Temperature control method, device and equipment for baking equipment and storage medium

Abstract

The invention belongs to the technical field of electric appliances, and particularly relates to a temperature control method, device and equipment of baking equipment and a storage medium, and the method comprises the steps that hyperspectral data of intra-cavity images of the baking equipment are acquired, and the intra-cavity images comprise a food material image and a cavity image; the hyperspectral data comprises spectral data of each pixel point of the intracavity image, and the spectral data of the pixel point comprises light intensity of different wavelengths of the pixel point; according to the hyperspectral data, color distribution information of the surface of the baked food material is obtained; acquiring temperature distribution information of a cavity of the baking equipment according to the hyperspectral data; according to the coloring distribution information and the temperature distribution information, temperature control parameters of the baking equipment are adjusted, and the baking equipment is controlled according to the adjusted temperature control parameters, so that the color distribution of the surfaces of the food materials is uniform, and the purpose that the color distribution of the surfaces of the food materials is uniform under different food material conditions and environment conditions is achieved. And dishes with uniform surface colors can be obtained.

Description

Technical Field

[0001] This application belongs to the field of electrical technology, and specifically relates to a temperature control method, device, equipment and storage medium for baking equipment. Background Technology

[0002] As people's living standards improve and their pursuit of a high-quality life continues, various cooking equipment are playing an increasingly important role in daily life, and users' requirements for cooking equipment are also getting higher and higher. For some dishes that require baking, users have certain requirements for the uniformity of color on the surface of the baked dish.

[0003] Existing baking equipment typically conducts cooking experiments on different dishes using different temperature control parameter values ​​before the equipment is put on the market. Based on the experimental results, the temperature control parameter value that produces a dish with good color uniformity is used as the preset temperature control parameter value for the corresponding dish.

[0004] However, due to the diversity of ingredients used and the differences in actual cooking scenarios, cooking ingredients using fixed preset temperature control parameters often makes it difficult to guarantee the uniformity of the surface color of the final product. In some cases, the surface of the finished product may be charred and blackened, while other parts may be too light in color, making it difficult to achieve the expected cooking effect. Summary of the Invention

[0005] This application provides a temperature control method, apparatus, device, and storage medium for baking equipment, to solve the problems of uneven surface color and poor cooking effect of dishes made by existing baking equipment.

[0006] In a first aspect, this application provides a temperature control method for a baking apparatus, the method comprising:

[0007] The hyperspectral data of the cavity image of the baking equipment is acquired, wherein the cavity image includes an image of the food and an image of the cavity, and the hyperspectral data includes the spectral data of each pixel in the cavity image, and the spectral data of each pixel includes the light intensity of different wavelengths of the pixel.

[0008] Based on the hyperspectral data, obtain the color distribution information on the surface of the baked food;

[0009] Based on the hyperspectral data, the temperature distribution information of the cavity of the baking equipment is obtained;

[0010] Based on the color distribution information and the temperature distribution information, the temperature control parameters of the baking equipment are adjusted, and the baking equipment is controlled according to the adjusted temperature control parameters to make the color distribution on the surface of the food uniform.

[0011] In one possible design, the intracavitary image is an image of the food ingredient; the step of obtaining the color distribution information of the surface of the baked food ingredient based on the hyperspectral data includes:

[0012] For any pixel in each pixel of the food image, the light intensity of the pixel at a first target wavelength is obtained based on the spectral data of the pixel, wherein the first target wavelength includes red light wavelength, blue light wavelength and green light wavelength;

[0013] The LAB channel value of the pixel is obtained based on the light intensity of the pixel at the first target wavelength;

[0014] Based on the LAB channel value of the pixel and the LAB channel value of each neighboring pixel, obtain the neighborhood color difference value of the pixel.

[0015] The color distribution information of the food surface is obtained based on the LAB channel value and neighborhood color difference value of each pixel.

[0016] In one possible design, obtaining the color distribution information of the food surface based on the neighborhood color difference value of each pixel includes:

[0017] Based on the neighborhood color difference value of each pixel, the color uniformity of the food surface is obtained, and it is determined whether the color uniformity of the food surface meets the preset uniformity condition.

[0018] If not, then based on the LAB channel value and neighborhood color difference value of each pixel, the color distribution information of the food surface is obtained, wherein the color distribution information includes multiple different coloring degrees and their corresponding food surface areas;

[0019] If so, the color distribution information of the food surface is obtained based on the LAB channel value of each pixel, wherein the color distribution information includes a coloring degree and its corresponding food surface area.

[0020] In one possible design, the intracavitary image is a cavity image; obtaining the temperature distribution information inside the inner cavity of the baking equipment based on the hyperspectral data includes:

[0021] For any pixel in each pixel of the cavity image, the light intensity of the pixel at multiple second target wavelengths is obtained based on the spectral data of the pixel.

[0022] The temperature value corresponding to the pixel is obtained based on each second target wavelength and the light intensity corresponding to each second target wavelength.

[0023] Based on the temperature value corresponding to each pixel, the temperature distribution information inside the cavity is obtained, wherein the temperature distribution information includes the temperature range corresponding to each preset region inside the cavity.

[0024] In one possible design, adjusting the temperature control parameters of the baking equipment based on the color distribution information and the temperature distribution information includes:

[0025] Based on the color distribution information, obtain each coloring region to be adjusted and its coloring degree;

[0026] Based on the spatial relationship between each coloring area to be adjusted and each operating heating device of the baking equipment, obtain the first target heating device corresponding to each coloring area to be adjusted;

[0027] Based on the temperature distribution information and the set temperature of the baking equipment, obtain each temperature zone to be adjusted and its temperature deviation degree;

[0028] Based on the spatial relationship between each temperature region to be adjusted and each operating heating device, obtain the second target heating device corresponding to each temperature region to be adjusted;

[0029] Based on the degree of coloring of each of the coloring areas to be adjusted and the degree of temperature deviation of each of the temperature areas to be adjusted, the operating parameters of each corresponding first target heating device and second target heating device are adjusted respectively.

[0030] In one possible design, adjusting the operating parameters of each corresponding first target heating device and second target heating device based on the coloring degree of each of the coloring areas to be adjusted and the temperature deviation degree of each of the temperature areas to be adjusted includes:

[0031] For any one of the coloring regions to be adjusted in each coloring region to be adjusted, a first correction coefficient corresponding to the coloring degree is obtained according to the coloring degree of the coloring region to be adjusted;

[0032] Based on the first correction coefficient corresponding to the degree of coloring, the operating parameters of the first target heating device corresponding to the coloring area to be adjusted are adjusted;

[0033] For any one of the temperature regions to be adjusted, a second correction coefficient corresponding to the degree of temperature deviation is obtained based on the degree of temperature deviation of the coloring region to be adjusted.

[0034] Based on the second correction coefficient corresponding to the temperature deviation, the operating parameters of the second target heating device corresponding to the coloring area to be adjusted are adjusted.

[0035] In one possible design, acquiring the hyperspectral data of the cavity image of the baking equipment includes:

[0036] Acquire the original intracavitary image of the baking equipment acquired by the hyperspectral imaging device, and based on the original intracavitary image, obtain the original size information and the coordinate data and spectral data of each original pixel point of the original intracavitary image;

[0037] Obtain the reconstruction size information of the intracavitary image and the coordinate data of each reconstructed pixel in the intracavitary image;

[0038] Based on the original size information, the coordinate data and spectral data of each original pixel, the reconstructed size information, and the coordinate data of each reconstructed pixel, the spectral data of each reconstructed pixel is obtained;

[0039] Based on the spectral data of each reconstructed pixel, hyperspectral data of the cavity image of the baking device is obtained.

[0040] Secondly, this application provides a control device for a baking apparatus, the device comprising:

[0041] The acquisition module is used to acquire hyperspectral data of the cavity image of the baking equipment, wherein the cavity image includes an image of the food and an image of the cavity, and the hyperspectral data includes the spectral data of each pixel in the cavity image, and the spectral data of each pixel includes the light intensity of different wavelengths of the pixel.

[0042] The identification module is used to obtain color distribution information on the surface of the baked food based on the hyperspectral data; and to obtain temperature distribution information of the cavity of the baking equipment based on the hyperspectral data.

[0043] The control module is used to adjust the temperature control parameters of the baking equipment according to the color distribution information and the temperature distribution information, and to control the baking equipment according to the adjusted temperature control parameters so as to make the color distribution on the surface of the food uniform.

[0044] Thirdly, this application provides an electronic device, including: a processor, and a memory communicatively connected to the processor;

[0045] The memory stores computer-executed instructions;

[0046] The processor executes computer execution instructions stored in the memory to implement the temperature control method for the baking apparatus as described in the first aspect.

[0047] Fourthly, this application provides a computer-readable storage medium storing computer-executable instructions, which, when executed by a processor, are used to implement the temperature control method for the baking apparatus as described in the first aspect.

[0048] Fifthly, this application provides a computer program product, including a computer program that, when executed by a processor, implements the temperature control method for a baking apparatus as described in the first aspect.

[0049] The temperature control method, apparatus, device, and storage medium for baking equipment provided in this application acquire hyperspectral data of an image inside the baking equipment cavity, wherein the image inside the cavity includes an image of the food and an image of the cavity, and the hyperspectral data includes spectral data of each pixel in the image inside the cavity, the spectral data of each pixel including the light intensity of different wavelengths of the pixel; based on the hyperspectral data, color distribution information of the surface of the food to be baked is acquired; based on the hyperspectral data, temperature distribution information of the cavity of the baking equipment is acquired; based on the color distribution information and the temperature distribution information, the temperature control parameters of the baking equipment are adjusted, and the baking equipment is controlled according to the adjusted temperature control parameters, so that the color distribution on the surface of the food is uniform, and dishes with uniform surface color can be obtained under different food conditions and environmental conditions. Attached Figure Description

[0050] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with this application and, together with the description, serve to explain the principles of this application.

[0051] Figure 1 This is a schematic diagram of the structure of a hyperspectral imaging device provided in this application;

[0052] Figure 2 Flowchart of the temperature control method for the baking equipment provided in this application Figure 1 ;

[0053] Figures 3a to 3b Flowchart of the temperature control method for the baking equipment provided in this application Figure 2 ;

[0054] Figure 4 This application provides a schematic diagram of the structure of a control device for a baking equipment.

[0055] Figure 5 This is a schematic diagram of the structure of an electronic device provided in this application.

[0056] The accompanying drawings illustrate specific embodiments of this application, which will be described in more detail below. These drawings and descriptions are not intended to limit the scope of the concept in any way, but rather to illustrate the concept of this application to those skilled in the art through reference to particular embodiments. Detailed Implementation

[0057] To make the objectives, technical solutions, and advantages of this application clearer, the technical solutions of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0058] As people's living standards improve and their pursuit of a high-quality life continues, various cooking equipment are playing an increasingly important role in daily life, and users' requirements for cooking equipment are also getting higher and higher. For some dishes that require baking, users have certain requirements for the uniformity of color on the surface of the baked dish.

[0059] Existing baking equipment typically conducts cooking experiments on different dishes using different temperature control parameter values ​​before the equipment is put on the market. Based on the experimental results, the temperature control parameter value that produces a dish with good color uniformity is used as the preset temperature control parameter value for the corresponding dish.

[0060] However, due to the diversity of ingredients used and the differences in actual cooking scenarios, cooking ingredients using fixed preset temperature control parameters often makes it difficult to guarantee the uniformity of the surface color of the final product. In some cases, the surface of the finished product may be charred and blackened, while other parts may be too light in color, making it difficult to achieve the expected cooking effect.

[0061] To address the aforementioned issues, this application proposes the following technical concept: When baking food using a baking device, a hyperspectral imaging system installed within the baking device collects hyperspectral data of the food and the cavity, processes and identifies the hyperspectral data to obtain color distribution information of the food and temperature distribution information of the cavity. This allows for the determination of the uniformity of color on the food surface and the temperature distribution within the cavity during the baking process. Based on the color distribution information of the food and the temperature distribution information of the cavity, the temperature control parameters of the baking device are adjusted accordingly to ensure uniform color distribution on the food surface. This allows for the production of dishes with uniform surface color under different food conditions and environmental conditions.

[0062] The following detailed description of specific embodiments illustrates how the technical solutions of this application solve the aforementioned technical problems. These specific embodiments may exist independently or in combination with each other. Similar or identical concepts or processes may not be repeated in some embodiments. The embodiments of this application will now be described in conjunction with the accompanying drawings.

[0063] Figure 1 This is a schematic diagram of the structure of a hyperspectral imaging device 100 provided in an embodiment of this application. This hyperspectral imaging device is applied to the aforementioned baking equipment. Figure 1 As shown, the hyperspectral imaging device 100 provided in this application embodiment includes: a heat-insulating lens 101, a spectral imaging chip 102, and a data processing circuit 103. The heat-insulating lens 101 is located at the top corner of the cavity of the baking equipment to improve the accuracy of spectral data acquisition. The spectral imaging chip 102 can be a tile-type spectral imaging chip, that is, it can acquire three-dimensional data including two-dimensional space and one-dimensional time in one step. Taking a 4-band tile-type spectral imaging chip structure as an example, for an image sensor containing M×N pixels, each spectral band occupies (M / 2×N / 2) pixels. These adjacent (M / 2×N / 2) pixels constitute a filter block. In the same filter block, the same spectral filter film structure is integrated on the sensor chip, that is, these (M / 2×N / 2) pixels have the same spectral selectivity. The number of microlens arrays in the heat-insulating lens 101 is the same as the number of filter blocks in the spectral imaging chip 102. That is, for the same target food, M×N pixels are imaged on different spectrally selective imaging blocks respectively, and finally, the target spectral image (M / 2×N / 2×4) is obtained under the processing of the data processing circuit 103. The hyperspectral imaging device 100 can acquire the original cavity image inside the cooking equipment. The control device of the baking equipment can then obtain the hyperspectral data of the cavity image of the baking equipment based on the acquired original cavity image, and process and identify the hyperspectral data to obtain the color distribution information of the food and the temperature distribution information of the cavity. In order to determine the color uniformity of the food surface and the temperature distribution of the cavity during the baking process, and adjust the temperature control parameters of the baking equipment accordingly based on the color distribution information of the food and the temperature distribution information of the cavity, so that the color distribution of the food surface is uniform. Under different food conditions and environmental conditions, dishes with uniform surface color can be obtained.

[0064] Understandably, hyperspectral images acquired by hyperspectral imaging devices contain spectral information of the food surface and cavity across multiple wavelength ranges. Specifically, hyperspectral images record not only the spatial information (coordinate data) of each pixel but also the light intensity (spectral data) of each pixel at different wavelengths. This spectral data can contain information on hundreds of wavelengths from visible to near-infrared light. Hyperspectral images typically exist in the form of hyperspectral data cubes (also known as data cubes or hyperspectral data cubes). A hyperspectral data cube is a three-dimensional data structure including spatial and spectral dimensions. The spatial dimensions, namely the X-axis and Y-axis, correspond to the two-dimensional plane of a traditional image, representing the horizontal and vertical pixel positions of the image; each pixel represents a specific location in the image. The spectral dimension is the third dimension of the data cube, representing spectral information at different wavelengths, with each wavelength corresponding to a specific spectral channel. Hyperspectral imaging devices can subdivide the spectral range (e.g., 400 nm to 1000 nm) into multiple narrowband channels (e.g., 300 channels), each recording the light intensity information at a specific wavelength. At each spatial location (X,Y), the spectral dimension records the spectral information at that location. Specifically, each pixel has a complete spectral curve, representing the light intensity distribution at different wavelengths. By selecting specific spectral channels, two-dimensional images of those channels can be generated. These images can be used to analyze object features at specific wavelengths. For example, in identifying the degree of coloration on the surface of food, images at specific wavelengths can reveal the color characteristics of the food, thus providing more information about the degree of coloration.

[0065] Figure 2 A flowchart illustrating the temperature control method for the baking equipment provided in this application embodiment. Figure 1 .like Figure 2 As shown, the method includes:

[0066] S201: Acquire hyperspectral data of the cavity image of the baking equipment.

[0067] The intracavitary image includes an image of the food and an image of the cavity. The hyperspectral data includes the spectral data of each pixel in the intracavitary image, and the spectral data of each pixel includes the light intensity of different wavelengths of the pixel.

[0068] Specifically, the control device of the baking equipment acquires the original intracavity image collected by the hyperspectral imaging device inside the baking equipment, and preprocesses the original intracavity image according to the image preprocessing algorithm to obtain the preprocessed intracavity image. The preprocessing may include image correction, image enhancement, and image denoising. The preprocessed intracavity image is then segmented according to the image segmentation algorithm. For example, the image corresponding to the food in the preprocessed intracavity image can be used as the food image, and the image corresponding to the background can be used as the intracavity image. The image segmentation algorithm may be, for example, based on the light intensity of a specific wavelength of each pixel in the image, marking two sets of continuous regions in the image that meet the preset food intensity range and the preset cavity intensity range, and acquiring two sets of pixels corresponding to the two sets of continuous regions and the spectral data of each pixel. The spectral data corresponding to the two sets of pixels are used as the spectral data of the food image and the spectral data of the intracavity image, respectively. This application embodiment does not impose any special limitations on the specific content of the image preprocessing algorithm and the image segmentation algorithm.

[0069] S202: Based on the hyperspectral data, obtain the color distribution information on the surface of the baked food.

[0070] The color distribution information can be, for example, each color type on the surface of the food and its corresponding different regions, or the color difference between the region corresponding to each color type on the surface of the food and its adjacent regions.

[0071] Specifically, the control device of the baking equipment obtains the color distribution information of the food surface according to the hyperspectral data of the food image and a preset color recognition algorithm. The image recognition algorithm may be, for example, selecting light intensity data of a first target wavelength related to color from the hyperspectral data, calculating the normalized difference index or color difference value of the food image based on the light intensity data of the first target wavelength related to color, and classifying the colors of each region on the food surface according to the normalized difference index or color difference value of the food image, and obtaining each color type on the food surface and its corresponding different regions as color distribution information. This application embodiment does not impose any special limitations on the specific content of the color recognition algorithm.

[0072] S203: Obtain the temperature distribution information of the cavity of the baking equipment based on the hyperspectral data.

[0073] The temperature information may be, for example, the temperature range corresponding to each region within the cavity.

[0074] Specifically, the control device of the baking equipment selects light intensity data of a second target wavelength related to temperature based on the hyperspectral data of the cavity image. Based on the light intensity data of the second target wavelength related to temperature, and based on the mapping relationship between different light intensity values ​​and temperature values ​​of the cavity at the second target wavelength, the temperature range corresponding to each region in the cavity image is determined as the temperature distribution information of the cavity of the baking equipment.

[0075] S204: Adjust the temperature control parameters of the baking equipment according to the color distribution information and the temperature distribution information, and control the baking equipment according to the adjusted temperature control parameters.

[0076] Specifically, the temperature control device of the baking equipment adjusts the temperature control parameters of the baking equipment according to the color distribution information and the temperature distribution information, following a preset control parameter adjustment algorithm. For example, when the color distribution information indicates uneven coloring, the power of the heating devices near the lighter-colored areas can be increased, while the heating devices near the darker-colored areas can be temporarily turned off; when the temperature distribution information indicates uneven temperature distribution within the cavity, the power of the heating devices near the excessively low-temperature areas can be increased, while the power of the heating devices near the excessively high-temperature areas can be decreased, etc., to ensure uniform color distribution on the surface of the food. This application embodiment does not impose any particular limitations on the specific content of the control parameter adjustment algorithm.

[0077] The temperature control method for baking equipment provided in this application embodiment acquires hyperspectral images of the baking equipment cavity using a hyperspectral imaging device installed inside the cavity. By processing and analyzing the hyperspectral data of the food and the cavity, color distribution information of the food surface and temperature distribution information of the cavity are obtained. This fully utilizes the rich information in the hyperspectral data, improving the accuracy of the color distribution information of the food surface and the temperature distribution information of the cavity. Based on the color distribution information of the food surface and the temperature distribution information of the cavity, the control parameters of the corresponding deheating device are adjusted in a targeted manner, so that the color distribution of the food surface is uniform, effectively ensuring the baking effect and improving the user experience.

[0078] Figures 3a to 3b A flowchart illustrating the temperature control method for the baking equipment provided in this application embodiment. Figure 2 .like Figure 3a and Figure 3b As shown, the method includes:

[0079] S301: Acquire the original intracavitary image of the baking equipment collected by the hyperspectral imaging device, and based on the original intracavitary image, acquire the original size information and the coordinate data and spectral data of each original pixel point of the original intracavitary image.

[0080] The original intracavitary image is a rectangular image, and the original size information includes the length and width of the original intracavitary image, that is, the number of pixels on the x-axis side and the number of pixels on the y-axis side of the original intracavitary image.

[0081] Specifically, the control device of the baking equipment processes and analyzes the original intracavitary image acquired by the hyperspectral imaging device inside the baking equipment, obtains the number of pixels on the x-axis side and the number of pixels on the y-axis side of the original intracavitary image as the original size information, and obtains the coordinate data and spectral data of each original pixel point of the original intracavitary image.

[0082] S302: Obtain the reconstruction size information of the reconstructed intracavitary image and the coordinate data of each reconstructed pixel point of the reconstructed intracavitary image.

[0083] The reconstructed intracavitary image is a rectangular image. The reconstruction size information includes the length and width of the rectangular image, that is, the number of pixels on the x-axis side and the number of pixels on the y-axis side of the reconstructed intracavitary image. The reconstruction size information and the coordinate data of each reconstructed pixel point of the reconstructed intracavitary image are preset values.

[0084] Specifically, the control device of the baking equipment obtains the reconstruction size information of the reconstruction cavity image and the coordinate data of each reconstruction pixel of the reconstruction cavity image from the corresponding storage location.

[0085] S303: Based on the original size information, the coordinate data and spectral data of each original pixel, the reconstructed size information, and the coordinate data of each reconstructed pixel, obtain the spectral data of each reconstructed pixel.

[0086] Specifically, the control device of the baking equipment, according to a preset super-resolution algorithm, obtains the spectral data of each reconstructed pixel based on the original size information, the coordinate data and spectral data of each original pixel, the reconstructed size information, and the coordinate data of each reconstructed pixel. The super-resolution algorithm can, for example, input the original size information, the coordinate data and spectral data of each original pixel, the reconstructed size information, and the coordinate data of each reconstructed pixel into a pre-trained image reconstruction model to obtain the spectral data of each reconstructed pixel output by the image reconstruction model. The image reconstruction model can be a deep learning model pre-trained based on an image spatial degradation model; alternatively, it can input the original size information, the coordinate data of each original pixel, the reconstructed size information, and the coordinate data of each reconstructed pixel into a coordinate value mapping function based on an interpolation algorithm to determine the target original pixel corresponding to each reconstructed pixel, and use the spectral data of the target original pixel corresponding to each reconstructed pixel as the spectral data of the reconstructed pixel. This application embodiment does not impose any special limitations on the specific content of the super-resolution algorithm.

[0087] S304: Obtain hyperspectral data of the cavity image of the baking device based on the spectral data of each reconstructed pixel.

[0088] Specifically, the control device of the baking equipment preprocesses the reconstructed cavity image according to the spectral data of each reconstructed pixel using an image preprocessing algorithm to obtain a preprocessed cavity image. The preprocessing may include image correction and image denoising. The preprocessed reconstructed cavity image is then segmented using an image segmentation algorithm. For example, the image corresponding to the food in the preprocessed reconstructed cavity image can be used as the food image, and the image corresponding to the background can be used as the cavity image. The image segmentation algorithm may, for example, mark two consecutive regions in the image that conform to a preset food intensity range and a preset cavity intensity range based on the light intensity of different wavelengths of each pixel, and obtain two sets of pixels corresponding to the two consecutive regions, along with the spectral data of each pixel. The spectral data corresponding to the two sets of pixels are then used as the spectral data of the food image and the spectral data of the cavity image, respectively. This application embodiment does not impose any special limitations on the specific content of the image preprocessing algorithm and the image segmentation algorithm.

[0089] S305: For any pixel in each pixel of the food image, obtain the light intensity of the pixel at the first target wavelength based on the spectral data of the pixel.

[0090] The first target wavelength includes red light wavelength, blue light wavelength and green light wavelength. For example, the red light wavelength can be 680 nanometers, the green light wavelength can be 540 nanometers, and the blue light wavelength can be 460 nanometers.

[0091] S306: Obtain the LAB channel value of the pixel based on the light intensity of the pixel at the first target wavelength.

[0092] Specifically, the control device of the baking equipment normalizes the light intensity of the pixel at the first target wavelength according to a preset RGB channel value range to obtain the channel value of the pixel in the RGB space. The normalized result of the light intensity of the red light wavelength corresponds to the R channel value, the normalized result of the light intensity of the green light wavelength corresponds to the G channel value, and the normalized result of the light intensity of the blue light wavelength corresponds to the B channel value. According to a preset mapping conversion formula, the channel value of the pixel in the RGB space is converted into the tristimulus value in the CIE 1931XYZ color space, and then the tristimulus value in the CIE 1931XYZ color space is mapped to the three channel values ​​in the CIELAB color space.

[0093] Understandably, the CIELAB color space, also written as L*a*b*, is a color space defined by the International Commission on Illumination (ICI) in 1976. It expresses color using three values: "L*" represents perceived brightness, and "a*" and "b*" represent the four unique colors perceived by human vision: red, green, blue, and yellow. CIELAB aims to be a perceptually unified space where a given numerical change corresponds to a similar perceptual color change. It is the most complete color model commonly used to describe all colors visible to the human eye. By obtaining the color differences between different parts of a food image through the LAB channel values ​​of each pixel, it can more accurately reflect the human eye's perception of color differences. Furthermore, the CIELAB space is a device-independent color space, meaning it does not depend on specific display devices or lighting conditions. This ensures consistency and comparability of color difference calculation results across different devices and environments.

[0094] S307: Based on the coordinate data and LAB channel value of the pixel, and the coordinate data and LAB channel value of each neighboring pixel of the pixel, obtain the neighborhood color difference value of the pixel.

[0095] Specifically, the control device of the baking equipment determines the neighborhood of the pixel based on the pixel's coordinate data and a preset neighborhood size. For each neighboring pixel within the neighborhood, the color difference between the pixel and its neighboring pixels is calculated according to the color difference calculation formula, based on the pixel's coordinate value and LAB channel value, and the coordinate values ​​and LAB channel values ​​of the neighboring pixels. The color difference calculation formula can be, for example, ΔE*ab or ΔE2000. This embodiment does not impose any particular limitation on the specific form of the color difference calculation formula. The average value of the color difference between the pixel and each neighboring pixel within the neighborhood is calculated and used as the neighborhood color difference value of the pixel.

[0096] S308: Obtain the color distribution information of the food surface based on the LAB channel value and neighborhood color difference value of each pixel.

[0097] Optionally, the specific steps for the control device of the baking equipment to obtain the color distribution information of the food surface based on the LAB channel value and the neighboring color difference value of each pixel can be, for example:

[0098] Step 1: Based on the neighborhood color difference value of each pixel, obtain the color uniformity of the food surface, and determine whether the color uniformity of the food surface meets the preset uniformity condition.

[0099] The color uniformity can be, for example, the ratio between the number of pixels with a neighboring color difference value greater than a preset color difference value and the total number of pixels. Taking the color difference value ΔE*ab as an example, the preset color difference value can be, for example, 2.3. The preset uniformity condition can be, for example, the ratio between the number of pixels with a neighboring color difference value greater than the preset color difference and the total number of pixels is less than a preset ratio. The preset ratio can be, for example, 10%.

[0100] Step 2: If not, obtain the color distribution information of the food surface based on the LAB channel value and neighborhood color difference value of each pixel.

[0101] The coloring distribution information includes multiple different coloring degrees and their corresponding food surface areas. The coloring degree can be represented by multiple predefined coloring levels, such as ten levels, with the coloring degree increasing progressively. Coloring level one is the lightest coloring degree, and coloring level ten is the darkest coloring degree.

[0102] Specifically, the control device of the baking equipment classifies each pixel according to its LAB channel value and neighborhood color difference value using a preset classification algorithm to determine the coloring degree corresponding to each pixel. The classification algorithm may, for example, construct a coloring feature vector based on the LAB channel value and neighborhood color difference value of each pixel, determine the coloring degree corresponding to the coloring feature vector using a K-means clustering algorithm, and use the coloring degree corresponding to the coloring feature vector as the coloring degree corresponding to the pixel. This application embodiment does not impose any specific limitations on the specific content of the classification algorithm. Based on the coloring degree corresponding to each pixel, connected component analysis is used to determine a set of multiple adjacent pixels of the same color with the same coloring degree, and the image region corresponding to each set of pixels of the same color is used as the food surface region corresponding to each coloring degree.

[0103] Step 3: If so, obtain the color distribution information of the food surface based on the LAB channel value of each pixel.

[0104] The coloring distribution information includes a coloring degree and its corresponding food surface area.

[0105] Specifically, the control device of the baking equipment classifies each pixel according to its LAB channel value using a preset classification algorithm to determine the coloring degree corresponding to each pixel. The classification algorithm may, for example, construct a coloring feature vector based on the LAB channel value of each pixel, determine the coloring degree corresponding to the coloring feature vector using a K-means clustering algorithm, and use the coloring degree corresponding to the coloring feature vector as the coloring degree corresponding to the pixel. This application embodiment does not impose any specific limitations on the specific content of the classification algorithm. Based on the number of pixels corresponding to each coloring degree, the coloring degree with the largest number of corresponding pixels is obtained as the coloring degree of the food surface. The entire area of ​​the food image is taken as the food surface area corresponding to that coloring degree.

[0106] S309: For any pixel in each pixel of the cavity image, obtain the light intensity of the pixel at multiple second target wavelengths based on the spectral data of the pixel.

[0107] The second target wavelength can be selected from the mid-infrared band, for example, 3 micrometers, 4 micrometers and 5 micrometers, which is suitable for measuring the temperature of high-temperature objects.

[0108] S310: Obtain the temperature value corresponding to the pixel point based on each of the second target wavelengths and the light intensity corresponding to each of the second target wavelengths.

[0109] Specifically, the control device of the baking equipment establishes radiative transfer equations for different second target wavelengths based on each second target wavelength and the corresponding light intensity. The radiative transfer equation for wavelength λ can be:

[0110] L(λ)=ε(λ)*B(λ,T)+(1-ε(λ))*L env (λ);

[0111] Where L(λ) is the light intensity of the cavity at wavelength λ; ε(λ) is the emissivity of the cavity surface at wavelength λ, which can be determined through prior experiments; B(λ, T) is the radiance of the blackbody at wavelength λ at temperature T, which can be calculated using Planck's law; L env (λ) is the radiance of the environment inside the cavity at wavelength λ, which can be measured in advance inside the cavity of a cooking device that is not in operation and has no food placed inside.

[0112] Based on the radiative transfer equations for different second target wavelengths, the temperature value corresponding to the pixel is solved by an inversion algorithm. The inversion algorithm can be, for example, the least squares method or the Newton-Raphson method, etc. This application embodiment does not impose any particular limitation on this.

[0113] S311: Obtain the temperature distribution information inside the cavity based on the temperature value corresponding to each pixel.

[0114] The temperature distribution information includes the temperature range corresponding to each preset region within the cavity.

[0115] Specifically, the control device of the baking equipment obtains the pixels corresponding to each preset area based on the coordinate range corresponding to each preset area; it obtains the area temperature value of each preset area based on the average temperature value of the pixels corresponding to each preset area; for any preset area, based on the preset temperature range and the area temperature value of the preset area, the preset temperature range to which the area temperature value belongs is taken as the temperature range corresponding to the preset area; the difference between the upper and lower boundaries of each preset temperature range is equal, for example, it can be 20 degrees.

[0116] S312: Based on the color distribution information, obtain each coloring region to be adjusted and its coloring degree.

[0117] Specifically, the control device of the cooking equipment determines the current target coloring degree based on the currently running baking program and runtime, as well as the preset correspondence between different runtime segments of different baking programs and different coloring degrees; based on the target coloring degree and the coloring distribution information, it obtains each non-target coloring degree and its corresponding food surface area, that is, each coloring area to be adjusted and its coloring degree.

[0118] S313: Based on the spatial relationship between each coloring area to be adjusted and each operating heating device of the baking equipment, obtain the first target heating device corresponding to each coloring area to be adjusted.

[0119] Specifically, for any one of the areas to be adjusted in each coloring area, the control device of the baking equipment obtains the spatial position of the area to be adjusted based on the viewing angle information of the hyperspectral imaging system, the placement position of the food (e.g., the number of rack layers), and the position coordinates of the area to be adjusted in the food image; obtains the spatial position of each operating heating device from the corresponding storage area; obtains the distance between the area to be adjusted and each operating heating device based on the spatial position of the area to be adjusted and the spatial position of each operating heating device, and designates the operating heating device closest to the area to be adjusted as the first target heating device for the area to be adjusted.

[0120] S314: Based on the temperature distribution information and the set temperature of the baking equipment, obtain each temperature zone to be adjusted and its temperature deviation degree.

[0121] The degree of temperature deviation can be characterized by different predefined temperature deviation levels, such as ten levels, with the degree of temperature deviation increasing progressively. Level 1 temperature deviation is 20 to 40 degrees, Level 2 temperature deviation is 40 to 60 degrees, and Level 10 temperature deviation is above 220 degrees.

[0122] Specifically, the control device of the cooking equipment reads the operating program information of the baking equipment to obtain the set temperature of the baking equipment; based on the temperature range corresponding to each preset area in the temperature distribution information and the set temperature, it determines whether the set temperature falls within the temperature range, and designates the preset areas where the set temperature does not fall within the temperature range as temperature areas to be adjusted; for any one of the temperature areas to be adjusted, the difference between the set temperature and the median of the temperature range is taken as the deviation temperature, and based on the deviation temperature range to which the deviation temperature belongs, and the preset correspondence between different deviation levels and deviation temperature ranges, the deviation level corresponding to the deviation temperature range to which the deviation temperature belongs is taken as the temperature deviation degree of the temperature area to be adjusted.

[0123] S315: Based on the spatial relationship between each temperature region to be adjusted and each operating heating device, obtain the second target heating device corresponding to each temperature region to be adjusted.

[0124] Specifically, for any one of the temperature zones to be adjusted, the control device of the baking equipment obtains the spatial position of the temperature zone to be adjusted and the spatial position of each operating heating device from the corresponding storage location; based on the spatial position of the temperature zone to be adjusted and the spatial position of each operating heating device, the distance between the temperature zone to be adjusted and each operating heating device is obtained, and the heating device closest to the temperature zone to be adjusted is taken as the second target heating device of the temperature zone to be adjusted.

[0125] S316: Based on the degree of coloring of each of the coloring areas to be adjusted and the degree of temperature deviation of each of the temperature areas to be adjusted, the operating parameters of each corresponding first target heating device and second target heating device are adjusted respectively.

[0126] Optionally, the specific method and steps for the control device of the baking equipment to adjust the operating parameters of each corresponding first target heating device and second target heating device according to the coloring degree of each of the coloring areas to be adjusted and the temperature deviation degree of each of the temperature areas to be adjusted can be as follows:

[0127] Step 1: For any one of the coloring regions to be adjusted, obtain the first correction coefficient corresponding to the coloring degree based on the coloring degree of the coloring region to be adjusted.

[0128] For example, if the coloring level of the second coloring area to be adjusted is level two and the target coloring level is level four, the control device of the baking equipment obtains the first correction coefficient corresponding to the current food type, coloring level of level two, and target coloring level of level four from a pre-stored lookup table, where the primary key of the lookup table is the food type, coloring level, and target coloring level, and the key value is the first correction coefficient. For example, it can be 1.1.

[0129] Step 2: Adjust the operating parameters of the first target heating device corresponding to the coloring degree according to the first correction coefficient.

[0130] Continuing with the example above, the first target heating device corresponding to the second coloring area to be adjusted can be, for example, heating tube three, whose operating parameter is steady-state load power. The current steady-state load power can be, for example, 600 watts. Then, according to the first correction coefficient, the steady-state load power of heating tube three is adjusted, and the adjusted steady-state load power = 600 * 1.1 = 660 watts.

[0131] Step 3: For any one of the temperature regions to be adjusted, obtain the second correction coefficient corresponding to the temperature deviation based on the degree of temperature deviation of the coloring region to be adjusted.

[0132] For example, if the temperature deviation of the temperature zone to be adjusted is level three, and the lower limit of the temperature range of the temperature zone to be adjusted is higher than the set temperature, that is, the temperature deviation state is too high, the control device of the baking equipment retrieves the second correction coefficient corresponding to the temperature deviation level of level three and the temperature deviation state of too high from a pre-stored lookup table, where the primary key of the lookup table is the temperature deviation level and the temperature deviation state, and the key value is the second correction coefficient. For example, it can be 0.8.

[0133] Step 4: Adjust the operating parameters of the second target heating device corresponding to the coloring area to be adjusted according to the second correction coefficient corresponding to the temperature deviation.

[0134] Continuing with the example above, the first target heating device corresponding to the temperature range to be adjusted can be, for example, heating tube two, whose operating parameter is steady-state load power. The current steady-state load power can be, for example, 800 watts. Then, according to the second correction coefficient, the steady-state load power of heating tube two is adjusted, and the adjusted steady-state load power = 800 * 0.8 = 640 watts.

[0135] Optionally, the control device of the baking equipment can cache the adjusted operating parameters and corresponding operating times of each operating heating device as optimized parameter information for the dish during the baking process of a certain dish. After the baking program ends, the device sends a feedback collection request to the user through the touch screen of the baking equipment or a user terminal associated with the baking equipment. The device obtains the user's feedback on the feedback collection request through the interactive device. If the feedback is satisfactory, the device stores the identifier of the dish and its optimized parameter information in the corresponding storage location. When the user uses the baking equipment to cook the dish again, the optimized parameter information of the dish is used as the initial control parameter information to control the baking equipment to cook the ingredients, thereby further reducing the probability of uneven color on the surface of the ingredients and uneven temperature distribution in the cavity during the cooking process, and improving the cooking effect.

[0136] The temperature control method for baking equipment provided in this application enhances the original intracavity image acquired by the hyperspectral imaging device using a super-resolution algorithm, thereby improving the resolution of the hyperspectral image and contributing to the accuracy of subsequent image recognition. By mapping the hyperspectral data of the first target wavelength to three-channel values ​​in the CIELAB space, and calculating the color difference value of each pixel in the CIELAB space based on these three-channel values, the color distribution information of the food surface is determined based on this color difference value. This allows the color distribution information to accurately reflect the human eye's perception of color differences on the food surface, further improving the accuracy and specificity of subsequent control parameter adjustments. By calculating the cavity temperature in different preset areas based on the spectral data of the second target wavelength, accurate measurement of the temperature in each area of ​​the cavity is achieved without the need for additional temperature sensors. By correcting the control parameters of the heating device corresponding to each area to be adjusted based on the color distribution information of the food surface and the temperature distribution information of the cavity, refined control of the temperature inside the cavity is achieved, further ensuring the uniformity of the food surface color and the appropriate degree of doneness of the food as a whole.

[0137] Figure 4 This is a schematic diagram of the structure of the control device for the baking equipment provided in the embodiments of this application; as shown below. Figure 4 As shown, this application provides a control device for a baking apparatus. The control device 400 for the baking apparatus includes:

[0138] The acquisition module 401 is used to acquire hyperspectral data of the cavity image of the baking equipment, wherein the cavity image includes an image of the food and an image of the cavity, and the hyperspectral data includes the spectral data of each pixel of the cavity image, and the spectral data of each pixel includes the light intensity of different wavelengths of the pixel.

[0139] The identification module 402 is used to obtain color distribution information on the surface of the baked food based on the hyperspectral data; and to obtain temperature distribution information of the cavity of the baking equipment based on the hyperspectral data.

[0140] The control module 403 is used to adjust the temperature control parameters of the baking equipment according to the color distribution information and the temperature distribution information, and to control the baking equipment according to the adjusted temperature control parameters so as to make the color distribution on the surface of the food uniform.

[0141] Optionally, the recognition module 402 is specifically configured to, for any one pixel in each pixel of the food image, obtain the light intensity of the pixel at a first target wavelength based on the spectral data of the pixel, wherein the first target wavelength includes red light wavelength, blue light wavelength, and green light wavelength; obtain the LAB channel value of the pixel based on the light intensity of the pixel at the first target wavelength; obtain the neighborhood color difference value of the pixel based on the LAB channel value of the pixel and the LAB channel values ​​of each neighboring pixel of the pixel; and obtain the color distribution information of the food surface based on the LAB channel value and the neighborhood color difference value of each pixel.

[0142] Optionally, the recognition module 402 is specifically configured to: obtain the color uniformity of the food surface based on the neighborhood color difference value of each pixel, and determine whether the color uniformity of the food surface meets a preset uniformity condition; when the color uniformity of the food surface does not meet the preset uniformity condition, obtain the color distribution information of the food surface based on the LAB channel value and neighborhood color difference value of each pixel, wherein the color distribution information includes multiple different coloring degrees and their corresponding food surface areas; when the color uniformity of the food surface meets the preset uniformity condition, obtain the color distribution information of the food surface based on the LAB channel value of each pixel, wherein the color distribution information includes a coloring degree and its corresponding food surface area.

[0143] Optionally, the recognition module 402 is specifically configured to, for any one pixel in each pixel of the cavity image, obtain the light intensity of the pixel at multiple second target wavelengths based on the spectral data of the pixel; obtain the temperature value corresponding to the pixel based on each second target wavelength and the light intensity corresponding to each second target wavelength; and obtain temperature distribution information within the cavity based on the temperature value corresponding to each pixel, wherein the temperature distribution information includes the temperature range corresponding to each preset region within the cavity.

[0144] Optionally, the control module 403 is specifically configured to: obtain each coloring region to be adjusted and its coloring degree according to the coloring distribution information; obtain a first target heating device corresponding to each coloring region to be adjusted according to the spatial relationship between each coloring region to be adjusted and each operating heating device of the baking equipment; obtain each temperature region to be adjusted and its temperature deviation degree according to the temperature distribution information and the set temperature of the baking equipment; obtain a second target heating device corresponding to each temperature region to be adjusted according to the spatial relationship between each temperature region to be adjusted and each operating heating device; and adjust the operating parameters of each corresponding first target heating device and second target heating device according to the coloring degree of each coloring region to be adjusted and the temperature deviation degree of each temperature region to be adjusted.

[0145] Optionally, the control module 403 is specifically configured to: for any one of the adjustable coloring regions, obtain a first correction coefficient corresponding to the coloring degree of the adjustable coloring region; adjust the operating parameters of the first target heating device corresponding to the adjustable coloring region according to the first correction coefficient corresponding to the coloring degree; for any one of the adjustable temperature regions, obtain a second correction coefficient corresponding to the temperature deviation degree of the adjustable coloring region; and adjust the operating parameters of the second target heating device corresponding to the adjustable coloring region according to the second correction coefficient corresponding to the temperature deviation.

[0146] Optionally, the acquisition module 401 is specifically used to acquire the original intracavitary image of the baking equipment collected by the hyperspectral imaging device, and based on the original intracavitary image, acquire original size information and coordinate data and spectral data of each original pixel in the original intracavitary image; acquire the reconstructed size information of the reconstructed intracavitary image and coordinate data of each reconstructed pixel in the reconstructed intracavitary image; based on the original size information, coordinate data and spectral data of each original pixel, the reconstructed size information, and coordinate data of each reconstructed pixel, acquire the spectral data of each reconstructed pixel; and based on the spectral data of each reconstructed pixel, acquire the hyperspectral data of the intracavitary image of the baking equipment.

[0147] Figure 5 This is a schematic diagram of the structure of an electronic device provided in an embodiment of this application, such as... Figure 5 As shown, the electronic device 500 includes at least one processor 501 and a memory 502; in addition, the electronic device 500 may also have a communication interface 504 for receiving and sending instructions.

[0148] The processor 501, memory 502 and communication interface 504 are connected via bus 503.

[0149] The computer program is stored in memory 502 and configured to be executed by processor 501 to implement this application. Figures 2-3b The temperature control method for the baking equipment provided in any corresponding embodiment;

[0150] Figure 5 The electronic device shown in the embodiment can be used to execute the technical solution of the above method embodiment. Its implementation principle and technical effect are similar, and will not be repeated here.

[0151] In addition, this embodiment also provides a computer-readable storage medium storing a computer program thereon, which is executed by a processor to implement the temperature control method of the baking equipment described above.

[0152] In the several embodiments provided by this invention, it should be understood that the disclosed apparatus and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection between apparatuses or units may be electrical, mechanical, or other forms.

[0153] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0154] Furthermore, the functional units in the various embodiments of the present invention can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or in the form of hardware plus software functional units.

[0155] The integrated units implemented as software functional units described above can be stored in a computer-readable storage medium. These software functional units, stored in a storage medium, include several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) or processor to execute partial steps of the methods of the various embodiments of the present invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0156] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the above-described division of functional modules is merely an example. In practical applications, the above functions can be assigned to different functional modules as needed, that is, the internal structure of the device can be divided into different functional modules to complete all or part of the functions described above. The specific working process of the device described above can be referred to the corresponding process in the foregoing method embodiments, and will not be repeated here.

[0157] The technical solutions of this application have been described above with reference to the preferred embodiments shown in the accompanying drawings. However, it is readily understood by those skilled in the art that the scope of protection of this application is obviously not limited to these specific embodiments. The above embodiments are only used to illustrate the technical solutions of this application and are not intended to limit them. Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some or all of the technical features therein. These modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of this application.

Claims

1. A temperature control method of a baking apparatus, characterized by, The method comprises: acquiring hyperspectral data of a cavity image of the baking equipment, wherein the cavity image comprises a food material image and a cavity image, the hyperspectral data comprises spectral data of each pixel point of the cavity image, and the spectral data of the pixel point comprises light intensity of different wavelengths of the pixel point; acquiring color distribution information of a baking food material surface according to the hyperspectral data; acquiring temperature distribution information of a cavity of the baking equipment according to the hyperspectral data; adjusting a temperature control parameter of the baking equipment according to the color distribution information and the temperature distribution information, and controlling the baking equipment according to the adjusted temperature control parameter to make the color distribution of the food material surface uniform.

2. The method of claim 1, wherein, The cavity image is a food material image; and the acquiring of the color distribution information of the baking food material surface according to the hyperspectral data comprises: for any one of each pixel point of the food material image, acquiring light intensity of the pixel point at a first target wavelength according to spectral data of the pixel point, wherein the first target wavelength comprises a red light wavelength, a blue light wavelength and a green light wavelength; acquiring LAB channel values of the pixel point according to the light intensity of the pixel point at the first target wavelength; acquiring neighborhood color difference values of the pixel point according to the LAB channel values of the pixel point and LAB channel values of each neighborhood pixel point of the pixel point; acquiring the color distribution information of the food material surface according to the LAB channel values and the neighborhood color difference values of each pixel point.

3. The method of claim 2, wherein, The acquiring of the color distribution information of the food material surface according to the neighborhood color difference values of each pixel point comprises: acquiring a color uniformity degree of the food material surface according to the neighborhood color difference values of each pixel point, and judging whether the color uniformity degree of the food material surface satisfies a preset uniformity condition; if not, acquiring the color distribution information of the food material surface according to the LAB channel values and the neighborhood color difference values of each pixel point, wherein the color distribution information comprises a plurality of different color degrees and corresponding food material surface regions; if yes, acquiring the color distribution information of the food material surface according to the LAB channel values of each pixel point, wherein the color distribution information comprises one color degree and a corresponding food material surface region.

4. The method of claim 1, wherein, The cavity image is a cavity image; and the acquiring of the temperature distribution information in the inner container of the baking equipment according to the hyperspectral data comprises: for any one of each pixel point of the cavity image, acquiring light intensity of the pixel point at a plurality of second target wavelengths according to spectral data of the pixel point; acquiring a temperature value corresponding to the pixel point according to each second target wavelength and light intensity corresponding to each second target wavelength; acquiring temperature distribution information in the cavity according to the temperature value corresponding to each pixel point, wherein the temperature distribution information comprises a temperature range corresponding to each preset region in the cavity.

5. The method of claim 1, wherein, The adjusting of the temperature control parameter of the baking equipment according to the color distribution information and the temperature distribution information comprises: According to the coloring distribution information, obtain each to-be-adjusted coloring area and its coloring degree; According to the spatial relationship between each to-be-adjusted coloring area and each operating heating device of the baking equipment, obtain a first target heating device corresponding to each to-be-adjusted coloring area; According to the temperature distribution information and the set temperature of the baking equipment, obtain each to-be-adjusted temperature area and its temperature deviation degree; According to the spatial relationship between each to-be-adjusted temperature area and each operating heating device, obtain a second target heating device corresponding to each to-be-adjusted temperature area; According to the coloring degree of each to-be-adjusted coloring area and the temperature deviation degree of each to-be-adjusted temperature area, adjust the operating parameters of each corresponding first target heating device and second target heating device, respectively.

6. The method of claim 5, wherein, The adjusting the operating parameters of each corresponding first target heating device and second target heating device according to the coloring degree of each to-be-adjusted coloring area and the temperature deviation degree of each to-be-adjusted temperature area, comprises: For any one of each to-be-adjusted coloring area, according to the coloring degree of the to-be-adjusted coloring area, obtain a first correction coefficient corresponding to the coloring degree; According to the first correction coefficient corresponding to the coloring degree, adjust the operating parameters of the first target heating device corresponding to the to-be-adjusted coloring area; For any one of each to-be-adjusted temperature area, according to the temperature deviation degree of the to-be-adjusted coloring area, obtain a second correction coefficient corresponding to the temperature deviation degree; According to the second correction coefficient corresponding to the temperature deviation, adjust the operating parameters of the second target heating device corresponding to the to-be-adjusted coloring area.

7. The method of claim 1, wherein, The obtaining the hyperspectral data of the cavity image of the baking equipment comprises: Obtaining an original cavity image of the baking equipment collected by a hyperspectral imaging device, and according to the original cavity image, obtaining original size information and coordinate data and spectral data of each original pixel point of the original cavity image; Obtaining reconstruction size information of a reconstructed cavity image and coordinate data of each reconstruction pixel point of the reconstructed cavity image; According to the original size information, the coordinate data and the spectral data of each original pixel point, the reconstruction size information, and the coordinate data of each reconstruction pixel point, obtaining the spectral data of each reconstruction pixel point; According to the spectral data of each reconstruction pixel point, obtaining the hyperspectral data of the cavity image of the baking equipment.

8. A control device of a baking apparatus, characterized by, The device comprises: An obtaining module is configured to obtain hyperspectral data of a cavity image of the baking equipment, wherein the cavity image comprises a food material image and a cavity image, and the hyperspectral data comprises spectral data of each pixel point of the cavity image, and the spectral data of the pixel point comprises light intensity of different wavelengths of the pixel point; An identifying module is configured to obtain coloring distribution information of a baking food material surface according to the hyperspectral data, and obtain temperature distribution information of a cavity of the baking equipment according to the hyperspectral data. A control module is configured to adjust a temperature control parameter of the baking device according to the color distribution information and the temperature distribution information, and control the baking device according to the adjusted temperature control parameter, so as to make the color distribution of the surface of the food material uniform.

9. An electronic device, comprising: The method comprises: a processor, and a memory connected with the processor in communication; the memory stores computer-executable instructions; the processor executes the computer-executable instructions stored in the memory to implement the method according to any one of claims 1-7.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer-executable instructions, and the computer-executable instructions are executed by the processor to implement the method according to any one of claims 1-7.

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