Container loading/unloading time estimation
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Solution Overview
Problem
Current systems lack efficient methods for estimating the time required to load or unload containers, which hinders resource planning and loading bay utilization in transportation facilities.
Innovation Solution
A container monitoring unit (CMU) captures 3D and 2D images to determine the active load/unload time and fullness of containers, using depth cameras and RGB cameras to estimate the Estimated Time of Completion (ETC) through processing and analysis.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If computerized systems are used to monitor and estimate loading/unloading time, then time estimation accuracy is improved, but device complexity increases
Solution Approach 1:
The system divides the container monitoring task into multiple independent components: depth cameras for 3D imaging, RGB cameras for visual data, processors for image analysis, and communication modules for data transmission. Each component performs a specific function, allowing the complex estimation task to be broken down into manageable segments that can be processed independently and combined for the final time estimation.
Solution Approach 2:
The monitoring system is designed to perform multiple functions: capturing 3D images of container contents, determining active load/unload time, calculating fullness percentage, and estimating time to completion. By integrating these diverse functions into a single multi-functional system, the patent reduces the need for separate dedicated devices for each task, thereby managing complexity while maintaining comprehensive monitoring capabilities.
2Productivity
If real-time monitoring of container loading/unloading is implemented, then productivity is improved, but use of energy increases
Solution Approach 1:
Instead of continuous monitoring, the system performs periodic image captures and processing cycles. The depth and RGB cameras take images at specific intervals or triggered by events such as container door opening/closing or significant changes in container contents. This periodic operation reduces energy consumption compared to continuous real-time monitoring while still providing timely updates for productivity improvement.
Solution Approach 2:
The system automatically processes captured images to determine container fullness and estimate time to completion without requiring constant human intervention or supervision. The processors autonomously analyze the image data, calculate metrics, and generate estimates, allowing the system to serve itself and reducing the energy overhead associated with manual monitoring and decision-making processes.
Data Source
AI summary
Embodiments of the present invention are generally directed to system and methods for estimating the time associated with completion of loading and/or unloading of a container. In an embodiment, the present invention is a method of estimating an estimated time to completion (ETC) of loading a container. The method includes: capturing, via an image capture apparatus, a three-dimensional image representative of a three-dimensional formation, the three-dimensional image having a plurality of points with three-dimensional point data; based at least in part on a first sub-plurality of the points, determining an active load time for the container; based at least in part on a second sub-plurality of the points, determining a fullness of the container; and estimating, by a controller, the ETC based on the active load time and on the fullness.