Artificial intelligence-based database optimization method and system

Abstract

The invention discloses a database optimization method and system based on artificial intelligence, and belongs to the technical field of database management, the implementation of the method comprises: data collection: collecting operation data of a database, including query logs, performance indexes and system resource use conditions; intelligent analysis: analyzing the collected data by using a machine learning algorithm, and identifying performance bottlenecks and optimization points; optimization strategy generation: automatically generating an optimization strategy according to an intelligent analysis result; optimization execution: applying the optimization strategy to a database system, and monitoring an optimization effect; and feedback learning: carrying out self-learning and adjustment according to an optimization execution result so as to improve the accuracy of a future optimization strategy. The method can improve database performance, realize automatic optimization, adapt to dynamic change, enhance user experience, lower technical threshold, improve resource utilization rate and realize sustainable development.

Description

Technical Field

[0001] The present invention relates to the technical field of database management, and in particular to a database optimization method and system based on artificial intelligence. Background Art

[0002] With the advent of the big data era, the explosive growth of data volume and the diversification of data storage structures have brought new challenges to database technology. In this context, database technology is constantly evolving in the direction of model expansion and architecture decoupling, and is developing in a trend of learning from each other's strengths and weaknesses and continuously integrating with technologies such as cloud computing, artificial intelligence, blockchain, privacy computing, and new hardware. Especially in the field of artificial intelligence, the application and development of AI technology has brought new ideas and methods for database optimization. At present, how to better apply artificial intelligence technology to database management systems to improve database performance and resource utilization is an urgent problem to be solved. Summary of the invention

[0003] The technical task of the present invention is to address the above shortcomings and provide an artificial intelligence-based database optimization method and system that can improve database performance, realize automated optimization, adapt to dynamic changes, enhance user experience, lower technical barriers, improve resource utilization, and achieve sustainable development.

[0004] The technical solution adopted by the present invention to solve its technical problem is:

[0005] A database optimization method based on artificial intelligence, the implementation of which includes:

[0006] Data collection: Collect database operation data, including query logs, performance indicators, system resource usage, etc.;

[0007] Intelligent analysis: Use machine learning algorithms to analyze the collected data and identify performance bottlenecks and optimization points;

[0008] Optimization strategy generation: automatically generating an optimization strategy based on the results of the intelligent analysis;

[0009] Optimization execution: Apply optimization strategies to the database system and monitor the optimization effects;

[0010] Feedback learning: Self-learning and adjustment based on the results of optimization execution to improve the accuracy of future optimization strategies.

[0011] Furthermore, the data collection specifically includes:

[0012] Log capture: Real-time capture of database query logs, including query statements, execution time, resource consumption, and other information;

[0013] Performance monitoring: monitor the real-time performance indicators of the database, including response time, throughput, number of concurrent connections, etc.;

[0014] Resource monitoring: Monitor the resource usage of the server, including CPU, memory, I / O and other resources, to assess the impact of the database on resources.

[0015] Furthermore, the intelligent analysis specifically includes:

[0016] Feature extraction: Extract key features from the collected data, including query frequency, response time distribution, resource usage peak, etc.

[0017] Pattern recognition: Use machine learning algorithms, including clustering and classification, to identify patterns and anomalies in data;

[0018] Bottleneck analysis: Analyze data and identify performance bottlenecks through deep learning models such as neural networks;

[0019] Causality Analysis: Use causal inference techniques to identify the root causes of performance issues.

[0020] Furthermore, the optimization strategy generation specifically includes:

[0021] Index optimization: add or delete indexes to improve query efficiency;

[0022] Query optimization: rewrite inefficient query statements;

[0023] Resource adjustment: adjust database configuration, including memory allocation, connection pool size, etc.;

[0024] Architecture adjustment: When necessary, adjust the database architecture, including database and table sharding, read-write separation, etc.

[0025] Furthermore, the optimization execution is specifically implemented by:

[0026] Automated deployment: Automatically deploy optimization strategies without manual intervention;

[0027] Rollback mechanism: when the optimization effect is not good, it will automatically roll back to the state before optimization;

[0028] Effect monitoring: Monitor the execution effect of the optimization strategy to ensure the achievement of optimization goals.

[0029] Furthermore, the feedback learning is specifically implemented as follows:

[0030] Effect evaluation: Evaluate the actual effect of the optimization strategy, including performance improvement, resource saving, etc.;

[0031] Model training: Update machine learning models with the latest data to improve the accuracy of analysis;

[0032] Strategy adjustment: Based on the results of the effect evaluation, adjust the optimization strategy generation algorithm to adapt to the new data pattern.

[0033] Furthermore, the method adopts a modular design, and modules can be added or replaced as needed; it supports a variety of database systems, including MySQL, Oracle, and MongoDB database systems; and it supports cloud-native deployment, which can be easily expanded to more cloud platforms and environments;

[0034] A user interface is provided for a user to monitor and manage the system, the user interface comprising:

[0035] Dashboard: Provides a real-time dashboard showing database performance and optimization status;

[0036] Alarm system: Provides alarm notifications when performance issues or optimization failures occur;

[0037] Configuration Management: Allows users to configure optimization strategies and system parameters.

[0038] The present invention also claims protection for a database optimization system based on artificial intelligence, comprising:

[0039] Data collection module, used to collect database operation data, including query logs, performance indicators, system resource usage, etc.;

[0040] Intelligent analysis module, which uses machine learning algorithms to analyze the collected data and identify performance bottlenecks and optimization points;

[0041] An optimization strategy generation module is used to automatically generate optimization strategies based on the results of the intelligent analysis module;

[0042] The optimization execution module is used to apply the optimization strategy to the database system and monitor the optimization effect;

[0043] Feedback learning module, which is used to self-learn and adjust according to the results of optimization execution to improve the accuracy of future optimization strategies;

[0044] The system specifically realizes database optimization through the above-mentioned artificial intelligence-based database optimization method.

[0045] The present invention also claims protection for an artificial intelligence-based database optimization implementation device, comprising: at least one memory and at least one processor;

[0046] The at least one memory is used to store a machine-readable program;

[0047] The at least one processor is used to call the machine-readable program to implement the above method.

[0048] The present invention also claims protection for a computer-readable medium having computer instructions stored thereon, which, when executed by a processor, causes the processor to perform the above method.

[0049] Compared with the prior art, the database optimization method and system based on artificial intelligence of the present invention have the following beneficial effects:

[0050] The present invention realizes intelligent and automatic database performance optimization, improves the operation efficiency of the database, reduces maintenance costs, and adapts to the ever-changing data environment.

[0051] The method and system have outstanding effects in improving database performance, reducing costs, automation and intelligence, stability and reliability, scalability and flexibility, improving user experience, and sustainable development. BRIEF DESCRIPTION OF THE DRAWINGS

[0052] Figure 1 It is a flowchart of a database optimization method based on artificial intelligence provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0053] The present invention will be further described below in conjunction with the accompanying drawings and specific embodiments.

[0054] The embodiment of the present invention provides a database optimization method based on artificial intelligence, and the implementation of the method includes:

[0055] 1. Data collection: collect database operation data, including query logs, performance indicators, system resource usage, etc.;

[0056] 2. Intelligent analysis: Use machine learning algorithms to analyze the collected data and identify performance bottlenecks and optimization points;

[0057] 3. Optimization strategy generation: automatically generate optimization strategies based on the results of the intelligent analysis;

[0058] 4. Optimization execution: Apply the optimization strategy to the database system and monitor the optimization effect;

[0059] 5. Feedback learning: Self-learning and adjustment based on the results of optimization execution to improve the accuracy of future optimization strategies.

[0060] The data collection is specifically implemented as follows:

[0061] Log capture: Real-time capture of database query logs, including query statements, execution time, resource consumption, and other information;

[0062] Performance monitoring: monitor the real-time performance indicators of the database, including response time, throughput, number of concurrent connections, etc.;

[0063] Resource monitoring: Monitor the server's CPU, memory, I / O and other resource usage to assess the impact of the database on resources.

[0064] The intelligent analysis is achieved through the following steps:

[0065] Feature extraction: Extract key features from the collected data, such as query frequency, response time distribution, resource usage peak, etc.

[0066] Pattern recognition: using machine learning algorithms such as clustering and classification to identify patterns and anomalies in data;

[0067] Bottleneck analysis: Analyze data and identify performance bottlenecks through deep learning models such as neural networks;

[0068] Causality Analysis: Use causal inference techniques to identify the root causes of performance issues.

[0069] The optimization strategy generation specifically includes:

[0070] Index optimization: add or delete indexes to improve query efficiency;

[0071] Query optimization: rewrite inefficient query statements;

[0072] Resource adjustment: adjust database configuration, such as memory allocation, connection pool size, etc.;

[0073] Architecture adjustment: When necessary, adjust the database architecture, such as splitting the database and tables, separating reads and writes, etc.

[0074] The optimization execution is specifically implemented as follows:

[0075] Automated deployment: Automatically deploy optimization strategies without manual intervention;

[0076] Rollback mechanism: when the optimization effect is not good, it will automatically roll back to the state before optimization;

[0077] Effect monitoring: Monitor the execution effect of the optimization strategy to ensure the achievement of optimization goals.

[0078] The feedback learning is specifically implemented as follows:

[0079] Effect evaluation: evaluate the actual effect of the optimization strategy, such as performance improvement, resource saving, etc.

[0080] Model training: Update machine learning models with the latest data to improve the accuracy of analysis;

[0081] Strategy adjustment: Based on the results of the effect evaluation, adjust the optimization strategy generation algorithm to adapt to the new data pattern.

[0082] This method uses big data processing frameworks such as Apache Spark for data collection and processing; uses machine learning platforms such as TensorFlow and PyTorch to build intelligent analysis models; uses database interfaces such as JDBC and ODBC to interact with various database systems; and is deployed on cloud platforms such as AWS and Azure to take advantage of the elasticity and scalability of cloud services.

[0083] Considering security and privacy protection, the collected data is encrypted to ensure the security of data transmission and storage; a strict access control policy is implemented to ensure that only authorized users can access the system; during the analysis and optimization process, the user's privacy information is protected from being leaked.

[0084] In terms of scalability and flexibility, this method adopts a modular design to add or replace modules as needed; it supports multiple database systems, including MySQL, Oracle, and MongoDB database systems; and it supports cloud-native deployment, which can be easily expanded to more cloud platforms and environments.

[0085] A user interface is provided for a user to monitor and manage the system, the user interface comprising:

[0086] Dashboard: Provides a real-time dashboard showing database performance and optimization status;

[0087] Alarm system: Provides alarm notifications when performance issues or optimization failures occur;

[0088] Configuration Management: Allows users to configure optimization strategies and system parameters.

[0089] Through the above technical solutions, this database optimization method can realize intelligent and automatic database performance optimization, improve the operation efficiency of the database, reduce maintenance costs, and adapt to the ever-changing data environment.

[0090] An embodiment of the present invention also provides an artificial intelligence-based database optimization system, which implements database optimization through the artificial intelligence-based database optimization method described in the above embodiment.

[0091] The system includes:

[0092] 1. Data collection module, used to collect database operation data, including query logs, performance indicators, system resource usage, etc.

[0093] The data collection module is the basis of the system and it works in the following ways:

[0094] Log capture: Real-time capture of database query logs, including query statements, execution time, resource consumption, and other information;

[0095] Performance monitoring: monitor the real-time performance indicators of the database, such as response time, throughput, number of concurrent connections, etc.;

[0096] Resource monitoring: Monitor the server's CPU, memory, I / O and other resource usage to assess the impact of the database on resources.

[0097] 2. Intelligent analysis module, which uses machine learning algorithms to analyze the collected data and identify performance bottlenecks and optimization points.

[0098] The intelligent analysis module is the core of the system, which is implemented through the following steps:

[0099] Feature extraction: Extract key features from the collected data, such as query frequency, response time distribution, resource usage peak, etc.

[0100] Pattern recognition: using machine learning algorithms such as clustering and classification to identify patterns and anomalies in data;

[0101] Bottleneck analysis: Analyze data and identify performance bottlenecks through deep learning models such as neural networks;

[0102] Causality Analysis: Use causal inference techniques to identify the root causes of performance issues.

[0103] 3. Optimization strategy generation module, used to automatically generate optimization strategies based on the results of the intelligent analysis module; including:

[0104] Index optimization: It is recommended to add or delete indexes to improve query efficiency;

[0105] Query optimization: It is recommended to rewrite inefficient query statements;

[0106] Resource adjustment: It is recommended to adjust the database configuration, such as memory allocation, connection pool size, etc.

[0107] Architecture adjustment: When necessary, it is recommended to adjust the database architecture, such as splitting the database and tables, separating reads and writes, etc.

[0108] 4. Optimization execution module, used to apply optimization strategies to the database system and monitor the optimization effects.

[0109] The specific implementation includes:

[0110] Automated deployment: Automatically deploy optimization strategies without manual intervention;

[0111] Rollback mechanism: when the optimization effect is not good, it will automatically roll back to the state before optimization;

[0112] Effect monitoring: Monitor the execution effect of the optimization strategy to ensure the achievement of optimization goals.

[0113] 5. Feedback learning module, which is used to conduct self-learning and adjustment based on the results of optimization execution to improve the accuracy of future optimization strategies. Specifically, it includes:

[0114] Effect evaluation: evaluate the actual effect of the optimization strategy, such as performance improvement, resource saving, etc.

[0115] Model training: Use the latest data to update machine learning models and improve the accuracy of analysis;

[0116] Strategy adjustment: Based on the results of the effect evaluation, adjust the optimization strategy generation algorithm to adapt to the new data pattern.

[0117] This system is implemented using the following technologies:

[0118] Data processing framework: Use big data processing frameworks such as Apache Spark for data collection and processing;

[0119] Machine learning platform: Use machine learning platforms such as TensorFlow and PyTorch to build intelligent analysis models;

[0120] Database interface: Use JDBC, ODBC and other database interfaces to interact with various database systems;

[0121] Cloud services: Deployed on cloud platforms such as AWS and Azure, taking advantage of the elasticity and scalability of cloud services.

[0122] When designing and implementing this system, consider security and privacy protection:

[0123] Data encryption: Encrypt the collected data to ensure the security of data transmission and storage;

[0124] Access control: Implement strict access control policies to ensure that only authorized users can access the system;

[0125] Privacy protection: During the analysis and optimization process, protect the user's privacy information from being leaked.

[0126] The system is designed to be highly scalable and flexible to accommodate different business needs:

[0127] Modular design: The system adopts modular design, which makes it easy to add or replace modules as needed;

[0128] Multi-database support: supports multiple database systems, such as MySQL, Oracle, MongoDB, etc.

[0129] Cloud-native support: Supports cloud-native deployment and can be easily expanded to more cloud platforms and environments.

[0130] The system provides a friendly user interface to facilitate users to monitor and manage the system:

[0131] Dashboard: Provides a real-time dashboard showing database performance and optimization status;

[0132] Alarm system: Provides alarm notifications when performance issues or optimization failures occur;

[0133] Configuration Management: Allows users to configure optimization strategies and system parameters.

[0134] Through the above technical solutions, this database optimization system realizes intelligent and automatic database performance optimization, improves the operation efficiency of the database, reduces maintenance costs, and adapts to the ever-changing data environment.

[0135] The embodiment of the present invention also provides a database optimization implementation device based on artificial intelligence, comprising: at least one memory and at least one processor;

[0136] The at least one memory is used to store a machine-readable program;

[0137] The at least one processor is used to call the machine-readable program to implement the artificial intelligence-based database optimization method described in the above embodiment.

[0138] The embodiment of the present invention further provides a computer-readable medium, on which computer instructions are stored, and when the computer instructions are executed by a processor, the processor executes the database optimization method based on artificial intelligence described in the above embodiment. Specifically, a system or device equipped with a storage medium can be provided, on which a software program code for implementing the functions of any of the above embodiments is stored, and a computer (or CPU or MPU) of the system or device reads and executes the program code stored in the storage medium.

[0139] In this case, the program code itself read from the storage medium can realize the function of any one of the above-mentioned embodiments, and thus the program code and the storage medium storing the program code constitute a part of the present invention.

[0140] The storage medium embodiments for providing the program code include a floppy disk, a hard disk, a magneto-optical disk, an optical disk (such as CD-ROM, CD-R, CD-RW, DVD-ROM, DVD-RAM, DVD-RW, DVD+RW), a magnetic tape, a non-volatile memory card, and a ROM. Alternatively, the program code can be downloaded from a server computer by a communication network.

[0141] In addition, it should be clear that the functions of any of the above embodiments can be implemented not only by executing the program code read by the computer, but also by enabling an operating system operating on the computer to complete part or all of the actual operations based on instructions from the program code.

[0142] In addition, it can be understood that the program code read from the storage medium is written to a memory provided in an expansion board inserted into the computer or written to a memory provided in an expansion unit connected to the computer, and then based on the instructions of the program code, a CPU installed on the expansion board or the expansion unit is enabled to perform part or all of the actual operations, thereby realizing the functions of any of the above-mentioned embodiments.

[0143] The present invention is shown and described in detail above through the accompanying drawings and preferred embodiments. However, the present invention is not limited to these disclosed embodiments. Based on the above multiple embodiments, those skilled in the art can know that the code review methods in the above different embodiments can be combined to obtain more embodiments of the present invention, and these embodiments are also within the protection scope of the present invention.

Claims

1. A database optimization method based on artificial intelligence, characterized in that: The implementation of this method includes: Data collection: Collect database operation data, including query logs, performance indicators, and system resource usage; Intelligent analysis: Use machine learning algorithms to analyze the collected data and identify performance bottlenecks and optimization points; Optimization strategy generation: automatically generating an optimization strategy based on the results of the intelligent analysis; Optimization execution: Apply optimization strategies to the database system and monitor the optimization effects; Feedback learning: Self-learning and adjustment based on the results of optimization execution to improve the accuracy of future optimization strategies.

2. The method for optimizing a database based on artificial intelligence according to claim 1, characterized in that: The data collection specifically includes: Log capture: Real-time capture of database query logs, including query statements, execution time, and resource consumption information; Performance monitoring: monitor the real-time performance indicators of the database, including response time, throughput, and number of concurrent connections; Resource monitoring: Monitor server resource usage, including CPU, memory, and I / O resources, to assess the impact of the database on resources.

3. The method for optimizing a database based on artificial intelligence according to claim 1, characterized in that: The intelligent analysis specifically includes: Feature extraction: Extract key features from the collected data, including query frequency, response time distribution, and resource usage peaks; Pattern recognition: using machine learning algorithms including clustering and classification algorithms to identify patterns and anomalies in data; Bottleneck analysis: Analyze data and identify performance bottlenecks through deep learning models; Causality Analysis: Use causal inference techniques to identify the root causes of performance issues.

4. The method for optimizing a database based on artificial intelligence according to claim 1, characterized in that: The optimization strategy generation specifically includes: Index optimization: add or delete indexes to improve query efficiency; Query optimization: rewrite inefficient query statements; Resource adjustment: adjust database configuration, including memory allocation and connection pool size; Architecture adjustment: When necessary, adjust the database architecture, including splitting the database and tables, and separating read and write operations.

5. The method for optimizing a database based on artificial intelligence according to claim 1, characterized in that: The optimization execution is specifically implemented as follows: Automated deployment: Automatically deploy optimization strategies without manual intervention; Rollback mechanism: when the optimization effect is not good, it will automatically roll back to the state before optimization; Effect monitoring: Monitor the execution effect of the optimization strategy to ensure the achievement of optimization goals.

6. The method for optimizing a database based on artificial intelligence according to claim 1, characterized in that: The feedback learning is specifically implemented as follows: Effect evaluation: Evaluate the actual effect of the optimization strategy, including performance improvement and resource saving; Model training: Update machine learning models with the latest data to improve the accuracy of analysis; Strategy adjustment: Based on the results of the effect evaluation, adjust the optimization strategy generation algorithm to adapt to the new data pattern.

7. The method for optimizing a database based on artificial intelligence according to claim 1, characterized in that: This method adopts modular design, adding or replacing modules as needed; it supports a variety of database systems, including MySQL, Oracle, and MongoDB database systems; And support cloud-native deployment; A user interface is provided for a user to monitor and manage the system, the user interface comprising: Dashboard: Provides a real-time dashboard showing database performance and optimization status; Alarm system: Provides alarm notifications when performance issues or optimization failures occur; Configuration Management: Allows users to configure optimization strategies and system parameters.

8. A database optimization system based on artificial intelligence, characterized in that: include: The data collection module is used to collect the operation data of the database, including query logs, performance indicators, and system resource usage; Intelligent analysis module, which uses machine learning algorithms to analyze the collected data and identify performance bottlenecks and optimization points; An optimization strategy generation module is used to automatically generate optimization strategies based on the results of the intelligent analysis module; The optimization execution module is used to apply the optimization strategy to the database system and monitor the optimization effect; Feedback learning module, which is used to self-learn and adjust according to the results of optimization execution to improve the accuracy of future optimization strategies; The system specifically realizes database optimization through the artificial intelligence-based database optimization method described in any one of claims 1 to 7.

9. A database optimization implementation device based on artificial intelligence, characterized in that: include: at least one memory and at least one processor; The at least one memory is used to store a machine-readable program; The at least one processor is used to call the machine-readable program to implement the method described in any one of claims 1 to 7.

10. A computer-readable medium, characterized in that The computer readable medium stores computer instructions, which, when executed by a processor, cause the processor to execute the method according to any one of claims 1 to 7.

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