Data backup method and device, equipment and medium

Abstract

The invention relates to the technical field of computers, and discloses a data backup method, device and equipment and a medium, which are applied to a data backup system, and comprise a data label generation module, a cloud resource analysis module, a cross-cloud scheduling module and a data backup module. Generating structured label data based on the key information of the to-be-backed-up data through a data label generation module; determining a target access frequency and a target confidentiality level of the structured tag data through a cloud resource analysis module, and determining path information; generating backup summary information based on the path information and the to-be-backed-up data through the cross-cloud scheduling module, and sending the backup summary information to the data backup module; and carrying out structured storage on the backup summary information through a data backup module, and storing the backup summary information to a distributed block chain so as to back up the to-be-backed-up data. The method can be applied to business program systems such as financial science and technology, medical health care and the like, and the efficiency, accuracy and compliance of data backup can be improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of computer, and in particular, to a data backup method, device, equipment and medium. BACKGROUND

[0002] In the strong regulatory field such as finance and medical treatment, the continuity, security and availability of data constitute the core guarantee of system operation and regulatory compliance. Especially in the insurance industry, in the face of complex heterogeneous cloud architecture deployment background, the traditional data backup and disaster recovery system has been difficult to meet the intelligent decision-making needs of storage strategy of multi-type data such as high-frequency transaction data, customer sensitive information and long-term archival materials.

[0003] The current commonly used backup scheme is mainly static configuration, which lacks the ability of dynamic classification and storage level allocation according to data access characteristics, security level and regulatory requirements, and is easy to cause waste of storage resources or delay in disaster recovery. At the same time, the traditional data integrity verification mostly relies on the one-way verification mechanism in the central database, such as hash algorithm verification comparison, which has the risk of malicious tampering or forging verification results, and it is difficult to realize truly credible data evidence in a multi-cloud environment. In addition, the data backup process often relies on manual intervention of technical personnel, and the process is not transparent and the response is not unified. Therefore, there is an urgent need for a data backup method that can improve the efficiency, accuracy and compliance of data backup. SUMMARY

[0004] The present application provides a data backup method, device, equipment and medium to solve the technical problem of low efficiency, accuracy and compliance of data backup in related technologies.

[0005] In a first aspect, a data backup method is provided, which is applied to a data backup system including a data tag generation module, a cloud resource analysis module, a cross-cloud scheduling module and a data backup module. The method comprises:

[0006] Obtaining backup data, generating structured tag data based on the key information of the backup data through the data tag generation module, and sending the structured tag data to the cloud resource analysis module; wherein the key information includes data fields, access behaviors and business attribution;

[0007] Determining the target access frequency and the target security level of the structured tag data through the cloud resource analysis module, determining the corresponding path information based on the target access frequency and the target security level, and sending the path information to the cross-cloud scheduling module; wherein the path information includes a backup path and a storage level;

[0008] The cross-cloud scheduling module generates backup summary information based on the path information and the data to be backed up, and sends the backup summary information to the data backup module; wherein, the backup summary information includes the hash value, timestamp, and cloud node number corresponding to the data to be backed up;

[0009] The data backup module performs structured storage of the backup summary information and stores the structured backup summary information in a distributed blockchain to back up the data to be backed up.

[0010] Secondly, a data backup device is provided, the data backup device comprising:

[0011] The data tag generation module is used to acquire the data to be backed up, generate structured tag data based on the key information of the data to be backed up, and send the structured tag data to the cloud resource analysis module; wherein, the key information includes data fields, access behavior, and business affiliation;

[0012] The cloud resource analysis module is used to determine the target access frequency and target security level of the structured tag data, determine the corresponding path information based on the target access frequency and target security level, and send the path information to the cross-cloud scheduling module; wherein, the path information includes backup path and storage level;

[0013] The cross-cloud scheduling module is used to generate backup summary information based on the path information and the data to be backed up, and send the backup summary information to the data backup module; wherein, the backup summary information includes the hash value, timestamp and cloud node number corresponding to the data to be backed up;

[0014] The data backup module is used to store the backup summary information in a structured manner and store the structured backup summary information to a distributed blockchain to back up the data to be backed up.

[0015] Thirdly, a computer device is provided, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the steps of the above-described data backup method.

[0016] Fourthly, a computer-readable storage medium is provided, which stores a computer program that, when executed by a processor, implements the steps of the above-described data backup method.

[0017] In the aforementioned data backup method, apparatus, computer equipment, and storage medium, the data backup method is applied to a data backup system. The data backup system includes a data tag generation module, a cloud resource analysis module, a cross-cloud scheduling module, and a data backup module. The method includes: first, acquiring the data to be backed up; generating structured tag data based on key information of the data to be backed up using the data tag generation module; and sending the structured tag data to the cloud resource analysis module; wherein the key information includes data fields, access behavior, and business affiliation. Further, the cloud resource analysis module can determine the target access frequency and target confidentiality level of the structured tag data, determine the corresponding path information based on the target access frequency and target confidentiality level, and send the path information to the cross-cloud scheduling module; wherein the path information includes the backup path and storage level. Thus, the cross-cloud scheduling module can generate backup summary information based on the path information and the data to be backed up, and send the backup summary information to the data backup module; wherein the backup summary information includes the hash value, timestamp, and cloud node number corresponding to the data to be backed up. Finally, the data backup module can perform structured storage of the backup summary information and store the structured backup summary information in a distributed blockchain for backing up the data to be backed up. In this invention, a data tagging module can extract key information from the data to be backed up, such as data fields, access behavior, and business affiliation, to generate structured tag data, which is beneficial for subsequent intelligent identification and processing. In the financial, healthcare, and elderly care fields, this step can achieve fine-grained classification of sensitive transaction data or patient information. Subsequently, the cloud resource analysis module determines the priority and sensitivity of data storage based on access frequency and confidentiality level, and then plans a reasonable backup path and storage hierarchy to achieve cold and hot data separation and compliant storage. On this basis, the cross-cloud scheduling module generates a backup digest containing hash value, timestamp, and cloud node number based on path information to ensure data traceability and integrity. Finally, the data backup module structures this digest information and writes it into a distributed blockchain system to improve data tamper resistance and credibility. The overall solution takes into account data characteristic identification, cloud resource optimization, and blockchain security, and is suitable for financial and medical information backup scenarios with high security and high audit requirements. Attached Figure Description

[0018] To more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings used in the description of the embodiments of the present invention will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0019] Figure 1 This is a schematic diagram of an application environment for a data backup method according to an embodiment of the present invention;

[0020] Figure 2 This is a flowchart illustrating a data backup method according to an embodiment of the present invention;

[0021] Figure 3 yes Figure 1 A schematic diagram of a specific implementation method for step S10;

[0022] Figure 4 This is a schematic diagram of a data backup device in one embodiment of the present invention;

[0023] Figure 5 This is a schematic diagram of the structure of a computer device according to an embodiment of the present invention;

[0024] Figure 6 This is another structural schematic diagram of a computer device according to one embodiment of the present invention. Detailed Implementation

[0025] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0026] The data backup method provided in this embodiment of the invention can be applied to, for example, Figure 1In this application environment, the client communicates with the server via a network. The server can obtain the data to be backed up from the client, and the data tag generation module generates structured tag data based on the key information of the data to be backed up, and sends the structured tag data to the cloud resource analysis module; wherein, the key information includes data fields, access behavior, and business affiliation; the cloud resource analysis module determines the target access frequency and target confidentiality level of the structured tag data, determines the corresponding path information based on the target access frequency and target confidentiality level, and sends the path information to the cross-cloud scheduling module; wherein, the path information includes the backup path and storage level; the cross-cloud scheduling module determines the path based on the path... The system generates a backup summary based on the information and the data to be backed up, and sends the backup summary to the data backup module. The backup summary includes the hash value, timestamp, and cloud node number corresponding to the data to be backed up. The data backup module then stores the backup summary in a structured format on a distributed blockchain to back up the data to be backed up. Finally, the backed-up data is fed back to the client. In this invention, a data tag generation module can extract key information from the data to be backed up, such as data fields, access behavior, and business affiliation, to generate structured tag data, which is beneficial for subsequent intelligent identification and processing. In the financial, healthcare, and elderly care fields, this step can achieve fine-grained classification of sensitive transaction data or patient information. Subsequently, the cloud resource analysis module determines the priority and sensitivity of data storage based on access frequency and confidentiality level, and then plans a reasonable backup path and storage level to achieve cold and hot data separation and compliant storage. Based on this, the cross-cloud scheduling module generates a backup summary containing a hash value, timestamp, and cloud node number based on the path information to ensure data traceability and integrity. Finally, the data backup module structures this summary information and writes it into the distributed blockchain system, improving data tamper resistance and trustworthiness. The overall solution balances data characteristic identification, cloud resource optimization, and blockchain security, making it suitable for high-security, high-audit-requirement financial and medical information backup scenarios. The client can be, but is not limited to, various personal computers, laptops, smartphones, tablets, and portable wearable devices. The server can be implemented using a standalone server or a server cluster consisting of multiple servers. The invention will be described in detail below through specific embodiments.

[0027] Please see Figure 2 As shown, Figure 2 This is a flowchart illustrating a data backup method provided in an embodiment of the present invention. The data backup method is applied to a data backup system, which includes a data tag generation module, a cloud resource analysis module, a cross-cloud scheduling module, and a data backup module. The method includes the following steps:

[0028] S10: Obtain the data to be backed up, generate structured tag data based on the key information of the data to be backed up through the data tag generation module, and send the structured tag data to the cloud resource analysis module.

[0029] The key information includes data fields, access behavior, and business affiliation.

[0030] For example, step S10 can preprocess the data to be backed up through the data tag generation module, extract key information, including data fields (such as data type and structure), access behavior (such as access frequency and method), and business affiliation (such as department or system), and then generate corresponding structured tag data.

[0031] The aforementioned structured tag data can serve as a basis for subsequent analysis and decision-making, facilitating the identification of core transaction data in financial systems or sensitive medical record information in healthcare and elderly care systems. This enables accurate classification and compliant backup, which is then transmitted to the cloud resource analysis module to drive subsequent path planning and strategy selection.

[0032] Among them, such as Figure 3 As shown, in step S10, namely obtaining the data to be backed up, the data tag generation module generates structured tag data based on the key information of the data to be backed up, including the following steps:

[0033] S11: Obtain the data to be backed up, identify the business affiliation of the data to be backed up through the data tag generation module, and extract the business identifier field based on the business affiliation.

[0034] S12: The data tag generation module maps the business identifier field to a preset tag library and combines it with the access behavior of the data to be backed up to obtain the target access frequency.

[0035] S13: The data tag generation module determines the data type and target confidentiality level of the data to be backed up based on the data fields and access behavior.

[0036] S14: The data tag generation module generates the structured tag data based on the target access frequency, the data type, and the target confidentiality level.

[0037] In step S11, after acquiring the data to be backed up, the data tag generation module can identify its business affiliation and extract the corresponding business identifier field, such as account services and payment clearing in a financial system, or outpatient and inpatient business modules in a medical system. Subsequently, in step S12, the data tag generation module maps this business identifier field to a preset tag library and, combined with the data's access behavior in actual operation, calculates the corresponding target access frequency, such as frequently accessed transaction data or frequently accessed archived information.

[0038] Furthermore, in step S13, the specific data type (such as structured forms, image data, log records, etc.) and corresponding target confidentiality level can be determined by combining the field structure and access behavior of the data to be backed up. For example, medical images can be determined to be high-confidentiality data, while user behavior logs are medium-confidentiality data. Based on this, step S14 can combine the access frequency, data type, and confidentiality level to generate structured tag data by the data tag generation module.

[0039] The aforementioned structured tag data not only helps to precisely match the optimal storage path in subsequent cloud resource allocation, but also supports the deployment of compliant and efficient data backup strategies in scenarios with extremely high requirements for data security and hierarchical management, such as finance, healthcare, and elderly care.

[0040] S20: The cloud resource analysis module determines the target access frequency and target confidentiality level of the structured tag data, determines the corresponding path information based on the target access frequency and target confidentiality level, and sends the path information to the cross-cloud scheduling module.

[0041] The path information includes the backup path and storage level.

[0042] For example, in step S20, after receiving the structured tag data, the cloud resource analysis module further analyzes the target access frequency and target confidentiality level as important parameters for evaluating data characteristics. Based on these two indicators, the system intelligently matches the most suitable backup path (e.g., private cloud, public cloud, or hybrid cloud environment) and the corresponding storage level (e.g., hot storage, cold storage, or archive storage) to achieve the optimal balance between data performance, cost, and security. This path information is then sent to the cross-cloud scheduling module to guide specific data backup actions, which is particularly suitable for scenarios such as low-latency backup of financial transaction data or high-confidentiality storage of medical, health, and elderly care data.

[0043] In some embodiments, determining the corresponding path information based on the target access frequency and the target confidentiality level includes: parsing the target access frequency and the target confidentiality level through the cloud resource analysis module to obtain the data type and compliance level corresponding to the data to be backed up; and analyzing the data type and compliance level corresponding to the data to be backed up through the multi-objective optimization model in the cloud resource analysis module to obtain the path information.

[0044] For example, the cloud resource analysis module can further identify the data type (such as images, text, logs, etc.) and compliance level (such as financial regulatory level or medical privacy protection level) corresponding to the data to be backed up by parsing the target access frequency and target confidentiality level in the structured tags. Subsequently, the module's built-in multi-objective optimization model combines these attributes for comprehensive analysis, weighing performance, security, and compliance costs, and intelligently generating optimal path information, including the most suitable backup path and storage level. This mechanism ensures that different types of data can achieve accurate and compliant distributed backup based on their sensitivity and access requirements in scenarios such as finance, healthcare, and elderly care.

[0045] S30: The cross-cloud scheduling module generates backup summary information based on the path information and the data to be backed up, and sends the backup summary information to the data backup module.

[0046] The backup summary information includes the hash value, timestamp, and cloud node number corresponding to the data to be backed up.

[0047] For example, in step S30, after receiving the path information, the cross-cloud scheduling module can generate corresponding backup summary information based on the data to be backed up, which is used to identify and track the backup status of the data. The backup summary information includes three parts: a hash value used to verify data integrity and prevent tampering during transmission or storage; a timestamp recording the specific time of backup, which helps with version control and auditing; and a cloud node number indicating the cloud resource node to which the data was actually allocated, supporting data location across regions or platforms.

[0048] The aforementioned summary information is then sent to the data backup module, providing standardized input for subsequent structured storage and blockchain registration. This is particularly suitable for scenarios requiring strong consistency and auditability, such as financial transaction records and medical records.

[0049] In some embodiments, generating backup summary information based on the path information and the data to be backed up by the cross-cloud scheduling module includes: packaging the data to be backed up and the path information into partitioned and segmented structure data by the cross-cloud scheduling module; generating a hash value for each partitioned and segmented structure data according to a hash calculation algorithm by the cross-cloud scheduling module, and binding a timestamp to the hash value; analyzing the partitioned and segmented structure data by the cross-cloud scheduling module to determine the cloud node number corresponding to the data to be backed up; and combining the hash value, the timestamp, and the cloud node number into the backup summary information by the cross-cloud scheduling module.

[0050] For example, the cross-cloud scheduling module first integrates the data to be backed up with path information to generate structured data units with logical partitioning and physical segmentation characteristics, thereby optimizing transmission and storage efficiency in cross-cloud environments. Subsequently, the module applies a hash algorithm to each partitioned segment of structured data to generate a unique hash value and binds a corresponding timestamp to each hash value, ensuring data verifiability and traceability over time. Next, through analysis of the structured data, the target cloud node number for storing each data segment can be determined, achieving reasonable allocation of cross-cloud resources. Finally, the hash value, timestamp, and cloud node number are combined to generate standardized backup summary information, ensuring data integrity and security while facilitating distributed tracking and intelligent recovery in scenarios requiring high availability and compliance, such as finance, healthcare, and elderly care.

[0051] S40: The backup summary information is structured and stored by the data backup module, and the structured backup summary information is stored in a distributed blockchain to back up the data to be backed up.

[0052] In some embodiments, the step of storing the backup summary information in a structured manner through the data backup module and storing the structured backup summary information to a distributed blockchain includes: converting the backup summary information into a blockchain-ready data structure through the data backup module; encrypting the blockchain-ready data structure through the data backup module and generating on-chain transaction records; identifying the on-chain transaction records as the structured backup summary information through the data backup module, and storing the structured backup summary information to the distributed blockchain.

[0053] For example, in step S40, to achieve secure and reliable storage of the data to be backed up, the data backup module first performs structured processing on the generated backup summary information to meet the format requirements of on-chain storage. Specifically, this process includes converting the backup summary information (such as hash values, timestamps, and cloud node numbers) into a standard on-chain data structure for efficient management and verification in the distributed blockchain system. This structured data can be used for transaction records in the financial sector or patient information indexes in the medical, health, and elderly care sectors, forming data identification units that are easy to share and verify across systems.

[0054] Subsequently, the data backup module encrypts the on-chain data structure to enhance its tamper-proof and privacy protection capabilities in a blockchain environment. Based on this, it generates on-chain transaction records to ensure that the data writing process has non-repudiation and timestamp authentication functions. The system officially confirms the on-chain transaction record as structured backup summary information and writes it into the distributed blockchain, realizing the traceability, verifiability, and high credibility of the data summary throughout its entire lifecycle.

[0055] The aforementioned technical means are applicable to the precise monitoring of fund flows in financial auditing scenarios, or the legal and compliant preservation of access history of sensitive data in medical, health and elderly care scenarios.

[0056] In some embodiments, the method further includes: determining the original data node corresponding to the data to be backed up based on the backup digest information using the data backup module; performing data recovery on the original data node using the data backup module to obtain the recovered data and the original hash value; performing consistency verification on the recovered data and the original hash value using the data backup module to obtain a consistency verification result; and if the consistency verification result is correct, storing the structured backup digest information to the distributed blockchain using the data backup module.

[0057] For example, to ensure the accuracy and reliability of backup data, the data backup module first uses the backup summary information to reverse locate the data node of the data to be backed up in the original storage environment, and performs a data recovery operation to extract the recovered data and its original hash value. Next, the recovered data and the original hash value are verified for consistency to determine whether the data has been tampered with or damaged during transmission, storage, and recovery. If the verification result shows that the data is completely consistent, i.e., the verification is error-free, it indicates that the backup process is safe and reliable. Subsequently, the structured backup summary information can be formally written into the distributed blockchain to achieve traceable and tamper-proof data storage management.

[0058] This technology is of great significance in financial, healthcare, and elderly care scenarios, as it can be used to ensure the audit compliance and long-term reliable storage of critical data such as transaction records and patient files.

[0059] In some embodiments, the data backup system further includes an anomaly identification module, and the method further includes: collecting anomaly logs and performance indicator information of the data to be backed up through the anomaly identification module; analyzing the anomaly logs and performance indicator information of the data to be backed up using an intelligent identification model through the anomaly identification module to obtain analysis results; generating strategy optimization suggestions based on the analysis results through the anomaly identification module, and sending the strategy optimization suggestions to the operation and maintenance management platform.

[0060] For example, to improve the stability and intelligent operation and maintenance capabilities of a data backup system, an anomaly identification module can be added, specifically for monitoring and analyzing the operational status of the data to be backed up. This module first collects anomaly logs (such as failed backup records, network interruptions, etc.) and performance metrics (such as response latency, resource utilization, etc.) related to the data to be backed up. Then, it uses an embedded intelligent identification model to perform in-depth analysis of the above data, extracting potential risk patterns and bottleneck causes. Based on the analysis results, targeted strategy optimization suggestions can be generated, such as adjusting backup frequency, optimizing path selection, or expanding resource configuration. These suggestions are then promptly sent to the operation and maintenance management platform to assist operation and maintenance personnel in implementing highly available and reliable data protection strategies in critical scenarios such as financial transaction systems or healthcare and elderly care platforms.

[0061] As can be seen, in the above solution, the data tagging module can extract key information from the data to be backed up, such as data fields, access behavior, and business affiliation, to generate structured tag data, which is beneficial for subsequent intelligent identification and processing. In the financial, healthcare, and elderly care sectors, this step can achieve fine-grained classification of sensitive transaction data or patient information. Subsequently, the cloud resource analysis module determines the priority and sensitivity of data storage based on access frequency and confidentiality level, and then plans a reasonable backup path and storage hierarchy to achieve cold and hot data separation and compliant storage. On this basis, the cross-cloud scheduling module generates a backup digest containing hash value, timestamp, and cloud node number based on path information to ensure data traceability and integrity. Finally, the data backup module structures this digest information and writes it into the distributed blockchain system to improve data tamper resistance and credibility. The overall solution takes into account data characteristic identification, cloud resource optimization, and blockchain security, and is suitable for financial and medical information backup scenarios with high security and high audit requirements.

[0062] It should be understood that the sequence number of each step in the above embodiments does not imply 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 invention.

[0063] In one embodiment, a data backup device is provided, which corresponds one-to-one with the data backup methods described in the above embodiments. For example... Figure 4 As shown, the data backup device includes a data tag generation module 101, a cloud resource analysis module 102, a cross-cloud scheduling module 103, and a data backup module 104. Detailed descriptions of each functional module are as follows:

[0064] The data tag generation module 101 is used to acquire the data to be backed up, generate structured tag data based on the key information of the data to be backed up, and send the structured tag data to the cloud resource analysis module 102; wherein, the key information includes data fields, access behavior, and business affiliation.

[0065] The cloud resource analysis module 102 is used to determine the target access frequency and target confidentiality level of the structured tag data, determine the corresponding path information based on the target access frequency and target confidentiality level, and send the path information to the cross-cloud scheduling module 103; wherein, the path information includes backup path and storage level;

[0066] The cross-cloud scheduling module 103 is used to generate backup summary information based on the path information and the data to be backed up, and send the backup summary information to the data backup module 104; wherein, the backup summary information includes the hash value, timestamp and cloud node number corresponding to the data to be backed up.

[0067] The data backup module 104 is used to store the backup summary information in a structured manner and store the structured backup summary information to a distributed blockchain to back up the data to be backed up.

[0068] The data tag generation module 101 is used to acquire the data to be backed up, identify the business affiliation of the data to be backed up, and extract a business identifier field based on the business affiliation; map the business identifier field to a preset tag library, and obtain a target access frequency by combining the access behavior of the data to be backed up; and determine the data type and target confidentiality level of the data to be backed up based on the data field and access behavior respectively; and generate the structured tag data according to the target access frequency, the data type and the target confidentiality level.

[0069] The cloud resource analysis module 102 is used to analyze the target access frequency and the target confidentiality level to obtain the data type and compliance level corresponding to the data to be backed up; and to analyze the data type and compliance level corresponding to the data to be backed up through a multi-objective optimization model to obtain the path information.

[0070] The cross-cloud scheduling module 103 is used to package the data to be backed up with the path information to generate partitioned and segmented structure data; generate a hash value for each partitioned and segmented structure data according to a hash calculation algorithm, and bind a timestamp to the hash value; and analyze the partitioned and segmented structure data to determine the cloud node number corresponding to the data to be backed up; and combine the hash value, the timestamp, and the cloud node number to form the backup summary information.

[0071] The data backup module 104 is used to convert the backup summary information into a blockchain-ready data structure; encrypt the blockchain-ready data structure and generate on-chain transaction records; identify the on-chain transaction records as the structured backup summary information; and store the structured backup summary information in the distributed blockchain.

[0072] In one embodiment, the data backup module 104 is further configured to: determine the original data node corresponding to the data to be backed up based on the backup summary information; perform data recovery on the original data node to obtain the recovered data and the original hash value; perform consistency verification on the recovered data and the original hash value to obtain a consistency verification result; if the consistency verification result is correct, store the structured backup summary information in the distributed blockchain.

[0073] In one embodiment, the data backup system further includes an anomaly identification module (not shown in the figure), which is also used to collect anomaly logs and performance indicator information of the data to be backed up; analyze the anomaly logs and performance indicator information of the data to be backed up using an intelligent identification model to obtain analysis results; generate strategy optimization suggestions based on the analysis results; and send the strategy optimization suggestions to the operation and maintenance management platform.

[0074] This invention provides a data backup device that can extract key information from the data to be backed up, such as data fields, access behavior, and business affiliation, through a data tag generation module, generating structured tag data that facilitates subsequent intelligent identification and processing. In the financial, healthcare, and elderly care sectors, this step enables fine-grained classification of sensitive transaction data or patient information. Subsequently, a cloud resource analysis module determines the priority and sensitivity of data storage based on access frequency and confidentiality level, thereby planning a reasonable backup path and storage hierarchy to achieve cold and hot data separation and compliant storage. Based on this, a cross-cloud scheduling module generates a backup digest containing hash values, timestamps, and cloud node numbers based on path information, ensuring data traceability and integrity. Finally, the data backup module structures this digest information and writes it into a distributed blockchain system, improving data tamper resistance and trustworthiness. The overall solution balances data characteristic identification, cloud resource optimization, and blockchain security, making it suitable for high-security and high-audit-requirement financial and medical information backup scenarios.

[0075] For specific limitations regarding the data backup device, please refer to the limitations on the data backup method described above, which will not be repeated here. Each module in the aforementioned data backup device can be implemented entirely or partially through software, hardware, or a combination thereof. These modules can be embedded in the processor of the computer device in hardware form or independent of it, or stored in the memory of the computer device in software form, so that the processor can call and execute the operations corresponding to each module.

[0076] In one embodiment, a computer device is provided, which may be a server, and its internal structure diagram may be as follows: Figure 5 As shown, the computer device includes a processor, memory, network interface, and database connected via a system bus. The processor provides computing and control capabilities. The memory includes non-volatile and / or volatile storage media and internal memory. The non-volatile storage media stores the operating system, computer programs, and database. The internal memory provides an environment for the operation of the operating system and computer programs stored in the non-volatile storage media. The network interface is used to communicate with external clients via a network connection. When the computer program is executed by the processor, it implements the functions or steps of a data backup method on the server side.

[0077] In one embodiment, a computer device is provided, which may be a client, and its internal structure diagram may be as follows: Figure 6 As shown, the computer device includes a processor, memory, network interface, display screen, and input devices connected via a system bus. The processor provides computing and control capabilities. The memory includes non-volatile storage media and internal memory. The non-volatile storage media stores the operating system and computer programs. The internal memory provides an environment for the operation of the operating system and computer programs stored in the non-volatile storage media. The network interface is used to communicate with an external server via a network connection. When the computer program is executed by the processor, it implements the functions or steps of a data backup method on the client side.

[0078] In one embodiment, a computer device is provided, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to perform the following steps:

[0079] The system acquires the data to be backed up, generates structured tag data based on the key information of the data to be backed up using the data tag generation module, and sends the structured tag data to the cloud resource analysis module; wherein, the key information includes data fields, access behavior, and business affiliation;

[0080] The cloud resource analysis module determines the target access frequency and target security level of the structured tag data, determines the corresponding path information based on the target access frequency and target security level, and sends the path information to the cross-cloud scheduling module; wherein, the path information includes backup path and storage level;

[0081] The cross-cloud scheduling module generates backup summary information based on the path information and the data to be backed up, and sends the backup summary information to the data backup module; wherein, the backup summary information includes the hash value, timestamp, and cloud node number corresponding to the data to be backed up;

[0082] The data backup module performs structured storage of the backup summary information and stores the structured backup summary information in a distributed blockchain to back up the data to be backed up.

[0083] In one embodiment, a computer-readable storage medium is provided having a computer program stored thereon, the computer program performing the following steps when executed by a processor:

[0084] The system acquires the data to be backed up, generates structured tag data based on the key information of the data to be backed up using the data tag generation module, and sends the structured tag data to the cloud resource analysis module; wherein, the key information includes data fields, access behavior, and business affiliation;

[0085] The cloud resource analysis module determines the target access frequency and target security level of the structured tag data, determines the corresponding path information based on the target access frequency and target security level, and sends the path information to the cross-cloud scheduling module; wherein, the path information includes backup path and storage level;

[0086] The cross-cloud scheduling module generates backup summary information based on the path information and the data to be backed up, and sends the backup summary information to the data backup module; wherein, the backup summary information includes the hash value, timestamp, and cloud node number corresponding to the data to be backed up;

[0087] The data backup module performs structured storage of the backup summary information and stores the structured backup summary information in a distributed blockchain to back up the data to be backed up.

[0088] It should be noted that the functions or steps that can be implemented by the computer-readable storage medium or computer device described above can be referred to the relevant descriptions on the server side and client side in the foregoing method embodiments. To avoid repetition, they will not be described one by one here.

[0089] Those skilled in the art will understand that all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium, and when executed, it can include the processes of the embodiments of the above methods. Any references to memory, storage, databases, or other media used in the embodiments provided in this application can include non-volatile and / or volatile memory. Non-volatile memory can include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), or flash memory. Volatile memory can include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in various forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), dual data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link DRAM (SLDRAM), RAMbus direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and RAMbus dynamic RAM (RDRAM), etc.

[0090] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the above-described division of functional units and modules is used as an example. In practical applications, the above functions can be assigned to different functional units and modules as needed, that is, the internal structure of the device can be divided into different functional units or modules to complete all or part of the functions described above.

[0091] The above-described embodiments are only used to illustrate the technical solutions of the present invention, and are not intended to limit it. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention, and should all be included within the protection scope of the present invention.

Claims

1. A data backup method, characterized in that, The data backup method is applied to a data backup system, which includes a data tag generation module, a cloud resource analysis module, a cross-cloud scheduling module, and a data backup module. The method includes: The system acquires the data to be backed up, generates structured tag data based on the key information of the data to be backed up using the data tag generation module, and sends the structured tag data to the cloud resource analysis module; wherein, the key information includes data fields, access behavior, and business affiliation; The cloud resource analysis module determines the target access frequency and target security level of the structured tag data, determines the corresponding path information based on the target access frequency and target security level, and sends the path information to the cross-cloud scheduling module; wherein, the path information includes backup path and storage level; The cross-cloud scheduling module generates backup summary information based on the path information and the data to be backed up, and sends the backup summary information to the data backup module; wherein, the backup summary information includes the hash value, timestamp, and cloud node number corresponding to the data to be backed up; The data backup module performs structured storage of the backup summary information and stores the structured backup summary information in a distributed blockchain to back up the data to be backed up.

2. The method according to claim 1, characterized in that, The process of acquiring the data to be backed up, and generating structured tag data based on the key information of the data to be backed up by the data tag generation module, includes: The data to be backed up is obtained, and the business affiliation of the data to be backed up is identified through the data tag generation module. Based on the business affiliation, the business identifier field is extracted. The data tag generation module maps the business identifier field to a preset tag library and combines this with the access behavior of the data to be backed up to obtain the target access frequency; and, The data tag generation module determines the data type and target security level of the data to be backed up based on the data fields and access behavior, respectively. The data tag generation module generates the structured tag data based on the target access frequency, the data type, and the target confidentiality level.

3. The method according to claim 1, characterized in that, The step of determining the corresponding path information based on the target access frequency and the target security level includes: The cloud resource analysis module analyzes the target access frequency and the target confidentiality level to obtain the data type and compliance level corresponding to the data to be backed up. The path information is obtained by analyzing the data types and compliance levels corresponding to the data to be backed up using the multi-objective optimization model in the cloud resource analysis module.

4. The method according to claim 1, characterized in that, The step of generating backup summary information based on the path information and the data to be backed up by the cross-cloud scheduling module includes: The cross-cloud scheduling module packages the data to be backed up with the path information to generate partitioned and segmented structured data. The cross-cloud scheduling module generates hash values ​​for the segmented structure data of each region according to a hash calculation algorithm, and binds a timestamp to the hash values; and, The cross-cloud scheduling module analyzes the segmented structure data to determine the cloud node number corresponding to the data to be backed up. The cross-cloud scheduling module combines the hash value, the timestamp, and the allowed node number into the backup summary information.

5. The method according to claim 1, characterized in that, The step of storing the backup summary information in a structured manner through the data backup module and storing the structured backup summary information to a distributed blockchain includes: The data backup module converts the backup summary information into a blockchain-compatible data structure. The data backup module encrypts the on-chain data structure and generates on-chain transaction records. The data backup module identifies the on-chain transaction records as the structured backup summary information and stores the structured backup summary information in the distributed blockchain.

6. The method according to claim 1, characterized in that, The method further includes: The data backup module determines the original data node corresponding to the data to be backed up based on the backup summary information. The data backup module is used to restore the original data node, obtaining the restored data and the original hash value. The data backup module performs consistency verification on the restored data and the original hash value to obtain the consistency verification result. If the consistency verification result is correct, the structured backup summary information is stored in the distributed blockchain through the data backup module.

7. The method according to claim 1, characterized in that, The data backup system also includes an anomaly detection module, and the method further includes: The anomaly identification module collects anomaly logs and performance metrics information of the data to be backed up. The anomaly identification module uses an intelligent identification model to analyze the anomaly logs and performance indicator information of the data to be backed up, and obtains the analysis results. The anomaly identification module generates strategy optimization suggestions based on the analysis results and sends the strategy optimization suggestions to the operation and maintenance management platform.

8. A data backup device, characterized in that, The data backup device includes: The data tag generation module is used to acquire the data to be backed up, generate structured tag data based on the key information of the data to be backed up, and send the structured tag data to the cloud resource analysis module; wherein, the key information includes data fields, access behavior, and business affiliation; The cloud resource analysis module is used to determine the target access frequency and target security level of the structured tag data, determine the corresponding path information based on the target access frequency and target security level, and send the path information to the cross-cloud scheduling module; wherein, the path information includes backup path and storage level; The cross-cloud scheduling module is used to generate backup summary information based on the path information and the data to be backed up, and send the backup summary information to the data backup module; wherein, the backup summary information includes the hash value, timestamp and cloud node number corresponding to the data to be backed up; The data backup module is used to store the backup summary information in a structured manner and store the structured backup summary information to a distributed blockchain to back up the data to be backed up.

9. A computer device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the computer program, it implements the steps of the data backup method as described in any one of claims 1 to 7.

10. A computer-readable storage medium storing a computer program, characterized in that, When the computer program is executed by a processor, it implements the steps of the data backup method as described in any one of claims 1 to 7.

Images