基于两级数据融合技术的煤矿粉尘监测研究
Research on Coal Mine Dust Monitoring Based on Two-stage Data Fusion Technology
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摘要: 为解决煤矿粉尘监测中传感器测量指标种类单一、测量数据量大等问题，提出了以粉尘浓度和粉尘粒度为监测对象，运用数据融合技术处理传感器信息的新方法。在建立数据融合两级结构模型的基础上，先应用基于矩阵分析的融合算法对同质源数据进行数据级融合，再应用D-S证据理论对异质源数据进行决策级融合，最终实现传感器信息的整合优化。试验和应用结果表明，该方法在完善粉尘表征评价指标的同时，显著提高了传感器信息的准确度和可信度。Abstract: In order to solve the problem of the single piece of sensors' measurement and the complex and repeated measured data in the coal mine dust monitoring, a new method that taking the dust concentration and dust particle size as the monitoring objects is proposed by using the data fusion technology to process the data of sensor. On the basis of the establishment of data fusion two-stage structure model, we first applied fusion algorithm based on matrix analysis to carry out data level information fusion of homogeneous data source, and then D-S evidence theory was applied to heterogeneous source data for decision level fusion. Finally, we realize the integration optimization of sensor information. Test and application results show that this method is perfect in dust characterization evaluation, which significantly improves the accuracy and reliability of the sensor information.
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Keywords:
- data fusion /
- dust in coal mine /
- real-time monitoring /
- matrix analysis /
- D-S evidence theory
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