Cooking device, control method and device thereof, computer device and medium

Abstract

The application relates to a cooking device and a control method and device thereof, computer equipment and a medium. The whole scheme obtains material weight distribution data and a material image, and on this basis, the material types and thickness distribution of different regions on a material bearing assembly are identified, so that the corresponding basic heating distance of different parts of the material can be determined according to the inherent characteristics of the different parts; further, the basic heating distance is individually corrected in combination with the input taste preference, so that the target heating distance capable of making the doneness of each region of the material uniform is obtained, the cooking function is started, and the pose of the material bearing assembly is adjusted in real time according to the target heating distance, so that different regions of the material are always in a heating position matched with the type, thickness and taste demand of the material itself in the cooking process, the local overcooking or undercooking phenomenon caused by the shape difference of the material or a single fixed heating distance is effectively avoided, and the problem of uneven material doneness in the cooking process is solved.

Description

Technical Field

[0001] This application relates to the field of smart home appliance technology, and in particular to a cooking device and its control method, apparatus, computer equipment, storage medium and computer program product. Background Technology

[0002] In existing cooking equipment (such as ovens, microwave ovens, and steam ovens), when processing large or layered ingredients, localized overcooking or undercooking often occurs. Specifically, the top of the food, being closer to the heat source, easily burns, while the bottom or center remains undercooked due to insufficient heating, resulting in poor overall cooking uniformity. Although users can manually adjust the baking tray position before cooking according to recipe suggestions, this operation is a static preset and cannot dynamically respond to the actual state of the food during heating, severely affecting the consistency and quality of the finished product.

[0003] A deeper contradiction lies in the fact that traditional cooking logic is "humans adapting to machines," meaning users must guess and adjust the placement of ingredients and cooking time based on fixed heating curves, rather than the machine actively adapting to the real-time changes in the ingredients. This lack of dynamic feedback mechanisms means that while high-end cooking equipment has sensors, it cannot form a closed-loop control system; while it has a display interface, it cannot make proactive decisions, making it difficult to meet modern families' pursuit of precise cooking and personalized flavors.

[0004] Although some existing cooking equipment is equipped with cameras, their functions are limited to food identification, doneness assessment, or remote monitoring, and they are not linked to the movement mechanism of the food-supporting components (such as trays) or the heating system for coordinated control. Therefore, when food is heated too quickly or too slowly in certain areas during cooking, the equipment cannot dynamically adjust the relative position of the food and the heat source or the heating power, thus failing to fundamentally solve the problem of uneven cooking. Summary of the Invention

[0005] Therefore, it is necessary to provide a cooking device, control method, apparatus, computer equipment, storage medium, and computer program product that can solve the problem of uneven cooking of food, in order to address the above-mentioned technical problems.

[0006] Firstly, this application provides a method for controlling a cooking device. The method includes:

[0007] Acquire food weight distribution data and food images; place food on the food-bearing component;

[0008] The area corresponding to the food carrier component is divided into N×M grids. Based on the food image, the food type and thickness of each grid are obtained grid by grid to generate food thickness distribution data.

[0009] Based on the type of food and the thickness distribution data of the food, determine the basic heating distance corresponding to the food in different areas;

[0010] The input taste preferences are obtained, and the heating distance of ingredients in different regions is corrected based on the taste preferences and the basic heating distance to obtain the target heating distance of ingredients in different regions.

[0011] Start the cooking function and adjust the position of the food-carrying component based on the target heating distance.

[0012] In one embodiment, determining the basic heating distance for different regions of food based on food type and food thickness distribution data includes:

[0013] Obtain the correspondence between preset food type, thickness, and basic heating distance;

[0014] Based on the food type, food thickness distribution data, and the preset correspondence between food type, thickness, and basic heating distance, the basic heating distance for food in different regions is determined.

[0015] In one embodiment, the input taste preference is obtained, and the heating distance of ingredients in different regions is corrected based on the taste preference and the basic heating distance to obtain the target heating distance of ingredients in different regions, including:

[0016] Respond to taste preference setting operations and obtain the input taste preference parameters;

[0017] Based on taste preference parameters, analyze the user's setting of doneness and the degree of caramelization on the surface of the food;

[0018] Obtain the heating distance correction coefficient for different types of ingredients at different degrees of doneness and charring of the ingredient surface;

[0019] The heating distance of ingredients in different regions is corrected based on the heating distance correction coefficient and the basic heating distance to obtain the target heating distance for ingredients in different regions.

[0020] In one embodiment, after activating the cooking function and adjusting the pose of the food-carrying component based on the target heating distance, the method further includes:

[0021] Obtain color data of different areas of ingredients during the cooking process, as well as color thresholds corresponding to taste preferences;

[0022] By comparing color data and corresponding color thresholds in different areas of the food, the pose of the food-bearing components is further corrected to reduce the risk of overcooking.

[0023] In one embodiment, color data and corresponding color thresholds are compared in different areas of the food to perform secondary correction on the pose of the food-supporting component, thereby reducing the risk of overcooking the food.

[0024] By comparing color data and corresponding color thresholds in different areas of the food, overcooked areas, critical areas, and substandard areas in different areas of the food can be identified.

[0025] Based on the color data of different regions of the ingredient, obtain the rate of color change in different regions of the ingredient;

[0026] Obtain the heating power curve during the cooking process, and based on the heating power curve and the rate of color change, identify the overcooked risk zone in the critical zone and the unqualified zone of the food.

[0027] The pose of the food-bearing component is further corrected based on the location of overcooked and overcooked risk areas in different regions of the food to reduce the risk of overcooking.

[0028] In one embodiment, the pose of the food-carrying component is further corrected based on the location of overcooked areas and overcooked risk areas in different regions of the food to reduce the risk of overcooking, including:

[0029] Obtain the edge coordinates of the overcooked area and the overcooked risk area in different regions of the food;

[0030] Based on the edge coordinates, the size of the food-carrying component, and the current rotation angle, calculate the minimum rotation angle to move the overcooked area and the overcooked risk area away from the center of the cooking equipment's hot zone.

[0031] The orientation of the food-bearing component is corrected a second time based on the minimum rotation angle.

[0032] Secondly, this application also provides a cooking equipment control device. The device includes:

[0033] The data acquisition module is used to acquire food weight distribution data and food images; the food is placed on the food carrying component;

[0034] The recognition module is used to divide the area corresponding to the food carrying component into N×M grids, and obtain the food type and food thickness corresponding to each grid based on the food image, and generate food thickness distribution data.

[0035] The heating distance determination module is used to determine the basic heating distance for ingredients in different regions based on the type of ingredients and the thickness distribution data of ingredients; it obtains the input taste preference, and corrects the heating distance of ingredients in different regions based on the taste preference and the basic heating distance to obtain the target heating distance of ingredients in different regions.

[0036] The control module is used to start the cooking function and adjust the position of the food-carrying component based on the target heating distance.

[0037] Thirdly, this application also provides a computer device. The computer device includes a memory and a processor, the memory storing a computer program, and the processor executing the computer program to perform the following steps:

[0038] Acquire food weight distribution data and food images; place food on the food-bearing component;

[0039] The area corresponding to the food carrier component is divided into N×M grids. Based on the food image, the food type and thickness of each grid are obtained grid by grid to generate food thickness distribution data.

[0040] Based on the type of food and the thickness distribution data of the food, determine the basic heating distance corresponding to the food in different areas;

[0041] The input taste preferences are obtained, and the heating distance of ingredients in different regions is corrected based on the taste preferences and the basic heating distance to obtain the target heating distance of ingredients in different regions.

[0042] Start the cooking function and adjust the position of the food-carrying component based on the target heating distance.

[0043] Fourthly, this application also provides a computer-readable storage medium. The computer-readable storage medium stores a computer program thereon, which, when executed by a processor, performs the following steps:

[0044] Acquire food weight distribution data and food images; place food on the food-bearing component;

[0045] The area corresponding to the food carrier component is divided into N×M grids. Based on the food image, the food type and thickness of each grid are obtained grid by grid to generate food thickness distribution data.

[0046] Based on the type of food and the thickness distribution data of the food, determine the basic heating distance corresponding to the food in different areas;

[0047] The input taste preferences are obtained, and the heating distance of ingredients in different regions is corrected based on the taste preferences and the basic heating distance to obtain the target heating distance of ingredients in different regions.

[0048] Start the cooking function and adjust the position of the food-carrying component based on the target heating distance.

[0049] Fifthly, this application also provides a computer program product. The computer program product includes a computer program that, when executed by a processor, performs the following steps:

[0050] Acquire food weight distribution data and food images; place food on the food-bearing component;

[0051] The area corresponding to the food carrier component is divided into N×M grids. Based on the food image, the food type and thickness of each grid are obtained grid by grid to generate food thickness distribution data.

[0052] Based on the type of food and the thickness distribution data of the food, determine the basic heating distance corresponding to the food in different areas;

[0053] The input taste preferences are obtained, and the heating distance of ingredients in different regions is corrected based on the taste preferences and the basic heating distance to obtain the target heating distance of ingredients in different regions.

[0054] Start the cooking function and adjust the position of the food-carrying component based on the target heating distance.

[0055] Sixthly, this application also provides a cooking device. It includes an adjustable food-carrying component and a controller, the controller using the aforementioned cooking device control method to control the position and orientation of the food-carrying component.

[0056] The aforementioned cooking equipment, control methods, devices, computer equipment, storage media, and computer program products acquire food weight distribution data and food images. Based on this, they identify the food type and thickness distribution in different regions of the N×M grids on the food-bearing component. This allows them to determine the corresponding basic heating distance for different parts of the food based on their inherent characteristics. Furthermore, by incorporating input taste preferences, they personalize this basic heating distance to obtain a target heating distance that ensures uniform cooking across all areas of the food. Based on this, the cooking function is activated, and the position of the food-bearing component is adjusted in real time according to this target heating distance. This ensures that different areas of the food are always in a heating position that matches their type, thickness, and taste requirements during cooking. This effectively avoids localized overcooking or undercooking caused by differences in food shape or a single fixed heating distance, achieving a shift from "static preset" to "dynamic adaptation," thus fundamentally solving the problem of uneven food cooking. Attached Figure Description

[0057] Figure 1 This is an application environment diagram of a cooking equipment control method in one embodiment;

[0058] Figure 2 This is a flowchart illustrating a cooking equipment control method in one embodiment;

[0059] Figure 3 This is a flowchart illustrating the cooking equipment control method in another embodiment;

[0060] Figure 4 This is a structural block diagram of a cooking equipment control device in one embodiment;

[0061] Figure 5 This is an internal structural diagram of a computer device in one embodiment.

[0062] Explanation of reference numerals in the attached figures:

[0063] 100. Oven; 120. Baking tray; 140. Controller. Detailed Implementation

[0064] To make the objectives, technical solutions, and advantages of this application clearer, the following detailed description is provided in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the scope of this application.

[0065] The cooking equipment control method provided in this application embodiment can be applied to, for example... Figure 1 The application environment is illustrated below. Taking an oven as an example, the oven 100 has a built-in adjustable baking tray 120 (food-carrying component), and also includes a controller 140. The controller 140 acquires food weight distribution data and food images; based on the food images, it identifies the food type and thickness distribution data in different areas of the baking tray 120; according to the food type and thickness distribution data, it determines the basic heating distance corresponding to different areas of the food; it acquires the input taste preference, and based on the taste preference and the basic heating distance, it corrects the heating distance of different areas of the food to obtain the target heating distance for different areas; it starts the cooking function and adjusts the position of the baking tray 120 based on the target heating distance. It is understood that the cooking device can also include microwave ovens, steam ovens, etc., and their control process is similar to the above, so it will not be described in detail here.

[0066] In one embodiment, such as Figure 2 As shown, a method for controlling a cooking device is provided, which is applied to... Figure 1 Taking controller 140 as an example, the explanation includes the following steps:

[0067] S100: Acquire food weight distribution data and food images; place food on the food carrier component.

[0068] The controller first acquires weight distribution data and image data of the food placed on the food-supporting component. The weight distribution data characterizes the weight differences of the food at different locations on the food-supporting component; for example, when the food is irregularly shaped or placed off-center, the pressure distribution on the support component varies in different areas. The food image is then used for subsequent visual analysis. In practical applications, various sensing elements can be configured within the cooking equipment. For example, a distributed weight sensor array can be integrated below the food-supporting component or within its supporting structure. This array can detect the pressure values ​​in different areas of the support component in real time, thereby generating weight distribution data. Simultaneously, a camera (such as a wide-angle camera or a fisheye camera) is installed inside the cooking equipment cavity to acquire image data of the food. The controller obtains the required weight distribution data and food image by reading the output signals of the aforementioned sensors.

[0069] S200: Divide the area corresponding to the food carrying component into N×M grids, obtain the food type and food thickness corresponding to each grid based on the food image, and generate food thickness distribution data.

[0070] After acquiring the food image, the controller uses a preset image recognition algorithm to process it. Specifically, algorithms including but not limited to image segmentation, feature extraction, and classification can be used to identify the type of food corresponding to different areas on the food-bearing component (e.g., distinguishing between meat, vegetable, and starchy food areas). Simultaneously, the controller calculates the thickness distribution data for each area based on geometric information in the image (such as shadows, textures, or combined with additional depth sensing information). This thickness distribution data reflects the height or thickness value at different points on the food, characterizing its three-dimensional shape. Furthermore, to obtain more accurate thickness information, the camera in the cooking device can be used in conjunction with a structured light projector. The structured light projector projects a grating with a specific pattern onto the food surface, the camera captures the grating image modulated by the food surface, and the controller generates a three-dimensional thickness distribution map of the food by calculating the grating deformation. For example, when processing a whole chicken, this step can identify that the chicken breast area is thicker than the chicken wing area and generate corresponding thickness distribution data.

[0071] Specifically, the controller first logically divides the planar area covered by the food-carrying component (i.e., the area where the food may be placed) into N rows and M columns of grid cells. Here, N and M are both integers greater than or equal to 2, and the grid shape can be rectangular, square, or other regular shapes. The granularity of the grid division (i.e., the values ​​of N and M) can be preset or dynamically adjusted according to the required recognition accuracy and computing resources. The purpose of grid division is to discretize continuous food image information into multiple independent sub-regions, thereby facilitating grid-by-grid analysis and differentiated processing of different parts of the food, avoiding the loss of local feature information due to overall averaging. After completing the grid division, the controller, for each grid cell, based on the food image obtained in step S100, identifies the corresponding food type and the food thickness of the grid region. Specifically, for each grid, the controller can extract image features (such as color, texture, shape, etc.) within the grid using an image segmentation algorithm and compare them with a preset food feature library to determine which type of food (e.g., meat, vegetables, starches, etc.) the grid belongs to. Simultaneously, the controller can calculate the thickness of the food within the grid using depth information from the image (such as data from binocular vision, structured light, or time-of-flight sensors). Furthermore, if the food thickness is uneven within the grid, the average or maximum value can be used as the representative thickness of that grid. After acquiring the food thickness for each grid, the controller integrates the thickness data from all grids to generate food thickness distribution data. This data can specifically be a food thickness distribution map corresponding to the entire food-bearing component area. This distribution map can be represented as a two-dimensional matrix, a heat map, or a contour map, visually reflecting the thickness differences at various locations within the food.

[0072] S300: Determine the basic heating distance for different regions of food based on the type of food and the thickness distribution data of the food.

[0073] After obtaining the type and thickness data of each region of the food, the controller determines the corresponding basic heating distance for different regions of the food based on a built-in preset mapping relationship. The basic heating distance can be understood as the reference spatial distance that a region should maintain between itself and the heating source under standard cooking conditions (such as default heating power and default ambient temperature) to ensure that food of that type and thickness reaches a basically uniformly cooked state. For example, for thicker meat regions, heat is difficult to penetrate, so they need to be placed closer to the heating source (i.e., a smaller basic heating distance); for thinner vegetables or edge regions, the basic heating distance can be set relatively larger to avoid overcooking. This mapping relationship can be obtained through experimental calibration or machine learning training.

[0074] S400: Obtain the input taste preference, and adjust the heating distance of ingredients in different regions based on the taste preference and the basic heating distance to obtain the target heating distance of ingredients in different regions.

[0075] To meet the personalized needs of different users, the controller further acquires the user's input taste preferences. These preferences can be received through the cooking equipment's interface (such as a touchscreen, knob, voice input, or mobile app), and may include, but are not limited to, levels of doneness (e.g., rare, medium, well-done), degree of caramelization (light, medium, heavy), and preference for a crispy exterior and tender interior. Based on these preferences, the controller modifies the base heating distance determined in step S300. For example, if the user prefers a heavily caramelized texture, the controller can shorten the heating distance corresponding to the surface area of ​​the food to enhance surface browning; if the user prefers a tender interior, the controller may even out the heating distance across different areas to avoid excessive localized heating. The modified heating distance is called the target heating distance, defined as the heating distance that ensures uniform doneness across all areas of the food and meets the user's preferences.

[0076] S500: Start the cooking function and adjust the position of the food-carrying component based on the target heating distance.

[0077] After determining and correcting the above parameters, the controller activates the cooking function, i.e., controls the heating device (such as a heating element, microwave generator, steam generator, etc.) to start working. Simultaneously, based on the target heating distance calculated in step S400, the controller generates and sends control commands in real time to adjust the posture of the food-carrying component. The posture includes at least the vertical height (lifting / lowering) and angular position (rotation) of the food-carrying component within a horizontal plane. By adjusting the posture, different areas of the food can be dynamically positioned at their corresponding target heating distances. In practical applications, multi-degree-of-freedom drive mechanisms can be configured within the cooking equipment. In a specific embodiment, the food-carrying component uses a disc-shaped baking pan and its supporting structure, connected by rollers to ensure stable operation. This drive mechanism integrates both lifting and rotation functions: a motor-driven conveyor belt lifting mechanism achieves the vertical displacement of the baking pan, while a gear transmission system drives a rotating mechanism, causing the baking pan to rotate around its central axis. The controller calculates the required displacement for each area based on the target heating distance and drives the aforementioned mechanisms.

[0078] The aforementioned cooking equipment control method acquires food weight distribution data and food images, and based on this, identifies the food type and thickness distribution in different regions of the N×M grids on the food-supporting component. This allows for the determination of the corresponding basic heating distance for different parts of the food, taking into account their inherent characteristics. Furthermore, this basic heating distance is personalized by incorporating input taste preferences, resulting in a target heating distance that ensures uniform cooking across all areas of the food. Based on this, the cooking function is activated, and the position of the food-supporting component is adjusted in real time according to this target heating distance, ensuring that different areas of the food are always in a heating position that matches their type, thickness, and taste requirements during cooking. This effectively avoids localized overcooking or uncooking caused by differences in food shape or a single fixed heating distance, achieving a shift from "static preset" to "dynamic adaptation," thus fundamentally solving the problem of uneven food cooking.

[0079] In one embodiment, such as Figure 3 As shown, based on the type of food and the thickness distribution data, the basic heating distance for different areas of food is determined as follows:

[0080] S320: Obtain the correspondence between preset food type, thickness, and basic heating distance.

[0081] The controller first acquires a pre-built mapping relationship stored in the cooking equipment's internal memory. This preset food type-thickness-basic heating distance mapping relationship refers to mapping data established through extensive experimental calibration for different food types (such as meat, vegetables, pastries, eggs, etc.) and their varying thicknesses. This mapping relationship characterizes the reference heating distance that should be set under standard cooking conditions for a specific type and thickness of food to ensure uniform cooking.

[0082] S340: Based on the food type, food thickness distribution data, and the preset correspondence between food type, thickness, and basic heating distance, determine the basic heating distance for food in different regions.

[0083] After obtaining the aforementioned preset correspondence, the controller takes the food type and thickness data of each region identified in step S200 as input, and performs matching or calculation based on the preset correspondence to determine the corresponding basic heating distance for each region. Specifically, for each position on the food-bearing component (e.g., each grid mentioned above), the controller queries the preset correspondence based on the food type and thickness value of that position. If a precise match exists, the corresponding basic heating distance is directly read; if no precise match exists, the corresponding basic heating distance is calculated through interpolation (such as linear interpolation or spline interpolation). Finally, the controller outputs a basic heating distance value for each region of the food.

[0084] In one embodiment, the input taste preference is obtained, and the heating distance of ingredients in different regions is corrected based on the taste preference and the basic heating distance to obtain the target heating distance of ingredients in different regions, including:

[0085] Step 1: Respond to the taste preference setting operation and obtain the input taste preference parameters.

[0086] The controller responds to the user's taste preference setting operation performed through the cooking equipment's interactive interface and obtains the taste preference parameters input by the user. The taste preference parameters are the user's expected description of the taste of the final cooked product, and their specific forms may include, but are not limited to: graded options (such as doneness levels: rare, medium, well-done; caramelization level: no caramelization, light, medium, heavy), numerical ratings (such as 1-10 points), or descriptive labels (such as "crispy on the outside and tender on the inside", "soft and sticky", etc.).

[0087] Step 2: Analyze the user's settings for doneness and the degree of caramelization on the surface of the ingredients based on taste preference parameters.

[0088] After obtaining the taste preference parameters, the controller parses these parameters, extracting two key dimensions: the user's desired internal doneness (i.e., the degree of doneness of the center of the food) and the user's desired degree of caramelization on the surface of the food. For example, when a user selects "medium done, medium caramelization," the controller interprets "medium done" as the target internal doneness value and "medium caramelization" as the target surface color or browning level value. It should be noted that the same taste preference parameter may implicitly include requirements for both dimensions simultaneously. For example, "crispy on the outside and tender on the inside" requires both a high degree of caramelization on the surface and a low degree of doneness inside.

[0089] Step 3: Obtain the heating distance correction coefficient for different types of ingredients at different degrees of doneness and the degree of caramelization on the surface of the ingredients.

[0090] The controller acquires a pre-built correction coefficient mapping relationship. This mapping relationship records, for different food types, the correction coefficients that need to be applied to the base heating distance to achieve specific internal doneness and specific surface caramelization. The correction coefficient can be a multiplier factor (e.g., 0.8, 1.2), an increment or decrement (e.g., -1 cm, +2 cm), or a non-linear mapping function. Because different foods have significantly different physicochemical properties such as thermal conductivity, moisture content, fat ratio, and protein structure, even for the same taste preference (e.g., both "medium-rare" and "medium caramelized"), the corresponding correction coefficients will differ. For example, the heating distance correction coefficient required to achieve "medium-rare" chicken breast differs from the correction coefficient required to achieve the same doneness in salmon. These correction coefficients cannot be uniformly calculated using theoretical formulas; instead, they need to be obtained through extensive experimental calibration for each food type during the research and development phase. Specifically, for the same ingredient, by testing different combinations of heating distance and heating time, the color change curve of the ingredient surface and the sensory evaluation results of the final product are recorded. The finished product is classified into different levels such as overcooked, perfect, and undercooked, and its caramelization and tenderness are quantified. In this way, the optimal heating distance correction coefficient corresponding to each taste level is determined in reverse.

[0091] Step 4: Correct the heating distance of ingredients in different regions based on the heating distance correction coefficient and the basic heating distance to obtain the target heating distance of ingredients in different regions.

[0092] After obtaining the correction coefficient, the controller applies the correction coefficient to the base heating distance determined in step S300, and corrects the heating distance of the food in different regions respectively, thereby obtaining the target heating distance for each region. The correction operation can be a multiplication operation (target distance = base distance × correction coefficient) or an addition operation (target distance = base distance + correction amount), depending on how the correction coefficient is defined.

[0093] In one embodiment, after activating the cooking function and adjusting the pose of the food-carrying component based on the target heating distance, the method further includes:

[0094] Step 1: Obtain color data of different areas of the ingredients during the cooking process, as well as the color threshold corresponding to taste preferences.

[0095] After the cooking process begins, the controller continuously acquires color data from different areas of the food surface, while simultaneously obtaining color thresholds corresponding to the user's input taste preferences. Color data refers to the real-time color information of the food surface captured by an image acquisition device (such as a camera), which can be expressed as quantitative indicators such as chromaticity values, RGB components, browning index, or Maillard reaction degree. The color threshold is a pre-set standard value used to determine whether the food surface has reached the user's desired level of caramelization or doneness. This color threshold corresponds one-to-one with the user's taste preferences: for example, if the user prefers "lightly caramelized," the corresponding color threshold is lower (lighter surface color); if the user prefers "heavily caramelized," the corresponding color threshold is higher (darker surface color). It should be noted that because different foods exhibit different color change patterns during heating, the color threshold needs to be calibrated separately for different food types. This calibration is usually completed during the research and development phase, through extensive experimental measurements of the color changes of various foods under different heating conditions, combined with sensory evaluation, to determine the optimal color threshold range corresponding to each taste preference level.

[0096] Step 2: Compare the color data and corresponding color thresholds in different areas of the food, and make secondary corrections to the pose of the food-bearing components to reduce the risk of overcooking.

[0097] If the current color data of a certain area has reached or exceeded the preset color threshold (i.e., there is a risk of overcooking), while the color of other areas has not yet met the standard, the controller determines that the area is overheated. At this time, the controller generates a secondary correction command to dynamically adjust the position of the food-bearing component. The secondary correction may include, but is not limited to, one or more of the following operations: lowering the overall height of the food-bearing component to move all areas away from the heating source; or moving the overcooked area out of the high heat radiation area by rotation, while moving the areas that have not yet met the standard into the high heat radiation area; or reducing the power of the local heating unit corresponding to the overcooked area (if the equipment supports zone heating). Through the above dynamic adjustments, the controller can reduce the heat intensity of the food just as the surface of the food reaches the desired color, preventing it from further developing into a scorched state, thereby effectively reducing the risk of overcooking.

[0098] In one embodiment, color data and corresponding color thresholds are compared in different areas of the food to perform secondary correction on the pose of the food-supporting component, thereby reducing the risk of overcooking the food.

[0099] Step 1: Compare the color data and corresponding color thresholds in different areas of the ingredient to identify overcooked, critical, and substandard areas in different regions of the ingredient.

[0100] The controller compares real-time color data from different areas of the food with a preset color threshold system, thus dividing the food surface into three categories. The color threshold system includes at least three levels: the target color threshold (corresponding to the color state desired by the user's taste preference), the overcooked threshold T1 (above the target color threshold T, indicating the food surface has already appeared or is about to char), and the basic doneness threshold T2 (below the target color threshold, indicating the food has not yet reached maturity). By comparing the current color value of each area with the above thresholds, the controller divides the food surface into: overcooked zone (color value exceeds the overcooked threshold, posing a risk of charring), critical zone (color value between the basic doneness threshold and the overcooked threshold, close to the target state but not yet overcooked), and undercooked zone (color value below the basic doneness threshold, not yet meeting basic ripeness requirements). The controller continuously collects color data from each grid. To quantify color changes, the controller can use the internationally recognized L... ab Feature extraction is performed in the color space, and the color difference value of each grid relative to the initial color is calculated using the color difference formula as follows:

[0101]

[0102] in, This is the current real-time color value. The preset target model color value (corresponding to the color state desired by the user's taste preference). Based on this color difference value, the controller performs region classification according to the following rules: when When, the grid is determined to be an overripe area; when When the grid is in the critical region, δ is determined to be a preset tolerance range, used to allow for margin during the cooking process to achieve a near-perfect taste; when At that time, the grid was determined to be a non-compliant area, among which This is the basic maturity threshold. (The above...) , , Parameters such as δ need to be calibrated through a large number of experiments for different ingredients during the research and development stage. For example, different combinations of heating distance and heating time can be tested on the same ingredient to record the final product state and associate it with the corresponding color value.

[0103] Step 2: Based on the color data of different areas of the ingredient, obtain the rate of color change in different areas of the ingredient.

[0104] The controller calculates the rate of color change over time for each region (e.g., each grid cell) based on continuously acquired multi-frame images of food. Specifically, the controller records the color values ​​of the same region at adjacent time points and obtains the rate of color change for that region through differential calculation (e.g., the amount of color change within time interval Δt divided by the time interval Δt). The rate of color change reflects the speed of browning or ripening on the surface of the food and is an important dynamic indicator for predicting the risk of overripeness in the future. For example, if a region has not yet reached the overripeness threshold, but its rate of color change is relatively fast, it means that the region will enter an overripe state in a short period of time.

[0105] Step 3: Obtain the heating power curve during the cooking process. Based on the heating power curve and the rate of color change, identify the overcooked risk zone in the critical zone and the unqualified zone of the ingredients.

[0106] The controller acquires the heating power curve (i.e., the trajectory of the heating device's output power over time) during the current cooking process. Then, the controller combines the heating power curve with the color change rate calculated in step 2 to assess the overcooking risk for each area in the critical and substandard zones. Specifically, based on the current color change rate and the current heating power, the controller can fit a future color change trend curve for each area. If the prediction shows that the color value of the area will reach or exceed the overcooking threshold within a future time window, the area is marked as an "overcooking risk zone." It is worth noting that the trend of the heating power curve affects the accuracy of the prediction: if the heating power curve shows that the power will further increase in the future, the currently predicted overcooking risk will accelerate; if the power will decrease in the future, the currently predicted overcooking risk may be mitigated.

[0107] Step 4: Based on the location of overcooked areas and overcooked risk areas in different regions of the food, the pose of the food-supporting component is further corrected to reduce the risk of overcooking.

[0108] After identifying the locations of overcooked and overcooked risk areas, the controller generates corresponding pose correction commands based on the spatial distribution of these areas on the food-bearing component. These corrections include, but are not limited to, rotation correction and height correction. For rotation correction, the controller calculates the minimum rotation angle required to move overcooked and overcooked risk areas away from high-heat radiation areas, while simultaneously moving substandard areas into high-heat radiation areas, and drives the food-bearing component to rotate. For height correction, when rotation correction cannot adequately balance the heating differences between areas, or when the overall color change rate of the food deviates from the expected trajectory, the controller further fine-tunes the vertical height of the food-bearing component to change the overall heat radiation intensity received by the food.

[0109] In one embodiment, the pose of the food-carrying component is further corrected based on the location of overcooked areas and overcooked risk areas in different regions of the food to reduce the risk of overcooking, including:

[0110] Step 1: Obtain the edge coordinates of the overcooked area and the overcooked risk area in different regions of the ingredient.

[0111] The controller first acquires the spatial location information of the identified overcooked and overcooked risk areas on the food-supporting component, and then further extracts the edge coordinates of these areas. Edge coordinates refer to the set of location points that constitute the outline boundary of the overcooked and overcooked risk areas, such as the boundary point coordinates of the grid cells covered by each area. By acquiring the edge coordinates, the controller can determine the specific distribution range and geometry of these areas on the supporting component, providing a precise spatial basis for subsequent calculation of rotation angles.

[0112] Step 2: Based on the edge coordinates, the size of the food-carrying component, and the current rotation angle, calculate the minimum rotation angle to move the overcooked area and the overcooked risk area away from the center of the cooking equipment's hot zone.

[0113] After obtaining the edge coordinates of the overcooked area and the overcooked risk area, the controller further obtains the dimensions (e.g., radius or side length) of the food-carrying component and its current rotation angle. Then, the controller combines these parameters to calculate a minimum rotation angle. The minimum rotation angle refers to the smallest angle required to move the overcooked area and the overcooked risk area away from the center of the heat field (i.e., the high heat radiation area) through rotation, based on the current pose of the carrying component. This angle calculation needs to consider the relative positional relationship between the edge of the overcooked area and the center of the heat field, as well as the spatial distribution changes of each area after rotation. By calculating the minimum rotation angle, the controller can achieve the avoidance of overcooked areas and the compensation of substandard areas with the most economical movement amplitude, avoiding unnecessary over-rotation and improving adjustment efficiency.

[0114] Step 3: Correct the position of the food-bearing component a second time based on the minimum rotation angle.

[0115] After calculating the minimum rotation angle, the controller generates a corresponding rotation control command, driving the food-carrying component to rotate according to that minimum rotation angle. After rotation, the overripe and overripe risk zones, which were originally located near the center of the heating zone, move to positions away from the center of the heating zone, thus entering a lower heat radiation area and slowing down their browning rate. At the same time, the substandard zones, which were originally located elsewhere, may rotate into the vicinity of the center of the heating zone, thereby obtaining higher heat radiation intensity and accelerating their ripening process.

[0116] Furthermore, after cooking is complete, the controller automatically records the ingredient parameters (including ingredient type, thickness distribution data, and weight distribution data), user-inputted taste preferences, the actual motion trajectory of the ingredient-carrying components (including lifting height sequence and rotation angle sequence), and the color evolution data of the ingredient surface (e.g., the curve of color difference value of each grid changing over time). If the user provides a taste evaluation of the finished product through the interactive interface (e.g., "overcooked," "just right," "undercooked," or a sub-rating), the controller associates and stores this evaluation with the recorded data for subsequent optimization and adjustment of initial parameters when cooking similar ingredients. Additionally, the cooking equipment uploads the recorded local cooking data and user evaluations to a cloud server via network connectivity. The cloud server aggregates a large amount of user feedback data from multiple cooking devices. When a large number of users report "overcooked" or "undercooked" abnormal results for a certain type of ingredient, the cloud automatically analyzes and generates a correction coefficient corresponding to that ingredient (e.g., a basic heating distance correction coefficient or a color threshold correction coefficient). This correction coefficient is then distributed to all networked cooking devices to optimize the initial cooking parameters for similar ingredients.

[0117] It should be understood that although the steps in the flowcharts of the above embodiments are shown sequentially according to the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless explicitly stated herein, there is no strict order restriction on the execution of these steps, and they can be executed in other orders. Moreover, at least some steps in the flowcharts of the above embodiments may include multiple steps or multiple stages. These steps or stages are not necessarily completed at the same time, but can be executed at different times. The execution order of these steps or stages is not necessarily sequential, but can be performed alternately or in turn with other steps or at least some of the steps or stages of other steps.

[0118] Based on the same inventive concept, this application also provides a cooking equipment control device for implementing the cooking equipment control method described above. The solution provided by this device is similar to the solution described in the above method; therefore, the specific limitations in one or more embodiments of the cooking equipment control device provided below can be found in the limitations of the cooking equipment control method described above, and will not be repeated here.

[0119] In one embodiment, such as Figure 4 As shown, a cooking equipment control device is provided, comprising:

[0120] Data acquisition module 200 is used to acquire food weight distribution data and food images; food is placed on food carrying component;

[0121] The recognition module 400 is used to divide the area corresponding to the food carrying component into N×M grids, and obtain the food type and food thickness corresponding to each grid based on the food image, and generate food thickness distribution data.

[0122] The heating distance determination module 600 is used to determine the basic heating distance corresponding to different regions of ingredients based on the type of ingredients and the thickness distribution data of ingredients; to obtain the input taste preference; and to correct the heating distance of ingredients in different regions based on the taste preference and the basic heating distance to obtain the target heating distance of ingredients in different regions.

[0123] The control module 800 is used to start the cooking function and adjust the position of the food-carrying component based on the target heating distance.

[0124] In one embodiment, the heating distance determination module 600 is further configured to obtain a preset correspondence between food type, thickness, and basic heating distance; and to determine the basic heating distance for food in different regions based on the food type, food thickness distribution data, and the preset correspondence between food type, thickness, and basic heating distance.

[0125] In one embodiment, the heating distance determination module 600 is further configured to respond to the taste preference setting operation, obtain the input taste preference parameters; analyze the doneness and the degree of caramelization on the surface of the food according to the taste preference parameters; obtain the heating distance correction coefficients for different doneness and caramelization on the surface of the food corresponding to different types of food; and correct the heating distance of the food in different regions according to the heating distance correction coefficients and the basic heating distance to obtain the target heating distance of the food in different regions.

[0126] In one embodiment, the control module 800 is also used to acquire color data of different areas of the food during the cooking process, as well as color thresholds corresponding to taste preferences; compare the color data and corresponding color thresholds in different areas of the food to make secondary corrections to the pose of the food-bearing components, so as to reduce the risk of overcooking the food.

[0127] In one embodiment, the control module 800 is further configured to compare color data and corresponding color thresholds in different regions of the food to identify overcooked areas, critical areas, and substandard areas in different regions of the food; obtain the color change rate of different regions of the food based on the color data of different regions of the food; obtain the heating power curve during the cooking process, and identify overcooked risk areas in the critical and substandard areas of the food based on the heating power curve and the color change rate; and perform secondary correction on the pose of the food-bearing component based on the position of the overcooked areas and overcooked risk areas in different regions of the food to reduce the risk of overcooking the food.

[0128] In one embodiment, the control module 800 is further configured to acquire the edge coordinates of the overcooked area and the overcooked risk area in different regions of the food; calculate the minimum rotation angle to move the overcooked area and the overcooked risk area away from the center of the hot field of the cooking equipment based on the edge coordinates, the size of the food carrying component and the current rotation angle; and correct the pose of the food carrying component a second time based on the minimum rotation angle.

[0129] Each module in the aforementioned cooking equipment control device can be implemented entirely or partially through software, hardware, or a combination thereof. These modules can be embedded in or independent of the processor in a computer device, or stored in the computer device's memory as software, so that the processor can call and execute the corresponding operations of each module.

[0130] In addition, this application also provides a cooking device. It includes an adjustable food-carrying component and a controller, the controller using the aforementioned cooking device control method to control the position and orientation of the food-carrying component.

[0131] In one embodiment, a computer device is provided, which may be a terminal, and its internal structure diagram may be as follows: Figure 5 As shown, the computer device includes a processor, memory, communication interface, display screen, and input devices connected via a system bus. The processor provides computing and control capabilities. The memory includes non-volatile storage media and internal memory. The non-volatile storage media stores the operating system and computer programs. The internal memory provides an environment for the operation of the operating system and computer programs stored in the non-volatile storage media. The communication interface is used for wired or wireless communication with external terminals; wireless communication can be achieved through Wi-Fi, mobile cellular networks, NFC (Near Field Communication), or other technologies. When the computer program is executed by the processor, it implements a method for controlling a cooking device. The display screen can be an LCD screen or an e-ink screen. The input devices can be a touch layer covering the display screen, buttons, a trackball, or a touchpad on the computer device's casing, or an external keyboard, touchpad, or mouse.

[0132] Those skilled in the art will understand that Figure 5 The structure shown is merely a block diagram of a portion of the structure related to the present application and does not constitute a limitation on the computer device to which the present application is applied. Specific computer devices may include more or fewer components than those shown in the figure, or combine certain components, or have different component arrangements.

[0133] In one embodiment, a computer device is provided, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the above-described cooking device control method.

[0134] In one embodiment, a computer-readable storage medium is provided having a computer program stored thereon, which, when executed by a processor, implements the above-described cooking equipment control method.

[0135] In one embodiment, a computer program product is provided, including a computer program that, when executed by a processor, implements the above-described cooking equipment control method.

[0136] Those skilled in the art will understand that all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium. When executed, the computer program can include the processes of the embodiments of the above methods. Any references to memory, databases, or other media used in the embodiments provided in this application can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM can take many forms, such as Static Random Access Memory (SRAM) or Dynamic Random Access Memory (DRAM). The databases involved in the embodiments provided in this application may include at least one type of relational database and non-relational database. Non-relational databases may include, but are not limited to, blockchain-based distributed databases. The processors involved in the embodiments provided in this application may be general-purpose processors, central processing units, graphics processing units, digital signal processors, programmable logic devices, quantum computing-based data processing logic devices, etc., and are not limited to these.

[0137] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0138] The above embodiments are merely illustrative of several implementation methods of this application, and their descriptions are relatively specific and detailed. However, they should not be construed as limiting the scope of this application. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of this application, and these all fall within the protection scope of this application. Therefore, the protection scope of this application should be determined by the appended claims.

Claims

1. A method for controlling a cooking device, characterized in that, The food-carrying component in the cooking device has an adjustable position; the method includes: Acquire food weight distribution data and food images; place the food in the food-supporting component; The area corresponding to the food carrying component is divided into N×M grids. Based on the food image, the food type and food thickness corresponding to each grid are obtained grid by grid to generate food thickness distribution data. Based on the food type and food thickness distribution data, determine the basic heating distance for food in different regions; The input taste preference is obtained, and the heating distance of ingredients in different regions is corrected based on the taste preference and the basic heating distance to obtain the target heating distance of ingredients in different regions. Start the cooking function and adjust the position of the food-carrying component based on the target heating distance.

2. The method according to claim 1, characterized in that, The step of determining the basic heating distance for different regions of food based on the food type and food thickness distribution data includes: Obtain the correspondence between preset food type, thickness, and basic heating distance; Based on the food type, the food thickness distribution data, and the preset correspondence between food type, thickness, and basic heating distance, the basic heating distance corresponding to food in different regions is determined.

3. The method according to claim 1, characterized in that, The process of obtaining the input taste preference, and then correcting the heating distance of ingredients from different regions based on the taste preference and the base heating distance to obtain the target heating distance for ingredients from different regions includes: Respond to taste preference setting operations and obtain the input taste preference parameters; Based on the taste preference parameters, analyze the cookedness set by the user and the degree of caramelization on the surface of the food; Obtain the heating distance correction coefficient for different types of ingredients at different degrees of doneness and charring of the ingredient surface; The heating distance of ingredients in different regions is corrected based on the heating distance correction coefficient and the base heating distance to obtain the target heating distance of ingredients in different regions.

4. The method according to claim 1, characterized in that, After adjusting the orientation of the food-carrying component based on the target heating distance, the cooking function activation further includes: Obtain color data of different areas of the ingredients during the cooking process, as well as the color threshold corresponding to the taste preference; By comparing color data and corresponding color thresholds in different areas of the food, the pose of the food-bearing component is corrected a second time to reduce the risk of overcooking the food.

5. The method according to claim 4, characterized in that, The step of comparing color data and corresponding color thresholds in different areas of the food to perform secondary correction on the pose of the food-supporting component, in order to reduce the risk of overcooking the food, includes: By comparing color data and corresponding color thresholds in different areas of the food, overcooked areas, critical areas, and substandard areas in different areas of the food can be identified. Based on the color data of different regions of the ingredient, obtain the rate of color change in different regions of the ingredient; The heating power curve during the cooking process is obtained, and the overcooked risk area in the critical zone and the substandard zone of the food is identified based on the heating power curve and the color change rate. The pose of the food-bearing component is further corrected based on the location of overcooked and overcooked risk areas in different regions of the food to reduce the risk of overcooking.

6. The method according to claim 5, characterized in that, The secondary correction of the food-supporting component pose based on the positions of overcooked and overcooked risk areas in different regions of the food, in order to reduce the risk of overcooking, includes: Obtain the edge coordinates of the overcooked area and the overcooked risk area in different regions of the food; Based on the edge coordinates, the size of the food-carrying component, and the current rotation angle, calculate the minimum rotation angle to move the overcooked area and the overcooked risk area away from the center of the cooking equipment's hot zone. The orientation of the food-bearing component is corrected a second time based on the minimum rotation angle.

7. A cooking equipment control device, characterized in that, The food-carrying component in the cooking device has an adjustable position; the device includes: The data acquisition module is used to acquire food weight distribution data and food images; the food is placed on the food carrying component; The identification module is used to divide the area corresponding to the food carrying component into N×M grids, and obtain the food type and food thickness corresponding to each grid based on the food image, and generate food thickness distribution data. The heating distance determination module is used to determine the basic heating distance corresponding to different regions of ingredients based on the ingredient type and ingredient thickness distribution data; obtain the input taste preference, and correct the heating distance of ingredients in different regions based on the taste preference and the basic heating distance to obtain the target heating distance of ingredients in different regions. The control module is used to start the cooking function and adjust the position of the food-carrying component based on the target heating distance.

8. A computer device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that, When the processor executes the computer program, it implements the steps of the method according to any one of claims 1 to 6.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by a processor, it implements the steps of the method according to any one of claims 1 to 6.

10. A cooking device, characterized in that, The device includes an adjustable food-carrying component and a controller, wherein the controller controls the position and orientation of the food-carrying component using the cooking device control method according to any one of claims 1 to 6.

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