摘 要：随着大数据时代的到来，数据库查询优化面临日益复杂的挑战，传统基于规则或统计的优化方法已难以满足动态多变的工作负载需求。为此，本研究提出一种基于人工智能的数据库自适应查询优化技术，旨在通过机器学习模型实时分析查询模式和数据分布特征，从而实现高效、灵活的查询计划生成。研究采用深度强化学习算法构建优化框架，结合神经网络对查询语句进行向量化表示，并引入在线学习机制以持续改进模型性能。实验结果表明，该方法在多种复杂查询场景下能够显著降低响应时间，平均提升系统性能达30%以上。此外，所提出的自适应机制有效解决了传统优化器中静态统计信息导致的偏差问题，为动态环境下的查询优化提供了新思路。本研究的主要贡献在于首次将深度强化学习应用于数据库查询优化领域，开创性地实现了智能化与自适应性的融合，为未来相关技术的发展奠定了理论与实践基础。
关键词：数据库查询优化；深度强化学习；自适应机制
Abstract：With the advent of the big data era, database query optimization is facing increasingly complex challenges, and traditional rule-based or statistical optimization methods are struggling to meet the demands of dynamically changing workloads. To address this issue, this study proposes an artificial intelligence-based adaptive query optimization technique for databases, which aims to generate efficient and flexible query plans by leveraging machine-learning models to analyze query patterns and data distribution characteristics in real time. A deep reinforcement learning algorithm is employed to construct the optimization fr amework, incorporating neural networks for vectorized representation of query statements and introducing an online learning mechanism to continuously enhance model performance. Experimental results demonstrate that this approach significantly reduces response time across various complex query scenarios, achieving an average system performance improvement of over 30%. Moreover, the proposed adaptive mechanism effectively resolves bias issues caused by static statistical information in traditional optimizers, offering new insights into query optimization in dynamic environments. The primary contribution of this research lies in its pioneering application of deep reinforcement learning to the field of database query optimization, innovatively integrating intelligence and adaptability, and establishing both theoretical and practical foundations for the future development of related technologies.
引言 1
一、数据库查询优化的基础理论 1
(一)查询优化的基本概念 1
(二)传统查询优化方法分析 2
(三)自适应查询优化的必要性 2
二、人工智能技术在查询优化中的应用 3
(一)机器学习算法概述 3
(二)深度学习在查询优化中的作用 3
(三)强化学习与自适应优化结合 4
三、自适应查询优化的关键技术研究 4
(一)查询执行计划动态调整机制 4
(二)数据分布感知的优化策略 5
(三)实时反馈驱动的优化模型 5
四、基于人工智能的自适应查询优化实践 6
(一)实验环境与数据集设计 6
(二)性能评估与结果分析 6
(三)系统实现与未来改进方向 7
结论 7
参考文献 9
致谢 9
关键词：数据库查询优化；深度强化学习；自适应机制
Abstract：With the advent of the big data era, database query optimization is facing increasingly complex challenges, and traditional rule-based or statistical optimization methods are struggling to meet the demands of dynamically changing workloads. To address this issue, this study proposes an artificial intelligence-based adaptive query optimization technique for databases, which aims to generate efficient and flexible query plans by leveraging machine-learning models to analyze query patterns and data distribution characteristics in real time. A deep reinforcement learning algorithm is employed to construct the optimization fr amework, incorporating neural networks for vectorized representation of query statements and introducing an online learning mechanism to continuously enhance model performance. Experimental results demonstrate that this approach significantly reduces response time across various complex query scenarios, achieving an average system performance improvement of over 30%. Moreover, the proposed adaptive mechanism effectively resolves bias issues caused by static statistical information in traditional optimizers, offering new insights into query optimization in dynamic environments. The primary contribution of this research lies in its pioneering application of deep reinforcement learning to the field of database query optimization, innovatively integrating intelligence and adaptability, and establishing both theoretical and practical foundations for the future development of related technologies.
Keywords: Database Query Optimization；Deep Reinforcement Learning；Adaptive Mechanism
引言 1
一、数据库查询优化的基础理论 1
(一)查询优化的基本概念 1
(二)传统查询优化方法分析 2
(三)自适应查询优化的必要性 2
二、人工智能技术在查询优化中的应用 3
(一)机器学习算法概述 3
(二)深度学习在查询优化中的作用 3
(三)强化学习与自适应优化结合 4
三、自适应查询优化的关键技术研究 4
(一)查询执行计划动态调整机制 4
(二)数据分布感知的优化策略 5
(三)实时反馈驱动的优化模型 5
四、基于人工智能的自适应查询优化实践 6
(一)实验环境与数据集设计 6
(二)性能评估与结果分析 6
(三)系统实现与未来改进方向 7
结论 7
参考文献 9
致谢 9
