基于机器学习的物联网设备异常检测系统设计与应用
摘 要
本文介绍了基于机器学习的物联网设备异常检测系统的设计和应用，旨在提高物联网设备的可靠性和效率。针对物联网设备大量数据的特点，本文从数据采集和处理、特征提取和选择、异常检测算法选择和模型评估和优化四个方面详细介绍了系统设计流程，并探讨了机器学习在工业制造、交通运输和高性能计算等领域中的具体应用，提高了设备运行安全性和生产效率。本文的贡献在于集成了机器学习和物联网技术，实现了对物联网设备运行状态的快速监测和预测，可为提高物联网设备可靠性和效率提供参考。
关键词：机器学习、物联网、异常检测
Abstract
This article introduces the design and application of a machine learning-based system for detecting anomalies in IoT devices with the aim of improving their reliability and efficiency. Considering the large amounts of data generated by IoT devices, this paper details the system design process from data collection and processing, feature extraction and selection, anomaly detection algorithm selection, and model evaluation and optimization. Furthermore, this paper explores the specific applications of machine learning in industrial manufacturing, transportation, and high-performance computing, enhancing equipment safety and production efficiency. The contribution of this study lies in integrating machine learning and IoT technology to enable fast monitoring and prediction of IoT device operational status, which can provide guidance for enhancing reliability and efficiency.
Keyword: machine learning、IoT、anomaly detection
目录
摘 要 1
Abstract 1
1绪论 2
2 相关的技术和原理 2
2.1 Internet技术综述 2
2.2仪器故障探测方法研究进展 2
2.3基本的机器学习 2
3 基于机器学习技术的 IoT设备异常识别方法研究 3
3.1资料的获取与加工 3
3.2特征的抽取与选取 3
3.3选取异常检测算法 3
4在 IoT设备异常识别中的应用 4
4.1 制造业领域 4
4.2 运输领域 4
4.3高性能计算 Domain 4
5结论 4
参考文献 6
摘 要
本文介绍了基于机器学习的物联网设备异常检测系统的设计和应用，旨在提高物联网设备的可靠性和效率。针对物联网设备大量数据的特点，本文从数据采集和处理、特征提取和选择、异常检测算法选择和模型评估和优化四个方面详细介绍了系统设计流程，并探讨了机器学习在工业制造、交通运输和高性能计算等领域中的具体应用，提高了设备运行安全性和生产效率。本文的贡献在于集成了机器学习和物联网技术，实现了对物联网设备运行状态的快速监测和预测，可为提高物联网设备可靠性和效率提供参考。
关键词：机器学习、物联网、异常检测
Abstract
This article introduces the design and application of a machine learning-based system for detecting anomalies in IoT devices with the aim of improving their reliability and efficiency. Considering the large amounts of data generated by IoT devices, this paper details the system design process from data collection and processing, feature extraction and selection, anomaly detection algorithm selection, and model evaluation and optimization. Furthermore, this paper explores the specific applications of machine learning in industrial manufacturing, transportation, and high-performance computing, enhancing equipment safety and production efficiency. The contribution of this study lies in integrating machine learning and IoT technology to enable fast monitoring and prediction of IoT device operational status, which can provide guidance for enhancing reliability and efficiency.
Keyword: machine learning、IoT、anomaly detection
目录
摘 要 1
Abstract 1
1绪论 2
2 相关的技术和原理 2
2.1 Internet技术综述 2
2.2仪器故障探测方法研究进展 2
2.3基本的机器学习 2
3 基于机器学习技术的 IoT设备异常识别方法研究 3
3.1资料的获取与加工 3
3.2特征的抽取与选取 3
3.3选取异常检测算法 3
4在 IoT设备异常识别中的应用 4
4.1 制造业领域 4
4.2 运输领域 4
4.3高性能计算 Domain 4
5结论 4
参考文献 6
