Cloud backup and recovery system and method for geological survey data

Abstract

The invention relates to the technical field of data backup and recovery, in particular to a cloud backup and recovery system and method for geological survey data, and the method comprises the steps: data collection: obtaining geological data through a data collection device; data preprocessing: processing the collected data through a data preprocessing module; data encryption: encrypting the processed data through Twofish symmetric encryption and ECC asymmetric encryption by a data encryption module; remote backup: the encrypted data is stored in a distributed storage system of a cloud backup module and managed by a MongoDB database, backup operation is controlled through a backup strategy and a trigger mechanism, and timing backup and event trigger backup are supported; intelligent data recovery: an intelligent data recovery module is responsible for incremental recovery and consistency verification of data, and data integrity is ensured by using a log recovery mechanism; and the user interface is used for providing backup strategy setting, state checking and recovering and log monitoring functions through a user interface so as to be operated and managed by a user.

Description

Technical Field

[0001] The present invention relates to the technical field of data backup and recovery, and in particular to a cloud-based backup and recovery system and method for geological survey data. Background Art

[0002] With the development of geological survey technology, the collection and management of geological data has become increasingly important. Traditional data backup methods usually rely on local storage, which easily leads to problems such as data loss risk and low backup efficiency. In the prior art, many geological survey data are stored in hard disks or LAN servers. The disadvantages of this method are: 1. Insufficient data security: Once the equipment is damaged or attacked by malicious attacks, it may lead to permanent loss of data. 2. Low backup efficiency: Manual backup is cumbersome and error-prone, and it is difficult to achieve high-frequency data backup. 3. Long recovery time: Data recovery operations are complicated and the recovery time is long, which affects the timeliness of engineering decisions. 4. Lack of flexibility: Traditional backup methods cannot flexibly respond to different types of data requirements, such as insufficient compatibility of multiple data formats such as geological models and geographic information. Therefore, there is an urgent need for a system that can automatically, quickly and safely back up and restore geological survey data to improve the reliability and efficiency of data management.

[0003] The information disclosed in this background technology section is only intended to deepen the understanding of the overall background technology of the present invention, and should not be regarded as acknowledging or suggesting in any form that the information constitutes the prior art already known to those skilled in the art. Summary of the invention

[0004] The purpose of the present invention is to provide a cloud backup and recovery system and method for geological survey data to solve the technical problems existing in the prior art.

[0005] In order to achieve the above object, the present invention adopts the following technical solutions:

[0006] In a first aspect, the present invention provides a cloud backup and recovery method for geological survey data, which comprises the following steps:

[0007] Data collection: Obtain geological data through data collection equipment;

[0008] Data preprocessing: The collected data is processed through the data preprocessing module;

[0009] Data encryption: The processed data is encrypted through the data encryption module using Twofish symmetric encryption and ECC asymmetric encryption;

[0010] Remote backup: Encrypted data is stored in the distributed storage system of the cloud backup module and managed by the MongoDB database. The backup operation is controlled by the backup strategy and trigger mechanism, supporting scheduled backup and event-triggered backup.

[0011] Intelligent data recovery: The intelligent data recovery module is responsible for incremental data recovery and consistency verification, and the log recovery mechanism is used to ensure data integrity;

[0012] User interface: The user interface provides backup policy settings, status viewing and recovery, and log monitoring functions for user operation and management.

[0013] Preferably, in the step of data acquisition, it includes: acquiring geological data in real time, including geological structure, soil moisture, temperature and pressure, by connecting with geological sensing detection elements.

[0014] Preferably, the step of data preprocessing includes: data format standardization and data cleaning.

[0015] Preferably, in the step of data encryption, the following steps are included:

[0016] Data grouping, including:

[0017] Divide geological survey data into 128-bit data blocks; use PKCS to fill and process data blocks;

[0018] Twofish encryption, including:

[0019] Key generation: Use CSPRNG to generate 256-bit keys; Feistel network: 16 rounds of encryption operations, including subkey addition, S-box substitution, and MDS matrix transformation; output ciphertext blocks;

[0020] ECC key encryption, including:

[0021] ECC key pair generation: select elliptic curve parameters to generate public and private key pairs; use the public key to encrypt the Twofish key; output the encrypted key pair;

[0022] Data encryption is complete.

[0023] Preferably, the cloud backup step includes the following steps:

[0024] Data monitoring, including:

[0025] Use inotify to monitor data file changes; detect data change rates and abnormal events;

[0026] Trigger backup operations, including:

[0027] Set trigger conditions: the data change rate exceeds the threshold or anomalies are detected; the backup process is automatically triggered;

[0028] Data transmission and storage, including:

[0029] Data is transmitted via HTTPS protocol and stored in the Amazon S3 distributed system using TLS encryption. A replica mechanism is used for redundant storage.

[0030] Cloud backup is complete.

[0031] Preferably, in the step of intelligent data recovery, the following steps are included:

[0032] Backup options, including:

[0033] The user selects the backup time point to be restored through the section, and the system searches for the backup file through the database;

[0034] Data decryption, including:

[0035] Get the encrypted data from the cloud, decrypt the Twofish key using the ECC private key, and then decrypt the data;

[0036] Incremental recovery, including:

[0037] The Rsync algorithm is used to achieve incremental recovery. By comparing the differences between the source data and the target data, only the changed parts are transmitted and restored.

[0038] Data consistency check, including:

[0039] After the recovery is complete, the SHA-256 hash function is used to generate a data checksum to verify data integrity;

[0040] Log recovery mechanism, including:

[0041] The system records every data change log, quickly locates and restores data, and uses a log-based recovery mechanism to replay the log to restore data when data is damaged or lost;

[0042] Performance improvements, including:

[0043] Through load balancing technology, data access requests are distributed, data access and recovery performance are optimized, and system response speed is improved. Load balancing is achieved through polling or minimum connection number strategy.

[0044] In a second aspect, the present invention provides a cloud backup and recovery system for geological survey data, which adopts the cloud backup and recovery method for geological survey data, including: data acquisition equipment, data preprocessing module, data encryption module, cloud backup module, intelligent recovery module, user interface module;

[0045] The data acquisition device is used for data acquisition and obtaining geological data; the data preprocessing module is used to process the collected data; the data encryption module encrypts the processed data through Twofish symmetric encryption and ECC asymmetric encryption; the cloud backup module is used to store the encrypted data in a distributed storage system, which is managed by a MongoDB database, and the backup operation is controlled by a backup strategy and a trigger mechanism, supporting scheduled backup and event-triggered backup; the intelligent recovery module is responsible for incremental recovery and consistency verification of data, and uses a log recovery mechanism to ensure data integrity; the user interface module is used to provide backup strategy settings, status viewing and recovery, and log monitoring functions.

[0046] By adopting the above technical solution, the present invention has the following beneficial effects:

[0047] (1) Multiple encryption technology: The present invention uses the Twofish symmetric encryption algorithm and the elliptic curve asymmetric encryption algorithm (ECC) to achieve multiple encryption of data. The Twofish algorithm encrypts data through the Feistel network structure and S-box substitution, providing efficient encryption performance and security. The ECC algorithm generates a public-private key pair through point operations on the elliptic curve, which is used to encrypt and transmit the Twofish key, ensuring the security of the key during transmission. The combination of the two can effectively protect the security of data during backup and transmission.

[0048] (2) Automatic cloud backup: The present invention realizes automatic data backup through cloud computing technology. It uses file system event monitoring (inotify) and database triggers to monitor data changes in real time and trigger backup operations. Users can customize the backup frequency and strategy. The system automatically executes the backup task according to the timing and event trigger strategy set by the user, ensuring that the geological survey data is backed up in time when changes occur, and ensuring the security and integrity of the data.

[0049] (3) Fast data recovery: The present invention integrates an intelligent data recovery algorithm and uses Rsync incremental recovery and log-based recovery mechanisms to achieve fast data recovery. The Rsync algorithm detects differences in data blocks and only transmits and recovers the changed parts, thereby shortening the recovery time. The log recovery mechanism records and replays data operation logs to achieve fast data recovery from the most recent backup point, ensuring the integrity and accuracy of the restored data.

[0050] (4) Distributed storage architecture: The present invention adopts a distributed storage architecture and stores data in multiple cloud nodes through cloud storage services. The consistent hashing algorithm is used for data sharding and load balancing to achieve efficient distribution and redundant storage of data, thereby improving data reliability and availability. This architecture can reduce the risk of single point failures and ensure that data is still available when a node fails. BRIEF DESCRIPTION OF THE DRAWINGS

[0051] In order to more clearly illustrate the specific implementation methods of the present invention or the technical solutions in the prior art, the drawings required for use in the specific implementation methods or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are some implementation methods of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0052] Figure 1 A schematic diagram of the architecture of the cloud-based backup and recovery system for geological survey data provided by the present invention;

[0053] Figure 2 The data encryption flow chart provided by the present invention;

[0054] Figure 3 A cloud backup flow chart provided by the present invention;

[0055] Figure 4 The data recovery flow chart provided by the present invention. DETAILED DESCRIPTION

[0056] The technical solution of the present invention will be described clearly and completely below in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0057] The specific implementation of the present invention is described in detail below in conjunction with the accompanying drawings. It should be understood that the specific implementation described here is only used to illustrate and explain the present invention, and is not used to limit the present invention.

[0058] Embodiment 1

[0059] Combination Figures 1 to 4As shown, this embodiment provides a cloud backup and recovery method for geological survey data, which includes the following steps: data acquisition: obtaining geological data through a data acquisition device; data preprocessing: processing the collected data through a data preprocessing module; data encryption: encrypting the processed data through Twofish symmetric encryption and ECC asymmetric encryption through a data encryption module; remote backup: the encrypted data is stored in a distributed storage system of a cloud backup module and managed by a MongoDB database. The backup operation is controlled by a backup strategy and a trigger mechanism, and scheduled backup and event-triggered backup are supported; intelligent data recovery: the intelligent data recovery module is responsible for incremental recovery and consistency verification of data, and a log recovery mechanism is used to ensure data integrity; user interface: backup strategy settings, status viewing and recovery, and log monitoring functions are provided through a user interface for user operation and management.

[0060] Specifically, this embodiment adopts the following technical solutions:

[0061] 1. Data Collection and Processing

[0062] 1.1. Data collection: Through the connection with geological sensors and equipment, geological data including geological structure, soil moisture, temperature, pressure, etc. are obtained in real time.

[0063] 1.2. Data format standardization: Data is stored in JSON format, and fields include timestamp, geographic location, data type and value.

[0064] {

[0065] "timestamp": "2023-08-04T00:00:00Z",

[0066] "latitude": 35.6895,

[0067] "longitude": 139.6917,

[0068] "data": {

[0069] "soil_moisture": 0.23,

[0070] "temperature": 298.15,

[0071] "pressure": 101.3

[0072] }

[0073] }

[0074] 1.3. Data cleaning:

[0075] 1.31. Remove noise: Use the Moving Average Filter to smooth the data. This method calculates the average value of consecutive data points to reduce data noise. The specific formula is as follows:

[0076]

[0077] in:

[0078] : The smoothed value of the i-th data point

[0079] n: window size

[0080] : Original data points

[0081] 1.32. Outlier detection and removal: Use the Z-score method to detect outliers. Calculate the Z-score of each data point:

[0082]

[0083] in:

[0084] : Data Points

[0085] : The mean of the data set

[0086] : The standard deviation of the data set

[0087] If (| | > 3), it is considered as an outlier and removed.

[0088] 1.33. Missing value filling: Linear interpolation is used to fill missing data. Assume that the missing value is The values ​​before and after are and , then the interpolation calculation is:

[0089]

[0090] in:

[0091] : Missing values

[0092] : The time point corresponding to the missing value

[0093] , : Before and after time points.

[0094] 2. Data encryption: Twofish symmetric encryption algorithm and elliptic curve asymmetric encryption algorithm (ECC) are used for encryption during data transmission and storage to ensure data security.

[0095] 2.1.Twofish symmetric encryption:

[0096] 2.11. Encryption process:

[0097] Key Generation: Generate 128, 192, or 256-bit keys using a cryptographically secure random number generator (CSPRNG).

[0098]

[0099] : Key

[0100] : Key length

[0101] 2.12. Data grouping: Divide the data into 128-bit blocks and use PKCS7 to fill in the data that is not a block.

[0102] 2.13. Feistel network encryption: Each data block undergoes 16 rounds of encryption operations, each round includes:

[0103] Subkey plus: XOR the data block with the wheel key.

[0104] S-box substitution: Use S-boxes for nonlinear substitution to improve security. S-boxes are fixed substitution matrices implemented by table lookup.

[0105] Maximum distance separable matrix (MDS matrix): The hybrid transformation enhances encryption strength and is implemented through matrix multiplication.

[0106]

[0107] in:

[0108] : Output vector

[0109] :MDS matrix

[0110] : Input vector

[0111] 2.14. Round function: nonlinear operation combined with S-box and MDS matrix to achieve complex transformation.

[0112]

[0113] in:

[0114] : Ciphertext block

[0115] : Encryption function

[0116] : Plaintext block

[0117] 2.2.ECC asymmetric encryption:

[0118] 2.21. Algorithm principle: ECC is based on the mathematical properties of elliptic curves and implements encryption operations through addition of points on the curve and scalar multiplication.

[0119] 2.22. Encryption process:

[0120] 1. Key pair generation: Select elliptic curve parameters to generate public and private key pairs.

[0121]

[0122] in:

[0123] : Public Key

[0124] : Private Key

[0125] : Elliptic curve base point

[0126] 2. Encryption operation: Encrypt the Twofish key using the public key.

[0127]

[0128] in:

[0129] : Encryption Key

[0130] : Session key (random number)

[0131] :Twofish key

[0132] 3. Decryption operation: Decrypt with the private key to recover the Twofish key.

[0133]

[0134] in:

[0135] , : Encryption key pair

[0136] : Private key.

[0137] 3. Cloud backup

[0138] The system realizes automatic backup of geological survey data through the cloud backup module. The backup process supports two modes: timing and event triggering. The system can automatically back up geological survey data in data updates and abnormal situations to ensure data security and high availability.

[0139] 3.1 Automatic backup strategy:

[0140] Scheduled backup: Users can set the backup frequency through the system interface, such as daily or weekly automatic backup. Scheduled tasks automatically perform backup operations at specified times through the Cron scheduler.

[0141] 3.2 Event-triggered backup: The system detects data changes or abnormal situations in real time, and automatically performs backup operations based on the set trigger conditions.

[0142] Trigger mechanism:

[0143] Real-time monitoring of data updates and exceptions is achieved by using file system event monitoring (inotify) and database triggers.

[0144] Trigger criteria:

[0145] Data change monitoring: Monitor data file changes in the file system through inotify. When file modification, creation, or deletion events are detected, determine whether the data update rate exceeds the set threshold. If it exceeds the threshold, the backup is triggered.

[0146] Anomaly detection: Monitor abnormal patterns in data tables (such as sudden data growth) in real time through database triggers. Once abnormal patterns or risk events are detected, backups are triggered immediately.

[0147] 3.3 Database usage:

[0148] The system uses MongoDB for backup management. MongoDB supports fast data read and write operations and horizontal expansion capabilities.

[0149] Operation statement:

[0150] Insert backup data:

[0151] db.backup.insert({

[0152] "timestamp": "2023-08-04T00:00:00Z",

[0153] "encrypted_data": "<encrypted_data> ",

[0154] "backup_status": "complete"

[0155] })

[0156] Update backup status:

[0157] javascript db.backup.update( {"timestamp": "2023-08-04T00:00:00Z"},{$set: {"backup_status": "verified"}} )

[0158] 3.4 Data Transmission:

[0159] The HTTPS protocol is used for data transmission, and the TLS (Transport Layer Security) protocol is used to encrypt data to ensure the security and integrity of data during transmission.

[0160] 3.5 Data Storage:

[0161] Data is stored in a distributed file system in the cloud, using data redundancy technology to achieve high availability.

[0162] 3.6 Data Redundancy:

[0163] Through the copy mechanism of data redundancy technology and the consistent hashing algorithm, the system achieves efficient data storage and redundant backup, ensuring high availability and reliability of data in the event of a single point of failure.

[0164] Copy mechanism:

[0165] Through the copy mechanism, data is redundantly stored on multiple nodes. The system uses the distributed storage system Amazon S3 to achieve data redundancy, ensuring that data is still available in the event of a single point of failure. Multiple copies are created for each data file when it is stored, and the copies are distributed in different geographical locations and physical nodes.

[0166] Implementation steps:

[0167] 1. Data Sharding: Split large files into smaller pieces for more efficient replication and storage.

[0168] 2. Replica creation: Create multiple replicas for each data shard, usually three, distributed on different storage nodes.

[0169] 3. Off-site storage: Amazon S3's cross-region replication feature ensures that data copies are stored in different geographical locations to improve disaster recovery capabilities.

[0170] Consistent hashing algorithm:

[0171] The consistent hashing algorithm is used to achieve load balancing and data distribution in distributed storage systems. It maps storage nodes and data shards to the same hash ring, making data distribution more uniform and stable. The system uses the consistent hashing algorithm to achieve load balancing and automatic data distribution, improving storage efficiency and data reliability.

[0172] Implementation steps:

[0173] 1. Hash ring construction: Map all storage nodes into a ring structure through a hash function.

[0174] 2. Data shard mapping: Perform a hash operation on each data shard to determine its position on the hash ring.

[0175] 3. Sharded storage: Data shards are allocated to the closest storage nodes based on the location of the hash ring.

[0176] 4. Node failure handling: When a node fails, the hash ring is used to remap data shards to other nodes to achieve fast data recovery and load redistribution.

[0177] 3.7 Load balancing and data distribution:

[0178] The distributed storage system uses Amazon S3's automatic load balancing function, and uses a consistent hashing algorithm to ensure that data is evenly distributed among different nodes, reducing the load pressure on a single node and improving the overall performance and reliability of the distributed storage system.

[0179] Implementation steps:

[0180] 1. Load monitoring: Monitor the load status of each storage node in real time and obtain node performance indicators through the API interface.

[0181] 2. Dynamic adjustment: Dynamically adjust the distribution of data shards based on load monitoring results to ensure balanced load and maximize resource utilization.

[0182] 4. Intelligent data recovery

[0183] The system supports rapid data recovery from the cloud to ensure data integrity and business continuity. The recovery process is as follows:

[0184] 4.1 Backup selection: The user selects the backup time point, and the system searches for backup files through the database.

[0185] 4.2 Data decryption: Get the encrypted data from the cloud, use the ECC private key to decrypt the Twofish key, and then decrypt the data.

[0186]

[0187] in:

[0188] : Original data after decryption

[0189] : Ciphertext data

[0190] 4.3 Incremental recovery:

[0191] The system uses the Rsync algorithm to achieve incremental recovery. Rsync is an efficient data synchronization and transmission algorithm that compares the differences between source data and target data and only transmits and recovers the changed parts.

[0192] Rsync algorithm principle:

[0193] The Rsync algorithm uses rolling checksum and strong checksum SHA-1 to detect data block differences and achieve data synchronization.

[0194] 1. Block partitioning: Divide the file into blocks of fixed size.

[0195] 2. Checksum calculation: Fast checksum and strong checksum are calculated for each block.

[0196] 3. Difference identification: Compare block checksums between source and target to identify changed blocks.

[0197] 4. Differential transfer: Only the identified changed blocks are transferred.

[0198] 5. Target merge: Merge the changed blocks on the target side to complete file synchronization.

[0199] Incremental recovery process: Identifies data changes since the last backup and restores only those changes, shortening recovery time.

[0200] 4.4 Data consistency check: After recovery is complete, the SHA-256 hash function is used to generate a data check code to verify data integrity.

[0201]

[0202] in:

[0203] : Hash function

[0204] : Raw data

[0205] : Recovered data

[0206] 4.5 Log recovery mechanism:

[0207] The system records every data change log to quickly locate and restore data. It uses a log-based recovery mechanism to replay the log to restore data when data is damaged or lost.

[0208] 1. Data change log:

[0209] The system records each data change operation by implementing a WAL (Write-Ahead Logging) mechanism. Each data change generates a log record, which includes the operation details such as data insertion, update, and deletion.

[0210] Implementation steps:

[0211] Logging: Before performing data operations, the system will write detailed operation information into a log file, including timestamp, operation type, data object ID, change content, etc.

[0212] Log synchronization: Synchronize the generated log information to cloud storage to ensure that the log data is still available when the local storage fails.

[0213] Log rotation: Regularly rotate log files, archive historical logs, and clean up expired log files to maintain storage efficiency and system performance.

[0214] 2. Log replay recovery:

[0215] When data is damaged or lost, the system restores the data to a specified point in time by replaying the data operations in the log file.

[0216] Implementation steps:

[0217] 1. Log playback startup: When data corruption or loss is detected, the system automatically starts the log playback mechanism.

[0218] 2. Log file loading: Read the log files stored in the cloud and load them in chronological order.

[0219] 3. Operation reproduction: According to the operation type and data content recorded in the log, perform data insertion, update or deletion operations in sequence to restore the data to a normal state.

[0220] 4. Data verification: After completing log playback, the system performs a data consistency check to ensure that the restored data is consistent with the original data.

[0221] 4.6 Performance optimization:

[0222] Through load balancing technology, data access requests are distributed, data access and recovery performance are optimized, and system response speed is improved. Load balancing can be achieved through polling or minimum number of connections strategy.

[0223] 1. Load balancing technology:

[0224] The system uses Nginx-based load balancing technology to distribute data access requests and improve system response speed and reliability.

[0225] Implementation steps:

[0226] 1. Load balancer configuration: Deploy Nginx load balancer in front of the server and configure polling and minimum connection number strategies.

[0227] 2. Request distribution: Dynamically distribute client requests to different storage nodes based on the configured load balancing strategy.

[0228] 3. Node monitoring: Monitor the load status of each storage node in real time, and obtain the response time and number of connections of the node through the API interface.

[0229] 4. Dynamic adjustment: Automatically adjust the request distribution strategy based on monitoring results to ensure balanced load and maximize resource utilization.

[0230] 5. Failover: When a node failure is detected, requests are automatically distributed to other available nodes to achieve seamless failover.

[0231] 5. Data Transmission and Security

[0232] During the data transmission and storage process, the system ensures data security through multiple encryption technologies and security protocols.

[0233] 5.1 Transmission Protocol:

[0234] The system uses the HTTPS protocol to ensure the security of data during transmission. HTTPS is a secure version of the HTTP protocol that encrypts data through the Transport Layer Security Protocol (TLS).

[0235] TLS protocol encryption:

[0236] Handshake process: When the client and the server establish a connection, a TLS handshake is performed. During this process, both parties exchange the protocol version, encryption algorithm and other necessary information supported by encryption to determine the encryption suite to be used.

[0237] Key exchange: Use the Elliptic Curve Diffie-Hellman (ECDH) algorithm to exchange keys and generate symmetric encryption keys. This key is used to encrypt subsequent data transmission to ensure the confidentiality and integrity of communications.

[0238] Data encryption transmission: After the handshake is completed, both parties use the generated symmetric key (Twofish encryption algorithm) to encrypt the transmitted data to ensure the integrity and confidentiality of the data.

[0239] Verify integrity: Use the message authentication code (MAC) mechanism to ensure that data has not been tampered with during transmission.

[0240] Network Firewall:

[0241] Deploy a network firewall and use software firewall iptables to monitor and filter traffic, monitor traffic in and out of the network, set access control rules, and prevent unauthorized data access. The firewall can identify and block suspicious activities and protect the system from network attacks.

[0242] Implementation steps:

[0243] Rule definition: Configure firewall rules to allow legitimate traffic to pass and block unauthorized access.

[0244] Stateful Monitoring: Enable stateful monitoring to track the status and behavior of the connection.

[0245] Log analysis: Regularly analyze firewall logs to detect and respond to suspicious activities.

[0246] 5.4 Intrusion Detection System (IDS):

[0247] Implement an intrusion detection system, deploy a network-based IDS, use Snort for real-time detection, and analyze network traffic and system activities in real time to identify potential intrusion attempts. IDS monitors logs, traffic patterns, and abnormal behaviors, and issues alerts in a timely manner to prevent data leaks.

[0248] Implementation steps:

[0249] Rule configuration: Configure detection rules based on known threats and signatures.

[0250] Real-time monitoring: Analyze network traffic and system logs in real time to detect abnormal activities.

[0251] Alert response: Once an intrusion attempt is detected, an alert is generated and response measures are taken in a timely manner.

[0252] 5.5 DDoS Protection:

[0253] The system adopts a DDoS protection strategy, using traffic analysis and load balancing technology, using Cloudflare DDoSProtection service to identify and mitigate distributed denial of service attacks. This strategy ensures the stability and availability of system services by automatically adjusting traffic and isolating abnormal traffic.

[0254] Implementation steps:

[0255] Traffic analysis: Monitor network traffic and identify abnormally high traffic peaks.

[0256] Automatic filtering: Automatically filter abnormal traffic based on preset thresholds and rules.

[0257] Load balancing: Evenly distribute traffic to multiple servers to prevent single point overload.

[0258] 5.6 Traffic Monitoring:

[0259] Use Wireshark as a traffic monitoring tool to continuously analyze the source, destination, and content of network traffic and identify abnormal behavior. By capturing and decoding network packets, Wireshark helps system administrators understand traffic patterns, detect security vulnerabilities in a timely manner, and take measures to prevent potential security threats.

[0260] Implementation steps:

[0261] Packet Capture: Capture packets on the network in real time.

[0262] Protocol Analysis: Decode and analyze various network protocols to understand traffic patterns.

[0263] Anomaly Detection: Detects and reports suspicious network behavior and potential security threats.

[0264] 6. User Interface

[0265] The system provides a user-friendly graphical interface for users to perform backup and recovery operations and system monitoring. The system uses modern Web technologies to develop the user interface, including HTML5, CSS3 and JavaScript, and uses the React framework to implement front-end interaction. The back-end uses Node.js and Express frameworks to provide API services, combined with MongoDB to store user configuration and log data.

[0266] Function support:

[0267] 6.1 Backup strategy settings:

[0268] 1. The system allows users to enter the backup plan and frequency through the React form component, and uses the API interface of the Express framework to receive requests.

[0269] 2. After the user sets the backup strategy in the interface, the front end sends the information to the back end via HTTP request. The back end processes the request and stores the backup strategy in the MongoDB database.

[0270] Backup status view:

[0271] 1. Use React's state management and Hooks to implement real-time data updates on the front end. The interface sends requests to the back end at fixed time intervals to obtain the latest backup status.

[0272] 2. The backend provides a RESTful API through Express to query the backup status data in the MongoDB database and return it to the frontend.

[0273] 3. The interface is updated in real time based on the received data, displaying the status and history of the current backup task, ensuring that users are aware of the backup status in a timely manner.

[0274] 6.3 Data recovery operation:

[0275] 1. The front end implements the recovery operation interface through React, allowing users to select the recovery time point and perform the recovery operation.

[0276] 2. After the user selects the restore option in the interface, the React component calls the restore API of the Node.js backend.

[0277] 3. The backend is responsible for decrypting the backup data and restoring the data to the specified state through the cloud backup module.

[0278] 6.4 Log Monitoring:

[0279] 1. The system uses React table components to display log data. The front end allows users to select a time range and request to view log information.

[0280] 2. After the user initiates a request, the Express API queries the relevant log data in MongoDB and returns it to the front end.

[0281] 3. The front-end table component parses the returned data and displays detailed logs in chronological order, making it convenient for users to monitor the entire process of backup and recovery operations.

[0282] Embodiment 2

[0283] This embodiment 2 provides a cloud backup and recovery system for geological survey data on the basis of embodiment 1. This embodiment 2 includes the technical content disclosed in embodiment 1. The technical content of this embodiment 2 that is the same as that of embodiment 1 will not be repeated. The following describes the differences between this embodiment 2 and embodiment 1. The cloud backup and recovery system for geological survey data includes: data acquisition equipment, data preprocessing module, data encryption module, cloud backup module, intelligent recovery module, and user interface module; the data acquisition equipment is used for data acquisition and acquisition of geological data; the data preprocessing module is used to process the collected data; the data encryption module encrypts the processed data through Twofish symmetric encryption and ECC asymmetric encryption; the cloud backup module is used to store the encrypted data in a distributed storage system, and is managed by a MongoDB database, and the backup operation is controlled through backup strategies and trigger mechanisms, supporting scheduled backup and event-triggered backup; the intelligent recovery module is responsible for incremental recovery and consistency verification of data, and uses a log recovery mechanism to ensure data integrity; the user interface module is used to provide backup strategy settings, status viewing and recovery, and log monitoring functions.

[0284] Embodiment 3

[0285] The third embodiment is further refined and applied on the basis of the first embodiment. The third embodiment includes the technical contents disclosed in the first embodiment. The same technical contents as those in the first embodiment are not repeated here. The differences between the third embodiment and the first embodiment are described below.

[0286] For example: Cloud backup and recovery of geological survey data for mountainous roads

[0287] The geological conditions of mountain roads in a certain province are complex, and geological disasters such as landslides and mud-rock flows occur frequently. In order to ensure the safe operation of the highway, it is decided to use the cloud-based backup and recovery system for geological survey data of the present invention to conduct real-time monitoring, automatic backup and intelligent recovery of geological data along the route.

[0288] The specific system deployment and implementation are as follows:

[0289] 1. Data Collection and Processing

[0290] 1.1 Data collection: Multiple geological sensors, including soil moisture sensors, tilt sensors, and seismic wave sensors, are installed along the highway. These sensors are connected to the central data collection system via a wireless network to collect geological data along the route in real time.

[0291] 1.2 Data format standardization: Sensor data is initially processed by the edge computing node and standardized into JSON format, with fields including timestamp, geographic location, and data value. For example:

[0292] {

[0293] "timestamp": "20230804T00:00:00Z",

[0294] "latitude": 35.6895,

[0295] "longitude": 139.6917,

[0296] "data": {

[0297] "soil_moisture": 0.23,

[0298] "inclination": 1.5,

[0299] "seismic_activity": 0.02

[0300] }

[0301] }

[0302] 1.3 Data cleaning: The system filters the collected data for noise and outliers, uses the moving average filter method to smooth the data, and uses the Zscore method to identify and eliminate outliers.

[0303] 1.4 Data encryption: The processed geological data is encrypted using the Twofish symmetric encryption algorithm and the ECC elliptic curve asymmetric encryption algorithm, and then transmitted to the cloud via the HTTPS protocol to ensure data security.

[0304] 2. Cloud backup

[0305] 2.1 Automatic backup strategy: The system is set to perform a scheduled backup every 6 hours. At the same time, the event trigger mechanism is enabled. When abnormal geological activities are detected or sensor data fluctuations exceed the threshold, the system will immediately trigger the backup operation.

[0306] 2.2 Data transmission and storage: The encrypted data is transmitted to the cloud storage via the TLS protocol and stored in the Amazon S3 distributed file system. The system uses data redundancy technology to ensure the high availability and reliability of data on different nodes through a copy mechanism and consistent hashing algorithm.

[0307] 3. Intelligent Data Recovery

[0308] 3.1 Backup selection and data decryption: When data recovery is required, the user selects the backup time point through the system interface. The system obtains the backup data from Amazon S3 and uses the ECC private key to decrypt the Twofish key to restore the original geological data.

[0309] 3.2 Incremental recovery: The system uses the Rsync algorithm to compare the differences between the backup data and the current data, and only transfers and restores the changed parts, significantly shortening the data recovery time.

[0310] 3.3 Data consistency check: After the recovery is completed, the system uses the SHA256 hash function to generate a data verification code to ensure that the recovered data is consistent with the original data, thereby ensuring the integrity and accuracy of the data.

[0311] 4. System monitoring and optimization

[0312] 4.1 Log monitoring: The system provides detailed log monitoring function through the user interface, and users can view the entire process of data backup, transfer and recovery. The log includes the time, data volume and success status of each backup and recovery operation.

[0313] 4.2 Load balancing: In order to improve the response speed and stability of the system, the system uses Nginx load balancer to evenly distribute data access requests and monitor the load status of each storage node to ensure efficient resource utilization and failover.

[0314] 5. Actual application effect

[0315] By applying this system, the geological disaster monitoring capability of the province's mountain roads has been significantly improved. The system realizes real-time collection and automatic backup of geological data, ensuring data security and high availability. In landslide warning, the system timely issues warning notices to management departments by analyzing sensor data and historical data in real time, reducing the impact and losses of potential disasters. The system's intelligent recovery function can quickly restore key data when data is lost or damaged, ensuring business continuity and safe operation of highways.

[0316] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or replace some or all of the technical features therein with equivalents. However, these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present invention.

Claims

1. A cloud backup and recovery method for geological survey data, characterized in that: The steps include: Data collection: Obtain geological data through data collection equipment; Data preprocessing: The collected data is processed through the data preprocessing module; Data encryption: The processed data is encrypted through the data encryption module using Twofish symmetric encryption and ECC asymmetric encryption; Remote backup: Encrypted data is stored in the distributed storage system of the cloud backup module and managed by the MongoDB database. The backup operation is controlled by the backup strategy and trigger mechanism, supporting scheduled backup and event-triggered backup. Intelligent data recovery: The intelligent data recovery module is responsible for incremental data recovery and consistency verification, and the log recovery mechanism is used to ensure data integrity; User interface: The user interface provides backup policy settings, status viewing and recovery, and log monitoring functions for user operation and management.

2. The cloud backup and recovery method for geological survey data according to claim 1, characterized in that: In the step data collection, including: By connecting with geological sensing detection elements, geological data including geological structure, soil moisture, temperature and pressure can be obtained in real time.

3. The cloud backup and recovery method for geological survey data according to claim 1, characterized in that: The data preprocessing step includes: data format standardization and data cleaning.

4. The cloud backup and recovery method for geological survey data according to claim 1, characterized in that: In the step data encryption, the following steps are included: Data grouping, including: Divide geological survey data into 128-bit data blocks; use PKCS to fill and process data blocks; Twofish encryption, including: Key generation: Use CSPRNG to generate 256-bit keys; Feistel network: 16 rounds of encryption operations, including subkey addition, S-box substitution, and MDS matrix transformation; output ciphertext blocks; ECC key encryption, including: ECC key pair generation: select elliptic curve parameters to generate public and private key pairs; use the public key to encrypt the Twofish key; output the encrypted key pair; Data encryption is complete.

5. The cloud backup and recovery method for geological survey data according to claim 1, characterized in that: In the step of cloud backup, the following steps are included: Data monitoring, including: Use inotify to monitor data file changes; detect data change rates and abnormal events; Trigger backup operations, including: Set trigger conditions: the data change rate exceeds the threshold or anomalies are detected; the backup process is automatically triggered; Data transmission and storage, including: Data is transmitted via HTTPS protocol and stored in the Amazon S3 distributed system using TLS encryption. A replica mechanism is used for redundant storage. Cloud backup is complete.

6. The cloud backup and recovery method for geological survey data according to claim 1, characterized in that: In the step intelligent data recovery, the following steps are included: Backup options, including: The user selects the backup time point to be restored through the section, and the system searches for the backup file through the database; Data decryption, including: Get the encrypted data from the cloud, decrypt the Twofish key using the ECC private key, and then decrypt the data; Incremental recovery, including: The Rsync algorithm is used to achieve incremental recovery. By comparing the differences between the source data and the target data, only the changed parts are transmitted and restored. Data consistency check, including: After the recovery is complete, the SHA-256 hash function is used to generate a data checksum to verify data integrity; Log recovery mechanism, including: The system records every data change log, quickly locates and restores data, and uses a log-based recovery mechanism to replay the log to restore data when data is damaged or lost; Performance improvements, including: Through load balancing technology, data access requests are distributed, data access and recovery performance are optimized, and system response speed is improved. Load balancing is achieved through polling or minimum connection number strategy.

7. A cloud backup and recovery system for geological survey data, characterized in that: The cloud backup and recovery method for geological survey data according to any one of claims 1 to 6 comprises: Data acquisition equipment, data preprocessing module, data encryption module, cloud backup module, intelligent recovery module, user interface module; The data acquisition device is used for data acquisition and obtaining geological data; the data preprocessing module is used to process the collected data; the data encryption module encrypts the processed data through Twofish symmetric encryption and ECC asymmetric encryption; the cloud backup module is used to store the encrypted data in a distributed storage system, which is managed by a MongoDB database, and the backup operation is controlled by a backup strategy and a trigger mechanism, supporting scheduled backup and event-triggered backup; the intelligent recovery module is responsible for incremental recovery and consistency verification of data, and uses a log recovery mechanism to ensure data integrity; the user interface module is used to provide backup strategy settings, status viewing and recovery, and log monitoring functions.

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