摘 要：
随着网络规模的持续扩展和复杂性的不断提升，传统网络故障诊断与恢复方法已难以满足现代网络对高效性和智能化的需求基于此背景，本研究旨在探索人工智能技术在网络故障诊断与恢复中的应用潜力，提出一种基于深度学习和强化学习的综合机制以应对这一挑战研究通过构建多层神经网络模型，结合时序数据分析和异常检测算法，实现了对网络故障的精准定位与分类同时，引入强化学习策略优化故障恢复路径选择过程，从而显著提升网络自愈能力实验结果表明，所提出的机制在故障诊断准确率和恢复效率方面均优于现有方法，特别是在大规模动态网络环境中表现出更强的适应性与鲁棒性本研究的主要创新点在于将深度学习的特征提取能力和强化学习的决策优化能力有机结合，形成了一种端到端的智能解决方案此外，该机制还支持在线学习与持续优化，为未来网络运维提供了新的思路总体而言，本研究不仅验证了人工智能技术在网络管理领域的可行性，也为后续相关研究奠定了理论与实践基础
关键词：网络故障诊断；深度学习；强化学习；故障恢复；智能运维
Research on Network Fault Diagnosis and Recovery Mechanism Based on Artificial Intelligence
Abstract: With the continuous expansion of network scale and increasing complexity, traditional methods for network fault diagnosis and recovery are struggling to meet the demands of modern networks for efficiency and intelligence. Against this backdrop, this study explores the application potential of artificial intelligence technologies in network fault diagnosis and recovery, proposing an integrated mechanism based on deep learning and reinforcement learning to address these challenges. By constructing a multi-layer neural network model and combining time-series data analysis with anomaly detection algorithms, the study achieves precise localization and classification of network faults. Additionally, reinforcement learning strategies are introduced to optimize the process of fault recovery path selection, thereby significantly enhancing the network's self-healing capabilities. Experimental results indicate that the proposed mechanism outperforms existing methods in terms of fault diagnosis accuracy and recovery efficiency, demonstrating stronger adaptability and robustness in large-scale dynamic network environments. The primary innovation of this research lies in the organic integration of deep learning's feature extraction capabilities and reinforcement learning's decision optimization capabilities, forming an end-to-end intelligent solution. Moreover, the mechanism supports online learning and continuous optimization, providing new insights for future network operations. Overall, this study not only verifies the feasibility of artificial intelligence technologies in the field of network management but also lays a theoretical and practical foundation for subsequent related research.
Keywords: Network Fault Diagnosis; Deep Learning; Reinforcement Learning; Fault Recovery; Intelligent Operation And Maintenance
目 录
1绪论 1
1.1网络故障诊断与恢复的研究背景 1
1.2基于人工智能研究的意义与价值 1
1.3国内外研究现状分析 1
1.4本文研究方法与技术路线 2
2人工智能在故障诊断中的应用基础 2
2.1故障诊断的基本概念与流程 2
2.2人工智能技术的分类与特点 3
2.3数据驱动的故障诊断模型构建 3
2.4深度学习在故障检测中的优势 4
2.5传统方法与AI方法的对比分析 4
3基于人工智能的网络故障诊断机制设计 5
3.1网络故障的主要类型与特征 5
3.2AI算法在故障识别中的应用 5
3.3面向实时性的故障诊断框架设计 6
3.4异常流量检测与模式识别技术 6
3.5故障诊断系统的性能评估指标 7
4网络故障恢复机制的智能化实现 7
4.1故障恢复的核心需求与挑战 7
4.2自动化恢复策略的设计原则 8
4.3基于强化学习的恢复路径优化 8
4.4动态资源调度与负载均衡机制 9
4.5恢复机制的实际案例分析 9
结论 10
参考文献 11
致 谢 12
