|本期目录/Table of Contents|

[1]杨珊,明俊桦,周智勇.基于改进的非线性GM(1,1)模型的职业病预测研究[J].中国安全生产科学技术,2018,14(1):111-116.[doi:10.11731/j.issn.1673-193x.2018.01.018]
　YANG Shan,MING Junhua,ZHOU Zhiyong.Study on prediction of occupational diseases based on improved nonlinear GM (1,1) model[J].JOURNAL OF SAFETY SCIENCE AND TECHNOLOGY,2018,14(1):111-116.[doi:10.11731/j.issn.1673-193x.2018.01.018]
点击复制

基于改进的非线性GM(1,1)模型的职业病预测研究
分享到：

《中国安全生产科学技术》[ISSN:1673-193X/CN:11-5335/TB]

卷:
14
期数:
2018年1期
页码:
111-116
栏目:
职业安全卫生管理与技术
出版日期:
2018-01-31

文章信息/Info

Title:
Study on prediction of occupational diseases based on improved nonlinear GM (1,1) model
文章编号:
1673-193X(2018)-01-0111-06
作者:
杨珊; 明俊桦; 周智勇
（中南大学 资源与安全工程学院，湖南 长沙 410083）
Author(s):
YANG Shan;  MING Junhua;  ZHOU Zhiyong
(School of Resources and Safety Engineering, Central South University, Changsha Hunan 410083, China)
关键词:
职业病; 传统GM(1; 1)模型; 弱化算子; 非线性; 预测精度
Keywords:
occupation diseases;  traditional GM (1; 1) model;  weakening operator;  nonlinear;  prediction accuracy
分类号:
X913
DOI:
10.11731/j.issn.1673-193x.2018.01.018
文献标志码:
A
摘要:
为了更准确地预测职业病，在传统GM(1,1)模型的基础上，提出改进的非线性GM(1,1)模型，在传统的GM(1,1)模型中引入弱化算子，将紧邻均值与原始数据之间的线性假设改为非线性假设，提高曲线的拟合度。以2005—2014年的全国职业病例数为研究对象，进行数据拟合和预测分析，其中以2014年职业病例数作为验证数据，并利用后验差比值和小误差概率2个参数，检验该改进模型的预测精度。由应用实例的分析结果可知：在职业病发病趋势的预测方面，改进的非线性GM(1,1)模型的预测精度提高到一级，曲线拟合度较高，预测得到2015年的职业病例数为34 900例。
Abstract:
In order to predict the occupation diseases more accurately, on the basis of the traditional GM (1,1) model, an improved nonlinear GM (1,1) model was proposed. The weakening operator was introduced into the traditional GM (1,1) model, and the linear hypothesis between the consecutive neighbors mean and the original data was changed into the nonlinear hypothesis, so as to improve the fitting degree of curves. Taking the case numbers of occupational diseases from 2005 to 2014 in China as the research object, the data fitting and prediction analysis were conducted, in which the case number of occupation diseases in 2014 was used as the verification data, and the posteriori error ratio and small error probability were used to test the prediction accuracy of the improved model. The results showed that in the prediction on the incidence trend of occupational diseases, the prediction accuracy of the improved nonlinear GM (1,1) model was improved to the first level, the fitting degree of curve was relatively high, and the case number of occupational diseases in 2015 was predicted to be 34900 cases.

参考文献/References:

［1］贾麟.全球职业安全健康问题与新形势［J］.中国安全生产,2014（11）:58-59. JIA Lin.Global occupational safety and health issues and the new situation［J］.China Occupational Safety and Health, 2014(11):58-59.
［2］乔庆梅, 柯常云. 我国职业病防治的现状与思考［J］. 中国医疗保险, 2011(12):59-62. QIAO Qingmei,KE Changyun.The present situation of occupational disease prevention and cure in china and thoughts on it［J］.China Health Insurance,2011(12):59-62.
［3］徐金梅．职业病防治现状分析及其对策［J］．西部探矿工程，2008，20（7）：239-241. XU Jinmei.The actuality analysis and countermeasure of prevention and cure of industrial disease in China［J］. West-China Exploration Engineering,2008,20(7):239-241.
［4］朱进平，梅震．职业卫生监督量化分级管理模式探讨［J］．工业卫生与职业病，2008，34（6）：378-381. ZHU Jinping,MEI Zhen.Discussion on quantitative classified management mode of occupational health supervision［J］.Industrial Health and Occupational Diseases,2008,34(6):378-381.
［5］SUN Y, SHAO H, WANG H. Occupational diseases prevention and control in China: a comparison with the United States［J］. Journal of Public Health, 2015, 23(6):379-386.
［6］恒川谦司, 刘宝龙, 高建明,等. 日本职业卫生管理及对中国的启示［J］. 中国安全生产科学技术, 2008, 4(1):116-119. TSUNEKAWA K, LIU Baolong, GAO Jianming，et al. Occupational health management in Japan and its enlightenment to China［J］.Journal of Safety Science and Technology,2008,4(1):116-119.
［7］LIN Y, LIU S. An introduction to grey systems: foundations, methodology and applications［J］. Kybernetes, 2003, 32(4):584-585.
［8］刘思峰. 灰色系统理论及其应用（第五版）［M］. 北京：科学出版社, 2010.
［9］王维,李建东.GM(1,1)灰色模型在职业病发病预测中的应用［J］.中国公共卫生管理,2014,30（6）：920-922. WANG Wei，LI Jiandong. Application of GM(1,1) grey model in prediction of occupational disease［J］．Chinese Journal of Public Health Management,2014,30(6): 920-922．
［10］李怡，张华东．改良灰色模型在职业病发病趋势上预测的分析研究［J］．预防医学情报杂志，2015，31（8）：630-634. LI Yi，ZHANG Huadong．Application of refined grey model in forecasting the of occupational disease［J］.Journal of Preventive Medicine Information,2015,31(8): 630-634．
［11］钱峰, 吕效国, 朱帆. 灰色GM(1,1)模型的改进模型在房地产价格指数预测中的应用［J］. 数学的实践与认识, 2009, 39(7):29-33. QIAN Feng,LYU Xiaoguo,ZHU Fan.The application of the improved grey GM (1,1) model in real estate price index prediction［J］.Mathematics in Practice and Theory,2009,39(7):29-33.
［12］郭晓君，李大治，褚海鸥，等．基于GM(1,1)改进模型的“两税”税收预测研究［J］．统计与决策，2014（4）：34-36. GUO Xioajun,LI Dazhi,CHU Haiou，et al. The study on the prediction of "Two Taxes" tax based on the improved GM(1,1) model［J］.Statistics and Decision,2014(4):34-36.
［13］时冬青, 宋文华, 张桂钏,等. 基于灰色GM(1,1)-马尔科夫模型的职业病预测研究［J］. 中国安全生产科学技术, 2017, 13(4):176-180. SHI Dongqing,SONG Wenhua,ZHANG Guichuan，et al.Study on prediction of occupational diseases based on grey GM(1,1)－ markov model［J］. Journal of Safety Science and Technology, 2017,13（4）：176-180.
［14］陈昌源, 戴冉, 杨婷婷,等. 基于改进 GM(1,1)模型的上海港集装箱吞吐量预测［J］. 船海工程, 2016, 45(4):153-156. CHEN Changyuan,DAI Ran,YANG Tingting, et al. Study on container throughput prediction of shanghai port based on improved GM(1,1) model［J］.Ship & Ocean Engineerine, 2016, 45(4): 153-156.

相似文献/References:

[1]徐桂芹.2000—2009年全国职业中毒状况规律分析和对策探讨[J].中国安全生产科学技术,2011,7(5):96.
　XU Gui-qin.Analysis and Control Proposal of Occupational Poisoning from 2000 – 2009 in China[J].JOURNAL OF SAFETY SCIENCE AND TECHNOLOGY,2011,7(1):96.
[2]李博,李树森,杨洪泽.园林工人割灌作业职业性肌肉骨骼疾患的工效学研究[J].中国安全生产科学技术,2013,9(11):174.[doi:10.11731/j.issn.1673-193x.2013.11.030]
　LI Bo,LI Shu sen,YANG Hong ze.Ergonomic research on workrelated muscular skeletal disorders by cutting irrigation operation of gardeners[J].JOURNAL OF SAFETY SCIENCE AND TECHNOLOGY,2013,9(1):174.[doi:10.11731/j.issn.1673-193x.2013.11.030]
[3]李旭东,苏世标,闫雪华,等.船舶制造业职业病危害风险和防护措施分析[J].中国安全生产科学技术,2013,9(3):87.[doi:10.11731/j.issn.1673-193x.2013.03.15]
　LI Xu dong,SU Shi biao,YAN Xue hua,et al.Analysis on risk and control measures of occupational disease in shipyard[J].JOURNAL OF SAFETY SCIENCE AND TECHNOLOGY,2013,9(1):87.[doi:10.11731/j.issn.1673-193x.2013.03.15]
[4]王淑云,匡 雅,曾向璟,等.纺织企业噪声职业危害风险评价及降噪措施[J].中国安全生产科学技术,2014,10(2):141.[doi:10.11731/j.issn.1673-193x.2014.02.024]
　WANG Shu yun,KUANG Ya,ZENG Xiang jing,et al.Risk assessment on occupational noise hazard and noise reduction measures in a textile mill[J].JOURNAL OF SAFETY SCIENCE AND TECHNOLOGY,2014,10(1):141.[doi:10.11731/j.issn.1673-193x.2014.02.024]
[5]陈建武,任颖,王学峰,等.职业病防护设施评价程序与评价方法研究[J].中国安全生产科学技术,2014,10(7):112.[doi:10.11731/j.issn.1673-193x.2014.07.020]
　CHEN Jian-wu,REN Ying,WANG Xue-feng,et al.Study on evaluation process and method of occupational disease prevention facilities[J].JOURNAL OF SAFETY SCIENCE AND TECHNOLOGY,2014,10(1):112.[doi:10.11731/j.issn.1673-193x.2014.07.020]
[6]时冬青,宋文华,张桂钏,等.基于灰色GM(1,1)-马尔科夫模型的职业病预测研究[J].中国安全生产科学技术,2017,13(4):176.[doi:10.11731/j.issn.1673-193x.2017.04.028]
　SHI Dongqing,SONG Wenhua,ZHANG Guichuan,et al.Study on prediction of occupational diseases based on grey GM(1,1)-Markov model[J].JOURNAL OF SAFETY SCIENCE AND TECHNOLOGY,2017,13(1):176.[doi:10.11731/j.issn.1673-193x.2017.04.028]
[7]张敏,徐李卉,刘拓,等.我国医护人员职业卫生防护体系研究[J].中国安全生产科学技术,2018,14(5):51.[doi:10.11731/j.issn.1673-193x.2018.05.008]
　ZHANG Min,XU Lihui,LIU Tuo,et al.Study on occupational health protection system for healthcare workers in China[J].JOURNAL OF SAFETY SCIENCE AND TECHNOLOGY,2018,14(1):51.[doi:10.11731/j.issn.1673-193x.2018.05.008]
[8]孙国翔,周川,杨小兵,等.1993—2016年我国职业中毒现状分析及防治对策[J].中国安全生产科学技术,2018,14(10):187.[doi:10.11731/j.issn.1673-193x.2018.10.030]
　SUN Guoxiang,ZHOU Chuan,YANG Xiaobing,et al.Analysis on current situation of occupational poisoning in China from 1993 to 2016 and prevention and control countermeasures[J].JOURNAL OF SAFETY SCIENCE AND TECHNOLOGY,2018,14(1):187.[doi:10.11731/j.issn.1673-193x.2018.10.030]

备注/Memo

备注/Memo:
国家自然科学基金青年基金项目(51404305)；中南大学中央高校基本科研业务费专项资金（2017zzts631）；国家自然科学基金资助项目（51504286）
更新日期/Last Update: 2018-02-12