Cooking utensil superheated steam system and temperature control method thereof

Abstract

The invention provides a cooking utensil superheated steam system and a temperature control method thereof, and relates to the technical field of steam cooking. The superheated steam system of the cooking utensil comprises the cooking utensil, a steam module, an ultrasonic heating module, a temperature monitoring module, a cooking data acquisition module, a data analysis module, a model prediction module, a display module, a dynamic adjustment heating strategy, a waste heat recovery module and a preheating box. Ultrasonic vibration is used for assisting heating, water molecules rapidly vibrate in a microwave field to generate heat, the waste heat recovery module captures heat generated in the cooking process through a heat exchanger and is used for preheating the next batch of food, the energy utilization efficiency is remarkably improved, energy waste and carbon emission are reduced, the heating strategy is dynamically adjusted, and the energy utilization rate is increased. According to the real-time sensor data and the prediction model, the heating power, the steam flow and the heating time parameters are automatically optimized, unnecessary energy consumption is avoided, and accurate temperature control and energy consumption optimization are achieved.

Description

Technical Field

[0001] The present invention relates to the technical field of steam cooking, and particularly to an overheated steam system for a cooking appliance and a temperature control method thereof. Background Art

[0002] Steam cooking is a cooking method that uses steam as the main heat conduction medium. Compared with traditional cooking methods such as frying, stir-frying, and roasting, steam cooking has higher efficiency, more uniform heating effect, and can better retain the nutritional components and original flavor of ingredients. The overheated steam system of a cooking appliance is an advanced cooking technology that heats water to the overheated steam state and uses its high-efficiency heat transfer characteristics and high-temperature characteristics to achieve a fast, efficient, and energy-saving cooking process.

[0003] During the use of the existing overheated steam system of cooking appliances, there are still some problems. Traditional cooking appliances have a slow heating speed, a long cooking time, and low efficiency. Traditional cooking appliances have high energy consumption and low energy utilization rate. Traditional cooking appliances have inaccurate temperature control and are prone to overcooking or undercooking of ingredients. Therefore, those skilled in the art have provided an overheated steam system for a cooking appliance and a temperature control method thereof to solve the problems raised in the above background art. Summary of the Invention

[0004] (1) Technical Problems to be Solved

[0005] In view of the deficiencies of the prior art, the present invention provides an overheated steam system for a cooking appliance and a temperature control method thereof, which solve the problems of slow heating speed, long cooking time, and low efficiency of traditional cooking appliances, high energy consumption and low energy utilization rate of traditional cooking appliances, and inaccurate temperature control of traditional cooking appliances, which are prone to overcooking or undercooking of ingredients.

[0006] (2) Technical Solutions

[0007] To achieve the above objectives, the present invention is realized through the following technical solutions: An overheated steam system for a cooking appliance, comprising: a cooking appliance, a steam module, an ultrasonic heating module, a temperature monitoring module, a cooking data acquisition module, a data analysis module, a model prediction module, a display module, a dynamic adjustment heating strategy, a waste heat recovery module, and a preheating box;

[0008] The ultrasonic heating module is arranged at the heating component position of the steam module. By the ultrasonic heating module, microwave heating is utilized to generate heat through the rapid vibration of water molecules in the microwave field, which can rapidly increase the water temperature. Compared with traditional resistance heating, microwave heating can heat water to the required temperature more quickly, thereby shortening the heating time;

[0009] The temperature monitoring module is used to monitor the temperatures of the steam module and the cooking appliance;

[0010] The cooking data collection module collects the user's cooking data, including the types of ingredients, cooking time, temperature settings, and user preferences. The data collection module works in coordination with the temperature monitoring module to ensure the comprehensiveness and accuracy of the data;

[0011] The data analysis module conducts in-depth analysis on the collected cooking data to identify the user's cooking habits and preferences. Using big data analysis techniques, it extracts valuable information to support personalized cooking suggestions and optimization plans;

[0012] The model prediction module predicts the cooking time and energy consumption based on historical data and real-time sensor data, and dynamically adjusts the heating strategy. By using machine learning algorithms and prediction models, it improves the prediction accuracy and the system response speed;

[0013] The dynamic adjustment of the heating strategy automatically adjusts the heating power, steam flow rate, and heating time parameters according to real-time sensor data and the prediction model. By using intelligent control algorithms, it achieves precise temperature control and energy consumption optimization;

[0014] The waste heat recovery module recovers the waste heat generated during the cooking process for preheating the water entering the steam generator or other uses. By using an efficient heat exchanger, it ensures the effective recovery and utilization of the waste heat.

[0015] Preferably, the preheating tank is used to store and preheat the water entering the steam generator. It works in coordination with the waste heat recovery module to preheat the water using the recovered waste heat, improving the heating efficiency.

[0016] Preferably, the display module provides a user interface to display cooking parameters, cooking progress, and energy consumption information. By using a touch screen and a graphical user interface, it provides an intuitive operation experience and real-time feedback.

[0017] Preferably, the cooking appliance includes basic components such as a steam generator, a cooking cavity, and steam nozzles, and is used to generate and utilize superheated steam for cooking.

[0018] A method for controlling the temperature of a superheated steam system of a cooking appliance includes the following steps:

[0019] S1. Data collection. The cooking data collection module collects the user's cooking data, including the types of ingredients, cooking time, temperature settings, and user preferences. The temperature monitoring module monitors the temperature of the steam module and the cooking appliance in real time and transmits the data to the data analysis module;

[0020] S2. Data analysis. The data analysis module conducts in-depth analysis on the collected data to identify the user's cooking habits and preferences. Using big data analysis techniques, it extracts valuable information to support personalized cooking suggestions and optimization plans;

[0021] S3. Heating, the ultrasonic heating module and the steam module work together to quickly heat the preheated water to the superheated steam state. The ultrasonic heating module uses microwave heating technology to generate heat through the rapid vibration of water molecules in the microwave field, shortening the heating time;

[0022] S4. Real-time monitoring, the temperature monitoring module monitors the temperature, humidity and steam pressure parameters during the cooking process in real time and transmits the data to the model prediction module;

[0023] S5. Prediction and adjustment, the model prediction module predicts the cooking time and energy consumption based on historical data and real-time sensor data. According to the prediction results, it dynamically adjusts the heating power, steam flow rate and heating time parameters, and uses intelligent control algorithms to achieve precise temperature control and energy consumption optimization;

[0024] S6. User interface, the display module provides a user interface to display cooking parameters, cooking progress and energy consumption information. Users can monitor the cooking process in real time and adjust parameters through the touch screen and graphical user interface;

[0025] S7. Feedback and optimization, users can receive personalized cooking suggestions and optimization plans provided by the system. The system further optimizes the cooking parameters and heating strategies according to the user's feedback and cooking results;

[0026] S8. Preheating, the waste heat recovery module recovers the waste heat generated during the cooking process. The preheating box uses the recovered waste heat to preheat the water entering the steam generator, and preheats the food to be cooked subsequently, thereby improving the heating efficiency.

[0027] Preferably, the waste heat recovery module further includes a heat exchanger, a control valve, a sensor and a heat storage device. During the cooking process, the steam generator generates superheated steam for cooking. After the steam releases heat in the cooking cavity, it will condense into water. A large amount of latent heat will be released during this condensation process, and this part of the heat is the source of waste heat. In addition, the waste gas generated during the cooking process also contains recoverable heat. The waste heat recovery module captures this waste heat through the heat exchanger. The heat exchanger is usually installed near the exhaust port of the steam generator or the cooking cavity to capture the waste heat to the maximum extent.

[0028] Preferably, the heat exchanger is internally designed with multiple channels. The waste heat fluid and the medium to be heated flow through these channels respectively. Through heat conduction and convection, the waste heat fluid transfers heat to the medium to be heated. The specific process is as follows:

[0029] A1. High-temperature steam or air passes through the hot side channel of the heat exchanger and transfers its heat to the metal wall of the heat exchanger;

[0030] A2. The medium to be heated passes through the cold side channel of the heat exchanger and absorbs the heat transferred from the metal wall, and the temperature rises.

[0031] Preferably, the model prediction module predicts the cooking time and energy consumption based on historical data and real-time sensor data. The detailed steps according to the prediction results are as follows:

[0032] B1. Data collection, collect historical cooking data, including types of ingredients, cooking time, temperature setting, user preferences, real-time sensor data, current temperature, current humidity, steam pressure, and ambient temperature;

[0033] B2. Data preprocessing, data cleaning, remove missing values, outliers, and noisy data, normalize data with different dimensions, and extract and select features useful for the prediction task, time features, temperature features, and sensor features;

[0034] B3. Model selection, select a suitable machine learning model for prediction. The decision tree model can handle multi-classification problems;

[0035] B4. Model training, use historical data for model training, and adjust model parameters to minimize the prediction error;

[0036] B5. Real-time prediction, according to real-time sensor data and current user input, use the trained model to predict the cooking time and energy consumption, predict the time required for the current ingredient to reach the target temperature at the current temperature, and the corresponding energy consumption.

[0037] Preferably, the dynamic adjustment automatically optimizes the heating power, steam flow rate, and heating time parameters according to the heating strategy predicted by the model to achieve precise temperature control and energy consumption optimization. The detailed steps are as follows:

[0038] Q1. Predict the cooking time and energy consumption. The model prediction module predicts the remaining time and energy consumption required for the current cooking task based on real-time sensor data and historical data, and uses the LSTM model to predict the temperature change trend in the future for a period of time;

[0039] Q2. Set target parameters, according to user settings and prediction results, set target parameters, target temperature, target humidity, and target steam pressure;

[0040] Q3. Calculate the error, calculate the error between the current temperature and the target temperature, and its algorithm formula is as follows:

[0041] e(t) = T target - T current ;

[0042] Q4. Select a control algorithm, according to system requirements and real-time data, select PID control. The proportional control of the PID:

[0043] P(t) = K p·e(t)

[0044] Integral control:

[0045]

[0046] Differential control:

[0047]

[0048] Total control output:

[0049] u(t) = P(t) + I(t) + D(t)

[0050] Adjust the heating power. According to the control output u(t), adjust the heating power P heat :

[0051]

[0052] where P max is the maximum heating power, u min and u max are the minimum and maximum values of the control output;

[0053] Adjust the steam flow rate. According to the heating power and the target steam pressure, adjust the steam flow rate F steam :

[0054] F steam = T remain + Δt

[0055] where Δt is the adjustment time increment.

[0056] (III) Advantageous effects

[0057] The present invention provides an overheated steam system for a cooking appliance and a temperature control method thereof. It has the following advantageous effects:

[0058] 1. In the present invention, through ultrasonic vibration-assisted heating, water molecules vibrate rapidly in the microwave field to generate heat, which can quickly increase the water temperature. Compared with traditional resistance heating, ultrasonic heating can heat water to the required temperature more quickly, significantly shorten the heating time, improve the heating efficiency, and utilize the high temperature and high energy density of overheated steam to heat food ingredients more quickly and evenly, shorten the cooking time, and improve the cooking efficiency.

[0059] 2. In the present invention, the cooking data collection module collects the cooking data of the user, and the data analysis module conducts in-depth analysis on the data to identify the cooking habits and preferences of the user, providing support for personalized cooking suggestions and optimization solutions. The model prediction module predicts the cooking time and energy consumption based on historical data and real-time sensor data, and dynamically adjusts the heating strategy. By using machine learning algorithms and prediction models, the prediction accuracy and system response speed are improved. The PID intelligent control algorithm is adopted to achieve precise temperature control and energy consumption optimization.

[0060] 3. In the present invention, the waste heat recovery module captures the heat generated during the cooking process through a heat exchanger and uses it for preheating the next batch of food, which significantly improves the energy utilization efficiency and reduces energy waste and carbon emissions.

[0061] 4. In the present invention, the data analysis and model prediction module provides personalized cooking suggestions and optimization solutions, helping users adjust cooking parameters and heating strategies according to different ingredients and cooking requirements, enhancing the user experience. The heating strategy is dynamically adjusted, and based on real-time sensor data and the prediction model, the heating power, steam flow rate, and heating time parameters are automatically optimized to avoid unnecessary energy consumption and achieve precise temperature control and energy consumption optimization. BRIEF DESCRIPTION OF THE DRAWINGS

[0062] Figure 1 is a schematic diagram of the system of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0063] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present invention.

[0064] Embodiment 1:

[0065] As Figure 1 shown, the embodiment of the present invention provides an overheated steam system for a cooking appliance, including: a cooking appliance, a steam module, an ultrasonic heating module, a temperature monitoring module, a cooking data collection module, a data analysis module, a model prediction module, a display module, a dynamically adjusted heating strategy, a waste heat recovery module, and a preheating box;

[0066] The ultrasonic heating module is set at the heating component position of the steam module. By using the ultrasonic heating module to heat with microwaves, heat is generated by the rapid vibration of water molecules in the microwave field, which can quickly increase the water temperature. Compared with traditional resistance heating, microwave heating can heat water to the required temperature more quickly, thus shortening the heating time. By utilizing the high temperature and high energy density of superheated steam, it can heat food ingredients more quickly and evenly, shortening the cooking time and improving cooking efficiency;

[0067] The temperature monitoring module is used to monitor the temperatures of the steam module and the cooking appliance;

[0068] The cooking data acquisition module collects the user's cooking data, including the types of food ingredients, cooking time, temperature settings, and user preferences. The data acquisition module works in coordination with the temperature monitoring module to ensure the comprehensiveness and accuracy of the data. The cooking data acquisition module collects the user's cooking data, and the data analysis module conducts in-depth analysis on the data to identify the user's cooking habits and preferences, providing support for personalized cooking suggestions and optimization solutions;

[0069] The data analysis module conducts in-depth analysis on the collected cooking data to identify the user's cooking habits and preferences. By using big data analysis techniques, valuable information is extracted to provide support for personalized cooking suggestions and optimization solutions. The data analysis and model prediction module provides personalized cooking suggestions and optimization solutions to help users adjust cooking parameters and heating strategies according to different food ingredients and cooking requirements, enhancing the user experience. The cooking data acquisition module collects the user's cooking data, and the data analysis module conducts in-depth analysis on the data to identify the user's cooking habits and preferences, providing support for personalized cooking suggestions and optimization solutions. The model prediction module, based on historical data and real-time sensor data, predicts the cooking time and energy consumption, and dynamically adjusts the heating strategy. By using machine learning algorithms and prediction models, the prediction accuracy and the system response speed are improved. The PID intelligent control algorithm is adopted to achieve precise temperature control and energy consumption optimization;

[0070] The model prediction module, based on historical data and real-time sensor data, predicts the cooking time and energy consumption, and dynamically adjusts the heating strategy. By using machine learning algorithms and prediction models, the prediction accuracy and the system response speed are improved;

[0071] The dynamic heating strategy adjusts the heating power, steam flow rate, and heating time parameters automatically according to real-time sensor data and prediction models. By using intelligent control algorithms, it achieves precise temperature control and energy consumption optimization. The data analysis and model prediction module provides personalized cooking suggestions and optimization solutions to help users adjust cooking parameters and heating strategies according to different ingredients and cooking requirements, enhancing the user experience. The dynamic heating strategy automatically optimizes the heating power, steam flow rate, and heating time parameters based on real-time sensor data and prediction models, avoiding unnecessary energy consumption and achieving precise temperature control and energy consumption optimization;

[0072] The waste heat recovery module recovers the waste heat generated during the cooking process for preheating the water entering the steam generator or other uses. By using a highly efficient heat exchanger, it ensures the effective recovery and utilization of waste heat. The waste heat recovery module captures the waste heat generated during the cooking process through a heat exchanger and uses it to preheat the water entering the steam generator or other purposes. This significantly improves the energy utilization efficiency, reducing energy waste and carbon emissions.

[0073] The preheating tank is used to store and preheat the water entering the steam generator. It works in coordination with the waste heat recovery module to preheat the water using the recovered waste heat, improving the heating efficiency.

[0074] The display module provides a user interface to display cooking parameters, cooking progress, and energy consumption information. By using a touch screen and a graphical user interface, it provides an intuitive operation experience and real-time feedback.

[0075] The cooking appliance includes basic components such as a steam generator, a cooking cavity, and steam nozzles, which are used to generate and utilize superheated steam for cooking.

[0076] A method for controlling the temperature of a superheated steam system in a cooking appliance includes the following steps:

[0077] S1. Data collection: The cooking data collection module collects the user's cooking data, including the type of ingredients, cooking time, temperature setting, and user preferences. The temperature monitoring module monitors the temperature of the steam module and the cooking appliance in real time and transmits the data to the data analysis module;

[0078] S2. Data analysis: The data analysis module conducts in-depth analysis on the collected data to identify the user's cooking habits and preferences. By using big data analysis techniques, it extracts valuable information to support personalized cooking suggestions and optimization solutions;

[0079] S3. Heating: The ultrasonic heating module and the steam module work together to quickly heat the preheated water to the superheated steam state. The ultrasonic heating module uses microwave heating technology to generate heat through the rapid vibration of water molecules in the microwave field, shortening the heating time;

[0080] S4. Real-time monitoring: The temperature monitoring module monitors the temperature, humidity, and steam pressure parameters during the cooking process in real time and transmits the data to the model prediction module;

[0081] S5. Prediction and adjustment: The model prediction module predicts the cooking time and energy consumption based on historical data and real-time sensor data. According to the prediction results, it dynamically adjusts the heating power, steam flow rate, and heating time parameters, and uses intelligent control algorithms to achieve precise temperature control and energy consumption optimization;

[0082] S6. User interface: The display module provides a user interface to display cooking parameters, cooking progress, and energy consumption information. Users can monitor the cooking process in real time and adjust parameters through the touch screen and graphical user interface;

[0083] S7. Feedback and optimization: Users can receive personalized cooking suggestions and optimization solutions provided by the system. The system further optimizes the cooking parameters and heating strategies based on the user's feedback and cooking results;

[0084] S8. Preheating: The waste heat recovery module recovers the waste heat generated during the cooking process. The preheating box uses the recovered waste heat to preheat the water entering the steam generator, and preheats the food to be cooked subsequently, thereby improving the heating efficiency.

[0085] The waste heat recovery module also includes a heat exchanger, a control valve, sensors, and a heat storage device. During the cooking process, the steam generator generates superheated steam for cooking. After the steam releases heat in the cooking cavity, it will condense into water, and a large amount of latent heat will be released during this condensation process. This part of the heat is the source of waste heat. In addition, the exhaust gas generated during the cooking process also contains recoverable heat. The waste heat recovery module captures this waste heat through the heat exchanger. The heat exchanger is usually installed near the exhaust port of the steam generator or the cooking cavity to capture the waste heat to the maximum extent.

[0086] The heat exchanger is internally designed with multiple channels. The waste heat fluid and the medium to be heated flow through these channels respectively. Through heat conduction and convection, the waste heat fluid transfers heat to the medium to be heated. The specific process is as follows:

[0087] A1. High-temperature steam or air passes through the hot side channel of the heat exchanger and transfers its heat to the metal wall of the heat exchanger;

[0088] A2. The medium to be heated passes through the cold side channel of the heat exchanger and absorbs the heat transferred from the metal wall, resulting in a temperature increase.

[0089] The detailed steps of the model prediction module predicting the cooking time and energy consumption based on historical data and real-time sensor data are as follows:

[0090] B1. Data collection, collect historical cooking data, including types of ingredients, cooking time, temperature settings, user preferences, real-time sensor data, current temperature, current humidity, steam pressure, and ambient temperature;

[0091] B2. Data preprocessing, data cleaning, remove missing values, outliers, and noisy data, normalize data with different dimensions, extract and select features useful for the prediction task, time features, temperature features, and sensor features;

[0092] B3. Model selection, select a suitable machine learning model for prediction, the decision tree model can handle multi-classification problems;

[0093] B4. Model training, use historical data for model training, adjust model parameters to minimize prediction errors;

[0094] B5. Real-time prediction, according to real-time sensor data and current user input, use the trained model to predict cooking time and energy consumption, predict the time required for the current ingredient to reach the target temperature at the current temperature, and the corresponding energy consumption.

[0095] Preferably, the detailed steps for dynamically adjusting and automatically optimizing the heating power, steam flow, and heating time parameters according to the heating strategy predicted by the model to achieve precise temperature control and energy consumption optimization are as follows:

[0096] Q1. Predict cooking time and energy consumption, the model prediction module predicts the remaining time and energy consumption required for the current cooking task based on real-time sensor data and historical data, and uses the LSTM model to predict the temperature change trend in the future for a period of time;

[0097] Q2. Set target parameters, according to user settings and prediction results, set target parameters, target temperature, target humidity, and target steam pressure;

[0098] Q3. Calculate the error, calculate the error between the current temperature and the target temperature, and its algorithm formula is as follows:

[0099] e(t) = T target - T current ;

[0100] Q4. Select a control algorithm, according to system requirements and real-time data, select PID control, the proportional control of PID:

[0101] P(t) = K p ·e(t)

[0102] Integral control:

[0103]

[0104] Derivative control:

[0105]

[0106] Total control output:

[0107] u(t) = P(t) + I(t) + D(t)

[0108] Adjust the heating power. According to the control output u(t), adjust the heating power P heat :

[0109]

[0110] where P max is the maximum heating power, u min and u max are the minimum and maximum values of the control output;

[0111] Adjust the steam flow rate. According to the heating power and the target steam pressure, adjust the steam flow rate F steam :

[0112] F steam = T remain + Δt

[0113] where Δt is the adjustment time increment.

[0114] Although the embodiments of the present invention have been shown and described, those of ordinary skill in the art can understand that various changes, modifications, substitutions, and variations can be made to these embodiments without departing from the principles and spirit of the present invention. The scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. An overheat steam system for a cooking appliance, characterized in that: Including: A cooking appliance, a steam module, an ultrasonic heating module, a temperature monitoring module, a cooking data collection module, a data analysis module, a model prediction module, a display module, a dynamic adjustment of the heating strategy, a waste heat recovery module and a preheating box; The ultrasonic heating module is arranged at the heating component position of the steam module. By using the ultrasonic heating module to heat with microwaves, heat is generated by the rapid vibration of water molecules in the microwave field, which can quickly increase the water temperature. Compared with traditional resistance heating, microwave heating can heat water to the required temperature more quickly, thus shortening the heating time; The temperature monitoring module is used to monitor the temperatures of the steam module and the cooking appliance; The cooking data collection module collects the user's cooking data, including the type of ingredients, cooking time, temperature setting and user preferences. The data collection module works in cooperation with the temperature monitoring module to ensure the comprehensiveness and accuracy of the data; The data analysis module deeply analyzes the collected cooking data, identifies the user's cooking habits and preferences, and uses big data analysis technology to extract valuable information to support personalized cooking suggestions and optimization solutions; The model prediction module predicts the cooking time and energy consumption based on historical data and real-time sensor data, and dynamically adjusts the heating strategy, using machine learning algorithms and prediction models to improve the prediction accuracy and the system response speed; The dynamic adjustment of the heating strategy automatically adjusts the heating power, steam flow rate and heating time parameters according to real-time sensor data and the prediction model, and uses intelligent control algorithms to achieve precise temperature control and energy consumption optimization; The waste heat recovery module recovers the waste heat generated during the cooking process for preheating the water entering the steam generator or other uses, and uses an efficient heat exchanger to ensure the effective recovery and utilization of the waste heat.

2. The superheated steam system of a cooking appliance according to claim 1, wherein: The preheating box is used to store and preheat the water entering the steam generator, and works in cooperation with the waste heat recovery module to preheat the water by using the recovered waste heat to improve the heating efficiency.

3. The superheated steam system of a cooking appliance according to claim 1, wherein: The display module provides a user interface to display cooking parameters, cooking progress and energy consumption information, and uses a touch screen and a graphical user interface to provide an intuitive operation experience and real-time feedback.

4. A superheated steam system for a cooking appliance according to claim 1, wherein: The cooking appliance includes basic components such as a steam generator, a cooking cavity, and steam nozzles, and is used to generate and utilize superheated steam for cooking.

5. A temperature control method for an overheated steam system of a cooking appliance, using an overheated steam system of a cooking appliance according to any one of claims 1 to 4, characterized in that: Including the following steps: S1. Data collection. The cooking data collection module collects the user's cooking data, including the type of ingredients, cooking time, temperature setting and user preferences. The temperature monitoring module monitors the temperatures of the steam module and the cooking appliance in real time and transmits the data to the data analysis module; S2. Data analysis. The data analysis module deeply analyzes the collected data, identifies the user's cooking habits and preferences, and uses big data analysis technology to extract valuable information to support personalized cooking suggestions and optimization solutions; S3. Heating. The ultrasonic heating module and the steam module work together to quickly heat the preheated water to the superheated steam state. The ultrasonic heating module uses microwave heating technology to generate heat through the rapid vibration of water molecules in the microwave field, shortening the heating time; S4. Real-time monitoring: The temperature monitoring module monitors the temperature, humidity, and steam pressure parameters during the cooking process in real time and transmits the data to the model prediction module. S5. Prediction and adjustment: The model prediction module predicts the cooking time and energy consumption based on historical data and real-time sensor data. According to the prediction results, it dynamically adjusts the heating power, steam flow rate, and heating time parameters, and uses intelligent control algorithms to achieve precise temperature control and energy consumption optimization. S6. User interface: The display module provides a user interface to display cooking parameters, cooking progress, and energy consumption information. Users can monitor the cooking process in real time and adjust parameters through the touch screen and graphical user interface. S7. Feedback and optimization: Users can receive personalized cooking suggestions and optimization plans provided by the system. The system further optimizes the cooking parameters and heating strategies based on the user's feedback and cooking results. S8. Preheating: The waste heat recovery module recovers the waste heat generated during the cooking process. The preheating tank uses the recovered waste heat to preheat the water entering the steam generator, preheating the food to be cooked subsequently, thereby improving the heating efficiency.

6. A temperature control method for an overheated steam system of a cooking appliance according to claim 5, characterized in that: The waste heat recovery module further includes a heat exchanger, a control valve, sensors, and a heat storage device. During the cooking process, the steam generator generates superheated steam for cooking. After the steam releases heat in the cooking cavity, it will condense into water, and a large amount of latent heat will be released during this condensation process. This part of the heat is the source of waste heat. In addition, the exhaust gas generated during the cooking process also contains recoverable heat. The waste heat recovery module captures this waste heat through the heat exchanger. The heat exchanger is usually installed near the exhaust port of the steam generator or the cooking cavity to capture the waste heat to the maximum extent.

7. A temperature control method for an overheated steam system of a cooking appliance according to claim 5, characterized in that: The heat exchanger is internally designed with multiple channels. The waste heat fluid and the medium to be heated flow through these channels respectively. Through heat conduction and convection, the waste heat fluid transfers heat to the medium to be heated. The specific process is as follows: A1. High-temperature steam or air passes through the hot-side channel of the heat exchanger and transfers its heat to the metal wall of the heat exchanger. A2. The medium to be heated passes through the cold-side channel of the heat exchanger and absorbs the heat transferred from the metal wall, resulting in a temperature increase.

8. A temperature control method for an overheated steam system of a cooking appliance according to claim 5, characterized in that: The model prediction module predicts the cooking time and energy consumption based on historical data and real-time sensor data. The detailed steps according to the prediction results are as follows: B1. Data collection: Collect historical cooking data, including food ingredient types, cooking time, temperature settings, user preferences, real-time sensor data, current temperature, current humidity, steam pressure, and ambient temperature. B2. Data preprocessing: Data cleaning, removing missing values, outliers, and noise data, normalizing data with different dimensions, and extracting and selecting features useful for the prediction task, such as time features, temperature features, and sensor features. B3. Model selection: Select a suitable machine learning model for prediction. The decision tree model can handle multi-classification problems. B4. Model training: Use historical data for model training and adjust the model parameters to minimize the prediction error. B5. Real-time prediction: Based on real-time sensor data and current user input, use the trained model to predict cooking time and energy consumption, predicting the time required for the current ingredients to reach the target temperature at the current temperature, and the corresponding energy consumption.

9. A method for controlling the temperature of an overheated steam system of a cooking appliance according to claim 5, characterized in that: The detailed steps for the dynamic adjustment to automatically optimize the heating power, steam flow rate, and heating time parameters according to the heating strategy predicted by the model to achieve precise temperature control and energy consumption optimization are as follows: Q1. Predict cooking time and energy consumption: The model prediction module predicts the remaining time and energy consumption required for the current cooking task based on real-time sensor data and historical data, and uses the LSTM model to predict the temperature change trend in the future for a period of time; Q2. Set target parameters: Set target parameters, target temperature, target humidity, and target steam pressure according to user settings and prediction results; Q3. Calculate the error: Calculate the error between the current temperature and the target temperature, and its algorithm formula is as follows: e(t) = T target -T current ; Q4. Select the control algorithm: According to system requirements and real-time data, select PID control. The proportional control of the PID: P(t) = K p ·e(t) Integral control: Derivative control: Total control output: u(t) = P(t) + I(t) + D(t) Adjust the heating power. According to the control output u(t), adjust the heating power P heat : Among them, P max is the maximum heating power, u min and u max are the minimum and maximum values of the control output; Adjust the steam flow rate According to the heating power and the target steam pressure, adjust the steam flow rate F steam : F steam = T remain + Δt where Δt is the adjustment time increment.

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