基于改进的LS-BSVR(least squares B support vector regression)算法对旅游地理经济进行分析预测。提出了时政指数、景区景点分布指数概念,设计了时政指数、景区景点分布指数,并应用于旅游地理经济进行分析预测。设计了用于分析预测的数据模式。数值实验和预测平台结果表明,改进的LS-BSVR算法和设计的旅游地理经济数据指标以及数据模式是有效的。
Based on spatio-temporal correlativity analysis method, the automatic identification techniques for data anomaly monitoring of coal mining working face gas are presented. The asynchronous correlative characteristics of gas migration in working face airflow direction are qualitatively analyzed. The calculation method of asynchronous correlation delay step and the prediction and inversion formulas of gas concentration changing with time and space after gas emission in the air return roadway are provided. By calculating one hundred and fifty groups of gas sensors data series from a coal mine which have the theoretical correlativity, the correlative coefficient values range of eight kinds of data anomaly is obtained. Then the gas moni- toring data anomaly identification algorithm based on spatio-temporal correlativity analysis is accordingly presented. In order to improve the efficiency of analysis, the gas sensors code rules which can express the spatial topological relations are sug- gested. The experiments indicate that methods presented in this article can effectively compensate the defects of methods based on a single gas sensor monitoring data.
