A control method and system for an Internet of Things air fryer

Abstract

The present invention discloses a control method and system for an Internet of Things air fryer, the method comprising: receiving and identifying the food material information of food to be cooked, setting the cooking parameters of the Internet of Things air fryer according to the food material information; cooking the food to be cooked based on the current cooking parameters, and monitoring the cooking state change of the food in real time; dynamically adjusting the current cooking parameters according to the cooking state change, and calculating the cooking quality index of the cooked food at the end of cooking; if the cooking quality index does not reach a preset threshold, adjusting the cooking time, continuing to perform the step of cooking the food to be cooked based on the current cooking parameters, until the cooking quality index reaches the preset threshold, ending cooking, and uploading the cooked food, cooking parameters and cooking quality index to the cloud in real time through the Internet of Things. By using the embodiments of the present invention, the intelligence and automation of the cooking process can be ensured, and a more personalized cooking experience can be provided to users through cloud data analysis.

Description

Technical Field

[0001] The present invention belongs to the technical field of air fryers, and in particular to a control method and system for an air fryer of the Internet of Things. Background Art

[0002] With the advancement of science and technology and the improvement of living standards, home appliances are developing in the direction of intelligence, automation and health. In the field of cooking equipment, traditional cooking methods are gradually being replaced by more efficient and convenient technologies. In particular, air fryers are widely popular due to their convenience and relatively healthy cooking methods. Air fryers use hot air circulation technology to make food have a similar flavor and taste to fried food without or with little oil, thereby reducing oil intake, which is in line with modern people's pursuit of healthy diet.

[0003] However, existing air fryer technology still has some limitations. First, users usually need to manually set the cooking temperature and time when using an air fryer, which requires high professional knowledge of different ingredients and personal tastes. Second, traditional air fryers lack real-time monitoring and adjustment functions, which may lead to unstable cooking results. Summary of the invention

[0004] The purpose of the present invention is to provide a control method and system for an Internet of Things air fryer to solve the deficiencies in the prior art. It can utilize technologies such as food identification, real-time monitoring, intelligent adjustment and cooking quality evaluation to ensure the intelligence and automation of the cooking process, and provide users with a more personalized cooking experience through cloud data analysis.

[0005] An embodiment of the present application provides a control method for an Internet of Things air fryer, the method comprising:

[0006] Receive and identify food information of food to be cooked, and set cooking parameters of the IoT air fryer according to the food information;

[0007] Cook the food to be cooked based on the current cooking parameters and monitor the cooking state changes of the food in real time;

[0008] dynamically adjusting current cooking parameters according to the cooking state changes, and calculating a cooking quality index of the cooked food at the end of cooking;

[0009] If the cooking quality index does not reach the preset threshold, the cooking time is adjusted, and the steps of cooking the food to be cooked based on the current cooking parameters and monitoring the changes in the cooking state of the food in real time are continued until the cooking quality index reaches the preset threshold. Cooking is terminated, and the cooked food, cooking parameters and cooking quality index are uploaded to the cloud in real time via the Internet of Things.

[0010] Optionally, the cooking parameters are adjusted as follows:

[0011] P_aft=P_cur·e^{-(alpha·DeltaT+beta·Delta H+gamma

[0012] ·DeltaV+delta·Delta C)}

[0013] Among them, the P_aft is the adjusted cooking parameter, the P_cur is the current cooking parameter, the Delta T, the Delta H and the Delta V are the temperature change, humidity change and volume change since the last adjustment, respectively, and the alpha, the beta and the gamma are the corresponding adjustment coefficients.

[0014] Optionally, the cooking quality index is calculated as follows:

[0015] CQI=omega_a·A+omega_n·N+omega_q·Q+omega_t·T

[0016] Among them, the CQI is a cooking quality index, A is an appearance index, N is a nutrient retention index, Q is a physical change index, T is a temperature distribution index, and omega_a, omega_n, omega_q, and omega_t are corresponding weighting coefficients.

[0017] Optionally, the calculation formula of the appearance index is:

[0018] A=(C_L+C_T+C_B) / 3

[0019] Among them, the C_L is the color score, the C_T is the texture score, and the C_B is the burnt degree score.

[0020] Optionally, the calculation formula of the nutrient retention index is:

[0021] N=(1-(Delta V+Delta M) / 2)·100

[0022] Wherein, the Delta V is the vitamin loss rate, and the Delta M is the mineral loss rate.

[0023] Optionally, the calculation formula of the physical change index is:

[0024] Q=lambda_w·Delta W / W_0+lambda_v·Delta V / V_0+lambda_c

[0025] Delta C

[0026] Among them, the Delta W is the weight change, the W_0 is the initial weight, the Delta V is the volume change, the V_0 is the initial volume, the Delta C is the crystallinity change, and the lambda_w, the lambda_v, and the lambda_c are corresponding weighting coefficients.

[0027] Optionally, the calculation formula of the temperature distribution index is:

[0028] T=eta_c·(T_c / T_s)+eta_u·U

[0029] Among them, T_c is the center temperature, T_s is the set temperature, U is the uniformity of heat distribution, and eta_c and eta_u are corresponding weighting coefficients.

[0030] Another embodiment of the present application provides a control system for an Internet of Things air fryer, the system comprising:

[0031] A receiving module, used to receive and identify the food information of the food to be cooked, and set the cooking parameters of the IoT air fryer according to the food information;

[0032] A cooking module, used to cook food to be cooked based on current cooking parameters and monitor changes in the cooking state of the food in real time;

[0033] a calculation module, used for dynamically adjusting current cooking parameters according to the cooking state change, and calculating a cooking quality index of the cooked food at the end of cooking;

[0034] The adjustment module is used to adjust the cooking time when the cooking quality index does not reach a preset threshold, continue to cook the food to be cooked based on the current cooking parameters, and monitor the changes in the cooking state of the food in real time until the cooking quality index reaches the preset threshold, end the cooking, and upload the cooked food, cooking parameters and cooking quality index to the cloud in real time through the Internet of Things.

[0035] Yet another embodiment of the present application provides a storage medium, wherein the storage medium stores a computer program, wherein the computer program is configured to execute any of the above methods when running.

[0036] Yet another embodiment of the present application provides an electronic device, including a memory and a processor, wherein the memory stores a computer program, and the processor is configured to run the computer program to execute any of the methods described above.

[0037] Compared with the prior art, the present invention provides a control method for an Internet of Things air fryer, which receives and identifies ingredient information of food to be cooked, sets cooking parameters of the Internet of Things air fryer according to the ingredient information; cooks the food to be cooked based on the current cooking parameters, and monitors changes in the cooking state of the food in real time; dynamically adjusts the current cooking parameters according to the changes in the cooking state, and calculates a cooking quality index of the cooked food at the end of cooking; if the cooking quality index does not reach a preset threshold, adjusts the cooking time, continues to cook the food to be cooked based on the current cooking parameters, and monitors changes in the cooking state of the food in real time until the cooking quality index reaches the preset threshold, ends cooking, and uploads the cooked food, cooking parameters, and cooking quality index to the cloud in real time through the Internet of Things, so that technologies such as ingredient recognition, real-time monitoring, intelligent adjustment, and cooking quality evaluation can be used to ensure the intelligence and automation of the cooking process, and provide users with a more personalized cooking experience through cloud data analysis. BRIEF DESCRIPTION OF THE DRAWINGS

[0038] Figure 1 A hardware structure block diagram of a computer terminal for a control method of an Internet of Things air fryer provided by an embodiment of the present invention;

[0039] Figure 2 A schematic flow chart of a control method for an Internet of Things air fryer provided by an embodiment of the present invention;

[0040] Figure 3 A schematic structural diagram of a control system of an Internet of Things air fryer provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0041] The embodiments described below with reference to the accompanying drawings are exemplary and are only used to explain the present invention, but should not be construed as limiting the present invention.

[0042] The embodiment of the present invention first provides a control method for an Internet of Things air fryer, which can be applied to electronic devices such as computer terminals, specifically ordinary computers, etc.

[0043] The following describes it in detail by taking running on a computer terminal as an example. Figure 1 The hardware structure block diagram of a computer terminal of a control method for an Internet of Things air fryer provided by an embodiment of the present invention. Figure 1 As shown, the computer terminal may include one or more ( Figure 1Only one is shown in the figure) a processor 102 (the processor 102 may include but is not limited to a processing device such as a microprocessor MCU or a programmable logic device FPGA) and a memory 104 for storing data. Optionally, the computer terminal may also include a transmission device 106 for communication functions and an input and output device 108. It can be understood by those skilled in the art that Figure 1 The structure shown is only for illustration and does not limit the structure of the above-mentioned computer terminal. Figure 1 More or fewer components as shown, or with Figure 1 Different configurations shown.

[0044] The memory 104 can be used to store software programs and modules of application software, such as program instructions / modules corresponding to the control method of the Internet of Things air fryer in the embodiment of the present application. The processor 102 executes various functional applications and data processing by running the software programs and modules stored in the memory 104, that is, implementing the above method. The memory 104 may include a high-speed random access memory, and may also include a non-volatile memory, such as one or more magnetic storage devices, flash memory, or other non-volatile solid-state memory. In some instances, the memory 104 may further include a memory remotely arranged relative to the processor 102, and these remote memories may be connected to the computer terminal via a network. Examples of the above-mentioned network include, but are not limited to, the Internet, an intranet, a local area network, a mobile communication network, and combinations thereof.

[0045] The transmission device 106 is used to receive or send data via a network. The specific example of the above network may include a wireless network provided by a communication provider of a computer terminal. In one example, the transmission device 106 includes a network adapter (Network Interface Controller, NIC), which can be connected to other network devices through a base station so as to communicate with the Internet. In one example, the transmission device 106 can be a radio frequency (RF) module, which is used to communicate with the Internet wirelessly.

[0046] See also Figure 2 , an embodiment of the present invention provides a control method for an Internet of Things air fryer, which may include the following steps:

[0047] S201, receiving and identifying the food information of the food to be cooked, and setting the cooking parameters of the IoT air fryer according to the food information;

[0048] Specifically, in order to receive and identify food information, the IoT air fryer is equipped with one or more sensors, such as image recognition cameras, weight sensors, RFID (Radio-Frequency Identification) readers, etc. These sensors can capture key attributes of food, such as size, shape, weight, barcode or RFID information. After receiving the food data, the air fryer processes and analyzes the information through a built-in microprocessor or connected to a cloud server. Using advanced image recognition and machine learning algorithms, the system can accurately identify the type of food, expected quality, and other relevant characteristics. In addition, the database in the system contains a large number of cooking parameters for different ingredients, such as temperature, time, and wind speed, which are optimized based on empirical data and expert advice. One implementation method may include:

[0049] 1. Ingredient image recognition: When the user puts the ingredients into the air fryer, the built-in camera captures the image of the ingredients. A convolutional neural network (CNN) model is used for image processing and ingredient recognition. CNN has been pre-trained to recognize common ingredients, and the recognition accuracy can be continuously optimized through deep learning.

[0050] 2. Ingredient weight measurement: The weight sensor detects the weight of the ingredients. This information is particularly important for determining cooking time and temperature, because ingredients of different weights may require different cooking conditions.

[0051] 3. RFID information reading: If the food packaging has an RFID tag, the air fryer's RFID reader can scan and obtain the information embedded in the tag, such as food type, weight, origin and optimal cooking conditions.

[0052] 4. Food information processing: The identified food information is processed by the built-in microprocessor or sent to the connected cloud server. On the cloud server, the most suitable cooking parameters are retrieved from the database based on the food information.

[0053] 5. Cooking parameter setting: The retrieved cooking parameters, including the required cooking time, temperature, and wind speed, are sent back to the IoT air fryer. The air fryer automatically sets the cooking mode based on these parameters.

[0054] 6. User Interface Interaction: After all parameters are set, the user interface displays the estimated cooking time and a start button. The user can choose to start cooking or adjust parameters (if they have specific cooking preferences).

[0055] The IoT air fryer that applies this automation technology algorithm can greatly simplify the user's operation process, while ensuring that the cooking results are more consistent and in line with personal tastes through data-driven optimization. Through truly intelligent operation, the IoT air fryer can not only improve the cooking quality, but also enhance the user's interactive experience, making cooking easier and more enjoyable.

[0056] S202, cooking the food to be cooked based on the current cooking parameters, and monitoring the cooking state changes of the food in real time;

[0057] Specifically, this step involves the implementation and monitoring of the food cooking process, with the goal of ensuring the consistency and real-time feedback of the cooking process. When cooking food, the IoT air fryer monitors and records various cooking state parameters of the food, such as temperature, humidity, weight and volume changes, through multiple built-in sensors. These data are not only used to adjust the cooking parameters to obtain the ideal cooking effect, but are also crucial for food quality analysis in subsequent steps. One implementation method may include:

[0058] 1. Temperature monitoring: The IoT air fryer is equipped with an accurate temperature sensor that can monitor the temperature in the cooking cavity in real time. The data collected by the sensor is transmitted to the cloud for analysis through a built-in microprocessor or wirelessly, ensuring that the food is always in the optimal temperature environment throughout the cooking process.

[0059] 2. Humidity monitoring: In addition to temperature, humidity is also one of the key parameters for regulating the cooking process. The humidity sensor in the air fryer can monitor the humidity changes in the cooking chamber in real time, which is especially important for certain ingredients that need to maintain moisture.

[0060] 3. Weight and volume change monitoring: Use load sensors and 3D volume measurement technology to track the weight and volume changes of food during the cooking process. Weight loss usually means the loss of water and fat, while volume changes may reflect the expansion or shrinkage of food, which are important indicators of cooking quality.

[0061] 4. Data analysis and processing: All real-time monitored data is fed into a processing unit, which may be a microcontroller inside the air fryer or a cloud server. Here, an automated algorithm compares the real-time data with the preset parameters to analyze whether adjustments are needed during the cooking process.

[0062] 5. Real-time adjustment of parameters: If the monitored data shows that the cooking status deviates from the optimal conditions, the system will automatically adjust the cooking parameters such as temperature, time or wind speed. For example, if the food temperature is lower than expected, the air fryer can increase the output of the heating element, or extend the cooking time to compensate.

[0063] 6. Continuous feedback loop: The above monitoring and adjustment is a continuous process. The system continues to collect and analyze data during the entire cooking process. This ensures that the cooking parameters are always synchronized with the actual state of the food, thereby optimizing the cooking results.

[0064] 7. Cloud and local collaboration: Optionally, the system can send real-time data to the cloud to optimize the adjustment strategy of cooking parameters through large-scale data processing and advanced machine learning models. At the same time, data collection in the cloud can help machine learning models continue to learn and improve, providing more accurate predictions for future cooking.

[0065] Through this constant real-time monitoring and intelligent adjustments, the IoT air fryer can automatically adjust during the cooking process to adapt to the real-time state of the ingredients. This approach not only improves cooking quality, but also reduces the need for user intervention, thus achieving a truly intelligent cooking experience.

[0066] S203, dynamically adjusting the current cooking parameters according to the cooking state change, and calculating the cooking quality index of the cooked food at the end of cooking;

[0067] Adjust cooking parameters. The design significance is to dynamically adjust the cooking settings of the air fryer according to the real-time changes of temperature, humidity, volume and other key parameters (such as color changes) during the cooking process to achieve the best cooking results.

[0068] Specifically, a cooking parameter adjustment method is:

[0069] P_aft=P_cur·e^{-(alpha·DeltaT+beta·Delta H+gamma

[0070] ·DeltaV+delta·Delta C)}

[0071] Among them, the P_aft is the adjusted cooking parameter, the P_cur is the current cooking parameter, the Delta T, the Delta H and the Delta V are the temperature change, humidity change and volume change since the last adjustment, respectively, and the alpha, the beta and the gamma are the corresponding adjustment coefficients.

[0072] -alpha (α): This coefficient reflects the degree of influence of temperature changes (Delta T) on cooking parameters. High temperature may speed up the cooking of food, while low temperature may require longer cooking time. The alpha value can be determined through experimental data analysis or set based on the characteristics of the ingredients and cooking experience. Example values ​​may be between 0.1 and 1, depending on the cooking characteristics of the ingredients.

[0073] - beta (β): An adjustment factor for humidity variation (Delta H), which represents the effect of humidity on the cooking effect during the cooking process. For example, higher humidity may make food more juicy, while lower humidity may accelerate dehydration. The determination of beta is also based on experimental data or expert experience, and example values ​​may be in the range of 0.05 to 0.5.

[0074] -gamma (γ): The adjustment factor of volume change (Delta V), which reflects the significance of food volume change on the adjustment of cooking parameters. The increase or decrease of food volume may mean the essential change of food state, which has an impact on cooking time and temperature. The specific value of gamma can be determined through experiments based on different ingredients and cooking methods, and may vary between 0.1 and 1.

[0075] -delta (δ): represents the adjustment factor for the effect of other key parameters (such as color change Delta C) on the cooking parameters. Color change may be related to the doneness of the food, thus affecting the required cooking time or temperature. The value of delta is set according to the type of food and the importance of color change during cooking. Example values ​​may be 0.1 to 0.5.

[0076] The e^(-x) in the formula is an exponential decay function, which is used to adjust the cooking parameters in a nonlinear manner, providing a smooth adjustment process to prevent sudden changes.

[0077] Specifically, the calculation formula of the cooking quality index (CQI) is to comprehensively evaluate the quality of cooked food. It takes into account multiple factors that affect the sensory and nutritional value of food, and combines these factors through weight coefficients to obtain a quantitative indicator. This indicator is intended to reflect the overall success of the cooking process. A calculation method of the cooking quality index can be:

[0078] CQI=omega_a·A+omega_n·N+omega_q·Q+omega_t·T

[0079] Among them, the CQI is a cooking quality index, A is an appearance index, N is a nutrient retention index, Q is a physical change index, T is a temperature distribution index, and omega_a, omega_n, omega_q, and omega_t are corresponding weighting coefficients.

[0080] -A (Appearance Index): Evaluates the appearance of food after cooking, including color, texture, and whether it is burnt. Appearance directly affects the attractiveness and appetite of food. Determination method and sample value: Usually scored by experts or using instruments such as color analyzers to comprehensively evaluate the score. The sample value can be in the range of 0-10.

[0081] -N (Nutrient Retention Index): Measures the retention of nutrients in food during cooking, especially heat-sensitive vitamins and minerals. Determination method and sample values: The proportion of nutrient loss can be determined by chemical analysis, and the sample value is expressed as a percentage, and a value close to 100% means that the nutrients are very well retained.

[0082] -Q (Physical Change Index): Evaluates changes in the physical properties of food, such as changes in weight, volume, and crystallinity. These physical changes can reflect the degree of cooking and taste of the food. Determination method and sample value: The change is calculated from the measured data. The sample value is usually a negative number, indicating a decrease in the physical quantity.

[0083] -T (Temperature Distribution Index): Measures the uniformity of the temperature distribution inside the food and its consistency with the set temperature. Temperature distribution has a great impact on the degree of doneness of the food and the balance of cooking. Determination method and sample value: The internal temperature distribution data is obtained through the temperature sensor. The sample value can be in the range of 0-1, where 1 means perfect temperature consistency and uniformity.

[0084] - omega_a, omega_n, omega_q, omega_t (weighting coefficient): represents the relative importance of each indicator to the overall cooking quality. Determination method and example value: can be determined based on expert experience or consumer surveys, and the example value can be between 0 and 1, and the sum is 1.

[0085] Among them, the appearance index (A) is used to quantify the appearance characteristics of food after cooking, which includes the color, texture and degree of burntness of the food. This index reflects the attractiveness and sensory quality of food, which are important factors in people's choice and enjoyment of food. A calculation formula for the appearance index can be:

[0086] A=(C_L+C_T+C_B) / 3

[0087] Among them, the C_L is the color score, the C_T is the texture score, and the C_B is the burnt degree score.

[0088] - C_L (color score): measures the beauty of food color and whether it meets expectations. Determination method and example value: objective evaluation can be performed using a colorimeter or visual evaluation. Example values ​​range from 0 (very bad) to 10 (perfect).

[0089] - C_T (texture score): evaluates the texture of the food surface or cut surface. Determination method and example value: It may be judged by hand or visually, or it can be quantitatively measured by texture analysis equipment. The example value is also between 0 and 10.

[0090] - C_B (burnt degree score): detects the degree of burntness of food, which is usually the result of cooking for too long. Determination method and example value: The score can be given by observing or tasting the food, and the example value ranges from 0 (completely burnt) to 10 (not burnt).

[0091] Among them, the nutrient retention index (N) is used to measure the retention of food nutrients during the cooking process, with a special focus on vitamins and minerals that are easily lost during the heating process. A calculation formula for the nutrient retention index can be:

[0092] M=(1-(Delta V+Delta M) / 2)·100

[0093] Wherein, the Delta V is the vitamin loss rate, and the Delta M is the mineral loss rate.

[0094] -ΔV (vitamin loss rate): reflects the percentage of vitamin content reduction during cooking. Determination method and example values: obtained through nutrient composition analysis, the example value range may be 0% (no loss) to 100% (complete loss).

[0095] -ΔM (mineral loss rate): Similar to vitamin loss rate, but for minerals. Determination method and example values: Also obtained through chemical analysis, the example values ​​are the same as vitamin loss rate.

[0096] -N is calculated by averaging the vitamin and mineral loss rates, then subtracting that value from 1 and multiplying by 100 to get the percentage of nutrient retained.

[0097] Among them, the physical change index (Q) evaluates the changes in weight, volume and other physical properties of food during the cooking process. These changes can affect the taste and appearance of the food and reflect the degree of cooking. A calculation formula for the physical change index can be:

[0098] Q=lambda_w·Delta W / W_0+lambda_v·Delta V / V_0+lambda_c

[0099] delta C

[0100] Among them, the Delta W is the weight change, the W_0 is the initial weight, the Delta V is the volume change, the V_0 is the initial volume, the Delta C is the crystallinity change, and the lambda_w, the lambda_v, and the lambda_c are corresponding weighting coefficients.

[0101] -ΔW (weight change): indicates the difference in weight of food before and after cooking. Determination method and sample value: measured by a precision scale. The sample value can be a negative number, indicating a decrease in weight.

[0102] -W_0 (initial weight): The weight of the food before cooking. Determination method and example value: Also obtained by weighing, with different example values ​​depending on the food.

[0103] -ΔV (volume change): represents the change in volume of food before and after cooking. Determination method and example value: can be obtained by measurement or calculation, and the example value depends on the type of food.

[0104] - V_0 (initial volume): The initial volume of the food. Determination method and example value: Determined by volume measurement. The specific example value depends on the characteristics of the food.

[0105] -ΔC (crystallinity change): reflects the changes in food structure during the cooking process, such as gelatinization of starch or denaturation of protein. Determination method and example value: obtained by methods such as X-ray diffraction or infrared spectroscopy analysis, and the example value is the percentage of the change rate.

[0106] - lambda_w, lambda_v, lambda_c (weighting coefficients): describe the weight of each physical variable on the Q value. Determination method and sample value: based on the importance of food characteristics and physical changes during the cooking process, the sample value is between 0-1, depending on the importance.

[0107] Among them, the temperature distribution index (T) is used to evaluate the uniformity of the temperature inside the food and the degree of compliance between the actual temperature and the set temperature. Uniform temperature distribution is essential to ensure the uniformity and quality of food cooking. A calculation formula for the temperature distribution index can be:

[0108] T=eta_c·(T_c / T_s)+eta_u·U

[0109] Among them, T_c is the center temperature, T_s is the set temperature, U is the uniformity of heat distribution, and eta_c and eta_u are corresponding weighting coefficients.

[0110] -T_c (core temperature): The temperature at the center of the food. Determination method and example value: Measured using a piercing temperature sensor. Example values ​​may vary depending on the food.

[0111] -T_s (set temperature): The target temperature set for the air fryer. Determination method and example value: Set according to the cooking guide or recipe, and the example value depends on the cooking requirements.

[0112] -U (heat distribution uniformity): indicates the uniformity of temperature distribution. Determination method and example value: obtained through multi-point temperature measurement analysis, 0 means completely uneven, 1 means completely uniform.

[0113] -eta_c, eta_u (weighting coefficients): define the relative importance of T_c and U in calculating T. Determination method and example value: set according to the detailed requirements of the cooking process and the characteristics of the food, and the example value is between 0-1.

[0114] This step is one of the most critical steps in the cooking process. It not only monitors the cooking status of the food in real time and makes corresponding adjustments to ensure the cooking quality, but also calculates the cooking quality index (CQI) of the food at the end of cooking. CQI covers multiple aspects such as appearance, nutrient retention, physical changes and temperature distribution, and it provides a quantitative indicator to evaluate the overall cooking effect.

[0115] S204, if the cooking quality index does not reach the preset threshold, adjust the cooking time, continue to cook the food to be cooked based on the current cooking parameters, and monitor the changes in the cooking state of the food in real time until the cooking quality index reaches the preset threshold, end cooking, and upload the cooked food, cooking parameters and cooking quality index to the cloud in real time through the Internet of Things.

[0116] Specifically, this step ensures that the cooking quality meets the user's expectations or preset standards through an iterative process. This method allows the air fryer to flexibly respond to various variables during the cooking process, such as ingredient size, initial temperature, humidity, etc. This method achieves a highly automated and personalized cooking experience by cyclically monitoring and adjusting until the preset cooking effect is achieved. The adaptability of this process is based on real-time data and pre-programmed algorithms, rather than a single static cooking program, which can significantly improve the consistency and reliability of cooking results. One implementation method may include:

[0117] Step 1: Real-time monitoring of cooking status

[0118] -Continuously monitor key parameters such as temperature, moisture, volume and color while food is cooking using built-in sensors or connected sensing devices.

[0119] -The monitored data is transmitted in real time to the microprocessor of the air fryer or uploaded to the cloud server through the Internet of Things.

[0120] Step 2: Evaluate the Cooking Quality Index (CQI)

[0121] - According to the above formula, the CQI under the current cooking status is calculated by the microprocessor or the cloud.

[0122] -Determine whether the current CQI has reached the preset cooking quality threshold.

[0123] Step 3: Adjust cooking time

[0124] - If the CQI does not reach the preset threshold, the system will adjust the cooking time according to the size of the gap and the trend. For example, if the CQI is much lower than the threshold, the cooking time will be increased accordingly; if it is close to the threshold, the cooking time will be increased appropriately.

[0125] - Adjusting cooking parameters, in particular cooking time, can be done according to a pre-set function or an algorithm based on historical data. For example, a linear or non-linear time adjustment strategy can be used.

[0126] Step 4: Continue cooking and monitor in real time

[0127] - Continue cooking the food to be cooked based on the current cooking parameters.

[0128] -At the same time, repeat step 1 to monitor the changes in the cooking status of the food in real time.

[0129] Step 5: Iterate until the preset threshold is reached

[0130] - Loop through steps 2 to 4 until the CQI reaches or exceeds a preset threshold.

[0131] - Throughout the process, the system continuously records changes in cooking parameters and CQI and refers to this data during the iteration process to optimize the adjustment of cooking time.

[0132] Step 6: Finish cooking and upload data

[0133] - Once the CQI reaches the preset threshold, end cooking.

[0134] - Upload the cooked food, cooking parameters and cooking quality index to the cloud in real time through the Internet of Things, so that this data can be accessed by users and provided for data analysis to optimize the parameter settings of future cooking.

[0135] This continuous adjustment and feedback loop enables the IoT air fryer to intelligently adapt to various cooking needs and provide users with a continuously improved cooking experience through analysis of cloud data.

[0136] It can be seen that the ingredient information of the food to be cooked is received and identified, and the cooking parameters of the IoT air fryer are set according to the ingredient information; the food to be cooked is cooked based on the current cooking parameters, and the cooking state changes of the food are monitored in real time; the current cooking parameters are dynamically adjusted according to the cooking state changes, and the cooking quality index of the cooked food is calculated at the end of cooking; if the cooking quality index does not reach the preset threshold, the cooking time is adjusted, and the steps of cooking the food to be cooked based on the current cooking parameters and monitoring the cooking state changes of the food in real time are continued until the cooking quality index reaches the preset threshold, and cooking is ended, and the cooked food, cooking parameters and cooking quality index are uploaded to the cloud in real time through the IoT, so that technologies such as ingredient identification, real-time monitoring, intelligent adjustment and cooking quality evaluation can be used to ensure the intelligence and automation of the cooking process, and through cloud data analysis, a more personalized cooking experience is provided to users.

[0137] Another embodiment of the present invention provides a control system for an Internet of Things air fryer, see Figure 3 , the system may include:

[0138] The receiving module 301 is used to receive and identify the food material information of the food to be cooked, and set the cooking parameters of the IoT air fryer according to the food material information;

[0139] A cooking module 302, used to cook the food to be cooked based on the current cooking parameters and monitor the cooking state changes of the food in real time;

[0140] A calculation module 303, used for dynamically adjusting the current cooking parameters according to the cooking state change, and calculating the cooking quality index of the cooked food at the end of cooking;

[0141] The adjustment module 304 is used to adjust the cooking time when the cooking quality index does not reach the preset threshold, continue to cook the food to be cooked based on the current cooking parameters, and monitor the changes in the cooking state of the food in real time until the cooking quality index reaches the preset threshold, end cooking, and upload the cooked food, cooking parameters and cooking quality index to the cloud in real time through the Internet of Things.

[0142] It can be seen that the ingredient information of the food to be cooked is received and identified, and the cooking parameters of the IoT air fryer are set according to the ingredient information; the food to be cooked is cooked based on the current cooking parameters, and the cooking state changes of the food are monitored in real time; the current cooking parameters are dynamically adjusted according to the cooking state changes, and the cooking quality index of the cooked food is calculated at the end of cooking; if the cooking quality index does not reach the preset threshold, the cooking time is adjusted, and the steps of cooking the food to be cooked based on the current cooking parameters and monitoring the cooking state changes of the food in real time are continued until the cooking quality index reaches the preset threshold, and cooking is ended, and the cooked food, cooking parameters and cooking quality index are uploaded to the cloud in real time through the IoT, so that technologies such as ingredient identification, real-time monitoring, intelligent adjustment and cooking quality evaluation can be used to ensure the intelligence and automation of the cooking process, and through cloud data analysis, a more personalized cooking experience is provided to users.

[0143] An embodiment of the present invention further provides a storage medium, in which a computer program is stored, wherein the computer program is configured to execute the steps of any of the above method embodiments when running.

[0144] Specifically, in this embodiment, the above storage medium may be configured to store a computer program for performing the following steps:

[0145] S201, receiving and identifying the food information of the food to be cooked, and setting the cooking parameters of the IoT air fryer according to the food information;

[0146] S202, cooking the food to be cooked based on the current cooking parameters, and monitoring the cooking state changes of the food in real time;

[0147] S203, dynamically adjusting the current cooking parameters according to the cooking state change, and calculating the cooking quality index of the cooked food at the end of cooking;

[0148] S204, if the cooking quality index does not reach the preset threshold, adjust the cooking time, continue to cook the food to be cooked based on the current cooking parameters, and monitor the changes in the cooking state of the food in real time until the cooking quality index reaches the preset threshold, end cooking, and upload the cooked food, cooking parameters and cooking quality index to the cloud in real time through the Internet of Things.

[0149] It can be seen that the ingredient information of the food to be cooked is received and identified, and the cooking parameters of the IoT air fryer are set according to the ingredient information; the food to be cooked is cooked based on the current cooking parameters, and the cooking state changes of the food are monitored in real time; the current cooking parameters are dynamically adjusted according to the cooking state changes, and the cooking quality index of the cooked food is calculated at the end of cooking; if the cooking quality index does not reach the preset threshold, the cooking time is adjusted, and the steps of cooking the food to be cooked based on the current cooking parameters and monitoring the cooking state changes of the food in real time are continued until the cooking quality index reaches the preset threshold, and cooking is ended, and the cooked food, cooking parameters and cooking quality index are uploaded to the cloud in real time through the IoT, so that technologies such as ingredient identification, real-time monitoring, intelligent adjustment and cooking quality evaluation can be used to ensure the intelligence and automation of the cooking process, and through cloud data analysis, a more personalized cooking experience is provided to users.

[0150] An embodiment of the present invention further provides an electronic device, including a memory and a processor, wherein the memory stores a computer program, and the processor is configured to run the computer program to execute the steps in any one of the above method embodiments.

[0151] Specifically, the electronic device may further include a transmission device and an input / output device, wherein the transmission device is connected to the processor, and the input / output device is connected to the processor.

[0152] Specifically, in this embodiment, the processor may be configured to perform the following steps through a computer program:

[0153] S201, receiving and identifying the food information of the food to be cooked, and setting the cooking parameters of the IoT air fryer according to the food information;

[0154] S202, cooking the food to be cooked based on the current cooking parameters, and monitoring the cooking state changes of the food in real time;

[0155] S203, dynamically adjusting the current cooking parameters according to the cooking state change, and calculating the cooking quality index of the cooked food at the end of cooking;

[0156] S204, if the cooking quality index does not reach the preset threshold, adjust the cooking time, continue to cook the food to be cooked based on the current cooking parameters, and monitor the changes in the cooking state of the food in real time until the cooking quality index reaches the preset threshold, end cooking, and upload the cooked food, cooking parameters and cooking quality index to the cloud in real time through the Internet of Things.

[0157] It can be seen that the ingredient information of the food to be cooked is received and identified, and the cooking parameters of the IoT air fryer are set according to the ingredient information; the food to be cooked is cooked based on the current cooking parameters, and the cooking state changes of the food are monitored in real time; the current cooking parameters are dynamically adjusted according to the cooking state changes, and the cooking quality index of the cooked food is calculated at the end of cooking; if the cooking quality index does not reach the preset threshold, the cooking time is adjusted, and the steps of cooking the food to be cooked based on the current cooking parameters and monitoring the cooking state changes of the food in real time are continued until the cooking quality index reaches the preset threshold, and cooking is ended, and the cooked food, cooking parameters and cooking quality index are uploaded to the cloud in real time through the IoT, so that technologies such as ingredient identification, real-time monitoring, intelligent adjustment and cooking quality evaluation can be used to ensure the intelligence and automation of the cooking process, and through cloud data analysis, a more personalized cooking experience is provided to users.

[0158] The above describes in detail the structure, features and effects of the present invention based on the embodiments shown in the drawings. The above is only a preferred embodiment of the present invention, but the present invention is not limited to the scope of implementation shown in the drawings. Any changes made according to the concept of the present invention, or modifications to equivalent embodiments with equivalent changes, which still do not exceed the spirit covered by the description and drawings, should be within the protection scope of the present invention.

Claims

1. A control method for an Internet of Things air fryer, characterized in that: The method comprises: Receive and identify food information of food to be cooked, and set cooking parameters of the IoT air fryer according to the food information; Cook the food to be cooked based on the current cooking parameters and monitor the cooking state changes of the food in real time; The current cooking parameters are dynamically adjusted according to the cooking state changes, and the cooking quality index of the cooked food is calculated at the end of cooking; the cooking quality index is calculated as follows: Wherein, the CQI is a cooking quality index, A is an appearance index, N is a nutrient retention index, Q is a physical change index, T is a temperature distribution index, and omega_a, omega_n, omega_q, and omega_t are corresponding weighting coefficients; the calculation formula of the physical change index is: Wherein, the Delta W is the weight change, the W_0 is the initial weight, the Delta V is the volume change, the V_0 is the initial volume, and the Delta C is the crystallinity change, which reflects the change in food structure during cooking. The crystallinity change includes: gelatinization of starch or denaturation of protein, and the crystallinity change is obtained by X-ray diffraction or infrared spectroscopy analysis; , , is the corresponding weighting coefficient; If the cooking quality index does not reach the preset threshold, the cooking time is adjusted, and the steps of cooking the food to be cooked based on the current cooking parameters and monitoring the changes in the cooking state of the food in real time are continued until the cooking quality index reaches the preset threshold. Cooking is terminated, and the cooked food, cooking parameters and cooking quality index are uploaded to the cloud in real time via the Internet of Things.

2. The method according to claim 1, characterized in that The cooking parameters are adjusted as follows: Among them, the P_aft is the adjusted cooking parameter, the P_cur is the current cooking parameter, the Delta T, the Delta H and the Delta V are the temperature change, humidity change and volume change since the last adjustment, respectively, and the alpha, the beta and the gamma are the corresponding adjustment coefficients.

3. The method according to claim 2, characterized in that The calculation formula of the appearance index is: Among them, the C_L is the color score, the C_T is the texture score, and the C_B is the burnt degree score.

4. The method according to claim 2, characterized in that: The calculation formula of the nutrient retention index is: Wherein, the Delta V is the vitamin loss rate, and the Delta M is the mineral loss rate.

5. The method according to claim 2, characterized in that: The calculation formula of the temperature distribution index is: Among them, T_c is the center temperature, T_s is the set temperature, U is the uniformity of heat distribution, and eta_c and eta_u are corresponding weighting coefficients.

6. A control system for an Internet of Things air fryer, characterized in that: The system comprises: A receiving module, used to receive and identify the food information of the food to be cooked, and set the cooking parameters of the IoT air fryer according to the food information; A cooking module, used to cook food to be cooked based on current cooking parameters and monitor changes in the cooking state of the food in real time; The calculation module is used to dynamically adjust the current cooking parameters according to the change of the cooking state, and calculate the cooking quality index of the cooked food at the end of cooking; the cooking quality index is calculated as follows: Wherein, the CQI is a cooking quality index, A is an appearance index, N is a nutrient retention index, Q is a physical change index, T is a temperature distribution index, and omega_a, omega_n, omega_q, and omega_t are corresponding weighting coefficients; the calculation formula of the physical change index is: Wherein, the Delta W is the weight change, the W_0 is the initial weight, the Delta V is the volume change, the V_0 is the initial volume, and the Delta C is the crystallinity change, which reflects the change in food structure during cooking. The crystallinity change includes: gelatinization of starch or denaturation of protein, and the crystallinity change is obtained by X-ray diffraction or infrared spectroscopy analysis; , , is the corresponding weighting coefficient; The adjustment module is used to adjust the cooking time when the cooking quality index does not reach a preset threshold, continue to cook the food to be cooked based on the current cooking parameters, and monitor the changes in the cooking state of the food in real time until the cooking quality index reaches the preset threshold, end the cooking, and upload the cooked food, cooking parameters and cooking quality index to the cloud in real time through the Internet of Things.

7. A storage medium, characterized in that: The storage medium stores a computer program, wherein the computer program is configured to execute the method according to any one of claims 1 to 5 when executed.

8. An electronic device, comprising a memory and a processor, characterized in that: A computer program is stored in the memory, and the processor is configured to run the computer program to perform the method according to any one of claims 1 to 5.

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