A Comprehensive Intelligent Monitoring System for Mines
A Comprehensive Intelligent Monitoring System for Mines
批准号:
536337-2018
负责人:
Fernando, Xavier
金额:
$5.83万
依托单位:
依托单位国家:
加拿大
项目类别:
Collaborative Research and Development Grants
财政年份:
2020
资助国家:
加拿大
项目状态:
已结题
起止时间:
2020-01-01 至 2021-12-31
中文摘要
每个工人的健康都受到生物、生理、心理和其他个人特征的影响。这一比例在压力很大的采矿部门要高得多,那里的工作艰巨,而且往往很危险。毫不奇怪,这种工作场所疲劳是采矿业各种事故数量增加的主要原因。每年,采矿业都会因这些事件而在人员生命和宝贵的基础设施方面遭受重大损失。 尽管采矿业只雇用了全球1%的劳动力,但却造成了全球约8%的工伤死亡(加拿大为8.2%)。不幸的是,其中许多是可以预防的。虽然技术进步和更严格的监管有助于减少采矿事故,但这一数字仍然太高,特别是采矿业雇用了40多万工人,其中大多数来自偏远社区。
以人为本的方法是理解问题和改善矿工福祉所必需的。 这反过来将减少采矿业职业危害的数量和影响。在这个项目中,将开发一个全面的远程监测系统,以收集矿工的生物医学数据以及真实的实时位置和环境数据。这些多传感器数据将被融合,并将开发机器学习算法来分析和解释收集到的数据。这将有助于更好地了解矿工有效工作的能力、环境因素、年龄、工作时间和其他生理条件之间的相互关系。其结果不仅将用于确保矿工的长期和短期健康,还将有助于减少事故,提高生产力,优化矿山资源利用。这将与PBE Canada合作完成,PBE Canada是矿山和隧道通信和监控系统的全球领导者。
英文摘要
Every worker's health is influenced by biological, physical, psychological, and other individual traits. This percentage is much higher in the stressful mining sector where work is arduous and often dangerous. No surprise, this workplace fatigue is the primary reason for increased number of various accidents in the mining industry. Every year, the mining industry sees significant losses in terms of human lives and valuable infrastructure due to these incidents. Despite employing only 1% of the global workforce, the mining industry is responsible for approximately 8% of work-related fatalities worldwide (8.2% in Canada). Unfortunately, many of these are preventable. Although technological advancements and tighter regulations have helped in alleviating mining incidents, still the number is too high, especially when over 400,000 worker mostly from remote communities are employed by the mining industry.
A human centric approach is necessary to understand the issues and to improve the well-being of miners. This in turn will reduce the number and the impact of occupational hazards in the mining industry. In this project, a comprehensive remote monitoring system will be developed to collect biomedical data from miners in addition to their location and environment data in real time. This multi-sensor data will be fused and Machine learning algorithms will be developed to analyze and interpret the collected data. This will create better understanding of the correlations among miners' ability to function efficiently, environmental factors, their age, work duration and other physiological conditions. The outcome will not only be used to ensure long- and short-term wellbeing of miners, it will also help to alleviate accidents and improve productivity and optimize resource usage in mines. This will be done in collaboration with PBE Canada, a global leader for communication and monitoring systems for mines and tunnels.
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