论文标题
薄雾和边缘计算工业以人为中心的网络物理系统5.0：一种经济高效的物联网热成像安全系统
Mist and Edge Computing Cyber-Physical Human-Centered Systems for Industry 5.0: A Cost-Effective IoT Thermal Imaging Safety System
论文作者
论文摘要
尽管全世界许多公司仍在努力适应行业4.0原则，但已经过渡到行业5.0。在这样的范式下，已经出现了以网络物理为中心的人类系统（CPHS）来利用运营商的能力，以实现复杂的制造系统的目标，以实现以人为本的以人为本，弹性和可持续性。本文首先介绍了行业5.0 CPHSS开发的基本概念，然后分析了最新的CPHS，并确定其主要的设计要求和关键的实施组件。此外，概述了此类CPHS的主要挑战。接下来，为了说明先前描述的概念，提出了现实世界中的5.0 CPHS。这样的CPH可以使依赖协作机器人和重型机械的制造过程中的操作员安全和操作跟踪提高。具体而言，提议的用例由一个研讨会组成，需要更明智地使用资源，并且人类接近检测确定何时应进行机械工作，以避免涉及此类机械的事件或事故。提出的CPHS利用了与智能雾化计算节点进行热图像并做出反应以防止工业安全问题的混合边缘计算体系结构。执行的实验表明，在选定的现实情况下，开发的CPHS算法能够以快速准确的方式（在少于10 ms的情况下使用低功率PI 3B）检测人类的存在（具有97.04％精度的10毫秒），因此可以成为许多行业5.0的有效解决方案，因此可以成为一个有效的解决方案。最后，本文提供了具体的准则，可以帮助未来的开发人员和经理克服在部署下一代CPHSs供智能和可持续制造的CPHS时会出现的挑战。
While many companies worldwide are still striving to adjust to Industry 4.0 principles, the transition to Industry 5.0 is already underway. Under such a paradigm, Cyber-Physical Human-centered Systems (CPHSs) have emerged to leverage operator capabilities in order to meet the goals of complex manufacturing systems towards human-centricity, resilience and sustainability. This article first describes the essential concepts for the development of Industry 5.0 CPHSs and then analyzes the latest CPHSs, identifying their main design requirements and key implementation components. Moreover, the major challenges for the development of such CPHSs are outlined. Next, to illustrate the previously described concepts, a real-world Industry 5.0 CPHS is presented. Such a CPHS enables increased operator safety and operation tracking in manufacturing processes that rely on collaborative robots and heavy machinery. Specifically, the proposed use case consists of a workshop where a smarter use of resources is required, and human proximity detection determines when machinery should be working or not in order to avoid incidents or accidents involving such machinery. The proposed CPHS makes use of a hybrid edge computing architecture with smart mist computing nodes that processes thermal images and reacts to prevent industrial safety issues. The performed experiments show that, in the selected real-world scenario, the developed CPHS algorithms are able to detect human presence with low-power devices (with a Raspberry Pi 3B) in a fast and accurate way (in less than 10 ms with a 97.04% accuracy), thus being an effective solution that can be integrated into many Industry 5.0 applications. Finally, this article provides specific guidelines that will help future developers and managers to overcome the challenges that will arise when deploying the next generation of CPHSs for smart and sustainable manufacturing.
