摘 要
随着工业4.0时代的到来，实时数据采集与分析在自动化流程中的重要性日益凸显。本研究旨在探索实时数据采集与分析技术在自动化流程中的应用效果及其优化策略。通过构建基于物联网的实时数据采集系统，结合机器学习算法对采集的数据进行实时分析，本研究提出了一种新型的自动化流程优化模型。研究结果表明，该模型能够显著提高生产效率，降低能耗，并有效预测设备故障。创新点在于将边缘计算与云计算相结合，实现了数据的分布式处理与集中式分析的有机结合，从而提高了数据处理的速度和准确性。主要贡献包括开发了一套完整的实时数据采集与分析框架，并通过实验验证了其在多个工业场景中的适用性和有效性。
关键词：实时数据采集；自动化流程优化；边缘计算
With the advent of the industry 4.0 era, the importance of real-time data acquisition and analysis in automated processes has become increasingly prominent. The purpose of this study is to explore the effectiveness of real-time data acquisition and analysis technology in automated processes and its optimization strategies. By constructing a real-time data acquisition system based on the Internet of Things and conducting real-time analysis of the collected data, a new automated process optimization model is proposed in this study. The results show that the model can significantly improve production efficiency, reduce energy consumption, and effectively predict equipment failure. The innovation lies in the combination of edge computing and cloud computing to realize the organic combination of distributed data processing and centralized analysis, so as to improve the speed and accuracy of data processing. Major contributions include the development of a complete set of real-time data acquisition and analysis fr amework, and the experimental validation of its applicability and effectiveness in multiple industrial scenarios.
Keywords: Real-time data acquisition; automatic process optimization; edge calculation
目 录
1 引言 1
2 实时数据采集技术基础 1
2.1 数据采集系统架构设计 1
2.2 传感器与数据接口技术 1
2.3 实时数据传输协议分析 2
3 自动化流程中的数据预处理 3
3.1 数据清洗与异常值处理 3
3.2 数据标准化与特征提取 3
3.3 实时数据流处理技术 4
4 数据分析在自动化中的应用 4
4.1 实时监控与预警系统构建 4
4.2 基于机器学习的预测分析 5
4.3 决策支持系统的实现方法 5
5 结论 6
致 谢 7
参考文献 8
随着工业4.0时代的到来，实时数据采集与分析在自动化流程中的重要性日益凸显。本研究旨在探索实时数据采集与分析技术在自动化流程中的应用效果及其优化策略。通过构建基于物联网的实时数据采集系统，结合机器学习算法对采集的数据进行实时分析，本研究提出了一种新型的自动化流程优化模型。研究结果表明，该模型能够显著提高生产效率，降低能耗，并有效预测设备故障。创新点在于将边缘计算与云计算相结合，实现了数据的分布式处理与集中式分析的有机结合，从而提高了数据处理的速度和准确性。主要贡献包括开发了一套完整的实时数据采集与分析框架，并通过实验验证了其在多个工业场景中的适用性和有效性。
关键词：实时数据采集；自动化流程优化；边缘计算
With the advent of the industry 4.0 era, the importance of real-time data acquisition and analysis in automated processes has become increasingly prominent. The purpose of this study is to explore the effectiveness of real-time data acquisition and analysis technology in automated processes and its optimization strategies. By constructing a real-time data acquisition system based on the Internet of Things and conducting real-time analysis of the collected data, a new automated process optimization model is proposed in this study. The results show that the model can significantly improve production efficiency, reduce energy consumption, and effectively predict equipment failure. The innovation lies in the combination of edge computing and cloud computing to realize the organic combination of distributed data processing and centralized analysis, so as to improve the speed and accuracy of data processing. Major contributions include the development of a complete set of real-time data acquisition and analysis fr amework, and the experimental validation of its applicability and effectiveness in multiple industrial scenarios.
Keywords: Real-time data acquisition; automatic process optimization; edge calculation
目 录
1 引言 1
2 实时数据采集技术基础 1
2.1 数据采集系统架构设计 1
2.2 传感器与数据接口技术 1
2.3 实时数据传输协议分析 2
3 自动化流程中的数据预处理 3
3.1 数据清洗与异常值处理 3
3.2 数据标准化与特征提取 3
3.3 实时数据流处理技术 4
4 数据分析在自动化中的应用 4
4.1 实时监控与预警系统构建 4
4.2 基于机器学习的预测分析 5
4.3 决策支持系统的实现方法 5
5 结论 6
致 谢 7
参考文献 8
