With the diversification of pit mine slope monitoring and the development of new technologies such as multisource data flow monitoring, normal alert log processing system cannot fulfil the log analysis expectation at the scale of big data. In order to make up this disadvantage, this research will provide an ensemble prediction algorithm of anomalous system data based on time series and an evaluation system for the algorithm. This algorithm integrates multiple classifier prediction algorithms and proceeds classified forecast for data collected, which can optimize the accuracy in predicting the anomaly data in the system. The algorithm and evaluation system is tested by using the microseismic monitoring data of an open-pit mine slope over 6 months. Testing results illustrate prediction algorithm provided by this research can successfully integrate the advantage of multiple algorithms to increase the accuracy of prediction. In addition, the evaluation system greatly supports the algorithm, which enhances the stability of log analysis platform.

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As faults in the solar water heaters are structurally complicated and highly correlated, an approach of fault diagnosis on the basis of support vector machine and D-S evidence theory has been proposed in this study, attempting to enhance the system's thermal efficiency and ensure its safety. In the approach presented, information of audio conditions, temperature at the outlet of solar thermal collectors, hourly flow and hourly heat transfer rate are accessible, which facilitate the feature evidence and are diagnosed by using "one-against-one" multi-class support vector machine. Experiments are conducted to diagnose fault information fusion and the results show that the diagnosis approach proposed in this study is of high reliability with fewer uncertainties, indicating that the approach is capable to recognize and diagnose solar water heater faults accurately.

Research on the particle size of blast piles has always been an essential issue in mining engineering. Reasonable blasting parameters can reduce mining costs and reduce the workload of secondary crushing, which can significantly improve mining efficiency. The usual particle size analysis methods include the sieving method, the large particle size statistical method and other manual measurement methods. Nevertheless, these methods have the disadvantages of a high labor intensity, low efficiency and low precision. This paper analyzes UAV image information based on the single-picture photogrammetry method of computer image processing technology. A two-dimensional empirical wavelet transform (EWT) is used for image noise reduction. The nonlocal multiscale fractional differential (NMFD) enhances the texture of dark images and uses superpixel image segmentation technology so that the processed image can meet the granularity statistical study requirements of blast piles. The research results show that the accuracy of the ore particle size distribution by the method proposed in this paper is more than 90%.