基于物联网的煤矿安全系统

R. Kodali, S. Sahu
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引用次数: 2

摘要

煤炭是最重要的燃料来源之一,对许多行业至关重要,通常被许多人视为黑钻石。它是世界上最重要的本土能源。为了满足不断增长的电力需求,挖掘越来越多的煤炭已成为印度的国家优先事项。但对于从事这项工作的人来说,这是一项危险的、可能致命的任务。煤矿工人面临着许多与环境有关的问题,如高温、高湿、有害气体的释放等等。这些因素为工人创造了一个相当危险的工作环境,从而对他们的生命构成严重威胁。为了减轻问题的严重程度,我们开发了一种模型,可以从矿井的不同位置感知这些环境参数,并向矿井控制室发出警报,以便在出现危险情况时采取适当的行动。这里,NodeMCU被用作不同传感器感知的这些参数的MQTT发布者。来自不同矿位的这些参数值通过MQTT发送,由树莓派订阅,树莓派在这里充当MQTT代理。当这些值超过一定的阈值时,可以通过电子邮件向矿控办公室发送警报,以便采取所需的行动。这也会产生警报,提醒工人撤离现场。这些发布的数据也可以存储在文件中,用于数据收集和大数据分析,以便开发更有效的系统,以提高效率并降低工人可能失去生命的风险。
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IoT based Safety System for Coal Mines
Coal is one of the most important fuel sources and is of paramount importance to many industries, often regarded by many as Black Diamond. It is the most important indigenous energy resource available in the world. Digging up more and more coal has become a national priority for India as it tries to meet its ever-increasing demand for electricity. But for the people who do this job, it is a dangerous and potentially lethal assignment. Workers in coal mines face many environment related problems such as high temperature, humidity, release of harmful gases to name a few. These factors create a rather dangerous environment for workers to work in and thus poses a serious risk to their very lives.As a means of reducing the intensity of the problem, we have developed a model which senses these environmental parameters from different locations in the mines and sends alerts to the mines control room so that appropriate actions can be taken whenever there seems to be a dangerous situation. Here NodeMCU is used as an MQTT publisher for these parameters sensed by the different sensors. The values of these parameters from different mine locations are sent via MQTT, by being subscribed by a Raspberry Pi which acts as an MQTT broker here. When these values exceed certain threshold values, an alert by email can be sent to the mine control office so that required actions can be taken. This also generates an alarm for the workers to evacuate the place. This published data can also be stored in files for data collection and big data analytics so that more efficient systems can be developed to improve the efficiency and reduce the risk of workers potentially losing their lives.
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