基于道路垃圾量分类的扫地机清洁参数的模糊控制算法
Haiying Wang1, Chenguang Wang2, Yang Ao2
1Key Laboratory of Road Construction Technology and Equipment of MOE, Chang'an University, Xi'an, 710064, China. whying@chd.edu.cn.
Scientific reports
|March 12, 2025
概括
这项研究介绍了街头扫地人员的智能模糊控制算法,根据道路垃圾量优化了清洁设置. 该系统实现了显著的节能,提高了城市清洁效率.
科学领域:
- 环境工程 环境工程
- 机器人和智能系统 机器人和智能系统
- 人工智能的人工智能
背景情况:
- 街头清扫人员在调整清洁参数以适应可变的道路垃圾水平方面面临着挑战,这导致运营效率低下和高能耗.
- 目前的系统缺乏智能来动态调整清洁设置,影响城市清洁和资源管理.
研究的目的:
- 开发和评估一种模糊控制算法,用于自适应式的街头扫地机清洁参数调整.
- 提高城市街道清洁操作的智能和能源效率.
主要方法:
- 使用YOLO (You Only Look Once) v5深度学习模型实时检测和分类道路表面的垃圾.
- 开发了一个模糊的控制模型,使用垃圾体积分级系数和扫地机速度来优化磁盘刷和风扇速度.
- 综合垃圾覆盖面和重量数据,以计算道路垃圾体积分类系数.
主要成果:
- 拟议的模糊控制算法证明了对街头扫地机清洁参数的自适应控制.
- 在相同的道路垃圾条件下,与传统轮控制相比,实现了29.77%的节能.
- 通过动态参数调整成功增强了街头扫地人员的操作智能.
结论:
- 模糊控制算法有效地解决了传统街头扫地机控制系统的局限性.
- 智能系统为改善城市环境中的能源效率和清洁性能提供了可行的解决方案.
- 这项研究为更智能,更可持续的城市维护技术铺平了道路.
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