自主循环的数据分析任务,以确定咖啡生产过程的中小微企业

IF 10.4 1区 计算机科学 Q1 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS
Jairo Fuentes , Jose Aguilar , Edwin Montoya
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引用次数: 0

摘要

咖啡生产需要一定程度的效率,以确保咖啡豆的质量,烘焙过程,总的来说,咖啡加工方法,达到财务和环境可持续性的目标。这需要监测和分析咖啡豆的特性,以及烘焙过程等方面的任务,以便中小微企业农业工业部门的利益相关者能够了解咖啡生产过程中发生的事情,并能够做出更好的决策来改进它。在前一篇文章中,提出了三个自主周期的数据分析任务,用于中小微企业生产链的自动化。这项工作旨在实例化在咖啡生产的情况下,负责识别在生产过程中转换的输入类型的自主循环。这个循环分析生产链的输入(数量、质量、季节性、耐久性、成本等),基于来自组织和环境的信息,建立要执行的生产过程。这种自主循环在咖啡生产中实例化,以确定要转换的输入类型(咖啡豆质量),并确定转换过程(烘焙过程中咖啡豆的减少程度和咖啡加工方法)。质量模型采用K-means技术定义,其Silhouette Index的性能为0.85;咖啡豆在烘焙过程中减少程度的预测模型采用Random Forest技术定义,其精度为0.81;最后,采用Logistic回归技术对“生产方法”进行识别,其质量性能的精度为0.72。其中最重要的发现是,基于机器学习技术的数据分析任务的自主循环能够研究咖啡生产的上下文数据,以确定要转换的输入类型和咖啡转换过程。另一个重要的发现是,自动循环允许生产过程自动化,从而改善了时间和咖啡加工。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Autonomous cycle of data analysis tasks for the determination of the coffee productive process for MSMEs
Coffee production needs certain levels of efficiency to ensure that the quality of the bean, the roasting process, and in general, the coffee processing methods, achieve financial and environmental sustainability objectives. This requires tasks of monitoring and analyzing of features of the coffee bean, and the roasting process, among other aspects, so that stakeholders of the agro-industrial sector of MSMEs can know what happens in the coffee production and can make better decisions to improve it. In a previous article, three autonomous cycles of data analysis tasks are proposed for the automation of the production chains of the MSMEs. This work aims to instantiate the autonomous cycle responsible for identifying the type of input to transform in the production process, in the case of coffee production. This cycle analyzes the inputs of the production chain (quantity, quality, seasonality, durability, cost, etc.), based on information from the organization and the context, to establish the production process to be carried out. This autonomous cycle is instanced in the coffee production to identify the type of input to transform (bean quality), and to determine the transformation process (level of decrease of the bean during the roasting process and coffee processing method). The quality model is defined by the K-means technique with a performance in the Silhouette Index of 0.85, the predictive model of the level of decrease of beans in the roasting process is defined by Random Forest with a performance in the accuracy of 0.81, and finally, the identification model of the "production method" is carried out by the Logistic Regression technique with a quality performance in the accuracy of 0.72. Among the most important findings is that the autonomous cycle of data analysis tasks based on machine learning techniques is capable of studying the contextual data of coffee production to identify the type of input to be transformed and the coffee transformation process. Another important finding is that the autonomous cycle allows the automation of the production process, leading to improved times and coffee processing.
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来源期刊
Journal of Industrial Information Integration
Journal of Industrial Information Integration Decision Sciences-Information Systems and Management
CiteScore
22.30
自引率
13.40%
发文量
100
期刊介绍: The Journal of Industrial Information Integration focuses on the industry's transition towards industrial integration and informatization, covering not only hardware and software but also information integration. It serves as a platform for promoting advances in industrial information integration, addressing challenges, issues, and solutions in an interdisciplinary forum for researchers, practitioners, and policy makers. The Journal of Industrial Information Integration welcomes papers on foundational, technical, and practical aspects of industrial information integration, emphasizing the complex and cross-disciplinary topics that arise in industrial integration. Techniques from mathematical science, computer science, computer engineering, electrical and electronic engineering, manufacturing engineering, and engineering management are crucial in this context.
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