With the advent of automation, many manufacturing industries have transitioned to data-centric methodologies, giving rise to an unprecedented influx of data during the manufacturing process. This data has become instrumental in analyzing the quality of manufacturing process and equipment. Engineers and data analysts, in particular, require extensive time-series data for seasonal cycle analysis. However, due to computational resource constraints, they are often limited to querying short-term data multiple times or resorting to the use of summarized data in which key patterns may be overlooked. This study proposes a novel solution to overcome these limitations; the advanced resolution-based pixel preemption data filtering (AR-PPF) algorithm. This technology allows for efficient visualization of time-series charts over long periods while significantly reducing the time required to retrieve data. We also demonstrates how this approach not only enhances the efficiency of data analysis but also ensures that key feature is not lost, thereby providing a more accurate and comprehensive understanding of the data.

翻译：随着自动化技术的兴起，许多制造业已转向以数据为中心的方法论，导致制造过程中数据量空前增长。这些数据对分析制造过程与设备质量至关重要。工程师与数据分析师尤其需要大量时间序列数据进行季节性周期分析。然而，受计算资源限制，他们往往只能多次查询短期数据，或被迫使用可能遗漏关键模式的汇总数据。本研究提出一种突破这些局限的创新解决方案：基于高级分辨率的像素抢占数据过滤（AR-PPF）算法。该技术能够实现长期时间序列图的高效可视化，同时显著缩短数据检索所需时间。我们还论证了该方法不仅能提升数据分析效率，更能确保关键特征不丢失，从而提供更精确、更全面的数据理解。