摘要
针对铀矿井下受限空间内氡及氡子体浓度分布特征,分析受限空间含氡作业环境对人体的危害机制,提出基于“人—机—环”互联自适应的铀矿智能通风降氡方案,架构基于ARDUINO的氡气监测及智能调控系统,设计系统的“硬件”和“软件”方案。该系统的核心硬件为ARDUINO开发板和ESP8266-WiFi模块,铀矿粉尘浓度、氡及其子体浓度、风速动态数据由相应传感器获取,通过各传感数据采集器以相应的通信协议传输至ARDUINO主控,ARDUINO主控输出指令至通风设备及报警装置。系统同时搭建网络云平台,实现环境监测的远程监控和智能通风系统的远程控制。该系统能够基于物联感知获取环境数据,进行智能控制逻辑运算并实现通风设备自适应调控响应,从而安全、高效、稳定、低耗地调控铀矿井下空气质量指数。
Aiming at the distribution characteristics of the concentrations of radon and radon progeny in the underground confined space of uranium mine,the hazardous mechanism of the operating environment containing radon on the human body in the confined space was analyzed,and an intelligent ventilation and radon reduction of uranium mine based on the“human-machine-environment”interconnection and self-adaptation was proposed.A radon monitoring and intelligent control system based on ARDUINO was constructed,and the“hardware”and“software”solutions of the system were designed.The core hardware of the system were the ARDUINO development board and the ESP8266-WiFi module.The dynamic data of the concentration of dust,the concentrations of radon and its progeny,and the wind speed in the uranium mine were acquired by the corresponding sensors,and they were transmitted to the ARDUINO master control through the sensor data collectors with the corresponding communication protocols,then the ARDUINO main control output the commands to the ventilation equipments and alarm devices.At the same time,the system built a network cloud platform to realize the remote monitoring of environmental monitoring and remote control of intelligent ventilation system.The system can obtain environmental data based on IoT perception,perform intelligent control logic operations,and realize adaptive control and response of ventilation equipment,thereby safely,efficiently,stably,and low-costly regulating the air quality index of uranium mines.
作者
耿新洋
袁正平
王富林
GENG Xinyang;YUAN Zhengping;WANG Fulin(School of Resource&Environment and Safety Engineering,University of South China,Hengyang Hunan 421001,China)
出处
《中国安全生产科学技术》
CAS
CSCD
北大核心
2022年第7期109-113,共5页
Journal of Safety Science and Technology
基金
国家自然科学基金项目(51904154)
湖南省自然科学基金项目(2020JJ5491)。
关键词
ARDUINO
铀矿井
环境监测
智能控制
ARDUINO
uranium mine
environmental monitoring
intelligent control