18276781. CONTAINER LOADING MANAGEMENT SYSTEM AND CONTAINER LOADING MANAGEMENT METHOD simplified abstract (NEC Corporation)
Contents
- 1 CONTAINER LOADING MANAGEMENT SYSTEM AND CONTAINER LOADING MANAGEMENT METHOD
- 1.1 Organization Name
- 1.2 Inventor(s)
- 1.3 CONTAINER LOADING MANAGEMENT SYSTEM AND CONTAINER LOADING MANAGEMENT METHOD - A simplified explanation of the abstract
- 1.4 Simplified Explanation
- 1.5 Potential Applications
- 1.6 Problems Solved
- 1.7 Benefits
- 1.8 Potential Commercial Applications
- 1.9 Possible Prior Art
- 1.10 Unanswered Questions
- 1.11 Original Abstract Submitted
CONTAINER LOADING MANAGEMENT SYSTEM AND CONTAINER LOADING MANAGEMENT METHOD
Organization Name
Inventor(s)
CONTAINER LOADING MANAGEMENT SYSTEM AND CONTAINER LOADING MANAGEMENT METHOD - A simplified explanation of the abstract
This abstract first appeared for US patent application 18276781 titled 'CONTAINER LOADING MANAGEMENT SYSTEM AND CONTAINER LOADING MANAGEMENT METHOD
Simplified Explanation
The loading container information input means accepts input of information on the target container. The Inquiring means transmits current loading state and information on the target container to the container loading planning device to inquire about the loading position of the target container. The evaluation means outputs an evaluation value for loading the target container at the received loading position. The output means outputs data including the loading state and information of the target container, the loading position of the target container, and the evaluation value as training data. The learning means learns the model by machine learning using the output training data. The loading position determination means determines the loading position of the target container using the learned model.
- Loading container information input means: Accepts information on the target container.
- Inquiring means: Transmits current loading state and target container information to inquire about loading position.
- Evaluation means: Outputs evaluation value for loading at received position.
- Output means: Outputs data including loading state, target container information, loading position, and evaluation value as training data.
- Learning means: Learns model through machine learning using training data.
- Loading position determination means: Determines loading position using learned model.
Potential Applications
This technology could be applied in automated container loading systems in warehouses or shipping ports.
Problems Solved
1. Streamlining the container loading process. 2. Improving efficiency and accuracy in loading containers.
Benefits
1. Increased productivity. 2. Reduced errors in loading containers.
Potential Commercial Applications
Optimizing container loading in logistics companies.
Possible Prior Art
There may be prior art related to automated container loading systems in the logistics industry.
Unanswered Questions
How does this technology handle different types of containers?
The abstract does not specify how the system adapts to various container sizes and shapes.
What is the accuracy rate of the evaluation value in determining the loading position?
The abstract does not mention the precision of the evaluation value in practical applications.
Original Abstract Submitted
The loading container information input means accepts input of information on the target container. The Inquiring means transmits current loading state and information on the target container to the container loading planning device to inquire about the loading position of the target container. The evaluation means outputs an evaluation value for loading the target container at the received loading position. The output means outputs data including the loading state and information of the target container, the loading position of the target container, and the evaluation value as training data. The learning means learns the model by machine learning using the output training data. The loading position determination means determines the loading position of the target container using the learned model.
