A human-computer interactive standardized cooking method and system

Abstract

The present invention discloses a human-computer interactive standardized cooking method and system, the method comprising: receiving cooking parameters input by a user through a human-computer interactive interface; analyzing the recipe requirements to generate a corresponding flavor component mapping matrix; determining a cooking heat output strategy according to the flavor component mapping matrix and the temperature control parameters; adjusting the heating element of the cooking device in real time according to the heat output strategy, and monitoring the preset indicators of the ingredients during the cooking process so that the preset indicators meet the preset standards. By using the embodiments of the present invention, a higher level of automation, precise control and user interaction can be achieved to adapt to diverse cooking needs, and to achieve standardized and personalized cooking of ingredients, and ultimately achieve healthy, delicious, nutritious and repeatable cooking results.

Description

Technical Field

[0001] The invention belongs to the technical field of electrical cooking, in particular to a human-machine interactive standardized cooking method and system. Background Art

[0002] As the pace of modern life accelerates, people are increasingly seeking fast, convenient and reliable cooking solutions. As an important part of people's daily lives, the quality and efficiency of cooking are of great significance to ensuring healthy eating and improving the quality of life. In traditional cooking methods, the cooking process often depends on the chef's experience and intuition, which makes the cooking results uncertain and often difficult to replicate specific flavors and qualities.

[0003] With the development of technology, people have begun to try to apply electronic and intelligent technologies to cooking equipment in order to achieve higher cooking standardization and precision. Some existing high-end cooking equipment can already provide basic temperature control and timing functions, but these functions still lack sufficient intelligence, are not sophisticated enough for the specific heating requirements of different ingredients, and do not have enough user interaction to adapt to personalized cooking needs. Summary of the invention

[0004] The purpose of the present invention is to provide a human-computer interactive standardized cooking method and system to solve the deficiencies in the prior art.

[0005] An embodiment of the present application provides a human-computer interactive standardized cooking method, the method comprising:

[0006] Receiving cooking parameters input by a user through a human-computer interaction interface, wherein the cooking parameters at least include: recipe requirements and temperature control parameters;

[0007] Analyze the recipe requirements and generate a corresponding flavor component mapping matrix, wherein the flavor component mapping matrix at least includes: an optimal heating temperature and an optimal heating time required for each ingredient;

[0008] Determining a heat energy output strategy for cooking according to the flavor component mapping matrix and the temperature control parameters;

[0009] The heating element of the cooking device is adjusted in real time according to the heat energy output strategy, and the preset indicators of the food during the cooking process are monitored so that the preset indicators meet the preset standards.

[0010] Optionally, monitoring preset indicators of ingredients during the cooking process so that the preset indicators meet preset standards includes:

[0011] Monitor the gradual thermal gradient along the volume of the food during the cooking process so that the gradual thermal gradient conforms to a preset gradient curve of the food, wherein the gradual thermal gradient is:

[0012] G_thermal_gradient=G_max*e^(-λ*T)+G_eq

[0013] Wherein, the G_thermal_gradient is the gradual thermal gradient, the G_max is the maximum thermal gradient, the λ is the thermal gradient attenuation constant, the T is the temperature, and the G_eq is the equilibrium thermal gradient.

[0014] Optionally, monitoring preset indicators of ingredients during the cooking process so that the preset indicators meet preset standards includes:

[0015] Monitor the temperature distribution of the food during cooking to achieve a preset ideal distribution of the food, wherein the temperature distribution is:

[0016] T_distribution(x,y,z,t)=T_initial+ΔT(x,y,z)*f(t,E_thermal_plan)

[0017] Among them, the T_distribution(x,y,z,t) is the temperature distribution, the T_initial is the initial temperature, the ΔT(x,y,z) is the temperature change adjusted based on the geometric position of the food (x,y,z), and the f(t,E_thermal_plan) is a preset function of the time t and the thermal energy output strategy E_thermal_plan.

[0018] Optionally, analyzing the recipe requirements to generate a corresponding flavor component mapping matrix includes:

[0019] Performing semantic analysis on the recipe requirements to extract key ingredients, cooking methods and personalized needs of users from the recipe requirements;

[0020] Using the food database and the output of semantic analysis, deep learning calculations are performed on each food to determine the optimal heating temperature and optimal heating time required for each food;

[0021] According to the optimal heating temperature and optimal heating time required for each ingredient, combined with the cooking method and the personalized needs of the user, a flavor component mapping matrix is ​​generated using a multivariate analysis tool, wherein the matrix includes: the optimal heating temperature and optimal heating time required for each ingredient, and the interaction influence information between the ingredients.

[0022] Another embodiment of the present application provides a human-computer interactive standardized cooking method system, the system comprising:

[0023] A receiving module, used to receive cooking parameters input by a user through a human-computer interaction interface, wherein the cooking parameters at least include: recipe requirements and temperature control parameters;

[0024] An analysis module, used to analyze the recipe requirements and generate a corresponding flavor component mapping matrix, wherein the flavor component mapping matrix at least includes: an optimal heating temperature and an optimal heating time required for each ingredient;

[0025] A determination module, used to determine a heat energy output strategy for cooking according to the flavor component mapping matrix and the temperature control parameters;

[0026] The adjustment module is used to adjust the heating element of the cooking device in real time according to the heat energy output strategy, and monitor the preset indicators of the ingredients during the cooking process so that the preset indicators meet the preset standards.

[0027] 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.

[0028] 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.

[0029] Compared with the prior art, the present invention provides a human-computer interactive standardized cooking method, which receives cooking parameters input by a user through a human-computer interaction interface, wherein the cooking parameters include at least: recipe requirements and temperature control parameters; analyzes the recipe requirements to generate a corresponding flavor component mapping matrix, wherein the flavor component mapping matrix includes at least: the optimal heating temperature and the optimal heating time required for each ingredient; determines the thermal energy output strategy for cooking according to the flavor component mapping matrix and the temperature control parameters; adjusts the heating elements of the cooking equipment in real time according to the thermal energy output strategy, and monitors the preset indicators of the ingredients during the cooking process so that the preset indicators meet the preset standards, thereby achieving a higher level of automation, precise control and user interaction to adapt to diverse cooking needs, and realize standardized and personalized cooking of ingredients, and ultimately achieves healthy, delicious, nutritious and repeatable cooking results. BRIEF DESCRIPTION OF THE DRAWINGS

[0030] Figure 1 A hardware structure block diagram of a computer terminal for a human-computer interactive standardized cooking method provided by an embodiment of the present invention;

[0031] Figure 2 A schematic diagram of a flow chart of a human-computer interactive standardized cooking method provided by an embodiment of the present invention;

[0032] Figure 3 A schematic diagram of the structure of a human-computer interactive standardized cooking method system provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0033] 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.

[0034] The embodiment of the present invention firstly provides a human-computer interactive standardized cooking method, which can be applied to electronic devices such as computer terminals, specifically ordinary computers, etc.

[0035] 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 for a human-computer interactive standardized cooking method provided by an embodiment of the present invention. Figure 1 As shown, the computer terminal may include one or more ( Figure 1 Only 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 are shown.

[0036] The memory 104 can be used to store software programs and modules of application software, such as program instructions / modules corresponding to the human-computer interactive standardized cooking method 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, to implement 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 examples, 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.

[0037] 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.

[0038] See also Figure 2 , an embodiment of the present invention provides a human-computer interactive standardized cooking method, which may include the following steps:

[0039] S201, receiving cooking parameters input by a user through a human-computer interaction interface, wherein the cooking parameters at least include: recipe requirements and temperature control parameters;

[0040] Specifically, one implementation may include:

[0041] Step 1: Create an augmented reality (AR) interface In order to provide a unique interactive experience, an augmented reality interface is designed as a platform for input parameters. Users can trigger this interactive interface through AR glasses or the camera on a smartphone, and the interface can display a virtual cooking environment and ingredients.

[0042] Step 2: 3D Ingredient Selection In the AR interface, users can see different types of ingredients on a virtual kitchen workbench. Users can select and operate these 3D ingredient models through gestures or touch screen, such as taking a virtual chicken breast to a virtual baking tray.

[0043] Step 3: Recipe parameter input Once the ingredients are selected, the interface will prompt the user to enter the recipe requirements. At this time, the user can describe his or her cooking requirements for the ingredients through voice commands (for example, saying "I want golden and crispy fried chicken skin"). The system will use natural language processing technology to parse the user's voice input and convert it into specific cooking parameters.

[0044] Step 4: Adjust temperature control parameters Next, the interface will display a virtual temperature controller or slider, and users can adjust the temperature setting through physical gestures such as pushing or sliding. The AR interface can display temperature changes and expected cooking results in real time, and can even simulate the changes in color and texture of ingredients during the cooking process.

[0045] Step 5: Confirmation and Preview After all parameters are entered, the system will provide a cooking preview, where users can see in the AR interface what the dish will look like based on the input parameters. Users can confirm the preview results or return to the previous step to adjust the parameters.

[0046] Step 6: Final Confirmation and Transmission Once the user is satisfied with the previewed cooking result, he / she confirms the input parameters with a simple gesture or command. The system then transmits the user-defined recipe requirements and temperature control parameters to the cooking device, ready for actual cooking.

[0047] This implementation uses augmented reality technology to enhance the user's interactive experience, allowing users to input and adjust cooking parameters in a more intuitive and closer to actual cooking. By combining 3D models, natural language processing and virtual temperature control operations, users can participate in the preparation of the cooking process in a new and unexpected interactive method.

[0048] S202, analyzing the recipe requirements and generating a corresponding flavor component mapping matrix, wherein the flavor component mapping matrix at least includes: an optimal heating temperature and an optimal heating time required for each ingredient;

[0049] Specifically, the recipe requirements can be semantically analyzed to extract key ingredients, cooking methods, and personalized needs of users from the recipe requirements; deep learning calculations can be performed on each ingredient using the ingredient database and the output of semantic analysis to determine the optimal heating temperature and optimal heating time required for each ingredient; based on the optimal heating temperature and optimal heating time required for each ingredient, combined with the cooking method and personalized needs of the user, a flavor component mapping matrix is ​​generated using a multivariate analysis tool, wherein the matrix includes: the optimal heating temperature and optimal heating time required for each ingredient, as well as information on the interaction effects between ingredients. An implementation method may include:

[0050] Step 1: Build a natural language processing system for recipe requirements You can develop an advanced natural language processing (NLP) system that can accept and process recipe requirements entered by users in natural language. This system uses a machine learning model (such as a long short-term memory network LSTM or a Transformer model) that has been trained on a large amount of recipe data and cooking guides to extract key information.

[0051] Step 2: Semantic parsing and key information extraction The NLP system is used to semantically parse the recipe requirements entered by the user, extracting key ingredients, required cooking techniques (such as roasting, frying, steaming, etc.), taste descriptions (such as spicy, light, sweet and sour, etc.), and any other information that may affect the cooking parameters. For example, the recipe requirements entered by the user may be "I hope to get a mixed fried rice containing matsutake and beef, with a taste that tends to be fresh and slightly spicy." The semantic analysis system will process this text and extract "matsutake" and "beef" as key ingredients, "mixed fried rice" as a cooking method, and "fresh and slightly spicy" as a taste description.

[0052] Step 3: Access a specific ingredient database. Each extracted key ingredient is matched to an extensive ingredient database. The database contains information about the characteristics of each ingredient, such as the necessary optimal heating temperature, heating time, and their interactions with other ingredients. The database information is continuously updated and refined through historical recipes, expert input, and user feedback. For example, it will contain the aroma release of matsutake mushrooms at different temperatures, the tenderness changes of beef at different cooking stages, etc.

[0053] Step 4: Deep learning calculation and flavor component mapping matrix creation For each ingredient, deep learning models are used to calculate its optimal heating temperature and time. These models consider the physical and chemical properties of the ingredients and are trained based on previous successful cooking experience. Model training includes identifying the optimal flavor release time and heating temperature of the ingredients at specific temperatures. Model training will also include the identification of chemical changes and flavor interactions that may occur when ingredients are combined.

[0054] Then, using multivariate analysis tools, combined with the user's personalized needs and cooking methods, a flavor component mapping matrix containing the optimal temperature control parameters for each ingredient is generated. The matrix also includes information on the interactions between ingredients, which is critical for precise control of taste and flavor. This matrix combines the heating characteristics of each ingredient with the optimal heating temperature and time required. For example, it may show that matsutake is best released by heating at 180°C for 4 minutes, while beef needs to be quickly stir-fried at 200°C for 3 minutes to achieve a slightly spicy taste and maintain tenderness. The matrix will be encoded in a format that can be easily parsed and utilized by cooking equipment.

[0055] Step 5: User Personalized Adjustment The generated flavor component mapping matrix is ​​fed back to the user in a visual way. The user can make subtle adjustments to the parameters in the matrix through the interactive interface to meet their personal taste preferences. The system allows users to adjust the heating temperature and time through a slider or direct input through an interactive graphical user interface. For example, if the user wants a slightly spicier dish, the system may recommend increasing the stir-fry time or temperature of the beef, or adding a specific variety of peppers. After the user selects and modifies the suggestions, the system will readjust the mapping matrix.

[0056] Step 6: Final Confirmation and Parameter Integration After the user completes the personalized adjustment, the system will ask for confirmation of the final cooking parameters. After confirmation, these parameters are integrated into the control system of the cooking device to prepare for the actual cooking process. Such cooking devices can be smart pots, smart ovens or other kitchen appliances that can automatically adjust temperature control parameters.

[0057] This specific implementation uses natural language processing and deep learning technology to convert the recipe requirements entered by the user into a usable flavor component mapping matrix. This method can not only provide a personalized cooking experience, but also continuously optimize the cooking parameters through learning to achieve more accurate cooking results. In this way, the cooking method can adapt to various complex recipe requirements and meet the user's unique preferences for food taste and texture.

[0058] S203, determining a cooking heat energy output strategy according to the flavor component mapping matrix and the temperature control parameters;

[0059] Specifically, one implementation may include:

[0060] Step 1: Obtaining the flavor component mapping matrix After the user inputs the cooking parameters through the human-computer interaction interface, the system has generated a flavor component mapping matrix reflecting the optimal heating temperature and time for each ingredient through the steps described above.

[0061] Step 2: Set temperature control parameters The user enters or selects temperature control parameters, which can be specific cooking temperatures, times, or more complex temperature curves, such as programmed temperature changes (eg, increase first and then decrease).

[0062] Step 3: Create a cooking simulation environment Create a computer simulation environment that can simulate the thermodynamic behavior of ingredients during the cooking process. The simulation will use the actual physical and chemical properties of the ingredients, including heat capacity, density, water evaporation rate, etc., which can come from scientific experiments or ingredient databases.

[0063] Step 4: Construct a thermal energy output strategy model. A thermal energy output strategy model is constructed using thermodynamics and control theory. The model will be able to calculate the thermal energy output curve (e.g., temperature versus time function) necessary to achieve the desired cooking result based on the input flavor component mapping matrix and temperature control parameters.

[0064] Step 5: Incorporate machine learning optimization strategies Use machine learning algorithms, such as reinforcement learning, to optimize the heat output strategy. The system will learn and adjust the heat output strategy in a simulated environment by simulating multiple rounds of cooking processes to maximize the release of food flavors and meet temperature control parameters.

[0065] Step 6: Prediction and adaptation of cooking strategies With the help of the machine learning model, the system predicts a set of thermal output strategies that details the required temperature and power output at each point in the cooking process. This set of strategies will allow different cooking devices to adjust their heating elements in real time.

[0066] Step 7: Verification and adjustment of heat output strategy The predicted strategy is verified in a virtual environment, simulating the actual cooking process and monitoring the preset indicators of the ingredients, such as temperature distribution and thermal gradient. The system can adjust the strategy to ensure that these indicators meet the preset standards.

[0067] Step 8: Implementation of cooking strategy The validated heat output strategy is sent to the cooking device, and the device’s embedded system controls its heating elements according to this strategy to accurately execute the cooking process.

[0068] Through these steps, the system can not only generate a thermal energy output strategy corresponding to the best flavor of the ingredients, but also adapt to the temperature control parameters set by the user to achieve personalized and standardized cooking.

[0069] S204, adjusting the heating element of the cooking device in real time according to the heat output strategy, and monitoring the preset indicators of the food during the cooking process to make the preset indicators meet the preset standards.

[0070] Specifically, a method for implementing real-time adjustment of the heating element of the cooking device according to the heat output strategy may include:

[0071] Step 1: Design a Dynamic Adjustment System A dynamic heating control system (DHCS) can be developed for the cooking device that receives the thermal output strategy and adjusts the heating elements in real time.

[0072] Step 2: Heat output strategy conversion The heat output strategy is digitized and converted into a sequence of control instructions that can be understood by the DHCS. These instructions determine the power level and duration of the heating elements at various stages of the cooking process.

[0073] Step 3: Heating element configuration Choose the appropriate type of heating element for the cooking equipment, such as electromagnetic induction heater, resistance heater or infrared heater, and ensure that they have the ability to respond quickly and precisely control. Ensure that the heating element layout can heat the food evenly or transfer heat through a heat-conducting medium (such as oil, water).

[0074] Step 4: Sensor Deployment Install high-precision temperature sensors in cooking equipment to monitor the real-time temperature of ingredients and the surface temperature of heating elements.

[0075] Step 5: Feedback Control Logic Establishment Feedback control logic is established to enable the system to adjust the power output of the heating elements in real time based on sensor readings to maintain consistency with the thermal energy output strategy.

[0076] Step 6: Implementing the real-time adjustment mechanism When the cooking process starts, the DHCS activates the heating elements according to the control instruction sequence and adjusts the power according to the temperature feedback of the ingredients and the predetermined time points throughout the cooking process.

[0077] Specifically, in one implementation, the preset indicator may be a gradual thermal gradient, and the gradual thermal gradient along the volume of the food during the cooking process is monitored so that the gradual thermal gradient conforms to a preset gradient curve of the food, wherein the gradual thermal gradient is:

[0078] G_thremal_gradient=G_max*e^(-λ*T)+G_eq

[0079] Among them, G_thermal_gradient is the gradual thermal gradient, which represents the rate of change of heat from one point to another along the volume of the food. This variable measures the rate of change of heat along the volume inside the food. Controlling this gradient is crucial to ensure that the food is cooked evenly instead of being fried on the surface and undercooked on the inside.

[0080] G_max is the maximum thermal gradient, which indicates the maximum rate of heat change along the volume of the food without any heat loss. It indicates the maximum rate at which the food absorbs heat at the beginning of cooking. For example, for grilled meat, the maximum thermal gradient may be 100℃ / cm, which means that the temperature difference from the edge of the meat to 1 cm inside can be as high as 100℃.

[0081] λ is the thermal gradient decay constant, which indicates the decay rate of heat along the food. This constant describes the rate at which the thermal gradient decreases as the temperature increases. For example, for water-containing food, this value may be higher because the evaporation of water causes the heat to disperse faster, assuming it is 0.1 / cm.

[0082] T is temperature, which indicates the temperature inside the food at a specific time. For example, 10 minutes after the start of cooking, the surface of the food may reach 200°C.

[0083] G_eq is the equilibrium thermal gradient, which indicates the constant rate of change of heat along the volume of the food when the system reaches thermal equilibrium. It refers to the constant rate of change of heat distribution along the volume of the food when the food reaches a stable state. For example, after the barbecue is over, the internal thermal gradient of the meat is stable at 5℃ / cm.

[0084] Designed to describe the gradual thermal gradient inside the food along the volume direction, this gradient determines how heat is transferred from the outside to the inside of the food, and how it changes with time and temperature. A specific implementation method can be as follows:

[0085] Step 1: Establish a mathematical model. Use the mathematical equation G_{thermal_gradient}=G_{max}*e^{(-λ*T)}+G_{eq} to establish a mathematical model of the gradual thermal gradient inside the food.

[0086] Step 2: Sensor Arrangement A distributed temperature sensor network is installed on the cooking equipment to measure the real-time temperature of different volume parts of the food in order to calculate the actual gradual thermal gradient.

[0087] Step 3: Determine the thermal gradient decay constant The thermal gradient decay constant λ of the cooking equipment and specific ingredients is determined experimentally. This value determines how fast the heat is transferred inside the ingredients.

[0088] Step 4: Real-time monitoring of thermal gradient Collect sensor data in real time, calculate the current G_{thermal_gradient}, and compare it with the preset gradient curve of the cooking object.

[0089] Step 5: Adjust heating strategy If the real-time G_{thermal_gradient} does not match the preset gradient curve, adjust the power output of the heating elements, which may include increasing or decreasing the temperature of specific heating elements, or changing their heating mode (such as pulse heating or continuous heating).

[0090] Step 6: Heat distribution optimization Computational fluid dynamics (CFD) can be used to simulate the heat flow inside the food to optimize the location and intensity of heating elements.

[0091] Step 7: Feedback Control System Integration Integrate the feedback information into the control system of the cooking equipment to ensure that the heating strategy can be automatically adjusted based on real-time data.

[0092] Step 8: Test and Validate Test and validate the cooking process in a simulation environment to ensure the accuracy and effectiveness of the monitoring and control systems.

[0093] Step 9: Implementation and Adjustment Adjust system parameters based on test results to ensure that the preset thermal gradient curve can be accurately simulated and implemented under different cooking conditions.

[0094] Step 10: User Interface Integration A user interface can be developed to allow the user to monitor the changing thermal gradient during the cooking process and visualize the cooking status.

[0095] Through the above steps, the gradual thermal gradient of the ingredients during the cooking process can be monitored and adjusted in real time to ensure that it complies with the preset gradient curve, thereby achieving standardized and personalized cooking results.

[0096] Specifically, in another implementation, the preset indicator may be a temperature distribution, and the temperature distribution of the food during the cooking process is monitored to achieve a preset ideal distribution of the food, wherein the temperature distribution is:

[0097] T_distribution(x,y,z,t)=T_initial+ΔT(x,y,z)*f(T,E_thermal_plan)

[0098] Among them, T_distribution(x,y,z,t) is the temperature distribution, which represents the temperature of the food point at position (x,y,z) at a specific time t. For example, t = 30 minutes and the selected monitoring point is the center of the food (x = 5cm, y = 5cm, z = 5cm), then T_{distribution}(5,5,5,30) will indicate the temperature of the point after 30 minutes of cooking.

[0099] T_initial is the initial temperature, the temperature of the food before cooking. For example, the initial temperature of chicken taken out of the refrigerator may be 4°C; the initial temperature of frozen steak taken out of the refrigerator may be -18°C, that is, T_{initial} = -18°C.

[0100] ΔT(x,y,z) is the temperature change adjusted based on the geometric position (x,y,z) of the food. For example, the temperature change may be larger at locations close to the heating element. For example, the surface of the steak in the center of the oven is closer to the heating element, and ΔT(0,0,0) may be 150°C, while the bottom edge far from the heating element ΔT(10cm,5cm,0) may only be 100°C.

[0101] f(t,E_thermal_plan) is a preset function of time t and thermal output strategy E_thermal_plan, which determines how the temperature distribution changes over time and the thermal output strategy. For example:

[0102] f(t,E_{thermal_plan})=(1-e^{-k*t})*sum_{i=1}^{n}{alpha_i*H(t-t_i)}

[0103] Wherein, t: time, which represents the time measurement since the start of the cooking process; E_{thermal_plan}: thermal energy output strategy, which consists of a series of heating events, each with a specific heating intensity and duration; k: time constant, which describes the response rate of temperature change over time, and its value is less than 1, for example, 0.3; n: the number of heating events; alpha_i: the heating intensity coefficient of the i-th heating event, which is determined according to actual needs, for example, 0.5 under conventional heating intensity; H(t-t_i): step function, which takes the value of 1 when t exceeds the start time t_i of event i, otherwise it takes the value of 0.

[0104] The change in the temperature of the food over time is caused by a series of heating events (each event can have a different heating intensity) superimposed on the initial temperature. These heating events contribute to the temperature change at their start time t_i and heat the food according to their heating intensity alpha_i. The step function H(t-t_i) ensures that the heating effect is only calculated after the start of a heating event.

[0105] Designed to describe the temperature distribution inside the food during the cooking process, this distribution provides the expected temperature value at any location inside the food at any given time. The steps of a specific implementation method can be as follows:

[0106] Step 1: Establish a temperature distribution model A temperature distribution model T_{distribution} can be developed to describe the internal temperature changes of ingredients under specific cooking settings.

[0107] Step 2: Real-time temperature measurement Use an embedded thermal imager or multi-point temperature sensor to capture real-time temperature data of food at different locations and time points.

[0108] Step 3: Define thermal output strategy Develop a thermal output strategy E_{thermal_plan}, which specifies in detail the heating element power adjustment at various stages of the cooking process.

[0109] Step 4: Programming the control system A control system can be developed that reads the thermal output strategy and real-time temperature and adjusts the heating elements based on the model T_{distribution}(x,y,z,t).

[0110] Step 5: Implement the preset function to design the function f(t,E_{thermal_plan} to calculate how much heat should be applied to each part of the food at any given point in time to meet the expected temperature distribution.

[0111] Step 6: Feedback adjustment By comparing the real-time measured temperature with the expected temperature distribution, the system will continuously adjust the power of the heating element until the actual temperature distribution matches the expected distribution.

[0112] Step 7: Verification and optimization Test the system under different ingredients and cooking conditions, and continuously optimize the model T_{distribution}(x,y,z,t) and function f(t,E_{thermal_plan} based on the feedback data.

[0113] Step 8: Safety monitoring adds overheating protection to ensure that the temperature of the food does not exceed the set safety upper limit.

[0114] Step 9: User Interaction Interface Develop a user interaction interface to visualize the temperature changes during the cooking process, allowing users to view the temperature distribution inside the ingredients in real time.

[0115] Through the above steps, the control system of the cooking device will be able to monitor the temperature distribution of the food in real time and adjust the heating element according to the preset temperature distribution model to ensure that the temperature distribution of the food during the cooking process reaches the preset ideal distribution of the food.

[0116] It can be seen that the cooking parameters input by the user through the human-computer interaction interface are received, wherein the cooking parameters include at least: recipe requirements and temperature control parameters; the recipe requirements are analyzed to generate a corresponding flavor component mapping matrix, wherein the flavor component mapping matrix includes at least: the optimal heating temperature and the optimal heating time required for each ingredient; the thermal energy output strategy for cooking is determined according to the flavor component mapping matrix and the temperature control parameters; the heating elements of the cooking equipment are adjusted in real time according to the thermal energy output strategy, and the preset indicators of the ingredients during the cooking process are monitored so that the preset indicators meet the preset standards, thereby achieving a higher level of automation, precise control and user interaction to adapt to diverse cooking needs, and realize standardized and personalized cooking of ingredients, and ultimately achieve healthy, delicious, nutritious and repeatable cooking results.

[0117] Another embodiment of the present invention provides a human-computer interactive standardized cooking method system, see Figure 3 , the system may include:

[0118] The receiving module 301 is used to receive cooking parameters input by the user through the human-computer interaction interface, wherein the cooking parameters at least include: recipe requirements and temperature control parameters;

[0119] An analysis module 302 is used to analyze the recipe requirements and generate a corresponding flavor component mapping matrix, wherein the flavor component mapping matrix at least includes: an optimal heating temperature and an optimal heating time required for each ingredient;

[0120] A determination module 303, for determining a heat energy output strategy for cooking according to the flavor component mapping matrix and the temperature control parameters;

[0121] The adjustment module 304 is used to adjust the heating element of the cooking device in real time according to the heat output strategy, and monitor the preset indicators of the ingredients during the cooking process so that the preset indicators meet the preset standards.

[0122] It can be seen that the cooking parameters input by the user through the human-computer interaction interface are received, wherein the cooking parameters include at least: recipe requirements and temperature control parameters; the recipe requirements are analyzed to generate a corresponding flavor component mapping matrix, wherein the flavor component mapping matrix includes at least: the optimal heating temperature and the optimal heating time required for each ingredient; the thermal energy output strategy for cooking is determined according to the flavor component mapping matrix and the temperature control parameters; the heating elements of the cooking equipment are adjusted in real time according to the thermal energy output strategy, and the preset indicators of the ingredients during the cooking process are monitored so that the preset indicators meet the preset standards, thereby achieving a higher level of automation, precise control and user interaction to adapt to diverse cooking needs, and realize standardized and personalized cooking of ingredients, and ultimately achieve healthy, delicious, nutritious and repeatable cooking results.

[0123] 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.

[0124] Specifically, in this embodiment, the above storage medium may be configured to store a computer program for performing the following steps:

[0125] S201, receiving cooking parameters input by a user through a human-computer interaction interface, wherein the cooking parameters at least include: recipe requirements and temperature control parameters;

[0126] S202, analyzing the recipe requirements and generating a corresponding flavor component mapping matrix, wherein the flavor component mapping matrix at least includes: an optimal heating temperature and an optimal heating time required for each ingredient;

[0127] S203, determining a cooking heat energy output strategy according to the flavor component mapping matrix and the temperature control parameters;

[0128] S204, adjusting the heating element of the cooking device in real time according to the heat output strategy, and monitoring the preset indicators of the food during the cooking process to make the preset indicators meet the preset standards.

[0129] It can be seen that the cooking parameters input by the user through the human-computer interaction interface are received, wherein the cooking parameters include at least: recipe requirements and temperature control parameters; the recipe requirements are analyzed to generate a corresponding flavor component mapping matrix, wherein the flavor component mapping matrix includes at least: the optimal heating temperature and the optimal heating time required for each ingredient; the thermal energy output strategy for cooking is determined according to the flavor component mapping matrix and the temperature control parameters; the heating elements of the cooking equipment are adjusted in real time according to the thermal energy output strategy, and the preset indicators of the ingredients during the cooking process are monitored so that the preset indicators meet the preset standards, thereby achieving a higher level of automation, precise control and user interaction to adapt to diverse cooking needs, and realize standardized and personalized cooking of ingredients, and ultimately achieve healthy, delicious, nutritious and repeatable cooking results.

[0130] 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.

[0131] 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.

[0132] Specifically, in this embodiment, the processor may be configured to perform the following steps through a computer program:

[0133] S201, receiving cooking parameters input by a user through a human-computer interaction interface, wherein the cooking parameters at least include: recipe requirements and temperature control parameters;

[0134] S202, analyzing the recipe requirements and generating a corresponding flavor component mapping matrix, wherein the flavor component mapping matrix at least includes: an optimal heating temperature and an optimal heating time required for each ingredient;

[0135] S203, determining a cooking heat energy output strategy according to the flavor component mapping matrix and the temperature control parameters;

[0136] S204, adjusting the heating element of the cooking device in real time according to the heat output strategy, and monitoring the preset indicators of the food during the cooking process to make the preset indicators meet the preset standards.

[0137] Specifically, the specific examples in this embodiment can refer to the examples described in the above embodiments and optional implementation modes, and this embodiment will not be described in detail here.

[0138] It can be seen that the cooking parameters input by the user through the human-computer interaction interface are received, wherein the cooking parameters include at least: recipe requirements and temperature control parameters; the recipe requirements are analyzed to generate a corresponding flavor component mapping matrix, wherein the flavor component mapping matrix includes at least: the optimal heating temperature and the optimal heating time required for each ingredient; the thermal energy output strategy for cooking is determined according to the flavor component mapping matrix and the temperature control parameters; the heating elements of the cooking equipment are adjusted in real time according to the thermal energy output strategy, and the preset indicators of the ingredients during the cooking process are monitored so that the preset indicators meet the preset standards, thereby achieving a higher level of automation, precise control and user interaction to adapt to diverse cooking needs, and realize standardized and personalized cooking of ingredients, and ultimately achieve healthy, delicious, nutritious and repeatable cooking results.

[0139] 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 human-computer interactive standardized cooking method, characterized in that: The method comprises: Receiving cooking parameters input by a user through a human-computer interaction interface, wherein the cooking parameters at least include: recipe requirements and temperature control parameters; Analyze the recipe requirements and generate a corresponding flavor component mapping matrix, wherein the flavor component mapping matrix at least includes: an optimal heating temperature and an optimal heating time required for each ingredient; Determining a heat energy output strategy for cooking according to the flavor component mapping matrix and the temperature control parameters; According to the heat output strategy, the heating element of the cooking device is adjusted in real time, and the preset index of the food during the cooking process is monitored so that the preset index meets the preset standard; the preset index of the food during the cooking process is monitored so that the preset index meets the preset standard, including: Monitor the gradual thermal gradient along the volume of the food during the cooking process so that the gradual thermal gradient conforms to a preset gradient curve of the food, wherein the gradual thermal gradient is: Wherein, the G_thermal_gradient is a gradual thermal gradient, the G_max is a maximum thermal gradient, the λ is a thermal gradient attenuation constant, the T is a temperature, and the G_eq is a balanced thermal gradient; or, the preset index of the food material during the cooking process is monitored so that the preset index meets the preset standard, including: Monitor the temperature distribution of the food during cooking to achieve a preset ideal distribution of the food, wherein the temperature distribution is: Among them, the T_distribution(x, y, z, t) is the temperature distribution, the T_initial is the initial temperature, the ∆T(x, y, z) is the temperature change adjusted based on the geometric position of the food (x, y, z), and the f(t, E_thermal_plan) is a preset function of the time t and the thermal energy output strategy E_thermal_plan.

2. The method according to claim 1, characterized in that The step of analyzing the recipe requirements and generating a corresponding flavor component mapping matrix includes: Performing semantic analysis on the recipe requirements to extract key ingredients, cooking methods and personalized needs of users from the recipe requirements; Using the food database and the output of semantic analysis, deep learning calculations are performed on each food to determine the optimal heating temperature and optimal heating time required for each food; According to the optimal heating temperature and optimal heating time required for each ingredient, combined with the cooking method and the personalized needs of the user, a flavor component mapping matrix is ​​generated using a multivariate analysis tool, wherein the matrix includes: the optimal heating temperature and optimal heating time required for each ingredient, and the interaction influence information between the ingredients.

3. A human-computer interactive standardized cooking method system, characterized in that: The system comprises: A receiving module, used to receive cooking parameters input by a user through a human-computer interaction interface, wherein the cooking parameters at least include: recipe requirements and temperature control parameters; An analysis module, used to analyze the recipe requirements and generate a corresponding flavor component mapping matrix, wherein the flavor component mapping matrix at least includes: an optimal heating temperature and an optimal heating time required for each ingredient; A determination module, used to determine a heat energy output strategy for cooking according to the flavor component mapping matrix and the temperature control parameters; The regulating module is used to regulate the heating element of the cooking device in real time according to the heat output strategy, and monitor the preset index of the food during the cooking process so that the preset index meets the preset standard; the regulating module is specifically used to: Monitor the gradual thermal gradient along the volume of the food during the cooking process so that the gradual thermal gradient conforms to a preset gradient curve of the food, wherein the gradual thermal gradient is: Wherein, the G_thermal_gradient is a gradual thermal gradient, the G_max is a maximum thermal gradient, the λ is a thermal gradient attenuation constant, the T is a temperature, and the G_eq is a balanced thermal gradient; or, the regulating module is specifically used for: Monitor the temperature distribution of the food during cooking to achieve a preset ideal distribution of the food, wherein the temperature distribution is: Among them, the T_distribution(x, y, z, t) is the temperature distribution, the T_initial is the initial temperature, the ∆T(x, y, z) is the temperature change adjusted based on the geometric position of the food (x, y, z), and the f(t, E_thermal_plan) is a preset function of the time t and the thermal energy output strategy E_thermal_plan.

4. The system according to claim 3, characterized in that The analysis module is specifically used for: Performing semantic analysis on the recipe requirements to extract key ingredients, cooking methods and personalized needs of users from the recipe requirements; Using the food database and the output of semantic analysis, deep learning calculations are performed on each food to determine the optimal heating temperature and optimal heating time required for each food; According to the optimal heating temperature and optimal heating time required for each ingredient, combined with the cooking method and the personalized needs of the user, a flavor component mapping matrix is ​​generated using a multivariate analysis tool, wherein the matrix includes: the optimal heating temperature and optimal heating time required for each ingredient, and the interaction influence information between the ingredients.

5. 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 2 when executed.

6. 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 2.

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