Kang Liangguo, Wu Chao, Wang Bing
School of Resources and Safety Engineering, Central South University, Changsha, China.
Safety & Security Theory Innovation and Promotion Center, Central South University, Changsha, China.
Front Psychol. 2019 Jul 9;10:1596. doi: 10.3389/fpsyg.2019.01596. eCollection 2019.
Big data is now widely used in many fields and is also widely applied to the integration of disciplines. Traditional methods of safety psychology are not well suited for analyzing psychological states, especially in the management of human factors in industrial production. Also, big data now becomes 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 to illustrate the challenges of applying big data in safety psychology. First, this paper puts forward the concept of BDSP and analyzes the difference between BDSP and traditional sample data. Subsequently, this paper summarizes the classification standard and basic characteristic of BDSP, explores the framework of BDSP and then constructs a three-dimensional structure of BDSP. Lastly, this paper discusses the challenges of using BDSP. This study is of great help to safety practitioners to solve psychological issues in the safety domain, and points out one of the research trends of human factor in industrial safety.
大数据如今在许多领域被广泛应用，同时也被广泛应用于学科整合。传统的安全心理学方法不太适合分析心理状态，尤其是在工业生产中的人为因素管理方面。此外，大数据现在成为通过分析大量心理数据挖掘相关见解的一种新方式。因此，本文旨在提出安全心理学大数据（BDSP）的概念，并阐述在安全心理学中应用大数据所面临的挑战。首先，本文提出了BDSP的概念，并分析了BDSP与传统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，探索了BDSP的框架，进而构建了BDSP的三维结构。最后，本文讨论了使用BDSP所面临的挑战。本研究对安全从业者解决安全领域的心理问题有很大帮助，并指出了工业安全中人为因素的研究趋势之一。
