作　　者： ; ;

机构地区： 中南大学资源与安全工程学院

出　　处： 《安全与环境学报》 2011年第6期250-254,共5页

摘　　要： 为了对区域性的四大安全生产指标进行宏观预测,建立了一个综合的预测模型。根据对安全生产状况影响的密切程度,应用统计的方法,计算了亿元GDP死亡率和影响因素之间的Spearman相关系数,从而确定了四大安全生产指标的主要影响因素。在对各预测模型比较的基础上,提出了区域性安全生产控制指标的预测模型。通过灰色预测法的GM(1,1)模型和曲线拟合的方法得出各安全生产指标影响因素的预测值,再利用多元回归法可将各种影响因素综合起来,通过曲线估计和转换后的多元线形回归模型对区域性安全生产控制指标的总体发展趋势进行预测。实例验证表明,减小第二产业比重,促进产业优化升级,适度控制城市率的迅速提升,并不断提高人民的文化素质和提升医疗卫生水平,能降低亿元GDP死亡率,提高安全生产水平。预测结果显示该预测方法具有较高的预测精度,且借助SPSS软件也具备较好的操作性。 The present paper is aimed to introduce a comprehensive prediction model proposed by its authors to carry out the macro prediction for the four regional safety-production indexes and necessary ways for their control. As is known, it has been an urgent need to provide a scientific basis for making regional safety-production plan and determine the corresponding production safety controlling index system in accordance with the influential degree for the safety-production conditions to be applied by the statistics, spearman correlation coefficient between the death rate per hundred million GDP and the influential factors worked out with SPSS. The conclusion of the spearman correlation coefficient shows that the main influential factors of death rate per hundred million GDP should be in proportion with the second industry, students at the institutions of higher learning per 10 000 persons, doctors per 1 000 persons, and urbanization rate. It is also possible to identify the main influential factors of the other three safety-production indexes in the same way. It is also possible to obtain the data properties of the influential factors and the prediction values of such factors through the GM（1,1） model of gray prediction method or curve estimation method based on the comparison to prediction models before the prediction model for the influential factors is established. Next, the influential factors of safety-production indexes are synthesized by using the multiple regression method so as to deduce the overall development trend of the regional production safety controlling indexes and make it easier to make the prediction following the curve estimation and the transformation of multiple regression model. Take Guangdong Province as a case study sample, based on the statistical data of death rate per hundred million GDP and its influential factors of the Province from 2000 to 2009, the prediction has been made based on the death rate per hundred million GDP of the 12th five-year plan. The sample collected shows that t

关 键 词： 安全管理工程 宏观预测 安全生产相对指标 影响因素 灰色预测 多元线性回归

领　　域： [环境科学与工程]