大数据在安全生产中的应用

Discussion over the big data to be applied in the safety production

大数据的诞生使人类具备了获取海量数据和处理规范化数据的能力,安全生产与经济社会发展密切相关,因此提升大数据技术在安全生产领域的应用至关重要。阐述了大数据的技术、应用和发展趋势,总结了目前安全生产中大数据的应用现状,探讨了大数据在安全生产中的应用方向。从数据源建设入手,提出了一个统一的安全生产大数据架构,以及采用分布式内存计算技术实现该架构的方法。

Thanks to the birth and growth of the big data, it has been made possible for human being to get access to large amounts of data and the capability to deal with the standardized data. Therefore, safety production and social economic development ought to be closely related to how for human beings to empower their own skills on dealing with the big data technology in the field of safety production, though the field of safe production has not yet been mature enough to be made a standardized data source. This article intends to demonstrates the technology application and development trend of the so-called big data. For this purpose, this article illustrated lots of different types of digital data to summarize how big data can be put into the safety production though success- ful application. Starting from the construction of big data re- sources, this article has proposed an integrated systematic data ar- chitecture for the safe production and through the method of dis- tributing computer memory based on the technology to implement the aforementioned architecture. To clarify the situation, we have made an exploration and approaches to how big data are used in the safety production from the following four aspects, that is, data acquisition, transmission, storage, the active intelligent safety monitoring system over the big data, the construction safety supervision data standard system, and the data supply and talent gap. In so-doing, the author has made a deep-going exploration and discussion on the direction of big data application of safety rules in production in order to identify and determine the application and development trend of big data, as well as summarize the current safety production status-in situ application. Furthermore, we have also suggested problems, such as how to solve problems on bigdata collection, classification, transmission and storage through Internet and cloud computing technology. Besides, the efficiency of such a kind of architecture should be able to＇ experience objective testament and verification through comparison with HDFS and simple registration. The practical tests indicate that the registration speed of the architecture tends to increase linearly with the number of nodes, which can be finished by increasing the speed of over 500 times of the HDFS at 30 nodes, though the data production procedure should be managed to be operated in a easy and smooth manner.