人工智能与采矿

2020年05月29日

来源:柴老师国际教育服务

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当您想到“数据”和“挖矿”这两个词时,毫无疑问,数据挖掘的思想首先出现。但是,正如我们在挖掘丰富的数据资源中发现的价值一样,我们也可以将处理数据的先进技术应用于现实世界的挖掘中,即从地球上提取自然资源。世界与数据资源一样,对自然资源的依赖程度也很高,因此有必要了解不断发展的人工智能和机器学习领域如何对采矿和自然资源提取领域产生影响。

采矿一直是危险的职业,因为开采矿物,天然气,石油和其他资源需要在可能对人类生命造成危险的条件下进行作业。为了获取我们需要的资源,我们越来越需要进入更恶劣的气候环境,例如海底深处或地球深处。因此,采矿和资源开采公司寻求各种机器人技术,自治系统和各种AI应用程序来最大程度地降低风险,最大化回报并减轻采矿对我们生态系统的环境影响就不足为奇了。

机器学习在采矿中的作用

采矿业使用大量昂贵的大型机械在矿场以及需要处理物料的地方进行广泛的操作。许多此类机器都有许多提供大量数据的传感器,这些数据可让您深入了解非常昂贵的机器的运行方式,运行条件以及完成特定任务的性能。保持机器正常运行对于确保采矿作业能够继续进行至关重要。任何停机时间或不必要的维护都将导致显着的成本和采矿操作的复杂性。

此外,使用更高级的分析功能使人们不必在潜在危险情况下深入矿山,以尝试评估问题并确认正在发生的事情。此外,使用预测分析可以实现从维护到购买设备的更具战略性和效率的运营。

减少环境影响

资源提取与它周围的环境密不可分。在不影响提取物周围的自然和地形的情况下,将其从地球上带走几乎是不可能的。但是,可以最大程度地减少这种影响,而机器学习和AI正是为此而努力。 Antoine解释说,在错误的地方挖掘或低效率地提取资源几乎没有什么好处。机器学习系统可帮助您更精确地分析地质和地形,以准确地确定您要挖掘的内容,并找到提取其的最佳方法,从而最大程度地为操作员带来收益并最大程度地减少对环境的总体影响。

机器学习和AI的其他用途可以应用于使用无人机镜头或卫星图像来持续监视废物和产出堆,以确保它们符合环境法规并最大程度地减少任何潜在的安全隐患。这些具有AI功能的计算机视觉系统不仅可以定期检查这些位置,还可以持续监视,发现潜在问题并提醒管理人员在问题变得危险之前解决问题。

When you think of the words “data” and “mine”, no doubt the idea of data mining comes first. However, just as much as we find value in mining the rich resources of data, so too can we apply the advanced techniques for dealing with data to real-world mining — that is, extracting natural resources from the earth. The world is just as dependent on natural resources as it is data resources, so it makes sense to see how the evolving areas of artificial intelligence and machine learning have an impact on the world of mining and natural resource extraction.

Mining has always been a dangerous profession, since extracting minerals, natural gas, petroleum, and other resources requires working in conditions that can be dangerous for human life. Increasingly, we are needing to go to harsher climates such as deep under the ocean or deep inside the earth to extract the resources we still need. It should come as little surprise then that mining and resource extraction companies are looking to robotics, autonomous systems, and AI applications of all sorts to minimize risk, maximize return, and also lessen the environmental impact that mining has on our ecosystem.

The role of machine learning in mining

The mining industry uses a lot of big, expensive machinery to perform a wide range of operations at the mine site as well as farther away when the materials need to be processed. Much of this machinery has many sensors that provide large volumes of data that give insights into how the very expensive machines are operating, the conditions in which they operate, and also insights into their performance on specific tasks. Keeping machines up and running is essential to making sure that the mining operation can continue. Any downtime or unnecessary maintenance will result in significant cost and complications for the mining operation.

In addition, the use of more advanced analytics prevents the need for humans to have to travel deep into the mine in potentially dangerous situations to try to evaluate problems and determine what is happening. Furthermore, the use of predictive analytics enables more strategic and efficient operations from maintenance to purchasing equipment.

Reducing the environmental impact

Resource extraction is inextricably linked to the environment around it. It’s near impossible to take something out of the earth without impacting the nature and topology surrounding that extraction. However, it is possible to minimize that impact, and machine learning and AI are helping to do just that. Antoine explains that there is very little benefit to dig in the wrong place or extract resources inefficiently. Machine learning systems are helping to analyze geology and topography with greater precision to identify what exactly you’re digging for and find the most optimal way in which to extract it, maximizing the benefit for the operator and minimizing the overall environmental impact.

Other uses of machine learning and AI can be applied to using drone footage or satellite imagery to keep a constant watch on waste and output piles to make sure that they are keeping in compliance with environmental regulations as well as minimizing any potential safety hazards. Rather than just spot checking these locations on a regular basis, these AI-enabled computer vision systems can keep a constant watch, spotting potential problems, and alerting management to solve problems before they become hazardous.

 

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