Heterogeneous database real-time synchronization method supporting full increment integration

Abstract

The invention discloses a method for supporting real-time synchronization of a full-increment integrated heterogeneous database, which comprises the following steps of: designing a uniform interface layer to configure a source database and a target database, and compiling a specific adapter for each supported database type through a database adapter mode; respectively initializing a log parser, a transaction manager and a data buffer area; reading and analyzing the newly added log record, only extracting data of a change part for each data change record, and putting the extracted incremental data into a data buffer area; performing data format conversion and establishing a mapping relation between the source data model and the target data model; packaging data generated by the data capture layer into a message, sending the message to a message queue, and receiving the sent message by a real-time synchronization engine; and monitoring the whole synchronization process, and storing logs generated by the data capture layer and the real-time synchronization engine into a target database by using a message queue. The method has the advantages of real-time performance and wide supportability.

Description

Technical Field

[0001] The present invention relates to the technical field of database management, and in particular to a method for real-time synchronization of heterogeneous databases supporting full-increment integration. Background Art

[0002] Nowadays, many organizations and enterprises use multiple different types of databases to store and manage their data. These databases may include relational databases, NoSQL databases, column-based databases, etc. In addition, these databases may be distributed in different geographical locations or even run on different cloud platforms, with different data storage and query methods. Therefore, real-time data synchronization has become a key technology to ensure that data between different databases remains consistent and real-time. However, due to heterogeneity and complexity, real-time data synchronization between heterogeneous databases faces some challenges.

[0003] There are currently several common methods to achieve data synchronization between heterogeneous databases:

[0004] Method 1: ETL tools (Extract Transform Load): ETL tools are usually used to extract data from one database and load it into another database after transformation and processing. Although ETL tools can achieve batch synchronization of data, they cannot meet the needs of real-time synchronization, resulting in a certain delay after the data is updated; and additional transformation and processing steps are required.

[0005] Method 2: Database replication and synchronization tools: Some database management systems provide built-in replication and synchronization functions, allowing data to be copied from one database instance to another. However, these tools are usually limited to specific types of databases and lack comprehensive support for heterogeneous databases.

[0006] Method 3: Message queue and event-driven architecture: Using message queue and event-driven architecture can achieve asynchronous data transmission between databases and provide a certain degree of real-time performance. However, this method usually requires the development of customized data processing and synchronization logic, and there may be risks of data loss or duplication.

[0007] It can be seen that the existing technology has the following defects in achieving real-time synchronization of heterogeneous databases:

[0008] 1. Insufficient real-time performance: Many existing data synchronization solutions cannot provide real-time data synchronization, resulting in a certain delay after data update, which cannot meet the needs of some application scenarios for real-time data.

[0009] 2. High complexity: Some solutions require additional data conversion, format mapping, and processing steps, which increases the complexity and cost of implementation and may cause problems with data consistency and accuracy.

[0010] 3. Performance bottleneck: Large-scale data synchronization may cause performance bottlenecks, especially when synchronizing across networks or cloud platforms, which may affect the system's response speed and throughput.

[0011] 4. Limited support: Some database replication and synchronization tools only support specific types of databases and cannot meet the comprehensive synchronization requirements between heterogeneous databases. Summary of the invention

[0012] In order to solve the above technical problems, the present invention provides a method for real-time synchronization of heterogeneous databases supporting full-increment integration, comprising:

[0013] Design a unified interface layer in the heterogeneous data access layer to support connections to multiple databases, and write a specific adapter for each supported database type through the database adapter mode to shield the differences between different databases;

[0014] Initialize the log parser, transaction manager and data buffer respectively; monitor the changes of the database log file, read and parse the newly added log records, determine the incremental data, and detect the start and end marks of the transaction in the log;

[0015] For each data change record, only the changed data is extracted, and the extracted incremental data is put into the data buffer;

[0016] When a transaction end mark is detected, checking data changes in the data buffer and publishing the data changes in the data buffer to the target database;

[0017] By converting the data format, the data of the source database is converted into a format acceptable to the target database; and by establishing a data mapping relationship between the source data model and the target data model, it is ensured that the data in different data models are represented and processed according to a unified standard;

[0018] The data generated by the data capture layer is encapsulated into messages and sent to the message queue, and the real-time synchronization engine receives the sent messages; the entire synchronization process is monitored, and the logs generated by the data capture layer and the real-time synchronization engine are stored in the target database by using the message queue.

[0019] Furthermore, the data capture layer monitors data changes in the source database in real time, and can adopt different capture strategies according to the characteristics of different databases, wherein the capture strategies include CDC connector-based or trigger-based capture or timestamp-based or auto-increment primary key ID-based capture.

[0020] Further, the method based on the CDC connector includes:

[0021] For databases that provide logging functionality, the CDC connector can capture data changes by analyzing the logs, converting the change operations in the logs into a processable data format, and only extracting new or changed data since the last synchronization to reduce the load of data transmission and processing.

[0022] Furthermore, the trigger-based capture method includes:

[0023] Set a trigger on the table or database of the source database. When the data changes, the capture operation is automatically triggered and the changed data is stored in a temporary table or queue.

[0024] Furthermore, the method of capturing based on timestamp or auto-increment primary key id includes:

[0025] During the data synchronization process, record the timestamp or auto-increment primary key ID of each synchronization;

[0026] During the next synchronization, the data that has changed since the last synchronization is filtered out according to the timestamp or the auto-increment primary key ID.

[0027] Furthermore, the reading and parsing of the newly added log records includes:

[0028] Using multiple threads or multiple processes to process the log parsing and data capture; wherein each thread or process is responsible for processing a portion of the log records;

[0029] Before data changes are published, the transaction manager is used to verify transaction integrity to ensure that data changes of each transaction are published only after submission to ensure data consistency.

[0030] Furthermore, the data in the source database is converted into a format acceptable to the target database by converting the data format, wherein the method for converting the data format includes:

[0031] Data cleaning: Improve data quality by removing duplicate data, correcting erroneous data, and filling missing values;

[0032] Data standardization: converting data into a unified format or standard to facilitate subsequent processing and analysis;

[0033] Data adaptation: Converting data into a format that meets the needs of a specific system or application.

[0034] Furthermore, by establishing a specific and clear data element correspondence between the source data model and the target data model, it is ensured that the data in different data models are represented and processed according to a unified standard, wherein the method for establishing a data mapping relationship between the source data model and the target data model includes manual data mapping or fully automatic data mapping or semi-automatic data mapping, wherein:

[0035] The manual data mapping is to establish a clear data correspondence manually;

[0036] The fully automatic data mapping is to automatically match data fields using natural language processing technology;

[0037] The semi-automatic data mapping is the result of the fully automatic data mapping. If an incorrect mapping occurs between fields, the mapping fields can be manually adjusted.

[0038] Furthermore, the monitoring of the entire synchronization process also includes designing an exception handling mechanism to ensure data integrity and synchronization reliability.

[0039] Furthermore, the exception handling mechanism includes:

[0040] Use a logging framework to record exception information and context;

[0041] Use database transaction management to ensure data consistency;

[0042] Use reasonable error responses and feedback mechanisms for user requests;

[0043] After catching the exception, perform resource cleanup to ensure that all allocated resources are released;

[0044] Set up monitoring and alarm systems to promptly notify relevant personnel when abnormalities occur.

[0045] Compared with the prior art, the present invention has the following beneficial effects:

[0046] 1. Improve real-time performance: Compared with traditional periodic polling or batch processing, the present invention introduces a log-based real-time capture mechanism, that is, directly parsing the database change log, which can capture data changes in real time and immediately trigger the synchronization process, meeting application scenarios with extremely high requirements for real-time performance.

[0047] 2. Simplify complexity and cost: The present invention can automatically identify and process format differences between different data sources through intelligent data conversion and field mapping mechanisms, reducing the need for manual configuration and customization of conversion logic; at the same time, through integrated and modular design, it reduces system complexity and maintenance costs.

[0048] 3. Breaking through performance bottlenecks: The use of parallel processing and distributed architecture can execute data synchronization tasks in parallel on multiple nodes, effectively dispersing processing pressure and improving the overall performance and throughput of the system.

[0049] 4. Wide support: Through the flexible database adapter mode and extensible plug-in architecture, developers can easily add support for new data sources according to actual needs, improving the availability and adaptability of the system. BRIEF DESCRIPTION OF THE DRAWINGS

[0050] Figure 1 It is a principle block diagram disclosed in an embodiment of the present invention;

[0051] Figure 2 This is a principle block diagram of the data capture layer disclosed in the embodiment of the present invention implemented by a CDC connector and taking MySQL binlog as an example;

[0052] Figure 3 It is a principle block diagram of the data capture algorithm disclosed in the embodiment of the present invention;

[0053] Figure 4 A timing diagram of a data capture algorithm disclosed in an embodiment of the present invention;

[0054] Figure 5 A flow chart of monitoring and logging disclosed in an embodiment of the present invention. DETAILED DESCRIPTION

[0055] In order to make the technical scheme and technical effect of the present invention clearer, the technical scheme in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments. Obviously, the described embodiments are only part of the embodiments of the present invention, rather than all of the embodiments.

[0056] The present invention aims to provide a method for real-time synchronization of heterogeneous databases that supports full-incremental integration to achieve efficient and low-latency data synchronization in a heterogeneous database environment, support full and incremental data synchronization, and ensure the consistency of the target database with the source database.

[0057] refer to Figure 1 , the present invention is explained from four aspects: heterogeneous data access layer, data capture layer, real-time synchronization engine, and monitoring and logging:

[0058] 1. Heterogeneous Data Access Layer

[0059] A unified interface layer is designed at the heterogeneous data access layer to support connections to multiple databases, such as relational databases, non-relational databases, file systems, message queues, and API interfaces, and to capture data from these data sources in real time or on a scheduled basis.

[0060] The heterogeneous data access layer uses the database adapter mode to write specific adapters for each supported database type to shield the differences between different databases. Select the data source type, configure the data source address, and if there is an account and password, you also need to configure the account and password; the system automatically selects the data source driver according to the data source type to connect to the database.

[0061] 2. Data Capture Layer

[0062] An efficient data capture algorithm is used to ensure that the data capture layer can maintain efficient performance in large data volumes and high concurrency environments. At the same time, when capturing data changes, the integrity and consistency of the transaction need to be considered to ensure that the captured data can accurately reflect the status of the source database.

[0063] refer to Figure 3-4 , the specific implementation methods include:

[0064] 2.1. Initialization

[0065] Initialize the log parser to parse the database log file.

[0066] Initializes the transaction manager, which detects and manages transaction boundaries.

[0067] Initialize the data buffer for temporary storage of captured data.

[0068] 2.2 Real-time log analysis

[0069] Monitor changes in database log files; when log files change, read new log records.

[0070] 2.3. Parsing log records

[0071] Parse the read log records to extract the specific content of data changes, such as INSERT, UPDATE, and DELETE operations; determine the incremental changes in data based on the operation type.

[0072] 2.4 Transaction Boundary Detection

[0073] When parsing log records, detect the start and end markers of the transaction; ensure that data changes are not published before the transaction ends to ensure data consistency.

[0074] 2.5. Incremental data extraction

[0075] For each data change record, only the changed data is extracted; and the extracted incremental data is placed in the data buffer.

[0076] 2.6 Parallel Processing

[0077] Use multi-threading or multi-processing to handle log parsing and data capture; each thread or process is responsible for processing a portion of log records to improve processing efficiency.

[0078] 2.7. Data consistency guarantee

[0079] Before data changes are published, use the transaction manager to check transaction integrity to ensure that data changes for each transaction are published only after submission to ensure data consistency.

[0080] 2.8 Data Release

[0081] When the transaction end marker is detected, the data changes in the data buffer are checked and published to the target database or other data processing system.

[0082] The capture strategy for the data capture layer can be based on a CDC connector, trigger capture, timestamp, or auto-increment primary key ID. The following describes each of them in detail:

[0083] a. Based on CDC connector

[0084] Currently, many database systems provide log functions to record all changes to the database, such as MySQL's binlog and Oracle's Redo Log. The CDC connector can capture data changes by analyzing these logs, converting the change operations in the logs into a processable data format, and only extracting new or changed data since the last synchronization, reducing the load of data transmission and processing.

[0085] The data CDC technology based on database logs is combined with real-time data conversion. Through the built-in large number of CDC connectors, data can be collected in real time without intrusion, and full and incremental data integration can be easily achieved.

[0086] refer to Figure 2 The following example uses MySQL binlog as an example:

[0087] After configuring the database, the system obtains the driver to establish a connection with the target data source. At the decision point of "whether there is data", the system checks whether there is available data in the target system. If so, the execution continues; otherwise, the execution ends.

[0088] In the next step of "Assembling Connection Information", the connection parameters are constructed based on the collected information, the database is connected, and if the connection is successful, the data is acquired through listening and the event is started to parse the binlog.

[0089] "Starting file status monitoring" is an important part of the process, which is responsible for monitoring changes in file status in the target system. After starting file status monitoring, create a verification thread that executes every three minutes, regularly check whether the system has read blocking conditions to ensure smooth reading. If so, continue to determine whether the collected file has expired; if not, continue to determine whether it is the last file to ensure that the log file has been read, and then close the listener.

[0090] b. Trigger-based capture

[0091] Trigger setting: Set a trigger on the table or database of the source database to automatically trigger the capture operation when the data changes.

[0092] Data capture: After the trigger is triggered, the corresponding capture logic is executed to store the changed data in a temporary table or queue.

[0093] c. Capture based on timestamp or auto-increment primary key id

[0094] Timestamp or auto-increment primary key ID record: During the data synchronization process, the timestamp or auto-increment primary key ID of each synchronization is recorded.

[0095] Data filtering: During the next synchronization, filter out the data that has changed since the last synchronization based on the timestamp or the auto-increment primary key ID.

[0096] 3. Data Conversion and Mapping Layer

[0097] 3.1 Data Conversion

[0098] The data conversion and mapping layer converts the data in the source database into a format acceptable to the target database through data format conversion, so as to handle issues such as data type differences and inconsistent field names; and by establishing a data mapping relationship between the source data model and the target data model, it ensures that data in different data models are represented and processed according to a unified standard. The methods for data format conversion include: data cleaning, data standardization, and data adaptation. Specifically:

[0099] Data cleaning: Improve data quality by removing duplicate data, correcting erroneous data, and filling missing values.

[0100] Data standardization: Converting data into a unified format or standard to facilitate subsequent processing and analysis.

[0101] Data adaptation: Converting data into a format that meets the needs of a specific system or application.

[0102] Common operations include:

[0103] Data type conversion: such as converting a string type to a numeric type.

[0104] Data splitting and merging: Split complex data structures into simple components, or merge data from multiple data sources into a whole.

[0105] Data aggregation: Summarizing or grouping data to produce a higher-level view of the data.

[0106] Data transformation: such as logarithmic transformation, exponential transformation, etc., to improve data readability and analysis capabilities.

[0107] 3.2 Data Mapping

[0108] A data mapping relationship is established between the source data model and the target data model. The implementation method may include: manual data mapping, fully automatic data mapping, or semi-automatic data mapping, which are mainly introduced below:

[0109] Manual data mapping: Establish clear data correspondence manually; suitable for scenarios with small data volume and relatively simple data structure.

[0110] Fully automatic data mapping: Use natural language processing technology to automatically match data fields to achieve efficient data mapping.

[0111] Semi-automatic data mapping: For the results of fully automatic data mapping, if there is an incorrect mapping between fields, you can manually adjust the mapping fields.

[0112] The main characteristics of the data mapping pattern are as follows:

[0113] Clear correspondence: Establish specific and clear data element correspondence between the source data model and the target data model.

[0114] Independence and flexibility: The data mapping pattern makes the persistent data storage layer, the data presentation layer in memory, and the data mapping itself independent of each other, which improves the flexibility and maintainability of the system.

[0115] Standardization and consistency: By defining clear mapping rules and logic, ensure that data in different data models are represented and processed according to unified standards.

[0116] The purpose of establishing a data mapping relationship between the source data model and the target data model is to improve data integration efficiency by reducing errors and manual intervention in the data integration process through clear data correspondence; to convert data into a format that is easier to analyze and query, increase query speed and reduce system load, thereby optimizing data analysis and query performance; and to facilitate cross-data source analysis and query by integrating data from multiple data sources into a unified data model, thereby supporting cross-data source analysis and query.

[0117] 4. Real-time synchronization engine

[0118] The data generated by the data capture layer is encapsulated into messages and sent to the message queue, such as a specific Topic of Kafka. The real-time synchronization engine receives the sent messages. Batch writing and transactional operations are supported to ensure the integrity and consistency of data, and asynchronous processing and load balancing of data can be achieved. Multi-threading technology is used to divide batch data into multiple parts and write them to the database in parallel, which can significantly improve the writing speed, but attention should be paid to the management of database connections and the synchronization of transactions.

[0119] V. Monitoring and Logging

[0120] Monitor the entire synchronization process and provide fault recovery capabilities. Write the monitored data to the target database clickhouse or elasticsearch. Clickhouse has the advantages of low latency, high throughput, high-performance query, and low resource consumption, making it very suitable for application scenarios that require high-speed data processing and real-time analysis. Figure 5 The collection, processing, storage, and query process of log data shown in the figure. The logs generated by the data capture layer and the real-time synchronization engine are stored in the target database, i.e., ClickHouse or ElasticSearch, by using message queues. The system can efficiently process large amounts of log data and provide powerful query and analysis capabilities.

[0121] During the data capture and writing process, you may encounter various abnormal situations, such as network interruption, database failure, etc. A perfect exception handling mechanism is designed to ensure data integrity and synchronization reliability. Specifically:

[0122] 5.1. Identify potential anomaly types

[0123] Network anomalies: such as connection timeout, network interruption, DNS resolution failure, etc.

[0124] Database exceptions: such as connection failure, SQL syntax error, transaction conflict, deadlock, etc.

[0125] System resource abnormalities: such as insufficient memory, insufficient disk space, high CPU usage, etc.

[0126] Business logic exceptions: such as data verification failure, business rule violation, etc.

[0127] 5.2. Design an exception handling strategy

[0128] Prevention strategy: Prevent exceptions from occurring through proper resource management and system monitoring, and set reasonable timeouts, resource quotas, and load balancing.

[0129] Capture strategy: Use try-catch blocks at key locations in the code to capture and handle expected exceptions, and ensure that the captured exception type is as specific as possible so that more targeted handling measures can be taken.

[0130] Logging strategy: Log all captured exceptions and their context information, such as timestamp, user ID, request parameters, etc., for subsequent analysis and debugging.

[0131] Rollback strategy: For operations involving database transactions, if an exception occurs, ensure that you can roll back to the state before the transaction started to maintain data consistency.

[0132] Retry strategy: For some recoverable exceptions, such as a brief network outage, the retry mechanism automatically retries the operation before the upper limit of the number of retries is reached.

[0133] 5.3. Exception handling mechanism processing method

[0134] Use the logging framework: Use Log4j to record exception information and context.

[0135] Database transaction management: Use JDBC transactions and Spring's declarative transaction management to ensure data consistency.

[0136] Error handling and feedback: For user requests, by using reasonable error responses and feedback mechanisms so that users can understand what happened and possibly take further action.

[0137] Resource cleanup: After catching an exception, make sure to release all allocated resources, such as database connections, file handles, etc.

[0138] Monitoring and Alerts: Set up monitoring and alert systems to promptly notify relevant personnel when abnormalities occur.

[0139] By comparing this embodiment with the prior art method of building an API layer and dual-writing Kafka, this embodiment has obvious advantages in terms of development workload, impact on existing applications, and monitoring and maintenance, as shown in the following table:

[0140]

[0141] This embodiment can support more than 100 database data sources, including MySQL, MongoDB, PostgresSQL, 0racle, SQL Server, Elasticsearch, Kafka, OceanBase, Cassandra, ASE, Gaussdb2000, MariaDB, ApacheDoris, StarRocks, Databend, DM, HBase, HIVE, MQ, KUDU, Greenplum, TiDB, Hana, TCP / IPClickHouse, File, CSV, etc. The data CDC technology based on database logs is combined with real-time data conversion and data mapping, and a large number of CDC connectors are built in, which can collect data in real time without intrusion, and easily realize full and incremental data integration.

[0142] Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. A method for real-time synchronization of heterogeneous databases supporting full-increment integration, characterized in that: include: Design a unified interface layer in the heterogeneous data access layer to support connections to multiple databases, and write a specific adapter for each supported database type through the database adapter mode to shield the differences between different databases; Initialize the log parser, transaction manager and data buffer respectively; monitor the changes of the database log file, read and parse the newly added log records, determine the incremental data, and detect the start and end marks of the transaction in the log; For each data change record, only the changed data is extracted, and the extracted incremental data is put into the data buffer; When a transaction end mark is detected, checking data changes in the data buffer and publishing the data changes in the data buffer to a target database; By converting the data format, the data of the source database is converted into a format acceptable to the target database; and by establishing a data mapping relationship between the source data model and the target data model, it is ensured that the data in different data models are represented and processed according to a unified standard; The data generated by the data capture layer is encapsulated into messages and sent to the message queue, and the real-time synchronization engine receives the sent messages; The entire synchronization process is monitored, and the logs generated by the data capture layer and the real-time synchronization engine are stored in the target database by using the message queue.

2. The method for real-time synchronization of heterogeneous databases supporting full-increment integration according to claim 1, characterized in that: The data capture layer monitors data changes in the source database in real time and can adopt different capture strategies according to the characteristics of different databases, wherein the capture strategies include CDC connector-based capture, trigger-based capture, or timestamp-based capture or auto-increment primary key ID capture.

3. The method for real-time synchronization of heterogeneous databases supporting full-increment integration according to claim 2 is characterized in that: The CDC connector-based method includes: For databases that provide logging functionality, the CDC connector can capture data changes by analyzing the logs, converting the change operations in the logs into a processable data format, and only extracting new or changed data since the last synchronization to reduce the load of data transmission and processing.

4. The method for real-time synchronization of heterogeneous databases supporting full-increment integration according to claim 2, characterized in that: The trigger-based capture method includes: Set a trigger on the table or database of the source database. When the data changes, the capture operation is automatically triggered and the changed data is stored in a temporary table or queue.

5. The method for real-time synchronization of heterogeneous databases supporting full-increment integration according to claim 2, characterized in that: The method of capturing based on timestamp or auto-increment primary key ID includes: During the data synchronization process, record the timestamp or auto-increment primary key ID of each synchronization; During the next synchronization, the data that has changed since the last synchronization is filtered out according to the timestamp or the auto-increment primary key ID.

6. The method for real-time synchronization of heterogeneous databases supporting full-increment integration according to claim 1, characterized in that: The reading and parsing of the newly added log records includes: Using multiple threads or multiple processes to process the log parsing and data capture; wherein each thread or process is responsible for processing a portion of the log records; Before data changes are published, the transaction manager is used to verify transaction integrity to ensure that data changes of each transaction are published only after submission to ensure data consistency.

7. The method for real-time synchronization of heterogeneous databases supporting full-increment integration according to claim 1, characterized in that: The data in the source database is converted into a format acceptable to the target database by converting the data format, wherein the method for converting the data format includes: Data cleaning: Improve data quality by removing duplicate data, correcting erroneous data, and filling missing values; Data standardization: converting data into a unified format or standard to facilitate subsequent processing and analysis; Data adaptation: Converting data into a format that meets the needs of a specific system or application.

8. The method for real-time synchronization of heterogeneous databases supporting full-increment integration according to claim 1, characterized in that: By establishing a specific and clear data element correspondence between the source data model and the target data model, it is ensured that the data in different data models are represented and processed according to a unified standard, wherein the method for establishing a data mapping relationship between the source data model and the target data model includes manual data mapping, fully automatic data mapping, or semi-automatic data mapping, wherein: The manual data mapping is to establish a clear data correspondence manually; The fully automatic data mapping is to automatically match data fields using natural language processing technology; The semi-automatic data mapping is the result of the fully automatic data mapping. If an incorrect mapping occurs between fields, the mapping fields can be manually adjusted.

9. The method for real-time synchronization of heterogeneous databases supporting full-increment integration according to claim 1, characterized in that: The monitoring of the entire synchronization process also includes designing an exception handling mechanism to ensure data integrity and synchronization reliability.

10. The method for real-time synchronization of heterogeneous databases supporting full-increment integration according to claim 9, characterized in that: The exception handling mechanism includes: Use a logging framework to record exception information and context; Use database transaction management to ensure data consistency; Use reasonable error responses and feedback mechanisms for user requests; After catching the exception, perform resource cleanup to ensure that all allocated resources are released; Set up monitoring and alarm systems to promptly notify relevant personnel when abnormalities occur.

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