Automatic cooking device and method
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Solution Overview
Problem
Conventional cooking devices require manual user input and are not optimized for varying food characteristics, leading to inefficient cooking processes.
Innovation Solution
An automatic cooking device equipped with a light emitter capable of emitting different wavelength bands, a photographing unit, and a processor that performs vision recognition and spectroscopic analysis to identify food materials and control the cooking process based on their characteristics.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If manual user input is required for cooking settings, then the device is easier to manufacture with simpler structure, but the ease of operation deteriorates and productivity decreases due to repeated manipulation
Solution Approach 1:
The cooking device automatically identifies food materials using image recognition and spectroscopic analysis, then autonomously determines and executes optimal cooking parameters without requiring user input. The system serves itself by independently completing the entire cooking process from identification to execution, eliminating manual intervention.
Solution Approach 2:
Manual mechanical input operations are replaced with automated optical and spectral analysis systems. The device uses light emitters, image sensors, and spectroscopic modules to automatically detect food characteristics and trigger appropriate cooking programs, substituting human mechanical interaction with automated sensing and control systems.
2Manufacturing precision
If standard recipes are used for cooking, then the device complexity is reduced, but the manufacturing precision deteriorates because it cannot account for variations in food material characteristics
Solution Approach 1:
The system analyzes local characteristics of the specific food material placed in the device using spectroscopic and imaging methods. Instead of applying generic cooking parameters, the device tailors cooking conditions to the precise composition, moisture content, and physical properties of the individual food item, ensuring optimal results for each specific case.
Solution Approach 2:
The device dynamically adjusts cooking parameters such as temperature, time, and power levels based on real-time analysis of food material properties. The system modifies cooking conditions according to detected characteristics like water content, fat composition, and thermal properties, rather than following fixed predetermined settings.
3Reliability
If the device does not analyze food material characteristics, then the device complexity is lower, but the reliability of cooking results deteriorates
Solution Approach 1:
The system implements a feedback loop where the cooking device continuously monitors food material characteristics through spectroscopic and imaging analysis, compares detected properties with optimal cooking parameters stored in memory, and automatically adjusts cooking conditions accordingly. This closed-loop control ensures reliable and consistent cooking results.
Solution Approach 2:
The device performs preliminary analysis of food material characteristics before the cooking process begins. By pre-identifying the food type, composition, and physical properties using light emission and spectroscopic methods, the system prepares and sets optimal cooking parameters in advance, ensuring reliable cooking outcomes from the start.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
The device autonomously determines the optimal cooking method, time, and temperature, minimizing user intervention and ensuring consistent cooking results by analyzing the food's composition and state in real-time.
Implementation Method 1
obtain characteristic information of the food material by performing spectroscopic analysis based on light reflected by emitting light of a wavelength band
Implementation Method 2
obtain characteristic information of the food material by performing spectroscopic analysis based on light reflected
Data Source
AI summary
The present disclosure relates to an artificial intelligence (AI) system for simulating functions, such as recognition and determination, of the human brain by using a machine learning algorithm such as deep learning, and an application thereof. Provided are an automatic cooking device and method for selectively emitting light of different wavelength bands to a food material, identifying the food material by obtaining information about the food material, based on reflected light, and controlling a cooking process of the food material.