Disaster recovery management method and system for database

Abstract

The invention discloses a disaster recovery management method and system for a database, and relates to the technical field of data processing, and the method comprises the following steps: obtaining database storage information, carrying out association analysis on storage data attributes according to the database storage information, and obtaining data attribute classification information. Correlation among different data is accurately analyzed through the data attribute topological relation graph, and the data core index corresponding to the intermediate node is obtained based on the initial node and the last node in the data attribute topological relation to which the intermediate node belongs by taking the intermediate node as a reference. Accurate evaluation of importance degrees of different attribute data is realized, timeliness and reliability of data disaster recovery are ensured, data disaster recovery is performed through to-be-stored data information, data attribute classification information and data disaster recovery center information, a proper data disaster recovery mode is selected through a data core index corresponding to the to-be-stored data, and data disaster recovery efficiency is improved. And the stability of data backup is ensured.

Description

Technical Field

[0001] The present invention relates to the technical field of data processing, and in particular to a disaster recovery management method and system for a database. Background Art

[0002] With the widespread adoption of IT technology, more and more companies are choosing to store their data on storage systems. The reliability of storage system data is crucial to any business. Frequent natural disasters (floods, fires, earthquakes, etc.) and man-made disasters (accidental deletion, misoperation, etc.) can significantly impact data security. In some cases, these can lead to business interruptions, requiring days to recover and damaging the company's reputation. In other cases, they can lead to the complete loss of data and disrupt operations. For example, system downtime at power companies can cause widespread power outages, causing inconvenience to residents and interrupting production, resulting in significant losses. Therefore, disaster recovery is essential for critical data. This involves backing up critical data to create backup files and saving them to local or remote data systems. In the event of data loss, data can be promptly restored by accessing the pre-stored backup files in the data system.

[0003] Current disaster recovery management methods for databases still have problems such as being unable to accurately assess the importance of stored data based on its attributes, and being unable to select appropriate backup methods based on data analysis results. Data is often directly divided into core data and non-core data based on its attributes. However, even if the data attributes are the same, the core data in different scenarios are completely different. Therefore, it cannot be applied to data storage in multiple scenarios. In addition, data backup is often performed at a fixed cycle or in a fixed manner, and cannot be dynamically adjusted according to the data to be backed up. Summary of the Invention

[0004] In order to solve the above technical problems, a database disaster recovery management method and system are provided. This technical solution solves the problems raised in the above background technology, such as the inability to accurately evaluate the importance of stored data based on the attributes of the stored data, and the inability to select a suitable backup method based on the data analysis results. The data is often directly divided into core data and non-core data based on the data attributes. However, even if the data attributes are the same, the core data in different scenarios are completely different. Therefore, it cannot be applied to data storage in multiple scenarios, and the data is often backed up at a fixed period or in a fixed manner, and cannot be dynamically adjusted according to the data to be backed up.

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

[0006] A database disaster recovery management method, comprising:

[0007] Acquiring database storage information, wherein the database storage information includes stored data attribute information;

[0008] According to the information stored in the database, the stored data attributes are analyzed for association and the data attribute classification information is obtained;

[0009] Obtaining data disaster recovery center information, including local disaster recovery centers and remote disaster recovery centers;

[0010] Monitor the data storage in the database to obtain information about the data to be stored, including attribute information of the data to be stored;

[0011] Data disaster recovery is performed based on the data information to be stored, data attribute classification information and data disaster recovery center information.

[0012] Preferably, performing association analysis on stored data attributes based on database storage information to obtain data attribute classification information specifically includes:

[0013] According to the stored data attribute information, based on data topology analysis, a data attribute topology relationship diagram is obtained, wherein each topology node in the data attribute topology relationship diagram corresponds to a data attribute;

[0014] Based on the data attribute topological relationship graph, taking any topological node as the benchmark, obtain the topological nodes adjacent to the node, take the topological node pointing to the node as the upper topological node of the node, and take the topological node pointed to by the node as the lower topological node of the node;

[0015] If a topological node does not have an upper-level topological node, the node is used as the initial node. If a topological node does not have a lower-level topological node, the node is used as the last node. If a topological node has both an upper-level topological node and a lower-level topological node, the node is used as the intermediate node.

[0016] Taking the intermediate node as the benchmark, based on the initial node and the last node in the data attribute topology relationship to which the intermediate node belongs, obtain the data core index corresponding to the intermediate node;

[0017] Based on data disaster recovery requirements, obtain the data core index threshold;

[0018] According to the data core index and the data core index threshold, determine whether the data attribute corresponding to the intermediate node is a core data attribute. If the data core index exceeds the data core index threshold, the data attribute corresponding to the intermediate node is a core data attribute. If the data core index does not exceed the data core index threshold, the data attribute corresponding to the intermediate node is a non-core data attribute.

[0019] The data attributes corresponding to the initial node are regarded as core data attributes, and the data attributes corresponding to the last node are regarded as non-core data attributes;

[0020] Based on core data attributes and non-core data attributes, the stored data attributes are classified to obtain data attribute classification information.

[0021] Preferably, taking the intermediate node as a benchmark and based on the initial node and the last node in the data attribute topology relationship to which the intermediate node belongs, obtaining the data core index corresponding to the intermediate node specifically includes:

[0022] According to the intermediate node, obtain the upper topology node and the lower topology node corresponding to the intermediate node;

[0023] Get the initial node and the last node in the data attribute topology relationship of the intermediate node;

[0024] Taking the initial node and the last node in the data attribute topological relationship of the intermediate node as the benchmark, according to the number of topological nodes between the intermediate node and any initial node or last node, obtain the number of topological layers corresponding to the intermediate node and each initial node or last node;

[0025] Get the maximum and minimum topological layers of the intermediate nodes and the initial node;

[0026] According to the maximum and minimum topological layers of the intermediate node and the initial node, the core indicator corresponding to the intermediate node is obtained;

[0027] Get the maximum and minimum topological layers of the intermediate nodes and the last node;

[0028] According to the maximum and minimum topological layers of the intermediate node and the last node, the non-core index corresponding to the intermediate node is obtained;

[0029] Obtain the data core index based on the upper-level topological node, lower-level topological node, core indicators, and non-core indicators corresponding to the intermediate node;

[0030] The calculation formula of the data core index is:

[0031]

[0032] Where Q is the data core index, A1 represents the number of upper-level topological nodes corresponding to the intermediate node, and A2 represents the number of lower-level topological nodes corresponding to the intermediate node. Indicates the core indicator corresponding to the intermediate node, d min (a1) represents the minimum topological layer number between the intermediate node and the initial node, d max (a1) represents the maximum number of topological layers between the intermediate nodes and the initial node, Indicates the non-core index corresponding to the intermediate node, d min (a2) represents the minimum topological layer number between the middle node and the last node, d max (a2) represents the maximum topological layer number between the middle node and the last node, β is the difference adjustment coefficient, and β = 0.6.

[0033] Preferably, obtaining the data core index threshold based on data disaster recovery requirements specifically includes:

[0034] Get the initial node information and the last node information;

[0035] Based on the initial node information, the adjacent intermediate node pointed to by the initial node is used as the first feature node;

[0036] According to the last node information, the topological layer number between each first characteristic node and the last node in the topological relationship to which it belongs is obtained;

[0037] The last node with the smallest number of topological layers is used as the last characteristic node corresponding to each first characteristic node;

[0038] The intermediate nodes adjacent to and pointing to the last feature node are used as the secondary feature nodes corresponding to the last feature node;

[0039] According to the secondary feature node, a secondary feature node that has a topological relationship with the first feature node is used as a second feature node;

[0040] Obtain the last data core index according to the data core index corresponding to the second feature node and the secondary feature node;

[0041] Based on the last feature node, the data core index corresponding to the first feature node is compared with the last data core index, and the larger value is used as the data core index of the initial node corresponding to the first feature node;

[0042] Based on the initial node information, the mean of the data core index of the initial node is used as the data core index threshold;

[0043] The core index of the last data is specifically:

[0044]

[0045] Where E is the last data core index, Q(i) represents the data core index corresponding to the i-th second feature node, Q(j) represents the data core index corresponding to the j-th secondary feature node, n is the total number of second feature nodes, and m is the total number of secondary feature nodes.

[0046] Preferably, performing data disaster recovery based on the data information to be stored, the data attribute classification information and the data disaster recovery center information specifically includes:

[0047] Based on the data disaster recovery center information, obtain the information of the disaster recovery center in the same city and the disaster recovery center in different locations;

[0048] According to the data information to be stored, obtaining attribute information of the data to be stored;

[0049] Based on the data attribute classification information and the attribute information of the data to be stored, the data to be stored is divided to obtain the core data to be stored and the non-core data to be stored;

[0050] Back up the data to be stored based on the core data to be stored, the non-core data to be stored, and the data disaster recovery center information;

[0051] Among them, for the core data to be stored, while the database stores the core data to be stored, it backs up the core data to be stored based on the information of the local disaster recovery center, and verifies the backed-up data to be stored;

[0052] If the verification is successful, the database will complete the storage of the core data to be stored and proceed to the next data storage. If the verification is unsuccessful, the database will resend the core data to be stored to the local disaster recovery center;

[0053] For non-core data to be stored, after the database completes storing the non-core data to be stored, it continues to store the next copy of data. The disaster recovery center in the same city backs up the non-core data to be stored based on asynchronous replication.

[0054] Preferably, backing up the data to be stored based on the core data to be stored, the non-core data to be stored and the data disaster recovery center information further includes:

[0055] For the core data to be stored, after the database completes the storage of the core data to be stored, it continues to store the next copy of the data, and the remote disaster recovery center information backs up the core data to be stored based on asynchronous replication;

[0056] For non-core data to be stored, obtain data disaster recovery cycle information based on data disaster recovery requirements;

[0057] Obtain historical data disaster recovery information based on the information from the remote disaster recovery center;

[0058] Based on data attribute classification information and historical data disaster recovery information, obtain historical non-core data within each data disaster recovery cycle;

[0059] The average value of the data core index of historical non-core data is used as the data disaster recovery feature value;

[0060] According to the data information to be stored, obtain the non-core data to be stored within a data disaster recovery cycle;

[0061] The average value of the data core index corresponding to the non-core data to be stored within a data disaster recovery cycle is used as the data disaster recovery value;

[0062] If the data disaster recovery value does not exceed the data disaster recovery characteristic value, incremental backup is performed on the non-core data to be stored within the data disaster recovery cycle;

[0063] If the data disaster recovery value exceeds the data disaster recovery characteristic value, a full backup of the non-core data to be stored within the data disaster recovery cycle will be performed.

[0064] Furthermore, a database disaster recovery management system is proposed to implement the above management method, including:

[0065] A main control module, the main control module is used to determine whether the data attribute corresponding to the intermediate node is a core data attribute based on the data core index and the data core index threshold, divide the data to be stored based on the data attribute classification information and the attribute information of the data to be stored, obtain the core data to be stored and the non-core data to be stored, classify the stored data attributes based on the core data attributes and the non-core data attributes, obtain the data attribute classification information, and back up the data to be stored based on the core data to be stored, the non-core data to be stored and the data disaster recovery center information;

[0066] An information acquisition module is used to obtain database storage information, stored data attribute information, data disaster recovery center information, local disaster recovery center information, and remote disaster recovery center information. Based on the stored data attribute information and data topology analysis, a data attribute topology relationship diagram is obtained. The module monitors the data storage in the database, obtains the data information to be stored, the attribute information of the data to be stored, and obtains historical data disaster recovery information based on the remote disaster recovery center information.

[0067] An evaluation module, wherein the evaluation module is used to obtain the non-core index corresponding to the intermediate node based on the maximum and minimum topological layers of the intermediate node and the last node, obtain the data core index based on the upper topological node, lower topological node, core index and non-core index corresponding to the intermediate node, obtain the last data core index based on the data core index corresponding to the second feature node and the secondary feature node, compare the data core index corresponding to the first feature node with the last data core index based on the feature last node, and use the larger value as the data core index of the initial node corresponding to the first feature node; based on the initial node information, use the average of the data core indexes of the initial nodes as the data core index threshold;

[0068] The display module interacts with the main control module and is used to output and display a data attribute topology diagram, data attribute classification information, data attribute information to be stored, and data disaster recovery center information.

[0069] Optionally, the main control module specifically includes:

[0070] A control unit, the control unit being configured to classify the attributes of the stored data based on the core data attributes and the non-core data attributes, obtain data attribute classification information, and back up the data to be stored based on the core data to be stored, the non-core data to be stored, and the data disaster recovery center information;

[0071] An information receiving unit, which interacts with the information acquisition module and the evaluation module to receive data and transmit it to the judgment unit;

[0072] A judgment unit is used to judge whether the data attribute corresponding to the intermediate node is a core data attribute based on the data core index and the data core index threshold, and to divide the data to be stored based on the data attribute classification information and the attribute information of the data to be stored, so as to obtain the core data to be stored and the non-core data to be stored.

[0073] Optionally, the information acquisition module specifically includes:

[0074] A first acquisition unit is configured to acquire database storage information, stored data attribute information, data disaster recovery center information, local disaster recovery centers, and remote disaster recovery centers, and acquire a data attribute topology relationship diagram based on the stored data attribute information and data topology analysis;

[0075] The second acquisition unit is used to monitor the data storage in the database, obtain the data information to be stored, the attribute information of the data to be stored, and obtain the historical disaster recovery information of the data based on the information of the remote disaster recovery center.

[0076] Optionally, the evaluation module specifically includes:

[0077] A first evaluation unit is configured to obtain a non-core indicator corresponding to the intermediate node based on the maximum and minimum topological layers of the intermediate node and the last node, and obtain a data core index based on the upper and lower topological nodes, the core indicator, and the non-core indicator corresponding to the intermediate node;

[0078] The second evaluation unit is used to obtain the last data core index based on the data core index corresponding to the second feature node and the secondary feature node, compare the data core index corresponding to the first feature node with the last data core index based on the feature last node, and take the larger value as the data core index of the initial node corresponding to the first feature node; based on the initial node information, take the average of the data core indexes of the initial nodes as the data core index threshold.

[0079] Compared with the prior art, the present invention has the following beneficial effects:

[0080] The present invention proposes a disaster recovery management method and system for a database, which accurately analyzes the relationship between different data through a data attribute topological relationship diagram, takes the intermediate node as a benchmark, and obtains the data core index corresponding to the intermediate node based on the initial node and the last node in the data attribute topological relationship to which the intermediate node belongs, thereby accurately evaluating the importance of data with different attributes, ensuring the timeliness and reliability of data disaster recovery, performing data disaster recovery through the information of data to be stored, data attribute classification information and data disaster recovery center information, and selecting an appropriate data disaster recovery method through the data core index corresponding to the data to be stored, thereby ensuring the stability of data backup. BRIEF DESCRIPTION OF THE DRAWINGS

[0081] Figure 1 This is a flow chart of a database disaster recovery management method proposed by the present invention;

[0082] Figure 2 This is a flow chart for obtaining data attribute classification information in the present invention;

[0083] Figure 3 This is a flowchart for obtaining the data core index in the present invention;

[0084] Figure 4 This is a flowchart for obtaining the data core index threshold in the present invention;

[0085] Figure 5 This is a structural block diagram of a database disaster recovery management system proposed by the present invention. DETAILED DESCRIPTION

[0086] The following description is intended to disclose the present invention so that those skilled in the art can implement the present invention. The preferred embodiments described below are merely examples, and those skilled in the art may conceive of other obvious variations.

[0087] Reference Figure 1 - Figure 4 As shown, a database disaster recovery management method in an embodiment of the present invention includes:

[0088] Acquiring database storage information, wherein the database storage information includes stored data attribute information;

[0089] According to the information stored in the database, the stored data attributes are analyzed for association and the data attribute classification information is obtained;

[0090] Specifically, based on the database storage information, the stored data attributes are analyzed for association to obtain data attribute classification information, including:

[0091] According to the stored data attribute information, based on data topology analysis, a data attribute topology relationship diagram is obtained, wherein each topology node in the data attribute topology relationship diagram corresponds to a data attribute;

[0092] Based on the data attribute topological relationship graph, taking any topological node as the benchmark, obtain the topological nodes adjacent to the node, take the topological node pointing to the node as the upper topological node of the node, and take the topological node pointed to by the node as the lower topological node of the node;

[0093] If a topological node does not have an upper-level topological node, the node is used as the initial node. If a topological node does not have a lower-level topological node, the node is used as the last node. If a topological node has both an upper-level topological node and a lower-level topological node, the node is used as the intermediate node.

[0094] Taking the intermediate node as the benchmark, based on the initial node and the last node in the data attribute topology relationship to which the intermediate node belongs, obtain the data core index corresponding to the intermediate node;

[0095] Based on data disaster recovery requirements, obtain the data core index threshold;

[0096] According to the data core index and the data core index threshold, determine whether the data attribute corresponding to the intermediate node is a core data attribute. If the data core index exceeds the data core index threshold, the data attribute corresponding to the intermediate node is a core data attribute. If the data core index does not exceed the data core index threshold, the data attribute corresponding to the intermediate node is a non-core data attribute.

[0097] The data attributes corresponding to the initial node are regarded as core data attributes, and the data attributes corresponding to the last node are regarded as non-core data attributes;

[0098] Based on core data attributes and non-core data attributes, the stored data attributes are classified to obtain data attribute classification information.

[0099] Specifically, taking the intermediate node as the benchmark, based on the initial node and the last node in the data attribute topology relationship to which the intermediate node belongs, the data core index corresponding to the intermediate node is obtained, specifically including:

[0100] According to the intermediate node, obtain the upper topology node and the lower topology node corresponding to the intermediate node;

[0101] Get the initial node and the last node in the data attribute topology relationship of the intermediate node;

[0102] Taking the initial node and the last node in the data attribute topological relationship of the intermediate node as the benchmark, according to the number of topological nodes between the intermediate node and any initial node or last node, obtain the number of topological layers corresponding to the intermediate node and each initial node or last node;

[0103] Get the maximum and minimum topological layers of the intermediate nodes and the initial node;

[0104] According to the maximum and minimum topological layers of the intermediate node and the initial node, the core indicator corresponding to the intermediate node is obtained;

[0105] Get the maximum and minimum topological layers of the intermediate nodes and the last node;

[0106] According to the maximum and minimum topological layers of the intermediate node and the last node, the non-core index corresponding to the intermediate node is obtained;

[0107] Obtain the data core index based on the upper-level topological node, lower-level topological node, core indicators, and non-core indicators corresponding to the intermediate node;

[0108] The calculation formula of the data core index is:

[0109]

[0110] Where Q is the data core index, A1 represents the number of upper-level topological nodes corresponding to the intermediate node, and A2 represents the number of lower-level topological nodes corresponding to the intermediate node. Indicates the core indicator corresponding to the intermediate node, d min (a1) represents the minimum topological layer number between the intermediate node and the initial node, d max (a1) represents the maximum number of topological layers between the intermediate nodes and the initial node, Indicates the non-core index corresponding to the intermediate node, d min (a2) represents the minimum topological layer number between the middle node and the last node, d max (a2) represents the maximum topological layer number between the middle node and the last node, β is the difference adjustment coefficient, and β = 0.6.

[0111] In this solution, by constructing a data attribute topology relationship diagram, the dependency relationship between data attributes is visualized, and the initial node (core data), intermediate nodes (need to be quantitatively evaluated) and last node (non-core data) are clearly distinguished. The core nature of data attributes is quantified from the two dimensions of dependency complexity and link position importance. A dynamic data classification system based on dependency relationships is constructed, which solves the problems of "fuzzy core data identification" and "extensive resource allocation" in traditional disaster recovery, achieves a precise match between disaster recovery strategy and data importance, and improves the disaster resistance and resource utilization efficiency of the database system. It is especially suitable for scenarios with complex data links and high disaster recovery requirements (such as finance, government affairs, and large enterprise-level databases).

[0112] It is understandable that the initial node, as basic data without upstream dependencies, directly determines the existence of downstream data and is naturally defined as core data, ensuring priority protection during disaster recovery. The last node, as terminal data without downstream dependencies, has a limited scope of influence and is classified as non-core data to reduce disaster recovery resource consumption. The intermediate nodes are quantitatively evaluated through the data core index, combined with their position in the topology (the number of upper and lower nodes, and the number of topological layers with the initial / last node), to accurately identify the intermediate links that are critical to the data link (such as high-frequency access and multi-dependent data attributes), avoid the traditional "one-size-fits-all" disaster recovery strategy, and realize on-demand resource allocation.

[0113] In this embodiment, the number of topological layers corresponding to the intermediate node and each initial node or the last node is specifically the number of topological nodes between the intermediate node and each initial node or the last node plus one. For example, if the number of topological nodes between the intermediate node B and the initial node A is C, then d AB =C+1.

[0114] Specifically, based on data disaster recovery requirements, obtain the data core index threshold, including:

[0115] Get the initial node information and the last node information;

[0116] Based on the initial node information, the adjacent intermediate node pointed to by the initial node is used as the first feature node;

[0117] According to the last node information, the topological layer number between each first characteristic node and the last node in the topological relationship to which it belongs is obtained;

[0118] The last node with the smallest number of topological layers is used as the last characteristic node corresponding to each first characteristic node;

[0119] The intermediate nodes adjacent to and pointing to the last feature node are used as the secondary feature nodes corresponding to the last feature node;

[0120] According to the secondary feature node, a secondary feature node that has a topological relationship with the first feature node is used as a second feature node;

[0121] Obtain the last data core index according to the data core index corresponding to the second feature node and the secondary feature node;

[0122] Based on the last feature node, the data core index corresponding to the first feature node is compared with the last data core index, and the larger value is used as the data core index of the initial node corresponding to the first feature node;

[0123] Based on the initial node information, the mean of the data core index of the initial node is used as the data core index threshold;

[0124] The core index of the last data is specifically:

[0125]

[0126] Where E is the last data core index, Q(i) represents the data core index corresponding to the i-th second feature node, Q(j) represents the data core index corresponding to the j-th secondary feature node, n is the total number of second feature nodes, and m is the total number of secondary feature nodes.

[0127] In this solution, through correlation analysis between the initial node (data source) and the last node (data end), the first characteristic node (the intermediate node directly downstream of the initial node) and the secondary characteristic node (the intermediate node directly upstream of the last node) in the data link are locked, ensuring that the threshold generation is based on real data dependencies rather than artificially preset or fixed numerical standards. The core index of the initial node is determined by the correlation strength between its directly downstream intermediate node (the first characteristic node) and the last node, avoiding the omission of core data due to ignoring the hub role of the intermediate node. The core index of the last data combines the core indexes of the second characteristic node (the secondary intermediate node related to the first characteristic node) and the secondary characteristic node (the direct upstream of the last node) to ensure that the key upstream nodes of the terminal data are reversely identified. By comparing the larger value of the core index of the first characteristic node and the last data, the core index of the initial node is used as the core index, and the average is taken to generate the threshold. This covers both strong dependency scenarios (such as single-layer direct association) and the common situation of multi-layer indirect dependency, preventing the threshold from being too high or too low.

[0128] It's understandable that for complex databases with multiple initial nodes (such as user, device, product, and other basic data), calculating the average core index of all initial nodes as the final threshold prevents the uniqueness of a single link from interfering with the global strategy. For example, in an e-commerce database, "user" and "product" serve as dual initial nodes, and the average core index of their downstream links can reflect the disaster recovery critical value of the entire data.

[0129] Obtaining data disaster recovery center information, including local disaster recovery centers and remote disaster recovery centers;

[0130] Monitor the data storage in the database to obtain information about the data to be stored, including attribute information of the data to be stored;

[0131] Data disaster recovery is performed based on the data information to be stored, data attribute classification information and data disaster recovery center information.

[0132] Specifically, data disaster recovery is performed based on the data to be stored, data attribute classification information, and data disaster recovery center information, including:

[0133] Based on the data disaster recovery center information, obtain the information of the disaster recovery center in the same city and the disaster recovery center in different locations;

[0134] According to the data information to be stored, obtaining attribute information of the data to be stored;

[0135] Based on the data attribute classification information and the attribute information of the data to be stored, the data to be stored is divided to obtain the core data to be stored and the non-core data to be stored;

[0136] Back up the data to be stored based on the core data to be stored, the non-core data to be stored, and the data disaster recovery center information;

[0137] Among them, for the core data to be stored, while the database stores the core data to be stored, it backs up the core data to be stored based on the information of the local disaster recovery center, and verifies the backed-up data to be stored;

[0138] If the verification is successful, the database will complete the storage of the core data to be stored and proceed to the next data storage. If the verification is unsuccessful, the database will resend the core data to be stored to the local disaster recovery center;

[0139] For non-core data to be stored, after the database completes storing the non-core data to be stored, it continues to store the next copy of data. The disaster recovery center in the same city backs up the non-core data to be stored based on asynchronous replication.

[0140] In this solution, core data is stored in the primary database and synchronized to the local disaster recovery center. Data verification (such as hash value comparison and transaction integrity checks) ensures that the backup is completely consistent with the primary database. This mechanism reduces the recovery point objective (RPO) of core data to 0 (no data loss) and the recovery time objective (RTO) to minutes, meeting the needs of scenarios such as finance and healthcare that require extremely high data consistency. For example, if the backup verification of bank transfer transaction data fails, the primary database will automatically roll back and retransmit it, avoiding catastrophic failures caused by "inconsistent primary and backup data."

[0141] Understandably, after the primary database is complete, non-core data (such as logs and historical reports) is backed up to the local disaster recovery center via asynchronous replication. This significantly improves database storage efficiency by eliminating the need to wait for backup completion before confirming writes. Asynchronous replication also supports batch data transfers during off-peak hours (such as nightly batch backups), avoiding competition for network and storage resources with core services and improving overall system stability.

[0142] Specifically, based on the core data to be stored, the non-core data to be stored, and the data disaster recovery center information, backing up the data to be stored also includes:

[0143] For the core data to be stored, after the database completes the storage of the core data to be stored, it continues to store the next copy of the data, and the remote disaster recovery center information backs up the core data to be stored based on asynchronous replication;

[0144] For non-core data to be stored, obtain data disaster recovery cycle information based on data disaster recovery requirements;

[0145] Obtain historical data disaster recovery information based on the information from the remote disaster recovery center;

[0146] Based on data attribute classification information and historical data disaster recovery information, obtain historical non-core data within each data disaster recovery cycle;

[0147] The average value of the data core index of historical non-core data is used as the data disaster recovery feature value;

[0148] According to the data information to be stored, obtain the non-core data to be stored within a data disaster recovery cycle;

[0149] The average value of the data core index corresponding to the non-core data to be stored within a data disaster recovery cycle is used as the data disaster recovery value;

[0150] If the data disaster recovery value does not exceed the data disaster recovery characteristic value, incremental backup is performed on the non-core data to be stored within the data disaster recovery cycle;

[0151] If the data disaster recovery value exceeds the data disaster recovery characteristic value, a full backup of the non-core data to be stored within the data disaster recovery cycle will be performed.

[0152] In this solution, based on data attribute classification information and historical data disaster recovery information, historical non-core data in each data disaster recovery cycle is obtained, and the data core index average of the historical non-core data is used as the data disaster recovery characteristic value. According to the information of the data to be stored, the non-core data to be stored in a data disaster recovery cycle is obtained, and the data core index average corresponding to the non-core data to be stored in a data disaster recovery cycle is used as the data disaster recovery value. According to the data disaster recovery value and the data disaster recovery characteristic value, an appropriate data disaster recovery method is selected to ensure the timeliness and stability of data backup. When the data disaster recovery value of the non-core data to be stored is ≤ the characteristic value (that is, the average data core index of the current cycle does not exceed the historical average), it means that the data change is small (such as log increment, historical report supplement), and only the difference is backed up to reduce backup time and storage space. When the data disaster recovery value is greater than the characteristic value (such as a large number of new types of logs generated during the business promotion period, and the data structure changes after the system upgrade), it means that the data characteristics have changed significantly, and a full backup is required to avoid the cumulative error of the incremental backup, thereby ensuring the integrity of the disaster recovery data.

[0153] Reference Figure 5 As shown, further, in combination with the above-mentioned database disaster recovery management method, a database disaster recovery management system is proposed, including:

[0154] A main control module, the main control module is used to determine whether the data attribute corresponding to the intermediate node is a core data attribute based on the data core index and the data core index threshold, divide the data to be stored based on the data attribute classification information and the attribute information of the data to be stored, obtain the core data to be stored and the non-core data to be stored, classify the stored data attributes based on the core data attributes and the non-core data attributes, obtain the data attribute classification information, and back up the data to be stored based on the core data to be stored, the non-core data to be stored and the data disaster recovery center information;

[0155] An information acquisition module is used to obtain database storage information, stored data attribute information, data disaster recovery center information, local disaster recovery center information, and remote disaster recovery center information. Based on the stored data attribute information and data topology analysis, a data attribute topology relationship diagram is obtained. The module monitors the data storage in the database, obtains the data information to be stored, the attribute information of the data to be stored, and obtains historical data disaster recovery information based on the remote disaster recovery center information.

[0156] An evaluation module, wherein the evaluation module is used to obtain the non-core index corresponding to the intermediate node based on the maximum and minimum topological layers of the intermediate node and the last node, obtain the data core index based on the upper topological node, lower topological node, core index and non-core index corresponding to the intermediate node, obtain the last data core index based on the data core index corresponding to the second feature node and the secondary feature node, compare the data core index corresponding to the first feature node with the last data core index based on the feature last node, and use the larger value as the data core index of the initial node corresponding to the first feature node; based on the initial node information, use the average of the data core indexes of the initial nodes as the data core index threshold;

[0157] The display module interacts with the main control module and is used to output and display a data attribute topology diagram, data attribute classification information, data attribute information to be stored, and data disaster recovery center information.

[0158] Main control module, specifically including:

[0159] A control unit, the control unit being configured to classify the attributes of the stored data based on the core data attributes and the non-core data attributes, obtain data attribute classification information, and back up the data to be stored based on the core data to be stored, the non-core data to be stored, and the data disaster recovery center information;

[0160] An information receiving unit, which interacts with the information acquisition module and the evaluation module to receive data and transmit it to the judgment unit;

[0161] A judgment unit is used to judge whether the data attribute corresponding to the intermediate node is a core data attribute based on the data core index and the data core index threshold, and to divide the data to be stored based on the data attribute classification information and the attribute information of the data to be stored, so as to obtain the core data to be stored and the non-core data to be stored.

[0162] Information acquisition module, specifically including:

[0163] A first acquisition unit is configured to acquire database storage information, stored data attribute information, data disaster recovery center information, local disaster recovery centers, and remote disaster recovery centers, and acquire a data attribute topology relationship diagram based on the stored data attribute information and data topology analysis;

[0164] The second acquisition unit is used to monitor the data storage in the database, obtain the data information to be stored, the attribute information of the data to be stored, and obtain the historical disaster recovery information of the data based on the information of the remote disaster recovery center.

[0165] Assessment modules include:

[0166] A first evaluation unit is configured to obtain a non-core indicator corresponding to the intermediate node based on the maximum and minimum topological layers of the intermediate node and the last node, and obtain a data core index based on the upper and lower topological nodes, the core indicator, and the non-core indicator corresponding to the intermediate node;

[0167] The second evaluation unit is used to obtain the last data core index based on the data core index corresponding to the second feature node and the secondary feature node, compare the data core index corresponding to the first feature node with the last data core index based on the feature last node, and take the larger value as the data core index of the initial node corresponding to the first feature node; based on the initial node information, take the average of the data core indexes of the initial nodes as the data core index threshold.

[0168] To sum up, the advantages of the present invention are: by storing data attribute information, based on data topology analysis, a data attribute topology relationship diagram is obtained, the relationship between different data is accurately analyzed through the data attribute topology relationship diagram, by taking the intermediate node as the benchmark, based on the initial node and the last node in the data attribute topology relationship to which the intermediate node belongs, the data core index corresponding to the intermediate node is obtained, the importance of different attribute data is accurately evaluated through the data core index, thereby ensuring the timeliness and reliability of data disaster recovery, performing data disaster recovery through the data information to be stored, data attribute classification information and data disaster recovery center information, selecting a suitable data disaster recovery method through the data core index corresponding to the data to be stored, thereby ensuring the stability of data backup.

[0169] The above shows and describes the basic principles, main features, and advantages of the present invention. Those skilled in the art should understand that the present invention is not limited to the above embodiments. The above embodiments and descriptions merely illustrate the principles of the present invention. Various changes and modifications may be made to the present invention without departing from the spirit and scope of the present invention. Such changes and modifications are intended to fall within the scope of the present invention. The scope of protection claimed by the present invention is defined by the appended claims and their equivalents.

Claims

1. A database disaster recovery management method, characterized in that: include: Acquiring database storage information, wherein the database storage information includes stored data attribute information; According to the information stored in the database, the stored data attributes are analyzed for association and the data attribute classification information is obtained; Obtaining data disaster recovery center information, including local disaster recovery centers and remote disaster recovery centers; Monitor the data storage in the database to obtain information about the data to be stored, including attribute information of the data to be stored; Data disaster recovery is performed based on the data information to be stored, data attribute classification information and data disaster recovery center information.

2. A database disaster recovery management method according to claim 1, characterized in that: The method of performing association analysis on stored data attributes based on database storage information to obtain data attribute classification information specifically includes: According to the stored data attribute information, based on data topology analysis, a data attribute topology relationship diagram is obtained, wherein each topological node in the data attribute topology relationship diagram corresponds to a data attribute; Based on the data attribute topological relationship graph, taking any topological node as the benchmark, obtain the topological nodes adjacent to the node, take the topological node pointing to the node as the upper topological node of the node, and take the topological node pointed to by the node as the lower topological node of the node; If a topological node does not have an upper-level topological node, the node is used as the initial node. If a topological node does not have a lower-level topological node, the node is used as the last node. If a topological node has both an upper-level topological node and a lower-level topological node, the node is used as the intermediate node. Taking the intermediate node as the benchmark, based on the initial node and the last node in the data attribute topology relationship to which the intermediate node belongs, obtain the data core index corresponding to the intermediate node; Based on data disaster recovery requirements, obtain the data core index threshold; According to the data core index and the data core index threshold, determine whether the data attribute corresponding to the intermediate node is a core data attribute. If the data core index exceeds the data core index threshold, the data attribute corresponding to the intermediate node is a core data attribute. If the data core index does not exceed the data core index threshold, the data attribute corresponding to the intermediate node is a non-core data attribute. The data attributes corresponding to the initial node are regarded as core data attributes, and the data attributes corresponding to the last node are regarded as non-core data attributes; Based on core data attributes and non-core data attributes, the stored data attributes are classified to obtain data attribute classification information.

3. A database disaster recovery management method according to claim 2, characterized in that: The method of obtaining the data core index corresponding to the intermediate node based on the initial node and the last node in the data attribute topology relationship of the intermediate node with the intermediate node as the benchmark specifically includes: According to the intermediate node, obtain the upper topology node and the lower topology node corresponding to the intermediate node; Get the initial node and the last node in the data attribute topology relationship of the intermediate node; Taking the initial node and the last node in the data attribute topological relationship of the intermediate node as the benchmark, according to the number of topological nodes between the intermediate node and any initial node or last node, obtain the number of topological layers corresponding to the intermediate node and each initial node or last node; Get the maximum and minimum topological layers of the intermediate nodes and the initial node; According to the maximum and minimum topological layers of the intermediate node and the initial node, the core indicator corresponding to the intermediate node is obtained; Get the maximum and minimum topological layers of the intermediate nodes and the last node; According to the maximum and minimum topological layers of the intermediate node and the last node, the non-core index corresponding to the intermediate node is obtained; Obtain the data core index based on the upper-level topological node, lower-level topological node, core indicators, and non-core indicators corresponding to the intermediate node; The calculation formula of the data core index is: Where Q is the data core index, A1 represents the number of upper-level topological nodes corresponding to the intermediate node, and A2 represents the number of lower-level topological nodes corresponding to the intermediate node. Indicates the core indicator corresponding to the intermediate node, d min (a1) represents the minimum topological layer number between the intermediate node and the initial node, d max (a1) represents the maximum number of topological layers between the intermediate nodes and the initial node, Indicates the non-core index corresponding to the intermediate node, d min (a2) represents the minimum topological layer number between the middle node and the last node, d max (a2) represents the maximum topological layer number between the middle node and the last node, β is the difference adjustment coefficient, and β = 0.

6.

4. A database disaster recovery management method according to claim 2, characterized in that: The data core index threshold is obtained based on the data disaster recovery requirements, specifically including: Get the initial node information and the last node information; Based on the initial node information, the adjacent intermediate node pointed to by the initial node is used as the first feature node; According to the last node information, the topological layer number between each first characteristic node and the last node in the topological relationship to which it belongs is obtained; The last node with the smallest number of topological layers is used as the last characteristic node corresponding to each first characteristic node; The intermediate nodes adjacent to and pointing to the last feature node are used as the secondary feature nodes corresponding to the last feature node; According to the secondary feature node, a secondary feature node that has a topological relationship with the first feature node is used as a second feature node; Obtain the last data core index according to the data core index corresponding to the second feature node and the secondary feature node; Based on the last feature node, the data core index corresponding to the first feature node is compared with the last data core index, and the larger value is used as the data core index of the initial node corresponding to the first feature node; Based on the initial node information, the mean of the data core index of the initial node is used as the data core index threshold; The core index of the last data is specifically: Where E is the last data core index, Q(i) represents the data core index corresponding to the i-th second feature node, Q(j) represents the data core index corresponding to the j-th secondary feature node, n is the total number of second feature nodes, and m is the total number of secondary feature nodes.

5. A database disaster recovery management method according to claim 1, characterized in that: The data disaster recovery is performed according to the data information to be stored, the data attribute classification information and the data disaster recovery center information, specifically including: Based on the data disaster recovery center information, obtain the information of the disaster recovery center in the same city and the disaster recovery center in different locations; According to the data information to be stored, obtaining attribute information of the data to be stored; Based on the data attribute classification information and the attribute information of the data to be stored, the data to be stored is divided to obtain the core data to be stored and the non-core data to be stored; Back up the data to be stored based on the core data to be stored, the non-core data to be stored, and the data disaster recovery center information; Among them, for the core data to be stored, while the database stores the core data to be stored, it backs up the core data to be stored based on the information of the local disaster recovery center, and verifies the backed-up data to be stored; If the verification is successful, the database will complete the storage of the core data to be stored and proceed to the next data storage. If the verification is unsuccessful, the database will resend the core data to be stored to the local disaster recovery center; For non-core data to be stored, after the database completes storing the non-core data to be stored, it continues to store the next copy of data. The disaster recovery center in the same city backs up the non-core data to be stored based on asynchronous replication.

6. A database disaster recovery management method according to claim 5, characterized in that: The backing up of the data to be stored based on the core data to be stored, the non-core data to be stored and the data disaster recovery center information further includes: For the core data to be stored, after the database completes the storage of the core data to be stored, it continues to store the next copy of the data, and the remote disaster recovery center information backs up the core data to be stored based on asynchronous replication; For non-core data to be stored, obtain data disaster recovery cycle information based on data disaster recovery requirements; Obtain historical data disaster recovery information based on the information from the remote disaster recovery center; Based on data attribute classification information and historical data disaster recovery information, obtain historical non-core data within each data disaster recovery cycle; The average value of the data core index of historical non-core data is used as the data disaster recovery feature value; According to the data information to be stored, obtain the non-core data to be stored within a data disaster recovery cycle; The average value of the data core index corresponding to the non-core data to be stored within a data disaster recovery cycle is used as the data disaster recovery value; If the data disaster recovery value does not exceed the data disaster recovery characteristic value, incremental backup is performed on the non-core data to be stored within the data disaster recovery cycle; If the data disaster recovery value exceeds the data disaster recovery characteristic value, a full backup of the non-core data to be stored within the data disaster recovery cycle will be performed.

7. A database disaster recovery management system, used to implement the management method according to any one of claims 1 to 6, characterized in that: include: A main control module, the main control module is used to determine whether the data attribute corresponding to the intermediate node is a core data attribute based on the data core index and the data core index threshold, divide the data to be stored based on the data attribute classification information and the attribute information of the data to be stored, obtain the core data to be stored and the non-core data to be stored, classify the stored data attributes based on the core data attributes and the non-core data attributes, obtain the data attribute classification information, and back up the data to be stored based on the core data to be stored, the non-core data to be stored and the data disaster recovery center information; An information acquisition module is used to obtain database storage information, stored data attribute information, data disaster recovery center information, local disaster recovery center information, and remote disaster recovery center information. Based on the stored data attribute information and data topology analysis, a data attribute topology relationship diagram is obtained. The module monitors the data storage in the database, obtains the data information to be stored, the attribute information of the data to be stored, and obtains historical data disaster recovery information based on the remote disaster recovery center information. An evaluation module, wherein the evaluation module is used to obtain the non-core index corresponding to the intermediate node based on the maximum and minimum topological layers of the intermediate node and the last node, obtain the data core index based on the upper topological node, lower topological node, core index and non-core index corresponding to the intermediate node, obtain the last data core index based on the data core index corresponding to the second feature node and the secondary feature node, compare the data core index corresponding to the first feature node with the last data core index based on the feature last node, and use the larger value as the data core index of the initial node corresponding to the first feature node; based on the initial node information, use the average of the data core indexes of the initial nodes as the data core index threshold; The display module interacts with the main control module and is used to output and display a data attribute topology diagram, data attribute classification information, data attribute information to be stored, and data disaster recovery center information.

8. A database disaster recovery management system according to claim 7, characterized in that: The main control module specifically includes: A control unit, the control unit being configured to classify the attributes of the stored data based on the core data attributes and the non-core data attributes, obtain data attribute classification information, and back up the data to be stored based on the core data to be stored, the non-core data to be stored, and the data disaster recovery center information; An information receiving unit, which interacts with the information acquisition module and the evaluation module to receive data and transmit it to the judgment unit; A judgment unit is used to judge whether the data attribute corresponding to the intermediate node is a core data attribute based on the data core index and the data core index threshold, and to divide the data to be stored based on the data attribute classification information and the attribute information of the data to be stored, so as to obtain the core data to be stored and the non-core data to be stored.

9. A database disaster recovery management system according to claim 7, characterized in that: The information acquisition module specifically includes: A first acquisition unit is configured to acquire database storage information, stored data attribute information, data disaster recovery center information, local disaster recovery centers, and remote disaster recovery centers, and acquire a data attribute topology relationship diagram based on the stored data attribute information and data topology analysis; The second acquisition unit is used to monitor the data storage in the database, obtain the data information to be stored, the attribute information of the data to be stored, and obtain the historical disaster recovery information of the data based on the information of the remote disaster recovery center.

10. A database disaster recovery management system according to claim 7, characterized in that: The evaluation module specifically includes: A first evaluation unit is configured to obtain a non-core indicator corresponding to the intermediate node based on the maximum and minimum topological layers of the intermediate node and the last node, and obtain a data core index based on the upper and lower topological nodes, the core indicator, and the non-core indicator corresponding to the intermediate node; The second evaluation unit is used to obtain the last data core index based on the data core index corresponding to the second feature node and the secondary feature node, compare the data core index corresponding to the first feature node with the last data core index based on the feature last node, and take the larger value as the data core index of the initial node corresponding to the first feature node; based on the initial node information, take the average of the data core indexes of the initial nodes as the data core index threshold.

Images