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Principles, Approaches and Challenges of Applying Big Data in Safety Psychology Research

机译:应用大数据在安全心理学研究中应用大数据的原则,方法和挑战

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摘要

Big data is a well-known data processing technology in use today which widely applied to the integration of disciplines. Traditional methods of safety psychology are not well suitable to analyze psychological state, especially for manage human factors in industrial production. Also, big data is now a new way to excavate related insight by analyzing a large amount of psychological data. So, this paper is to propose the concept of big data of safety psychology (BDSP) and illustrate the challenge of big data in safety psychology. First, this paper is going to put forward the connotation of BDSP and analyze the difference between BDSP and sample data of safety psychology. Subsequently, this paper summarizes classification standard and basic characteristics of BDSP, then explore the framework of BDSP and construct the three-dimensional structures of BDSP. Lastly, this paper discusses the challenge of BDSP. This study was great help safety practitioner to solve psychological issue in safety domain, which point out one of the study trends of human factor in industrial safety.
机译:大数据是今天使用的众所周知的数据处理技术,广泛应用于学科的整合。传统的安全心理方法不太适合分析心理状态,特别是在工业生产中管理人类因素。此外,大数据现在是通过分析大量心理数据来挖掘相关洞察力的新方法。因此,本文提出了安全心理学(BDSP)大数据的概念,并说明了安全心理学中大数据的挑战。首先,本文将提出BDSP的内涵,并分析安全心理学的BDSP和样本数据之间的差异。随后,本文总结了BDSP的分类标准和基本特征,然后探索BDSP的框架并构建BDSP的三维结构。最后,本文讨论了BDSP的挑战。这项研究是伟大的帮助安全从业者解决安全领域的心理问题,这指出了工业安全人为因素的研究趋势之一。

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