现代纺织技术2025，Vol.33Issue(7)：54-64,11.DOI:10.12477∕j.att.202409036
基于数字孪生的纺纱车间能耗预测系统研究与应用
Research and application of an energy consumption prediction system for spinning workshops based on digital twins
摘要
Abstract
This paper proposes a novel energy consumption prediction system based on digital twin technology,with the objective of addressing the issues of low visualization and high energy consumption in spinning.The system aims to enhance the accuracy of energy consumption predictions in spinning workshops through intelligent means.Given the multitude of factors that influence energy consumption in the spinning process,including the type of raw cotton and the spindle speed,precise prediction of energy consumption not only facilitates the optimization of production processes but also enables significant cost savings for enterprises.The system architecture proposed in this text is comprised of four layers:the physical layer,the perception layer,the twin layer,and the application layer.The physical layer refers to the actual physical equipment and environment,which serves as the foundation for all data collection.The perception layer involves various sensors and other data acquisition devices responsible for real-time collection of data from the physical layer.The twin layer establishes virtual models to simulate various variables and conditions in the actual production process,thereby creating a"digital twin"corresponding to the physical entity.The application layer is concerned with the process of supporting decision-making through data analysis and processing. In this system,the combination of Convolutional Neural Networks(CNN)and Bidirectional Long Short-Term Memory(BiLSTM)is utilized to explore the potential correlations between different data dimensions in the spinning process.CNN is particularly adept at processing spatial features,such as pattern recognition in images,while BiLSTM is well-suited to the task of capturing dynamic changes in timeseries data.Through this combination,the model becomes more flexible and efficient in handling complex and variable production data.To further enhance the model's capacity for generalization,the system also introduces the Attention Mechanism.The mechanism enables the model to focus on the most salient aspects of the input data,thereby better understanding and processing complex data structures.Finally,the Fully Connected Layer is responsible for integrating the features extracted from the previous layers to output the final prediction result.The results demonstrate that this CNN-BiLSTM-Attention model exhibits extremely high accuracy in energy consumption prediction,reaching 98.21%,with prediction errors all controlled within 2.5%.In comparison to models such as CNN-BiLSTM,the new model demonstrates superior performance in both prediction accuracy and error control,suggesting that the incorporation of the Attention Mechanism can indeed significantly improve model performance. In conclusion,the energy consumption prediction system based on digital twin technology offers a novel approach to addressing the energy consumption prediction challenge in the conventional spinning process.It not only enhances the accuracy of energy consumption predictions but also offers powerful technical support for future production management,energy conservation,and emission reduction.This achievement is of great significance for promoting the development of the textile industry in a more intelligent and green direction.关键词
数字孪生/能耗预测/卷积神经网络/双向长短时记忆网络/注意力机制Key words
digital twin/energy consumption prediction/convolutional neural network/bidirectional long short-term memory/attention mechanism分类
轻工纺织引用本文复制引用
鲜灿龙,袁逸萍,晁永生,赵飞阳,杨海龙..基于数字孪生的纺纱车间能耗预测系统研究与应用[J].现代纺织技术,2025,33(7):54-64,11.基金项目
新疆自治区科技计划项目-重点研发专项(2022B01057-2,2023B01027-2) （2022B01057-2,2023B01027-2）
