计算技术与自动化2026，Vol.45Issue(2)：182-188,7.DOI:10.16339/j.cnki.jsjsyzdh.202602029
基于有监督深度学习的设备数字化模型的构建与实现
Construction and Implementation of Device Digitization Model Based on Supervised Deep Learning
晋世仲1
作者信息
- 1. 国家能源集团新能源技术研究院有限公司,北京 102209
- 折叠
摘要
Abstract
In order to improve the security and reliability of the digital system for equipment,this article uses an im-proved fuzzy control algorithm through a data acquisition module and a digital model module,which can automatically adjust the motion parameters of sensors and collect equipment data in real-time using multiple sensors.The digital model module adopts improved convolutional neural network algorithm and back propagation algorithm,greatly improving the generaliza-tion ability and accuracy of the model.After testing,the peak signal-to-noise ratio of the collected images by the data acqui-sition module of the system is 39.13,the color distortion evaluation of the equipment model is 0.0382,and the structural similarity evaluation of the equipment model is 0.8962,which meets the requirements of digital monitoring and management of equipment.关键词
有监督深度学习/模糊控制算法/传感器/反向传播算法Key words
supervised deep learning/fuzzy control algorithm/sensors/back propagation algorithm分类
信息技术与安全科学引用本文复制引用
晋世仲..基于有监督深度学习的设备数字化模型的构建与实现[J].计算技术与自动化,2026,45(2):182-188,7.
