Perishable product containing device, method of operating the perishable product containing device and computer program
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Solution Overview
Problem
The challenge of effectively controlling the freshness of perishable products is complex due to variations among different products and their initial freshness levels, making uniform environmental control ineffective.
Innovation Solution
A perishable product containing device that employs reinforcement learning using freshness information from sensors to automatically adjust environmental conditions, optimizing the inside environment to minimize freshness decline over time.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If uniform environmental control conditions are applied to all perishable products, then the control system is simple to operate, but the freshness control effectiveness deteriorates due to product variations
Solution Approach 1:
The system applies different environmental control conditions to different perishable products based on their individual characteristics. Sensors measure specific parameters (temperature, humidity, gas composition) for each product, and the control system adjusts conditions locally for each product type rather than applying uniform conditions to all products, thereby maintaining high freshness control effectiveness while keeping the interface simple for users.
Solution Approach 2:
The environmental control conditions are dynamically adjusted based on real-time sensor measurements and product characteristics. The system continuously monitors freshness indicators and modifies temperature, humidity, and gas composition accordingly, transitioning from static uniform control to dynamic adaptive control that responds to product-specific needs.
2Reliability
If reinforcement learning is implemented to optimize freshness control, then the freshness preservation improves, but the device complexity increases
Solution Approach 1:
The reinforcement learning system enables the control device to automatically optimize its own operation without external intervention. The system learns optimal control strategies through continuous interaction with the environment, using sensor feedback to improve freshness preservation autonomously. This self-learning capability reduces the need for complex manual configuration while enhancing performance over time.
Solution Approach 2:
The system implements continuous feedback loops where sensors monitor freshness indicators and environmental conditions, and this information feeds back to the reinforcement learning algorithm. The algorithm uses this feedback to adjust control actions and improve freshness preservation. The feedback mechanism structures the complexity in a manageable way, transforming it into an adaptive intelligent system rather than a statically complex one.
Data Source
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AI summary
Reinforcement learning is performed on control conditions of the perishable product environment by using information regarding freshness of perishable products by a freshness sensor to automatically control the perishable product environment. There are provided: a freshness determination section 520 acquiring information regarding freshness of a perishable product contained in a storage container; and an analysis section 530 learning, by reinforcement learning, an inside environment of the storage container for the freshness of the perishable product acquired by the freshness determination section 520 to decide a reward used in the learning. The analysis section 530 decides the reward based on the decrease in the freshness over a certain period of time under the inside environment for the freshness determined based on the freshness acquired by the freshness determination section 520. Then, the analysis section 530 learns the inside environment for the freshness based on the decided reward.