Range hood control method based on flame image, range hood and intelligent linkage system

Abstract

The invention relates to a range hood control method based on a flame image, a range hood and an intelligent linkage system. The method comprises the following steps: acquiring the flame image; the flame image is obtained by acquiring flames generated during combustion of gas on a stove by an image sensor; the stove and the range hood are oppositely arranged; analyzing flames in the flame image to obtain turbulence characteristics reflecting dynamic changes of the flames; according to the turbulence characteristics, gas flow information is determined, the air volume gear of the range hood is adjusted based on the gas flow information, the problem that the reliability of air volume control over the range hood is poor is solved, the gas flow condition is fed back efficiently and reliably through the technical means of image processing, and the control reliability of the range hood is improved. According to the invention, accurate pre-judgment of the change of the oil smoke amount in the cooking process is realized, the conditions of adjustment lag and wrong shutdown are avoided, the reliability of intelligent control is improved, and better cooking experience is brought to users.

Description

Technical Field

[0001] This application relates to the field of smart home appliance technology, and in particular to a range hood control method based on flame images, a range hood and a smart linkage system. Background Technology

[0002] In modern kitchens, range hoods are indispensable appliances. Their core function is to efficiently extract cooking fumes, odors, and heat generated during cooking by using negative pressure created by a fan. Especially during high-heat cooking methods like stir-frying, a large amount of fumes are generated instantly, and emissions are particularly intense. At this time, users often need to concentrate on operating the stove and find it difficult to simultaneously manually adjust the range hood's airflow, easily leading to inadequate fume extraction. Therefore, achieving intelligent control of range hoods is a key aspect of improving the user experience.

[0003] Traditional solutions typically involve installing infrared thermometers on the range hood to non-contactly measure the temperature of the pot and burners above the cooktop. The on / off status of the cooktop is determined by temperature changes, which then controls the range hood's operation and airflow. However, this method measures the average temperature over a large area of ​​the cooktop surface. Localized temperature increases during opening and closing have minimal impact on the measurement results. Furthermore, when the pot lid is on, the measured value is easily averaged by the surrounding cooler areas. Therefore, existing solutions often fail to accurately reflect the cooktop's operating status, leading to issues such as delayed start-up and accidental shutdown of the range hood.

[0004] There is currently no effective solution to the problem of insufficient reliability in the control of range hoods in the linkage between range hoods and stoves in related technologies. Summary of the Invention

[0005] This embodiment provides a range hood control method based on flame images, a range hood, and an intelligent linkage system to solve the problem of poor control reliability of range hoods in related technologies.

[0006] In a first aspect, this embodiment provides a range hood control method based on flame images, the method comprising:

[0007] Acquire a flame image; the flame image is obtained by an image sensor capturing the flame generated when gas burns on the stove; the stove is positioned opposite the range hood;

[0008] The flames in the flame images are analyzed to obtain turbulent characteristics that reflect the dynamic changes of the flames;

[0009] Based on the turbulence characteristics, the gas flow rate information is determined, and based on the gas flow rate information, the fan speed setting of the range hood is adjusted.

[0010] In some embodiments, the flame in the flame image is analyzed to obtain turbulent features reflecting the dynamic changes of the flame; including:

[0011] Based on two adjacent frames of the flame image, calculate the coordinate offset of each edge point in the flame outline;

[0012] The turbulence intensity of the flame is calculated based on each of the coordinate offsets; the turbulence intensity is used to characterize the turbulence features.

[0013] In some embodiments, based on two adjacent frames of the flame image, the coordinate offset of each edge point in the flame contour is calculated, including:

[0014] Edge detection is performed on two adjacent frames of the flame image to obtain two flame contour images;

[0015] Based on the two frames of the flame outline image, the coordinate offset of each edge point in the flame outline is calculated.

[0016] In some embodiments, edge detection is performed on two adjacent frames of the flame image to obtain two flame contour images, including:

[0017] The two adjacent frames of the flame image are preprocessed to obtain two grayscale flame images; the preprocessing includes grayscale processing and filtering.

[0018] Edge detection is performed on the two frames of the flame grayscale image to obtain two frames of flame outline image.

[0019] In some embodiments, based on two frames of the flame outline image, the coordinate offset of each edge point in the flame outline is calculated, including:

[0020] Extract the edge points of the two flame contours from the two frames of the flame contour images respectively;

[0021] Pair the edge points of the two flame contours to obtain the coordinate set of the paired points;

[0022] Based on the set of coordinates of the paired points, the Euclidean distance between the paired points is calculated to obtain the coordinate offset of each edge point in the flame profile.

[0023] In some embodiments, based on the turbulence characteristics, gas flow information is determined, and based on the gas flow information, the fan speed of the range hood is adjusted, including:

[0024] Based on the turbulence intensity, the flow rate level of the gas is determined;

[0025] Based on the aforementioned flow rate setting, the corresponding airflow setting of the range hood is adjusted.

[0026] In some embodiments, the flame in the flame image is analyzed to obtain turbulent features reflecting the dynamic changes of the flame, including:

[0027] For each pixel in the flame image, calculate the probability distribution of grayscale values;

[0028] Based on the probability distribution of the grayscale values, the entropy value of the flame pattern is calculated; the entropy value of the flame pattern is used to characterize the turbulence features.

[0029] In some embodiments, based on the turbulence characteristics, gas flow information is determined, and based on the gas flow information, the fan speed of the range hood is adjusted, including:

[0030] The flow rate of the gas is calculated by substituting the entropy value of the flame pattern into a preset flow rate formula; the flow rate formula is a preset relationship between the flame entropy value and the gas flow rate.

[0031] Based on the flow rate value, the airflow level of the range hood is adjusted accordingly.

[0032] Secondly, this embodiment provides a range hood, including: a fan drive module and a control module;

[0033] The fan drive module is connected to the control module and is used to adjust the air volume level;

[0034] The control module is used to implement the steps of the method described in any one of the first aspects.

[0035] Thirdly, this embodiment provides a smart linkage system for range hoods and cooktops, including: an image sensor, a cooktop, and the range hood mentioned in the third aspect.

[0036] Compared with related technologies, the range hood control method, range hood, and intelligent linkage system based on flame images provided in this embodiment acquire flame images; the flame images are obtained by an image sensor collecting the flames generated when gas burns on the stove; the stove and the range hood are set relative to each other; the flames in the flame images are analyzed to obtain turbulence characteristics; based on the turbulence characteristics, gas flow information is determined; based on the gas flow information, the fan speed of the range hood is adjusted, solving the problem of poor reliability in range hood fan speed control. Through image processing technology, the gas flow situation is efficiently and reliably fed back, enabling accurate prediction of changes in the amount of oil fumes during cooking, avoiding adjustment lag and accidental shutdown, improving the reliability of intelligent control, and bringing a better cooking experience to users.

[0037] Details of one or more embodiments of this application are set forth in the following drawings and description to make other features, objects and advantages of this application more readily apparent. Attached Figure Description

[0038] The accompanying drawings, which are included to provide a further understanding of this application and form part of this application, illustrate exemplary embodiments and are used to explain this application, but do not constitute an undue limitation of this application. In the drawings:

[0039] Figure 1 This is a hardware structure block diagram of the terminal of the range hood control method based on flame images in the embodiments of this application;

[0040] Figure 2 This is a flowchart illustrating the range hood control method based on flame images in an embodiment of this application.

[0041] Figure 3 This is a schematic diagram showing the change in gas flow rate when the gas stove adjusts its flame in an embodiment of this application.

[0042] Figure 4 This is a schematic diagram of the structure of the range hood in the embodiments of this application;

[0043] Figure 5 This is a schematic diagram of the intelligent linkage system between the range hood and stove in the embodiments of this application;

[0044] Figure 6 This is a flowchart illustrating the range hood control method based on flame images in a preferred embodiment of this application.

[0045] Figure 7 This is a structural block diagram of the range hood control device based on flame images in the embodiments of this application.

[0046] Reference numerals: 102, processor; 104, memory; 106, transmission device; 108, input / output device; 71, image acquisition module; 72, image analysis module; 73, airflow regulation module. Detailed Implementation

[0047] To better understand the purpose, technical solution, and advantages of this application, the application is described and illustrated below in conjunction with the accompanying drawings and embodiments.

[0048] Unless otherwise defined, the technical or scientific terms used in this application shall have the general meaning understood by one of ordinary skill in the art to which this application pertains. Words such as “a,” “an,” “an,” “the,” “the,” and “these” used in this application do not indicate quantitative limitation and may be singular or plural. The terms “comprising,” “including,” “having,” and any variations thereof used in this application are intended to cover non-exclusive inclusion; for example, a process, method, system, product, or device that comprises a series of steps or modules (units) is not limited to the listed steps or modules (units) but may include steps or modules (units) not listed, or may include other steps or modules (units) inherent to these processes, methods, products, or devices. Words such as “connected,” “linked,” and “coupled” used in this application are not limited to physical or mechanical connections but may include electrical connections, whether direct or indirect. “Multiple” used in this application refers to two or more. “And / or” describes the relationship between related objects, indicating that three relationships may exist; for example, “A and / or B” can represent: A alone, A and B simultaneously, and B alone. Normally, the character " / " indicates that the objects before and after it are in an "or" relationship. The terms "first," "second," "third," etc., used in this application are merely to distinguish similar objects and do not represent a specific order of objects.

[0049] The method embodiments provided in this example can be executed on a terminal, computer, or similar computing device. For example, it can run on a terminal. Figure 1 This is a hardware structure block diagram of the terminal for the range hood control method based on flame images in this embodiment. (See diagram for example.) Figure 1 As shown, a terminal may include one or more ( Figure 1 Only one is shown in the diagram. A processor 102 and a memory 104 for storing data are also included. The processor 102 may be, but is not limited to, a microprocessor (MCU) or a programmable logic device (FPGA). The terminal may also include a transmission device 106 for communication functions and an input / output device 108. Those skilled in the art will understand that… Figure 1 The structure shown is for illustrative purposes only and does not limit the structure of the terminal described above. For example, the terminal may also include components that are larger than... Figure 1 The more or fewer components shown, or having the same Figure 1 The different configurations shown are illustrated.

[0050] The memory 104 can be used to store computer programs, such as application software programs and modules, like the computer program corresponding to the flame image-based range hood control method in this embodiment. The processor 102 executes various functional applications and data processing by running the computer program stored in the memory 104, thereby implementing the aforementioned method. The memory 104 may include high-speed random access memory and may also include non-volatile memory, such as one or more magnetic storage devices, flash memory, or other non-volatile solid-state memory. In some instances, the memory 104 may further include memory remotely located relative to the processor 102, and these remote memories can be connected to the terminal via a network. Examples of such networks include, but are not limited to, the Internet, corporate intranets, local area networks, mobile communication networks, and combinations thereof.

[0051] The transmission device 106 is used to receive or send data via a network. This network includes a wireless network provided by the terminal's communication provider. In one example, the transmission device 106 includes a Network Interface Controller (NIC), which can connect to other network devices via a base station to communicate with the Internet. In another example, the transmission device 106 can be a Radio Frequency (RF) module used for wireless communication with the Internet.

[0052] This embodiment provides a range hood control method based on flame images. Figure 2 This is a flowchart of the range hood control method based on flame images in this embodiment, as shown below. Figure 2 As shown, the process includes the following steps:

[0053] Step S210: Acquire a flame image; the flame image is obtained by an image sensor capturing the flame generated when the gas burns on the stove; the stove and the range hood are set opposite each other.

[0054] Specifically, an image sensor is used to detect the flame of the cooktop at appropriate locations behind the cooktop and below the range hood (ensuring the sensor's field of view covers the flame area). When the flame burns and emits light, the image sensor converts the light signal intensity into a corresponding electrical signal, which is then represented as an image, thus obtaining a flame image. The image sensor can be a CMOS sensor with a protective cover. The protective cover is made of high-temperature resistant, highly transparent quartz glass, which protects the sensor from oil fume corrosion and high-temperature damage without affecting the transmission of the flame light signal.

[0055] Step S220: Analyze the flame in the flame image to obtain turbulence characteristics that reflect the dynamic changes of the flame.

[0056] Specifically, turbulence features are used to reflect the dynamic changes in the flicker frequency and intensity of flame patterns, including turbulence intensity and the entropy value of the flame patterns. Turbulence intensity is obtained based on the changes at various positions along the edge of the flame profile at adjacent time points. Within the calibrated normal range, a higher turbulence intensity generally indicates more intense dynamic changes in the flame, and correspondingly, a higher gas flow rate. The entropy value of the flame patterns is calculated based on the probability distribution of each pixel. Within the calibrated normal range, a higher entropy value indicates more turbulent flame patterns, and correspondingly, a higher gas flow rate.

[0057] Step S230: Based on the turbulence characteristics, determine the gas flow information, and adjust the range hood's airflow level according to the gas flow information.

[0058] Specifically, Figure 3 This diagram illustrates the changes in gas flow rate when adjusting the gas stove's flame intensity. Increased gas flow leads to more intense combustion, resulting in a faster and more intense flickering frequency of the flame pattern, reflecting increased turbulence; conversely, decreased flow results in a slower flame. Therefore, by rapidly calculating gas flow information based on the current turbulence characteristics, changes in cooking fume levels can be predicted, providing a reliable basis for intelligent adjustment of the range hood's fan speed, effectively improving kitchen ventilation and enhancing the cooking environment.

[0059] In this embodiment, a flame image is acquired; the flame image is obtained by an image sensor capturing the flame generated during gas combustion on the stove; the stove and range hood are positioned relative to each other; the flame in the flame image is analyzed to obtain turbulence characteristics; based on the turbulence characteristics, gas flow information is determined; based on the gas flow information, the range hood's fan speed is adjusted, solving the problem of poor reliability in range hood fan speed control. No damage to the gas pipeline is required; changes in gas flow can be accurately detected using the light signal characteristics of the flame itself, making installation convenient and easy to implement. Simultaneously, the turbulence analysis has high sensitivity, can quickly respond to changes in gas flow, and promptly link with the range hood to improve kitchen ventilation, achieving intelligent control of the kitchen environment and providing users with a better experience.

[0060] In some embodiments, the flame in the flame image is analyzed to obtain turbulent features reflecting the dynamic changes of the flame; including:

[0061] Step S310: Based on two adjacent frames of flame images, calculate the coordinate offset of each edge point in the flame outline.

[0062] Specifically, within one image acquisition cycle, multiple frames of flame images are acquired, the flame contours in the flame images are identified, and the coordinate set of the corresponding edge points is obtained. Based on the coordinate sets of two adjacent flame images, the coordinate offset of the edge points is calculated using the following formula:

[0063] ;

[0064] Where, △x i This represents the coordinate offset of the i-th edge point, in pixels; x i and y i Let (n) and (n+1) represent the coordinates of the i-th edge point in the x and y directions, respectively; let (n) and (n+1) represent the frame numbers.

[0065] Step S320: Calculate the turbulence intensity of the flame based on each coordinate offset; the turbulence intensity is used to characterize the turbulence features.

[0066] Specifically, the root mean square of the coordinate offsets corresponding to all edge points is calculated to obtain the turbulence intensity. The calculation formula is as follows:

[0067] ;

[0068] Where I represents turbulence intensity, Δx i Let represent the coordinate offset of the i-th edge point, and n represent the number of edge points. In other implementations, the root mean square of the coordinate offsets in multiple consecutive adjacent frames can be calculated, and the turbulence intensity can be obtained by combining and analyzing the multiple root mean squares.

[0069] In this embodiment, the turbulence intensity calculated from the coordinate offset of the edge point is used to improve the feedback accuracy of the flame combustion state, thereby enhancing the reliability of the range hood control.

[0070] In some embodiments, the coordinate offset of each edge point in the flame contour is calculated based on two adjacent flame images, including:

[0071] Step S311: Perform edge detection on two adjacent flame images to obtain two flame contour images.

[0072] Specifically, the Canny operator can be used for edge detection, which utilizes gradient information to determine edge locations. During the calculation, the original flame image is first smoothed using Gaussian filtering (e.g., convolution to eliminate noise interference). Then, the gradient magnitude (the absolute value of the difference in grayscale values ​​between adjacent pixels, reflecting the rate of grayscale change; areas with large grayscale changes may be edges) and direction (determined by the gradient direction) are calculated. Finally, a double-threshold method (setting a high threshold Th and a low threshold Tl; edges greater than Th are strong edges, and those between Tl and Th are potential edges) is used to detect and track edges, ultimately obtaining the flame contour image, thereby determining the range of the flame region.

[0073] Step S312: Based on two frames of flame outline images, calculate the coordinate offset of each edge point in the flame outline.

[0074] Specifically, the flame outline image is a binary image. The frame difference method is used to extract and calculate the edge pixels in the binary image to obtain the coordinate offset.

[0075] In some embodiments, edge detection is performed on two adjacent flame images to obtain two flame contour images, including:

[0076] Step S311-a: Preprocess two adjacent flame images to obtain two grayscale flame images; the preprocessing includes grayscale processing and filtering.

[0077] Specifically, light intensity information is extracted from the RGB image. This light intensity information undergoes grayscale processing and filtering to extract the grayscale value of each pixel, resulting in a grayscale image of the flame. This reduces the amount of data and facilitates subsequent calculations. The grayscale processing formula is: Gray = 0.299 × R + 0.587 × G + 0.114 × B; where R, G, and B represent the intensity values ​​of the red, green, and blue components of each pixel in the image, respectively. This linear combination method converts the color image to a grayscale image. Since the human eye is most sensitive to green and least sensitive to blue, different components are assigned different weights.

[0078] Step S311-b: Perform edge detection on the two frames of flame grayscale images to obtain two frames of flame outline images.

[0079] Specifically, the flame outline image is a binary image, where the thin white lines represent the flame outline, and the coordinates of edge points can be extracted based on the thin white lines.

[0080] In some embodiments, the coordinate offsets of each edge point in the flame contour are calculated based on two frames of flame contour images, including:

[0081] Step S312-a: Extract the edge points of the two flame contours from the two flame contour images respectively.

[0082] Step S312-b: Pair the edge points of the two flame outlines to obtain the coordinate set of the paired points.

[0083] Step S312-c: Based on the coordinate set of the paired points, calculate the Euclidean distance between the paired points to obtain the coordinate offset of each edge point in the flame profile.

[0084] Specifically, the number of flame edge points in two adjacent frames may not be the same, and in order to avoid the calculated coordinate offset from being completely distorted due to simple sequence matching (e.g., mismatch between the flame top point and the bottom point), the sub-function of the edge detection contour can use the nearest neighbor matching algorithm or contour recalculation to avoid the distortion problem.

[0085] The principle of nearest neighbor matching is as follows: in the (n+1)th frame, for each edge point P in the nth frame... i (n) Find the point P that is spatially closest. i (n+1) is used as its unique corresponding point; if there is no sufficiently close point, then the P is discarded. i (n), not included in this statistic; the calculation steps include: 1. Obtain the point set P(n) = {P1(n), ..., Pn} in the nth frame. k (n)}; 2. The point set P(n+1) = {P1(n+1), ..., P} is obtained in the (n+1)th frame. L (n+1)}; 3. For each P i (n) Calculate the minimum distance; dmin = min‖P i (n) – P j (n+1)‖2, where j∈[1,L]; if dmin≤dTH (dTH is an empirical threshold, such as 3 pixels), then it is considered a pair; otherwise, it is discarded; 4. Finally, the matching set M={(i,j)} is obtained, and the effective coordinate offset is calculated based on the matching set M.

[0086] The principle of contour resampling is as follows: First, resample the two contour segments at equal intervals according to their arc lengths to forcibly generate a fixed number of points, and then align them directly according to their sequence numbers. The steps include: 1. Perform spline interpolation on the contour of the nth frame, and resample it into N points according to the arc length Δs: Q1(n)…Q N (n); 2. Resample the (n+1)th frame into N points: Q1(n+1)...Q N (n+1); 3. Direct pairing (Q) i (n),Q i (n+1)); 4. Then calculate based on the number of matching points reused.

[0087] In some embodiments, gas flow information is determined based on turbulence characteristics, and the fan speed of the range hood is adjusted according to the gas flow information, including:

[0088] Step S330: Determine the flow rate level of the gas based on the turbulence intensity.

[0089] Step S340: Adjust the airflow level of the range hood according to the flow rate level.

[0090] Specifically, the detected turbulence intensity I is compared with the pre-set threshold range of I corresponding to different speed settings. For example, three gas flow speed settings are set: low, medium, and high, with corresponding I values ​​in the ranges [I1...]. min ,I1 max ]、[I2 min I2 max ]、[I3 min ,I3 max[Range]. When the detected value falls within a certain range, the range hood's airflow is adjusted to the corresponding level to achieve intelligent airflow regulation and effective smoke extraction.

[0091] In this embodiment, the range hood's speed control is quickly linked by setting the speed level, thereby improving control efficiency.

[0092] In some embodiments, the flame in the flame image is analyzed to obtain turbulent features reflecting the dynamic changes of the flame, including:

[0093] Step S410: Obtain the grayscale image corresponding to the flame image; calculate the distribution probability of grayscale values ​​for each pixel in the grayscale image.

[0094] Specifically, the grayscale image corresponding to the flame image is divided into m×n pixel regions, each pixel region representing one pixel; the grayscale value of each pixel is g. i,j (Value range 0-255), calculate the probability of grayscale value appearing at each pixel. , which is the probability distribution of gray values, where G is the sum of the gray values ​​of all pixels in the image.

[0095] Step S420: Calculate the entropy value of the flame pattern based on the probability distribution of gray values; the entropy value of the flame pattern is used to characterize the turbulence features.

[0096] Specifically, the entropy value S of the flame pattern is defined as:

[0097] ;

[0098] Where m×n represents the number of pixels, p i,j represents the probability distribution of the grayscale value of the (i,j)th pixel. S reflects the degree of disorder in the flame pattern; changes in gas flow rate will cause changes in the flame combustion state, thus leading to changes in entropy. When the gas flow rate increases, the flame burns more violently, the flame pattern changes more complexly, and the entropy increases; conversely, the entropy decreases.

[0099] In this embodiment, the entropy value of the flame pattern is directly related to the spatiotemporal complexity of flame combustion, and it has strong resistance to interference from illumination and noise; moreover, real-time diagnosis is achieved through a single frame image, reducing the amount of computation.

[0100] In some embodiments, gas flow information is determined based on turbulence characteristics, and the fan speed of the range hood is adjusted according to the gas flow information, including:

[0101] Step S430: Substitute the entropy value of the flame pattern into the preset flow rate formula to calculate the flow rate of the gas; the flow rate formula is the preset relationship between the flame entropy value and the gas flow rate.

[0102] Step S440: Adjust the range hood's airflow level according to the flow rate value.

[0103] Specifically, based on extensive preliminary experimental data, a correlation can be established between the flame entropy value S and the gas flow rate Q. This relationship can typically be approximated as a linear one.

[0104] Q = a × S + b;

[0105] Where a and b are fitting coefficients, which can be obtained through experimental calibration. The entropy S of the flame pattern is calculated in real time from the flame image, and the current gas flow rate Q can be estimated by substituting it into Q=a×S+b.

[0106] In this embodiment, the current gas flow rate is accurately quantified by using a pre-fitted relationship between flame entropy and gas flow rate, thereby improving detection accuracy.

[0107] This embodiment provides a range hood, such as Figure 4 As shown, the range hood includes a fan drive module and a control module. The fan drive module is connected to the control module and is used to adjust the airflow level; the control module is used to implement the steps of the range hood control method based on flame images in any of the above embodiments.

[0108] In this embodiment, flame pattern turbulence analysis technology is used to accurately detect changes in gas flow during cooking, enabling early prediction of changes in oil fume during cooking. This provides a basis for intelligent adjustment of the range hood's airflow level, effectively improving kitchen smoke extraction and enhancing the cooking environment.

[0109] This embodiment provides a smart linkage system for range hoods and cooktops, such as Figure 5 As shown, the intelligent linkage system includes: an image sensor, a cooktop, and the range hood in the above embodiment.

[0110] In this embodiment, there is no need to intrude into the gas supply pipeline, which solves the problem of insufficient accuracy of non-contact gas flow monitoring, realizes indirect measurement of airflow based on combustion dynamic characteristics, and eliminates the need for sensors to transmit signals, only receiving signals, resulting in lower power consumption.

[0111] The present embodiment will now be described and illustrated through preferred embodiments.

[0112] This preferred embodiment provides a smart linkage system for range hoods and cooktops, such as... Figure 5 As shown, the intelligent linkage system includes: an image sensor, a cooktop, and a range hood. The range hood includes a fan drive module and a control module. The fan drive module is connected to the control module and is used to adjust the fan speed; the control module is also connected to the image sensor and is used to implement the steps of the range hood control method based on flame images, see [link to relevant documentation]. Figure 6 The method specifically includes the following steps:

[0113] S1. Control the image sensor to start and initialize.

[0114] S2. If the image sensor detects a flame, it acquires a flame image according to the set period T and continues to execute step S3; if the image sensor does not detect a flame, it maintains the current state or prompts the user.

[0115] S3. Preprocess the flame images to obtain two frames of flame grayscale images; the preprocessing includes grayscale processing and filtering.

[0116] S4. Use the Canny operator to perform edge detection on the grayscale image of the flame to obtain the flame outline image.

[0117] S5. Based on two adjacent flame contour images, calculate the coordinate offset of each edge point in the flame contour. Specifically, this includes: extracting the edge points of the two flame contours from the two adjacent flame contour images; pairing the edge points of the two flame contours to obtain the coordinate set of the paired points {p_n, p_n+1}, where p_n = {(x_i, y_i)_n} and p_n+1 = {(x_i, y_i)_n+1}; and calculating the Euclidean distance between the paired points according to their sequence number based on the coordinate set of the paired points to obtain the coordinate offset of each edge point in the flame contour.

[0118] S6. Calculate the turbulence intensity of the flame based on each coordinate offset; the turbulence intensity is used to characterize the turbulence features.

[0119] S7. Determine the flow rate level of the gas based on the turbulence intensity.

[0120] S8. Adjust the range hood's airflow level according to the flow rate setting.

[0121] S9. Continuously monitor the changes in turbulence intensity and repeat steps S7 to S8.

[0122] In this preferred embodiment, the problem of insufficient accuracy in non-contact gas flow monitoring is solved, and indirect gas measurement based on combustion dynamic characteristics is realized. In particular, by utilizing the coordinate offset of each edge point in the flame profile, the sensitivity to the inherent light ripple turbulence characteristics of the flame is improved, thereby quickly and accurately responding to changes in gas flow information. This enables intelligent linkage between the cooktop and the range hood, improving kitchen ventilation. Furthermore, this preferred embodiment does not require intrusion into the gas supply pipeline, making installation convenient and easy to implement. It also eliminates the need for sensors to transmit signals, only receiving signals, resulting in lower power consumption.

[0123] It should be noted that the steps shown in the above process or in the flowchart of the accompanying figures can be executed in a computer system such as a set of computer-executable instructions, and although a logical order is shown in the flowchart, in some cases the steps shown or described may be executed in a different order than that shown here.

[0124] This embodiment also provides a range hood control device based on flame images, which is used to implement the above embodiments and preferred embodiments; details already described will not be repeated. The terms "module," "unit," "subunit," etc., used below refer to combinations of software and / or hardware that achieve a predetermined function. Although the device described in the following embodiments is preferably implemented in software, hardware implementation, or a combination of software and hardware, is also possible and contemplated.

[0125] Figure 7 This is a structural block diagram of the range hood control device based on flame images in this embodiment, as shown below. Figure 7 As shown, the device includes: an image acquisition module 71, an image analysis module 72, and an airflow regulation module 73.

[0126] Image acquisition module 71 is used to acquire flame images; the flame images are obtained by the image sensor capturing the flames generated when the gas burns on the stove; the stove and the range hood are set opposite each other;

[0127] Image analysis module 72 is used to analyze the flame in the flame image to obtain turbulence characteristics that reflect the dynamic changes of the flame;

[0128] The air volume adjustment module 73 is used to determine the gas flow information based on turbulence characteristics, and adjust the air volume level of the range hood according to the gas flow information.

[0129] It should be noted that the above modules can be functional modules or program modules, and can be implemented through software or hardware. For modules implemented through hardware, the above modules can reside in the same processor; or the above modules can be located in different processors in any combination.

[0130] It should be noted that the specific examples in this embodiment can refer to the examples described in the above embodiments and optional implementations, and will not be repeated in this embodiment.

[0131] Furthermore, in conjunction with the flame image-based range hood control method provided in the above embodiments, this embodiment can also provide a storage medium for implementation. This storage medium stores a computer program; when executed by a processor, the computer program implements any of the flame image-based range hood control methods described in the above embodiments.

[0132] It should be understood that the specific embodiments described herein are merely illustrative of the application and not intended to limit it. All other embodiments derived by those skilled in the art based on the embodiments provided in this application without inventive effort are within the scope of protection of this application.

[0133] Obviously, the accompanying drawings are merely some examples or embodiments of this application. Those skilled in the art can apply this application to other similar situations based on these drawings without any creative effort. Furthermore, it is understood that although the work done in this development process may be complex and lengthy, for those skilled in the art, certain design, manufacturing, or production modifications made based on the technical content disclosed in this application are merely conventional technical means and should not be considered as insufficient disclosure of this application.

[0134] The term "embodiment" in this application refers to a specific feature, structure, or characteristic described in connection with an embodiment that may be included in at least one embodiment of this application. The appearance of this phrase in various places in the specification does not necessarily imply the same embodiment, nor does it imply that it is mutually exclusive with or independent of other embodiments. It will be clearly or implicitly understood by those skilled in the art that the embodiments described in this application may be combined with other embodiments without conflict.

[0135] The embodiments described above are merely illustrative of several implementation methods of this application, and while the descriptions are relatively specific and detailed, they should not be construed as limiting the scope of patent protection. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of this application, and these all fall within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the appended claims.

Claims

1. A method for controlling a range hood based on flame images, characterized in that, The method includes: Acquire a flame image; the flame image is obtained by an image sensor capturing the flame generated when gas burns on the stove; the stove is positioned opposite the range hood; The flames in the flame images are analyzed to obtain turbulent characteristics that reflect the dynamic changes of the flames; Based on the turbulence characteristics, the gas flow rate information is determined, and the fan speed setting of the range hood is adjusted according to the gas flow rate information.

2. The range hood control method based on flame images according to claim 1, characterized in that, Analyzing the flame in the flame image yields turbulent characteristics reflecting the dynamic changes of the flame; including: Based on two adjacent frames of the flame image, calculate the coordinate offset of each edge point in the flame outline; The turbulence intensity of the flame is calculated based on each of the coordinate offsets; the turbulence intensity is used to characterize the turbulence features.

3. The range hood control method based on flame images according to claim 2, characterized in that, Based on two adjacent frames of the flame image, the coordinate offset of each edge point in the flame contour is calculated, including: Edge detection is performed on two adjacent frames of the flame image to obtain two flame contour images; Based on the two frames of the flame outline image, the coordinate offset of each edge point in the flame outline is calculated.

4. The range hood control method based on flame images according to claim 3, characterized in that, Edge detection is performed on two adjacent frames of the flame image to obtain two flame contour images, including: The two adjacent frames of the flame image are preprocessed to obtain two grayscale flame images; the preprocessing includes grayscale processing and filtering. Edge detection is performed on the two frames of the flame grayscale image to obtain two frames of flame outline image.

5. The range hood control method based on flame images according to claim 3, characterized in that, Based on the two frames of the flame contour image, the coordinate offset of each edge point in the flame contour is calculated, including: Extract the edge points of the two flame contours from the two frames of the flame contour images respectively; Pair the edge points of the two flame contours to obtain the coordinate set of the paired points; Based on the set of coordinates of the paired points, the Euclidean distance between the paired points is calculated to obtain the coordinate offset of each edge point in the flame profile.

6. The range hood control method based on flame images according to claim 2, characterized in that, Based on the turbulence characteristics, the gas flow rate information is determined, and the fan speed setting of the range hood is adjusted according to the gas flow rate information, including: Based on the turbulence intensity, the flow rate level of the gas is determined; Based on the aforementioned flow rate setting, the corresponding airflow setting of the range hood is adjusted.

7. The range hood control method based on flame images according to claim 1, characterized in that, Analyzing the flame in the flame image yields turbulent characteristics reflecting the dynamic changes of the flame, including: Obtain the grayscale image corresponding to the flame image; For each pixel in the grayscale image, calculate the probability distribution of the grayscale value; Based on the probability distribution of the grayscale values, the entropy value of the flame pattern is calculated; the entropy value of the flame pattern is used to characterize the turbulence features.

8. The range hood control method based on flame images according to claim 7, characterized in that, Based on the turbulence characteristics, the gas flow rate information is determined, and the fan speed setting of the range hood is adjusted according to the gas flow rate information, including: The flow rate of the gas is calculated by substituting the entropy value of the flame pattern into a preset flow rate formula; the flow rate formula is a preset relationship between the flame entropy value and the gas flow rate. Based on the flow rate value, the airflow level of the range hood is adjusted accordingly.

9. A range hood, characterized in that, include: Fan drive module and control module; The fan drive module is connected to the control module and is used to adjust the air volume level; The control module is used to perform the steps of the method according to any one of claims 1 to 8.

10. A smart linkage system for range hoods and cooktops, characterized in that, include: Image sensor, cooktop, and range hood as claimed in claim 9.

Images