摘要
针对行业内被动式木门窗扇及窗框等集成材根据订单要求完成定长截断、四面刨铣后,需要对尺寸各异的门窗材进行优化分类堆叠的现状设计柔性分类智能远程控制系统。通过Modbus通信协议及多种传感器协同完成现场数据的实时采集,运用消息队列遥测传输(Message Queuing Telemetry Transport,MQTT)协议和通信模块将采集的数据上传至云平台,终端用户通过手机App或Web端实时查看系统运行数据及进行远程操控,实现基于物联网的远程监控与报警。结果显示,设计的控制系统尺寸信息采集系统的鲁棒性较高,系统故障率低,同时提高分类堆叠效率。控制系统的设计实现了门窗材定制化分类堆叠的自动化、数据实时采集与传输以及远程监控,为实木加工的智能化与信息化应用提供了技术支撑。
In view of the situation that integrated materials such as passive wood doors,window sashes,window frames in the industry need to optimize the classification and stacking of doors and windows of different sizes after completing fixed-length cutting and four-sided milling according to the order requirements,a flexible classification intelligent remote control system is designed.Real time collection of on-site data is achieved through the Modbus communication protocol and the collaboration of multiple sensors.The collected data is uploaded to the cloud platform using the Message Queuing Telemetry Transport(MQTT)protocol and communication module.End users can view system operation data in real time and perform remote control through mobile APPs or web terminals,achieving remote monitoring and alarm based on the Internet of Things.The results show that the designed control system for collecting size information has high robustness,low system failure rate,and improved classification and stacking efficiency.The design of the control system realizes the automation of customized classification and stacking of door and window materials,real-time data collection and transmission,and remote monitoring,providing technical support for the intelligent and information-based application of solid wood processing.
作者
石昌玉
丁禹程
曲文
任长清
吴哲
杨春梅
SHI Changyu;DING Yucheng;QU Wen;REN Changqing;WU Zhe;YANG Chunmei(College of Mechanical and Electrical Engineering,Northeast Forestry University,Harbin 150040,China)
出处
《森林工程》
北大核心
2024年第3期162-169,共8页
Forest Engineering
基金
黑龙江省重点研发项目(2022ZX01A17)
中央高校基本科研业务费专项资金项目资助(2572020DR12)。
关键词
定制化门窗材
分类堆叠
智能化
远程监控
被动式建筑
Customized windows and doors
categorized stacking
intelligent
remote monitoring
passive building