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安全信息学:信息时代大有可为和永恒的安全科学新领域

已有 4287 次阅读 2019-12-27 00:45 |系统分类:论文交流

安全信息学:信息时代大有可为和永恒的安全科学新领域

 

王秉|文

1.中南大学资源与安全工程学院,长沙:4100832.中南大学安全理论创新与促进研究中心,长沙:410083

     

    本人博士期间,一直从事安全信息学基础理论研究。最近,发表了一篇题为“Safety informatics as a new, promising and sustainable area of safety science in the information age”的英文文章,算是将“Safety informatics(安全信息学)”这一学术新概念和新学科真正推向了国际。这篇文章是国际上首篇系统介绍安全信息学的研究论文,内容非常丰富,可带你了解安全信息学的来龙去脉和全貌。通过阅读这篇文章,你就会做到对安全信息学这门新学科心中有数。这里,扼要介绍这篇文章。


一、文章来源

Bing Wang, Chao Wu. Safety informatics as a new, promising and sustainable area of safety science in the information age [J]. Journal of Cleaner Production, Available online 23 December 2019, DOI: 119852. 10.1016/j.jclepro.2019.119852

文章链接  Safety informatics as a new, promising and sustainable area of safety science in the information age

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二、期刊信息

Journal of Cleaner ProductionSCI收录期刊,JCR一区,中科院一区

三、文章要点

1)安全信息学是信息时代大有可为的一个安全科学新领域。

2)分析了安全信息学的发展过程。

3)回顾了现有的安全信息学研究。

4讨论了安全信息学研究的局限性和未来发展方向。

四、文章摘要

安全是当代人类健康、损失预防、环境保护、可持续性和清洁生产相关讨论中的一个中心维度和话题。在信息时代,特别是大数据时代,安全信息是不可或缺的安全策略,安全信息学已成为安全科学领域的一个重要研究方向和热点问题。近年来,安全信息学作为安全科学的一个新领域,受到了越来越多的关注,并随着对该学科的不断研究而得到了长足发展。本文的3个主要研究目的是:1)分析安全信息学的历史发展过程;2)回顾安全信息学的研究进展;3)回顾安全信息学领域的研究局限并提出未来的发展方向。首先,将安全信息学的发展过程化分为4个典型阶段,即萌芽阶段(1940年至1980年)、初兴阶段(1980年至1990年)、形成阶段(1990年至2010年)与深化发展阶段(2010年至今,乃至未来)。然其次,从7方面(即安全信息学学科建设、理论安全信息模型、基于安全信息的事故致因理论、基于安全信息的安全管理、安全大数据、安全情报与安全信息技术)出发,对安全信息学研究进行综述。最后,讨论安全信息学研究的局限性和未来发展方向。安全信息学的未来研究和发展重点是研究支撑安全4.0(计算安全科学)与智慧安全(包括精准安全)的理论、方法与技术

五、文章关键词

安全科学;信息科学;安全信息;安全信息学;安全4.0

六、文章主要图表

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Fig. 1.  Development process of safety informatics.

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Fig. 2.  Development process of safety science.

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Fig. 3.  The main research fields of safety informatics.

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Fig. 4.  SI-SB (Safety Information-Safety Behavior) system safety model (adopted from Wang and Wu)

Table 1.

List and summary of representative research of the discipline construction of safety informatics.

Year

Reference

Summary of research

1995

He (1995)

He put   forward an important conclusion that, the safety information highway is the future   direction of safety management science.

2005

Wang and Wang (2005)

They   discussed the status quo and outlook of intelligence and security   informatics. In their study, safety informatics research in some safety fields   (such as public safety) were mentioned.

2007

Li and Chen (2007)

They   discussed the importance, connotation, extension, and system of safety   management information system discipline.

2012

Zhang and Zhu (2012)

Safety   information engineering is a branch of safety informatics. They pointed out   that safety information engineering is a vital professional course for safety   technology and engineering majors, and proposed a framework for the safety   information engineering course.

2015

Huang et al. (2015)

They proposed   six core principles of safety informatics, namely safety information quality   principle, safety information grade principle, safety information sharing   principle, safety information prevention, and control principle, safety   information feedback principle, and safety information supervision principle.

2017

Luo and Wu (2017)

They put   forward the research methodology of safety informatics, which includes some   specific research methods of safety informatics (e.g., the collection method,   the summary method, the statistic method, etc.), and the general procedure   for safety informatics research.

2018

Wang and Wu (2018a)

They analyzed   reasons for the establishment of safety informatics. According to their   analysis, the establishment of safety informatics has a profound theoretical   and practical basis. Meanwhile, they deeply explored the   ten basic disciplinary issues of safety informatics (including its definition,   connotation, attribute, foundation, research object, etc.).

Table 2.

Representative theoretical safety information models.

No.

Model Type

Main researches

1

Safety information cognition model

     Leiter and Rheinberger (2016)   developed a model of how athletes engaged in risky sports value safety   information;

     Wu (2017) constructed a general safety information cognition   model to reveal the mechanism and fault mode of safety information   transmission in complex systems;

     Luo and Wu (2018) proposed a safety information cognition approach   to reveal the accident mechanism, and

     Lei Yu et al. (2018) developed a safety information cognitive   process model.

2

Safety information flow model

     Westrum (2014) summed   up his research and understanding of safety information flow. We think that his   opinions on the safety information flow can provide an important insight into   research on the safety information flow model;

     Wang and Wu (2018a) suggested   that, in a system, the safety material flow, the safety energy flow, and the   safety behavior flow can be unified into the safety information flow, and

     Wu and Huang (2019) built   a system safety information flow structure model.

3

Safety information supply model

     Many researches (e.g., Drupsteen and Boustras, 2016; Ferguson et al., 2011)   pointed out that effective safety information supply is essential to ensure safety;

     Wang and Wu (2018b) proposed   a theoretical model for safety information supply, and

     Shang (2017)   discussed a public safety information sharing model, and

     Kaza and Chen (2008) surveyed   public safety information sharing initiatives.

4

Safety information literacy (including safety   data literacy) model

     Wang and Wu (2018c) proposed   the concept of safety information literacy for the first time, and   constructed a safety information literacy model (according to their model, safety   information literacy mainly includes four aspects, namely, safety information   demand consciousness, safety information acquisition ability, safety   information evaluation ability, and safety information utilization ability);

     Yang (2012) explored   and constructed a model for information literacy of   safety professionals in safety management, and

     Wang et al. (2019b)   presented a theoretical framework for data literacy for safety professionals   in safety management.

5

Safety information behavior model

     Wang and Wu (2018d)   proposed the concept of safety information behavior for the first time,   and constructed a meta model for safety information behavior which explained   the mechanism of safety information behavior, that is ‘safety information   need→safety information demand→safety information motivation→safety   information seeking behavior→safety information utilization behavior’, and

     Huang et al. (2017a) constructed   a conceptual model of individual safety information capacity.

6

Safety information communication, diffusion or   transmission model

     Wincek (2011)   put forth a concise communication method for critical process safety   information;

     Xia (2015) built   a quality and safety information diffusion model, and

     Many scholars (e.g., Xiao,   2018; Zhou et al., 2008)   developed product or food safety information transmission models.

7

Safety information collection and analysis   model

     The Merseyside Accident Information Model (Davies and Manning, 1994a, Davies and Manning, 1994b)   can collect all available accident data without writing, typing or coding,   and accident data are immediately available for analysis.

8

Safety information feedback model

     Wu (1983) built   a safety information feedback model.

Table 3.

Accident causation models from a safety information perspective.

Model type

No.

References

Year

Model

Brief explanation

Individual

level

1

Surry (1969)

1969

Surry model

The error   in human information processing can cause dangers.

2

Cui (1995)

1970

Hale model

An   accident occurs when humans do not respond appropriately to the actual   situation of the event (namely, safety information).

3

Qin and Peng (2005)

1972

Wigglesworth model

Various   information constantly acts on people’s senses, stimulating them. If people   react appropriately to the stimulus, an accident will not occur. On the   contrary, if people react to the stimulus incorrectly or improperly, risks   may occur, which may cause accidents.

4

Lawrence (1974)

1974

Lawrence model

Human   error is the cause of accidents in gold mining. The most dominant of human   errors are failures to perceive warnings (namely, warning information) of   danger.

5

Qin and Peng (2005)

1978

Anderson model

Based on   the extension and amendment of the Surry model, the Anderson model was   proposed. It is similar to the Surry model.

6

Rasmussen (1987), and Katsakiori et al. (2009)

1987

SRK framework

SRK (Skill-, Rule-, and Knowledge-based behavior) framework   distinguishes between three different levels of human cognitive control of   the environment.

7

Hale and Glendon (1987),   as well as Lacroix and Dejoy (1989)

1987

Hale and Glendon model

Hale and   Glendon’s model is concerned with safety perceptions and   decisions, and is based on the attribution theory which focuses on how people   process information in determining the causality of events.

8

Hollnagel (1998) and Katsakiori et al. (2009)

1998

CREAM

CREAM (Cognitive Reliability and Error Analysis Method) describes   the full context in which errors and accidents occur.

Organizational(systemic) level

9

Leveson (2004)

2004

STAMP model

STAMP   (System-Theoretic Accident Model and Process) regards systems as interrelated   components that are kept in a state of dynamic equilibrium by feedback loops   of information and control.

10

Zhao and Zhou (2012)

2012

Accident causation model based on safety   information missing

Safety   information is an informational expression of various factors of accidents.   The lack of safety information is the main potential cause of accidents.

11

Li et al. (2017)

2017

Multilevel safety information asymmetry model

Information   asymmetry is the main cause of accidents in the organization.

12

Wang and Wu (2018e)

2018

FDA accident model

FDA   (Forecast-Decision-Action) accident model points out that, in the absence of   safety spoofing, the lack of safety information is the main cause of failures   in safety forecast, safety decision-making, and safety action.

13

Wu and Huang (2019)

2019

Accident model based on information flow

The role   of information flow in accident causation is profound. The breakdown of   information flow (such as the failure of information acquisition) can potentially   cause an accident.

Table 4.

List of typical applications of safety big data in safety management.

No.

Area

Main researches

1

Safety   decision-making

     Huang et al. (2018b) developed a   conceptual framework for big data-driven safety decision-making, and analyzed   the influencing factors of safety decision-making based on big data, and

       To use data-driven safety   decision-making to realize smart safety management in the era of big data, Wang et al. (2019c) stated the definition,   benefits, theoretical foundations, fundamental elements, and influencing   factors of data-driven safety decision-making.

2

Safety monitoring

       Shi and Abdel-Aty (2015) discussed the   application of big data in urban highway safety monitoring, and

     Bychkov et al. (2016) discussed the   application of big data in ground-based safety monitoring.

3

Accident   investigation and analysis

     Huang et al. (2017b) proposed a new paradigm for   accident investigation and analysis in the era of big data, and

     Huang and Zhou (2019) developed a mine accident   prediction and analysis approach based on multimedia big data.

4

Safety risk   prevention and control

     Walker and Strathie (2016) suggested that the big data and   ergonomics methods is a new paradigm for tackling strategic transport safety   risks, and

       Cao et al. (2017) and Wang et al. (2018) discussed safety risk early-warning   and governance based on big data.

5

Emergency   management

     Ragini et al. (2018) highlighted that the real-time   categorization and classification of social media big data can ensure   effective disaster response and recovery,

     Guo and Liu (2016) conducted a study on emergency   data quality governance in the era of big data, and

       Many other studies (e.g., Pang, 2015; Ma and Mao, 2015; Wu, 2017) explored the application of big   data in emergency management.

Table 5.

List of typical safety information technologies.

No.

Area

Examples of main   safety information technologies

1

Safety monitoring

GIS, GPS, sensor technology,   database technology, network technology, communication technology, etc.

2

Accident (disaster)   prevention and control

Computer simulation   technology, database technology, virtual reality technology, image   measurement technology, big data technology, etc.

3

Safety risk   management

Machine learning   technology, big data technology, assisted decision-making support system, artificial   neural network technology, etc.

4

Safety education and   training

Multimedia   technology, computer network technology, animation technology, virtual   reality technology, visualization technology, etc.

5

Emergency management

Internet of things   technology, network technology, assisted decision-making support system,   visualization technology, database technology, artificial intelligence   technology, etc.

附:课题组部分系列安全信息学研究成果

[1]            王秉,吴超著.安全信息学[M]. 北京:机械工业出版社,2020.(待版)

[2]            Bing Wang, Chao Wu. Demystifying safety-related intelligence in safety management: Some key questions answered from a theoretical perspective[J]. Safety Science, 2019, 120: 932–940.

[3]            Bing Wang, Chao Wu, Lang Huang. Data literacy for safety professionals in safety management: A theoretical perspective on basic questions and answers[J]. Safety Science, 2019, 117: 15–22.

[4]            Bing Wang, Chao Wu, Lang Huang, Liangguo Kang. 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: 1595-1604.

[5]            Bing Wang, Chao Wu, Bo Shi, Lang Huang. Evidence-based safety (EBS) management: A new approach to teaching the practice of safety management (SM) [J]. Journal of Safety Research, 2017, 63(12):21-28.

[6]            Lei Yu, Wu Chao, Feng Yanxi, Wang Bing. Optimization of multi-level safety information cognition (SIC): A new approach to reducing the systematic safety risk[J]. Reliability Engineering and System Safety, 2019, 190: 106497.

[7]            Lang Huang, Chao Wu, Bing Wang. Challenges, opportunities and paradigm of applying big data to production safety management: From a theoretical perspective[J]. Journal of Cleaner Production, 2019, 231:592-599.

[8]            Lang Huang, Chao Wu, Bing Wang, Qiumei Ouyang. Big-data-driven safety decision-making: A conceptual framework and its influencing factors[J]. Safety Science, 2018, 109: 46-56.

[9]            Chao Wu, Lang Huang. A new accident causation model based on information flow and its application in Tianjin Port fire and explosion accident[J]. Reliability Engineering and System Safety, 2019, 182: 73–85.

[10]            Lang Huang, Chao Wu, Bing Wang, Qiumei Ouyang. A new paradigm for accident investigation and analysis in the era of big data[J]. Process Safety Progress, 2018, 37(1): 42-48.

[11]            Lang Huang, Chao Wu, Bing Wang, Lin Huanhuan. An unsafe behavior formation mechanism based on risk perception [J]. Hum. Factors Man. 2019,29:109–117.

[12]            Qiumei Ouyang, Chao Wu, Lang Huang. Methodologies, principles and prospects of applying big data in safety science research[J]. Safety Science, 2018, 101: 60-71.

[13]            Tongyuan Luo, Chao Wu. Safety information cognition: A new methodology of safety science in urgent need to be established[J]. Journal of Cleaner Production, 2019, 209: 1182-1194.

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[35]            王秉,刘华森,吴超.情报主导的突发事件防控研究[J/OL].信息资源管理学报:1-8[2019-12-27].http://kns.cnki.net/kcms/detail/42.1812.G2.20191217.1412.006.html.

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