摘要
矿山属于高危行业,其事故致因和演化机理十分复杂,分形理论为解决复杂性问题提供了可能。通过对我国近年来矿山行业事故时序进行研究发现,矿山事故时间序列具有明显的分形特征,其饱和关联维数为6.390,说明要恰当描述其变化特征进行动力系统建模至少需要6～7个状态变量;该时间序列的Kolmogorov熵近似为0.0586,说明该混沌动力系统的平均可预测时间尺度为17个月。该研究结果可为建立矿山事故的时序预测预报模型提供依据。
Mining belongs to high-risk industries. Its accident causes and evolution mechanism are very complex and the fractal theory has made it possible to solve these complex problems. It is found through the study on the time series of the mine accidents in China in recent years that the mine accident time series has evident fractal characteristics, and its steady correlative dimension is 6. 390, indicating that at least 6 state variables are needed in describing the change characteristics of mine accidents and in constructing a dynamic system. The Kolmogorov entropy of the mine accident time series is approximately 0. 058 6, indicating that the mine accident dynamic system is chaotic and its average forecastable time scale is about 17 months. The study results can be used as the basis for constructing the forecast model of mine accidents time series.
出处
《金属矿山》
CAS
北大核心
2007年第12期111-114,共4页
Metal Mine
基金
陕西省教育厅专项科研基金项目(编号:06JK091)
关键词
矿山事故
时间序列
分形
相空间重构
Kohnogorov熵
Mine accidents, Time series, Fractal dimension, Reconstruction of phase space, Kohnogorov entropy
