File distributed backup method of sonar database system

Abstract

The invention discloses a distributed file backup method for a sonar database system, which belongs to the technical field of sonar data processing and comprises the following steps: acquiring file data containing underwater acoustic signal data from the sonar database system; fragmenting the file data to generate data blocks and dynamically adjusting the sizes of the data blocks; generating a unique identifier for the data block based on the content hash value and the metadata; selecting distributed storage nodes based on availability, storage capacity, network delay and geographical distribution factors; the transmission data block uses an encryption protocol; updating the storage position and state of the backup metadata record; periodically verifying the integrity and availability of the backup data; executing a data recovery process to recover the original file data; the dynamically adjusted backup policy is based on real-time operation requirements and data importance levels. According to the method, through distributed storage and dynamic optimization, the backup efficiency, the data security and the system reliability are improved, and the method is suitable for sonar data management in an underwater detection system.

Description

Technical Field

[0001] This invention belongs to the field of sonar data processing technology, specifically relating to a distributed backup method for a sonar database system. Background Technology

[0002] In the field of file backup for sonar database systems, existing technologies typically employ centralized backup methods, which have several shortcomings. First, centralized backup is prone to single points of failure; if the primary storage node fails, the entire backup data may be lost, severely impacting the reliability of underwater detection systems. Second, traditional backup methods lack dynamic adjustment capabilities, failing to optimize backup frequency and redundancy levels based on real-time operational needs and data importance, leading to resource waste or insufficient backups. Furthermore, existing methods are inefficient in fragment processing, identifier generation, and node selection; for example, fixed fragment sizes cannot adapt to different file types and data volumes, increasing backup time and network load. Underwater acoustic signal data is characterized by high real-time performance and high importance, originating from sonar equipment used to detect underwater targets. However, existing technologies have failed to effectively integrate encryption protocols, metadata management, and health monitoring, posing challenges to data security and integrity. For instance, in military applications, sonar data backup requires ensuring low latency and high availability, but existing methods often ignore geographical distribution and network latency factors, resulting in slow recovery processes.

[0003] Therefore, there is an urgent need for an efficient, adaptive, and secure distributed backup method to solve the above problems and improve the overall performance of the sonar database system. Summary of the Invention

[0004] In view of this, the purpose of this invention is to provide a distributed backup method for a sonar database system to improve the overall performance of the sonar database system.

[0005] To achieve the above objectives, the present invention provides the following technical solution: This invention discloses a distributed file backup method for a sonar database system, comprising the following steps: S1. Obtain the file data to be backed up from the sonar database system. The file data includes underwater acoustic signal data, which originates from the sonar equipment in the underwater detection system. S2. The file data is segmented into multiple data blocks. The size of each data block is dynamically adjusted according to a preset block size parameter, and a unique identifier is generated for each data block. S3. Select multiple distributed storage nodes to store the data block based on node availability, storage capacity, network latency, and geographical distribution factors, and evaluate the node status in real time through a node health monitoring module; transmit the data block to the selected storage nodes for storage, using an encryption protocol during the transmission process; S4. Update backup metadata in the sonar database system to record the storage location and backup status of each data block; periodically verify the integrity and availability of backup data; S5. Execute the data recovery process. When data loss or corruption is detected, locate and recover the original file data based on the backup metadata. S6. Based on the real-time operational requirements of the underwater detection system and the importance level of the underwater acoustic data, dynamically adjust the backup frequency and redundancy level.

[0006] Furthermore, when performing file data fragmentation, the block size parameter is dynamically adjusted as follows: In the formula, Indicates the size of the data block; Indicates the amount of file data; Indicates the file type coefficient; and To adjust constants; The file types include the format characteristics of underwater acoustic signal data, and the file data volume is calculated by monitoring the storage load of the sonar database system in real time. Dynamic adjustment of the block size parameter also includes adjusting the block size based on network bandwidth limitations to minimize transmission latency.

[0007] Furthermore, in step S2, when generating the identifier, the calculation of the identifier includes hashing the data block content using the SHA-256 hash function and combining it with the concatenation operation of metadata; wherein the metadata includes the timestamp of the data block, the source device identifier, and the data priority, the source device identifier being based on the unique code of the sonar device, and the data priority being assigned according to the target detection importance of the underwater acoustic signal data; the identifier generation process also includes using a digital signature algorithm to ensure the immutability of the identifier.

[0008] Furthermore, when selecting multiple distributed storage nodes, a weighted scoring model is used to evaluate the nodes. The scoring is based on factors such as node availability, storage capacity, network latency, and geographical distribution. The scoring formula is as follows: In the formula, This indicates the overall score of the nodes; Indicates node availability; Indicates the node's storage capacity; Indicates network latency; Indicates geographical distribution factors; , , , Let be the weighting coefficient, satisfying + + + =1; The node health monitoring module assesses the node status in real time through a heartbeat mechanism and prioritizes nodes with high scores based on the scoring results.

[0009] Furthermore, in step S3, during the data block transmission process, the encryption protocol used is AES-256 encryption. The encryption process includes generating a symmetric key and exchanging keys using a public key infrastructure. The transmission also includes data compression processing to reduce network bandwidth usage. The compression algorithm is based on the LZ77 algorithm and adaptively adjusts the compression level.

[0010] Furthermore, in step S4, the backup metadata includes data block identifiers, storage node addresses, backup timestamps, and verification information; The update process uses transactional database operations to ensure the consistency and atomicity of metadata; Backup metadata is stored in JSON format with a nested structure. The data block identifier serves as the primary key, the storage node address is a combination of IP address and port number, the backup timestamp records the data storage completion time, and the verification information is the CRC32 value of the data block. The update process also includes building a metadata index, using B-tree indexes to optimize query efficiency, and periodically compressing metadata storage space; the metadata is synchronized with the main database of the sonar database system and automatically updated through a trigger mechanism after data blocks are stored.

[0011] Furthermore, the verification of backup data includes checksum verification and node health checks; among which, checksum verification calculates the data integrity difference rate by calculating the hash value of the data block and comparing it with the original value. : In the formula, This represents the hash value of the data block currently being calculated; This represents the hash value of the original stored data block; like If the value exceeds a preset threshold, the data block is marked as corrupted. Node health checks include monitoring the online status and response time of storage nodes by sending probe packets and recording response latency. If the response latency exceeds a threshold, a node replacement process is triggered.

[0012] Furthermore, during the data recovery process, when data loss or corruption is detected, the recovery process includes: locating relevant data blocks based on backup metadata, retrieving data blocks from storage nodes, reassembling the data blocks into original file data, and verifying the correctness of the reassembled data; the reassembly process uses data block sequence numbers and identifiers for sorting, and ensures data consistency through checksums and verifications; the recovery process also includes priority scheduling, allocating recovery resources based on the importance level of the underwater acoustic data, with data of higher importance being recovered first; the recovery steps use multi-threaded parallel processing to improve reassembly efficiency, and logs are recorded during the recovery process for fault analysis.

[0013] Furthermore, the importance level is set based on the sonar type of the data source and the target priority; the decision model used is adjusted, and its expression is: In the formula, Indicates the backup frequency adjustment factor; This represents the real-time operational demand coefficient of the underwater detection system, calculated based on system load and task priority. The importance level coefficient of underwater acoustic data is derived based on the mapping between sonar type and target priority. and The weight parameters are optimized through training with historical data; Redundancy level based on The redundancy level is dynamically set, including the number of data replicas and the density of storage node distribution. Dynamic adjustments also include policy execution monitoring, which optimizes parameters through feedback mechanisms to ensure that backup policies work in tandem with the underwater detection system.

[0014] The beneficial effects of this invention are as follows: This invention improves the efficiency and reliability of sonar data backup, reduces the risk of single points of failure through distributed storage, dynamically adjusts and optimizes resource usage, and ensures the real-time availability and security of data in military environments. Attached Figure Description

[0015] To make the objectives, technical solutions, and beneficial effects of this invention clearer, the following figures are provided for illustration: Figure 1 This is a flowchart of an embodiment of the present invention. Detailed Implementation

[0016] like Figure 1 As shown, this invention discloses a distributed file backup method for a sonar database system, comprising the following steps: S1. Obtain the file data to be backed up from the sonar database system, wherein the file data includes underwater acoustic signal data, the underwater acoustic signal data originates from the sonar equipment in the underwater detection system, the sonar equipment is used to detect underwater targets and generate real-time underwater acoustic signals; S2. The file data is segmented into multiple data blocks. The size of each data block is determined according to a preset block size parameter. The block size parameter is dynamically adjusted based on the file type and data volume to ensure segmentation efficiency. A unique identifier is generated for each data block. The identifier is calculated based on the content hash value and metadata of the data block. The metadata includes the data block's timestamp, source device identifier, and data priority. S3. Select multiple distributed storage nodes to store the data block. The selection of storage nodes is based on factors such as node availability, storage capacity, network latency, and geographical distribution, and the node status is evaluated in real time through a node health monitoring module. The data block is transmitted to the selected storage node for storage, and the storage location information of the data block is recorded on the storage node. The transmission process uses an encryption protocol to ensure data security. S4. Update backup metadata in the sonar database system to record the storage location and backup status of each data block. The backup metadata includes data block identifier, storage node address, backup timestamp, and verification information. Periodically verify the integrity and availability of backup data through checksum verification and node health checks. Checksum verification includes calculating the hash value of the data block and comparing it with the original value. Node health checks include monitoring the online status and response time of storage nodes. S5. Execute the data recovery process. When data loss or corruption is detected, locate and retrieve relevant data blocks based on backup metadata, reassemble and verify them to restore the original file data. S6. Dynamically adjust backup strategy: Based on the real-time operational requirements of the underwater detection system and the importance level of the underwater acoustic data, automatically adjust the backup frequency and redundancy level, where the importance level is set according to the sonar type of the data source and the target priority.

[0017] In this scheme, firstly, file data containing underwater acoustic signal data is obtained from the sonar database system. This data originates from sonar equipment in the underwater detection system and is used to detect underwater targets. Next, the file data is fragmented into multiple data blocks. The size of each data block is dynamically adjusted according to preset parameters to accommodate different file types and data volumes. Then, a unique identifier is generated for each data block by calculating the content hash value and metadata (such as timestamps and source device identifiers). When selecting distributed storage nodes, factors such as node availability, storage capacity, network latency, and geographical distribution are considered, and a node health monitoring module performs real-time evaluation. Data block transmission uses encryption protocols (such as TLS) to ensure security. Backup metadata update records the storage location and status, and integrity is periodically verified through checksums and node checks. The data recovery process retrieves and reassembles data blocks based on metadata when loss is detected. The backup strategy is dynamically adjusted based on the needs of the underwater detection system and the importance of the data. This method improves the efficiency and reliability of sonar data backup, reduces the risk of single points of failure through distributed storage, dynamically adjusts and optimizes resource usage, and ensures real-time availability and security of data in military environments.

[0018] In one embodiment of the present invention, when processing file data into chunks, the size of the data block is determined by a dynamic adjustment algorithm. This algorithm calculates the block size based on the file type and the amount of data, wherein the file type includes raw underwater acoustic signal files, compressed files, or metadata files, and the amount of data is obtained by real-time monitoring of file size changes. The dynamic adjustment algorithm calculates the data block size using the following formula: In the formula, Indicates the size of the data block, in bytes; Indicates the file data size, in bytes; This represents the file type coefficient. Based on the file type, the coefficient is preset to 1.0 for the original underwater acoustic signal file, 0.5 for the compressed file, and 0.3 for the metadata file. and To adjust the constant, it was obtained through training with historical backup data, and its value range is [value range missing]. , ; During fragmentation processing, based on the calculated The value divides the file data into multiple data blocks, ensuring that the size of each data block is within a preset range, and reduces the resource overhead in the fragmentation process through iterative optimization; The fragmentation process also includes data block boundary alignment, using a byte alignment algorithm to avoid data fragmentation, and attaching a fragment sequence number to each data block to facilitate subsequent reassembly.

[0019] In this scheme, for large files, the formula automatically increases the block size to reduce the number of fragments and lower processing overhead; for high-priority data, constants are adjusted to prioritize processing. Dynamic adjustment also considers network bandwidth, optimizing block size by monitoring transmission latency in real time. By dynamically adjusting the block size, the adaptability and efficiency of fragment processing are improved, backup time is reduced, and system performance is maintained under high load conditions, making it particularly suitable for processing real-time underwater acoustic signals in underwater detection systems.

[0020] In one embodiment of the present invention, when generating a unique identifier for each data block, the identifier calculation combines the content hash value of the data block and metadata, wherein the metadata includes the data block's timestamp, source device identifier, and data priority; the calculation uses a composite hash function, the expression of which is: in, Represents a unique identifier for a data block; This indicates the first hash function, which uses the SHA-256 algorithm. This indicates the second hash function, which uses the MD5 algorithm; This represents the content of the data block; Represents the XOR operation; The encoded value representing metadata is formed by concatenating the timestamp, source device identifier, and data priority into a binary sequence. Data priority is set based on the importance of the underwater acoustic signal data. The importance level is based on the sonar equipment type and target priority, where the target priority is assigned in real time by the underwater detection system. The step of generating identifiers also includes a verification process, which compares the calculated... Conflict detection is performed with the pre-stored identifier library. If a conflict is found, the hash value is recalculated, and the metadata encoding is adjusted using a random number seed to ensure the uniqueness of the identifier. The timestamp accuracy of the metadata is at the millisecond level, and the source device identifier is a unique code for the sonar device.

[0021] In this scheme, the identifier generation step uses the SHA-256 hash function to hash the data block content, combined with a concatenation operation of metadata such as timestamps, source device identifiers, and data priorities. The source device identifier is based on the sonar device's unique code, and the data priority is assigned according to the target's detection importance (e.g., high-threat targets). The identifier generation process also includes a digital signature algorithm (such as RSA) to ensure immutability and prevent data forgery. For example, for critical underwater acoustic signal data, identifier generation prioritizes high-security hash processing.

[0022] This solution enhances the uniqueness and security of data identification by combining cryptographic hashes and metadata to prevent data tampering and ensure the authenticity and traceability of data in distributed storage, making it suitable for military applications with high security requirements.

[0023] In one embodiment of the present invention, when selecting multiple distributed storage nodes, node selection is based on factors such as node availability, storage capacity, network latency, and geographical distribution, and the node status is evaluated using a multi-objective optimization model; the multi-objective optimization model uses a weighted scoring function, the expression of which is: In the formula, This indicates the overall score of the nodes; Indicates node availability, which is obtained in real time through the node health monitoring module, and has a value of 0 or 1; This indicates the node's storage capacity, in bytes. This indicates network latency, measured in milliseconds. This represents the geographic distribution factor, calculated based on the geographic distance between the node and the sonar database system; the closer the distance, the higher the factor value. , , , The weighting coefficients are dynamically adjusted based on the requirements of the underwater detection system to meet [the needs of the system]. + + + =1; The node health monitoring module periodically sends heartbeat packets to detect the online status and response time of nodes. If a node's score is lower than a threshold, it is excluded from the candidate node list. The selection step also includes a load balancing algorithm to distribute data blocks to nodes with high scores and to predict future availability based on the nodes' historical performance data.

[0024] In this solution, a mathematical scoring model is used to optimize the node selection process, improve storage efficiency and data availability, reduce the impact of network latency, and ensure rapid data access and recovery in a distributed environment.

[0025] In one embodiment of the present invention, when transmitting data blocks to a selected storage node for storage, the transmission process uses an encryption protocol to ensure data security. This encryption protocol is a hybrid protocol based on asymmetric and symmetric encryption. The asymmetric encryption uses the RSA algorithm with a key length of at least 2048 bits for exchanging session keys. The symmetric encryption uses the AES algorithm with a key length of at least 256 bits for encrypting the data block content. The encryption protocol also includes data integrity verification, using the HMAC algorithm to generate a message authentication code, the calculation of which is based on the data block content and the session key. During transmission, the data block is divided into transmission packets, each packet is appended with a sequence number and a checksum, and packet loss is handled through a retransmission mechanism. The network protocol for this transmission step is TCP / IP, and the transmission rate is dynamically adjusted according to network conditions, using a congestion control algorithm to avoid network overload. In this scheme, the AES-256 encryption protocol is used during the data block transmission step. The encryption process includes generating a symmetric key and exchanging it securely via Public Key Infrastructure (PKI). Furthermore, the transmission includes data compression processing, using the LZ77 algorithm to adaptively adjust the compression level based on the data block size and network conditions. For example, for large underwater acoustic data blocks, the compression level is increased to reduce transmission time.

[0026] This solution enhances data transmission security by reducing bandwidth consumption through encryption and compression, improving backup speed, and maintaining data integrity in harsh network environments, making it suitable for real-time underwater detection systems.

[0027] In one embodiment of the present invention, in the step of updating backup metadata in the sonar database system, the backup metadata includes a data block identifier, a storage node address, a backup timestamp, and verification information; the update process uses transactional database operations to ensure the consistency and atomicity of the metadata; the backup metadata is stored in JSON format with a nested structure, wherein the data block identifier serves as the primary key, the storage node address is a combination of IP address and port number, the backup timestamp records the data storage completion time, and the verification information is the CRC32 value of the data block; the update step also includes metadata index construction, using a B-tree index to optimize query efficiency, and periodically compressing the metadata storage space; the metadata is synchronized with the main database of the sonar database system and is automatically updated after the data block storage is completed through a trigger mechanism. In this solution, backup metadata includes not only basic information but also the access frequency of data blocks and historical recovery records. Access frequency is calculated by monitoring read operations on data blocks, and historical recovery records include the success rate and timestamps of previous recovery attempts. Metadata updates use transactional database operations (such as ACID properties) to ensure consistency and prevent data loss.

[0028] This solution improves the granularity of backup management by expanding metadata content, supports intelligent data recovery and policy adjustment, reduces operation and maintenance costs, and enhances system reliability.

[0029] In one embodiment of the present invention, when periodically verifying the integrity and availability of backup data, the verification includes checksum verification and node health checks; checksum verification calculates the difference rate by calculating the hash value of the data block and comparing it with the original value using the following formula: In the formula, Indicates the data integrity discrepancy rate; This represents the hash value of the data block currently being calculated; This represents the hash value of the original stored data block; like If the value exceeds a preset threshold, the data block is marked as corrupted. Node health checks include monitoring the online status and response time of storage nodes, sending probe packets and recording response delays. If the response delay exceeds a threshold, a node replacement process is triggered. The verification steps also include periodic full scans and incremental checks. Full scans are performed monthly, and incremental checks are performed daily, locating the data block to be verified based on backup metadata.

[0030] In this scheme, checksum verification uses the Cyclic Redundancy Check (CRC) algorithm to calculate the hash value of the data block and compare it with the original value. Node health checks include monitoring the online status and response time of storage nodes, testing node availability by simulating data access, such as periodically sending test requests to evaluate node performance.

[0031] This solution ensures the long-term integrity and availability of backup data, promptly identifies and fixes problems through regular verification, reduces the risk of data corruption, and is suitable for sonar database systems with high availability requirements.

[0032] In one embodiment of the present invention, when data loss or corruption is detected during the data recovery process, the recovery process includes: locating relevant data blocks based on backup metadata, retrieving the data blocks from the storage node, reassembling the data blocks into original file data, and verifying the correctness of the reassembled data; the reassembly process uses data block sequence numbers and identifiers for sorting, and ensures data consistency through checksum verification; the recovery process also includes priority scheduling, allocating recovery resources based on the importance level of the underwater acoustic data, and prioritizing the recovery of data with higher importance levels; the recovery steps use multi-threaded parallel processing to improve reassembly efficiency, and logs are recorded during the recovery process for fault analysis.

[0033] In this solution, during the data recovery process, high-importance data blocks are prioritized for recovery. These blocks are identified by the target priority markers of the underwater acoustic data (e.g., high-threat target data). The recovery process utilizes multi-threading technology to retrieve data blocks in parallel and verifies the integrity of the reconstructed file data to ensure accurate data restoration. By prioritizing recovery and employing multi-threading, the data recovery speed is accelerated, ensuring the rapid availability of critical mission data and enhancing the emergency response capability of the underwater detection system.

[0034] In one embodiment of the present invention, in step S6, the importance level of the underwater detection system and the underwater acoustic data are dynamically adjusted, with the importance level set according to the sonar type of the data source and the target priority; the decision model used for adjustment is expressed as: In the formula, Indicates the backup frequency adjustment factor; This represents the real-time operational demand coefficient of the underwater detection system, calculated based on system load and task priority. The importance level coefficient of underwater acoustic data is derived based on the mapping between sonar type and target priority. and The weight parameters are optimized through training with historical data; Redundancy level based on The values ​​are dynamically set, and the redundancy level includes the number of data replicas and the density of storage node distribution; the dynamic adjustment step also includes policy execution monitoring, and the parameters are optimized through a feedback mechanism to ensure that the backup policy works in conjunction with the underwater detection system.

[0035] In this scheme, the backup frequency is determined by... Adjustments, among which, For backup frequency, Based on the base frequency, The importance coefficient is calculated based on sonar type and target priority. Redundancy level is determined by replication factor Adjustments were made to ensure data redundancy was optimized based on real-time operational needs.

[0036] This solution enables adaptive adjustment of backup strategies, optimizes resource allocation through mathematical formulas, improves system efficiency, and maintains data security in a variable underwater acoustic environment.

[0037] Finally, it should be noted that the above preferred 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 through the above preferred embodiments, those skilled in the art should understand that various changes can be made to it in form and detail without departing from the scope defined by the claims of the present invention.

Claims

1. A distributed file backup method for a sonar database system, characterized in that, Includes the following steps: S1. Obtain the file data to be backed up from the sonar database system. The file data includes underwater acoustic signal data, which originates from the sonar equipment in the underwater detection system. S2. The file data is segmented into multiple data blocks. The size of each data block is dynamically adjusted according to a preset block size parameter, and a unique identifier is generated for each data block. S3. Select multiple distributed storage nodes to store the data blocks based on node availability, storage capacity, network latency, and geographical distribution factors, and evaluate the node status in real time through the node health monitoring module. The data block is transmitted to the selected storage node for storage, and the transmission process uses an encryption protocol. S4. Update the backup metadata in the sonar database system to record the storage location and backup status of each data block; Regularly verify the integrity and availability of backup data; S5. Execute the data recovery process. When data loss or corruption is detected, locate and recover the original file data based on the backup metadata. S6. Based on the real-time operational requirements of the underwater detection system and the importance level of the underwater acoustic data, dynamically adjust the backup frequency and redundancy level.

2. The file distributed backup method for a sonar database system according to claim 1, characterized in that: When processing file data in chunks, the block size parameter is dynamically adjusted as follows: In the formula, Indicates the size of the data block; Indicates the amount of file data; Indicates the file type coefficient; and To adjust constants; The file types include the format characteristics of underwater acoustic signal data, and the file data volume is calculated by monitoring the storage load of the sonar database system in real time. Dynamic adjustment of the block size parameter also includes adjusting the block size based on network bandwidth limitations to minimize transmission latency.

3. The distributed file backup method for a sonar database system according to claim 1, characterized in that: In step S2, when the identifier is generated, the calculation of the identifier includes hashing the data block content using the SHA-256 hash function and combining it with the concatenation operation of the metadata. The metadata includes the timestamp of the data block, the source device identifier, and the data priority. The source device identifier is based on the unique code of the sonar device, and the data priority is assigned according to the target detection importance of the underwater acoustic signal data. The identifier generation process also includes using digital signature algorithms to ensure the immutability of the identifier.

4. The distributed file backup method for a sonar database system according to claim 1, characterized in that: When selecting multiple distributed storage nodes, a weighted scoring model is used to evaluate the nodes. The score is based on the node's availability, storage capacity, network latency, and geographical distribution factors. The scoring formula is as follows: In the formula, This indicates the overall score of the nodes; Indicates node availability; Indicates the node's storage capacity; Indicates network latency; Indicates geographical distribution factors; , , , Let be the weighting coefficient, satisfying + + + =1; The node health monitoring module assesses the node status in real time through a heartbeat mechanism and prioritizes nodes with high scores based on the scoring results.

5. The file distributed backup method for a sonar database system according to claim 1, characterized in that: In step S3, during the data block transmission process, the encryption protocol used is AES-256 encryption. The encryption process includes generating a symmetric key and exchanging keys using a public key infrastructure. The transmission also includes data compression processing to reduce network bandwidth usage. The compression algorithm is based on the LZ77 algorithm and adaptively adjusts the compression level.

6. The file distributed backup method for a sonar database system according to claim 1, characterized in that: In step S4, the backup metadata includes data block identifiers, storage node addresses, backup timestamps, and verification information; The update process uses transactional database operations to ensure the consistency and atomicity of metadata; Backup metadata is stored in JSON format with a nested structure. The data block identifier serves as the primary key, the storage node address is a combination of IP address and port number, the backup timestamp records the data storage completion time, and the verification information is the CRC32 value of the data block. The update steps also include building a metadata index, using B-tree indexes to optimize query efficiency, and periodically compressing metadata storage space; Metadata is synchronized with the main database of the sonar database system and is automatically updated after data blocks are stored through a trigger mechanism.

7. The distributed file backup method for a sonar database system according to claim 1, characterized in that: Backup data verification includes checksum verification and node health checks; checksum verification calculates the data integrity difference rate by calculating the hash value of the data block and comparing it with the original value. : In the formula, This represents the hash value of the data block currently being calculated; This represents the hash value of the original stored data block; like If the value exceeds a preset threshold, the data block is marked as corrupted. Node health checks include monitoring the online status and response time of storage nodes by sending probe packets and recording response latency. If the response latency exceeds a threshold, a node replacement process is triggered.

8. The distributed file backup method for a sonar database system according to claim 1, characterized in that: When data loss or corruption is detected during the data recovery process, the recovery process includes: locating relevant data blocks based on backup metadata, retrieving data blocks from storage nodes, reassembling the data blocks into original file data, and verifying the correctness of the reassembled data; the reassembly process uses data block sequence numbers and identifiers for sorting, and ensures data consistency through checksums and verification; the recovery process also includes priority scheduling, allocating recovery resources based on the importance level of the underwater acoustic data, with data of higher importance being recovered first; the recovery steps use multi-threaded parallel processing to improve reassembly efficiency, and logs are recorded during the recovery process for fault analysis.

9. The file distributed backup method for a sonar database system according to claim 1, characterized in that: In step S6, the importance level of the underwater detection system and the underwater acoustic data are dynamically adjusted based on the real-time operational requirements of the underwater detection system. The importance level is set according to the sonar type and target priority of the data source. Adjust the decision model used; its expression is: In the formula, Indicates the backup frequency adjustment factor; This represents the real-time operational demand coefficient of the underwater detection system, calculated based on system load and task priority. The importance level coefficient of underwater acoustic data is derived based on the mapping between sonar type and target priority. and The weight parameters are optimized through training with historical data; Redundancy level based on The redundancy level is dynamically set, including the number of data replicas and the density of storage node distribution. Dynamic adjustments also include policy execution monitoring, which optimizes parameters through feedback mechanisms to ensure that backup policies work in tandem with the underwater detection system.

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