关键词: 多传感器, 信息融合, 相空间重构, 加权一阶局域法, 融合预测

Abstract: For single sensor data mining prediction problem of the existence of one-sidedness, proposed the multi-sensor data mining prediction model of combining of information fusion technology and phase-space reconstruction technology. A variety of underground sensors, including gas concentration, wind speed, temperature sensors, are fusion forecasted. To many types of sensor time series data for the study, the first using the method of information fusion, respectively, followed by all kinds of data sensor data level fusion, feature level fusion; Then using the method of correlation integral the integration of two sensor data, respectively, to determine the time delay τ and embedding dimension m two parameters for the reconstruction phase; Finally, combined with the techniques of multivariate phase space reconstruction, fusion phase space the various types of sensor data, using the predictive models based on the weight one-rank local-region of K-Means clustering of multi-sensor data. The data is from the coal mines in Shanxi Province and the New King Wu Yi mine, collection of nearly 20G data to the gas concentration, wind speed, temperature experiment three sensor data, the results show that: For the feature level fusion, the data every 15 minutes period of time after fusion to be effective as a measure of the characteristics of this period, after the prediction model calculations, compared with the time period ,5 minutes, 10 minutes, 20 minutes, the error is minimum ESS=0.003, compared with the current minimum error value of 0.05, the error is greatly decreased, therefore, the integration forecasts’ better, it can more accurately predict the future after 15 minutes of sensor data, people have sufficient time to further provide for the safety assessment of underground basis for making decision.

Key words: multi-sensor, information fusion, phase space reconstruction, weight one-rank local-region, fusion prediction