{"title":"Research on Fire Risk Coupling of Petrochemical Enterprises Based on Improved N-K Model","authors":"Qingqing Lv, Haomiao Ning, Kezhen Chen, Jihong Ye","doi":"10.1109/ICBASE53849.2021.00060","DOIUrl":null,"url":null,"abstract":"Petrochemical companies are prone to fire accidents. In order to study the fire risk coupling of petrochemical companies, the traditional N-K model combined with the analytic hierarchy process is used to replace the accident probability with the frequency of risk factors to calculate the risk coupling degree. Research shows that the improved N-K model makes up for the limitation of the traditional N-K model that requires a large amount of data support; the more coupled risk factors, the greater the risk value; human factors must be paid attention to when managing petrochemical companies.","PeriodicalId":51314,"journal":{"name":"Big Data","volume":"68 1","pages":"290-295"},"PeriodicalIF":2.6000,"publicationDate":"2021-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Big Data","FirstCategoryId":"94","ListUrlMain":"https://doi.org/10.1109/ICBASE53849.2021.00060","RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS","Score":null,"Total":0}
引用次数: 0
Abstract
Petrochemical companies are prone to fire accidents. In order to study the fire risk coupling of petrochemical companies, the traditional N-K model combined with the analytic hierarchy process is used to replace the accident probability with the frequency of risk factors to calculate the risk coupling degree. Research shows that the improved N-K model makes up for the limitation of the traditional N-K model that requires a large amount of data support; the more coupled risk factors, the greater the risk value; human factors must be paid attention to when managing petrochemical companies.
Big DataCOMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS-COMPUTER SCIENCE, THEORY & METHODS
CiteScore
9.10
自引率
2.20%
发文量
60
期刊介绍：
Big Data is the leading peer-reviewed journal covering the challenges and opportunities in collecting, analyzing, and disseminating vast amounts of data. The Journal addresses questions surrounding this powerful and growing field of data science and facilitates the efforts of researchers, business managers, analysts, developers, data scientists, physicists, statisticians, infrastructure developers, academics, and policymakers to improve operations, profitability, and communications within their businesses and institutions.
Spanning a broad array of disciplines focusing on novel big data technologies, policies, and innovations, the Journal brings together the community to address current challenges and enforce effective efforts to organize, store, disseminate, protect, manipulate, and, most importantly, find the most effective strategies to make this incredible amount of information work to benefit society, industry, academia, and government.
Big Data coverage includes:
Big data industry standards,
New technologies being developed specifically for big data,
Data acquisition, cleaning, distribution, and best practices,
Data protection, privacy, and policy,
Business interests from research to product,
The changing role of business intelligence,
Visualization and design principles of big data infrastructures,
Physical interfaces and robotics,
Social networking advantages for Facebook, Twitter, Amazon, Google, etc,
Opportunities around big data and how companies can harness it to their advantage.
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