Cooking Parameter Control for Real-Time Nutritional Target Adjustment
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Solution Overview
Problem
Existing cooking control methods fail to appropriately control the nutrition of a dish, as they do not account for fluctuations in nutritional components during cooking and require manual adjustment of cooking parameters.
Innovation Solution
A control method that acquires target nutritional information, adjusts cooking parameters based on this information, and continuously monitors and modifies parameters to achieve desired nutritional values, using machine learning models to predict and correct nutritional outcomes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If cooking parameters are manually adjusted based on ingredient variations, then cooking flexibility is improved, but user operation complexity increases and time consumption increases
Solution Approach 1:
The cooking device automatically detects ingredient variations using sensors (weight, image, moisture content) and self-adjusts cooking parameters (temperature, time, power) without requiring manual user intervention. The control unit processes detected information and modifies cooking programs autonomously, enabling the system to serve itself in adapting to ingredient variations.
Solution Approach 2:
The system implements continuous feedback loops where sensors monitor ingredient properties during cooking, the control unit compares actual values with target values, and cooking parameters are dynamically adjusted based on the feedback. This closed-loop control ensures the dish achieves desired nutritional outcomes despite initial variations.
2Manufacturing precision
If cooking parameters are manually adjusted to account for ingredient variations, then nutritional control is improved, but time consumption increases
Solution Approach 1:
The system performs preliminary detection of ingredient properties (weight, moisture content, image recognition) before cooking begins and pre-calculates the required cooking parameters. This advance preparation eliminates the need for time-consuming manual adjustments during the cooking process while ensuring precise nutritional outcomes.
Solution Approach 2:
The patent replaces manual mechanical adjustment of cooking parameters with automated sensor-based detection and electronic control. Sensors detect ingredient properties, and the control unit electronically adjusts cooking parameters, substituting the manual mechanical process with an automated electromechanical system that is both faster and more precise.
3Device complexity
If cooking parameters are fixed according to recipe specifications, then device simplicity is maintained, but nutritional outcome reliability deteriorates when ingredient amounts vary
Solution Approach 1:
The system transitions from fixed static cooking parameters to dynamic adjustable parameters. The control unit modifies temperature, time, and power settings in real-time based on detected ingredient variations, allowing the cooking process to adapt dynamically while maintaining a relatively simple device structure through automated control logic.
Data Source
AI summary
A control method includes acquiring target nutritional information; acquiring initial cooking parameter information used for cooking by a cooking device, based on the acquired target nutritional information; acquiring cooking-in-progress nutritional information of an item to be cooked when the cooking by the cooking device is being performed; changing the initial cooking parameter information to modified cooking parameter information including one or more modified cooking parameters, based on the target nutritional information and the cooking-in-progress nutritional information; and outputting a control signal including the modified cooking parameter information.