Oven control method and device, electronic equipment and storage medium

Abstract

The embodiment of the invention provides an oven control method and device, electronic equipment and a storage medium. The method comprises the steps that cooking environment information and image information of food in an oven are obtained; extracting edge color information of the food based on the image information; determining the coking degree of the food based on the edge color information; the oven is controlled according to the coking degree and the cooking environment information, the situation that cooking is achieved depending on the cooking experience of a user is avoided, and therefore the cooking efficiency is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of oven control, and in particular to an oven control method, an oven control device, an electronic device and a computer-readable storage medium. Background Art

[0002] Traditional ovens rely mainly on user experience and timers to judge the doneness of food during the cooking process. This method has large uncertainties and errors, which can easily lead to overcooked or undercooked food, affecting the final cooking effect. At the same time, although some existing smart ovens have preset programs, they still lack the ability to monitor the real-time status of food and dynamically adjust, and cannot adapt to different ingredients and cooking needs. Summary of the invention

[0003] The embodiments of the present invention provide an oven control method, device, electronic device and computer-readable storage medium to overcome the above problems or at least partially solve the above problems.

[0004] The embodiment of the present invention discloses an oven control method, which is characterized by comprising:

[0005] Acquiring cooking environment information and image information of the food in the oven;

[0006] Extracting edge color information of the food based on the image information;

[0007] determining the degree of carbonization of the food based on the edge color information;

[0008] The oven is controlled according to the browning degree and the cooking environment information.

[0009] Optionally, it also includes:

[0010] A Gaussian filtering operation is performed on the image information.

[0011] Optionally, the step of extracting edge color information of the food based on the image information includes:

[0012] Performing edge detection on the image information after the Gaussian filtering operation, and extracting edge contour information of the food from the image information;

[0013] Extracting pixel points of the edge of the food according to the edge contour information;

[0014] Convert the pixel point from the red, green and blue color space to the hue, saturation and brightness color space, and obtain the hue, saturation and brightness of the pixel point in the hue, saturation and brightness color space;

[0015] Determine a first target color channel based on the hue, the saturation, and the brightness, and determine a first target pixel point associated with the first target color channel from the pixel points;

[0016] The color feature of the edge is extracted based on the first target pixel.

[0017] Optionally, the step of determining the degree of carbonization of the food based on the edge color information comprises:

[0018] Set color thresholds according to different food types and cooking states;

[0019] The degree of carbonization of the food is determined according to the color feature and the color threshold.

[0020] Optionally, the step of extracting edge color information of the food based on the image information includes:

[0021] Performing edge detection on the image information after the Gaussian filtering operation, and extracting edge contour information of the food from the image information;

[0022] Extracting pixel points of the edge of the food according to the edge contour information; the pixel points are pixel points from the red, green and blue color space;

[0023] Separating the color channels of the red, green and blue color space, and determining the red channel of the red, green and blue color space as the second target color channel;

[0024] Determine, from the pixel points, a second target pixel point associated with the second target color channel;

[0025] Read the red value of the second target pixel.

[0026] Optionally, the step of determining the degree of carbonization of the food based on the edge color information comprises:

[0027] Set color thresholds according to different food types and cooking states;

[0028] Determine the number of burnt pixels of the second target pixel whose red value exceeds the color threshold;

[0029] An edge coking ratio is determined based on the number of coked pixels, and a coking degree is determined by the edge coking ratio.

[0030] Optionally, it also includes:

[0031] determining a central region of the food;

[0032] Obtaining the central color mean of the pixels in the central area;

[0033] The maturity of the food is determined by the central color mean and a preset maturity color value.

[0034] Optionally, the method is applied to a smart oven, the smart oven is equipped with a heating device, and the step of controlling the oven according to the coking degree and the cooking environment information comprises:

[0035] The heating device is controlled according to the doneness, the coking degree and the cooking environment information.

[0036] Optionally, it also includes:

[0037] generating a maturity index parameter based on the maturity, the coking degree and the cooking environment information;

[0038] When the maturity index parameter meets the preset condition, the heating device is controlled to stop.

[0039] The embodiment of the present invention further discloses an oven control device, comprising:

[0040] An information acquisition module, used to acquire cooking environment information and image information of the food in the oven;

[0041] An edge color information extraction module, used to extract edge color information of the food based on the image information;

[0042] A coking degree determination module, used to determine the coking degree of the food based on the edge color information;

[0043] The oven control module is used to control the oven according to the coking degree and the cooking environment information.

[0044] The embodiment of the present invention further discloses an electronic device, comprising a processor, a communication interface, a memory and a communication bus, wherein the processor, the communication interface and the memory communicate with each other via the communication bus;

[0045] The memory is used to store computer programs;

[0046] The processor is used to implement the method described in the embodiment of the present invention when executing the program stored in the memory.

[0047] The embodiment of the present invention further discloses a computer-readable storage medium having instructions stored thereon, which, when executed by one or more processors, enables the processors to execute the method described in the embodiment of the present invention.

[0048] The embodiments of the present invention include the following advantages:

[0049] In the embodiment of the present invention, the following beneficial effects are achieved by acquiring cooking environment information and image information of the food in the oven; extracting edge color information of the food based on the image information; determining the degree of coking of the food based on the edge color information; and controlling the oven according to the degree of coking and the cooking environment information:

[0050] Intelligence: The system can automatically adjust cooking parameters according to the actual state of the food to achieve intelligent cooking.

[0051] Accuracy: Through image processing and data analysis, the degree of food burnt can be accurately assessed.

[0052] Real-time: The system can monitor the cooking status of food in real time and make dynamic adjustments.

[0053] Adaptability: The system can be adjusted accordingly based on different foods and cooking needs. BRIEF DESCRIPTION OF THE DRAWINGS

[0054] Figure 1 is a flow chart of steps of an oven control method provided in an embodiment of the present invention;

[0055] Figure 2 is a flow chart of an oven control method provided in an embodiment of the present invention;

[0056] Figure 3 is a structural block diagram of an oven control device provided in an embodiment of the present invention;

[0057] Figure 4 is a hardware structure block diagram of an electronic device provided in an embodiment of the present invention;

[0058] Figure 5 is a schematic diagram of a computer-readable medium provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0059] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, the present invention is further described in detail below with reference to the accompanying drawings and specific embodiments.

[0060] With the improvement of living standards, ovens are increasingly used in home cooking. Traditional ovens mainly rely on user experience and timers to control the heating time and temperature of food, which can easily lead to overcooked or undercooked food. Although some high-end ovens have introduced preset cooking programs and temperature probes, these functions still cannot monitor and dynamically adjust the doneness of food in real time.

[0061] In recent years, the development of artificial intelligence and image recognition technology has provided new possibilities for the intelligentization of ovens. Some smart ovens are equipped with built-in cameras, which use image processing algorithms to analyze the appearance characteristics of food and determine its maturity. However, the internal environment of the oven is complex, and factors such as light changes and steam can affect the image quality, resulting in inaccurate recognition results. In addition, existing image recognition algorithms are mostly limited to simple edge detection and color recognition, and cannot fully consider the impact of environmental parameters such as temperature and humidity on the maturity of food.

[0062] Therefore, a smarter solution is urgently needed that can not only monitor the maturity of food in real time, but also dynamically adjust the heating method based on environmental parameters to ensure that the food is evenly cooked and improve the cooking effect and user experience. This is exactly the starting point of this application, which realizes a comprehensive and intelligent oven control system through advanced image recognition and multi-parameter monitoring.

[0063] Traditional ovens rely mainly on user experience and timers to judge the doneness of food during the cooking process. This method has large uncertainties and errors, which can easily lead to overcooked or undercooked food, affecting the final cooking effect. At the same time, although some existing smart ovens have preset programs, they still lack the ability to monitor the real-time status of food and dynamically adjust, and cannot adapt to different ingredients and cooking needs.

[0064] In view of the above problems, the embodiment of the present invention proposes an intelligent oven food maturity monitoring and control system based on image recognition technology. Through the built-in camera and image processing algorithm, the appearance characteristics of the food are collected and analyzed in real time, and the maturity of the food is accurately judged in combination with the temperature, humidity and other environmental parameters inside the oven, and the heating method and time are automatically adjusted. This innovation solves the problem that traditional ovens cannot be monitored and adjusted dynamically in real time, improves the accuracy and efficiency of cooking, and ensures that the food achieves the best cooking effect.

[0065] Specifically, firstly, the built-in camera and image processing algorithm can monitor the appearance characteristics of food in real time and accurately judge the maturity of food, avoiding the problem of over-cooking or under-cooking caused by traditional ovens relying on experience and timers. Secondly, the system dynamically adjusts the heating method and time based on the temperature, humidity and other environmental parameters inside the oven to ensure that the food is evenly cooked and achieve the best cooking effect.

[0066] In addition, the smart oven also has a user-friendly interface and operating system, which allows users to easily set and monitor the cooking process through the touch screen or mobile devices. The preset multiple cooking programs further simplify the operation, allowing users with no cooking experience to easily make delicious dishes.

[0067] Reference Figure 1, shows a flow chart of the steps of an oven control method provided in an embodiment of the present invention, which may specifically include the following steps:

[0068] Step 101, obtaining cooking environment information and image information of food in the oven;

[0069] Step 102, extracting edge color information of the food based on the image information;

[0070] Step 103, determining the degree of carbonization of the food based on the edge color information;

[0071] Step 104: Control the oven according to the coking degree and the cooking environment information.

[0072] In a specific implementation, the embodiments of the present invention can be applied to an oven equipped with a smart oven food maturity monitoring and control system. The oven may include a high-resolution camera, a processor, a temperature sensor, a humidity sensor, a gas sensor, a heating device (including upper and lower and side heating tubes), a rotating device and a user interface (such as a touch screen and a mobile device control interface).

[0073] 1. High-resolution camera;

[0074] Function: Capture images of food inside the oven in real time and provide raw data for image recognition and analysis.

[0075] Features: High resolution can capture subtle changes in food, such as edge burnt, color changes, etc.

[0076] 2. Processor;

[0077] Function: Process image data, control heating device, realize human-computer interaction, etc.

[0078] Requirements: A high-performance processor that can quickly process image data and respond to user instructions in real time.

[0079] 3. Temperature sensor;

[0080] Function: To measure the temperature inside the oven and provide temperature feedback to the system in order to control the heating power.

[0081] Location: Usually installed in different locations inside the oven to monitor the temperature of different areas.

[0082] 4. Humidity sensor;

[0083] Function: To measure the humidity inside the oven, to control the amount of steam and maintain a suitable humidity environment in the oven.

[0084] Location: Usually installed close to the heating element.

[0085] 5. Gas sensor;

[0086] Function: Monitor the concentration of gases inside the oven, such as carbon dioxide, carbon monoxide, etc., to ensure cooking safety and optimize combustion efficiency.

[0087] Location: Usually mounted on the rear or side of the oven.

[0088] 6. Heating device;

[0089] Function: Provide heat for food to achieve cooking.

[0090] type:

[0091] Upper and lower heating tubes: provide upper and lower heating, suitable for cooking most foods.

[0092] 7. Rotating device;

[0093] Function: Rotate the baking tray or grill to ensure that the food is heated evenly.

[0094] Type: Can be motor driven or mechanically driven.

[0095] 8. User interface;

[0096] Function: Human-computer interaction interface, through which users set cooking parameters and monitor the cooking process.

[0097] type:

[0098] Touch screen: intuitive and easy to operate.

[0099] Mobile device control interface: The oven can be remotely controlled via a mobile phone APP.

[0100] Working principle of each device

[0101] Image acquisition: A high-resolution camera continuously captures images of the oven interior.

[0102] Image processing: The processor analyzes the image and extracts edge information, color information, etc.

[0103] Sensor data collection: Temperature sensors, humidity sensors, and gas sensors collect environmental data in real time.

[0104] Data fusion: Fusion analysis of image information and sensor data to determine the cooking status of food.

[0105] Control heating: Based on the analysis results, control the power and heating method of the heating device.

[0106] User interaction: Users can set cooking parameters through the touch screen or mobile phone APP and monitor the cooking process in real time.

[0107] System advantages:

[0108] Intelligence: Intelligent cooking is achieved through image recognition and data analysis.

[0109] Precise control: The heating parameters can be dynamically adjusted according to the actual state of the food to ensure the cooking effect.

[0110] User-friendly: The touch screen and mobile APP are simple to operate and convenient for users.

[0111] Safe and reliable: Gas sensors can monitor gas leaks to ensure safe use.

[0112] The smart oven achieves intelligent control of the cooking process through the reasonable combination of hardware configuration and sophisticated design of software algorithms, providing users with a more convenient and smarter cooking experience.

[0113] The embodiment of the present invention can obtain cooking environment information and image information of the food in the oven;

[0114] Purpose: To collect all the raw data needed in the food cooking process.

[0115] Beneficial effects:

[0116] Comprehensive data collection: Provide basic data for subsequent image processing and data analysis.

[0117] Real-time monitoring: By acquiring data in real time, you can always understand the cooking status of the food so as to make dynamic adjustments.

[0118] Exemplarily, the cooking environment information may include:

[0119] Temperature information obtained by the temperature sensor;

[0120] effect:

[0121] Real-time monitoring of oven temperature: ensure that the temperature inside the oven is always maintained within the set range.

[0122] Determine the internal temperature of food: By monitoring the temperature changes in the oven, you can indirectly infer the temperature changes inside the food and thus determine whether the food is cooked.

[0123] Control heating power: Based on the information fed back by the temperature sensor, the system can automatically adjust the heating power to achieve the best cooking effect.

[0124] Humidity information obtained by the humidity sensor;

[0125] effect:

[0126] Monitor the humidity in your oven: Humidity affects how quickly water evaporates from food, which can affect the taste and texture of your food.

[0127] Controlling the heating method: When baking bread, cakes and other foods that need to maintain a certain humidity, the humidity sensor can help the system adjust the heating method to prevent the food from being too dry. By monitoring the humidity changes, the evaporation rate of the food can be inferred. The use of humidity data combined with temperature data can more accurately assess the maturity of the food. For example, the crispness of the roasted chicken skin and the moistness of the inside can be judged by the rate of change of humidity and temperature.

[0128] Gas composition information obtained by the gas sensor;

[0129] effect:

[0130] Monitoring the gas composition in the oven: Some gas sensors can detect the concentration of gases such as carbon dioxide and carbon monoxide in the oven.

[0131] Determine combustion conditions: By monitoring gas composition, it is possible to determine whether the gas is fully burned, thereby optimizing combustion efficiency. By monitoring changes in the concentration of carbon dioxide and water vapor, the decomposition and reaction process of the food can be inferred. Using gas composition data in combination with temperature and humidity data, the maturity of food can be more comprehensively assessed. For example, when baking bread, changes in carbon dioxide concentration can reflect the degree of yeast fermentation, thereby determining the expansion and maturity of the bread.

[0132] Safety guarantee: Carbon monoxide sensor can detect carbon monoxide leakage in time to ensure safe use.

[0133] The role of various information in subsequent calculations can include:

[0134] Establish a temperature-time curve: By recording temperature data at different time points, you can establish a temperature-time curve for food, providing a reference for judging the maturity of food.

[0135] Combining temperature information with image information can more accurately judge the internal state of food. For example, when the surface of the food has been charred but the internal temperature has not yet reached the set value, the system can appropriately reduce the heating power.

[0136] Control the amount of steam through humidity information. In some cooking methods, steam needs to be injected into the oven. The humidity sensor can help the system control the amount of steam to achieve the best cooking effect.

[0137] To judge the state of food, humidity information can reflect the water loss of food, thereby helping the system to determine whether the food is too dry or too wet.

[0138] Optimize combustion through gas information: By monitoring the gas composition, the gas ratio can be adjusted to achieve the most complete combustion.

[0139] Safety protection: The carbon monoxide sensor can detect carbon monoxide leakage in time to ensure safe use.

[0140] Other possible sensors:

[0141] Light sensor: It can detect the light intensity in the oven and is used to determine the color changes of food.

[0142] Pressure sensor: can measure the pressure inside the oven and is used for some special cooking methods, such as high pressure cooking.

[0143] Information fusion and decision making:

[0144] By integrating the information obtained by the above sensors, a more comprehensive food cooking model can be established. By analyzing and processing these data, the system can make more accurate judgments, such as:

[0145] Determine whether food is cooked: By combining image information, temperature information, and humidity information, you can more accurately determine whether food is cooked.

[0146] Predict cooking time: Based on information such as food type, weight, initial temperature, etc., combined with historical data, the cooking time of food can be predicted.

[0147] Optimize the cooking process: By monitoring and analyzing the cooking process in real time, the heating method, temperature and humidity can be dynamically adjusted to achieve the best cooking effect.

[0148] The cooking environment information provides the smart oven with rich perception data, which can help the system better understand the cooking status of the food, thereby achieving smarter and more precise cooking control. By integrating image information and environmental information, a more complete cooking model can be established to provide users with a better cooking experience.

[0149] The embodiment of the present invention can extract edge color information of the food based on the image information;

[0150] Purpose: To extract the color features of food edges from images as an important basis for judging the degree of food burnt.

[0151] Beneficial effects:

[0152] Accurately locate the burnt area: By analyzing the edge color, you can accurately locate the burnt part of the food surface.

[0153] Objective evaluation: Color information is an intuitive indicator that can objectively reflect the degree of cooking of food.

[0154] The embodiment of the present invention can determine the degree of carbonization of the food based on the edge color information;

[0155] Objective: To quantitatively evaluate the degree of food burnt based on the extracted edge color information.

[0156] Beneficial effects:

[0157] Quantitative evaluation: Convert the degree of coking into quantifiable values ​​for easy computer processing and analysis.

[0158] Timely feedback: Through real-time monitoring of the degree of carbonization, cooking parameters can be adjusted in time to avoid excessive carbonization or undercooking of food.

[0159] The Canny edge detection algorithm is a classic edge detection algorithm that detects edges in an image through a series of steps:

[0160] Noise Suppression: Use a Gaussian filter to smooth the image and remove noise.

[0161] Gradient calculation: Calculate the gradient magnitude and direction of the image to determine the location of the edge.

[0162] Non-maximum suppression: Suppress non-maximum pixels to refine edges.

[0163] Dual Threshold Detection: Use two thresholds to determine strong and weak edges.

[0164] Edge joining: Connect weak edges to form a complete edge.

[0165] Application in smart oven:

[0166] 1. Extract food edges:

[0167] Image preprocessing: First, the collected food images are preprocessed, such as denoising and grayscale conversion, to improve the accuracy of edge detection.

[0168] Canny algorithm application: The preprocessed image is then input into the Canny algorithm, which calculates the edge of the image based on the set parameters, such as the size of the Gaussian filter, the gradient threshold, etc.

[0169] 2. Analyze edge color information:

[0170] Color space conversion: Convert the image from RGB color space to HSV or Lab color space. The V channel in HSV color space represents brightness, while the S channel represents saturation. Their perception of color is more consistent with the visual characteristics of the human eye. The Lab color space is more consistent with the color perception of the human eye.

[0171] Red channel analysis: Burning usually causes the food surface to darken in color, especially the red channel value will increase significantly. Therefore, by analyzing the red channel value of edge pixels, it is possible to determine whether the edge is burnt.

[0172] Other color channel analysis: In addition to the red channel, the values ​​of other color channels can also be analyzed to obtain more comprehensive color information.

[0173] 3. Determination of coking degree:

[0174] Pixel statistics: Count the number of edge pixels whose red channel values ​​exceed a certain threshold.

[0175] Ratio calculation: Calculate the ratio of the above pixel number to the total edge pixel number, that is, the burnt pixel ratio.

[0176] Threshold setting: Set different burnt degree thresholds according to different food types and cooking requirements. When the burnt pixel ratio exceeds the threshold, the edge of the food is considered to be burnt.

[0177] 4. Coking degree as an indicator:

[0178] Feedback system: The ratio of the degree of charring can be used as an important indicator to feed back to the system. When the degree of charring is too high, the system can adjust the heating method or time to avoid excessive charring of the food.

[0179] Determine food status: Combined with other sensor data, such as temperature and humidity, the overall status of food, such as whether it is cooked, can be more accurately determined.

[0180] The purpose of selecting the red channel is:

[0181] Color characteristics of caramelization: When food is caramelized, a caramelization reaction occurs on its surface, causing the color to darken, especially the value of the red channel will increase significantly.

[0182] Robustness: Compared with other color channels, the red channel is less sensitive to lighting changes and therefore has better robustness.

[0183] Simple and efficient: You only need to analyze the value of the red channel to quickly determine whether the edge is burnt, with high calculation efficiency.

[0184] By extracting the edge of food through the Canny edge detection algorithm and analyzing the red channel value of the edge pixel, the degree of food burn can be effectively determined. This method is simple, efficient, and robust, and is very suitable for use in smart ovens.

[0185] The embodiment of the present invention can control the oven according to the coking degree and the cooking environment information.

[0186] Purpose: To dynamically adjust the oven's heating method and time based on the food's charring degree and cooking environment information to achieve the best cooking effect.

[0187] Beneficial effects:

[0188] Intelligent control: The system can automatically adjust cooking parameters according to the actual state of the food without human intervention.

[0189] Optimize cooking results: Through precise control, you can ensure that the food is heated evenly inside and out, achieving the best taste and flavor.

[0190] For example, suppose a steak is being grilled. Through image processing, the system detects that the charring degree of the edge of the steak has reached the set threshold, and the temperature sensor shows that the internal temperature of the steak is 10°C away from the set temperature.

[0191] System decision-making and execution process:

[0192] Judging that the degree of carbonization is too high: The system judges that the upper edge of the cow has begun to carbonize based on the proportion of carbonized pixels.

[0193] Judging that the internal temperature is insufficient: The temperature sensor data shows that the internal temperature of the steak has not reached the set temperature.

[0194] Adjust heating parameters:

[0195] Reduce the power of the upper heating tube: Since the edge has begun to coke, the system reduces the power of the upper heating tube to reduce heating of the surface.

[0196] Increase the power of the lower heating tube: In order to increase the internal temperature of the steak as quickly as possible, the system increases the power of the lower heating tube.

[0197] Adjust fan speed: To make heat distribution more even, the system can adjust the fan speed appropriately.

[0198] Continuous Monitoring: The system continuously monitors the steak’s charring and internal temperature and makes adjustments based on real-time data.

[0199] Stop heating: When the carbonization degree and internal temperature of the steak reach the set value, the system will automatically stop heating and prompt the user.

[0200] Through the above methods, based on image processing and data analysis, intelligent monitoring and control of the food cooking process is achieved. Specifically, the system obtains image information of food through the camera and extracts edge color features to determine the degree of carbonization of the food. At the same time, the system also collects cooking environment information, such as temperature, humidity, etc. Based on this information, the system can dynamically adjust the heating method and time of the oven to ensure that the food reaches the best cooking state.

[0201] In the embodiment of the present invention, the following beneficial effects are achieved by acquiring cooking environment information and image information of the food in the oven; extracting edge color information of the food based on the image information; determining the degree of coking of the food based on the edge color information; and controlling the oven according to the degree of coking and the cooking environment information:

[0202] Intelligence: The system can automatically adjust cooking parameters according to the actual state of the food to achieve intelligent cooking.

[0203] Accuracy: Through image processing and data analysis, the degree of food burnt can be accurately assessed.

[0204] Real-time: The system can monitor the cooking status of food in real time and make dynamic adjustments.

[0205] Adaptability: The system can be adjusted accordingly based on different foods and cooking needs.

[0206] Based on the above embodiment, a variant embodiment of the above embodiment is proposed. It should be noted that in order to make the description concise, only the differences from the above embodiment are described in the variant embodiment.

[0207] In an optional embodiment of the present invention, it also includes:

[0208] A Gaussian filtering operation is performed on the image information.

[0209] In a smart oven, the food images captured by the camera are often interfered by various noises, such as:

[0210] Sensor noise: Random noise generated by the camera sensor itself.

[0211] Uneven lighting: Different areas have different lighting intensities, resulting in inconsistent image brightness.

[0212] Environmental interference: Environmental factors such as dust and water vapor can affect image quality.

[0213] These noises will affect subsequent image processing, such as edge detection, color recognition, etc., thereby reducing the accuracy of the system's judgment of food status. Therefore, before image processing, image preprocessing is usually required to remove noise and improve image quality.

[0214] Gaussian filtering is a linear smoothing filter that is often used to remove high-frequency noise in images, such as salt and pepper noise. It uses a two-dimensional Gaussian function as the convolution kernel to perform convolution operations with the image to achieve image smoothing.

[0215] Gaussian function: The Gaussian function is a bell-shaped curve, where the weight of the central pixel is the largest and the farther away from the center, the smaller the weight. This makes Gaussian filtering effective in smoothing images while preserving edge information.

[0216] Convolution operation: Slide the Gaussian filter on the image, and the value of each pixel is replaced by the weighted average of its neighboring pixels, with the weight determined by the Gaussian function.

[0217] The role of Gaussian filtering in smart ovens

[0218] Noise reduction: Gaussian filtering can effectively remove high-frequency noise in the image, making the image smoother.

[0219] Edge protection: Since the center pixel of the Gaussian filter has the largest weight, the edge information can be better preserved.

[0220] Improve image quality: By removing noise and enhancing image contrast, the accuracy of subsequent image processing can be improved.

[0221] The implementation process of Gaussian filtering:

[0222] Choose the appropriate filter size and standard deviation: The larger the filter size, the more obvious the smoothing effect, but edge details will also be lost. The standard deviation determines the shape of the Gaussian function. The larger the standard deviation, the flatter the Gaussian curve and the stronger the smoothing effect.

[0223] Generate Gaussian Filter: Generates a two-dimensional Gaussian matrix with the selected size and standard deviation.

[0224] Image convolution: Convolve the Gaussian filter with the image to obtain the filtered image.

[0225] After Gaussian filtering, the noise in the image is significantly reduced and the image becomes smoother.

[0226] Other preprocessing methods for image preprocessing:

[0227] In addition to Gaussian filtering, there are other commonly used image preprocessing methods, such as:

[0228] Median filtering: mainly used to remove salt and pepper noise.

[0229] Bilateral filtering: It can better preserve edge information while smoothing the image.

[0230] Histogram equalization: used to enhance image contrast.

[0231] As a classic image preprocessing method, Gaussian filtering plays an important role in smart ovens. By removing image noise and improving image quality, it provides a reliable foundation for subsequent image processing. In practical applications, appropriate filter parameters can be selected and multiple filtering methods can be combined according to different needs to achieve the best preprocessing effect.

[0232] In an optional embodiment of the present invention, the step of extracting edge color information of the food based on the image information includes:

[0233] Performing edge detection on the image information after the Gaussian filtering operation, and extracting edge contour information of the food from the image information;

[0234] Extracting pixel points of the edge of the food according to the edge contour information;

[0235] Convert the pixel point from the red, green and blue color space to the hue, saturation and brightness color space, and obtain the hue, saturation and brightness of the pixel point in the hue, saturation and brightness color space;

[0236] Determine a first target color channel based on the hue, the saturation, and the brightness, and determine a first target pixel point associated with the first target color channel from the pixel points;

[0237] The color feature of the edge is extracted based on the first target pixel.

[0238] In a specific implementation, the embodiment of the present invention may perform the following operations after performing preprocessing operations such as Gaussian filtering on the image information to extract the edge color information of the food:

[0239] Edge detection: Use edge detection algorithms such as the Canny operator to perform edge detection on the preprocessed image and extract the edge contour of the food in the image.

[0240] Edge pixel extraction: Based on the results of edge detection, all pixels belonging to the edges of food in the image are extracted.

[0241] Color space conversion: Convert the original image from RGB color space to HSV color space. HSV color space is more suitable for describing the perceptual characteristics of color, which is convenient for subsequent color analysis.

[0242] Edge pixel color acquisition: For the extracted edge pixels, obtain their H, S, and V values ​​in the HSV color space. The H value represents the hue, the S value represents the saturation, and the V value represents the brightness.

[0243] First target color channel selection: Select the appropriate color channel for analysis based on actual needs. For example, if you want to determine whether food is burnt, you usually focus on the pixels corresponding to the red area in the H channel.

[0244] RGB color space: red, green, and blue color space;

[0245] HSV color space: Hue, Saturation, Brightness color space, sometimes referred to as HSB color space (B stands for Brightness);

[0246] A simple explanation of the two color spaces:

[0247] RGB color space:

[0248] It is one of the most commonly used color spaces, based on the mixing of the three primary colors of red, green, and blue in different proportions to produce various colors.

[0249] Widely used in computer graphics and image processing.

[0250] RGB values ​​are usually represented by three numbers between 0 and 255, corresponding to the intensity of the red, green, and blue channels respectively.

[0251] HSV color space:

[0252] It is a color space designed based on the intuitive characteristics of color, which is more in line with the perception of the human eye.

[0253] HSV stands for Hue, Saturation, and Value.

[0254] Hue indicates the type of color, such as red, green, etc.; saturation indicates the purity of the color, the higher the value, the purer the color; value indicates the brightness of the color.

[0255] The purpose of using HSV color space in image processing is:

[0256] More in line with human eye perception: The three components of the HSV color space are more in line with people's intuitive understanding of color. Therefore, in color-related image processing tasks, such as color segmentation and color tracking, it is often more effective than the RGB color space.

[0257] Color representation is more intuitive: In the HSV color space, changing the hue can change the color, changing the saturation can change the color purity, and changing the lightness can change the color brightness. This representation method is more in line with human cognition of color.

[0258] The RGB color space is an additive color model based on physical principles, while the HSV color space is a model based on human eye perception.

[0259] In different image processing tasks, choosing an appropriate color space can improve processing efficiency and accuracy.

[0260] The reasons for choosing the HSV color space are:

[0261] Intuitiveness: The HSV color space is more consistent with people's perception of color and is easier to understand and analyze.

[0262] Robustness: Insensitive to lighting changes and more suitable for describing the color characteristics of objects.

[0263] Convenient color segmentation: In the HSV color space, specific color areas can be segmented by setting thresholds for hue, saturation, and brightness.

[0264] The Canny operator is a classic and efficient edge detection algorithm that accurately detects edges in an image through the following steps:

[0265] Noise Suppression: Use a Gaussian filter to smooth the image and reduce noise interference.

[0266] Gradient calculation: Calculate the gradient magnitude and direction of the image to determine the location and direction of the edge.

[0267] Non-maximum suppression: suppress non-maximum points along the gradient direction and refine the edges.

[0268] Double threshold detection: Use two thresholds to connect edges, ensuring that the detected edges are continuous and complete.

[0269] Advantages of Canny operator:

[0270] High accuracy: It can effectively detect the real edges in the image.

[0271] High positioning accuracy: The detected edge position is accurate.

[0272] Strong noise resistance: It is highly robust to noise.

[0273] 1. The purpose of edge detection is to find the area where the pixel value changes dramatically in the image, that is, the edge of the object. For food images, the edge usually represents the change of the outline or surface texture of the food.

[0274] Method: Canny operator is a commonly used edge detection algorithm, which can effectively detect the edges in the image through Gaussian filtering, gradient calculation, non-maximum suppression and double threshold processing.

[0275] Beneficial effect: Through edge detection, we can accurately locate the position of food in the image, providing a basis for subsequent color analysis.

[0276] 2. The purpose of edge pixel extraction is to extract specific pixels from the detected edges.

[0277] Method: According to the edge image output by the Canny operator, traverse each pixel point. If the gray value of the pixel point is greater than the set threshold, the pixel point is considered to belong to the edge.

[0278] Beneficial effect: The extracted edge pixels provide a direct data source for subsequent color analysis.

[0279] 3. The purpose of color space conversion is to convert the image from RGB color space to HSV color space in order to better describe the color information.

[0280] Method: The RGB color space is based on the three primary colors of red, green and blue, while the HSV color space is more in line with the perception of the human eye and decomposes color into three components: hue, saturation and brightness.

[0281] Beneficial effects:

[0282] In the HSV color space, hue H is more suitable for describing color categories, such as red, green, etc.

[0283] Saturation S indicates the purity of color and can be used to distinguish whether the color is bright or not.

[0284] Brightness V represents the brightness or darkness of the color. By converting to the HSV color space, we can analyze the color characteristics of food more intuitively.

[0285] 4. The purpose of edge pixel color acquisition is to obtain the color value of the edge pixel in the HSV color space.

[0286] Method: For each edge pixel, query its corresponding H, S, and V values ​​in the HSV color space.

[0287] Beneficial effect: These color values ​​will be used for subsequent color feature extraction and analysis.

[0288] 5. The purpose of the first target color channel selection is to determine the specific color channel used for analysis.

[0289] Method: Select the appropriate color channel according to the features to be detected. For example, if you want to detect whether food is burnt, you usually focus on the pixels corresponding to the red area in the H channel, because burnt food will darken the color of the surface and increase the red component.

[0290] Beneficial effect: By selecting the appropriate color channel, the color information related to the target feature can be extracted more accurately.

[0291] 6. The purpose of color feature extraction is to extract useful features from the color values ​​of edge pixels.

[0292] method:

[0293] Statistical analysis: Calculate the distribution of edge pixels in the selected color channel, such as calculating the proportion of red pixels.

[0294] Histogram analysis: Draw a color histogram and observe the pattern of color distribution.

[0295] Beneficial effects: The extracted color features can be used to characterize the state of food, such as the degree of carbonization, freshness, etc.

[0296] Through the above steps, rich color information can be extracted from food images and used to judge the state of the food.

[0297] In an optional embodiment of the present invention, the step of determining the degree of carbonization of the food based on the edge color information comprises:

[0298] Set color thresholds according to different food types and cooking states;

[0299] The degree of carbonization of the food is determined according to the color feature and the color threshold.

[0300] In a specific implementation, the embodiment of the present invention can set color thresholds according to different food types and cooking states, with the purpose of establishing a quantitative standard for the degree of carbonization of different foods at different cooking stages.

[0301] Beneficial effects:

[0302] Highly targeted: Different foods have different carbonization behaviors. Setting targeted thresholds can improve the accuracy of judgment.

[0303] Flexible adaptation: As the cooking time goes by, the color of the food will change. By dynamically adjusting the threshold, the real-time status of the food can be more accurately reflected.

[0304] Personalized settings: Users can adjust the threshold according to their taste preferences to achieve a personalized cooking experience.

[0305] The embodiment of the present invention can also determine the degree of burntness of the food according to the color threshold, and its purpose is to compare the extracted color feature with the preset threshold to determine whether the food is burnt and the degree of burntness.

[0306] Beneficial effects:

[0307] Automated judgment: The degree of coking is automatically judged by computer algorithms without manual intervention.

[0308] Real-time feedback: The cooking status of food can be monitored in real time, and cooking parameters can be adjusted in time to avoid over-burning or undercooking of food.

[0309] Data-driven: Models trained on large amounts of data can improve the accuracy of judgments and continuously optimize algorithms.

[0310] Specific implementation steps and methods:

[0311] 1. Color feature extraction:

[0312] Statistical analysis: Calculate the distribution of edge pixels in a specific color channel (such as the H channel).

[0313] Histogram analysis: Draw a color histogram to observe the peaks and valleys of color distribution.

[0314] 2.Threshold setting:

[0315] Empirical value: Set the initial threshold based on previous experimental data and industry standards.

[0316] Adaptive Threshold: Dynamically adjusts the threshold to accommodate changes in food color as the cooking process progresses.

[0317] Machine Learning: Use machine learning algorithms to automatically learn the optimal threshold based on a large amount of training data.

[0318] 3. Determination of coking degree:

[0319] Single threshold judgment: If the color value of the edge pixel exceeds the set threshold, the pixel is considered to be burnt.

[0320] Interval judgment: Divide the color value into multiple intervals, corresponding to different degrees of coking.

[0321] Probability model: Use the probability model to calculate the probability of food being in different carbonization states.

[0322] Optionally, the embodiment of the present invention can also determine the degree of coking in the following manner.

[0323] Build a color database: collect image data of different foods at different cooking stages and extract their color features.

[0324] Determine characteristic parameters: According to the characteristics of the food, select appropriate color characteristic parameters, such as the mean value and standard deviation of the H channel.

[0325] Classification: The degree of carbonization of food is divided into multiple levels, such as: raw, half-cooked, cooked, overcooked, etc.

[0326] Determine the threshold: For each level, determine the corresponding threshold range based on the distribution of color features.

[0327] Real-time monitoring: During the cooking process, the color features of food images are extracted in real time and compared with the preset thresholds.

[0328] Judgment result: According to the comparison result, determine the degree of carbonization of the food and give corresponding feedback.

[0329] Lighting conditions: Lighting affects the color of the image, so lighting compensation needs to be considered.

[0330] Food type: The material, shape, size, etc. of different foods will affect their color changes.

[0331] Camera parameters: Camera parameters such as resolution and white balance will affect image quality.

[0332] Threshold optimization: Machine learning methods can be used to automatically optimize the threshold through a large amount of training data.

[0333] By setting a reasonable color threshold and combining it with image processing technology, the degree of food charring can be automatically determined. This is of great significance to smart ovens, as it can not only improve the success rate of cooking, but also provide users with a smarter and more convenient cooking experience.

[0334] In an optional embodiment of the present invention, the step of extracting edge color information of the food based on the image information includes:

[0335] Performing edge detection on the image information after the Gaussian filtering operation, and extracting edge contour information of the food from the image information;

[0336] Extracting pixel points of the edge of the food according to the edge contour information; the pixel points are pixel points from the red, green and blue color space;

[0337] Separating the color channels of the red, green and blue color space, and determining the red channel of the red, green and blue color space as the second target color channel;

[0338] Determine, from the pixel points, a second target pixel point associated with the second target color channel;

[0339] Read the red value of the second target pixel.

[0340] Optionally, the step of determining the degree of carbonization of the food based on the edge color information comprises:

[0341] Set color thresholds according to different food types and cooking states;

[0342] Determine the number of burnt pixels of the second target pixel whose red value exceeds the color threshold;

[0343] An edge coking ratio is determined based on the number of coked pixels, and a coking degree is determined by the edge coking ratio.

[0344] In practical applications, burnt pixels usually show higher red channel values, because the burnt area usually becomes darker or even appears black. The embodiment of the present invention can determine the degree of burnt by calculating the burnt ratio.

[0345] In a specific implementation, the embodiment of the present invention can count the burnt pixels: count the number of pixels whose red channel value exceeds the color threshold among all edge pixels, and record it as the number of burnt pixels. Calculate the burnt ratio: divide the number of burnt pixels by the total number of edge pixels to obtain the edge burnt ratio.

[0346] The edge carbonization ratio, the color difference in the center area, and the environmental parameters inside the oven are combined to form a comprehensive maturity evaluation index. Based on the comprehensive evaluation index, the heating method of the oven is dynamically adjusted.

[0347] Exemplarily, the embodiment of the present invention can directly extract edge color information of food based on the RGB color space.

[0348] 1. After preprocessing the image (Gaussian filtering to remove noise), use edge detection algorithms such as the Canny operator to find the area where the pixel value changes dramatically in the image, that is, the edge of the food, so as to find the boundary of the food in the image.

[0349] Example: Perform a Gaussian filter on a picture of grilled steak, and then use the Canny operator to detect the outline of the steak.

[0350] 2. From the detected edges, obtain the coordinates of each edge pixel to obtain the RGB values ​​of these pixels.

[0351] Example: For the detected steak edge, get the (R,G,B) value of each edge pixel.

[0352] 3. Split the RGB image into three single-channel images (R, G, B), and select the red channel (the second target color channel) as the main analysis object.

[0353] Example: Split the RGB values ​​of the steak image into three matrices: R, G, and B. Then focus only on the R matrix, and the pixels in the R matrix are the second target pixels.

[0354] 4. From the extracted edge pixels, filter out pixels with higher red channel values.

[0355] Example: For each pixel on the edge of a steak, if its R value is greater than a certain threshold (e.g. 180), the pixel is considered to be a burnt pixel.

[0356] 5. Get the R value of the selected coked pixel points.

[0357] Example: Read the R values ​​of all pixels that satisfy the R value greater than 180.

[0358] 6. For different types of food and different degrees of cooking, the color of the burnt may appear different, so different thresholds need to be set. For example, the burnt color of beef may be different from the burnt color of bread.

[0359] For example, for steak, you might set the R value threshold to 180; for bread, you might want to set a higher threshold.

[0360] 7. Count the number of pixels whose R values ​​exceed the threshold.

[0361] Example: Count the number of pixels in a steak image whose R value is greater than 180.

[0362] 8. Divide the number of burnt pixels by the total number of edge pixels to get the edge burnt ratio. Based on this ratio, the burnt degree of the food can be determined. For example, if the burnt ratio exceeds 80%, it can be considered that the steak is over-burnt.

[0363] Example: Assuming that there are 1000 pixels on the edge of a steak, and 800 of them have R values ​​exceeding 180, the charring ratio is 80%, and it can be determined that the steak is over-charred.

[0364] The process of extracting edge information from the image, analyzing the edge color, and judging the degree of food charring based on color features. The whole process involves image preprocessing, edge detection, color space conversion, threshold setting, and statistical analysis, which can achieve real-time monitoring and control of food cooking status.

[0365] In an optional embodiment of the present invention, it also includes:

[0366] determining a central region of the food;

[0367] Obtaining the central color mean of the pixels in the central area;

[0368] The maturity of the food is determined by the central color mean and a preset maturity color value.

[0369] 1. The purpose of determining the central area of ​​the food is to narrow the analysis scope of the image to the most representative area of ​​the food.

[0370] Beneficial effects:

[0371] Improve accuracy: Avoid interference information such as image edges and background, so that color analysis can be more focused on the food itself.

[0372] Reduce the amount of calculation: Only analyze the central area, which reduces the amount of calculation and improves the processing speed.

[0373] 2. The purpose of obtaining the central color mean of the pixels in the central area is to calculate the average color of the central area.

[0374] Beneficial effects:

[0375] Representativeness: The central color mean can better represent the overall color characteristics of the central area.

[0376] Simplify calculations: Convert a large amount of pixel information into a simple color value to facilitate subsequent comparisons and calculations.

[0377] 3. The purpose of determining the maturity of the food by using the central color mean and the preset mature color value is to compare the actual color of the food with the preset mature color to determine whether the food is mature.

[0378] Beneficial effects:

[0379] Objective evaluation: The maturity of food is judged by numerical color differences, avoiding errors caused by subjective judgment.

[0380] Automation: Maturity judgment can be automatically completed through computer programs to improve efficiency.

[0381] In a specific implementation, the maturity can be determined in the following manner.

[0382] Center area extraction: directly extract the pixels in the center area by specifying the coordinate range of the image.

[0383] Color space conversion: Convert RGB color space to Lab color space. Lab color space is more in line with human eye perception, L represents brightness, a represents color components from green to red, and b represents color components from blue to yellow.

[0384] Color mean calculation: Average the Lab values ​​of all pixels in the central area to obtain the Lab color mean of the central area.

[0385] Color difference calculation: Use the CIE76 color difference formula or the CIE2000 color difference formula to calculate the color difference between the center color mean and the preset mature color value to obtain the deltaE value. The smaller the deltaE value, the closer the two colors are.

[0386] Maturity judgment: The calculated deltaE value is compared with the preset threshold. If the deltaE value is less than the threshold, the food is considered ripe.

[0387] The purpose of choosing Lab color space is:

[0388] Perceptual uniformity: Arithmetic changes in the Lab color space appear visually as arithmetic changes, which is more consistent with human perception.

[0389] Device independence: The Lab color space is device-independent and is not affected by device display.

[0390] Color difference calculation: The Lab color space provides a variety of color difference calculation formulas, which can more accurately measure the difference between two colors.

[0391] Exemplarily, the maturity of food may be determined in the following manner.

[0392] 1. Extract the central area. Assuming the image size is 800x600 pixels, we choose the central area to be 200x150 pixels.

[0393] 2. Calculate the color mean of the central area. Assume that the calculated BGR color mean is (150, 100, 50).

[0394] 3. Convert to Lab color model. Assume that the mean value of the converted Lab color is (65, 140, 80).

[0395] 4. Calculate the color difference, assuming the preset mature color Lab value is (60,128,128). Assume the calculated deltaE value is 12

[0396] Evaluate the maturity of food and evaluate the maturity of food based on the deltaE value. Suppose we set the maturity threshold to 10. Since the deltaE value is 12, which is greater than the threshold of 10, the system will determine that the food is not yet mature.

[0397] By analyzing the color of the central area and comparing it with the preset mature color, the maturity of food can be automatically judged. This method is simple, efficient, and has good robustness. Further research can be conducted in the future to improve the accuracy of judgment by combining multi-feature fusion, machine learning and other technologies.

[0398] Optionally, the method is applied to a smart oven, the smart oven is equipped with a heating device, and the step of controlling the oven according to the coking degree and the cooking environment information comprises:

[0399] The heating device is controlled according to the doneness, the coking degree and the cooking environment information.

[0400] The embodiment of the present invention can be comprehensively evaluated and dynamically adjusted based on the maturity, the degree of coking and the cooking environment information: the system calculates a comprehensive maturity score by comprehensively analyzing multiple indicators such as the degree of coking at the edge of the food, the color difference in the center and the temperature inside the oven. Based on this score, the system can adjust the heating method of the oven in real time. For example, when it is detected that the edge of the food has begun to coke, but the center is not yet cooked, the system will reduce the power of the upper heating tube and increase the power of the lower heating tube or the side heating tube to ensure that the inside of the food is heated evenly. In addition, if it is found that the food is heated unevenly, the system will improve this situation by controlling the rotation of the baking tray.

[0401] Optionally, it also includes:

[0402] generating a maturity index parameter based on the maturity, the coking degree and the cooking environment information;

[0403] When the maturity index parameter meets the preset condition, the heating device is controlled to stop.

[0404] During the entire cooking process, the system will continuously analyze the image of the food and update the maturity score in real time based on the analysis results. Users can intuitively view the cooking process of the food through the touch screen or mobile device, including real-time images and system adjustment status. Once the maturity of the food reaches the preset value, the system will automatically stop heating and issue a prompt sound or push notification to remind the user that the food has been cooked. This intelligent cooking method not only ensures the taste and quality of the food, but also greatly improves cooking efficiency.

[0405] The embodiment of the present invention also provides an intelligent oven system based on image recognition technology, which aims to solve the problem that traditional ovens cannot monitor and dynamically adjust the maturity of food in real time. The system uses a built-in camera and image processing algorithm to monitor the appearance of food in real time, and conducts a comprehensive analysis based on environmental parameters such as temperature and humidity to accurately judge the maturity of food. According to the real-time monitoring results, the system can dynamically adjust the heating method and time to ensure that the food is evenly mature. The touch screen and mobile device control interface provide a user-friendly interface for simplified operation. In addition, the system has a self-learning function and can continuously optimize performance based on historical data and user feedback. These innovations significantly improve the intelligence and accuracy of the oven, ensure that the food achieves the best cooking effect, and enhance the user experience.

[0406] In order to enable those skilled in the art to better understand the embodiment of the present invention, an example is used below to illustrate the embodiment of the present invention.

[0407] refer to Figure 2 , Figure 2is a flow chart of an oven control method provided in an embodiment of the present invention;

[0408] The smart oven system consists of several key components, including a high-resolution camera, a processor, a temperature sensor, a humidity sensor, a gas sensor, a heating device (including upper and lower and side heating tubes), a rotating device, and a user interface (such as a touch screen and a mobile device control interface).

[0409] 1. First, the system uses a high-resolution camera to collect images of food in real time, and uses image preprocessing techniques such as Gaussian filtering to remove noise from the image to ensure image clarity. Then, the system uses the Canny edge detection algorithm to extract the edge contour of the food and analyze the color information of these edge pixels, especially the value of the red channel, to determine whether the edge is burnt. The proportion of burnt pixels is counted and used as an indicator of the degree of edge burnt.

[0410] 2. At the same time, the system will also perform color comparison analysis on the center area of ​​the food. First, the center area of ​​the image is divided and the center part of the food is automatically segmented using an image segmentation algorithm. Then, the color mean of the center area is calculated and converted to the Lab color model for more accurate color analysis. The system uses the CIEDE2000 color difference formula to calculate the difference between the center area color and the preset mature color, which is used to evaluate the maturity of the food.

[0411] 3. In addition to image recognition, the system also monitors the environmental parameters inside the oven in real time through temperature sensors, humidity sensors and gas sensors. The temperature sensor is used to detect the real-time temperature inside the oven, the humidity sensor monitors the humidity level, and the gas sensor detects the gas composition inside the oven, such as carbon dioxide and water vapor concentration.

[0412] 4. By comprehensively analyzing the degree of charring at the edge of the food, the color difference in the center, and the environmental parameters inside the oven, the system forms a comprehensive maturity assessment index. Based on this index, the system can dynamically adjust the heating method of the oven. If it is found that the edge of the food has been charred, but the center color has not reached the expected level, the system will reduce the power of the upper heating tube and increase the power of the lower heating tube or the side heating tube. In addition, if it is detected that a part of the food is heated unevenly, the system will also control the rotation of the baking tray to ensure that all parts of the food are heated evenly.

[0413] 5. Throughout the cooking process, the system continuously monitors the status of the food, updates the comprehensive score in real time, and dynamically adjusts the heating parameters. Users can view real-time monitoring images and system adjustment status through the touch screen or mobile device to ensure that the food reaches the optimal state of maturity. Once the food reaches the preset maturity, the system will automatically stop heating and prompt the user that the food has been cooked.

[0414] By combining image recognition technology and environmental parameter monitoring, the smart oven system can determine the maturity of food in real time, dynamically adjust the heating method and rotate the heating position to ensure that the food is evenly cooked, thereby improving cooking quality and user experience.

[0415] It should be noted that, for the sake of simplicity, the method embodiments are described as a series of action combinations, but those skilled in the art should be aware that the embodiments of the present invention are not limited by the order of the actions described, because according to the embodiments of the present invention, certain steps can be performed in other orders or simultaneously. Secondly, those skilled in the art should also be aware that the embodiments described in the specification are all preferred embodiments, and the actions involved are not necessarily required by the embodiments of the present invention.

[0416] Reference Figure 3 , shows a structural block diagram of an oven control device provided in an embodiment of the present invention, which may specifically include the following modules:

[0417] An information acquisition module 301 is used to acquire cooking environment information and image information of the food in the oven;

[0418] An edge color information extraction module 302 is used to extract edge color information of the food based on the image information;

[0419] A coking degree determination module 303, used to determine the coking degree of the food based on the edge color information;

[0420] The oven control module 304 is used to control the oven according to the coking degree and the cooking environment information.

[0421] As for the device embodiment, since it is basically similar to the method embodiment, the description is relatively simple, and the relevant parts can be referred to the partial description of the method embodiment.

[0422] In addition, an embodiment of the present invention further provides an electronic device, including: a processor, a memory, and a computer program stored in the memory and executable on the processor. When the computer program is executed by the processor, the various processes of the above-mentioned oven control method embodiment are implemented, and the same technical effect can be achieved. To avoid repetition, it will not be described here.

[0423] The embodiment of the present invention further provides a computer-readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, each process of the oven control method embodiment described above is implemented, and the same technical effect can be achieved. To avoid repetition, it is not described here. The computer-readable storage medium is, for example, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk.

[0424] Figure 4 A schematic diagram of the hardware structure of an electronic device for implementing various embodiments of the present invention.

[0425] The electronic device 400 includes but is not limited to: a radio frequency unit 401, a network module 402, an audio output unit 403, an input unit 404, a sensor 405, a display unit 406, a user input unit 407, an interface unit 408, a memory 409, a processor 410, and a power supply 411. Those skilled in the art will appreciate that Figure 4 The electronic device structure shown in the figure does not constitute a limitation on the electronic device, and the electronic device may include more or fewer components than shown in the figure, or combine certain components, or arrange the components differently. In the embodiments of the present invention, the electronic device includes but is not limited to a mobile phone, a tablet computer, a laptop computer, a PDA, a vehicle-mounted terminal, a wearable device, and a pedometer.

[0426] It should be understood that in the embodiment of the present invention, the radio frequency unit 401 can be used for receiving and sending signals during information transmission or communication. Specifically, after receiving downlink data from the base station, it is sent to the processor 410 for processing; in addition, uplink data is sent to the base station. Generally, the radio frequency unit 401 includes but is not limited to an antenna, at least one amplifier, a transceiver, a coupler, a low noise amplifier, a duplexer, etc. In addition, the radio frequency unit 401 can also communicate with the network and other devices through a wireless communication system.

[0427] The electronic device provides users with wireless broadband Internet access through the network module 402, such as helping users to send and receive emails, browse web pages, and access streaming media.

[0428] The audio output unit 403 can convert the audio data received by the RF unit 401 or the network module 402 or stored in the memory 409 into an audio signal and output it as sound. Moreover, the audio output unit 403 can also provide audio output related to a specific function performed by the electronic device 400 (for example, a call signal reception sound, a message reception sound, etc.). The audio output unit 403 includes a speaker, a buzzer, a receiver, etc.

[0429] The input unit 404 is used to receive audio or video signals. The input unit 404 may include a graphics processor (GPU) 4041 and a microphone 4042, and the graphics processor 4041 processes the image data of a static picture or video obtained by an image capture device (such as a camera) in a video capture mode or an image capture mode. The processed image frame can be displayed on the display unit 406. The image frame processed by the graphics processor 4041 can be stored in the memory 409 (or other storage medium) or sent via the radio frequency unit 401 or the network module 402. The microphone 4042 can receive sound and can process such sound into audio data. The processed audio data can be converted into a format output that can be sent to a mobile communication base station via the radio frequency unit 401 in the case of a telephone call mode.

[0430] The electronic device 400 also includes at least one sensor 405, such as a light sensor, a motion sensor, and other sensors. Specifically, the light sensor includes an ambient light sensor and a proximity sensor, wherein the ambient light sensor can adjust the brightness of the display panel 4061 according to the brightness of the ambient light, and the proximity sensor can turn off the display panel 4061 and / or the backlight when the electronic device 400 is moved to the ear. As a type of motion sensor, the accelerometer sensor can detect the magnitude of acceleration in each direction (generally three axes), and can detect the magnitude and direction of gravity when stationary, which can be used to identify the posture of the electronic device (such as horizontal and vertical screen switching, related games, magnetometer posture calibration), vibration recognition related functions (such as pedometer, tapping), etc.; the sensor 405 can also include a fingerprint sensor, a pressure sensor, an iris sensor, a molecular sensor, a gyroscope, a barometer, a hygrometer, a thermometer, an infrared sensor, etc., which will not be repeated here.

[0431] The display unit 406 is used to display information input by the user or information provided to the user. The display unit 406 may include a display panel 4061, which may be configured in the form of a liquid crystal display (LCD), an organic light-emitting diode (OLED), or the like.

[0432] The user input unit 407 can be used to receive input digital or character information, and to generate key signal input related to user settings and function control of the electronic device. Specifically, the user input unit 407 includes a touch panel 4071 and other input devices 4072. The touch panel 4071, also known as a touch screen, can collect the user's touch operation on or near it (such as the user's operation on the touch panel 4071 or near the touch panel 4071 using any suitable object or accessory such as a finger, stylus, etc.). The touch panel 4071 may include two parts: a touch detection device and a touch controller. Among them, the touch detection device detects the user's touch orientation, detects the signal brought by the touch operation, and transmits the signal to the touch controller; the touch controller receives the touch information from the touch detection device, converts it into the contact point coordinates, and then sends it to the processor 410, receives the command sent by the processor 410 and executes it. In addition, the touch panel 4071 can be implemented using various types such as resistive, capacitive, infrared and surface acoustic waves. In addition to the touch panel 4071, the user input unit 407 may also include other input devices 4072. Specifically, other input devices 4072 may include but are not limited to a physical keyboard, function keys (such as volume control keys, switch keys, etc.), a trackball, a mouse, and a joystick, which are not described in detail here.

[0433] Furthermore, the touch panel 4071 may be overlaid on the display panel 4061. When the touch panel 4071 detects a touch operation on or near it, it is transmitted to the processor 410 to determine the type of the touch event. Then, the processor 410 provides a corresponding visual output on the display panel 4061 according to the type of the touch event. Figure 4 In the figure, the touch panel 4071 and the display panel 4061 are two independent components to realize the input and output functions of the electronic device. However, in some embodiments, the touch panel 4071 and the display panel 4061 can be integrated to realize the input and output functions of the electronic device, which is not limited here.

[0434] The interface unit 408 is an interface for connecting an external device to the electronic device 400. For example, the external device may include a wired or wireless headset port, an external power supply (or battery charger) port, a wired or wireless data port, a memory card port, a port for connecting a device with an identification module, an audio input / output (I / O) port, a video I / O port, a headphone port, etc. The interface unit 408 may be used to receive input (e.g., data information, power, etc.) from an external device and transmit the received input to one or more elements within the electronic device 400 or may be used to transmit data between the electronic device 400 and an external device.

[0435] The memory 409 can be used to store software programs and various data. The memory 409 can mainly include a program storage area and a data storage area, wherein the program storage area can store an operating system, at least one application required for a function (such as a sound playback function, an image playback function, etc.), etc.; the data storage area can store data created according to the use of the mobile phone (such as audio data, a phone book, etc.), etc. In addition, the memory 409 can include a high-speed random access memory, and can also include a non-volatile memory, such as at least one disk storage device, a flash memory device, or other volatile solid-state storage devices.

[0436] The processor 410 is the control center of the electronic device. It uses various interfaces and lines to connect various parts of the entire electronic device. It executes various functions of the electronic device and processes data by running or executing software programs and / or modules stored in the memory 409, and calling data stored in the memory 409, so as to monitor the electronic device as a whole. The processor 410 may include one or more processing units; preferably, the processor 410 may integrate an application processor and a modem processor, wherein the application processor mainly processes the operating system, user interface, and application programs, etc., and the modem processor mainly processes wireless communications. It is understandable that the above-mentioned modem processor may not be integrated into the processor 410.

[0437] The electronic device 400 may also include a power supply 411 (such as a battery) for supplying power to each component. Preferably, the power supply 411 may be logically connected to the processor 410 through a power management system, thereby implementing functions such as charging, discharging, and power consumption management through the power management system.

[0438] In addition, the electronic device 400 includes some functional modules not shown, which will not be described in detail here.

[0439] It should be noted that, in this article, the terms "include", "comprises" or any other variations thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device. In the absence of further restrictions, an element defined by the sentence "comprises a ..." does not exclude the existence of other identical elements in the process, method, article or device including the element.

[0440] Through the description of the above implementation methods, those skilled in the art can clearly understand that the above-mentioned embodiment methods can be implemented by means of software plus a necessary general hardware platform, and of course by hardware, but in many cases the former is a better implementation method. Based on such an understanding, the technical solution of the present invention, or the part that contributes to the prior art, can be embodied in the form of a software product, which is stored in a storage medium (such as ROM / RAM, a magnetic disk, or an optical disk), and includes a number of instructions for enabling a terminal (which can be a mobile phone, a computer, a server, an air conditioner, or a network device, etc.) to execute the methods described in each embodiment of the present invention.

[0441] like Figure 5 As shown, in another embodiment provided by the present invention, a computer-readable storage medium 501 is further provided, in which instructions are stored. When the computer-readable storage medium is run on a computer, the computer executes the oven control method described in the above embodiment.

[0442] The embodiments of the present invention are described above in conjunction with the accompanying drawings, but the present invention is not limited to the above-mentioned specific implementation modes, which are merely illustrative rather than restrictive. Under the guidance of the present invention, ordinary technicians in this field can also make many forms without departing from the scope of protection of the present invention and the claims, all of which are within the protection of the present invention.

[0443] Those of ordinary skill in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed in the embodiments of the present invention can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of the present invention.

[0444] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working processes of the systems, devices and units described above can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0445] In the embodiments provided in the present application, it should be understood that the disclosed devices and methods can be implemented in other ways. For example, the device embodiments described above are only schematic. For example, the division of the units is only a logical function division. There may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.

[0446] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0447] In addition, each functional unit in each embodiment of the present invention may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit.

[0448] If the functions are implemented in the form of software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, or the part of the technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium, including several instructions for a computer device (which can be a personal computer, server, or network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present invention. The aforementioned storage medium includes: various media that can store program codes, such as USB flash drives, mobile hard disks, ROM, RAM, magnetic disks, or optical disks.

[0449] The above is only a specific embodiment of the present invention, but the protection scope of the present invention is not limited thereto. Any person skilled in the art can easily think of changes or substitutions within the technical scope disclosed by the present invention, which should be included in the protection scope of the present invention. Therefore, the protection scope of the present invention should be based on the protection scope of the claims.

Claims

1. An oven control method, characterized in that: include: Acquiring cooking environment information and image information of the food in the oven; Extracting edge color information of the food based on the image information; determining a degree of carbonization of the food based on the edge color information; The oven is controlled according to the browning degree and the cooking environment information.

2. The method according to claim 1, characterized in that Also includes: A Gaussian filtering operation is performed on the image information.

3. The method according to claim 2, characterized in that The step of extracting edge color information of the food based on the image information comprises: Performing edge detection on the image information after the Gaussian filtering operation, and extracting edge contour information of the food from the image information; Extracting pixel points of the edge of the food according to the edge contour information; Convert the pixel point from the red, green and blue color space to the hue, saturation and brightness color space, and obtain the hue, saturation and brightness of the pixel point in the hue, saturation and brightness color space; Determine a first target color channel based on the hue, the saturation, and the brightness, and determine a first target pixel point associated with the first target color channel from the pixel points; The color feature of the edge is extracted based on the first target pixel.

4. The method according to claim 3, characterized in that The step of determining the degree of carbonization of the food based on the edge color information comprises: Set color thresholds according to different food types and cooking states; The degree of carbonization of the food is determined according to the color feature and the color threshold.

5. The method according to claim 2, characterized in that: The step of extracting edge color information of the food based on the image information comprises: Performing edge detection on the image information after the Gaussian filtering operation, and extracting edge contour information of the food from the image information; Extracting pixel points of the edge of the food according to the edge contour information; the pixel points are pixel points from the red, green and blue color space; Separating the color channels of the red, green and blue color space, and determining the red channel of the red, green and blue color space as the second target color channel; Determine, from the pixel points, a second target pixel point associated with the second target color channel; Read the red value of the second target pixel.

6. The method according to claim 5, characterized in that The step of determining the degree of carbonization of the food based on the edge color information comprises: Set color thresholds according to different food types and cooking states; Determine the number of burnt pixels of the second target pixel whose red value exceeds the color threshold; An edge coking ratio is determined based on the number of coked pixels, and a coking degree is determined by the edge coking ratio.

7. The method according to claim 4 or 6, characterized in that: Also includes: determining a central region of the food; Obtaining the central color mean of the pixels in the central area; The maturity of the food is determined by the central color mean and a preset maturity color value.

8. The method according to claim 7, characterized in that The method is applied to a smart oven, the smart oven is equipped with a heating device, and the step of controlling the oven according to the coking degree and the cooking environment information comprises: The heating device is controlled according to the doneness, the coking degree and the cooking environment information.

9. The method according to claim 8, characterized in that Also includes: generating a maturity index parameter based on the maturity, the coking degree and the cooking environment information; When the maturity index parameter meets the preset condition, the heating device is controlled to stop.

10. An oven control device, characterized in that: include: An information acquisition module, used to acquire cooking environment information and image information of the food in the oven; An edge color information extraction module, used to extract edge color information of the food based on the image information; A coking degree determination module, used to determine the coking degree of the food based on the edge color information; The oven control module is used to control the oven according to the coking degree and the cooking environment information.

11. An electronic device, characterized in that: It includes a processor, a communication interface, a memory and a communication bus, wherein the processor, the communication interface and the memory communicate with each other through the communication bus; The memory is used to store computer programs; The processor is used to implement the method according to any one of claims 1 to 9 when executing the program stored in the memory.

12. A computer-readable storage medium having instructions stored thereon, which, when executed by one or more processors, cause the processors to perform the method according to any one of claims 1 to 9.

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