Range hood and control method, device and storage medium thereof

Abstract

The application discloses an extractor hood, a control method and device thereof and a storage medium. The extractor hood comprises a visual detection sensor and a fan, the visual detection sensor is arranged above a cooking bench, the method comprises the following steps: acquiring image data generated by the visual detection sensor; determining smoke data corresponding to the image data based on color space data of the image data; determining a target running gear of the fan based on the smoke data and gear threshold values of at least two running gears; and controlling the fan to run in the target running gear. In this way, the smoke data is detected for the first time, the detection efficiency of the oil fume is improved, the intelligent running of the extractor hood is ensured, and the possibility of escape of the oil fume is reduced.

Description

Technical Field

[0001] This application relates to the field of oil fume removal, and more particularly to a range hood and its control method, device and storage medium. Background Technology

[0002] A range hood is a kitchen appliance used to purify the kitchen environment. It is typically installed above the stove and quickly removes waste from combustion and harmful fumes produced during cooking, venting them outdoors. It also condenses and collects these fumes, reducing pollution, purifying the air, and providing safety features such as protection against poisoning and explosions.

[0003] In real-world households, users often forget to turn on the range hood. To improve user experience, related technologies can use infrared light to intelligently detect smoke and extract it. However, infrared detectors typically have a limited field of view or angle. Light outside this range may be blocked by the lens edge or fail to focus on the detector, affecting detection accuracy and causing delays in range hood operation and smoke extraction, leading to the escape of cooking fumes. Summary of the Invention

[0004] In view of this, embodiments of this application provide a range hood and its control method, device and storage medium, which aim to ensure the intelligent operation of the range hood and reduce the escape of oil fumes.

[0005] The technical solution of this application embodiment is implemented as follows:

[0006] In a first aspect, embodiments of this application provide a control method for a range hood, the range hood comprising: a visual detection sensor and a fan, the visual detection sensor being disposed on a cooktop, the method comprising:

[0007] Acquire the image data generated by the visual inspection sensor;

[0008] Based on the color space data of the image data, determine the smoke data corresponding to the image data;

[0009] Based on the smoke data and the threshold values ​​of at least two operating gears, the target operating gear of the fan is determined;

[0010] Control the fan to operate at the target operating speed.

[0011] In some embodiments, determining the smoke data corresponding to the image data based on the color space data of the image data includes:

[0012] Based on the first color space data of the image data, a color space conversion is performed to generate the second color space data of the image data.

[0013] Based on the second color space data, smoke data corresponding to the image data is determined. In some embodiments, the step of performing color space conversion based on the first color space data of the image data to generate the second color space data of the image data includes:

[0014] Obtain the first color space type corresponding to the first color space data;

[0015] Based on the first color space type, determine the second color space type to be converted;

[0016] Based on the conversion rules corresponding to the second color space type, the first color space data is converted to generate the second color space data.

[0017] In some embodiments, the step of performing color space conversion on the first color space data based on the conversion rules corresponding to the second color space type to generate second color space data includes:

[0018] Obtain the first color pixel feature data corresponding to the first color space data;

[0019] Based on the first color pixel feature data and the conversion rule, the second color pixel feature data of the second color space data is determined;

[0020] The second color space data is generated based on the second color pixel feature data.

[0021] In some embodiments, determining the smoke data corresponding to the image data based on the second color space data includes:

[0022] Based on the second color space data and the smoke color range values, a third color space data is determined, wherein the third color space data is the second color space data within the smoke color range values;

[0023] The smoke data is generated based on the third color space data.

[0024] In some embodiments, the method further includes:

[0025] Based on the second color pixel feature data of the second color space data, determine the mean and variance of the second color pixel feature data;

[0026] Based on the mean and the variance, the upper and lower limits of the smoke color range values ​​are generated;

[0027] The numerical values ​​of the smoke color range are determined based on the upper and lower limits.

[0028] In some embodiments, generating the smoke data based on the third color space data includes:

[0029] The third color space data is subjected to numerical normalization.

[0030] Bitwise operations are performed on the third color space data after numerical normalization and the first color space data to generate the smoke data representing the size of the oil fume area.

[0031] In some embodiments, determining the target operating level of the fan based on the smoke data and at least two operating level thresholds includes:

[0032] Based on the size information of the image corresponding to the smoke data and the image data, determine the proportion of the current oil fume area to the area of ​​the image;

[0033] Based on the ratio information and the gear threshold, the target operating gear of the fan is determined.

[0034] In some embodiments, the method further includes:

[0035] If it is determined that the current ratio information is greater than or equal to the first gear threshold, then the fan is switched to the operating gear corresponding to the first gear threshold, where the first gear threshold is the switching threshold for operating gears above the fan speed of the current operating gear.

[0036] If it is determined that the current ratio information is less than the second gear threshold, then after determining that the difference between the second gear threshold and the current ratio information is greater than or equal to a set value, the fan is switched to the operating gear corresponding to the second gear threshold, where the second gear threshold is the switching threshold for the operating gear below the fan speed of the current operating gear.

[0037] Secondly, embodiments of this application provide a control device for a range hood, the range hood including: a visual detection sensor disposed on the stovetop, the device including:

[0038] The acquisition module is used to acquire image data generated by the visual inspection sensor;

[0039] The determination module is used to determine the smoke data corresponding to the image data based on the color space data of the image data; and to determine the target operating level of the fan based on the smoke data and the level thresholds of at least two operating levels.

[0040] The control module is used to control the fan to operate at the target operating level.

[0041] Thirdly, embodiments of this application provide a range hood, comprising: a visual detection sensor and a fan, wherein the visual detection sensor is disposed on the stovetop, and the range hood further comprises: a processor and a memory for storing a computer program capable of running on the processor, wherein...

[0042] The processor is configured to execute the steps of the method described in the first aspect when running a computer program.

[0043] Fourthly, embodiments of this application provide a computer storage medium storing a computer program, which, when executed by a processor, implements the steps of the method described in the first aspect.

[0044] The technical solution provided in this application embodiment offers a control method for a range hood. The range hood includes a visual detection sensor and a fan. The visual detection sensor is mounted on the stovetop. The method includes: acquiring image data generated by the visual detection sensor; determining smoke data corresponding to the image data based on the color space data of the image data; determining a target operating level of the fan based on the smoke data and at least two operating level thresholds; and controlling the fan to operate at the target operating level.

[0045] In this way, by acquiring images and generating smoke data based on the generated image data, smoke data can be detected in real time, avoiding the detection delay problem of infrared detection technology and improving the detection efficiency of cooking fumes. Based on the smoke data and the gear threshold, the operating gear of the range hood fan can be determined to ensure that the range hood can operate intelligently and reduce the possibility of cooking fumes escaping. Attached Figure Description

[0046] Figure 1 This is a schematic flowchart illustrating a control method for a range hood provided in an embodiment of this application.

[0047] Figure 2 A schematic diagram of the oil fume detection process of a range hood provided as an application example of this application;

[0048] Figure 3 This is a schematic diagram of the characteristics of cooking fumes provided as an application example of this application;

[0049] Figure 4 A schematic diagram illustrating that the cooking appliance provided in this application does not produce oil fumes;

[0050] Figure 5 A schematic diagram illustrating the generation of oil fumes by a cooking appliance provided as an application example of this application;

[0051] Figure 6 A schematic diagram of smoke before bitwise operations is provided as an application example of this application.

[0052] Figure 7 A schematic diagram of smoke after bitwise operations is provided as an application example of this application.

[0053] Figure 8 A schematic diagram of smoke before median filtering is provided as an application example of this application;

[0054] Figure 9 A schematic diagram of smoke after median filtering, provided as an application example of this application;

[0055] Figure 10 This is a schematic diagram of the control device for a range hood provided in an embodiment of this application;

[0056] Figure 11 This is a schematic diagram of the structure of a range hood provided in an embodiment of this application. Detailed Implementation

[0057] The present application will now be described in further detail with reference to the accompanying drawings and embodiments.

[0058] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs. The terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the application.

[0059] This application provides a control method for a range hood, which includes a visual detection sensor and a fan. The visual detection sensor is mounted on the cooktop. Here, the visual detection sensor refers to a device used to collect and process visual information; it is an important component of a machine vision system. The visual sensor includes a camera or video camera. Mounted on the cooktop, the visual sensor is primarily used to monitor fumes and other gases generated during cooking. The fan is the core component of the range hood, responsible for providing suction to extract fumes, steam, and odors generated during cooking from the cooktop area and discharge them into the external environment.

[0060] This application provides a control method for a range hood, referring to... Figure 1 Specifically, it includes the following steps:

[0061] Step 110: Acquire image data generated by the visual inspection sensor.

[0062] Here, image data refers to digitized visual information captured by visual inspection sensors such as cameras. Image data includes pixels, resolution, and color space type, among other things.

[0063] Here, a pixel refers to the basic unit of image display, the smallest element that makes up digital images and videos. Each pixel represents a fixed small point on the screen or in an image, possessing a specific color and brightness. Each pixel has brightness values ​​for one or more color channels (such as red, green, and blue). Image resolution defines the size of the image, usually expressed in pixels (width and height), such as 640x480, 1920x1080, etc. A color space is a mathematical model or system used to describe and represent color.

[0064] Step 120: Determine the smoke data corresponding to the image data based on the color space data of the image data. Here, when identifying the smoke data corresponding to the image data, it is first necessary to analyze the color space data of the image. Color space data refers to the way colors are represented in an image; different color spaces help to highlight different types of features in the image.

[0065] Here, different color spaces use different coordinate systems and parameters to define colors, and these parameters are usually related to human eye perception of color. Color spaces include at least one of the following: RGB (Red, Green, Blue) color space and HSV (Hue, Saturation, Value) color space.

[0066] Here, based on the color space data of the image data, feature information representing smoke in the image is identified and extracted to determine which parts of the image contain smoke data, thereby determining the smoke data corresponding to the image data.

[0067] Step 130: Determine the target operating level of the fan based on the smoke data and the threshold values ​​of at least two operating levels.

[0068] Here, the operating speed of the fan determines its ability to extract and expel smoke; different speeds correspond to different fan speeds or suction levels. The speed thresholds are preset based on factors such as smoke level and fan performance. Each speed threshold represents a specific smoke handling capacity; smoke below the threshold can be handled by a lower fan speed, while smoke above the threshold requires a higher fan speed.

[0069] Here, the embodiments of this application include at least two operating speed thresholds, each corresponding to a different fan speed or suction level. The speed thresholds can be adjusted according to factors such as smoke level, environmental conditions, and equipment performance.

[0070] Here, the area of ​​the smoke data can be compared with the threshold values ​​of at least two operating levels. If the area of ​​the smoke data is lower than the lowest threshold value, the fan may remain at the lowest operating level or be turned off. If the area of ​​the smoke data is higher than the highest threshold value, the fan should be set to the highest operating level to extract and expel smoke at maximum capacity.

[0071] Step 140: Control the fan to operate at the target operating level.

[0072] Here, based on the aforementioned smoke data and the threshold values ​​for at least two operating speeds, the target operating speed of the fan can be determined. A command for the target operating speed is generated and sent to the fan. Upon receiving the command, the fan begins adjusting its operating parameters. For example, this may include changing the motor speed, adjusting the valve opening, or adjusting the blade angle to achieve the desired suction capacity at the target speed.

[0073] In this way, by acquiring images and generating smoke data based on the generated image data, smoke data can be detected in real time, avoiding the detection delay problem of infrared detection technology and improving the detection efficiency of oil fumes; and based on the smoke data and the gear threshold, the operating gear of the range hood fan can be determined to ensure the intelligent operation of the range hood and reduce the possibility of oil fumes escaping.

[0074] In some embodiments, determining smoke data corresponding to the image data based on the color space data of the image data includes:

[0075] Based on the first color space data of the image data, a color space conversion is performed to generate the second color space data of the image data.

[0076] Based on the second color space data, determine the smoke data corresponding to the image data.

[0077] Here, RGB is based on the additive color mixing principle, which produces other colors by combining different brightness levels of the three basic colors: red, green, and blue. It is widely used in devices such as electronic displays and televisions. HSV colors are described by three parameters: hue, saturation, and value, which is more in line with human intuitive understanding of color.

[0078] Here, color space conversion refers to the process of converting color values ​​in one color space to equivalent color values ​​in another color space. In practical applications, different color spaces are suitable for different applications and devices, and the human eye's perception of color may differ in different color spaces.

[0079] Assuming the first color space data is RGB data, in order to better match human intuitive understanding of color and facilitate color adjustment and selection, this embodiment of the application converts the RGB data into HSV data to generate the second color space data of the image data.

[0080] Here, based on the second color space data, it is possible to determine which parts of the image data correspond to smoke, and generate smoke data corresponding to the image data. This smoke data is used to characterize the smoke conditions in the current environment, including information such as smoke concentration, distribution, and movement.

[0081] Thus, by converting the image from the first color space to the second color space, which is more suitable for smoke detection, the visual features of smoke can be highlighted more effectively, and the smoke data corresponding to the image data can be determined based on the second color space data, thereby improving the distinguishability and accuracy of the smoke data.

[0082] In some embodiments, based on the first color space data of the image data, a color space conversion is performed to generate the second color space data of the image data, including:

[0083] Obtain the first color space type corresponding to the first color space data;

[0084] Based on the first color space type, determine the second color space type to be converted;

[0085] Based on the conversion rules corresponding to the second color space type, the first color space data is converted to generate the second color space data.

[0086] Here, the color space type used by the current image data is obtained before performing color space conversion on the first color space. For example, the determination and identification of the color space type can be achieved by using a specific color space detection algorithm.

[0087] Here, the second color space type to be converted can be determined based on the first color space data and a preset mapping relationship. This mapping relationship represents the correspondence between the first and second color space types. In practical applications, users can preset the color space type mapping relationship according to actual needs and device characteristics.

[0088] Here, based on the second color space type, the corresponding conversion rules can be determined. These conversion rules include color space conversion formulas or algorithms. These conversion rules are usually generated based on color theory and mathematical models, and are used to map values ​​from one color space to another.

[0089] For example, the RGB color space uses a linear combination of three color components to represent color. Every color is related to these three components, and these components are highly correlated. Therefore, continuously changing colors is not intuitive; adjusting the color of an image requires changing all three components. Images captured in natural environments are easily affected by natural lighting, occlusion, and shadows, meaning they are quite sensitive to brightness. Furthermore, all three components of the RGB color space are closely related to brightness; that is, as long as the brightness changes, all three components will change accordingly, without a more intuitive way of expressing this.

[0090] The HSV color space is closer to people's perceptual experience of color than RGB. It intuitively expresses the hue, vividness, and brightness of colors, facilitating color comparison. HSV represents a color image in three parts: Hue (representing color information, i.e., the position of the color in the spectrum), Saturation (representing how close the color is to the spectral color; higher saturation indicates a deeper color), and Value (determining the lightness or darkness of a color in the color space; higher value indicates a brighter color).

[0091] Assuming the first color space data is RGB data, in order to better match human intuitive understanding of color and facilitate color adjustment and selection, this embodiment of the application converts the RGB data into HSV data to generate the second color space data of the image data.

[0092] Thus, by successfully converting the data from the first color space to the second color space, the smoke has more distinct characteristics in the second color space, allowing for better extraction and identification of smoke regions.

[0093] In some embodiments, based on the conversion rules corresponding to the second color space type, the first color space data is converted to generate the second color space data, including:

[0094] Obtain the first color pixel feature data corresponding to the first color space data;

[0095] Based on the first color pixel feature data and the conversion rules, the second color pixel feature data of the second color space data is determined;

[0096] Second color space data is generated based on the second color pixel feature data.

[0097] Here, the first color pixel feature data refers to the feature data obtained by analyzing and describing the color information of each pixel in the first color space data. This feature data can reflect the color distribution in the image. Here, there can be multiple first color pixel feature data. For example, in the RGB color space, the color pixel feature data has three feature values: red (R), green (G), and blue (B).

[0098] Here, the first color pixel feature data is transformed based on the transformation rules to determine the second color pixel feature data of the second color space data. Based on the second color pixel feature data, the second color space data is generated. The second color pixel feature data corresponds to the first color pixel feature data.

[0099] For example, suppose the color pixel feature data of the RGB color space is converted into color pixel feature data of HSV. The color pixel feature data of HSV includes three feature values, namely H, S and V values.

[0100] Thus, by determining the second color pixel feature data of the second color space data based on the first color pixel feature data and the conversion rules, and generating the second color space data based on the second color pixel feature data, it is more conducive to highlighting the specific features of smoke and improving the ability to distinguish smoke from other objects.

[0101] In some embodiments, determining smoke data corresponding to image data based on second color space data includes:

[0102] Based on the second color space data and the smoke color range, the third color space data is determined. The third color space data is the second color space data within the smoke color range.

[0103] Smoke data is generated based on data from the third color space.

[0104] Here, the smoke color range is generally used to characterize the typical color features of smoke in this color space. This range can be a continuous color range or a set of discrete color points or regions.

[0105] Here, based on the second color space data and the smoke color range, the third color space data consists of color values ​​filtered from the second color space data that fall within the smoke color range. These filtered data are more likely to represent the presence of smoke. Simultaneously, a mask is created based on the determination of whether each pixel is within the smoke color range; a binary mask is created. Typically, pixels belonging to the smoke color range are marked as 255, and pixels not belonging to the smoke color range are marked as 0.

[0106] Here, element-wise multiplication (or bitwise AND operation) is performed between the created mask and the second color space data. This preserves pixels belonging to the smoke color range while setting pixels not belonging to the smoke color range to the background color (e.g., black or transparent). Smoke data is then generated based on the third color space data, which can be used for subsequent smoke detection and analysis operations. This helps reduce noise and interference, and improves the accuracy and efficiency of smoke detection.

[0107] In some embodiments, the method further includes:

[0108] Based on the second color space data, determine the mean and variance of the second color pixel feature data;

[0109] Based on the mean and variance values, generate the upper and lower limits of the smoke color range;

[0110] The smoke color range is determined based on the upper and lower limits.

[0111] Here, the mean and variance of the second color pixel feature data can be determined based on the second color pixel feature data. For example, assume the second color pixel feature data includes hue (H), saturation (S), lightness (V), or other relevant features. For each feature dimension, the mean and variance for that feature dimension are calculated.

[0112] Here, the mean can be represented by M, and the variance by S. The upper limit of the H dimension is TH1 = M + S; the lower limit is TH2 = MS. Based on these upper and lower limits, the smoke color range is determined. That is, the smoke color range is [TH1, TH2].

[0113] Thus, by using the second color pixel feature data based on the second color space data to determine the mean and variance values ​​and generate the smoke color range, the accuracy of the smoke color range is improved, which is beneficial to improving the accuracy and efficiency of smoke detection.

[0114] In some embodiments, smoke data is generated based on third color space data, including:

[0115] The data in the third color space is subjected to numerical normalization.

[0116] Bitwise operations are performed on the third color space data and the first color space data after numerical normalization to generate smoke data representing the size of the oil fume area.

[0117] Here, each pixel value in the third color space data is normalized, transforming it to the range of 0-1. This helps eliminate the influence of different pixel value ranges, making subsequent processing more consistent and accurate.

[0118] Here, bitwise operations are performed between the normalized third color space data and the first color space data. For example, a bitwise AND operation can be performed on the corresponding pixel values ​​in the normalized third color space data and the first color space data. This preserves pixels that represent smoke in both color spaces.

[0119] Thus, the third color space data can be numerically normalized using the above method, and bitwise operations can be performed with the first color space data to generate smoke data representing the size of the oil fume region. This method combines information from different color spaces, improving the accurate quantification of the smoke region and enhancing the detection efficiency and accuracy of smoke images.

[0120] In some embodiments, the target operating level of the fan is determined based on smoke data and at least two operating level thresholds, including:

[0121] Based on the size information of the image corresponding to the smoke data and image data, determine the proportion of the current oil fume area to the image area;

[0122] Based on the proportional information and the gear threshold, the target operating gear of the fan is determined.

[0123] Here, the image size information corresponding to the image data includes the image's width and height. Based on the smoke data and the image size information corresponding to the image data, the proportion of the current smoke area to the image area is determined. For example, the smoke data includes pixels with non-zero values; the number of these pixels is determined, and the proportion of the current smoke area to the image area is the ratio of the number of pixels to the image area.

[0124] Here, multiple threshold levels can be set, such as low, medium, and high, corresponding to different threshold levels for the proportion of oil fume areas, thereby determining the target operating level.

[0125] Thus, this embodiment calculates the proportion of the current oil fume area in the image based on smoke data and image size information, and determines the target operating level of the fan based on this proportion and a preset level threshold. This enables dynamic adjustment of the fan's operating intensity based on the actual oil fume situation, thereby more effectively removing oil fumes and improving environmental quality and equipment energy efficiency.

[0126] In some embodiments, the method further includes:

[0127] If the current ratio information is determined to be greater than or equal to the first gear threshold, then switch the fan to the operating gear corresponding to the first gear threshold. The first gear threshold is the switching threshold for the operating gear above the fan speed of the current operating gear.

[0128] If the current ratio information is determined to be less than the second gear threshold, then after determining that the difference between the second gear threshold and the current ratio information is greater than or equal to a set value, the fan is switched to the operating gear corresponding to the second gear threshold. The second gear threshold is the switching threshold for the operating gear below the fan speed of the current operating gear.

[0129] Here, during operation, if the current ratio is determined to be greater than or equal to the first-level threshold, it indicates that the smoke concentration has increased and a higher level of smoke removal is required. The fan is then switched to the operating level corresponding to the first-level threshold, which is the switching threshold for operating levels above the current fan speed. Thus, when there is a large amount of oil smoke, the range hood automatically rises to a higher level, ensuring rapid and effective smoke removal and improving work efficiency.

[0130] Here, if the current ratio information is determined to be less than the second-level threshold, it indicates that the smoke concentration has decreased. However, the speed adjustment is not performed immediately. In some embodiments, to avoid repeated fluctuations in smoke, the fan is switched to the operating speed corresponding to the second-level threshold only after the difference between the second-level threshold and the current ratio information is greater than or equal to a set value. The second-level threshold is the switching threshold for operating speeds below the current operating speed. In other words, the current ratio information must be less than the second-level threshold and the difference between it and the second-level threshold must be greater than or equal to a set value before switching to the operating speed corresponding to the second-level threshold. This effectively avoids frequent switching of operating speeds when smoke fluctuates.

[0131] For example, in practical applications, a hysteresis range needs to be set. For instance, a ratio of 10-40% corresponds to range hood setting 1, 40-70% to setting 2, and 70-100% to setting 3. When the smoke increases and the range hood upgrades, it should upgrade normally once the corresponding setting is reached. Conversely, when the smoke decreases, the difference between the smoke level and the preset lower limit of the recommended setting must be greater than 10%. That is, when downgrading from setting 3 to setting 2, the ratio must be less than 60%. This prevents repeated fluctuations in the range hood's setting.

[0132] This avoids frequent fluctuations and unnecessary switching of the range hood's settings, ensuring stable operation of the equipment and reducing the impact on users.

[0133] The embodiments of this application will now be described in detail with reference to an application example.

[0134] While modern range hoods offer increasingly diverse functions, their core function remains smoke extraction. In reality, users often forget to turn on the range hood. Current smart detection systems typically use infrared light, which, due to its limited detection angle, usually places the sensor at the rear (near the exhaust vent). Regardless of the specific placement, this results in delayed smoke extraction, allowing cooking fumes to escape.

[0135] Based on this, this application example provides a method for detecting cooking fumes in a range hood. In this example, the intelligent range hood measures smoke primarily through an image-based solution, overcoming the reaction delay problem of existing infrared smoke detection methods. This application example includes a camera (i.e., the aforementioned visual sensor) on the range hood (i.e., the aforementioned cooking exhaust fan), directly facing the cooktop. It can capture images of the cooking fumes produced on the cooktop. Because the color characteristics of cooking fumes are more prominent than the background, the characteristics of cooking fumes can be collected in real time.

[0136] The RGB color space (the first color space mentioned above) uses a linear combination of three color components to represent color. Every color is related to these three components, and these components are highly correlated. Therefore, continuously changing colors is not intuitive; adjusting the color of an image requires changing all three components. Images captured in natural environments are easily affected by natural lighting, occlusion, and shadows, meaning they are quite sensitive to brightness. Since all three components of the RGB color space are closely related to brightness, any change in brightness will cause a corresponding change in all three components, and there is no more intuitive way to express this. The HSV color space (the second color space mentioned above) is closer to people's perceptual experience of color than RGB. It very intuitively expresses the hue, vividness, and lightness / darkness of a color, facilitating color comparison. The HSV representation of a color image consists of three parts: Hue (representing color information, i.e., the position of the color in the spectrum), Saturation (representing how close the color is to the spectral color; higher saturation indicates a deeper color), and Value (determining the lightness / darkness of the color in the color space; higher value indicates a brighter color).

[0137] This application example provides a schematic diagram of the workflow for detecting kitchen fumes in a range hood, such as... Figure 2 As shown, the steps are as follows:

[0138] Step 201: Initialization.

[0139] Step 202: Determine if the range hood is running. If yes, proceed to step 203; otherwise, proceed to step 201.

[0140] Step 203: Collect images.

[0141] Here, image data generated by the visual inspection sensor is acquired, such as... Figure 3 As shown, Figure 3 This is a schematic diagram of the characteristic images of cooking fumes captured by a camera (i.e., the aforementioned image data).

[0142] Step 204: Read the image.

[0143] Based on this image, the feature image is read to obtain RGB color data (i.e., the aforementioned first color space).

[0144] Step 205: Convert the image RGB data to HSV colors.

[0145] In practical applications, color space conversion is performed based on the first color space data of the image data to generate the second color space data of the image data; this application example converts RGB data to the HSV color space (and the aforementioned second color space data).

[0146] Step 206: Label the data according to the preset threshold.

[0147] In practical applications, based on the second color pixel feature data of the second color space data, the mean and variance values ​​of the second color pixel feature data are determined; based on the mean and variance values, the upper and lower limits of the smoke color interval values ​​are generated; based on the upper and lower limits, the smoke color interval values ​​are determined.

[0148] For example, the mean M and variance S of the H, S, and V components in the feature image are obtained. That is, the upper threshold TH1 = M + S; the lower threshold TH2 = MS for the smoke image. These upper and lower thresholds are used to separate smoke from the background in real-world situations. The upper and lower thresholds are determined through actual testing and debugging, and then fixedly written into the software.

[0149] Step 207: Data marking is completed, forming mask data (i.e., the aforementioned third color data).

[0150] In practical applications, the third color space data is determined based on the second color space data and the smoke color range values. The third color space data is the second color space data within the smoke color range values.

[0151] After the range hood is turned on, the camera starts working and captures images as follows: Figure 4 and Figure 5 As shown. Figure 4 This is a diagram illustrating cooking appliances that do not produce oil fumes. Figure 5This is a diagram illustrating the fumes produced by cooking appliances. The MCU reads the image and converts the data to the HSV color space. It marks the valid data according to the preset upper and lower thresholds TH1 / TH2. The valid data here is the aforementioned second color space data, and a mask data is formed (data between the upper and lower thresholds is set to 255, and data below the lower threshold or above the upper threshold is set to 0).

[0152] Step 208: Bitwise operations between mask data and RGB data.

[0153] In practical applications, bitwise operations are performed on the third color space data and the first color space data after numerical normalization to generate smoke data that represents the size of the oil fume area.

[0154] For example, performing bitwise operations between the mask data and the original RGB data of the image yields the extraction result. However, in practice, many impurities and noise still exist. Figure 6 and Figure 7 . Figure 6 This is a schematic diagram of the smoke before bitwise operations are performed. Figure 7 A diagram showing the smoke after bitwise operations.

[0155] Step 209: Median filtering.

[0156] The extracted image is then subjected to median filtering. This yields a relatively ideal result. For example... Figure 8 and Figure 9 As shown, Figure 8 This is a schematic diagram of the smoke before median filtering. Figure 9 This is a schematic diagram of the smoke after median filtering.

[0157] Step 210: Calculate the percentage of white dots in the image.

[0158] The amount of cooking fumes can be represented by the proportion of the image occupied by the white dots. In practice, different fume levels are set according to the range hood's settings. A hysteresis range also needs to be set. For example, 10-40% corresponds to range hood level 1, 40-70% to level 2, and 70-100% to level 3. When the fumes increase, the range hood should automatically adjust to the corresponding level. Conversely, when the fumes decrease, the difference between the fumes and the preset lower limit of the hysteresis range should be greater than 10%. That is, when reducing from level 3 to level 2, the fumes must be less than 60% of the preset level. This prevents repeated fluctuations in the range hood's settings.

[0159] Step 211: Adjust the range hood setting according to the amount of smoke.

[0160] This application example uses an image-based method to detect smoke as soon as it occurs, solving the problem of delay in current infrared smoke detection. This prevents the possibility of cooking fumes escaping and improves the user experience.

[0161] To implement the method of the embodiments of this application, the embodiments of this application also provide a control device for a range hood. For example... Figure 10 As shown, the control device 1000 of the range hood includes: an acquisition module 1010, a determination module 1020, and a control module 1030. The acquisition module 1010 is used to acquire image data generated by a visual inspection sensor; the determination module 1020 is used to determine the smoke data corresponding to the image data based on second color space data; and to determine the target operating level of the fan based on the smoke data and at least two operating level thresholds; the control module 1030 is used to control the fan to operate at the target operating level.

[0162] In some embodiments, the control device of the range hood further includes a conversion module 1140, which is used to perform color space conversion based on the first color space data of the image data to generate the second color space data of the image data; the determination module 1020 is also used to determine the smoke data corresponding to the image data based on the second color space data.

[0163] In some embodiments, the acquisition module 1010 is further configured to acquire the first color space type corresponding to the first color space data; the determination module 1020 is configured to determine the second color space type to be converted based on the first color space type; and the conversion module 1040 is configured to perform color space conversion on the first color space data based on the conversion rules corresponding to the second color space type to generate the second color space data.

[0164] In some embodiments, the acquisition module 1010 is further configured to acquire the first color pixel feature data corresponding to the first color space data; the determination module 1020 is further configured to determine the second color pixel feature data of the second color space data based on the first color pixel feature data and the conversion rules; the control device of the range hood further includes a generation module 1050, configured to generate the second color space data based on the second color pixel feature data.

[0165] In some embodiments, the determining module 1020 is further configured to determine third color space data based on the second color space data and the smoke color range, wherein the third color space data is the second color space data within the smoke color range; the generating module 1050 is further configured to generate smoke data based on the third color space data.

[0166] In some embodiments, the determining module 1020 is further configured to determine the mean and variance of the second color pixel feature data based on the second color pixel feature data of the second color space data; the generating module 1050 is further configured to generate the upper limit and lower limit of the smoke color range based on the mean and variance; and the determining module 1020 is further configured to determine the smoke color range based on the upper limit and lower limit.

[0167] In some embodiments, the control device of the range hood further includes a processing module 1060 for performing numerical normalization processing on the third color space data; the generation module 1050 is further used to perform bit operations on the numerically normalized third color space data and the first color space data to generate smoke data representing the size of the smoke area.

[0168] In some embodiments, the determining module 1020 is further configured to determine the proportion of the current oil fume area to the area of ​​the image based on the size information of the image corresponding to the smoke data and the image data; and to determine the target operating speed of the fan based on the proportion information and the speed threshold.

[0169] In some embodiments, the control device of the range hood further includes a switching module 1070, configured to switch the fan to the operating level corresponding to the first level threshold if it is determined that the current proportional information is greater than or equal to a first level threshold, wherein the first level threshold is the switching threshold for operating levels above the fan speed of the current operating level; and to switch the fan to the operating level corresponding to the second level threshold after determining that the difference between the second level threshold and the current proportional information is greater than or equal to a set value, wherein the second level threshold is the switching threshold for operating levels below the fan speed of the current operating level.

[0170] In practical applications, the acquisition module 1010, determination module 1020, control module 1030, conversion module 1040, generation module 1150, processing module 1060, and switching module 1170 can be implemented by the processor in the range hood's control device. Of course, the processor needs to run the computer program in the memory to implement its functions.

[0171] It should be noted that the control device for the range hood provided in the above embodiments is only illustrated by the division of the above-described program modules. In actual applications, the above processing can be assigned to different program modules as needed, that is, the internal structure of the device can be divided into different program modules to complete all or part of the processing described above. Furthermore, the control device for the range hood and the control method embodiments for the range hood provided in the above embodiments belong to the same concept, and their specific implementation process can be found in the method embodiments, which will not be repeated here.

[0172] Based on the hardware implementation of the above program modules, and in order to implement the method of the embodiments of this application, the embodiments of this application also provide a range hood. Figure 11 The diagram shows only an exemplary structure of the range hood, not the entire structure; implementation is possible as needed. Figure 11 The structure shown may be part or all of the structure.

[0173] like Figure 11 As shown, the range hood 1100 provided in this embodiment includes at least one processor 1101, a memory 1102, a user interface 1103, and at least one network interface 1104. The various components in the range hood 1100 are coupled together via a bus system 1105. It can be understood that the bus system 1105 is used to realize the connection and communication between these components. In addition to a data bus, the bus system 1105 also includes a power bus, a control bus, and a status signal bus. However, for clarity, in... Figure 11 The general designated all buses as Bus System 1105.

[0174] The user interface 1103 may include a monitor, keyboard, mouse, trackball, click wheel, buttons, touchpad, or touch screen.

[0175] The memory 1102 in this embodiment is used to store various types of data to support the operation of the range hood. Examples of such data include any computer program used to operate the range hood.

[0176] The range hood control method disclosed in this application can be applied to, or implemented by, the processor 1101. The processor 1101 may be an integrated circuit chip with signal processing capabilities. During implementation, each step of the range hood control method can be completed through integrated logic circuits in the hardware of the processor 1101 or through software instructions. The processor 1101 can be a general-purpose processor, a digital signal processor (DSP), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The processor 1101 can implement or execute the methods, steps, and logic block diagrams disclosed in the embodiments of this application. The general-purpose processor can be a microprocessor or any conventional processor. The steps of the method disclosed in the embodiments of this application can be directly manifested as execution by a hardware decoding processor, or execution by a combination of hardware and software modules in the decoding processor. The software module can be located in a storage medium, which is located in memory 1102. The processor 1101 reads the information in memory 1102 and, in conjunction with its hardware, completes the steps of the range hood control method provided in this application embodiment.

[0177] In an exemplary embodiment, the range hood may be implemented by one or more application-specific integrated circuits (ASICs), DSPs, programmable logic devices (PLDs), complex programmable logic devices (CPLDs), field-programmable gate arrays (FPGAs), general-purpose processors, controllers, microcontrollers (MCUs), microprocessors, or other electronic components to perform the aforementioned method.

[0178] It is understood that memory 1102 can be volatile memory or non-volatile memory, or both. Non-volatile memory can be read-only memory (ROM), programmable read-only memory (PROM), erasable programmable read-only memory (EPROM), electrically erasable programmable read-only memory (EEPROM), ferromagnetic random access memory (FRAM), flash memory, magnetic surface memory, optical disc, or compact disc read-only memory (CD-ROM); magnetic surface memory can be disk storage or magnetic tape storage. Volatile memory can be random access memory (RAM), which is used as an external cache. By way of example, but not limitation, many forms of RAM are available, such as Static Random Access Memory (SRAM), Synchronous Static Random Access Memory (SSRAM), Dynamic Random Access Memory (DRAM), Synchronous Dynamic Random Access Memory (SDRAM), Double Data Rate Synchronous Dynamic Random Access Memory (DDRSDRAM), Enhanced Synchronous Dynamic Random Access Memory (ESDRAM), Sync Link Dynamic Random Access Memory (SLDRAM), and Direct Rambus Random Access Memory (DRRAM).The memories described in the embodiments of this application are intended to include, but are not limited to, these and any other suitable types of memories.

[0179] In an exemplary embodiment, this application also provides a storage medium, namely a computer storage medium, specifically a computer-readable storage medium, such as a memory 1102 that stores a computer program. The computer program can be executed by the processor 1101 of the range hood to complete the steps of the method in this application embodiment. The computer-readable storage medium can be a ROM, PROM, EPROM, EEPROM, Flash Memory, magnetic surface memory, optical disc, or CD-ROM, etc.

[0180] It should be noted that terms such as "first" and "second" are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence.

[0181] Furthermore, the technical solutions described in the embodiments of this application can be combined arbitrarily without conflict.

[0182] The above are merely specific embodiments of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

Claims

1. A control method of a range hood, characterized by, The range hood includes a visual detection sensor and a fan, wherein the visual detection sensor is disposed on the cooktop, and the method includes: Acquire the image data generated by the visual inspection sensor; Based on the first color space data of the image data, a color space conversion is performed to generate the second color space data of the image data. Based on the second color pixel feature data of the second color space data, determine the mean and variance of the second color pixel feature data; Based on the mean and the variance, generate upper and lower limits for the smoke color range values; Based on the upper and lower limits, the numerical values ​​of the smoke color range are determined; Based on the second color space data and the smoke color range values, a third color space data is determined, wherein the third color space data is the second color space data within the smoke color range values; The third color space data is subjected to numerical normalization. Bitwise operations are performed on the third color space data after numerical normalization and the first color space data to generate smoke data representing the size of the oil fume area; Based on the smoke data and the threshold values ​​of at least two operating gears, the target operating gear of the fan is determined; Control the fan to operate at the target operating speed.

2. The method according to claim 1, characterized in that, The process of converting the first color space data of the image data to a second color space data of the image data includes: Obtain the first color space type corresponding to the first color space data; Based on the first color space type, determine the second color space type to be converted; Based on the conversion rules corresponding to the second color space type, the first color space data is converted to generate the second color space data.

3. The method according to claim 2, characterized in that, The step of converting the first color space data to the second color space data based on the conversion rules corresponding to the second color space type includes: Obtain the first color pixel feature data corresponding to the first color space data; Based on the first color pixel feature data and the conversion rule, the second color pixel feature data of the second color space data is determined; The second color space data is generated based on the second color pixel feature data.

4. The method according to claim 1, characterized in that, Determining the target operating level of the fan based on the smoke data and at least two operating level thresholds includes: Based on the size information of the image corresponding to the smoke data and the image data, determine the proportion of the current oil fume area to the area of ​​the image; Based on the ratio information and the gear threshold, the target operating gear of the fan is determined.

5. The method according to claim 4, characterized in that, The method further includes: If it is determined that the current ratio information is greater than or equal to the first gear threshold, then the fan is switched to the operating gear corresponding to the first gear threshold, where the first gear threshold is the switching threshold for operating gears above the fan speed of the current operating gear. If it is determined that the current ratio information is less than the second gear threshold, then after determining that the difference between the second gear threshold and the current ratio information is greater than or equal to a set value, the fan is switched to the operating gear corresponding to the second gear threshold, where the second gear threshold is the switching threshold for the operating gear below the fan speed of the current operating gear.

6. A control device for a range hood, characterized in that, The range hood includes: a visual detection sensor and a fan; the visual detection sensor is mounted on the cooktop; the device includes: The acquisition module is used to acquire image data generated by the visual inspection sensor; The determining module is configured to: perform color space conversion based on the first color space data of the image data to generate second color space data of the image data; determine the mean and variance of the second color pixel feature data based on the second color space data; generate an upper limit and a lower limit of the smoke color interval values ​​based on the mean and the variance values; determine the smoke color interval values ​​based on the upper limit and the lower limit values; determine third color space data based on the second color space data and the smoke color interval values, wherein the third color space data is the second color space data within the smoke color interval values; perform numerical normalization processing on the third color space data; perform bitwise operations on the numerically normalized third color space data and the first color space data to generate smoke data representing the size of the oil fume area; and determine the target operating level of the fan based on the smoke data and at least two operating level thresholds. The control module is used to control the fan to operate at the target operating level.

7. A range hood, characterized in that, The range hood includes: a visual detection sensor and a fan. The visual detection sensor is mounted on the cooktop. The range hood also includes: a processor and a memory for storing computer programs that can run on the processor. The processor, when running a computer program, performs the steps of the method according to any one of claims 1 to 5.

8. A computer storage medium storing a computer program, characterized in that, When the computer program is executed by a processor, it implements the steps of the method according to any one of claims 1 to 5.

Images