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Deep-level quality management based on big data analytics with case study

机译:基于大数据分析和案例研究的深度质量管理

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The Big data analytics gives new chances to the enterprises to enhance their management and manufacturing levels. A solution with case study is proposed to accomplish deep-level quality management based on big data analytics. First, the implementation of big data analytics based on industrial process data is illustrated with case study illustration. Through the analysis and feature extraction of off-line data, the corresponding reference model library is constructed, which can be used for real-time processing of unlabeled data in the industrial field. The cluster, outlier and other data indicators we can get from the calculation and analysis would have a certain guiding significance for the enterprise's deep-level quality management. Then, the distributed memory computing engine based on Spark, and the implementation of web application platform based on Spring MVC framework is also described. This can help to get higher performance and unrestricted abilities for data analytics. Meanwhile, good visibility and human-data interface can be achieved.
机译:大数据分析为企业提供了新的机会,以提高他们的管理和制造水平。建议采用案例研究解决基于大数据分析的深层质量管理。首先,用案例研究说明说明了基于工业过程数据的大数据分析的实现。通过离线数据的分析和特征提取,构造了相应的参考模型库,可用于工业领域未标记数据的实时处理。我们可以从计算和分析中获得的集群,异常值和其他数据指示器对企业的深度级别质量管理具有一定的指导意义。然后,还描述了基于Spring MVC框架的基于火花的分布式存储器计算引擎和Web应用程序平台的实现。这有助于获得更高的数据分析的性能和不受限制的能力。同时,可以实现良好的可视性和人类数据界面。

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