基于特征点压缩原理的矿井异常监测数据模拟自动生成模型
Automatic Generation Model for Coal Mine Alonormal Monitoring Data Based on Characteristic Point Compression
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摘要: 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。Abstract: Diagnosis and inspection on the coal mine ventilation system suffers the lack of effective and timely debugging data. In this paper, the time series of abnormal data from many mine ventilation monitoring systems are analyzed, and several types of abnormal monitoring curves are deeply investigated. By combining the data compression theory and the random function, we propose a characteristic point compression-based model （CPC） that can automatically generate abnormal coal mine monitoring data via free sampling and DP global sampling. Based on .Net and C#, a platform is proposed to simulate coal mine ventilation anomaly in real-time. Simulations are performed on several typical anomalies. The obtained anomaly curves are quite consistent with the actual abnormal data, and the proposed model can generate abnormal ventilation monitoring data for relevant researches.
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