Apparatus for calculating temperature setting, system for low temperature treatment, method of calculating temperature setting, and program for calculating temperature setting

The patent applies dynamics by transitioning from a fixed set temperature to a dynamic set temperature that automatically adjusts based on real-time heat load conditions. The system continuously monitors heat load parameters (ambient temperature, humidity, ventilation) and dynamically recalculates the set temperature using a learned model, ensuring the cargo core temperature remains within the required range despite varying external conditions.

The patent implements feedback by using the learned model to predict the relationship between heat load and cargo core temperature, then using this prediction to automatically adjust the set temperature. The system continuously feeds back temperature deviation information and heat load changes to the control algorithm, enabling closed-loop control that maintains temperature reliability without manual intervention.

The patent applies self-service by enabling the temperature control system to automatically adjust the set temperature without manual intervention. The learned model autonomously processes heat load data and calculates appropriate set temperature adjustments, making the system self-regulating and eliminating the need for operators to manually respond to heat load changes during marine transport.

The patent replaces manual mechanical temperature adjustment with an automated intelligent control system. Instead of operators manually changing temperature settings based on observations, the system uses a learned model (machine learning algorithm) to automatically calculate and apply set temperature adjustments, substituting human judgment with an automated decision-making system that responds instantly to heat load changes.

The patent applies preliminary action by pre-training the learned model with historical data before actual temperature control operations. The model learns the complex relationship between heat load parameters and cargo core temperature in advance, storing this knowledge for rapid deployment during marine transport. This preliminary learning phase separates the complex computational work from the real-time control operation, reducing the apparent system complexity during actual use.

The patent utilizes parameter changes by having the learned model process multiple heat load parameters (ambient temperature, humidity, ventilation volume) and transform them into an optimized set temperature parameter. The model dynamically adjusts the set temperature based on the combined influence of these parameters, converting complex multi-parameter input into a single actionable control parameter that maintains cargo core temperature reliability.