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

摘要： 针对单传感器煤矿数据预测存在的片面性问题，提出将信息融合技术与相空间重构技术相结合的多传感器煤矿数据的预测模型。对井下多种传感器，包括瓦斯浓度、风速、温度传感器，进行融合预测。以多类传感器时序数据为研究对象，首先利用信息融合的方法分别对各类传感器数据依次进行数据层融合、特征层融合；然后采用关联积分方法对两级融合之后的传感器数据分别确定相重构的时间延迟τ和嵌入维数m两个参数；最后结合多变量相空间重构技术，将各类传感器数据融合重构相空间，运用基于K-Means聚类的加权一阶局域法构建多传感器数据的预测模型。数据来源于山西省阳泉煤矿，采集了近20G数据，以瓦斯浓度、风速、温度三种传感器数据进行实验，实验结果表明：对于特征层的融合，每15分钟时间段内的数据经融合后可有效作为衡量这段时间内的特征，经过预测模型计算后，与时间段为5分钟、10分钟、20分钟相比较误差达到最小ESS=0.003，较目前的最小误差值0.05，误差大大下降，故融合预测效果较好，可以较准确地预测未来15分钟后的传感器数据，可有充足时间进一步为井下的安全评估提供决策依据。

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