摘 要：随着数据库系统在现代信息技术中的核心地位日益凸显，其运行稳定性与可靠性成为研究热点。本研究旨在通过机器学习技术实现数据库故障的精准预测与自动化自愈，以提升系统运维效率并降低人工干预成本。研究采用监督学习与无监督学习相结合的方法，构建了多维度特征提取模型，用于捕捉数据库运行状态中的潜在异常模式，并设计了一种基于强化学习的自愈策略优化算法，能够根据预测结果动态调整修复方案。实验结果表明，该方法可将故障预测准确率提升至95%以上，同时显著缩短平均修复时间（MTTR），达到传统方法的两倍效率。本研究的主要创新点在于首次提出融合时序分析与深度学习的故障诊断框架，并实现了端到端的自动化运维流程，为智能化数据库管理提供了新思路。研究成果对推动数据库运维从被动响应向主动预防转变具有重要意义。
关键词：数据库故障预测；机器学习；自愈策略
Abstract：As database systems increasingly occupy a central position in modern information technology, their operational stability and reliability have become research focal points. This study aims to achieve precise prediction of database failures and automated self-healing through machine learning technologies, thereby enhancing system maintenance efficiency and reducing the cost of human intervention. By integrating supervised and unsupervised learning approaches, a multi-dimensional feature extraction model was developed to capture latent anomaly patterns in database operational states. Additionally, a reinforcement learning-based self-healing strategy optimization algorithm was designed, which dynamically adjusts repair plans according to predictive outcomes. Experimental results demonstrate that this method can elevate the accuracy of failure prediction to over 95%, while significantly shortening the mean time to repair (MTTR) to twice the efficiency of conventional methods. A key innovation of this research lies in the first proposal of a fault diagnosis fr amework that integrates time-series analysis with deep learning, enabling an end-to-end automated operation and maintenance process. This study provides new insights into intelligent database management and plays a crucial role in advancing database maintenance from passive response toward proactive prevention.
引言 1
一、数据库故障预测的基础理论 1
(一)数据库故障类型分析 1
(二)机器学习在故障预测中的应用 2
(三)故障预测的关键技术挑战 2
二、基于机器学习的故障预测模型构建 3
(一)数据采集与预处理方法 3
(二)特征选择与优化策略 3
(三)模型训练与性能评估 4
三、数据库自愈技术的设计与实现 4
(一)自愈技术的基本原理 4
(二)故障诊断与定位算法 5
(三)自动修复机制的设计 5
四、系统验证与实际应用案例分析 6
(一)实验环境与数据集介绍 6
(二)预测与自愈效果评估 6
(三)实际部署中的问题与优化 7
结论 7
参考文献 9
致谢 9
关键词：数据库故障预测；机器学习；自愈策略
Abstract：As database systems increasingly occupy a central position in modern information technology, their operational stability and reliability have become research focal points. This study aims to achieve precise prediction of database failures and automated self-healing through machine learning technologies, thereby enhancing system maintenance efficiency and reducing the cost of human intervention. By integrating supervised and unsupervised learning approaches, a multi-dimensional feature extraction model was developed to capture latent anomaly patterns in database operational states. Additionally, a reinforcement learning-based self-healing strategy optimization algorithm was designed, which dynamically adjusts repair plans according to predictive outcomes. Experimental results demonstrate that this method can elevate the accuracy of failure prediction to over 95%, while significantly shortening the mean time to repair (MTTR) to twice the efficiency of conventional methods. A key innovation of this research lies in the first proposal of a fault diagnosis fr amework that integrates time-series analysis with deep learning, enabling an end-to-end automated operation and maintenance process. This study provides new insights into intelligent database management and plays a crucial role in advancing database maintenance from passive response toward proactive prevention.
Keywords: Database Fault Prediction；Machine Learning；Self-Healing Strategy
引言 1
一、数据库故障预测的基础理论 1
(一)数据库故障类型分析 1
(二)机器学习在故障预测中的应用 2
(三)故障预测的关键技术挑战 2
二、基于机器学习的故障预测模型构建 3
(一)数据采集与预处理方法 3
(二)特征选择与优化策略 3
(三)模型训练与性能评估 4
三、数据库自愈技术的设计与实现 4
(一)自愈技术的基本原理 4
(二)故障诊断与定位算法 5
(三)自动修复机制的设计 5
四、系统验证与实际应用案例分析 6
(一)实验环境与数据集介绍 6
(二)预测与自愈效果评估 6
(三)实际部署中的问题与优化 7
结论 7
参考文献 9
致谢 9
