یک معماری برای تحلیل فرآیندها در کارخانه‌های هوشمند بر اساس تکنیک‌های کلان داده، فرآیندکاوی و یادگیری ماشین

چکیده مقاله :

با توجه به ماهیت کارخانه­‌های هوشمند و به­‌کارگیری فناوری­‌های نوین مانند سیستم­‌های سایبری فیزیکی، رایانش ابری، اینترنت اشیاء و غیره در این­‌گونه محیط­‌ها، حجم داده­‌های تولید شده بصورت تصاعدی افزایش یافته است. بنابراین، پردازش بلادرنگ حجم زیادی از داده‌­های با سرعت بالا به منظور تحلیل فرآیندها، مساله‌ای سخت و چالش­‌برانگیز است. در این شرایط، فناوری­‌های تحلیل کلان داده­‌ها به عنوان ابزاری قدرتمند می‌­توانند نقش مهمی را در کنترل فرآیندها ایفا نمایند. در این پژوهش، یک معماری مبتنی بر ترکیب تکنیک­‌های کلان داده‌­ها، فرآیندکاوی و یادگیری ماشین برای تحلیل فرآیندها در کارخانه­‌های هوشمند ارائه شده است که امکان تحلیل دقیق و بلادرنگ فرآیندها در این­‌گونه محیط­‌ها را فراهم می‌آورد. در حقیقت، این معماری از ابزارهای قدرتمند تحلیل کلان داده و تکنیک‌های نوینی مانند فرآیندکاوی بهره می‌برد و با به‌کارگیری الگوریتم رگرسیون لجستیک به عنوان یک ابزار یادگیری ماشین، قادر به استخراج بینش‌های ارزشمند از داده‌های تولید شده در این محیط‌ها است. نتایج ارزیابی کارایی معماری پیشنهادی نشان می­‌دهد که این معماری توانایی بالایی در تحلیل­‌های بلادرنگ فرآیندها از طریق ترکیب تکنیک­‌های پیشرفته تحلیل داده فراهم می­‌آورد.

چکیده انگلیسی :

Due to the nature of smart factories and the use of new technologies such as cyber-physical systems, cloud computing, the Internet of Things, etc. in such environments, the volume of data generated has increased exponentially. Therefore, real-time processing of large volumes of high-speed data for process analysis is a difficult and challenging problem. In this case, big data analysis technologies, as a powerful tool, can play an important role in controlling processes. In this research, an architecture based on a combination of big data, process mining, and machine learning techniques for analyzing processes in smart factories is presented, which enables accurate and real-time analysis of processes in such environments. In fact, this architecture utilizes powerful big data analysis tools and new techniques such as process mining and by employing the logistic regression algorithm as a machine learning tool, it is able to extract valuable insights from the data generated in these environments. The results of the performance evaluation of the proposed architecture show that this architecture provides high capabilities in real-time process analysis through the combination of advanced data analysis techniques

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