基于大数据分析的多算法融合滤波方法  

Multi-algorithm Fusion Filtering Method Based on Big Data Analysis

作　　者：邵艳鸣 秦绪祥 SHAO Yan-ming;QIN Xu-xiang(SAIC-Volkswagen Co.,Ltd.,Shanghai 201805,China)

机构地区：[1]上汽大众汽车有限公司,上海201805

出　　处：《现代涂料与涂装》2025年第1期33-36,共4页Modern Paint & Finishing

摘　　要：某涂装车间调漆间实时采集的废溶剂回收缸数据存在周期性异常波动问题。从异常检测算法、常规滤波算法以及多算法融合方法等测试了不同算法对于该液位曲线的滤波效果。最终的测试结果显示,一种基于大数据分析的多算法融合方法整合了各滤波算法的优点,有效解决了废溶剂液位数据失真问题,为后续的溶剂降耗研究提供了良好的数据基础。同时,该方法可推广至车间类似设备,依托大数据分析方法处理液位扰动问题,为后续大数据分析的研究提供高质量数据。In response to the periodic abnormal fluctuations in the waste solvent recovery tank data collected in real-time from a paint mixing room in a certain paint workshop,this paper tested different algorithms,including anomaly detection algorithms,conventional filtering algorithms,and multi-algorithm fusion methods,to filter the liquid level curve.The final test results showed that a self-designed multi-algorithm fusion method integrated the advantages of various filtering algorithms and effectively solved the problem of disto rtion in the water-based waste solvent level data,providing a good data foundation for subsequent research on reducing water consumption in waste solvents.At the same time,this method can be applied to related equipment in various paint workshops,relying on big data analysis methods to deal with liquid level disturbance problems and providing high-quality data for subsequent big data analysis research.

关 键 词：大数据分析 液位滤波 水性废溶剂 

分 类 号：TQ639[化学工程—精细化工]