Method and system for determining an oil filter of an extractor hood, extractor hood, computer readable storage medium

Abstract

The application relates to the technical field of intelligent household appliances, and discloses a method for determining an oil screen of an extractor hood, which comprises the following steps: simulating cooking oil fume corresponding to a target smoke source; obtaining cooking oil fume information under the condition that the cooking oil fume reaches a steady state and the air volume of the extractor hood is constant; and determining an oil screen parameter of the extractor hood according to the cooking oil fume information. The method simulates cooking oil fume corresponding to a target smoke source, obtains cooking oil fume information when the cooking oil fume is in a steady state and the air volume of the extractor hood remains unchanged, and determines an oil screen parameter based on the cooking oil fume information. Therefore, the determination of the oil screen parameter can balance the filtering efficiency of oil fume particles and the size of air resistance. In this way, the determined oil screen parameter can improve the smoke suction effect. The application further discloses a system for determining an oil screen of an extractor hood, an extractor hood and a computer readable storage medium.

Description

Technical Field

[0001] This application relates to the field of smart home appliance technology, such as a method, system, range hood, and computer-readable storage medium for determining the oil filter of a range hood. Background Technology

[0002] Currently, range hoods, as kitchen exhaust devices, reduce user contact with oily fumes during cooking, while also optimizing air quality in the kitchen. The exhaust efficiency of a range hood is crucial for improving and optimizing its performance. The design of the oil filter parameters significantly impacts its exhaust effectiveness.

[0003] The related technology discloses a design method for a noise reduction and protection net for a range hood, including the following steps:

[0004] S1. Through geometric parametric modeling, the flow field inside the range hood is simulated and calculated using finite element analysis software. A coordinate system is established, and the coordinates of the first endpoint, the second endpoint, and the third endpoint are simulated in the coordinate system. S2. The first endpoint, the second endpoint, the third endpoint, and the origin of the coordinate system are connected to form the first surface and the second surface.

[0005] In the process of implementing the embodiments of this disclosure, at least the following problems were found in the related art:

[0006] Related technologies use simulation calculations to determine the internal flow field of range hoods and design protective meshes for noise reduction. However, the method for determining the mesh that improves smoke extraction efficiency is not disclosed.

[0007] It should be noted that the information disclosed in the background section above is only used to enhance the understanding of the background of this application, and therefore may include information that does not constitute prior art known to those skilled in the art. Summary of the Invention

[0008] To provide a basic understanding of some aspects of the disclosed embodiments, a brief summary is given below. This summary is not intended as a general commentary, nor is it intended to identify key / important components or describe the scope of protection of these embodiments, but rather as a prelude to the detailed description that follows.

[0009] This disclosure provides a method, system, range hood, and computer-readable storage medium for determining the oil filter parameters of a range hood, so that the determined oil filter parameters can improve smoke extraction performance.

[0010] In some embodiments, the method includes: simulating cooking fumes corresponding to a target smoke source; acquiring cooking fume information when the cooking fumes reach a stable state and the range hood airflow is constant; and determining the oil filter parameters of the range hood based on the cooking fume information.

[0011] In some embodiments, the system includes: an oil fume generating device for generating cooking oil fumes corresponding to a target smoke source; an oil fume information acquiring device for acquiring cooking oil fume information when the cooking oil fumes reach a stable state and the range hood airflow is constant; and a determining device for determining the oil filter parameters of the range hood based on the cooking oil fume information.

[0012] In some embodiments, the range hood includes an oil filter, which is determined based on the aforementioned method for determining the oil filter of the range hood.

[0013] In some embodiments, the computer-readable storage medium stores program instructions that, when executed, cause a computer to perform the aforementioned method for determining the oil filter of a range hood.

[0014] The method, system, range hood, and computer-readable storage medium for determining the oil filter of a range hood provided in this disclosure can achieve the following technical effects:

[0015] This embodiment simulates cooking fumes corresponding to a target smoke source and acquires cooking fume information when the cooking fumes are in a steady state and the range hood airflow remains constant. Based on the cooking fume information, oil filter parameters are determined. This allows the determined oil filter parameters to balance the filtration efficiency of particulate matter and the magnitude of air resistance. Thus, the determined oil filter parameters improve the smoke extraction effect.

[0016] The above general description and the description below are exemplary and illustrative only and are not intended to limit this application. Attached Figure Description

[0017] One or more embodiments are illustrated by way of example with reference to the accompanying drawings. These illustrations and drawings do not constitute a limitation on the embodiments. Elements having the same reference numerals in the drawings are shown as similar elements. The drawings are not to be scaled. And wherein:

[0018] Figure 1 This is a schematic diagram of a method for determining the oil filter of a range hood, provided in an embodiment of this disclosure;

[0019] Figure 2 This is a schematic diagram of another method for determining the oil filter of a range hood provided in an embodiment of this disclosure;

[0020] Figure 3 This is a schematic diagram of the opening area of ​​an oil mesh provided in an embodiment of this disclosure;

[0021] Figure 4 This is a schematic diagram of another method for determining the oil filter of a range hood provided in an embodiment of this disclosure;

[0022] Figure 5This is a schematic diagram of another method for determining the oil filter of a range hood provided in an embodiment of this disclosure;

[0023] Figure 6 This is a schematic diagram of a system for determining the oil filter of a range hood, provided in an embodiment of this disclosure;

[0024] Figure 7 This is a schematic diagram of a device for determining the oil filter of a range hood, provided in an embodiment of this disclosure. Detailed Implementation

[0025] To provide a more detailed understanding of the features and technical content of the embodiments of this disclosure, the implementation of the embodiments of this disclosure will be described in detail below with reference to the accompanying drawings. The accompanying drawings are for illustrative purposes only and are not intended to limit the embodiments of this disclosure. In the following technical description, for ease of explanation, several details are used to provide a full understanding of the disclosed embodiments. However, one or more embodiments may still be implemented without these details. In other cases, well-known structures and devices may be simplified in their depiction to simplify the drawings.

[0026] The terms "first," "second," etc., used in the specification, claims, and accompanying drawings of this disclosure are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate for the embodiments of this disclosure described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion.

[0027] Unless otherwise stated, the term "multiple" means two or more.

[0028] In this embodiment of the disclosure, the character " / " indicates that the objects before and after it are in an "or" relationship. For example, A / B means: A or B.

[0029] The term "and / or" describes an association between objects, indicating that three relationships can exist. For example, A and / or B means: A or B, or A and B.

[0030] The term "correspondence" can refer to an association or binding relationship. The correspondence between A and B means that there is an association or binding relationship between A and B.

[0031] In the experimental testing space, the generation and diffusion of cooking fumes under various cooking scenarios, as well as the changes in the flow field of cooking fumes under the action of a range hood, were simulated. The experimental equipment included a range hood, a heating source, and cookware; the heating source included a stove or other device capable of heating the cookware. Image acquisition devices were installed on both sides of the cookware to acquire the velocity parameters of the cooking fume particles. For example, one image acquisition device was installed on the front and one on the side of the cookware. A fume sampler was installed above the cookware to acquire the particle size parameters of the cooking fume particles.

[0032] Optionally, the image acquisition device includes a high-speed camera. When obtaining the velocity parameters of oil fume particles using particle image velocimetry, tracer particles are injected into the oil fume flow field and illuminated by a laser light source. The high-speed camera then acquires images, and the velocity parameters of the oil fume particles are obtained after image analysis and processing. Two high-speed cameras are calibrated to ensure that the frontal and side images are time-synchronized and spatially aligned.

[0033] The processor determines the mapping relationship between the initial velocity and particle size parameters of oil fume particles and the smoke source, including:

[0034] The processor acquires the initial velocity and particle size parameters of the oil fume particles in the target space.

[0035] The processor determines the smoke source in the target space based on the initial velocity and particle size parameters of the oil fume particles.

[0036] Here, the target space mainly refers to the space where the oil fume is located during the oil fume experimental test, and the initial velocity of the oil fume particles refers to the generation velocity of the oil fume particles. Cooking scenarios include various cooking states such as stewing, stir-frying, and deep-frying, and the parameters of the smoke source generated in each scenario are different. That is, the parameters of the oil fume particles are different (in this embodiment, the parameters of the oil fume particles include the initial velocity and particle size parameters). Therefore, the smoke source is determined by simulating the smoke generation of different cooking scenarios through experimental testing. Images and parameters of oil fume particles are collected during the oil fume generation process, and the initial velocity of the oil fume particles is obtained by processing and analyzing the images. The particle size parameters of the oil fume particles are obtained by analyzing the oil fume particle parameters. In this way, the initial velocity and particle size parameters of the oil fume particles for the corresponding cooking scenario are obtained. Simultaneously, based on the obtained initial velocity and particle size parameters of the oil fume particles, the smoke source is determined, thereby determining the cooking scenario. This allows us to determine the mapping relationship between the initial velocity and particle size parameters of the oil fume particles and the smoke source, which helps improve the accuracy of the analysis when analyzing the smoke exhaust effect of the range hood based on smoke source information.

[0037] It should be noted that the images of oil fume particles are acquired based on the plane containing the rim of the cookware as the initial surface, i.e., images of the location where the cookware emits smoke are collected. Analysis and processing of these images allows us to determine the initial velocity of the oil fume particles. Understandably, as the oil fume diffuses, its initial velocity changes. At this point, the velocity of the oil fume particles no longer accurately represents their initial velocity. Therefore, image acquisition primarily focuses on capturing images of oil fume particles along and near the rim of the cookware. Furthermore, to ensure the accuracy of the obtained particle size parameters, multiple oil fume samplers can be used to acquire these parameters.

[0038] Thus, by collecting particulate matter parameters of cooking fumes under specific cooking scenarios, the initial velocity and particle size parameters of the particulate matter are obtained. Based on the obtained initial velocity and particle size parameters, the smoke source is determined, and the cooking scenario is defined. This allows for the determination of the mapping relationship between the initial velocity and particle size parameters of the particulate matter and the smoke source. Consequently, the smoke extraction effect of the range hood under various cooking conditions can be accurately simulated based on the smoke source information.

[0039] Optionally, the processor acquires the initial velocity of the oil fume particles within the target space, including:

[0040] The processor uses particle image velocimetry to calculate the velocity field of oil fume particles within the target space, thereby obtaining the initial velocity of the oil fume particles within the target space. Alternatively,

[0041] The processor obtains the initial velocity of the oil fume particles in the target space, including:

[0042] The processor uses an image acquisition device to acquire images of oil fume particles generated in the target space; the oil fume images include multiple frames.

[0043] The processor processes the acquired oil fume images to obtain the grayscale peak distribution of the images; and calculates the time-averaged velocity field based on the grayscale peak distribution to obtain the initial velocity of oil fume particles in the target space.

[0044] There are two methods to obtain the initial velocity of oil fume particles. Method one, as described earlier, uses particle image velocimetry to calculate the velocity field of the oil fume particles in the target space to obtain their initial velocity. Specifically, tracer particles are injected into the oil fume flow field and illuminated by a laser light source. A high-speed camera then acquires images, which are analyzed to obtain the velocity field of the oil fume particles. The average velocity is then calculated based on this velocity field, thus yielding the initial velocity of the oil fume particles.

[0045] Method 2: Acquire multiple consecutive frames of oil fume images when particulate matter is generated. When the background of the oil fume experiment is black, the oil fume appears white. The acquired color oil fume images are converted to grayscale to obtain grayscale images. The shades of gray in the grayscale image represent different concentrations of oil fume. Therefore, based on the grayscale peak distribution of the images, the displacement of relevant peaks between adjacent frames can be calculated to obtain the instantaneous velocity field. Then, time averaging is performed on the instantaneous velocity field to obtain the time-averaged velocity field, thus obtaining the initial velocity of the oil fume particles in the target space. This method does not require the injection of tracer particles, making it more convenient, easier to use, and lower in cost compared to Method 1.

[0046] Optionally, the processor processes the acquired oil fume image to obtain the grayscale peak distribution of the image, including:

[0047] The processor converts the acquired oil fume images to grayscale before dividing them into grids.

[0048] The processor calculates the grayscale value of each grid cell, and the grayscale value of each grid cell is a grayscale sequence that changes over time.

[0049] The processor performs a Fourier transform on the grayscale values ​​to obtain the grayscale peak distribution of the image.

[0050] Here, the acquired color oil fume images are processed. Specifically, this includes: converting the color image (i.e., RGB image) to a grayscale image, such as using Python's OpenCV to convert an RGB image to a grayscale image. Discretizing the grayscale image, i.e., dividing the grayscale image into a grid. Calculating the grayscale value of each grid, such as by calculating the average value of each grid. Performing the above processing on each frame of the image, the grayscale values ​​corresponding to the same grid in consecutive frames change over time, yielding a grayscale sequence for each grid over time. Then, a Fourier transform is performed on the grayscale values, mapping them from the spatial domain to the frequency domain. The shift of the correlation peak is detected using a cross-correlation function; the distribution change of the image's grayscale peaks can be obtained through time variation. This completes the processing of the oil fume image for subsequent calculation of the initial velocity of oil fume particles.

[0051] Optionally, the processor calculates the time-averaged velocity field based on the grayscale peak distribution to obtain the initial velocity of the oil fume particles in the target space, including:

[0052] The processor obtains the instantaneous velocity field based on the displacement of the grayscale peaks of adjacent frames.

[0053] The processor calculates the average value of the instantaneous velocity field within a preset time period, obtains the time-averaged velocity field, and uses the time-averaged velocity field as the initial velocity of oil fume particles in the target space.

[0054] Here, after obtaining the grayscale peak value of the oil fume image, the instantaneous velocity field is obtained based on the displacement of the grayscale peak values ​​of adjacent frames. Specifically, for two adjacent frames, if oil fume particles move within one or more grids, the cross-correlation function will show a peak at the point of movement (i.e., a correlation peak). The displacement of the coordinates can be determined using the correlation function, and the quotient of the displacement and the time interval is the instantaneous velocity field. The time interval is obtained based on the image frame rate. Repeating the above process for all grids yields the instantaneous velocity field for the entire image region. Then, the time-averaged velocity field is obtained by averaging the data from multiple frames within a preset time period. The preset time period should generally not be too long, and can be between 25s and 50s. Thus, by processing multiple frames within a preset time period, the time-averaged velocity field, i.e., the initial velocity of the oil fume particles, is calculated. This can counteract interference from external conditions or some image noise.

[0055] Additionally, it should be noted that for frontal and side images of cooking fumes, the instantaneous velocity field can be calculated separately. Then, the instantaneous velocity fields from the two perspectives can be combined to obtain a more accurate velocity field. Alternatively, the grayscale data from the frontal and side images of cooking fumes can be fused to generate a three-dimensional grayscale image, and the instantaneous velocity field of the three-dimensional field can be calculated.

[0056] Optionally, the processor calculates the grayscale value for each grid cell by including:

[0057] The processor maps the two-dimensional grids corresponding to the oil fume images from two perspectives at the same moment to the same three-dimensional grid.

[0058] The processor fuses gray values ​​at the same grid location in the 3D mesh to calculate the gray value of each grid.

[0059] The higher the gray value, the higher the concentration of oil fume particles.

[0060] As mentioned earlier, images of cooking fumes are captured from the front and sides of the cookware, i.e., from a first-person perspective and a second-person perspective, respectively. Both the first-person and second-person perspective images are two-dimensional images. Therefore, data from different perspectives at the same time need to be processed, i.e., the first-person and second-person perspective images are mapped onto the same three-dimensional grid. The grayscale value of each grid cell is calculated within the three-dimensional grid. The higher the concentration of cooking fumes, the larger the grayscale value. For example, in the grayscale image, 255 represents white, and 1–254 represents different shades of gray. Against a black background, cooking fumes appear white. Therefore, the higher the concentration of cooking fumes, the larger the grayscale value.

[0061] Furthermore, in some embodiments, when mapping a 2D mesh to a 3D mesh, grayscale values ​​can be fused based on viewpoint weights. As an example, the weight for the first viewpoint is 0.6, and the weight for the second viewpoint is 0.4. The weights can be set based on the reliability of the image corresponding to each viewpoint.

[0062] Optionally, the processor performs grayscale conversion on the acquired oil fume images, including:

[0063] The processor converts each pixel in the color image of cooking fumes into a grayscale value.

[0064] The processor removes non-smoke areas from the image based on a grayscale threshold to obtain the grayscale converted image.

[0065] Here, the color image of cooking fumes is converted to a grayscale image, where each grid cell in the image is represented by a two-dimensional array. Each image is then a two-dimensional matrix. For example, if the coordinates of a grid cell in the image are (m, n), and the corresponding pixel value is f(m, n), the image can be represented as an M×N two-dimensional matrix, where M is the number of rows and N is the number of columns. After converting the image to grayscale, a grayscale threshold can be set to remove non-cooking fume areas from the image. If the grayscale value of a non-cooking fume area is small, a suitable grayscale threshold can be set to delete the non-cooking fume area from the image. This reduces the computational load and improves the processing speed.

[0066] Optionally, the processor acquires the particle size parameters of the oil fume particles in the target space, including:

[0067] The processor acquires the particle size distribution and particle size percentage of oil fume particles at multiple locations within the target space.

[0068] The processor calculates the average values ​​of particle size distribution and particle size percentage, and uses the average values ​​of particle size distribution and particle size percentage as the particle size parameters of oil fume particles.

[0069] To improve the accuracy of data acquisition, multiple oil fume samplers are set up within the target space to collect particulate matter size parameters at different locations. Specifically, particle size distribution data is collected, and the proportion of each particle size segment is calculated based on this data. This process is repeated for the data collected at each location, and then the average value of the particle size distribution and proportion across multiple locations is calculated. As an example, oil fume particles with a diameter range of 0.03–9.990 μm were collected and divided into several segments: 0.03–0.063, 0.063–0.109, 0.109–0.173, 0.173–0.267, 0.267–0.407, 0.407–0.655, 0.655–1.021, 1.021–1.655, 1.655–2.520, 2.250–4.085, 4.085–6.560, and 6.560–9.990. The proportion of particle size in each of these segments was statistically analyzed to obtain the particle size parameters of the oil fume particles.

[0070] Optionally, based on particle size distribution, the proportion of PM1.0 (particle size ≤ 1.0 μm) and PM2.5 (particle size ≤ 2.5 μm) particles can be statistically analyzed. The proportion of these two types of particles can characterize the smoke source.

[0071] Optionally, the processor determines the mapping relationship between the initial velocity and particle size parameters of the oil fume particles and the smoke source, including:

[0072] The processor acquires the initial velocity and particle size parameters of the oil fume particles in the target space.

[0073] When the initial velocity meets the first velocity condition and the particle size parameter meets the first particle size condition, the processor determines the smoke source in the target space as the first smoke source.

[0074] If the initial velocity meets the second velocity condition and the particle size parameter meets the second particle size condition, the processor determines the smoke source in the target space as the second smoke source.

[0075] Here, the corresponding smoke source is determined based on initial velocity and particle size parameters. The smoke sources mainly include two types: stir-frying and frying. Specifically, the particle size distribution in the stir-fry smoke source information is significantly higher than that in the frying smoke source, and the initial velocity of the oil fume particles in stir-frying is also significantly higher than that in frying. Therefore, velocity and particle size conditions are set to distinguish between these two smoke sources. The velocity condition includes a velocity threshold, and the particle size condition includes particle size percentage thresholds for PM1.0 and PM2.5 particles. As an example, the first particle size condition includes a PM2.5 particle size percentage greater than or equal to 90% and a PM1.0 particle size percentage greater than or equal to 80%. The second particle size condition includes a PM2.5 particle size percentage greater than or equal to 85%, and a PM1.0 particle size percentage range of (68%, 80%). The first velocity condition is greater than or equal to 0.8 m / s, and the velocity range for the second velocity condition is (0.5 m / s, 0.8 m / s). In this way, the smoke source can be determined based on set conditions. Simultaneously, the corresponding smoke source information can be obtained. Therefore, based on accurate smoke source information, the smoke extraction effect of the range hood under various cooking conditions can be simulated, improving the accuracy of effect evaluation.

[0076] Based on the previously established correspondence between the target smoke source and the particulate matter parameters of cooking fumes, cooking fumes can be simulated. Combined with... Figure 1 As shown in the embodiments of this disclosure, a method for determining the oil filter of a range hood is provided, including:

[0077] S101, the processor simulates the cooking fumes corresponding to the target smoke source.

[0078] S102, when the cooking fumes reach a stable state and the range hood's airflow remains constant, the processor acquires cooking fume information.

[0079] S103, the processor determines the oil filter parameters of the range hood based on the cooking fume information.

[0080] Here, a target simulation model is used to simulate the cooking fumes corresponding to the target smoke source. Specifically, based on the target smoke source, the corresponding particulate matter parameters and cooking temperature parameters are determined. These parameters are then input into the target simulation model to simulate the cooking fumes and obtain the fume flow field. The specific implementation of this step is detailed above and will not be repeated here.

[0081] When cooking fumes reach a stable state (i.e., the fume flow field converges) and the range hood's airflow is constant, cooking fume information is acquired. Here, range hood airflow measurement refers to maintaining a constant airflow parameter in the target simulation model; the range hood's operating parameters are also part of the target simulation model's input parameters. This avoids inaccurate cooking fume information due to variations in the range hood's airflow. Acquiring cooking fume information includes obtaining information such as the shape, escape information, and velocity of the cooking fumes. In other words, it involves acquiring cooking fume information that affects fume parameters, thereby determining the range hood's oil filter parameters based on this information. Here, the range hood's oil filter parameters have initial parameters; that is, the target simulation model is based on a range hood with an oil filter that has initial parameters. The initial oil filter values ​​are determined based on range hood information (such as model information). Generally, the initial oil filter parameters are that the oil filter holes are evenly distributed across the oil filter. Under the initial oil filter parameters, the oil filter parameters are adjusted and determined based on the cooking fume information. This results in better oil filter parameters, improving the range hood's smoke extraction effect.

[0082] The oil filter parameters include the layout, location, size / area of ​​the oil filter holes. Based on cooking fume information, adjusting these parameters specifically includes: when the information indicates that the fume velocity is weakening and cannot be effectively drawn into the range hood, adjusting the layout and size of the oil filter holes to increase suction power for that portion of the fume, thus ensuring effective fume extraction. When the information indicates that cooking fume is escaping, adding more oil filter holes based on the location of the escaping fume can increase the open area of ​​the oil filter, thereby increasing the fume collection area and reducing fume escape. When the information indicates that cooking fume is concentrated, the oil filter holes corresponding to areas where the fume is not concentrated can be blocked or their area reduced. This avoids large area of ​​oil filter holes in these areas, which could affect the overall suction power of the range hood. While reducing the area of ​​the oil filter holes can increase air resistance, the impact on the filtration efficiency of areas with less fume is negligible.

[0083] Furthermore, the target smoke source can be either a first smoke source or a second smoke source. Optionally, the target smoke source is the first smoke source. It is understandable that if the determined oil filter parameters result in good smoke extraction from the range hood when the target smoke source is the first smoke source, then good smoke extraction can also be guaranteed for other smoke sources. Therefore, the first smoke source is preferred as the target smoke source.

[0084] The method for determining the oil filter of a range hood, as provided in this embodiment, simulates cooking fumes corresponding to a target smoke source and acquires cooking fume information when the cooking fumes are in a steady state and the range hood airflow remains constant. Based on the cooking fume information, oil filter parameters are determined. This ensures that the determined oil filter parameters meet the requirements for filtration efficiency of particulate matter and wind resistance. Thus, the determined oil filter parameters improve the smoke extraction effect.

[0085] Optionally, in step S012, the cooking fume information includes: the shape, distribution, and velocity of the cooking fumes. The velocity of the cooking fumes includes the velocity of fumes at a preset location.

[0086] Here, the morphology of cooking fumes refers to their flow state during generation, diffusion, and capture; the distribution of cooking fumes refers to their diffusion over time in the vertical and horizontal directions; and the velocity of cooking fumes is primarily the velocity of the particulate matter at a preset height from the grease filter. Cooking fumes have an initial velocity upon generation, which gradually decreases as they diffuse. To ensure the effective smoke extraction of the grease filter, a certain velocity must be maintained for the fumes at the preset height, assuming a constant airflow from the range hood. If the velocity at this preset position is less than the preset velocity, that portion of the fumes cannot be drawn into the range hood. Therefore, the velocity of the fumes at the preset position affects the grease filter parameters. The preset height depends on the model of the range hood.

[0087] Meanwhile, the shape and distribution of cooking fumes also affect the parameters of the oil filter. For example, areas with higher concentrations of cooking fumes have more openings in the oil filter; areas with lower concentrations have fewer openings or even none. The shape of the cooking fumes also influences the setting of the opening areas in the oil filter. Therefore, cooking fume information includes multiple factors affecting the oil filter parameters. Determining oil filter parameters by considering these multiple factors is more reliable and accurate, and helps improve smoke extraction efficiency.

[0088] Combination Figure 2 As shown in the embodiments of this disclosure, another method for determining the oil filter of a range hood is provided, including:

[0089] S101, the processor simulates the cooking fumes corresponding to the target smoke source.

[0090] S102, when the cooking fumes reach a stable state and the range hood's airflow remains constant, the processor acquires cooking fume information. This information includes the shape, distribution, and velocity of the cooking fumes.

[0091] S131, the processor determines the opening area of ​​the oil mesh based on the shape and distribution of cooking fumes.

[0092] S132, the processor determines the position and area of ​​the oil mesh holes in the opening area based on the shape and speed of the cooking fumes.

[0093] Here, based on the aforementioned information on cooking fumes, the parameters of the oil mesh are determined. Specifically, based on the morphology and distribution of the cooking fumes, the opening area of ​​the oil mesh is determined. The opening area refers to the region within the oil mesh where the mesh holes are arranged; this region is a continuous, closed area. In other words, the opening area is a region determined based on the outermost mesh holes. For example, as shown... Figure 3As shown, areas A and B in the oil filter have oil mesh holes. The perforated area is the region formed by connecting the outermost oil holes in areas A and B (i.e., the gray area in the figure is the perforated area). The perforated area determines the smoke collection range of the range hood. Generally, the larger the perforated area, the better the smoke collection effect of the range hood and the less smoke escapes.

[0094] Based on the shape and velocity of cooking fumes, the location and area of ​​the oil mesh holes in the perforated area are determined. The shape of the cooking fumes influences the location and area of ​​the oil mesh holes. The shape of the cooking fumes characterizes the density of fumes in a space; areas with higher fumes density should have oil mesh holes for effective smoke extraction. Areas with low or negligible fumes density will not be able to extract much smoke even with oil mesh holes. These areas are considered ineffective smoke extraction zones, and oil mesh holes can be omitted in these zones. For example, for ceiling-mounted range hoods, oil mesh holes can be installed on both sides of the oil mesh area, while the middle area can be left un-grown (e.g.,...). Figure 3 ).

[0095] The velocity of cooking fumes affects the area of ​​the mesh screen openings, which includes the area of ​​each individual opening. In a cross-section at a preset location, if the velocity of cooking fumes in a certain area is low, the area of ​​the mesh screen openings in that area needs to be reduced to increase suction power and ensure that the cooking fumes in that area are effectively drawn into the range hood. Conversely, if the velocity of cooking fumes is high, the area of ​​the mesh screen openings can be maintained or slightly increased to improve fume collection while maintaining suction power.

[0096] Optionally, in step S132, the processor determines the position and area of ​​the oil mesh holes in the opening area based on the shape and speed of the cooking fumes, including:

[0097] The shape of cooking fumes is positively correlated with the position and area of ​​the oil mesh.

[0098] The speed of cooking fumes is negatively correlated with the area of ​​the mesh openings in the oil filter.

[0099] As mentioned earlier, the shape and velocity of cooking fumes affect the position and area of ​​the mesh screen openings. Specifically, the shape of the cooking fumes is positively correlated with the position and area of ​​the mesh screen openings, while the velocity of the cooking fumes is negatively correlated with the area of ​​the mesh screen openings. Therefore, the mesh screen parameters can be determined by comprehensively analyzing the information about the cooking fumes.

[0100] Optionally, the cooking fume information also includes information on fume escape; after step S132, it further includes:

[0101] S133, the processor adjusts the area and layout of the oil mesh holes at the corresponding positions based on the location, amount, and velocity of the oil fumes.

[0102] Here, cooking fume information also includes fume escape information. This information includes the location and amount of fume escape. The location of fume escape indicates an unreasonable layout of the oil filter mesh at that location. The layout of the oil filter mesh refers to the arrangement of the mesh holes, such as single or multiple rows; each row may include multiple columns of mesh holes; and the shape and / or area of ​​the mesh holes in multiple rows may differ. Understandably, fume escape indicates poor fume collection and insufficient suction power. Good fume collection requires a large mesh hole area, while strong suction power requires a small mesh area. Therefore, in this case, the oil filter mesh layout can be designed based on the fume escape location. For example, if the fume escape location is at the front of the range hood, multiple rows of mesh holes can be installed. The area of ​​the front row of mesh holes (closest to the front of the range hood) is larger than the area of ​​the rear row, and the proportion of the front row mesh holes in the arrangement direction is significantly smaller than that of the rear row. In this way, the large area of ​​the front oil fume mesh helps to collect smoke, while the small area of ​​the rear oil fume vents helps to improve suction power.

[0103] The amount of oil fume escaping and the corresponding oil fume velocity are used to adjust the area of ​​the oil filter holes at the corresponding positions. To avoid the oil filter holes being too large or too small, the area of ​​the oil filter holes is adjusted based on the amount and velocity of the oil fume escaping. Specifically, the amount of oil fume escaping is positively correlated with increasing the area of ​​the oil filter holes, and the difference between the oil fume velocity and the preset velocity is positively correlated with decreasing the area of ​​the oil filter holes. The larger the difference, the slower the oil fume velocity, and the greater the reduction in the area of ​​the oil filter holes. In this way, the adjusted oil filter holes can balance smoke collection and suction power, improving the smoke extraction effect of the range hood.

[0104] Combination Figure 5 As shown in the embodiments of this disclosure, another method for determining the oil filter of a range hood is provided, including:

[0105] S101, the processor simulates the cooking fumes corresponding to the target smoke source.

[0106] S102, when the cooking fumes reach a stable state and the range hood's airflow remains constant, the processor acquires cooking fume information.

[0107] S103, the processor determines the oil filter parameters of the range hood based on the cooking fume information.

[0108] S204, the processor obtains the number of oil fume particles captured by each oil mesh hole.

[0109] S205, the processor adjusts the area of ​​the oil mesh holes according to the number of oil fume particles captured by each oil mesh hole.

[0110] Here, after determining the parameters of the range hood's oil filter, the target simulation model can be used again to simulate the oil filter's ability to filter oil fumes. Specifically, the simulation obtains the number of oil fume particles captured by each oil filter hole. Using static mesh partitioning, each oil filter hole is a mesh cell, and the number of oil fume particles inhaled into the oil filter hole per unit time or a preset time period is counted. Then, based on the number of oil fume particles captured by each oil filter hole, the area of ​​the oil filter hole is adjusted. More specifically, the average number of oil fume particles captured by each oil filter hole is obtained, and the difference between the number of oil fume particles captured by each oil filter hole and the average number is calculated. If the absolute value of the difference is greater than a difference threshold, the corresponding oil filter hole area is adjusted. This makes the adjusted oil filter hole more effective at capturing oil fumes.

[0111] Optionally, in step S205, the processor adjusts the area of ​​the oil mesh holes according to the number of oil fume particles captured by each oil mesh hole, including:

[0112] If the difference between the number of oil fume particles captured by the target oil mesh and the number of oil fume particles captured by the adjacent oil mesh is less than a first threshold, the processor reduces the area of ​​the target oil mesh.

[0113] If the difference between the number of oil fume particles captured by the target oil mesh hole and the number of oil fume particles captured by the adjacent oil mesh hole is greater than the second threshold, the processor increases the area of ​​the target oil mesh hole.

[0114] Here, the absolute values ​​of the first threshold and the second threshold can be the same or different. The first threshold is a value less than zero, and the second threshold is a value greater than zero. The number of oil fume particles captured by the target oil mesh aperture is compared with the number captured by adjacent oil mesh apertures. This is because the number of oil fume particles captured by an oil mesh aperture is related to the position of the aperture; the closer to the center of the oil fume area, the more oil fume particles are captured, and vice versa. Therefore, to avoid inappropriately correcting the area of ​​the oil mesh apertures, this embodiment compares the number of oil fume particles captured by the target oil mesh aperture and adjacent oil mesh apertures. Optionally, when there are multiple oil mesh apertures adjacent to the target oil mesh aperture, the average number of oil fume particles captured by the multiple oil mesh apertures can be obtained. The number of oil fume particles captured by the target oil mesh aperture is compared with the average number. This improves the accuracy of the comparison.

[0115] Specifically, when the difference is less than the first threshold, it indicates that the number of oil fume particles in the target oil mesh hole is significantly less than that in the adjacent oil mesh holes. In this case, the area of ​​the target oil mesh hole can be reduced. Similarly, when the difference is greater than the second threshold, it indicates that the number of oil fume particles in the target oil mesh hole is significantly greater than that in the adjacent oil mesh holes. In this case, the area of ​​the target oil mesh hole can be increased. In this way, by optimizing the area of ​​each oil mesh hole, the overall smoke extraction effect of the oil mesh is improved.

[0116] Combination Figure 6 As shown in the figure, this disclosure provides a system 200 for determining the oil filter of a range hood, including an oil fume generating device 201, an oil fume information acquisition device 202, and a determining device 203. The oil fume generating device 201 is configured to generate cooking oil fumes corresponding to a target smoke source; the oil fume information acquisition device 202 is configured to acquire cooking oil fume information when the cooking oil fume reaches a stable state and the airflow of the range hood is constant; the determining device 203 is configured to determine the oil filter parameters of the range hood based on the cooking oil fume information.

[0117] The system 200 for determining the oil filter of a range hood, as provided in this embodiment, simulates cooking fumes corresponding to a target smoke source and acquires cooking fume information when the cooking fumes are in a steady state and the range hood airflow remains constant. Based on the cooking fume information, oil filter parameters are determined. This ensures that the determined oil filter parameters can balance the filtration efficiency of oil fume particles and the magnitude of air resistance. Thus, the determined oil filter parameters can improve the smoke extraction effect.

[0118] Combination Figure 7 As shown, this disclosure provides an apparatus 300 for determining the grease filter of a range hood, including a processor 301 and a memory 302. Optionally, the apparatus may further include a communication interface 303 and a bus 304. The processor 301, communication interface 303, and memory 302 can communicate with each other via the bus 304. The communication interface 303 can be used for information transmission. The processor 301 can call logical instructions in the memory 302 to execute the method for determining the grease filter of a range hood described in the above embodiment.

[0119] Furthermore, the logic instructions in the aforementioned memory 302 can be implemented as software functional units and, when sold or used as independent products, can be stored in a computer-readable storage medium.

[0120] The memory 302, as a computer-readable storage medium, can be used to store software programs and computer-executable programs, such as program instructions / modules corresponding to the methods in the embodiments of this disclosure. The processor 301 executes functional applications and data processing by running the program instructions / modules stored in the memory 302, that is, it implements the method for determining the oil filter of the range hood in the above embodiments.

[0121] The memory 302 may include a program storage area and a data storage area. The program storage area may store the operating system and applications required for at least one function; the data storage area may store data created based on the use of the terminal device. Furthermore, the memory 302 may include high-speed random access memory and may also include non-volatile memory.

[0122] This disclosure provides a computer-readable storage medium storing computer-executable instructions configured to perform the above-described method for determining the oil filter of a range hood.

[0123] The technical solutions of this disclosure can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes one or more instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the method described in this disclosure. The aforementioned storage medium can be a non-transitory storage medium, such as a USB flash drive, external hard drive, read-only memory (ROM), random access memory (RAM), magnetic disk, or optical disk, etc., and other media capable of storing program code.

[0124] The foregoing description and accompanying drawings fully illustrate embodiments of this disclosure to enable those skilled in the art to practice them. Other embodiments may include structural, logical, electrical, procedural, and other changes. The embodiments represent only possible variations. Individual components and functions are optional unless explicitly required, and the order of operation may vary. Parts and features of some embodiments may be included in or replace parts and features of other embodiments. Moreover, the terminology used in this application is for describing embodiments only and is not intended to limit the claims. As used in the description of embodiments and claims, the singular forms “a,” “an,” and “the” are intended to equally include the plural forms unless the context clearly indicates otherwise. Similarly, the term “and / or” as used in this application means including one or more of the associated listed items and all possible combinations thereof. Additionally, when used in this application, the term "comprise" and its variations "comprises" and / or "comprising" refer to the presence of stated features, integrals, steps, operations, elements, and / or components, but do not exclude the presence or addition of one or more other features, integrals, steps, operations, elements, components, and / or groups thereof. Without further limitations, an element defined by the phrase "comprises a..." does not exclude the presence of other identical elements in the process, method, or apparatus that includes said element. In this document, each embodiment may focus on the differences from other embodiments, and similar or identical parts between embodiments can be referred to mutually. For methods, products, etc., disclosed in the embodiments, if they correspond to the method section disclosed in the embodiments, the relevant parts can be referred to the description of the method section.

[0125] Those skilled in the art will recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of the embodiments of this disclosure. Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working processes of the systems, devices, and units described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here.

[0126] The methods and products (including but not limited to devices and equipment) disclosed in the embodiments herein can be implemented in other ways. For example, the device embodiments described above are merely illustrative. For instance, the division of units may be merely a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. In addition, the coupling or direct coupling or communication connection between the shown or discussed units may be through some interfaces, and the indirect coupling or communication connection between devices or units may be electrical, mechanical, or other forms. The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units, that is, they may be located in one place or distributed across multiple network units. Some or all of the units may be selected to implement this embodiment according to actual needs. Furthermore, the functional units in the embodiments of this disclosure may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit.

[0127] The flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to embodiments of this disclosure. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of code containing one or more executable instructions for implementing a specified logical function. In some alternative implementations, the functions marked in the blocks may occur in a different order than that shown in the drawings. For example, two consecutive blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. In the descriptions corresponding to the flowcharts and block diagrams in the accompanying drawings, the operations or steps corresponding to different blocks may also occur in a different order than disclosed in the description, and sometimes there is no specific order between different operations or steps. For example, two consecutive operations or steps may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. Each block in a block diagram and / or flowchart, and combinations of blocks in a block diagram and / or flowchart, can be implemented using a dedicated hardware-based system that performs the specified function or action, or using a combination of dedicated hardware and computer instructions.

Claims

1. A method for determining the oil filter of a range hood, characterized in that, include: Simulate cooking fumes corresponding to the target smoke source; Under conditions where cooking fumes reach a stable state and the range hood's airflow remains constant, information on cooking fumes is acquired. This information includes the shape, distribution, and velocity of the cooking fumes, with the velocity of the fumes including the velocity at a preset location. Based on the cooking fume information, the oil filter parameters of the range hood are determined; wherein, based on the shape and distribution of the cooking fume, the opening area of ​​the oil filter is determined; based on the shape and velocity of the cooking fume, the position and area of ​​the oil filter holes in the opening area are determined.

2. The method according to claim 1, characterized in that, Based on the shape and velocity of cooking fumes, the position and area of ​​the oil mesh holes in the opening area are determined, including: The shape of cooking fumes is positively correlated with the position and area of ​​the oil mesh openings; The speed of cooking fumes is negatively correlated with the area of ​​the oil mesh.

3. The method according to claim 1, characterized in that, The cooking fume information also includes fume escape information; determining the range hood's oil filter parameters based on the cooking fume information also includes: Adjust the area and layout of the oil fume mesh holes at the corresponding locations based on the location, amount, and velocity of the oil fumes.

4. The method according to any one of claims 1 to 3, characterized in that, The oil filter parameters include the oil filter area; after determining the oil filter parameters of the range hood, the following are also included: Obtain the number of oil fume particles captured by each oil mesh hole; The area of ​​the oil mesh is adjusted based on the number of oil fume particles captured by each mesh opening.

5. The method according to claim 4, characterized in that, Based on the amount of oil fume particles captured by each oil filter, the area of ​​the oil filter holes is adjusted, including: If the difference between the number of oil fume particles captured by the target oil mesh and the number of oil fume particles captured by the adjacent oil mesh is less than a first threshold, the area of ​​the target oil mesh is reduced. If the difference between the number of oil fume particles captured by the target oil mesh hole and the number of oil fume particles captured by the adjacent oil mesh hole is greater than the second threshold, the area of ​​the target oil mesh hole is increased.

6. A system for determining the oil filter of a range hood, characterized in that, The system comprising the method for determining the oil filter of a range hood as described in any one of claims 1 to 5, wherein the system includes: A fume generating device is used to generate cooking fumes corresponding to a target fume source; a fume information acquisition device is used to acquire cooking fume information when the cooking fumes reach a stable state and the range hood airflow is constant; wherein, the cooking fume information includes: the shape, distribution and velocity of the cooking fumes; the velocity of the cooking fumes includes the velocity of the fumes at a preset location; A determining device is used to determine the oil filter parameters of a range hood based on cooking fume information; wherein, the opening area of ​​the oil filter is determined based on the shape and distribution of the cooking fume; and the position and area of ​​the oil filter holes in the opening area are determined based on the shape and velocity of the cooking fume.

7. A computer-readable storage medium storing program instructions, characterized in that, When the program instructions are executed, they cause the computer to perform the method for determining the oil filter of a range hood as described in any one of claims 1 to 5.

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