Setting temperature calculation device, low temperature processing system, setting temperature calculation method, and setting temperature calculation program
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Solution Overview
Problem
The challenge of maintaining a consistent low temperature treatment during marine transport of fruits, such as lemons and grapefruits, is hindered by significant changes in heat load, making it difficult to adhere to quarantine regulations without manual and frequent adjustments of the set temperature.
Innovation Solution
A set temperature calculating apparatus that uses machine learning to infer cargo core temperatures based on data correlating with heat load, set temperature, and environmental conditions, automatically adjusting the set temperature to maintain the required low temperature treatment conditions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual temperature control is used in container storage, then the set temperature can be adjusted according to heat load changes, but frequent manual adjustments are required and human error risk increases
Solution Approach 1:
The system enables self-service temperature control by automatically inferring cargo core temperature from container temperature and heat load data, calculating required set temperature adjustments, and executing control actions without manual intervention. The controller autonomously adapts to heat load changes and maintains temperature compliance throughout the quarantine period.
Solution Approach 2:
The system implements feedback control by continuously monitoring container temperature, heat load conditions, and inferred cargo core temperature, then adjusting the set temperature based on the difference between inferred temperature and target temperature. This closed-loop feedback ensures reliable temperature maintenance while eliminating manual operation.
2Ease of operation
If fixed set temperature is used in container storage, then operation is simple, but the cargo core temperature may exceed the reference temperature when heat load changes
Solution Approach 1:
The system transitions from static fixed temperature control to dynamic adaptive control. The controller continuously infers cargo core temperature and adjusts the set temperature in real-time based on heat load changes and temperature deviations, ensuring compliance while maintaining operational simplicity through automation.
Solution Approach 2:
The system dynamically changes the set temperature parameter based on inferred cargo core temperature and heat load conditions. By automatically adjusting this critical parameter, the system maintains temperature compliance during heat load variations without requiring manual intervention or complex user input.
3Adaptability or versatility
If traditional control methods are used, then the system is simple, but it cannot adapt to daily ambient temperature differences and energy savings
Solution Approach 1:
The system replaces traditional mechanical temperature control with an intelligent control system that uses machine learning models and environmental data processing. This substitution enables adaptation to ambient temperature differences and heat load changes while maintaining reasonable system complexity through automated algorithms.
Solution Approach 2:
The system performs preliminary actions by inferring cargo core temperature and calculating optimal set temperature adjustments before temperature deviations occur. This predictive approach allows the controller to proactively adapt to environmental changes and prevent compliance violations.
Data Source
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AI summary
A set temperature calculating apparatus, a low temperature treatment system, a set temperature calculating method, and a set temperature calculating program are provided for realizing a low temperature treatment. The set temperature calculating apparatus includes: a first obtaining unit configured to obtain data correlating with a heat load in a container storage; a second obtaining unit configured to obtain a set temperature when performing temperature control in the container storage; and a learning unit configured to learn a cargo core temperature in the container storage according to a data set including a combination of the data correlating with the heat load and the set temperature.