Digital Twin Cargo Loading Control for Mixed Logistics Facilities
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Solution Overview
Problem
Conventional logistics systems rely on worker experience for cargo loading, leading to inefficiencies and safety issues, particularly in varying cargo sizes and shapes, and lack integrated monitoring and control across different types of logistics facilities.
Innovation Solution
A digital twin-based automated logistics facility operation system that synchronizes actual and virtual environments, using IoT interfaces, a digital twin server, and packaging simulators to optimize cargo deployment and loading sequences, integrating with cyber-physical systems for real-time control and monitoring.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If conventional cargo loading methods relying on worker experience are used, then flexibility in handling various cargo types is maintained, but loading efficiency and safety deteriorate due to variability in worker skill levels
Solution Approach 1:
The patent creates a digital twin (virtual copy) of the physical cargo loading environment, including 3D models of cargo items, loading containers, and robotic manipulators. This virtual replica allows for simulation and optimization of loading sequences without affecting actual operations, enabling efficient planning while maintaining safety through virtual testing of different scenarios.
Solution Approach 2:
The system performs preliminary simulation and optimization of cargo loading sequences in the virtual environment before executing actual loading operations. The cargo loading sequence is determined through simulation that considers cargo weight, volume, shape, and destination, allowing the system to pre-plan optimal loading arrangements that maximize efficiency and ensure safety before physical implementation.
2Ease of operation
If individual control facilities and workers are required for different types of logistics automated facilities, then specific tasks can be performed by specialized personnel, but system complexity and operational coordination requirements increase
Solution Approach 1:
The patent merges multiple control facilities and functions into a unified digital twin-based control system. The virtual environment integrates representations of various logistics facilities (automated warehouses, loading platforms, transport vehicles) and consolidates control functions into a single platform that can coordinate all operations through centralized simulation and sequence determination, reducing the need for multiple independent control systems.
Solution Approach 2:
The digital twin system serves multiple functions simultaneously: it acts as a simulation environment for optimizing loading sequences, a monitoring system for tracking cargo and facility status in real-time, a coordination platform for managing multiple automated facilities, and a planning tool for determining optimal cargo deployment. This multi-functional approach eliminates the need for separate specialized control facilities for each function.
3Adaptability or versatility
If conventional logistics loading algorithms depend on worker experience, then adaptability to different cargo configurations is possible, but loading time and cost increase due to manual intervention and trial-and-error
Solution Approach 1:
The patent replaces the mechanical system of human workers manually planning and executing cargo loading with an automated computational system. The digital twin environment uses algorithms to automatically determine optimal loading sequences based on cargo characteristics (weight, volume, shape, destination) and facility constraints, eliminating the need for human workers to manually plan each loading operation while maintaining adaptability to different cargo configurations.
Solution Approach 2:
The system dynamically adjusts loading parameters such as cargo placement position, loading sequence, and robot movement paths based on real-time data about cargo properties (weight, volume, shape) and destination requirements. The digital twin simulation evaluates multiple parameter combinations to identify the optimal loading configuration, automatically adapting to different cargo types and configurations without requiring manual intervention.
Data Source
AI summary
An automated logistics facility operation system may include an interface configured to support heterogeneous communication protocols with respect to various automated logistics facilities operated in a logistics terminal and collect facility data in real time, a server configured to mirror the facility data of an actual logistics facility and a virtual environment according to the facility data uploaded from the interface, a packaging simulator configured to derive a cargo deployment sequence and disposal location within a designated space, through a loading algorithm utilizing the facility data of the server, and a client device capable of commanding a loading sequence of the cargo matching a packaging simulation result and a loading work within the designated space.