Cargo Space Item Arrangement Using ML Classification
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Commercial vehicles face challenges in efficiently loading and arranging items of varying shapes, sizes, fragility, and perishability, leading to potential damage during transport and inefficient use of cargo space, as well as inconvenient access to items at drop-off locations.
Innovation Solution
A system utilizing machine learning models to classify items based on fragility, perishability, and available cargo space, predicting item boundaries, and determining optimal arrangements considering destination locations, with sensors and barcode scanners providing data for processing engines to visualize and optimize item placement.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If items are loaded into cargo space without considering fragility and weight attributes, then loading process is simple and fast, but fragile items may be damaged by heavy items placed on top
Solution Approach 1:
The system performs preliminary classification of items by fragility and weight attributes before loading, using machine learning models to analyze item characteristics and determine safe stacking arrangements in advance, preventing damage during transport
Solution Approach 2:
The patent replaces manual judgment and physical arrangement methods with automated machine learning models that classify items and determine optimal placements based on detected attributes, reducing loading process complexity while improving reliability
2Ease of operation
If items are arranged in cargo space without considering pickup and drop-off locations, then arrangement process is simple, but items to be unloaded may be inconveniently positioned requiring numerous items to be moved for access
Solution Approach 1:
The system determines optimal item arrangements in advance by considering pickup and drop-off locations, using machine learning models to predict which items need frequent access and positioning them for convenient retrieval before transport begins
Solution Approach 2:
The patent applies different arrangement strategies to different items based on their destination locations and access requirements, optimizing the local positioning of each item group according to its specific needs rather than using a uniform arrangement approach
3Productivity
If items are loaded without optimizing cargo space utilization, then loading process is simple and fast, but additional items cannot fit into cargo space without rearranging existing items
Solution Approach 1:
The system performs preliminary optimization of cargo space arrangement by analyzing item dimensions and cargo space capacity, using machine learning models to determine the most space-efficient configuration before loading commences
Solution Approach 2:
The patent utilizes three-dimensional spatial analysis and machine learning models to optimize item placement across multiple dimensions within the cargo space, maximizing utilization by efficiently packing items in height, width, and depth rather than simple linear arrangement
Data Source
AI summary
A method, computing system, and computer program product are provided. Items at a source location are detected and classified, with respect to fragility and perishability, based on characteristics of the each respective item and is performed by trained machine learning models. Item boundaries are predicted based on applying respective data regarding points on a surface of the each respective item to a trained second machine learning model to predict the item boundaries. The each respective item is classified into a respective group with respect to an available volume of the cargo space based on sensor data of the cargo space, the classified fragility and perishability, the predicted item boundaries, and a third machine learning model. An arrangement of the items in the cargo space is determined based on the group classifications and a corresponding destination location associated with the each respective item and is visualized relative to the cargo space.