to the memory usage M; the disk read and write speed parameter refers to the disk read and write speed R io ; The I / O operations per second parameter refers to the I / O operations per second I; the data update frequency parameter refers to the number of updates per hour F; the data growth rate parameter refers to the data growth rate G; the recovery time target parameter refers to the recovery time target T rto The recovery point target parameter refers to the recovery point target T rpo ; The key length parameter refers to the key length K; the access control list quantity parameter refers to the access control list quantity A.

[0007] Preferably, the data growth rate specifically refers to the amount of new data added per day, the number of access control lists specifically refers to the number of users, user groups or roles used to record access rights to the backup data, the recovery point objective specifically refers to the longest tolerable time interval for data loss, that is, when a disaster or failure occurs, the maximum time difference between the time point to which the data is restored and the time point when the failure occurs, and the recovery time objective specifically refers to the maximum time that the business system is allowed to be interrupted after a disaster or failure occurs.

[0008] Preferably, the standardization process specifically refers to Z-score standardization process, which converts the original data into a standardized Z-Score value by calculating the mean and standard deviation of the original data.

[0009] Preferably, the text output value includes a backup time assessment coefficient, a resource demand assessment coefficient, a data security assessment coefficient, a backup strategy effectiveness coefficient, and a big data backup multi-factor fusion performance coefficient.

[0010] Preferably, the mathematical model of the backup time evaluation coefficient is as follows: , where TB refers to the backup time evaluation coefficient, D refers to the backup data volume, B refers to the available bandwidth, P refers to the packet loss rate, L refers to the network delay, and R io Refers to the disk read and write speed; The mathematical model of the resource demand assessment coefficient is as follows: , where R refers to the resource demand assessment coefficient, C refers to the CPU usage, M refers to the memory usage, F refers to the number of updates per hour, and G refers to the data growth rate; The mathematical model of the data security assessment coefficient is as follows: ,in refers to the security assessment coefficient, K refers to the key length, and A refers to the number of access control lists; The mathematical model of the backup strategy effectiveness coefficient is as follows: ,in refers to the backup strategy effectiveness coefficient, TB refers to the backup time evaluation coefficient, T rto Refers to the recovery time objective, T rpo Refers to Recovery Point Objective; The mathematical model of the big data backup multi-factor fusion performance coefficient is as follows: ,in It refers to the multi-factor fusion performance coefficient of big data backup, TB refers to the backup time evaluation coefficient, and R refers to the resource demand evaluation coefficient. Refers to the safety assessment factor, Refers to the backup strategy effectiveness coefficient, , , , Refers to the weight coefficient.

[0011] Preferably, the weight coefficient , , , It is calculated by the entropy method, and the specific calculation steps of the entropy method are as follows: Data standardization: Since the measurement units of various indicators may be different, in order to eliminate the impact of the dimension, the collected data needs to be standardized. Commonly used standardization methods include Z-score standardization and range method standardization; Calculate the proportion of indicators: Calculate the proportion of the zth sample value under the jth indicator to the sum of all sample values ​​of the indicator. This step is the basis for the subsequent calculation of information entropy; Calculate information entropy: According to the definition and calculation formula of information entropy, calculate the information entropy ej of the jth indicator. The smaller the information entropy, the greater the degree of variation of the indicator and the greater the amount of information provided; Calculate information entropy redundancy: Information entropy redundancy dj is the complement of information entropy, that is, dj=1-ej. The greater the information entropy redundancy, the more information the indicator contains, and the greater the impact on the comprehensive evaluation; Calculate weight: Calculate the weight of each indicator in the comprehensive evaluation according to its information entropy redundancy. The weight coefficient is the ratio of the information entropy redundancy of each indicator to the sum of the information entropy redundancy of all indicators.

[0012] Preferably, the screening logic is as follows: preliminarily screen out text output values ​​whose big data backup multi-factor fusion performance coefficient values ​​are less than the big data backup multi-factor fusion performance coefficient preset values, and then compare the backup time assessment coefficient, resource requirement assessment coefficient, data security assessment coefficient and backup strategy effectiveness coefficient in the screened text output values ​​with the corresponding preset values, to help screen out the specific reasons that affect the big data backup multi-factor fusion performance coefficient value.

[0013] Preferably, the preset value is obtained based on the analysis of daily data backup practices, and is a critical threshold for evaluating the backup time, resource demand, data security, policy effectiveness, and the combined impact of these four aspects of the data block under the current backup method. When the big data backup multi-factor fusion performance coefficient value of the data block is lower than the big data backup multi-factor fusion performance coefficient preset value, it indicates that the backup method of the data block is not effective, and there may be a long backup time, large resource demand, poor data security, and poor policy effectiveness. By comparing the backup time evaluation coefficient, resource demand evaluation coefficient, data security evaluation coefficient, and backup policy effectiveness coefficient with the corresponding preset values, the specific reasons affecting the data backup method can be found.

[0014] Preferably, the data block corresponding information includes the data block number, the data block corresponding backup method number, the data block first key text and the text output value; It should be specifically explained in this embodiment that the user information terminal is an information output device that receives the data backup judgment result, including a smart phone, a tablet, a computer and other devices that can remotely realize remote communication; Technical effects and advantages of the present invention: 1. The present invention innovatively designs a multi-dimensional comprehensive evaluation model, which comprehensively covers key factors such as backup time, resource utilization, data integrity, security, and recovery capability. It breaks through the limitation of traditional technology that only focuses on a single or a few dimensions, and can achieve a comprehensive and accurate evaluation of the big data backup effect; 2. Based on the above-mentioned precise evaluation capability, the present invention can clearly and accurately grasp the actual effect of the big data backup method, thereby providing a scientific decision-making basis for it, effectively promoting the iterative optimization of the big data backup method, and timely discovering the resource bottleneck according to the evaluation results, and adjusting the backup strategy in a targeted manner to improve resource utilization; if it is found that there are potential risks in the data recovery link, the recovery mechanism can be quickly improved to enhance the recovery capability. This continuous optimization mechanism based on precise evaluation can significantly improve the overall performance of the big data backup method, ensuring that it always remains efficient, stable and secure under complex and changeable network environments and massive data processing requirements, and promoting the technological progress and application expansion of the entire big data backup field. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] Figure 1 It is a schematic diagram of the overall structure of the present invention. DETAILED DESCRIPTION

[0016] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0017] As attached Figure 1 A big data backup method based on a complex network environment is shown, including S1: data backup feature information acquisition, S2: data backup information mining, S3: data standardization processing, S4: data backup effect analysis, S5: data backup effect judgment, S6: judgment result screening; The S1 data backup characteristic information acquisition: numbering the data blocks backed up by different data backup methods and recording them as 1, 2, 3...i...n in sequence, filtering the data backup characteristic text according to the data block number, and obtaining the data backup characteristic text data corresponding to the data block number; The S2 data backup information mining includes a data backup time information mining step, a data backup resource requirement information mining step, a data security assessment information mining step, and a backup strategy effectiveness information mining step, and is used to select key texts from the data backup feature text data to obtain a first key text; It should be specifically explained in this embodiment that the first key text specifically refers to: available bandwidth parameter, backup data volume parameter, network delay parameter, packet loss rate parameter, CPU usage parameter, memory usage parameter, disk read and write speed parameter, I / O operation number parameter per second, data update frequency parameter, data growth rate parameter, recovery time target parameter, recovery point target parameter, key length parameter, access control list number parameter; The available bandwidth parameter refers to the available bandwidth B; The backup data volume parameter refers to the backup data volume D; The network delay parameter refers to the network delay L; The packet loss rate parameter refers to the packet loss rate P; The CPU usage parameter refers to the CPU usage C; The memory usage parameter refers to the memory usage M; The disk read / write speed parameter refers to the disk read / write speed R io ; The I / O operations per second parameter refers to the I / O operations per second I; The data update frequency parameter refers to the number of updates per hour F; The data growth rate parameter refers to the data growth rate G; The recovery time objective parameter refers to the recovery time objective T rto ; The recovery point objective parameter refers to the recovery point objective T rpo ; The key length parameter refers to the key length K; The access control list quantity parameter refers to the access control list quantity A; It should be specifically explained in this embodiment that the data growth rate specifically refers to the amount of new data added every day, the number of access control lists specifically refers to the number of users, user groups or roles that are used to record access rights to the backup data, the recovery point objective specifically refers to the longest time interval that can be tolerated for data loss, that is, the maximum time difference between the time point to which the data is restored and the time point when the failure occurs when a disaster or failure occurs, and the recovery time objective specifically refers to the maximum time that the business system is allowed to be interrupted after the disaster or failure occurs; The S3 data standardization process is used to perform standardization processing and analysis on the first key text data to obtain the second key text; It should be specifically explained in this embodiment that the standardization process specifically refers to Z-score standardization process, which converts the original data into a standardized Z-Score value by calculating the mean and standard deviation of the original data.

[0018] The S4 data backup effect analysis: importing the second key text data into the mathematical model of the backup time evaluation coefficient, the mathematical model of the resource demand evaluation coefficient, the mathematical model of the data security evaluation coefficient, the mathematical model of the backup strategy effectiveness coefficient and the mathematical model of the big data backup multi-factor fusion performance coefficient to obtain a text output value based on the second key text; The text output value includes the backup time assessment coefficient, resource demand assessment coefficient, data security assessment coefficient, backup strategy effectiveness coefficient and big data backup multi-factor fusion performance coefficient; The mathematical model of the backup time evaluation coefficient is as follows: , where TB refers to the backup time evaluation coefficient, D refers to the backup data volume, B refers to the available bandwidth, P refers to the packet loss rate, L refers to the network delay, and R io Refers to the disk read and write speed; The mathematical model of the resource demand assessment coefficient is as follows: ,where R refers to the resource demand assessment coefficient, C refers to the CPU usage, M refers to the memory usage, F refers to the number of updates per hour, and G refers to the data growth rate; The mathematical model of the data security assessment coefficient is as follows: ,in refers to the security assessment coefficient, K refers to the key length, and A refers to the number of access control lists; The mathematical model of the backup strategy effectiveness coefficient is as follows: ,in refers to the backup strategy effectiveness coefficient, TB refers to the backup time evaluation coefficient, T rto Refers to the recovery time objective, T rpo Refers to Recovery Point Objective; The mathematical model of the big data backup multi-factor fusion performance coefficient is as follows: ,in It refers to the multi-factor fusion performance coefficient of big data backup, TB refers to the backup time evaluation coefficient, and R refers to the resource demand evaluation coefficient. Refers to the safety assessment factor, Refers to the backup strategy effectiveness coefficient, , , , Refers to the weight coefficient; It should be specifically explained in this embodiment that the weight coefficient , , , It is calculated by the entropy method, and the specific calculation steps of the entropy method are as follows: Data standardization: Since the measurement units of various indicators may be different, in order to eliminate the impact of the dimension, the collected data needs to be standardized. Commonly used standardization methods include Z-score standardization and range method standardization; Calculate the proportion of indicators: Calculate the proportion of the zth sample value under the jth indicator to the sum of all sample values ​​of the indicator. This step is the basis for the subsequent calculation of information entropy; Calculate information entropy: According to the definition and calculation formula of information entropy, calculate the information entropy ej of the jth indicator. The smaller the information entropy, the greater the degree of variation of the indicator and the greater the amount of information provided; Calculate information entropy redundancy: Information entropy redundancy dj is the complement of information entropy, that is, dj=1-ej. The greater the information entropy redundancy, the more information the indicator contains, and the greater the impact on the comprehensive evaluation; Calculate weight: Calculate the weight of each indicator in the comprehensive evaluation according to its information entropy redundancy. The weight coefficient is the ratio of the information entropy redundancy of each indicator to the sum of the information entropy redundancy of all indicators.

[0019] The S5 data backup effect judgment: compare the backup time assessment coefficient, resource demand assessment coefficient, data security assessment coefficient, backup strategy effectiveness coefficient and big data backup multi-factor fusion performance coefficient with the backup time assessment coefficient preset value, resource demand assessment coefficient preset value, data security assessment coefficient preset value, backup strategy effectiveness coefficient preset value and big data backup multi-factor fusion performance coefficient preset value respectively, and screen out data blocks with big data backup multi-factor fusion performance coefficient values ​​lower than the preset values; What needs to be specifically explained in this embodiment is that the screening logic is as follows: preliminarily screen out text output values ​​whose big data backup multi-factor fusion performance coefficient value is less than the big data backup multi-factor fusion performance coefficient preset value, and then compare the backup time evaluation coefficient, resource demand evaluation coefficient, data security evaluation coefficient and backup strategy effectiveness coefficient in the screened text output value with the corresponding preset values, to help screen out the specific reasons that affect the big data backup multi-factor fusion performance coefficient value; What needs to be specifically explained in this embodiment is that the preset value is obtained based on the analysis of daily data backup practices, and is a critical threshold for evaluating the backup time, resource demand, data security, policy effectiveness, and the comprehensive impact of these four aspects of the data block under the current backup method. When the big data backup multi-factor fusion performance coefficient value of the data block is lower than the big data backup multi-factor fusion performance coefficient preset value, it indicates that the backup method of the data block is not effective, and there may be a long backup time, large resource demand, poor data security, and poor policy effectiveness. By comparing the backup time evaluation coefficient, resource demand evaluation coefficient, data security evaluation coefficient, and backup policy effectiveness coefficient with the corresponding preset values, the specific reasons affecting the data backup method can be found.

[0020] S6: Output filtering information: used to import the corresponding information of the filtered data blocks into the user information terminal.

[0021] It should be specifically explained in this embodiment that the data block corresponding information includes the data block number, the backup method number corresponding to the data block, the first key text of the data block and the text output value; It should be specifically explained in this embodiment that the user information terminal is an information output device that receives the data backup judgment result, including a smart phone, a tablet, a computer and other devices that can remotely realize remote communication; Secondly: In the drawings of the embodiments disclosed in the present invention, only the structures related to the embodiments disclosed in the present invention are involved, and other structures can refer to the general design. In the absence of conflict, the same embodiment and different embodiments of the present invention can be combined with each other; Finally: The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the protection scope of the present invention.

Claims

1. A big data backup method based on a complex network environment, characterized in that: include: S1: Acquisition of data backup feature information, S2: Data backup information mining, S3: Data standardization processing, S4: Data backup effect analysis, S5: Data backup effect judgment, S6: Judgment result screening; The S1 data backup characteristic information acquisition: numbering the data blocks backed up by different data backup methods and recording them as 1, 2, 3...i...n in sequence, filtering the data backup characteristic text according to the data block number, and obtaining the data backup characteristic text data corresponding to the data block number; The S2 data backup information mining includes a data backup time information mining step, a data backup resource requirement information mining step, a data security assessment information mining step, and a backup strategy effectiveness information mining step, and is used to select key texts from the data backup feature text data to obtain a first key text; The S3 data standardization process is used to perform standardization processing and analysis on the first key text data to obtain the second key text; The S4 data backup effect analysis: importing the second key text data into the mathematical model of the backup time evaluation coefficient, the mathematical model of the resource demand evaluation coefficient, the mathematical model of the data security evaluation coefficient, the mathematical model of the backup strategy effectiveness coefficient and the mathematical model of the big data backup multi-factor fusion performance coefficient to obtain a text output value based on the second key text; The S5 data backup effect judgment: compare the backup time assessment coefficient, resource demand assessment coefficient, data security assessment coefficient, backup strategy effectiveness coefficient and big data backup multi-factor fusion performance coefficient with the backup time assessment coefficient preset value, resource demand assessment coefficient preset value, data security assessment coefficient preset value, backup strategy effectiveness coefficient preset value and big data backup multi-factor fusion performance coefficient preset value respectively, and screen out data blocks with big data backup multi-factor fusion performance coefficient values ​​lower than the preset values; S6: Output filtering information: used to import the corresponding information of the filtered data blocks into the user information terminal.

2. The method for backing up large data in a complex network environment according to claim 1, characterized in that: The first key text specifically refers to: available bandwidth parameter, backup data volume parameter, network delay parameter, packet loss rate parameter, CPU usage parameter, memory usage parameter, disk read and write speed parameter, I / O operations per second parameter, data update frequency parameter, data growth rate parameter, recovery time objective parameter, recovery point objective parameter, key length parameter, access control list quantity parameter; The available bandwidth parameter refers to the available bandwidth B; the backup data volume parameter refers to the backup data volume D; the network delay parameter refers to the network delay L; the packet loss rate parameter refers to the packet loss rate P; the CPU usage rate parameter refers to the CPU usage rate C; the memory usage rate parameter refers to the memory usage rate M; The disk read / write speed parameter refers to the disk read / write speed R io ; The I / O operations per second parameter refers to the I / O operations per second I; the data update frequency parameter refers to the number of updates per hour F; the data growth rate parameter refers to the data growth rate G; the recovery time target parameter refers to the recovery time target T rto The recovery point target parameter refers to the recovery point target T rpo ; The key length parameter refers to the key length K; the access control list quantity parameter refers to the access control list quantity A.

3. The method for backing up large data in a complex network environment according to claim 1, characterized in that: The mathematical model of the backup time evaluation coefficient is as follows: , where TB refers to the backup time evaluation coefficient, D refers to the backup data volume, B refers to the available bandwidth, P refers to the packet loss rate, L refers to the network delay, and R io Refers to the disk read and write speed.

4. The method for backing up large data in a complex network environment according to claim 1, characterized in that: The mathematical model of the resource demand assessment coefficient is as follows: , where R refers to the resource demand assessment coefficient, C refers to the CPU usage, M refers to the memory usage, F refers to the number of updates per hour, and G refers to the data growth rate.

5. The method for backing up large data in a complex network environment according to claim 1, characterized in that: The mathematical model of the data security assessment coefficient is as follows: ,in refers to the security assessment coefficient, K refers to the key length, and A refers to the number of access control lists.

6. The method for backing up large data in a complex network environment according to claim 1, characterized in that: The mathematical model of the backup strategy effectiveness coefficient is as follows: , where E refers to the backup strategy effectiveness coefficient, TB refers to the backup time evaluation coefficient, and T rto Refers to the recovery time objective, T rpo Refers to Recovery Point Objective.

7. The method for backing up large data in a complex network environment according to claim 1, characterized in that: The mathematical model of the big data backup multi-factor fusion performance coefficient is as follows: ,in It refers to the multi-factor fusion performance coefficient of big data backup, TB refers to the backup time evaluation coefficient, and R refers to the resource demand evaluation coefficient. Refers to the safety assessment factor, Refers to the backup strategy effectiveness coefficient, , , , Refers to the weight coefficient.

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