摘要
为了有效地提高造纸企业智能调度的性能和效率,结合人工智能深度学习方法,针对造纸企业传感器数据融合探测、诊断决策和装载机调度等任务建立数据融合网络模型,提取数据的关联性特征,提高数据融合的精度和效率,并在此基础上开展生产物流智能调度研究。
In order to improve the performance and efficiency of intelligent scheduling in papermaking enterprises effectively,in this paper,combined with the deep learning method of artificial intelligence,establishes a data fusion network model for the tasks of sensor data fusion detection,diagnosis decision and loader scheduling in paper enterprises,extracts the correlation characteristics of data,improves the accuracy and efficiency of data fusion,and carries out the research on intelligent scheduling of production logistics on this basis.
作者
张海涛
唐美玲
刘锋
朱定欢
Zhang Haitao;Tang Meiling;Liu Feng;Zhu Dinghuan(College of Science,Hunan University of Technology,Zhuzhou,Hunan 412000,China)
出处
《计算机时代》
2022年第1期5-7,共3页
Computer Era
基金
湖南省2020年大学生创新创业训练计划资助项目(湘教通〔2020〕191号)(S202011535012)。
关键词
造纸企业
数据融合
生产物流
智能调度
papermaking enterprises
data fusion
production logistics
intelligent scheduling
