Automated Hoist Level Load Lifter Enhanced with AI for Material Handling

This research study presents a novel automated load lifter system designed to revolutionize material transportation efficiency within buildings. The system integrates cutting-edge AI-based load classification technology seamlessly, enhancing resource allocation and enabling proactive adjustments to meet dynamic demands. Leveraging a sophisticated network of sensors and advanced data processing modules, the system captures and transmits real-time floor-level data and load weight information, empowering managers to make informed decisions swiftly. A distinguishing feature of this system is its AI-driven load classification capability, which discerns between different load types based on shape, size, and material composition, facilitating strategic load distribution and risk mitigation. Moreover, this capability enhances structural assessments, providing valuable insights into load impacts on the building's integrity. By combining traditional load monitoring with AI-driven load classification, the system offers a comprehensive solution for material handling operations. Real-time data transmission enables agile decision-making, while historical data analysis identifies trends for proactive workflow optimization. This groundbreaking system represents a significant advancement in material handling technology, poised to elevate efficiency, safety, and productivity standards in material transportation within buildings, thereby establishing a new benchmark for future innovations.