Data backup method and system, electronic equipment and storage medium

Abstract

The invention relates to the technical field of data backup, in particular to a data backup method and system, electronic equipment and a storage medium. The method comprises the following steps: firstly, collecting use and environment data of a data source, and analyzing through a preset deep learning model to obtain a real-time backup demand; then querying historical backup data, and optimizing and adjusting real-time requirements; and finally, executing backup according to the optimized strategy. The backup opportunity can be accurately mastered, self-optimization can be continuously carried out according to historical experience, and the backup accuracy and efficiency are remarkably improved.

Description

Technical Field

[0001] The present application relates to the technical field of data backup, and in particular to a data backup method, system, electronic device and storage medium. Background Art

[0002] With the rapid development of information technology and the explosive growth of data volume, data backup plays a vital role in modern enterprises and organizations. An effective data backup strategy can not only protect valuable information assets, but also ensure business continuity and prevent huge losses caused by data loss or damage. Therefore, the development of advanced data backup methods has become an important research topic in the current field of information technology.

[0003] At present, common data backup methods mainly include scheduled backup and incremental backup. Scheduled backup performs a complete backup of the entire data source at a preset time interval, while incremental backup only backs up the data that has changed since the last backup. These methods trigger backup operations through a predetermined backup plan or based on simple data change detection to ensure data security and recoverability.

[0004] Existing data backup methods often adopt fixed backup strategies, which cannot be flexibly adjusted according to the actual usage of the data source and environmental changes. This situation needs to be further improved. Summary of the invention

[0005] In order to solve the problem that existing data backup is not flexible enough, the present application provides a data backup method, system, electronic device and storage medium, which adopt the following technical solutions: In a first aspect, the present application provides a data backup method, comprising the following steps: Collect usage data and environment data of the data source, and obtain usage information of the data source based on the usage data and environment data; Analyze the usage information based on a preset deep learning model to determine the real-time backup requirements of the data source; Query the historical operation database of the backup system to obtain the historical backup data of the data source; Based on the historical backup data, the real-time backup requirement is adjusted to obtain an optimized backup strategy; The data source is backed up according to the optimized backup strategy.

[0006] By adopting the above technical solution, in large enterprise data centers, the traditional fixed-cycle backup strategy may lead to unnecessary frequent backups during business off-peak periods, and cannot respond in time when data changes dramatically, resulting in the risk of data loss; this application first collects the usage and environmental data of the data source, and analyzes the real-time backup requirements through a preset deep learning model; then queries the historical backup data and optimizes and adjusts the real-time requirements; finally, the backup is performed according to the optimized strategy; it can not only accurately grasp the backup timing, but also continuously optimize itself based on historical experience, significantly improving the accuracy and efficiency of the backup.

[0007] Optionally, the usage data includes data access frequency, data modification frequency and data size change, and the environmental data includes system load, network status, storage capacity and time information.

[0008] By adopting the above technical solution, traditional backup systems may only perform backups based on fixed time intervals, ignoring the actual usage of data and dynamic changes in the system environment, resulting in backups during high load periods that affect system performance, or failure to respond in a timely manner when data changes dramatically; this application collects usage data including data access frequency, modification frequency and size changes, as well as environmental data such as system load, network conditions, storage capacity and time information; through comprehensive analysis of multi-dimensional data, it is possible to more accurately judge backup needs and the best time for backup.

[0009] Optionally, analyzing the usage information based on a preset deep learning model to determine the real-time backup requirements of the data source specifically includes the following steps: Using a recurrent neural network to extract features from the usage information, and obtaining data change frequency deep features and data importance deep features of the data source; According to the data change frequency depth characteristics and the data importance depth characteristics, a real-time backup requirement of the data source is generated.

[0010] By adopting the above technical solution, in financial transaction systems, the importance and change frequency of data may change dramatically with market fluctuations. Traditional static analysis methods are difficult to accurately capture this dynamic feature, resulting in failure to back up non-critical data in a timely manner or excessive backup at critical moments. This application first uses a recurrent neural network to extract features from usage information to obtain deep features of data change frequency and data importance; then generates real-time backup requirements based on the deep features; and can automatically learn and adapt to complex data change patterns without the need to manually set fixed rules or thresholds.

[0011] Optionally, query the historical operation database of the backup system to obtain historical backup data of the data source, which specifically includes the following steps: Determine characteristic parameters of the current data source, and retrieve similar historical backup records in the historical operation database according to the characteristic parameters; Sort the retrieved historical backup records and generate a historical backup relevance list; Select relevant historical backup records from the historical backup relevance list, and extract the backup strategy of the relevant historical backup records, wherein the backup strategy includes backup frequency, backup mode, and resource usage; Perform statistical analysis on the backup strategy to generate historical backup data.

[0012] By adopting the above technical solution, in a large data center, different types of data sources may have similar backup demand patterns, but traditional methods often cannot effectively utilize these historical experiences, resulting in each backup decision needing to be recalculated, which not only wastes computing resources but also fails to benefit from past experience; this application first determines the characteristic parameters of the current data source and retrieves similar records in the historical database; then sorts the retrieval results to generate a relevance list; then selects relevant records and extracts key information including backup frequency, method and resource usage; finally, generates historical backup data through statistical analysis; establishes a dynamic learning mechanism that can continuously accumulate and utilize historical experience to achieve continuous optimization of backup strategies.

[0013] Optionally, based on the historical backup data, the real-time backup requirement is adjusted to obtain an optimized backup strategy, which specifically includes the following steps: Based on the historical backup data, obtaining a historical backup efficiency index and a historical backup strategy of the data source; Compare the current real-time backup requirements with historical backup efficiency indicators, and mark situations with similar comparison results as similar backup scenarios; Based on the historical backup strategy of the similar backup scenario, the backup parameters of the current data source are adjusted, including the backup frequency, backup mode and resource allocation; Based on the adjusted backup parameters, the real-time backup requirements corresponding to the similar backup scenarios are modified to obtain an optimized backup strategy.

[0014] By adopting the above technical solution, in the IT environment of a large enterprise, data sources of different departments may present similar backup demand patterns due to business cycles or data characteristics, but traditional methods are often unable to identify these patterns and effectively utilize them, resulting in backup strategies that are either too conservative, wasting resources, or too aggressive, affecting data security. This application first extracts efficiency indicators and strategies based on historical backup data; then compares current demand with historical indicators to identify similar backup scenarios; then adjusts current backup parameters based on historical strategies for similar scenarios; finally, modifies real-time backup demand based on the adjusted parameters to obtain an optimized backup strategy; establishes a dynamic, adaptive strategy optimization mechanism that can accurately capture the characteristics of different backup scenarios and make corresponding adjustments.

[0015] Optionally, the method further comprises the following steps: Build a data backup system architecture, which includes local real-time mirror backup, remote hot backup center and cold backup storage warehouse; Based on the data backup system architecture, a data distribution strategy is designed, and the data distribution strategy automatically allocates backup resources according to the importance level of the data; According to the data distribution strategy, the storage device, the network device and the backup software of the data source are associated to obtain an associated backup component; Based on the associated backup components, generating a corresponding estimated maintenance time and maintenance plan; According to the maintenance plan, periodic simulated recovery drills are performed to verify the connectivity of all backup paths and the timeliness of data restoration.

[0016] By adopting the above technical solution, since traditional data backup systems often lack comprehensive architecture design and verification mechanisms, it is difficult to ensure comprehensive protection and rapid recovery of data in the face of complex and changeable IT environments; this application first constructs a multi-level backup architecture that includes local real-time mirroring, off-site hot standby and cold standby storage; then designs an intelligent distribution strategy based on data importance; then associates storage, network and software components to form a complete backup link; then generates a maintenance plan based on the associated components; finally, verifies the effectiveness of the system through regular simulated recovery drills; not only provides a comprehensive backup architecture design, but also introduces automated resource allocation and regular verification mechanisms, which can significantly improve the comprehensiveness, reliability and recovery efficiency of data backup.

[0017] In a second aspect, the present application provides a data backup system, comprising: A data collection module, used to collect usage data and environment data of a data source, and obtain usage information of the data source based on the usage data and environment data; A real-time backup demand judgment module, used to analyze the usage information based on a preset deep learning model to judge the real-time backup demand of the data source; The historical backup data acquisition module is used to query the historical operation database of the backup system and obtain the historical backup data of the data source; A real-time backup requirement adjustment module, used to adjust the real-time backup requirement based on the historical backup data to obtain an optimized backup strategy; The data backup module is used to back up the data source according to the optimized backup strategy.

[0018] In a third aspect, the present application provides an electronic device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the steps of the above-mentioned data backup method when executing the computer program.

[0019] In a fourth aspect, the present application provides a computer-readable storage medium having a computer program stored thereon, wherein the computer program implements the steps of the above-mentioned data backup method when executed by a processor.

[0020] In summary, the present application includes at least one of the following beneficial technical effects: 1. This application first collects the usage and environment data of the data source, and analyzes the real-time backup requirements through the preset deep learning model; then queries the historical backup data and optimizes and adjusts the real-time requirements; finally, the backup is executed according to the optimized strategy; it can not only accurately grasp the backup timing, but also continuously optimize itself according to historical experience, significantly improving the accuracy and efficiency of the backup; 2. This application first uses a recurrent neural network to extract features from usage information to obtain deep features of data change frequency and data importance; then generates real-time backup requirements based on the deep features; it can automatically learn and adapt to complex data change patterns without manually setting fixed rules or thresholds; 3. This application first extracts efficiency indicators and strategies based on historical backup data; then compares current needs with historical indicators to identify similar backup scenarios; then adjusts current backup parameters based on historical strategies for similar scenarios; finally, modifies real-time backup needs based on the adjusted parameters to obtain optimized backup strategies; and establishes a dynamic and adaptive strategy optimization mechanism that can accurately capture the characteristics of different backup scenarios and make corresponding adjustments. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] Figure 1 It is a flowchart of a data backup method according to an embodiment of the present application; Figure 2 It is a flowchart of step S200 in a data backup method according to an embodiment of the present application; Figure 3 It is a flowchart of step S300 in a data backup method according to an embodiment of the present application; Figure 4 It is a flowchart of step S400 in a data backup method according to an embodiment of the present application; Figure 5 It is a flowchart of the associated backup components in a data backup method according to an embodiment of the present application; Figure 6 This is a module diagram of a data backup system according to an embodiment of the present application; Figure 7 It is a diagram of the internal structure of an electronic device according to an embodiment of the present application. DETAILED DESCRIPTION

[0022] The terms used in the following embodiments of the present application are only for the purpose of describing specific embodiments, and are not intended to be used as limitations to the present application. As used in the specification and appended claims of the present application, the singular expressions "one", "a kind of", "said", "above", "the" and "this" are intended to also include plural expressions, unless there is a clear indication to the contrary in the context. It should also be understood that the term "and / or" used in the present application refers to any or all possible combinations comprising one or more listed items.

[0023] In the following, the terms "first" and "second" are used for descriptive purposes only and are not to be understood as suggesting or implying relative importance or implicitly indicating the number of the indicated technical features. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more of the features, and in the description of the embodiments of the present application, unless otherwise specified, "plurality" means two or more.

[0024] The embodiments of the present application are further described in detail below in conjunction with the drawings in the specification.

[0025] In the first aspect, the present application provides a data backup method, referring to Figure 1 , including the following steps: S100: Collect usage data and environment data of a data source, and obtain usage information of the data source based on the usage data and environment data.

[0026] In this embodiment, the data backup method refers to an intelligent data protection strategy, which dynamically adjusts the backup strategy by analyzing the usage and environmental conditions of the data source in real time and combining historical backup experience.

[0027] Specifically, the method first collects usage data and environmental data of the data source, which reflect the current status of the data source and the operating environment in which it is located. Usage data includes data access frequency, data modification frequency and data size change, which can reflect the importance and change speed of data. Environmental data includes system load, network status, storage capacity and time information, which will affect the execution efficiency and feasibility of the backup operation.

[0028] S200: Analyze usage information based on a preset deep learning model to determine real-time backup requirements of a data source.

[0029] In this embodiment, the deep learning model refers to an advanced machine learning algorithm based on a neural network that can extract features from complex data and make intelligent decisions. The real-time backup requirement refers to specific requirements such as the backup frequency, backup scope, and backup priority required according to the current data source status.

[0030] Specifically, this method uses a pre-trained deep learning model to analyze the usage information of the data source. The model may be a multi-layer recurrent neural network that can capture the time series characteristics of the data usage pattern. The input of the model is the pre-processed usage information, and the output is the evaluation result of the real-time backup demand of the data source. For example, the model may determine that some data sets need more frequent backups based on the access frequency and modification frequency of the data, or predict the storage space that may be needed in the future based on the trend of data size changes.

[0031] S300: query the historical operation database of the backup system to obtain historical backup data of the data source.

[0032] In this embodiment, the historical operation database refers to a data warehouse storing past backup operation records, including detailed information of each backup.

[0033] Specifically, the system will retrieve similar backup records from the historical operation database based on the characteristics of the current data source, such as data type and scale, including information such as the backup frequency, backup method, and resource consumption of similar data sources in the past. For example, for a large database, the system will search for the backup history of databases of the same size in the past to understand key indicators such as the average backup time and required storage space.

[0034] S400. Based on the historical backup data, the real-time backup requirements are adjusted to obtain an optimized backup strategy.

[0035] In this embodiment, the optimized backup strategy refers to a backup solution that can maximize resource utilization efficiency while meeting data security requirements.

[0036] Specifically, the system compares and analyzes the real-time backup needs determined by the deep learning model with historical backup data. For example, if the model recommends incremental backups every hour, but historical data shows that this frequency may cause excessive system load, the optimized strategy may be adjusted to backups every two hours, and full backups are scheduled during periods of low system load.

[0037] S500. Back up the data source according to the optimized backup strategy.

[0038] Specifically, the system will automatically schedule and execute backup tasks based on the optimized backup strategy. For example, for an important business database, the system may perform a full backup at 3 a.m. every day and an incremental backup at every hour.

[0039] In one embodiment, referring to Figure 2 In step S200, the use information is analyzed based on the preset deep learning model to determine the real-time backup requirements of the data source, which specifically includes the following steps: S210. Use a recurrent neural network to extract features from usage information to obtain data change frequency deep features and data importance deep features of the data source.

[0040] In this embodiment, the recurrent neural network is suitable for processing sequence data and time series problems. The data change frequency deep features and data importance deep features of the data source refer to high-level abstract features extracted by the deep learning model, which can better represent the dynamic change law and importance of the data source.

[0041] Specifically, the system uses a pre-trained recurrent neural network model to process the usage information of the data source. For example, for an enterprise's customer management system database, RNN can extract periodic high-frequency modification patterns from the usage data of the past few months, such as the frequent updates of customer billing data during the financial settlement period at the end of each month. For the deep features of data importance, RNN evaluates the business importance of data by analyzing factors such as data access frequency, access user level, and data relevance. For example, it can identify high-value data such as core customer information or key transaction records.

[0042] S220. Generate real-time backup requirements for data sources based on the data change frequency depth characteristics and the data importance depth characteristics.

[0043] In this embodiment, the real-time backup requirement of the data source refers to the most suitable backup strategy suggestion dynamically generated by the system based on the current data status and usage mode.

[0044] Specifically, the system inputs the extracted data change frequency deep features and data importance deep features into a decision model. The decision model is a rule-based system, such as a random forest or support vector machine. The decision model takes these deep features into account and generates specific backup recommendations. For example, for data with high change frequency but low importance, the system may recommend a high-frequency incremental backup strategy; while for data with low change frequency but extremely high importance, the system may recommend a real-time synchronous backup strategy.

[0045] In one embodiment, referring to Figure 3 In step S300, querying the historical operation database of the backup system to obtain the historical backup data of the data source specifically includes the following steps: S310: Determine characteristic parameters of the current data source, and retrieve similar historical backup records in the historical operation database based on the characteristic parameters.

[0046] In this embodiment, characteristic parameters refer to a group of key indicators that can describe the characteristics of a data source, including but not limited to data source type, data volume, average access frequency, data change rate, etc.

[0047] Specifically, the system first analyzes the various indicators of the current data source and generates a feature vector. Then, the system uses this feature vector to perform a similarity search in the historical operation database to find the closest historical backup record in the feature space. In this way, the system can quickly locate past backup cases that are similar to the current data source.

[0048] S320: Sort the retrieved historical backup records to generate a historical backup relevance list.

[0049] In this embodiment, the historical backup relevance list refers to a set of historical backup records sorted according to the degree of similarity.

[0050] Specifically, the system will perform multi-dimensional similarity calculations on the retrieved historical backup records, including the matching degree of feature parameters, the proximity of time, and the score of backup effect. For example, for the matching degree of feature parameters, the system may use cosine similarity or Euclidean distance to measure the proximity of the current data source and the historical record in the feature space. For the proximity of time, the system may give higher weights to the most recent backup records because they may better reflect the current technical and environmental conditions.

[0051] S330: Select relevant historical backup records from the historical backup relevance list, and extract the backup strategy of the relevant historical backup records.

[0052] Among them, the backup strategy includes backup frequency, backup method and resource usage.

[0053] In this embodiment, the relevant historical backup records refer to the most valuable record subset selected from the historical backup relevance list. The backup strategy includes backup frequency, backup mode and resource usage.

[0054] Specifically, the system selects the top N records in the relevance list, or selects records whose relevance scores exceed a certain threshold. Detailed backup strategy information is extracted from the selected records. For example, for backup frequency, the system may find that historically similar data sources usually adopt a strategy of full backup once a day and incremental backup once an hour. For backup methods, the system may notice that snapshot technology is effective on specific types of data sources. For resource usage, the system may find that on average each backup in the historical records consumes 10% of CPU resources and 20% of network bandwidth.

[0055] S340: Perform statistical analysis on the backup strategy to generate historical backup data.

[0056] Specifically, the system will perform multi-dimensional statistical analysis on the extracted backup strategies. For example, for backup frequency, the system will calculate the average interval time between full backup and incremental backup and their distribution. For backup methods, the system will analyze the usage ratios of different backup technologies (such as full backup, differential backup, incremental backup, etc.) and their respective success rates. For resource usage, the system will calculate the average resource consumption level and its fluctuation range.

[0057] Furthermore, the system may also analyze the changing trends of these policies over time, such as whether the backup frequency increases as the amount of data grows.

[0058] In one embodiment, referring to Figure 4 In step S400, based on the historical backup data, the real-time backup requirements are adjusted to obtain an optimized backup strategy, which specifically includes the following steps: S410: Based on the historical backup data, obtain the historical backup efficiency index and historical backup strategy of the data source.

[0059] In this embodiment, the historical backup efficiency index refers to a series of quantitative indicators that reflect the performance and effect of past backup operations, including but not limited to backup completion time, resource occupancy rate, data compression ratio, backup success rate, etc. The historical backup strategy refers to the specific backup plan adopted in similar scenarios in the past, including detailed information such as backup frequency, backup method, and resource allocation.

[0060] Specifically, the system extracts this key information from historical backup data. For example, for a large enterprise's financial database, the system may find that its historical backup efficiency indicators show that the average full backup time for every 100GB of data is 30 minutes, the incremental backup time is 5 minutes, and the average CPU usage during the backup process is 40%. At the same time, the historical backup strategy may show that this type of database usually adopts a full backup once a week, four incremental backups per day, and allocates 50% of the available system resources during the backup window.

[0061] S420: Compare the current real-time backup demand and the historical backup efficiency index, and mark situations with similar comparison results as similar backup scenarios.

[0062] In this embodiment, the similar backup scenario refers to a situation where the current real-time backup demand and the historical backup efficiency index are highly similar in key features.

[0063] Specifically, the system will compare the real-time backup requirements of the current data source with the extracted historical backup efficiency indicators in multiple dimensions. For example, suppose there is a 500GB database that needs to be backed up, and the system estimates the backup time to be 150 minutes, while there is a 480GB database in the historical records, and its backup time is 145 minutes. The system will calculate the similarity between the two situations in terms of data volume and backup time. If the similarity exceeds the preset threshold (such as 90%), the system will mark the current situation as a similar backup scenario. This marking not only considers a single indicator, but also comprehensively considers multiple factors. For example, the system will also compare indicators such as data change rate and system load in current and historical situations. Only when multiple key indicators show a high degree of similarity will the system determine that this is a similar backup scenario.

[0064] S430: Based on historical backup strategies of similar backup scenarios, adjust backup parameters of the current data source, including backup frequency, backup mode, and resource allocation.

[0065] In this embodiment, adjusting the backup parameters of the current data source refers to optimizing and fine-tuning the current backup plan based on historical experience of similar backup scenarios.

[0066] Specifically, the system analyzes historical backup strategies in similar backup scenarios and compares them with current backup needs. For example, if historical strategies show that one full backup and four incremental backups per day are best in similar situations, and the current strategy only has one full backup and two incremental backups, the system recommends increasing the number of incremental backups. For backup methods, if historical data shows that for databases of similar size, the use of snapshot technology can significantly reduce backup time, the system recommends switching from traditional logical backups to snapshot backups. In terms of resource allocation, if historical data shows that backing up in the early morning hours when system load is lower can achieve better performance, the system recommends adjusting the backup time window.

[0067] S440: Based on the adjusted backup parameters, modify the real-time backup requirements corresponding to similar backup scenarios to obtain an optimized backup strategy.

[0068] In this embodiment, the optimized backup strategy refers to the final backup plan after adjustment and modification, and the optimized backup strategy generated by the system includes specific operation instructions.

[0069] In one embodiment, referring to Figure 5 , the method further comprises the following steps: S510. Build a data backup system architecture, which includes local real-time mirror backup, off-site hot backup center, and cold backup storage warehouse.

[0070] In this embodiment, the data backup system architecture is a multi-level, multi-dimensional backup system design, which aims to provide comprehensive data protection and rapid recovery capabilities. Local real-time mirror backup is used to handle rapid recovery in emergency situations, the remote hot backup center ensures business continuity when the local system fails completely, and the cold backup storage warehouse is used for long-term data archiving and compliance requirements.

[0071] Specifically, the system will design this three-tier backup architecture based on the specific needs and resource conditions of the enterprise. For example, for a large financial institution, local real-time mirror backup may use a high-speed SSD storage array, and achieve millisecond-level data synchronization through synchronous replication technology. The off-site hot backup center may be located in another data center 100 kilometers away, using asynchronous replication technology to maintain near real-time synchronization of data, and equipped with sufficient computing resources to quickly take over the business when the main system fails. The cold backup storage warehouse may use a large-capacity tape library or object storage system to regularly archive important data and may be located in a more distant geographical location. Through this multi-level architectural design, the system can provide corresponding solutions for different levels of disaster recovery.

[0072] S520. Based on the data backup system architecture, a data distribution strategy is designed. The data distribution strategy automatically allocates backup resources according to the importance level of the data.

[0073] In this embodiment, the data distribution strategy is an intelligent rule system for automatically allocating and scheduling backup resources according to the importance and urgency of the data.

[0074] Specifically, the system first classifies and rates all the enterprise's data. For example, data may be divided into four levels: critical business data, important configuration data, daily operational data, and archived data. For the highest level of critical business data, the system automatically allocates the maximum backup bandwidth, uses multiple backup technologies (such as real-time mirroring + regular snapshots), and ensures that the data exists locally, in the hot backup center, and in the cold backup warehouse. For lower-level daily operational data, the system only performs daily incremental backups locally and regularly transfers summary data to the cold backup warehouse. This resource allocation strategy based on importance not only optimizes the efficiency of backup resource use, but also ensures that the most critical data can be restored as quickly as possible in the event of a disaster.

[0075] S530: According to the data distribution strategy, the storage device, network device and backup software of the data source are associated to obtain an associated backup component.

[0076] In this embodiment, associating backup components refers to logically associating various hardware and software resources of the data source to form a complete backup solution.

[0077] Specifically, the system will intelligently combine various backup resources according to the data distribution strategy. For example, for an important database server, the system will associate its primary storage device with a high-performance backup storage array, specify an independent backup network interface, and configure specialized database backup software. These components are associated into a logical unit to form a complete backup solution. The system will assign a unique identifier to each such associated backup component and record its configuration information, performance parameters, and interdependencies. In this way, the system can better manage and monitor the entire backup process, quickly locate potential problems, and make flexible adjustments when necessary.

[0078] S540: Generate a corresponding estimated maintenance time and maintenance plan based on the associated backup components.

[0079] In this embodiment, the estimated maintenance time and maintenance plan are systematic maintenance plans formulated based on the characteristics and requirements of the associated backup components.

[0080] Specifically, the system analyzes the characteristics of each associated backup component, such as hardware life, software update cycle, data growth trend, etc., to estimate the required maintenance frequency and duration. For example, for a local mirror backup system using SSD, a comprehensive performance check and data consistency verification may be required every quarter, and it is estimated that each time will take 4 hours. For a tape library system, cleaning and calibration may be required once a month, and it is estimated that each time will take 2 hours. Based on these estimates, the system will generate a detailed maintenance schedule that specifies the maintenance window, specific operation steps, and required resources for each component.

[0081] S550. According to the maintenance plan, perform periodic simulated recovery drills to verify the connectivity of all backup paths and the timeliness of data restoration.

[0082] In this embodiment, simulated recovery drills are an important measure to actively verify the effectiveness of the backup system. By regularly performing these drills, potential problems can be discovered in a timely manner to ensure that the backup and recovery process can proceed smoothly when an actual disaster occurs.

[0083] Specifically, the system will automatically perform a series of simulated recovery operations regularly according to the maintenance plan. For example, a non-critical data set may be selected every month to simulate the scenario of recovery from a local mirror. A larger-scale drill may be conducted every quarter to simulate the process of restoring the entire business system from an off-site hot standby center. In these drills, the system will verify the connectivity of all backup paths, including checking network links, accessibility of storage devices, etc. At the same time, the system will also test the timeliness of data restoration and record the entire process time from the start of recovery to system availability.

[0084] It should be understood that the size of the serial numbers of the steps in the above embodiments does not mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present application.

[0085] In a second aspect, the present application provides a data backup system. The data backup system of the present application is described below in combination with the above-mentioned data backup method.

[0086] Reference Figure 6 , a data backup system, comprising: The data collection module is used to collect usage data and environmental data of the data source, and obtain usage information of the data source based on the usage data and environmental data.

[0087] Among them, usage data includes data access frequency, data modification frequency and data size change, and environmental data includes system load, network status, storage capacity and time information.

[0088] A real-time backup demand judgment module is used to analyze usage information based on a preset deep learning model to judge the real-time backup demand of the data source; The historical backup data acquisition module is used to query the historical operation database of the backup system and obtain the historical backup data of the data source; The real-time backup demand adjustment module is used to adjust the real-time backup demand based on the historical backup data to obtain an optimized backup strategy; The data backup module is used to back up the data source according to the optimized backup strategy.

[0089] In one embodiment, the real-time backup demand determination module includes: A feature extraction unit, used to extract features from usage information using a recurrent neural network, and obtain data change frequency deep features and data importance deep features of the data source; The demand generation unit is used to generate real-time backup requirements for data sources according to the data change frequency depth characteristics and data importance depth characteristics.

[0090] In one embodiment, the historical backup data acquisition module includes: A characteristic parameter determination unit, used to determine the characteristic parameters of the current data source; A historical record retrieval unit, used to retrieve similar historical backup records in the historical operation database according to characteristic parameters; A sorting unit, used to sort the retrieved historical backup records and generate a historical backup relevance list; A policy extraction unit, used to select relevant historical backup records from the historical backup relevance list and extract the backup policy of the relevant historical backup records, wherein the backup policy includes backup frequency, backup mode and resource usage; The statistical analysis unit is used to perform statistical analysis on the backup strategy and generate historical backup data.

[0091] In one embodiment, the real-time backup demand adjustment module includes: An efficiency index acquisition unit, used to acquire a historical backup efficiency index and a historical backup strategy of a data source based on historical backup data; A scenario matching unit is used to compare the current real-time backup requirements with the historical backup efficiency indicators, and mark the situations with similar comparison results as similar backup scenarios; A parameter adjustment unit, used to adjust the backup parameters of the current data source, including backup frequency, backup mode, and resource allocation, based on historical backup strategies of similar backup scenarios; The demand modification unit is used to modify the real-time backup demand corresponding to the similar backup scenario based on the adjusted backup parameters to obtain an optimized backup strategy.

[0092] In one embodiment, the system further comprises: The architecture building module is used to build a data backup system architecture, which includes local real-time mirror backup, remote hot backup center, and cold backup storage warehouse; The strategy design module is used to design data distribution strategies based on the data backup system architecture. The data distribution strategies automatically allocate backup resources according to the importance level of the data. A component association module is used to associate the storage device, network device and backup software of the data source according to the data distribution strategy to obtain the associated backup component; A maintenance plan generation module, used to generate corresponding estimated maintenance time and maintenance plan based on associated backup components; The drill execution module is used to perform periodic simulated recovery drills according to the maintenance plan to verify the connectivity of all backup paths and the timeliness of data restoration.

[0093] In one embodiment, the present application provides an electronic device, which may be a server, and its internal structure diagram may be as follows: Figure 7 As shown. The electronic device includes a processor, a memory and a network interface connected through a system bus. Among them, the processor of the electronic device is used to provide computing and control capabilities. The memory of the electronic device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program and a database. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The database of the electronic device is used to store data. The network interface of the electronic device is used to communicate with an external terminal through a network connection. When the computer program is executed by the processor, a data backup method is implemented.

[0094] Those skilled in the art will understand that Figure 7 The structure shown in the figure is merely a block diagram of a partial structure related to the scheme of the present application, and does not constitute a limitation on the electronic device to which the scheme of the present application is applied. The specific electronic device may include more or fewer components than shown in the figure, or combine certain components, or have a different arrangement of components.

[0095] In one embodiment, an electronic device is further provided, including a memory and a processor, wherein a computer program is stored in the memory, and the processor implements the steps in the above method embodiments when executing the computer program.

[0096] Those of ordinary skill in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by instructing the relevant hardware through a computer program, and the above-mentioned computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to memory, storage, database or other media used in the embodiments provided in this application can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), tape, floppy disk, flash memory or optical memory, etc. Volatile memory can include random access memory (RAM) or external cache memory. As an illustration and not limitation, RAM can be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM).

[0097] The above are all preferred embodiments of the present application, and the protection scope of the present application is not limited thereto. Therefore, any equivalent changes made according to the structure, shape, and principle of the present application should be included in the protection scope of the present application.

Claims

1. A data backup method, characterized in that: The steps include: Collect usage data and environment data of the data source, and obtain usage information of the data source based on the usage data and environment data; Analyze the usage information based on a preset deep learning model to determine the real-time backup requirements of the data source; Query the historical operation database of the backup system to obtain the historical backup data of the data source; Based on the historical backup data, the real-time backup requirement is adjusted to obtain an optimized backup strategy; The data source is backed up according to the optimized backup strategy.

2. The data backup method according to claim 1, characterized in that: The usage data includes data access frequency, data modification frequency and data size change, and the environmental data includes system load, network status, storage capacity and time information.

3. The data backup method according to claim 1, characterized in that: Analyzing the usage information based on a preset deep learning model to determine the real-time backup requirements of the data source specifically includes the following steps: Using a recurrent neural network to extract features from the usage information, and obtaining data change frequency deep features and data importance deep features of the data source; According to the data change frequency depth characteristics and the data importance depth characteristics, a real-time backup requirement of the data source is generated.

4. The data backup method according to claim 1, characterized in that: Query the historical operation database of the backup system to obtain the historical backup data of the data source, which specifically includes the following steps: Determine characteristic parameters of the current data source, and retrieve similar historical backup records in the historical operation database according to the characteristic parameters; Sort the retrieved historical backup records and generate a historical backup relevance list; Select relevant historical backup records from the historical backup relevance list, and extract the backup strategy of the relevant historical backup records, wherein the backup strategy includes backup frequency, backup mode, and resource usage; Perform statistical analysis on the backup strategy to generate historical backup data.

5. The data backup method according to claim 4, characterized in that: Based on the historical backup data, the real-time backup requirement is adjusted to obtain an optimized backup strategy, which specifically includes the following steps: Based on the historical backup data, obtaining a historical backup efficiency index and a historical backup strategy of the data source; Compare the current real-time backup requirements with historical backup efficiency indicators, and mark situations with similar comparison results as similar backup scenarios; Based on the historical backup strategy of the similar backup scenario, the backup parameters of the current data source are adjusted, including the backup frequency, backup mode and resource allocation; Based on the adjusted backup parameters, the real-time backup requirements corresponding to the similar backup scenarios are modified to obtain an optimized backup strategy.

6. The data backup method according to claim 1, characterized in that: The method further comprises the steps of: Build a data backup system architecture, which includes local real-time mirror backup, remote hot backup center and cold backup storage warehouse; Based on the data backup system architecture, a data distribution strategy is designed, and the data distribution strategy automatically allocates backup resources according to the importance level of the data; According to the data distribution strategy, the storage device, the network device and the backup software of the data source are associated to obtain an associated backup component; Based on the associated backup components, generating a corresponding estimated maintenance time and maintenance plan; According to the maintenance plan, periodic simulated recovery drills are performed to verify the connectivity of all backup paths and the timeliness of data restoration.

7. A data backup system, characterized in that: include: A data collection module, used to collect usage data and environment data of a data source, and obtain usage information of the data source based on the usage data and environment data; A real-time backup demand judgment module, used to analyze the usage information based on a preset deep learning model to judge the real-time backup demand of the data source; The historical backup data acquisition module is used to query the historical operation database of the backup system and obtain the historical backup data of the data source; A real-time backup requirement adjustment module, used to adjust the real-time backup requirement based on the historical backup data to obtain an optimized backup strategy; The data backup module is used to back up the data source according to the optimized backup strategy.

8. An electronic device, characterized in that: The method comprises a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the steps of the data backup method according to any one of claims 1 to 6 when executing the computer program.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the data backup method according to any one of claims 1 to 6 are implemented.

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