强化学习在动态环境园区改造决策中的应用分析——以北京某重型电机厂141单体检测鉴定为例
Application of reinforcement learning in dynamic environment park renovation decision-making—A case study of the inspection and identification of 141 units in a heavy machinery factory in Beijing
董道武1
作者信息
- 1. 中电投工程研究检测评定中心有限公司,北京 100097
- 折叠
摘要
文章以北京某重型电机厂的改造项目为例,探讨了强化学习在老旧工业园区更新中的应用.研究显示,自主开发的Ntgale Ad系统通过半数字化手段提高了改造决策的效率.同时,减震隔震技术满足了园区特殊需求,如教育和医疗领域的长期规划.这项研究展示了强化学习在动态环境下的实用性,为城市工业园区的更新提供了新视角.
Abstract
This paper discusses the application of reinforcement learning in the renovation of old industrial parks,taking the renovation project of a heavy machinery factory in Beijing as an example.The study demonstrates that the independently developed Ntgale Ad system enhances decision-making efficiency in park renovations through semi-digitization.Additionally,the application of seismic isolation technology meets specific needs of the park,such as long-term planning in education and healthcare sectors.This research highlights the practicality of reinforcement learning in dynamic environments and offers a new perspective on the renewal of urban industrial parks.
关键词
工业园区/更新/半数字化/减震隔震Key words
industrial park/renewal/semi-digitalization/shock absorption and isolation引用本文复制引用
出版年
2024
NETL
NSTL
万方数据