摘 要
随着工业4.0时代的到来，自动化生产线的能耗问题日益凸显，成为制约制造业可持续发展的重要因素。本研究针对传统能耗监测方法精度不足、实时性差等问题，提出了一种基于物联网和大数据分析的能耗监测与优化技术体系。通过部署多源传感器网络，实现了对生产线各环节能耗数据的实时采集；结合改进的深度神经网络算法，构建了高精度的能耗预测模型；在此基础上，设计了基于遗传算法的多目标优化策略，实现了生产参数的自适应调节。实验结果表明，该系统在保证生产效率的前提下，可降低能耗15%-20%，预测准确率达到95%以上。本研究的创新点在于：首次将边缘计算技术应用于生产线能耗监测，显著提升了数据处理效率；提出了基于动态权重的多目标优化算法，有效解决了传统方法难以兼顾能效与产能的问题。研究成果为制造业节能减排提供了新的技术路径，具有重要的理论价值和实践意义。
关键词：能耗监测 大数据分析 深度神经网络 多目标优化
Abstract
With the advent of the Industry 4.0 era, the energy consumption of automated production lines has become increasingly prominent, and has become an important factor restricting the sustainable development of manufacturing. Aiming at the problems such as insufficient accuracy and poor real-time performance of traditional energy consumption monitoring methods, this study proposed a technical system of energy consumption monitoring and optimization based on the Internet of Things and big data analysis. Through the deployment of multi-source sensor network, the real-time acquisition of energy consumption data of each link of the production line is realized. Combined with the improved deep neural network algorithm, a high-precision energy consumption prediction model is constructed. On this basis, a multi-ob jective optimization strategy based on genetic algorithm is designed to realize the adaptive adjustment of production parameters. The experimental results show that the system can reduce energy consumption by 15%-20% and the prediction accuracy is over 95% under the premise of ensuring the production efficiency. The innovation of this research is that the edge computing technology is applied to the energy consumption monitoring of the production line for the first time, which significantly improves the data processing efficiency; A multi-ob jective optimization algorithm based on dynamic weights is proposed, which effectively solves the problem that traditional methods are difficult to balance energy efficiency and productivity. The research results provide a new technical path for energy conservation and emission reduction in manufacturing industry, which has important theoretical value and practical significance.
Keywords: Energy consumption monitoring; Big data analysis; Deep neural network; Multi-ob jective optimization
目 录
1 引言 1
2 自动化生产线能耗监测技术分析 1
2.1 能耗数据采集方法与系统架构 1
2.2 实时监测技术在生产线中的应用 2
2.3 能耗数据分析与可视化方法 2
3 自动化生产线能耗优化策略研究 3
3.1 基于设备运行状态的节能控制策略 3
3.2 生产调度优化对能耗的影响分析 3
3.3 能源管理系统集成与优化方案 3
4 自动化生产线能耗监测与优化系统实现 4
4.1 系统总体设计与功能模块划分 4
4.2 关键技术的实现与应用验证 4
4.3 系统性能评估与案例分析 5
5 结论 5
致 谢 7
参考文献 8
