![]() The cross-layer graph-based modeling of IoTManA facilitates the implemented management system (IoTManS) to detect and identify root causes of typically distributed failures occurring in IoT solutions. IoTManA has been implemented in a set of software components named IoT management system (IoTManS) and tested in two scenarios-Smart Agriculture and Smart Cities-showing that it can significantly contribute to harnessing the complexity of managing IoT solutions. Our architecture provides a cross-layer graph-based view of the end-to-end path between devices and the cloud. This paper proposes a novel four-layer IoT Management Architecture (IoTManA) that encompasses various aspects of a distributed infrastructure for managing, controlling, and monitoring software, hardware, and communication components, as well as dataflows and data quality. IoT management approaches focus on devices and connectivity, thus lacking a comprehensive understanding of the different software, hardware, and communication components that comprise an IoT-based solution. The management of IoT solutions is a complex task due to their inherent distribution and heterogeneity. In general, compared with traditional supply chain management, the constructed smart supply chain improves the quality and efficiency of sand factory operations, and all indicators of the designed system have achieved satisfactory results. Experiments show that the most critical indicator in the system, the accuracy rate of sand type identification, is above 98%, and the sand type identification time is only 0.022 s. To verify the performance of the constructed system, a sand factory simulation platform is established. In order to relieve the pressure of network bandwidth, reduce system delay, and improve system operation efficiency, we use edge-computing technology to process data at the edge. Along the supply chain, the deep learning model is used to realize the automatic identification of sand, avoiding the disadvantages of human identification, while improving the quality of sand factory operations. In this paper, a smart supply chain management system (SSCMS) is constructed to realize the intelligence and automatization of the management of sand factories, using edge-computing and deep learning techniques. However, the smart sand factories are hardly involved in previously reported studies, which is inconsistent with related studies on smart factories and the Industrial Internet of Things (IIoT). A role has been played in achieving intelligent management by constructing a smart supply chain. The digital thread ties together a connected ecosystem for manufacturing and, with the addition of domain expertise and intelligence, enables continuous learning and improvement.The diminishing natural sand has facilitated the booming of the sand manufacturing industry, and intelligent management of sand factories, in a time- and cost-efficient way, has become a growing tendency for the future. Our hardware solutions use metrology to bring real-world physical attributes to the digital thread to improve the accuracy of operations. Together, our manufacturing intelligence software solutions create a digital thread throughout the manufacturing process, enabling the entire organisation to take a holistic approach and work together with speed and confidence to achieve the desired outcomes. Through an unparalleled portfolio of digital manufacturing technologies spanning CAE solutions for design and engineering, CAD CAM and complementary software for production applications, metrology hardware and software solutions, as well as data management and analytics tools, we empower technology users throughout the process with deep and actionable insight into product quality, ensuring that quality drives productivity. Our manufacturing intelligence technology enables manufacturers to access, analyse and actively use data from all the key stages of the manufacturing process. ![]() Hexagon's Manufacturing Intelligence division helps customers put data to work to improve productivity and efficiency while embedding quality throughout the product lifecycle. Continuously improving productivity is essential for competitive success. While specific industries have their own unique challenges and motivations, productivity is central to manufacturers across the board.
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