Method and device for analyzing grease separation degree of range hood and computer readable storage medium

Abstract

The invention relates to the technical field of intelligent household appliances, and discloses a method for analyzing the grease separation degree of a range hood, and the method comprises the steps: determining a target smoke source, so as to obtain a lampblack particulate matter parameter and a cooking temperature parameter corresponding to the target smoke source; and inputting the oil smoke particulate matter parameters and the cooking temperature parameters into a target simulation model so as to carry out oil smoke capture simulation to obtain the oil separation degree of the range hood. According to the method, the input parameters are closer to the actual cooking scene, and the simulation accuracy can be improved. Therefore, the grease separation degree of the range hood is accurately calculated, calculation of the grease separation degree is simplified, and performance optimization of the range hood is facilitated. The invention further discloses a device for analyzing the oil separation degree of the range 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, apparatus, and computer-readable storage medium for analyzing the grease separation degree of a range hood. Background Technology

[0002] Currently, range hoods, as kitchen exhaust devices, reduce user contact with oily fumes during cooking, while optimizing air quality in the kitchen. Grease separation rate is a crucial performance indicator for range hoods. Typically, the grease separation rate is tested in a laboratory setting by introducing an aerosol containing standard grease into the range hood and calculating the difference in grease mass before and after separation using a gravimetric method. The expensive weighing equipment used in this method increases experimental costs.

[0003] The related technology discloses a method for detecting the grease separation degree of a range hood. The method involves preparing a test chamber, a dripping device, and the range hood under test, placing the range hood inside the test chamber; connecting an exhaust pipe to the outlet of the range hood; setting up a lower dilution device and a lower sampling pump next to the test chamber and connected to it; setting up an upper dilution device and an upper sampling pump next to the exhaust pipe and connected to it; connecting the outputs of both the lower and upper sampling pumps to a laser particle counter; turning on the range hood and starting the dripping device to begin the dripping test; during the dripping test, the laser particle counter measures the number of oil fume particles larger than 0.5 micrometers in the sampling gas from the lower and upper sampling pumps, respectively, and recording these as a1 and b1; then calculating the grease separation percentage FZ (%) of the range hood under test using the following formula.

[0004] In the process of implementing the embodiments of this disclosure, at least the following problems were found in the related art:

[0005] Although the related technologies have reduced the detection cost, they still require specialized testing equipment, making the calculation of oil separation degree cumbersome.

[0006] 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

[0007] 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.

[0008] This disclosure provides a method, apparatus, and computer-readable storage medium for analyzing the grease separation degree of a range hood, thereby simplifying the calculation of the grease separation degree.

[0009] In some embodiments, the method includes: determining a target smoke source to obtain the oil fume particulate matter parameters and cooking temperature parameters corresponding to the target smoke source; and inputting the oil fume particulate matter parameters and cooking temperature parameters into a target simulation model to perform oil fume capture simulation to obtain the grease separation degree of the range hood.

[0010] In some embodiments, the apparatus includes a processor and a memory storing program instructions, the processor being configured to, when executing the program instructions, perform the aforementioned method for analyzing the grease separation degree of a range hood.

[0011] In some embodiments, the computer-readable storage medium stores program instructions that, when executed, cause a computer to perform the aforementioned method for analyzing the grease separation degree of a range hood.

[0012] The method, apparatus, and computer-readable storage medium for analyzing the grease separation degree of a range hood provided in this disclosure can achieve the following technical effects:

[0013] Determining the corresponding particulate matter parameters based on the target smoke source helps improve the accuracy of these parameters in the target cooking scenario. Simultaneously, cooking temperature parameters are introduced, as they affect the distribution of particulate matter, smoke emission rate, and thus the changes in the smoke flow field, consequently influencing the simulation results. This makes the input parameters closer to the actual cooking scenario, contributing to improved simulation accuracy. Consequently, the grease separation degree of the range hood can be calculated more accurately, simplifying the calculation and aiding in the optimization of range hood performance.

[0014] The above general description and the description below are exemplary and illustrative only and are not intended to limit this application. Attached Figure Description

[0015] 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:

[0016] Figure 1 This is a schematic diagram of a method for analyzing the grease separation degree of a range hood, provided in an embodiment of this disclosure;

[0017] Figure 2 This is a schematic diagram of another method for analyzing the grease separation degree of a range hood provided in an embodiment of this disclosure;

[0018] Figure 3 This is a graph showing the relationship between the particle size distribution and mass fraction of oil fume particles provided in an embodiment of this disclosure;

[0019] Figure 4 This is a schematic diagram of the process of inputting parameters into a target simulation model to obtain the grease separation degree of a range hood in the method provided in this embodiment of the disclosure;

[0020] Figure 5 This is a schematic diagram of another method for analyzing the grease separation degree of a range hood provided in an embodiment of this disclosure;

[0021] Figure 6 This is a schematic diagram of an apparatus for analyzing the grease separation degree of a range hood, provided in an embodiment of this disclosure. Detailed Implementation

[0022] 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.

[0023] 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.

[0024] Unless otherwise stated, the term "multiple" means two or more.

[0025] 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.

[0026] 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.

[0027] 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.

[0028] 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.

[0029] 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.

[0030] The processor determines the mapping relationship between the initial velocity and particle size parameters of oil fume particles and the smoke source, including:

[0031] The processor acquires the initial velocity and particle size parameters of the oil fume particles in the target space.

[0032] The processor determines the smoke source in the target space based on the initial velocity and particle size parameters of the oil fume particles.

[0033] 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.

[0034] 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.

[0035] 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.

[0036] Optionally, the processor acquires the initial velocity of the oil fume particles within the target space, including:

[0037] 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,

[0038] The processor obtains the initial velocity of the oil fume particles in the target space, including:

[0039] 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.

[0040] 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.

[0041] 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.

[0042] 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.

[0043] Optionally, the processor processes the acquired oil fume image to obtain the grayscale peak distribution of the image, including:

[0044] The processor converts the acquired oil fume images to grayscale before dividing them into grids.

[0045] 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.

[0046] The processor performs a Fourier transform on the grayscale values ​​to obtain the grayscale peak distribution of the image.

[0047] 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.

[0048] 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:

[0049] The processor obtains the instantaneous velocity field based on the displacement of the grayscale peaks of adjacent frames.

[0050] 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.

[0051] 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.

[0052] 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.

[0053] Optionally, the processor calculates the grayscale value for each grid cell by including:

[0054] 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.

[0055] The processor fuses gray values ​​at the same grid location in the 3D mesh to calculate the gray value of each grid.

[0056] The higher the gray value, the higher the concentration of oil fume particles.

[0057] 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.

[0058] 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.

[0059] Optionally, the processor performs grayscale conversion on the acquired oil fume images, including:

[0060] The processor converts each pixel in the color image of cooking fumes into a grayscale value.

[0061] The processor removes non-smoke areas from the image based on a grayscale threshold to obtain the grayscale converted image.

[0062] 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, the coordinates of a grid cell in the image are (m, n), and the corresponding pixel value is f(m, n). This 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. For example, 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.

[0063] Optionally, the processor acquires the particle size parameters of the oil fume particles in the target space, including:

[0064] The processor acquires the particle size distribution and particle size percentage of oil fume particles at multiple locations within the target space.

[0065] 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.

[0066] 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.

[0067] 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.

[0068] 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:

[0069] The processor acquires the initial velocity and particle size parameters of the oil fume particles in the target space.

[0070] 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.

[0071] 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.

[0072] 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.

[0073] Based on the previously established correspondence between the target smoke source and the particulate matter parameters of cooking fumes, combined with... Figure 1 As shown in the embodiments of this disclosure, a method for analyzing the grease separation degree of a range hood is provided, comprising:

[0074] S101, the processor determines the target smoke source to obtain the corresponding oil fume particulate matter parameters and cooking temperature parameters.

[0075] S102, the processor inputs the oil fume particulate matter parameters and cooking temperature parameters into the target simulation model to perform oil fume capture simulation and obtain the oil separation degree of the range hood.

[0076] Here, analyzing the smoke extraction effect of a range hood requires first defining the cooking scenario to identify the target smoke source. The smoke sources generated under different cooking scenarios vary, affecting the accuracy of the parameter input for the target simulation model. Therefore, identifying the target smoke source allows us to obtain the corresponding particulate matter parameters and cooking temperature parameters. The correspondence between the target smoke source and the particulate matter parameters is detailed above. The correspondence between the target smoke source and the cooking temperature parameters is obtained experimentally. For example, under the target cooking scenario, the temperatures of the cookware and heat source are measured. The mapping relationship between the target smoke source and the cooking temperature parameters is saved and retrieved when needed.

[0077] The parameters of oil fume particles and cooking temperature are input into the target simulation model to obtain simulation results. The simulation results are then calculated and statistically analyzed to obtain the grease separation degree of the range hood. The grease separation degree is primarily calculated based on the oil fume particles captured on the inner surface of the range hood, i.e., the oil fume particles adhere to the inner wall surface after impacting it. Specifically, the total number of oil fume particles is determined; this total number of particles is a set parameter and one of the input parameters of the target simulation model. Thus, the grease separation degree of the range hood can be calculated based on the oil fume particles captured on the inner surface and the total number of oil fume particles. The more oil fume particles captured on the inner surface, the better the grease separation degree. The inner surface includes the inner wall surface of the range hood body and the inner wall surface of the air duct, etc.

[0078] Furthermore, the target simulation model includes fluid dynamics and discrete models. The fluid dynamics model simulates the flow field of cooking fumes, while the discrete model performs calculations. The parameters input to the target simulation model include not only the parameters of cooking fume particles and cooking temperature, but also other factors affecting fume diffusion, such as airflow parameters, range hood parameters, and operating parameters. This makes the simulation more closely resemble the actual scenario.

[0079] The method for analyzing the grease separation degree of a range hood, as provided in this disclosure, determines the corresponding particulate matter parameters based on the target smoke source, which helps improve the accuracy of these parameters under the target cooking scenario. Simultaneously, the cooking temperature parameter is introduced, as it affects the distribution and smoke generation rate of the particulate matter, thus influencing the change in the particulate matter flow field and consequently the simulation results. This makes the input parameters closer to the actual cooking scenario, contributing to improved simulation accuracy. Consequently, the grease separation degree of the range hood is calculated more accurately, simplifying the calculation and contributing to the optimization of range hood performance.

[0080] Optionally, in step S101, the parameters of oil fume particulate matter include the initial velocity, particle size distribution, and particle size ratio of the oil fume particulate matter.

[0081] Among them, the parameters of oil fume particulate matter and the target smoke source have a mapping relationship.

[0082] Here, there is a correspondence between the parameters of oil fume particulate matter and the target smoke source. The parameters of oil fume particulate matter include the initial velocity, particle size distribution, and particle size percentage of the oil fume particles. Specifically, the target smoke source is either a first smoke source or a second smoke source. The first smoke source is the smoke source corresponding to the stir-frying scenario, and the second smoke source is the smoke source corresponding to the stir-frying scenario. The initial velocity corresponding to the first smoke source is greater than the initial velocity corresponding to the second smoke source; and within the same particle size range, the particle size percentage of the first smoke source is greater than that of the second smoke source. The particle size range includes one or more particle size distributions. For example, the particle size range is the particle size range of PM2.5 and PM1.0 particles.

[0083] Optionally, in step S101, the cooking temperature parameters include the stove temperature and the pot temperature;

[0084] The cookware temperature includes the inner and outer surface temperatures of the cookware, and both the inner and outer surface temperatures include multiple annular temperature zones centered on the center of the cookware, with each annular temperature zone corresponding to a specific temperature.

[0085] Here, cooking temperature parameters include stove temperature and cookware temperature. Stove temperature refers to the combustion temperature of combustibles on the stove, while cookware temperature includes the inner and outer surface temperatures. The cookware temperature comprises multiple annular temperature zones centered on the cookware's center. The closer the annular temperature zone is to the center, the higher its temperature. Generally, the outer surface temperature of the cookware is slightly higher than the inner surface temperature. Furthermore, the cookware temperature is correlated with the stove temperature. The more annular temperature zones there are, the more accurate the cookware temperature readings. Thus, by simulating the impact of combustion on the generation and diffusion of particulate matter during cooking using cookware and stove temperatures, the simulation more closely approximates the actual cooking process.

[0086] In addition, cookware temperature can be obtained through experimental testing. Specifically, the cookware is heated using a stove, and during the heating process, one or more thermocouples are used to measure the temperature at various locations on the inner and outer surfaces of the cookware. This allows for the acquisition of temperature distribution information on the inner and outer surfaces of the cookware. The annular temperature region may also include only one area.

[0087] Optionally, in step S102, the processor inputs the particulate matter parameters and cooking temperature parameters into the target simulation model to perform a fume capture simulation to obtain the grease separation degree of the range hood, including:

[0088] S121, the processor inputs the particulate matter parameters of cooking fumes and the cooking temperature parameters into the discrete model.

[0089] S122, The processor sets and initializes the boundary conditions of the discrete model to calculate the simulated oil fume flow field.

[0090] S123, the processor calculates the grease separation degree of the range hood after the simulated oil fume flow field converges.

[0091] Here, the target simulation model includes a fluid dynamics model and a discrete model. Input parameters mainly refer to the input parameters of the discrete model, including parameters related to oil fume particles and cooking temperature. The oil fume particle parameters include not only the initial velocity, particle size distribution, and particle size distribution mentioned above, but also parameters related to the composition of oil fume particles. After inputting the parameters into the discrete model, boundary conditions are set and initialized to calculate the simulated oil fume flow field. After the oil fume flow field converges (i.e., reaches a steady state), the data of oil fume particles captured in the inner cavity are statistically analyzed to calculate the grease separation degree of the range hood. This ensures the accuracy of the data and obtains a stable flow field. By controlling the model parameters and boundary conditions, the reliability of the range hood effect simulation is ensured. Furthermore, in this embodiment, the grease separation degree is calculated using oil fume particle data after the simulated oil fume flow field converges. Compared to existing technologies, this is more convenient and has lower experimental costs.

[0092] Optionally, in S121, the processor inputs the particulate matter parameters and cooking temperature parameters into the discrete model, including:

[0093] The processor sets the composition of oil fume particles as heavy oil particles and the particle source as a surface jet source.

[0094] The processor fits the mass distribution of each quantity concentration corresponding to the particle size range of the oil fume particles to obtain the average particle size and distribution coefficient of the oil fume particles.

[0095] The processor inputs the average particle size and distribution coefficient of the oil fume particles into the discrete model.

[0096] Here, the particulate matter composition of cooking fumes consists of heavy oil particles, and the particle source is a surface jet source. Simultaneously, the number concentrations of each particle size range are calculated based on the particle size distribution and particle size distribution ratio. Specifically, the cooking fume particulate matter data is fitted to a Rosin-Rammler distribution, and the fitting calculations... Characteristic particle size for obtaining number concentration distribution And the distribution index n. Where Y is the cumulative distribution function, and ds is the particle size. The total number of oil fume particles is a set value. After the total number of oil fume particles is determined, the mass fraction corresponding to each particle size can be calculated based on the data concentration corresponding to each particle size range. Then, based on the mass fraction and the median particle size (the median particle size is calculated based on the upper and lower limits of each particle size range), the mass-average particle size is calculated. For example, the mass-average particle size of oil fume particles is 2.83 μm, and the distribution index is 2.35. Figure 3 The figure shows the relationship between the particle size distribution and mass fraction of oil fume particles. In the figure, ds represents the particle size, and Y(-) represents the mass fraction.

[0097] Furthermore, inputting oil fume particulate matter parameters and cooking temperature parameters into the discrete model also includes inputting initial velocity values, particle size parameters, and cookware temperature. For example, when the target smoke source is the first smoke source, the initial velocity of the oil fume particles is 0.8 m / s, the proportion of PM2.5 particles is greater than or equal to 90%, and the proportion of PM1.0 particles is greater than or equal to 80%. The cookware temperature range is [100℃, 600℃]. When the target smoke source is the second smoke source, the initial velocity of the oil fume particles is 0.5 m / s, the proportion of PM2.5 particles is greater than or equal to 85%, and the proportion of PM1.0 particles is greater than or equal to 70%. The cookware temperature range is [100℃, 450℃]. Thus, determining the simulation input parameters based on the target smoke source can improve the accuracy of the model simulation.

[0098] Furthermore, after determining the corresponding parameter values ​​of the oil fume particles and the cookware temperature, the total number of smoke-generating particles is set and input into the discrete model based on these parameters and cookware temperature. Specifically, under the same target smoke source, the higher the values ​​of the oil fume particle parameters and the cookware temperature, the greater the total number of smoke-generating particles.

[0099] Optionally, in S122, the processor sets the boundary conditions for the discrete model, including:

[0100] The processor sets the range hood outlet as the pressure outlet boundary, the door and window positions in the kitchen model as the pressure inlet boundary, the walls of the kitchen model as non-slip insulated walls, and the gas outlet interface as the velocity inlet boundary.

[0101] The processor sets the inner wall surface of the range hood as the collection surface for oil fume particles.

[0102] Here, the boundary conditions include pressure inlet / outlet, wall boundary, and velocity inlet boundary. Specifically, the range hood outlet is the pressure outlet boundary, which can simulate the distribution of oil fumes when the range hood is off. The kitchen door and window positions are pressure inlet boundaries, and the doors and windows are in the open state. The walls of the kitchen model and the stove are non-slip wall boundaries, where the walls of the kitchen model are non-slip adiabatic wall boundaries. The gas outlet interface is the velocity inlet interface; when a single burner is working, the gas outlet of that single burner is set as the velocity inlet boundary. Simultaneously, in this embodiment, the inner wall of the range hood is set as the oil fume particle collection surface to capture oil fume particles impacting the collection surface, thereby calculating the grease separation degree. Understandably, oil fumes, under the action of the range hood fan, throw out grease particles, which are then adsorbed after colliding with the collection surface to obtain grease. Therefore, the oil fume particles on the collection surface can characterize the grease interception situation. After setting the boundary conditions, the above boundary conditions are initialized. For example, the gas outlet velocity ranges from 0 m / s to 1 m / s, and the gas temperature ranges from 100℃ to 600℃, etc.

[0103] Optionally, in step S123, the processor calculates the grease separation degree of the range hood after the simulated oil fume flow field converges, including:

[0104] The processor counts the number of oil fume particles captured by the oil fume particle collection surface.

[0105] The processor obtains the total number of smoke-generating particles; the total number of smoke-generating particles is determined based on the target smoke source.

[0106] The processor uses the ratio of the number of captured oil fume particles to the total number of smoke particles as the grease separation degree of the range hood.

[0107] Here, after the oil fume flow field converges, the number of oil fume particles captured by the collection surface is counted using the target simulation model. Simultaneously, the total number of smoke-generating particles is obtained, with the setting for the total number of smoke-generating particles described above. Thus, the grease separation degree of the range hood can be calculated as: number of captured oil fume particles / total number of smoke-generating particles. In this way, the grease separation degree is calculated based on the oil fume particles captured by the collection surface. Compared to existing technologies, this method is more convenient and lower in cost.

[0108] Combination Figure 4 As shown in the embodiments of this disclosure, a method for analyzing the grease separation degree of a range hood is provided, comprising:

[0109] S101, the processor determines the target smoke source to obtain the particulate matter parameters and cooking temperature parameters of the target smoke source.

[0110] S203, the processor builds a kitchen model and imports the built kitchen model into the target simulation model to simulate the flow field of cooking fumes.

[0111] S204, the processor uses a static mesh to divide the oil fume flow field into a mesh.

[0112] S102, the processor inputs the parameters of oil fume particles and cooking temperature parameters into the target simulation model to simulate the smoke extraction effect and obtain the grease separation degree of the range hood.

[0113] Here, a kitchen model is established and imported into the target simulation model to simulate the oil fume flow field. In existing technologies, the simulation of the oil fume flow field is based on the range hood area; however, the kitchen space has a certain influence on the oil fume flow field. To improve the accuracy of the oil fume flow field simulation, a kitchen model is established, including the size of the kitchen space, the placement of doors and windows, and the locations of range hoods, stoves, etc. After importing the kitchen model into the target simulation model, the geometric boundaries of the fluid domain (air region) and solid domain (such as range hoods, stoves, etc.) in the oil fume flow field can be determined. Then, a static mesh can be used to mesh the oil fume flow field, that is, to discretize the continuous geometric space. Thus, after inputting parameters into the model, the flow field characteristics can be analyzed using the mesh elements. Optionally, the kitchen model is a standard kitchen model with dimensions of 3.5m * 2.5m * 2.5m. However, the kitchen model dimensions in this embodiment are not specified here and can be set according to requirements.

[0114] In this embodiment of the disclosure, static meshing is preferably used for mesh generation, which can accurately capture the details of the oil fume flow field, simplify model setup, and avoid numerical dispersion problems. In some embodiments, dynamic meshing can also be used for mesh generation.

[0115] Combination Figure 5 As shown in the embodiments of this disclosure, another method for analyzing the grease separation degree of a range hood is provided, including:

[0116] S101, the processor determines the target smoke source to obtain the corresponding oil fume particulate matter parameters and cooking temperature parameters.

[0117] S102, the processor inputs the oil fume particulate matter parameters and cooking temperature parameters into the target simulation model to perform oil fume capture simulation and obtain the oil separation degree of the range hood.

[0118] S303, during the operation of the range hood, the processor adjusts the fan and / or cooling module of the range hood according to the grease separation degree of the range hood in order to optimize the grease separation degree.

[0119] The cooling module is located inside the range hood and is used to regulate the temperature of the inner wall of the range hood.

[0120] Here, after obtaining the grease separation degree of the range hood, the fan and / or cooling module of the range hood can be adjusted during operation based on this degree. A higher grease separation degree results in better range hood performance. When simulation analysis shows that a certain type of range hood has a low grease separation degree (e.g., the grease separation degree is below the threshold), the operating parameters of the range hood can be adjusted to improve the grease separation degree. Specifically, the fan speed can be increased to enhance the centrifugal force and eject more grease. The temperature of the cooling module can be lowered to reduce the temperature of the inner wall of the range hood, increasing grease condensation and ensuring effective grease interception. This improves the performance of the range hood.

[0121] Combination Figure 6 As shown, this disclosure provides an apparatus 100 for analyzing the grease separation degree of a range hood, including a processor 101 and a memory 102. Optionally, the apparatus may further include a communication interface 103 and a bus 104. The processor 101, communication interface 103, and memory 102 can communicate with each other via the bus 104. The communication interface 103 can be used for information transmission. The processor 101 can call logical instructions in the memory 102 to execute the method for analyzing the grease separation degree of a range hood described in the above embodiment.

[0122] Furthermore, the logical instructions in the aforementioned memory 102 can be implemented as software functional units and, when sold or used as independent products, can be stored in a computer-readable storage medium.

[0123] The memory 102, 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 101 executes functional applications and data processing by running the program instructions / modules stored in the memory 102, that is, it implements the method for analyzing the grease separation degree of the range hood in the above embodiments.

[0124] The memory 102 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 102 may include high-speed random access memory and may also include non-volatile memory.

[0125] This disclosure provides a computer-readable storage medium storing computer-executable instructions configured to perform the above-described method for analyzing the grease separation degree of a range hood.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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 analyzing the grease separation degree of a range hood, characterized in that, include: Identify the target smoke source to obtain the corresponding particulate matter parameters and cooking temperature parameters for the target smoke source; The parameters of particulate matter and cooking temperature are input into the target simulation model to simulate the oil fume capture and obtain the grease separation degree of the range hood.

2. The method according to claim 1, characterized in that, The parameters of particulate matter and cooking temperature are input into the target simulation model to simulate the smoke extraction effect and obtain the grease separation degree of the range hood, including: Input the parameters of particulate matter in cooking fumes and the parameters of cooking temperature into the discrete model; Set and initialize the boundary conditions of the discrete model to calculate the simulated oil fume flow field; The grease separation degree of the range hood is calculated after the simulated oil fume flow field converges.

3. The method according to claim 2, characterized in that, Define the boundary conditions for the discrete model, including: The range hood outlet is set as the pressure outlet boundary, and the door and window positions in the kitchen model are set as the pressure inlet boundaries; the walls of the kitchen model are non-slip insulated walls; the gas outlet interface is set as the velocity inlet boundary. The inner wall surface of the range hood is set as the collection surface for oil fume particles.

4. The method according to claim 2, characterized in that, After the simulated oil fume flow field converges, the grease separation degree of the range hood is calculated, including: Count the number of oil fume particles captured by the oil fume particulate matter collection surface; Obtain the total number of smoke-generating particulate matter; the total number of smoke-generating particulate matter is determined based on the target smoke source; The ratio of the number of captured oil fume particles to the total number of smoke-generating particles is used as the grease separation degree of the range hood.

5. The method according to claim 1, characterized in that, The parameters of oil fume particulate matter include the initial velocity, particle size distribution, and particle size ratio of the oil fume particles; Among them, the initial velocity, particle size distribution, particle size ratio of oil fume particles and the target smoke source have a mapping relationship.

6. The method according to claim 1, characterized in that, Cooking temperature parameters include stove temperature and cookware surface temperature; The cookware temperature includes the inner and outer surface temperatures of the cookware, and both the inner and outer surface temperatures include multiple annular temperature zones centered on the center of the cookware, with each annular temperature zone corresponding to a specific temperature.

7. The method according to claim 1, characterized in that, Before inputting the particulate matter parameters and cooking temperature parameters into the target simulation model, the following steps are also included: A kitchen model is created and then imported into the target simulation model to simulate the flow field of cooking fumes. The oil fume flow field is divided into grids using a static grid type.

8. The method according to any one of claims 1 to 7, characterized in that, After obtaining the grease separation efficiency of the range hood, the following is also included: During the operation of the range hood, the fan and / or cooling module of the range hood are adjusted according to the grease separation degree of the range hood in order to improve the grease separation degree; The cooling module is located inside the range hood and is used to regulate the temperature of the inner wall of the range hood.

9. A device for analyzing the grease separation degree of a range hood, comprising a processor and a memory storing program instructions, characterized in that, The processor is configured to, when running the program instructions, execute the method for analyzing the grease separation degree of a range hood as described in any one of claims 1 to 8.

10. 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 analyzing the grease separation degree of a range hood as described in any one of claims 1 to 8.

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