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针对矿井通风系统诊断或健康体检研究缺少及时有效调试数据的实际情况,通过分析大量矿井监测监控系统的异常数据时间序列,深入研究了监测曲线的几种异常表现类型。运用数据压缩原理与随机函数相结合的方法,提出了基于自由采样法和DP原理全局采样法的矿井异常监测数据自动生成的特征点压缩模型(CPC模型)。并基于.NET开发平台,采用C#编程语言开发了矿井通风异常实时模拟平台。通过对典型异常类型的模拟,得出的异常模拟曲线与真实异常数据基本吻合,表明特征点压缩模型可以产生通风安全监测异常模拟数据供相关研究使用。
Aiming at the fact that there is a lack of timely and effective debugging data for mine ventilation system diagnosis or health examination, by analyzing a great deal of time series of abnormal data of mine monitoring and control system, several abnormal performance types of monitoring curve are deeply studied. Using the method of data compression combined with random function, a feature point compression model (CPC model) that is automatically generated based on the free sampling method and DP global sampling method is proposed. And based on the.NET development platform, the C # programming language was used to develop a mine ventilation abnormal real-time simulation platform. Through the simulation of typical anomalies, the anomalous simulated curves are basically consistent with the real anomaly data, which shows that the feature point compression model can generate abnormal simulation data of ventilation safety monitoring for the related research.