基于虚拟现实的数字孪生:药用大麻的案例研究

IF 3.7 Q2 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE Big Data and Cognitive Computing Pub Date : 2023-05-10 DOI:10.3390/bdcc7020095
Orestis Spyrou, W. Hurst, C. Verdouw
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引用次数: 1

摘要

数字双胞胎是现实生活中物体的数字等价物。它们允许生产商在出现(预期)偏差时立即采取行动,并根据真实数据模拟干预措施的效果。数字孪生和扩展现实技术(包括增强现实、混合现实和虚拟现实技术)相结合,是应对高度监管的作物生产挑战的有前景的解决方案,即现代药用大麻生产环境的复杂性,由于立法的变化,大麻生产环境不断增长。大麻农场不仅必须满足非常高的质量标准和监管要求,还必须应对高产量和市场的不确定性,包括能源方面的考虑。因此,该研究的主要贡献包括用于药用大麻生产的基于扩展现实的数字双胞胎的架构设计和概念验证,这在瓦赫宁根大学数字双胞胎会议上得到了演示。采用方便抽样的方法招募了30名对申请提供反馈的参与者。研究结果表明,尽管70%的参与者不熟悉这个概念,但80%的参与者对创新和创造力持积极态度。
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Virtual Reality-Based Digital Twins: A Case Study on Pharmaceutical Cannabis
Digital Twins are digital equivalents of real-life objects. They allow producers to act immediately in case of (expected) deviations and to simulate effects of interventions based on real-life data. Digital Twin and eXtended Reality technologies (including Augmented Reality, Mixed Reality and Virtual Reality technologies), when coupled, are promising solutions to address the challenges of highly regulated crop production, namely the complexity of modern production environments for pharmaceutical cannabis, which are growing constantly as a result of legislative changes. Cannabis farms not only have to meet very high quality standards and regulatory requirements but also have to deal with high production and market uncertainties, including energy considerations. Thus, the main contributions of the research include an architecture design for eXtended-Reality-based Digital Twins for pharmaceutical cannabis production and a proof of concept, which was demonstrated at the Wageningen University Digital Twins conference. A convenience sampling method was used to recruit 30 participants who provided feedback on the application. The findings indicate that, despite 70% being unfamiliar with the concept, 80% of the participants were positive regarding the innovation and creativity.
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来源期刊
Big Data and Cognitive Computing
Big Data and Cognitive Computing Business, Management and Accounting-Management Information Systems
CiteScore
7.10
自引率
8.10%
发文量
128
审稿时长
11 weeks
期刊最新文献
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