Abstract: This paper delves into the innovative application research of digital twin technology in the field of heavy-duty shelving. With the advent of Industry 4.0, the warehousing and logistics industry is increasingly demanding higher standards for shelf structural safety and operational efficiency. Leveraging its capabilities in virtual-real mapping, dynamic sensing, and intelligent decision-making, digital twin technology offers a groundbreaking technological approach to managing heavy-duty shelving systems.

This study systematically outlines the overall architecture of a real-time load monitoring and early-warning system built on digital twin technology. The system leverages 3D modeling to digitally map the physical structure of shelving units, while integrating multi-type sensor networks to continuously collect critical parameters such as structural stress, deformation, and cargo weight distribution. Together, these components form a dynamic data model that covers the entire lifecycle of the shelving system. In terms of functional design, the system not only offers real-time data visualization but also employs machine learning algorithms to deeply analyze historical data, automatically identifying abnormal load patterns and generating tiered warning alerts. Compared to traditional monitoring methods, this system boasts three key advantages: First, it shifts from passive maintenance to proactive prevention, significantly reducing the risk of shelf collapse by providing early warnings more than 72 hours in advance. Second, by utilizing digital thread technology, it breaks down data silos across design, production, and operations, enhancing management efficiency throughout the entire supply chain. Finally, the system supports simulation-based optimization using the digital twin, offering a scientific foundation for improving shelving structures and optimizing warehouse layouts. The findings of this research provide a replicable technical solution for building intelligent safety management systems in the warehousing industry, holding significant practical value in driving the digital transformation of logistics equipment.

Real-time Load Monitoring and Early Warning System for Shelves

1. System Architecture Setup
Digital twin technology creates virtual models that mirror physical entities, accurately mapping structural parameters and material properties of heavy-duty shelving into the virtual space. By deploying various sensors—such as strain-gauge pressure sensors—onto critical areas of the shelves, these devices act like sensitive antennae, continuously capturing real-time force data from each node and transmitting it back to the data processing center via IoT communication protocols. At the data center, advanced algorithms are employed to clean, integrate, and analyze the massive datasets, simultaneously updating the state of the digital twin model to achieve seamless virtual-real interaction. For instance, when goods are placed on a specific shelf level, the corresponding pressure readings instantly appear visually in the virtual model, enabling managers to intuitively grasp the distribution of forces across the structure.

II. Real-Time Monitoring Function Demonstration
At the heart of this system lies its real-time capability. Powered by a high-speed data acquisition module, it refreshes the monitoring display multiple times per second, accurately showing the current load weight of each storage bin. If any area approaches or exceeds the preset safety threshold, the system automatically activates both audible and visual alarms, while simultaneously highlighting the affected zone on the monitoring interface to alert staff for immediate intervention. Additionally, the system’s historical data storage feature allows users to trace load-change trends from any past period, aiding in the analysis of long-term usage patterns and enabling proactive identification of potential risk areas. For instance, during peak e-commerce promotional periods when shipment volumes surge dramatically, the system can dynamically track fluctuations in shelf loads, ensuring that operations remain safe and well-organized.

III. Effectiveness of the Early Warning Mechanism
Early warnings are not mere notifications—they are sophisticated, intelligence-driven decision-support tools. Built-in risk assessment engines analyze a combination of factors, such as shelving design standards and cargo types, to deliver tiered alert messages. For instance, in cases of mild overloading, the system suggests adjusting the layout of nearby storage locations; however, in severe overload situations, it automatically locks down affected aisles, halting all storage and retrieval activities until the underlying risks are eliminated. Additionally, the system’s simulation feature allows users to virtually test the effectiveness of various emergency response plans, helping to refine and optimize their strategies. In the event of sudden disasters like earthquakes that cause structural deformation, the system swiftly generates tailored reinforcement recommendations based on real-time feedback, minimizing potential damage to the greatest extent possible.

     The real-time heavy-duty shelf load monitoring and early-warning system, powered by digital twin technology, is reshaping the paradigm of warehouse safety management through digital means—transforming traditional static protection into dynamic, intelligent control, and injecting strong momentum into the steady growth of the logistics industry.