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Ensemble Prediction Algorithm of Anomaly Monitoring Based on Big Data Analysis Platform of Open-Pit Mine Slope

机译：基于露天矿边坡大数据分析平台的异常监测集合预测算法

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摘要

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.
机译：随着矿井边坡监测的多样化和多源数据流监测等新技术的发展，常规的警报日志处理系统无法满足大数据规模的日志分析预期。为了弥补这一缺点，本研究将提供一种基于时间序列的系统异常数据的集成预测算法和该算法的评估系统。该算法集成了多种分类器预测算法，并对收集到的数据进行了分类预测，可以优化预测系统中异常数据的准确性。使用露天矿山边坡6个月以上的微震监测数据对算法和评估系统进行了测试。测试结果表明，该研究提供的预测算法可以成功整合多种算法的优势，提高预测的准确性。此外，评估系统极大地支持了该算法，从而提高了日志分析平台的稳定性。

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