已有 2505 次阅读 2018-12-13 18:18 |系统分类:论文交流




Methodologies, principles and prospects of applying big data in safety science research

Abstract: It is clear that big data has numerous potential impacts in many fields. However, few papers discussed its applications in the field of safety science research. Additionally, there exist many problems that cannot be ignored when big data is applied to safety science, most outstanding of which is lack of universal supporting theory that guides how to apply big data to safety science research like methods, principles and approaches, etc. In other terms, it is not enough for big data to be viewed as a strong enabler for safety science applications mainly due to lack of universal and basic theory from the perspective of safety science. Considering the above analyzes, the two key objectives of this paper are: (1) to propose the connotation of safety big data (SBD) and its applying rules, methods and principles, and (2) to put forward some application prospects and challenges of big data to safety science research seen from theoretical research. First, by comparing SBD and traditional safety small data (SSD) from four aspects including theoretical research, typical research method, specific analysis method and processing mode, this paper puts forward the definition and connotation of SBD. Subsequently this paper further summarizes and extracts the application rules and methods of SBD. And then nine principles of SBD are explored and their relationship and application are addressed from the view of theory architecture and working framework in data processing flow. At last, this paper also discusses the potential applications and some hot issues of SBD. Overall, this paper will play an essential role in supporting the application of SBD. In addition, it will fill in the theory gaps in the field of SBD beyond traditional safety statistics, and further enriches the contents of safety science.

引用格式:Ouyang Q , Wu C , Huang L . Methodologies, principles and prospects of applying big data in safety science research[J]. Safety Science, 2018, 101: 60-71.

Big-data-driven safety decision-making: A conceptual framework and its influencing factors 

Abstract: Safety data and information are the most valuable assets for organizations’ safety decision-making (SDM), especially in the era of big data (BD). In this study, a conceptual framework for SDM based on BD, known as BD-driven SDM, was developed and its detailed structure and elements as well as strategies were presented. Other theoretical and practical contributions include: (a) the description of the meta-process and interdisciplinary research area of BD-driven SDM, (b) the design of six types of general analytics and five types of special analytics for SBD mining according to different requirements of safety management applications, (c) the analysis of influencing factors of BD-driven SDM, and (d) the discussion of advantages and limitations in this research as well as suggestions for future research. The results obtained from this study are of important implications for research and practice on BD-driven SDM. 

引用格式:Huang L, Wu C, Wang B, et al. Big-data-driven safety decision-making: A conceptual framework and its influencing factors[J]. Safety Science, 2018, 109: 46-56.


A new paradigm for accident investigation and analysis in the era of big data

 Abstract: The advent of the era of Big Data has spawned a new research paradigm and has transformed the outlook of numerous fields in science and engineering. Similarly, as one of the important fields of safety science, the area of accident investigation also has great opportunities for leveraging Big Data to its advantage. With this in mind, in this paper, the influencing factors of accident investigation were analyzed. Then, the definition of Safety-related Big Data (SRBD) was analyzed, and a four-layer pyramidal structure consisting of SRBD, Safety Information (SI), Safety Law (SL), and Safety Knowledge (SK) was constructed. Based on this, the conceptual model of the accident investigation paradigm based on SRBD was proposed. Moreover, the opportunities offered by the proposed new paradigm were argued from three aspects, which are ‘tools,’ ‘inputs,’ and ‘constraints.’ Lastly, the proposed paradigm was applied in a case study. Results show that the proposed paradigm can provide a novel method for accident investigation and analysis. The presented paper aims to explore the great expectations for accident investigation in the era of Big Data.

引用格式:Huang L, Wu C, Wang B, et al. A new paradigm for accident investigation and analysis in the era of big data[J]. Process Safety Progress, 2017, 37(1): 42-48.


Using data-driven safety decision-making to realize smart safety management in the era of big data: A theoretical perspective on basic questions and their answers

 Abstract: How to make an effective safety decision is always a topic of intense interest in the safety management field. Safety-Related Data (SRD) are the most valuable assets for organizations’ Safety Decision-Making (SDM), especially in the era of big data. This paper focuses on the potentially important value of SRD in SDM, and aims to systematically answer some fundamental questions concerning a new paradigm for SDM, known as data-driven SDM, from a theoretical perspective. These questions examine (1) what it is, (2) what its benefits are, (3) what its theoretical foundations are, (4) what its fundamental elements consist of, (5) what factors influencing it are, and (6) how the organization should implement it and realize smart safety management by using it. Other theoretical and practical contributions include a discussion of the problems of traditional SDM approaches and how to solve them, a rationale for creating and studying data-driven SDM, and suggestions for future research. This paper is the first to study the basic questions of data-driven SDM specifically, thus its results hold important implications for future research and practice on data-driven SDM and smart safety management.

引用格式:Wang B, Wu C, Huang L, et al. Using data-driven safety decision-making to realize smart safety management in the era of big data: A theoretical perspective on basic questions and their answers[J]. Journal of Cleaner Production, 2019, 210(2): 595-1604.




引用格式:黄浪, 吴超, 王秉. 大数据视阈下的系统安全理论建模范式变革[J]. 系统工程理论与实践, 2018, 38(7): 1877-1887. [Huang Lang, Wu Chao, Wang Bing. Paradigm shifting of system safety theoretical modeling under the perspective of big data[J]. System Engineering – Theory & Practice, 2018, 38(7): 1877-1887.]




引用格式:王秉, . 基于安全大数据的安全科学创新发展探讨[J]. 科技管理研究, 2017, (1): 37-43. [WANG Bing, WU Chao. Study on the Innovation Research of Safety Science Based on the Safety Big Data[J]. Science and Technology Management Research, 2017, (1): 37-43.]




引用格式:欧阳秋梅, 吴超. 安全大数据共享影响因素分析及其模型构建[J]. 中国安全生产科学技术, 2017, 13(2): 27-32. [OUYANG Qiumei, WU Chao. Study on influencing factors of safety big data sharing and its model construction[J]. Journal of Safety Science and Technology, 2017, 13(2): 27-32.]



 摘要: 为从安全生产大数据中挖掘安全规律并最终提炼安全生产基础原理,首先在对安全生产大数据的定义及其内涵进一步阐释基础上,提出安全生产大数据采集的定义,并将其分解为3个过程; 然后提出安全生产大数据的5W2H采集法,并对其内涵(采集原因,使用主体、采集者及采集对象,采集数据类型,采集边界,采集时间,采集数据量及其采集方法) 进行详细分析;最后以思维路径为主、过程路径和技术路径为辅,建立安全生产大数据采集的一般模式。结果表明,安全生产大数据采集模式的研究可为安全生产大数据的存储、处理及其应用提供基础。

引用格式:欧阳秋梅,吴超. 安全生产大数据的5W2H采集法及其模式研究[J]. 中国安全生产科学技术, 2016, 12(12): 22-27. [OUYANG Qiumei, WU Chao. Research on 5W2H acquisition method and mode of big data for work safety[J]. Journal of Safety Science and Technology, 2016, 12(12): 22-27.]



 摘要: 为促进大数据的理论、方法、技术等在安全科学研究中的应用,通过阐述现阶段安全数据、安全大数据及安全小数据的新内涵,用比较方法,提炼出安全大数据和安全小数据在理论、方法、具体分析和处理模式4方面的主要区别,阐述现阶段安全大数据存在的局限性和安全小数据存在的必要性;基于此,提炼出安全大数据与安全小数据运用的4条一般规律和5种互为借鉴的一般方法。结果表明,安全大数据和安全小数据相辅相成,提炼出的安全规律和互用方法有普适性和实用性,拓宽了安全统计学的研究领域。

引用格式: 欧阳秋梅, 吴超. 从大数据和小数据中挖掘安全规律的方法比较[J]. 中国安全科学学报, 2016, 26(7): 1-6. [OUYANG Qiumei, WU Chao. Comparison between methods for extracting safety rules from big data and small data[J]. China Safety Science Journal, 2016, 26(7): 1-6.]



 摘要: 为推广大数据在安全科学领域的应用,首先分析安全大数据应用原理的内涵;其次,基于安全大数据应用的3个价值来源,提炼出安全数据全样本、安全数据核心、安全数据隐含、安全科学导向、安全价值转化、安全关联交叉、安全资源整合、安全超前预测、安全容量维度等9条安全大数据应用的基础原理;最后,建立安全大数据应用原理的理论体系,以及基于安全大数据处理流程的作用框架。结果表明,9条基础原理的提出及其理论体系和作用框架的构建,可为大数据在安全科学领域的应用提供理论指导。

引用格式:欧阳秋梅, 吴超, 黄浪. 大数据应用于安全科学领域的基础原理研究[J]. 中国安全科学学报, 2016, 26(11): 13-18. [OUYANG Qiumei WU Chao HUANG Lang. Research on basic principles of applications of big data in field of safety science[J]. China Safety Science Journal, 2016, 26(11): 13-18.]



 摘要: 为促进大数据的理论、方法、技术等应用于安全科学研究中,用比较法找出大数据与传统安全统计数据在理论、方法、具体分析和处理模式等4个方面的区别。采用文献法,先将大数据分类,然后详细论述大数据在安全科学理论、“3E对策”、事故发展过程等3项研究中的应用;最后对大数据在安全科学领域的研究热点和重点进行展望。结果表明,大数据具有广阔的应用空间,它与传统安全统计数据的理论、方法和技术各有一定的适用范围和条件,应视实际情况令两者取长补短、相互借鉴、互为利用,为安全科学研究服务。

引用格式: 欧阳秋梅, 吴超. 大数据与传统安全统计数据的比较及其应用展望[J]. 中国安全科学学报, 2016, 26(3): 1-7. [OUYANG Qiumei, WU Chao. On comparison between big data and traditional safety statistics and big data's application prospects[J]. China Safety Science Journal, 2016, 26(3): 1-7.]



 摘要: 为探索复杂安全系统降维方法和模式,基于安全系统数据场视角,对复杂安全系统降维理论模型开展研究。首先,将数据场概念引入复杂安全系统,并从宏观和微观2个层面阐述安全系统数据场的内涵及其4个基本要素; 其次,分析数据场在安全系统中产生的3种效应模式及其导致事故发生的内在机制; 最后,以复杂安全系统降维过程中的降维、降变和降容为主线,构建复杂安全系统降维理论模型。结果表明,数据场理论及其效应模式适用于描述和阐释复杂安全系统的降维过程与规律,自聚类、维内降容、维间降维、维内()降变是复杂安全系统降维的4个关键过程。

引用格式: 欧阳秋梅, 吴超. 复杂安全系统数据场及其降维理论模型[J]. 中国安全科学学报, 2017, 27(8): 32-37. [OUYANG Qiumei, WU Chao. Research on model for dimension reduction of complex safety system based on data field[J]. China Safety Science Journal, 2017, 27(8): 32-37.]


 摘要: 为提高循证安全( EBS) 决策的可靠性,寻找安全决策的最佳证据支撑,首先,基于大数据和 EBS 相关理论,提出安全证据的概念; 其次,分析大数据视域下安全证据的特点,总结大数据视域下的 5 级安全证据体系; 在此基础上,提出大数据视域下的 EBS 管理模式,并分析其具体内涵和其中的安全信息流向; 最后,探讨大数据在 EBS 中的应用前景及优势,并给出 3 点促进意见。结果表明: 大数据的应用是 EBS 发展的新方向,提出的 EBS 管理模式和 5 级安全证据体系有助于为 EBS发展提供理论指导。

引用格式:闪顺章, 吴超, 王从陆, . 大数据视域下循证安全管理模式研究[J]. 中国安全科学学报, 2018, 28(6): 7-12. [SHAN Shunzhang, WU Chao, WANG Conglu, et al. Research on evidence-based safety management mode from perspective of big data[J]. China Safety Science Journal, 2018, 28(6): 7-12.]

下一篇:“Safety Differently”是什么意思?
收藏 IP: 110.53.160.*| 热度|

1 王秉

该博文允许注册用户评论 请点击登录 评论 (0 个评论)


Archiver|手机版|科学网 ( 京ICP备07017567号-12 )

GMT+8, 2022-8-12 16:28

Powered by

Copyright © 2007- 中国科学报社